From 0166a0982d81b8edefbbbf11bc02808a542975e9 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 17 Jul 2024 15:48:43 -0500
Subject: [PATCH 01/52] updates git ignore and zoning map

---
 .gitignore                              |   6 +-
 notebooks/01-energy-utility.ipynb       | 372 ++++++++++++++++++
 notebooks/gis_notebooks/kc-zoning.ipynb | 483 ++++++++++++++++++++----
 3 files changed, 780 insertions(+), 81 deletions(-)

diff --git a/.gitignore b/.gitignore
index 6a6e352..db65f80 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,9 @@
 cjest-data
-
+.snakemake/
+data
+01-energy-utility.ipynb
+02-census.ipynb
+puma_maps.ipynb
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/notebooks/01-energy-utility.ipynb b/notebooks/01-energy-utility.ipynb
index 96df9b3..fd3938f 100644
--- a/notebooks/01-energy-utility.ipynb
+++ b/notebooks/01-energy-utility.ipynb
@@ -53,6 +53,378 @@
     "eia_service_path = Path(\"../../spatial-data/Electric_Retail_Service_Territories/Electric_Retail_Service_Territories.shp\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: total: 234 ms\n",
+      "Wall time: 2.04 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "query_url = (\"https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Retail_Service_Territories/\"\n",
+    "             \"FeatureServer/0/query?where=STATE%20%3D%20'KS'%20OR%20STATE%20%3D%20'MO'&outFields=CNTRL_AREA,\"\n",
+    "             \"PLAN_AREA,HOLDING_CO,NET_GEN,PURCHASED,RETAIL_MWH,WSALE_MWH,TOTAL_MWH,TRANS_MWH,CUSTOMERS,YEAR,\"\n",
+    "             \"NET_EX,NAME,REGULATED,STATE,ID,NAICS_CODE,NAICS_DESC&outSR=4326&f=json\")\n",
+    "service_gdf = gpd.read_file(query_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: total: 328 ms\n",
+      "Wall time: 1.95 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ks_gdf = gpd.read_file(states.KS.shapefile_urls()['county'])\n",
+    "mo_gdf = gpd.read_file(states.MO.shapefile_urls()['county'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CNTRL_AREA</th>\n",
+       "      <th>PLAN_AREA</th>\n",
+       "      <th>HOLDING_CO</th>\n",
+       "      <th>NET_GEN</th>\n",
+       "      <th>PURCHASED</th>\n",
+       "      <th>RETAIL_MWH</th>\n",
+       "      <th>WSALE_MWH</th>\n",
+       "      <th>TOTAL_MWH</th>\n",
+       "      <th>TRANS_MWH</th>\n",
+       "      <th>CUSTOMERS</th>\n",
+       "      <th>YEAR</th>\n",
+       "      <th>NET_EX</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>REGULATED</th>\n",
+       "      <th>STATE</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>NAICS_CODE</th>\n",
+       "      <th>NAICS_DESC</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SOUTHWEST POWER POOL</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>KANSAS CITY POWER &amp; LIGHT CO</td>\n",
+       "      <td>14911882.0</td>\n",
+       "      <td>5624474.0</td>\n",
+       "      <td>14223892.0</td>\n",
+       "      <td>5957441.0</td>\n",
+       "      <td>20536356.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>562180.0</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>EVERGY METRO</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
+       "      <td>MULTIPOLYGON (((-94.66607 38.27027, -94.66941 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SOUTHWEST POWER POOL</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>WESTAR ENERGY</td>\n",
+       "      <td>9324681.0</td>\n",
+       "      <td>1566609.0</td>\n",
+       "      <td>9181326.0</td>\n",
+       "      <td>1628176.0</td>\n",
+       "      <td>10891290.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>334500.0</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>EVERGY KANSAS SOUTH, INC</td>\n",
+       "      <td>REGULATED</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>10005</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
+       "      <td>POLYGON ((-95.50819 38.42458, -95.50818 38.415...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SOUTHWEST POWER POOL</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>FREESTATE ELECTRIC COOP INC</td>\n",
+       "      <td>214.0</td>\n",
+       "      <td>287728.0</td>\n",
+       "      <td>270021.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>287942.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18451.0</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>FREESTATE ELECTRIC COOP</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>10019</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
+       "      <td>MULTIPOLYGON (((-95.61781 38.76753, -95.62164 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ASSOCIATED ELECTRIC COOPERATIVE, INC.</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>CITY OF LAMAR - (MO)</td>\n",
+       "      <td>50225.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>CITY OF LAMAR - (MO)</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>MO</td>\n",
+       "      <td>10057</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
+       "      <td>POLYGON ((-94.28309 37.49345, -94.28324 37.493...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>SOUTHWEST POWER POOL</td>\n",
+       "      <td>SOUTHWEST POWER POOL (SPP)</td>\n",
+       "      <td>CITY OF KENNETT - (MO)</td>\n",
+       "      <td>111178.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>-999999.0</td>\n",
+       "      <td>CITY OF KENNETT - (MO)</td>\n",
+       "      <td>NOT AVAILABLE</td>\n",
+       "      <td>MO</td>\n",
+       "      <td>10152</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
+       "      <td>MULTIPOLYGON (((-90.02360 36.26603, -90.02347 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              CNTRL_AREA                   PLAN_AREA  \\\n",
+       "0                   SOUTHWEST POWER POOL               NOT AVAILABLE   \n",
+       "1                   SOUTHWEST POWER POOL               NOT AVAILABLE   \n",
+       "2                   SOUTHWEST POWER POOL               NOT AVAILABLE   \n",
+       "3  ASSOCIATED ELECTRIC COOPERATIVE, INC.               NOT AVAILABLE   \n",
+       "4                   SOUTHWEST POWER POOL  SOUTHWEST POWER POOL (SPP)   \n",
+       "\n",
+       "                     HOLDING_CO     NET_GEN  PURCHASED  RETAIL_MWH  WSALE_MWH  \\\n",
+       "0  KANSAS CITY POWER & LIGHT CO  14911882.0  5624474.0  14223892.0  5957441.0   \n",
+       "1                 WESTAR ENERGY   9324681.0  1566609.0   9181326.0  1628176.0   \n",
+       "2   FREESTATE ELECTRIC COOP INC       214.0   287728.0    270021.0  -999999.0   \n",
+       "3          CITY OF LAMAR - (MO)     50225.0  -999999.0   -999999.0  -999999.0   \n",
+       "4        CITY OF KENNETT - (MO)    111178.0  -999999.0   -999999.0  -999999.0   \n",
+       "\n",
+       "    TOTAL_MWH  TRANS_MWH  CUSTOMERS  YEAR    NET_EX                      NAME  \\\n",
+       "0  20536356.0        0.0   562180.0  2020       0.0              EVERGY METRO   \n",
+       "1  10891290.0        0.0   334500.0  2020       0.0  EVERGY KANSAS SOUTH, INC   \n",
+       "2    287942.0        0.0    18451.0  2020       0.0   FREESTATE ELECTRIC COOP   \n",
+       "3   -999999.0  -999999.0  -999999.0  2020 -999999.0      CITY OF LAMAR - (MO)   \n",
+       "4   -999999.0  -999999.0  -999999.0  2020 -999999.0    CITY OF KENNETT - (MO)   \n",
+       "\n",
+       "       REGULATED STATE     ID NAICS_CODE  \\\n",
+       "0  NOT AVAILABLE    KS  10000       2211   \n",
+       "1      REGULATED    KS  10005       2211   \n",
+       "2  NOT AVAILABLE    KS  10019       2211   \n",
+       "3  NOT AVAILABLE    MO  10057       2211   \n",
+       "4  NOT AVAILABLE    MO  10152       2211   \n",
+       "\n",
+       "                                          NAICS_DESC  \\\n",
+       "0  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
+       "1  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
+       "2  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
+       "3  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
+       "4  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  MULTIPOLYGON (((-94.66607 38.27027, -94.66941 ...  \n",
+       "1  POLYGON ((-95.50819 38.42458, -95.50818 38.415...  \n",
+       "2  MULTIPOLYGON (((-95.61781 38.76753, -95.62164 ...  \n",
+       "3  POLYGON ((-94.28309 37.49345, -94.28324 37.493...  \n",
+       "4  MULTIPOLYGON (((-90.02360 36.26603, -90.02347 ...  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "service_gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wdt_gdf = ks_gdf[ks_gdf['NAME10']=='Wyandotte']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wdt_service = service_gdf.sjoin(wdt_gdf.to_crs(epsg=4326))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['CNTRL_AREA', 'PLAN_AREA', 'HOLDING_CO', 'NET_GEN', 'PURCHASED',\n",
+       "       'RETAIL_MWH', 'WSALE_MWH', 'TOTAL_MWH', 'TRANS_MWH', 'CUSTOMERS',\n",
+       "       'YEAR', 'NET_EX', 'NAME', 'REGULATED', 'STATE', 'ID', 'NAICS_CODE',\n",
+       "       'NAICS_DESC', 'geometry', 'index_right', 'STATEFP10', 'COUNTYFP10',\n",
+       "       'COUNTYNS10', 'GEOID10', 'NAME10', 'NAMELSAD10', 'LSAD10', 'CLASSFP10',\n",
+       "       'MTFCC10', 'CSAFP10', 'CBSAFP10', 'METDIVFP10', 'FUNCSTAT10', 'ALAND10',\n",
+       "       'AWATER10', 'INTPTLAT10', 'INTPTLON10'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wdt_service.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(38.95, 39.25)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "wdt_gdf.plot(ax=ax, color='lightgray', zorder=2, fc='None', ec='k')\n",
+    "wdt_service.plot(ax=ax, column='HOLDING_CO', legend=True, legend_kwds=dict(loc='lower left'))\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "ax.set_xlim(-95,-94.5)\n",
+    "ax.set_ylim(38.95, 39.25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# service_gdf[service_gdf['STATE'].isin(['KS','MO'])].plot(alpha=0.4)\n",
+    "fig, ax = plt.subplots()\n",
+    "service_gdf[service_gdf['NAME'].isin(['EVERGY METRO'])].plot(ax=ax, alpha=1, zorder=2)\n",
+    "ks_gdf[ks_gdf['NAME10']=='Wyandotte'].plot(ax=ax, color='grey', alpha=.1, zorder=1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 48,
diff --git a/notebooks/gis_notebooks/kc-zoning.ipynb b/notebooks/gis_notebooks/kc-zoning.ipynb
index 2a44cbb..405ce25 100644
--- a/notebooks/gis_notebooks/kc-zoning.ipynb
+++ b/notebooks/gis_notebooks/kc-zoning.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,16 +31,300 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kck_wards = gpd.read_file(\"https://maps.wycokck.org/gisdata/shp/ward_prec_py.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_ward = '06'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VTD</th>\n",
+       "      <th>VTD_S</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>CITY_CODE</th>\n",
+       "      <th>CITY_PREF</th>\n",
+       "      <th>WARD</th>\n",
+       "      <th>PRECINCT</th>\n",
+       "      <th>BPU</th>\n",
+       "      <th>UG</th>\n",
+       "      <th>KS_HOUSE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BPU_At_Lg1</th>\n",
+       "      <th>BPU_At_Lg2</th>\n",
+       "      <th>BPU_At_Lg3</th>\n",
+       "      <th>DATE_MOD</th>\n",
+       "      <th>DATE_ADDED</th>\n",
+       "      <th>MOD_BY</th>\n",
+       "      <th>ADDED_BY</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>KC06-02</td>\n",
+       "      <td>600310</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>1</td>\n",
+       "      <td>KC</td>\n",
+       "      <td>06</td>\n",
+       "      <td>02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>37</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>46433.543935</td>\n",
+       "      <td>5.578922e+07</td>\n",
+       "      <td>POLYGON ((2257706.000 296463.281, 2258080.500 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>KC06-01</td>\n",
+       "      <td>600300</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>1</td>\n",
+       "      <td>KC</td>\n",
+       "      <td>06</td>\n",
+       "      <td>01</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>37</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>ccooley</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>30244.383320</td>\n",
+       "      <td>4.474095e+07</td>\n",
+       "      <td>POLYGON ((2274619.250 294173.125, 2274657.750 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VTD   VTD_S         CITY CITY_CODE CITY_PREF WARD PRECINCT BPU UG  \\\n",
+       "68  KC06-02  600310  Kansas City         1        KC   06       02   3  2   \n",
+       "74  KC06-01  600300  Kansas City         1        KC   06       01   3  2   \n",
+       "\n",
+       "   KS_HOUSE  ... BPU_At_Lg1 BPU_At_Lg2 BPU_At_Lg3    DATE_MOD  DATE_ADDED  \\\n",
+       "68       37  ...       None       None       None  2023-01-09  2023-01-09   \n",
+       "74       37  ...       None       None       None  2023-01-09  2023-01-09   \n",
+       "\n",
+       "        MOD_BY    ADDED_BY    Shape_Leng    Shape_Area  \\\n",
+       "68  GIS_EDITOR  GIS_EDITOR  46433.543935  5.578922e+07   \n",
+       "74     ccooley  GIS_EDITOR  30244.383320  4.474095e+07   \n",
+       "\n",
+       "                                             geometry  \n",
+       "68  POLYGON ((2257706.000 296463.281, 2258080.500 ...  \n",
+       "74  POLYGON ((2274619.250 294173.125, 2274657.750 ...  \n",
+       "\n",
+       "[2 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale = kck_wards[kck_wards['WARD'] == armourdale_ward]\n",
+    "armourdale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>VTD</th>\n",
+       "      <th>VTD_S</th>\n",
+       "      <th>CITY_CODE</th>\n",
+       "      <th>CITY_PREF</th>\n",
+       "      <th>WARD</th>\n",
+       "      <th>PRECINCT</th>\n",
+       "      <th>BPU</th>\n",
+       "      <th>UG</th>\n",
+       "      <th>KS_HOUSE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BPU_Member</th>\n",
+       "      <th>BPU_At_Lg1</th>\n",
+       "      <th>BPU_At_Lg2</th>\n",
+       "      <th>BPU_At_Lg3</th>\n",
+       "      <th>DATE_MOD</th>\n",
+       "      <th>DATE_ADDED</th>\n",
+       "      <th>MOD_BY</th>\n",
+       "      <th>ADDED_BY</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CITY</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Kansas City</th>\n",
+       "      <td>POLYGON ((2274657.750 293923.782, 2274677.000 ...</td>\n",
+       "      <td>KC06-02</td>\n",
+       "      <td>600310</td>\n",
+       "      <td>1</td>\n",
+       "      <td>KC</td>\n",
+       "      <td>06</td>\n",
+       "      <td>02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>37</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>46433.543935</td>\n",
+       "      <td>5.578922e+07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                      geometry      VTD  \\\n",
+       "CITY                                                                      \n",
+       "Kansas City  POLYGON ((2274657.750 293923.782, 2274677.000 ...  KC06-02   \n",
+       "\n",
+       "              VTD_S CITY_CODE CITY_PREF WARD PRECINCT BPU UG KS_HOUSE  ...  \\\n",
+       "CITY                                                                   ...   \n",
+       "Kansas City  600310         1        KC   06       02   3  2       37  ...   \n",
+       "\n",
+       "            BPU_Member BPU_At_Lg1 BPU_At_Lg2 BPU_At_Lg3    DATE_MOD  \\\n",
+       "CITY                                                                  \n",
+       "Kansas City       None       None       None       None  2023-01-09   \n",
+       "\n",
+       "             DATE_ADDED      MOD_BY    ADDED_BY    Shape_Leng    Shape_Area  \n",
+       "CITY                                                                         \n",
+       "Kansas City  2023-01-09  GIS_EDITOR  GIS_EDITOR  46433.543935  5.578922e+07  \n",
+       "\n",
+       "[1 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale.dissolve(\"CITY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kc_zones = gpd.read_file(kc_zones_url)"
+    "# kc_zones = gpd.read_file(kc_zones_url, bbox=(2.26e6,287900,2.275e6,294500))\n",
+    "kc_zones = gpd.read_file(kc_zones_url, mask=armourdale.dissolve(\"CITY\"))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -53,7 +337,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -64,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -116,145 +400,145 @@
        "      <td>Heavy Industrial District</td>\n",
        "      <td>None</td>\n",
        "      <td>32282</td>\n",
-       "      <td>44025</td>\n",
-       "      <td>40495</td>\n",
-       "      <td>545</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>44025 is in Northwest corner</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>NO</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>227094.483091</td>\n",
-       "      <td>6.904394e+07</td>\n",
-       "      <td>MULTIPOLYGON (((2275610.250 318174.562, 227830...</td>\n",
+       "      <td>13380.173937</td>\n",
+       "      <td>2.017360e+06</td>\n",
+       "      <td>MULTIPOLYGON (((2263627.750 292663.281, 226362...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>R-1(B)</td>\n",
-       "      <td>Single Family District</td>\n",
-       "      <td>2005-04-06</td>\n",
-       "      <td>0-32-05</td>\n",
+       "      <td>M-3</td>\n",
+       "      <td>Heavy Industrial District</td>\n",
+       "      <td>None</td>\n",
+       "      <td>45043</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>2820</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>From R-2 Case # 32282</td>\n",
        "      <td>NO</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>512.869032</td>\n",
-       "      <td>1.643630e+04</td>\n",
-       "      <td>POLYGON ((2263758.500 296379.969, 2263724.500 ...</td>\n",
+       "      <td>4294.963937</td>\n",
+       "      <td>4.933658e+05</td>\n",
+       "      <td>POLYGON ((2254872.499 297255.187, 2254877.499 ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>R-1(B)</td>\n",
-       "      <td>Single Family District</td>\n",
-       "      <td>2005-04-06</td>\n",
-       "      <td>0-32-05</td>\n",
+       "      <td>M-3</td>\n",
+       "      <td>Heavy Industrial District</td>\n",
+       "      <td>None</td>\n",
+       "      <td>45701</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>2820</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>From R-2 Case # 32282</td>\n",
        "      <td>NO</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>824.222595</td>\n",
-       "      <td>3.643753e+04</td>\n",
-       "      <td>POLYGON ((2263838.250 296700.031, 2263709.749 ...</td>\n",
+       "      <td>753.562992</td>\n",
+       "      <td>3.208129e+04</td>\n",
+       "      <td>POLYGON ((2271434.999 291624.125, 2271441.000 ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>R-1(B)</td>\n",
-       "      <td>Single Family District</td>\n",
-       "      <td>2005-04-06</td>\n",
-       "      <td>0-32-05</td>\n",
+       "      <td>M-2</td>\n",
+       "      <td>General Industrial District</td>\n",
+       "      <td>None</td>\n",
+       "      <td>56119</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>2820</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>From R-2 Case # 32282</td>\n",
        "      <td>NO</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>836.139727</td>\n",
-       "      <td>3.654443e+04</td>\n",
-       "      <td>POLYGON ((2264740.500 296896.656, 2264687.999 ...</td>\n",
+       "      <td>429.868788</td>\n",
+       "      <td>1.148440e+04</td>\n",
+       "      <td>POLYGON ((2269504.750 290045.344, 2269498.500 ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>R-1(B)</td>\n",
-       "      <td>Single Family District</td>\n",
-       "      <td>2005-04-06</td>\n",
-       "      <td>0-32-05</td>\n",
+       "      <td>CP-0</td>\n",
+       "      <td>Planned Nonretail Business District</td>\n",
+       "      <td>None</td>\n",
+       "      <td>65831</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>2820</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>From R-2 Case # 32282</td>\n",
        "      <td>NO</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>498.761145</td>\n",
-       "      <td>1.556200e+04</td>\n",
-       "      <td>POLYGON ((2263722.999 297037.281, 2263693.499 ...</td>\n",
+       "      <td>568.157502</td>\n",
+       "      <td>1.925364e+04</td>\n",
+       "      <td>POLYGON ((2268886.000 290452.812, 2268862.000 ...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  ZONEDIST                   ZONENAME    APPRDATE  ORD_NO1 ORD_NO2 ORD_NO3  \\\n",
-       "0      M-3  Heavy Industrial District        None    32282   44025   40495   \n",
-       "1   R-1(B)     Single Family District  2005-04-06  0-32-05    None    None   \n",
-       "2   R-1(B)     Single Family District  2005-04-06  0-32-05    None    None   \n",
-       "3   R-1(B)     Single Family District  2005-04-06  0-32-05    None    None   \n",
-       "4   R-1(B)     Single Family District  2005-04-06  0-32-05    None    None   \n",
+       "  ZONEDIST                             ZONENAME APPRDATE ORD_NO1 ORD_NO2  \\\n",
+       "0      M-3            Heavy Industrial District     None   32282    None   \n",
+       "1      M-3            Heavy Industrial District     None   45043    None   \n",
+       "2      M-3            Heavy Industrial District     None   45701    None   \n",
+       "3      M-2          General Industrial District     None   56119    None   \n",
+       "4     CP-0  Planned Nonretail Business District     None   65831    None   \n",
        "\n",
-       "  PET_NO1 PET_NO2 PET_NO3                         NOTES SPLIT_ZONE ICOMAPATTR  \\\n",
-       "0     545    None    None  44025 is in Northwest corner         NO       None   \n",
-       "1    2820    None    None         From R-2 Case # 32282         NO       None   \n",
-       "2    2820    None    None         From R-2 Case # 32282         NO       None   \n",
-       "3    2820    None    None         From R-2 Case # 32282         NO       None   \n",
-       "4    2820    None    None         From R-2 Case # 32282         NO       None   \n",
+       "  ORD_NO3 PET_NO1 PET_NO2 PET_NO3 NOTES SPLIT_ZONE ICOMAPATTR DATE_MOD  \\\n",
+       "0    None    None    None    None  None         NO       None     None   \n",
+       "1    None    None    None    None  None         NO       None     None   \n",
+       "2    None    None    None    None  None         NO       None     None   \n",
+       "3    None    None    None    None  None         NO       None     None   \n",
+       "4    None    None    None    None  None         NO       None     None   \n",
        "\n",
-       "  DATE_MOD DATE_ADDED MOD_BY ADDED_BY     Shape_Leng    Shape_Area  \\\n",
-       "0     None       None   None     None  227094.483091  6.904394e+07   \n",
-       "1     None       None   None     None     512.869032  1.643630e+04   \n",
-       "2     None       None   None     None     824.222595  3.643753e+04   \n",
-       "3     None       None   None     None     836.139727  3.654443e+04   \n",
-       "4     None       None   None     None     498.761145  1.556200e+04   \n",
+       "  DATE_ADDED MOD_BY ADDED_BY    Shape_Leng    Shape_Area  \\\n",
+       "0       None   None     None  13380.173937  2.017360e+06   \n",
+       "1       None   None     None   4294.963937  4.933658e+05   \n",
+       "2       None   None     None    753.562992  3.208129e+04   \n",
+       "3       None   None     None    429.868788  1.148440e+04   \n",
+       "4       None   None     None    568.157502  1.925364e+04   \n",
        "\n",
        "                                            geometry  \n",
-       "0  MULTIPOLYGON (((2275610.250 318174.562, 227830...  \n",
-       "1  POLYGON ((2263758.500 296379.969, 2263724.500 ...  \n",
-       "2  POLYGON ((2263838.250 296700.031, 2263709.749 ...  \n",
-       "3  POLYGON ((2264740.500 296896.656, 2264687.999 ...  \n",
-       "4  POLYGON ((2263722.999 297037.281, 2263693.499 ...  "
+       "0  MULTIPOLYGON (((2263627.750 292663.281, 226362...  \n",
+       "1  POLYGON ((2254872.499 297255.187, 2254877.499 ...  \n",
+       "2  POLYGON ((2271434.999 291624.125, 2271441.000 ...  \n",
+       "3  POLYGON ((2269504.750 290045.344, 2269498.500 ...  \n",
+       "4  POLYGON ((2268886.000 290452.812, 2268862.000 ...  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,14 +549,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "kc_zones.plot(ax=ax, ec='k', fc='None')\n",
+    "kc_zones[kc_zones['ZONENAME'].isin(['Single Family District','Two Family District'])].plot(ax=ax, column='ZONENAME',categorical=True, legend=True, \n",
+    "              legend_kwds=dict(ncols=4, loc=(0.15,-0.)), cmap='jet_r')\n",
+    "armourdale.dissolve(\"CITY\").plot(ax=ax, fc='lightgray', ec='k', zorder=-1)\n",
+    "# ax.minorticks_on()\n",
+    "# ax.grid(color='k')\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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qnqr8R9CzQE5MPY7nL+dRKBRUKFeByZMn4+TkFO/njo3g4GDmzp3L77//TsmSJVm+fDkFChSwdFlCCCGEEEIIC0lRwbNSpUrY2dkxY8aMr36sXq9n+vTpbNy8EYPeQLGexakwrhK2aWzjodL3+T/05/jko1xadhGlSkl1j+qMHz8eOzu7eD93bFy4cIHx48fz6NEjRowYwciRI7GysrJ0WUIIIYQQQogElqKCp6urK82aNaNXr14xbiMgIIDvv/+e4yePo9KqKD+6AiW/K43G5vNDd2PL744fxyYc4eqaK6i1aurXqc+oUaOwto67/UfjWkREBEuWLGHZsmXkzJmT5cuXU7ZsWUuXJYQQQgghhEhAKSZ4Rq9oO2PGDOrUqRPr9h48eMCQIUPwunkTO1dbqk73wL1dIRTK+F/N1fe6L0fHHcZr4w001hqaNmrK0KFD0Wq18X7umLp16xbjx4/n6tWr9OnThx9++AF7e3tLlyWEEEIIIYRIACkmeJ4+fZoyZcqwYcMG8uXLF2ftnjp1itFjRvPc5zlpCqal+pyaZKuWMNugPL/ow+HRh7iz8zZWNla0adWG7777DqVSmSDn/1oGg4G1a9cyf/58UqdOzeLFi+PkIoAQQgghhBAicUucCSUeeHl5AZAlS5Y4bbdMmTLs/2s/Y8aMIeR+MOuqr2Z97bW8uPoiTs/zMa5F0tFiRys6nupC+nIZWLFiBWXKlWHhwoUYjcZ4P//XUqlUtG/fnk2bNpExY0bq1q1Lu3bt8PX1tXRpQgghhBBCiHiUYno8R44cycqVK9m3b1+8ncNoNDJ79mzW/r6WyPBIinQtSqWJlbFLnzBDSh8cfsDhEQd4fPIxtva29Ozek06dOiXIub+WyWRi27ZtzJw5E4VCwYwZM+jSpUui7a0VQgghhBBCxFyKCZ5NmjTBx8eHJUuWxPu5QkJCGDVqFAcOHUChVlJ2eDnKDCmL1i7+52CaTCbu7bvLwREHeH7BBwcnBwb0G0CLFi3i/dwx4efnx6xZs9i2bRtly5Zl8eLFFCxY0NJlCSGEEEIIIeJQigme+fPnp0iRIowcOTLBzvnkyROGDBnCtevXsEllQ+UpVSncuQhKdfz36plMJm5vu8WhkQd4ef0lzqmcGTp4KA0aNIj3c8fE2bNnmTx5Mg8ePGDgwIGMHz8eW9v436pGCCGEEEIIEf9SRPDU6/XodDqGDh1K69atE/z8Fy5c4PsR3/P0yVNc8qSi+uwa5KiTE4Ui/lfANRlN3PjjOodHHeS192vSuqZlxPcjqF69eryf+2tFRkayYsUKlixZQtq0afn5558TbVAWQgghhBBCfLkUETzv3LlDrly5WLJkiUX3kNy+fTvTpk8jwD+ALFWyUG1WDdIVS58g5zbqjVxdc4UjYw4R8CiAWrVqMXPmzAQ599d69OgRP/zwA8eOHaNhw4bMnz+fTJkyWbosIYQQQgghRAyliJVcole0zZYtYbY5+ZQGDRpw9MhRevTowbNTz1hefCnb2m/B/6F/vJ9bqVZSqGNhOp3uAgqIiIiI93PGVKZMmVi4cCEzZ87k5MmT5MuXj9mzZ6PX6y1dmhBCCCGEECIGUkTwvHnzJjqdDldXV0uXglKppG/fvhw/epy6dety4/fr/JJzAQdH/E2Yf1i8n//679cBGDhwYLyfKzYUCgW1atVi69atNGzYkKFDh1K8eHFOnTpl6dKEEEIIIYQQXylFBE8vLy+yZs2aIHMqv5S1tTXTp09n3559FClYhFMzTrIw23zOLTiLIdIQL+c0mUxcWOKJa1pXi/f+fik7OztGjBjB2rVrMRgMlCtXjh49evD69WtLlyaEEEIIIYT4QikmeGbJksXSZXxU2rRp+e2331i3dh2ujq7s67eHxXkXcnOzF3E9/fbZ2ae8uvGS5s2bx2m7CaFAgQKsWbOG77//nrVr15I3b16WL1+OwRA/IV0IIYQQQggRd1JM8EzsPXwFChRgx/YdzJ49G8UbBX82+YP/lVvJk1OP4+wcl5ZfRK1V07lz5zhrMyGpVCratGnD1q1bKV68OF27dqVw4cLs2rUrzkO6EEIIIYQQIu4k++Dp5+fHy5cvE33wjFajRg2OHD7CgAEDeHX5Jb+VXcHmFn/y+m7shpZGhkRydfUVChYoiFarjaNqLSNt2rTMmDGDdevWodPpqFevHh4eHpw7d87SpQkhhBBCCCE+ItkHz5s3bwKWX9H2a3Xr1o3jR4/TuHFjbm+9xaI8C9k/aB+hfqExas9r43UigyPp27dvHFdqOe7u7ixbtoyff/6ZR48eUbJkSdq0acO9e/csXZoQQgghhBDiHSkmeGbOnNnClXw9rVbLxIkTObD/AGVKlubsT2f4OetPnJp5En3Y120tcmHJBRydHClVqlQ8VWsZCoWCSpUqsXHjRiZMmMD+/fvJkycPAwcO5NWrV5YuTwghhBBCCEEKCJ5eXl64ublhY2Nj6VJizNnZmSVLlrBxw0YyuWbiwLD9LMr1M9fWXcVk/PzcRr/br3h8/BF1atdJgGotQ61W06RJE3bs2EHPnj1ZunQpOXLkYMaMGYSGxqyXWAghhBBCCBE3UkTwTKwr2n6t3Llzs2XzFn5e8DPacC1b22xmRcllPDzy4D8fd2n5RZRqJX369EmgSi1Hp9PRvXt3duzYQd26dRk1ahS5c+fmt99+kxVwhRBCCCGEsJAUETyzZs1q6TLiVKVKlTh44CDff/89Abf8WV35f/zxze+8uvnyg2ONeiOXll0ke9bsODk5JXyxFpIqVSpGjhzJli1byJcvH506daJo0aLs3bvX0qUJIYQQQgiR4iTr4BkZGYm3t3eSW1joS7Vt25bjR4/TunVr7u29y5L8i9jTZzfBL4LNx3jvuUOIbwhdu3a1YKWWkyVLFmbPns3q1avRaDTUrl2bGjVqcOHCBUuXJoQQQgghRIqRrIPnvXv30Ov1ya7H811qtZqRI0dy+OBhKlaoyMUlnizMNp/jU44SGRLJxaUXsLa1pn79+pYu1aIKFy7MypUrmTdvHt7e3hQrVoxGjRrx999/yx6gQgghhBBCxDOFKRl/6t62bRsNGzZk//79uLq6WrqcBPHgwQOGDh3KDa8bqG3U6EP01KhRg9mzZ1u6tERDr9ezdetW1qxZw+3bt8mbNy99+vShQ4cOODg4WLo8IYQQQggRDy5cuEDWrFlxdna2dCkpUrLu8YzeSmXs2LEsXbo0RSwukyVLFjZs2MCypctI7ZgaG1sd33//vaXLSlTUajVNmzblzz//ZPny5WTKlInvvvsONzc3evfuzfXr1y1dohBCCCGEiCMnT56kevXqFCtWLMVOP0sMknXwDA4ORq1Wo1QqmT9/Pl26dOHp06eWLitBlCpVir/2/cWZU6dJmzatpctJlBQKBSVLlmT27Nns3buXtm3b8scff1CgQAGqVq3Kpk2b0Ou/br9UIYQQQgiROJw5c4batWtTrlw5Hj16RPPmzdm8eTPXrl2zdGkpUrIeams0GgkJCcHOzo5jx47Rtm1b/Pz8GD16NPXq1bN0eSIRioyM5K+//mL9+vVcuHABNzc3evXqxbfffisBXgghhBAiCfD09GTs2LHs3LmTHDly0LNnT2rWrInBYKBevXp4eHiwevVqS5eZ4iTr4Plv/v7+9O7dm7Vr11KvXj1GjRqFvb29pcsSiZSXlxfr169n586dGI1GmjdvTp8+fShTpgwKhcLS5QkhhBBCiHdcvnyZsWPHsnXrVrJmzUrPnj2pXbs2KpXKfMy6deuYNm0aN2/eJGfOnBasNuVJUcEz2po1a+jduzd2dnZMnTqVYsWKWbokkYj5+/uzZcsWfv/9dx49ekTRokXp0aMHzZs3x8XFxdLlCSGEEEKkaNeuXWPChAn88ccfZMqUiZ49e1K3bl3UavUHx4aFhVG3bl0aNGjAsmXLLFBtypUigyfA/fv3adu2LadOnaJbt2707NkTjUZj6bJEImY0Gjl27Bjr16/n+PHjqFQqatWqRdu2bWnQoAG2traWLlEIIYQQIsXw8vJi4sSJrF+/nvTp09OjRw8aNGjw2c/0v/32G3PnzuXOnTtkyZIlgaoVKTZ4QtS2GtOmTWP8+PHkz5+fadOmkTlzZkuXJZKAly9fsnfvXnbv3s2lS5ewtbWlUaNGtGnThho1ashFDCGEEEKIeHLnzh0mTpzImjVrSJs2Ld9++y2NGzf+4s9fISEh1K5dm1atWrFw4cJ4rlZES9HBM9rp06dp06YNPj4+fP/99zRq1Ejm8Ikv9ujRI3bv3s2uXbvw9vYmVapUNG/enHbt2lG2bFmUymS9eLQQQgghRIJ4+fIlo0ePZunSpbi4uPDtt9/StGlTtFrtV7f166+/snjxYu7evUuGDBnioVrxbxI83woMDGTAgAGsWLGCGjVqMG7cOBwdHS1dlkhCTCYTt27dYufOnezevRsfHx8yZ85MmzZtaNOmDQULFrR0iUIIIYQQSY5er2fx4sWMGTMGvV5Pjx49aNmyJdbW1jFuMzAwkFq1atGtWzdmz54dh9WKT5Hg+S8bN26ke/fuaLVafvjhB0qVKmXpkkQSZDQa8fT0ZNeuXfz111+8efOGPHnyUKdOHWrWrEmlSpVkTqgQQgghxGccOXKEvn37cvXqVRo3bkz//v1JlSqV+edLly5lya9LGD9uPHXr1v2qthcsWMCqVau4f/8+adKkievSxb9I8PyIx48f0759ew4fPkynTp3o16+fzNkTMRYZGcmJEyf4+++/OXnyJD4+Pmi1WsqXL0+tWrWoUaMGRYoUkSG5QgghhBBvPX78mKFDh7J+/XoKFSrEiBEjcHd3f++YPn36cOTIEQAyuGVg7569X3WON2/eUKtWLb777jumTJkSZ7WLj5Pg+QlGo5FZs2YxatQocuXKxdSpU8mePbulyxJJnMlk4t69e5w8eZITJ05w7tw5QkJCSJ06NTVq1KBmzZrUqFEDNzc3S5cqhBBCCJHgwsPDmT17NpMnT8bGxobvvvuOb7755oML9N26deP06dOUG1EerYMVh0cdZOeOnWTKlOmrzjd79mw2btzIgwcPcHZ2jsunIv5FgudneHp60rp1ax4+fMjQoUNp3ry5LDwk4kxkZCQXL17k5MmTnDx5kmvXrmEymciXLx+1atWSYblCCCGESDF27txJ//79efjwIW3atKFnz57Y29t/cNy4cePYtGkTlSZVocLoigQ9D2K+21w8qngwd+7crzrny5cvqV27NqNGjWLMmDFx9EzEx0jw/AIhISEMGjSIxYsXU6VKFSZMmICLi4ulyxLJ0OvXrzl9+rS5RzR6WG7p0qXx8PDAw8OD0qVLY2VlZelShRBCCCHihMlkYvz48UycOJEyZcowYsSIT4403LFjByNHjaRwlyLUWVLP3CH0R6Pfebz/EadOnPrq6UtTp05lz5493L9//6NBV8QNCZ5fYdu2bXTt2hWFQsHEiROpUKGCpUsSydi7w3LPnDnD+fPn8ff3x8bGhnLlyuHh4UHVqlUpUaKEzEEWQgghRJKk1+vp1asXS5cuZcCAAebP2h8TFBREFY8qOOV2otOZrqi0KvPP7uy8zYb665kwYQJNmjT5qhp8fHyoW7cuU6ZMYejQobF6PuLTJHh+JR8fHzp27Mi+ffto164d3333nfQ+iQRhMBi4efMmZ8+e5cyZM3h6ehIUFISdnR0VKlQw94gWKVIElUr1+QaFEEIIISwoNDSUli1bsmvXLsaPH0+jRo3+8/hu3bpx5twZul74lrQFXd/7mclo4ie3uaS3S8fmzZu/upbx48dz9OhR7t+/j42NzVc/XnyeBM8YMBqNLFiwgGHDhpE5c2amTZtG7ty5LV2WSGH0ej3Xr1/nzJkznDlzhgsXLhAWFoaTkxOVK1ematWqVK1aFXd3d1kxVwghhBCJip+fH/Xr1+fixYvMmjWLihUr/ufxXl5etGjZgtKDy+Axo/pHj/l7yF+c++ksp06c+uo9Ph89ekSDBg2YM2cO/fr1+6rHii8jwTMWrly5QuvWrbl9+zYDBw6kTZs28gFfWExERARXrlzhzJkznD17lkuXLhEREUHq1KmpUqUK1apVo27dumTOnNnSpQohhBAiBXv48CG1atXi+fPn/PzzzxQsWPCzj2nZsiV3Ht6h76MBWDt9PFQ+Of2E38os5/vvv6dt27ZfXdfIkSPx9PTE29tbRjTGAwmesRQWFsbw4cP56aefKF++PJMmTZINaEWiEBYWxqVLl8w9oleuXMFgMFCgQAHq169P3bp1KVeuHGq12tKlCiGEECIFMJlM/O9//2PQoEHY2NiwaNEismbN+tnHvXz5kmrVq1F6aBmqTq326fZjOdz23r17NG7cmBkzZjBo0KCvfrz4bxI848iePXvo1KkTERERTJgwgapVq1q6JCHeExAQwMmTJzly5AjHjx/n1atXODo6UqtWLerVq0ft2rVJmzatpcsUQgghRDJ09+5dunfvzt9//039+vUZNmzYF++bOXHiRP744w/63O+HYxan/zx2/+C/OD8/ZsNtASZPnszevXvx9vaWXSzimATPOOTr60uXLl3YsWMHLVq0YMiQITI5WSRKRqOR69evc/ToUY4ePcqVK1dQKBSUKFGCevXqUbduXYoXLy5Dx4UQQggRK3q9nrlz5zJ27FicnZ0ZO3Ys5cuX/6o2qlWvhiqTis5nu332WB/PZywvvpQ+ffrQs2fPr6731atX1K9fn27dujFnzpyvfrz4NAmeccxkMrFkyRIGDhxIunTpmDZtGvnz57d0WUL8p5cvX3L8+HGOHj3KiRMnCAwMJG3atNSpU4d69epRo0YNnJycLF2mEEIIIZKQCxcu0LVrVy5dukTbtm3p27cvOp3uq9rQ6/UUL1mcciPLU2lClc8ebzKZWJR7ITah1uzdszdGdS9ZsoRFixZx48YNcuTIEaM2xIekOyOOKRQKevTogaenJ46OjrRr144VK1ZgNBotXZoQn5Q6dWoaNmzIzJkzOXz4MCtWrKBevXqcOHGCFi1akDp1aipXrsyMGTO4du0acr1KCCGEEJ8SGhrK8OHDKVmyJMHBwaxevZphw4Z9degEOHHiBEa9kUwVv2xxRIVCQeEuhXn27BnPnz//6vMBtG/fHhcXF0aOHBmjx4uPkx7PeBQREcHo0aOZOXMmpUqVYvLkyaRLl87SZQnxVZ4+fcqxY8c4cuQIp0+fJiwsjMyZM1O3bl1q1qxJlSpVvniOhhBCCCGStwMHDvDtt9/y+PFjevbsSadOndBoNDFub/bs2axYsYL+PgOxc7X7oscEPglgfqZ51Ktbj2nTpsXovFu3bmX06NGcOHGCsmXLxqgN8T4JngngwIEDdOjQgaCgIMaOHUvNmjUtXZIQMRIeHs7Zs2c5evQox44d4+HDhygUCooWLUr16tXx8PCgQoUK2NraWrpUIYQQQiSg169fM3jwYFasWEGJEiUYO3Ys2bJli3W7/fr149jpYwwN/h6FQvHFj9tQfz1PDj3m1IlTMVqzwmAw0LJlS1xcXDh+/PhXnVt8nATPBOLn58e3337Lpk2baNSoEd9//718OBdJ3pMnTzhz5gynT5/mzJkz+Pr6otFoKF26NNWqVaNatWqULl0arVZr6VKFEEIIEQ9MJhMbN26kX79+hISEMGjQIJo0aRJnCxS2b98e7xfe9Hvy3Vc97s7O22yov56JEyfSuHHjGJ375MmTdO/enT///JMmTZrEqA3xDwmeCchkMrFy5Ur69etHqlSpmDp1KoUKFbJ0WULECZPJxL179zh9+jSnT5/m7NmzBAQEoNPpKF++vLlHtGjRoqhUKkuXK4QQQohYevz4Mb1792b79u1Ur16dESNGxPnWbM2bN+eF0ZeeN3t/1eOMBiMLs87HUeHIrl27Ynz+3r178/TpU65fvy4X0mNJgqcF3LlzhzZt2uDp6UmvXr3o1q2bfBAXyY7BYMDLy8vcI+rp6UloaChOTk6ULVuWokWLUrhwYYoUKUKOHDnk/4AQQgiRROj1eubPn8/YsWPR6XSMGDGC6tWrx8u5mjVrhq/p5VcHT4AT045zeNRBdu7YSaZMmWJ0/tu3b9OsWTPmzJlD//79Y9SGiCLB00IiIyOZOHEiP/zwA0WKFOGHH37Azc3N0mUJEW8iIyO5fPkyp0+f5sqVK9y6dYsXL14AoNPpcHd3N4fRwoULU6hQIezsvmwRASGEEEIkjJMnT9KzZ0+uXLlCy5Yt6devHw4ODvF2vg4dOnDr6S0G+Az66scGvwhmfsa5lC5RmiVLlsS4hvHjx3Pw4EG8vb1le7lYkOBpYceOHaNt27b4+fkxatQo6tevb+mShEgwfn5+3Lx503y7desWd+/eRa/Xo1AoyJ49O0WKFKFAgQJkyJDBfEufPj1p06ZFrVZb+ikIIYQQKcKrV68YPnw4y5Ytw93dndGjR1OgQIF4P+/IkSPZvnMHw0K+R2319e/7u3vu5PLySxw7cizGF7R9fX2pV68effv2ZcaMGTFqQ0jwTBT8/f3p06cPa9asoW7duowePRp7e3tLlyWERURERODt7f1eGL1//z4vX758bz9cpVJJmjRp3guk0bdMmTKROXNmMmfOLP+XhBBCiFgwGo389ttvDBs2jPDwcPr370/z5s0TbIrMhg0bmDRpEl3OdyNdsfRf/fiXXi9Zku8XmjdvztixY2Ncxy+//MKyZcvw8vIia9asMW4nJZPgmYisWbOG3r17Y2dnx9SpUylWrJilSxIi0TAYDPj5+eHr6/ve7cWLF7x8+dL8538HVCcnJzJlykSWLFnMYTRz5sxkypSJjBkzYm1t/cU1mEwmjEYjBoMhxjeVSoVKpUKtVptv//7zu/dptVrs7OywsbGRpdyFEEIkqCtXrtCzZ09OnDhB/fr1GTx4MKlTp07QGl6/fk2lSpWo+2t9inQrGqM21tZYzYtTzzl5/GSMV9sNCQmhfv361KhRgzVr1sSojZROgmcic//+fdq2bcupU6fo1q0bPXv2jNWmu0KkNHq9Hl9fX549e4aPjw/Pnj0zfx/954CAAEuX+dVUKhX29vbmm4ODg/lr9PcZM2bE3d0dd3d3MmbMKEFVCCFEjAQFBTF+/Hjmzp1LlixZGDVqFKVKlbJYPSXLlCRnk1x8879GMXr8nV232VBvPcOGDaN9+/YxruPPP/9k/PjxnD17lhIlSsS4nZRKgmcipNfrmTZtGuPHjyd//vxMmzaNzJkzW7osIZKN4OBgfHx8eP78OXq9/qseq1AoUKlUKJXKT379r5/9u8dUr9eb7/v39waDgcjISIKDgwkJCSEoKIjg4OBP3p49e0ZwcDAADg4O5M+fn4IFC5rDqLu7e5wvcy+EECL5MJlMbN68mf79+/Py5Ut69OhBp06dLN4J0r59e27cu8HAV0NQqr6+x9JkMrG82FKCvAM5cexEjHs9DQYDzZs3J3369Bw6dEgu8H4lCZ6J2OnTp2nTpg0+Pj58//33NGrUSP6BCyE+yWg04uPjw507d7h9+zZ37tzB29sbb29vIiIiAEidOjXu7u7vBdKCBQvKXFghhEjh7t69S9++fdm9ezeVKlVixIgRZMyY0dJlAbBp0ybGjRtH+2OdyFQ+ZtuieO+5w+911tGvXz+6d+8e41qOHj1K79692bp1K998802M20mJJHgmcoGBgQwYMIAVK1ZQo0YNxo0bh6Ojo6XLEkIkIXq9nkePHnHnzh1zKPX29ubBgwcYDAYAsmXLRpEiRShUqJB5O5ts2bLF+KqwEEKIpCE8PJyZM2cyefJknJ2dGT58OB4eHomqs0Ov11OiVAlKDSpN1WnVYtSGyWTitzIreH3NjxPHTsR4ZXyTyUT37t3x8/Pj6tWrFu8NTkokeCYRGzdupHv37mi1Wn744QeLjrMXQiQPERER3L17l1u3bplXEL516xZ+fn4A2NnZ4e7u/t7eqoUKFZLeUSGESCYOHDhAr169uHv3Lu3bt6dnz57odDpLl/VRdevWJUQXQg+v3jFu48HhB6yp8j/atGnDiBEjYtyOl5cXLVq04Oeff6ZXr14xbielkeCZhDx+/Jj27dtz+PBhOnXqRL9+/eQqixAiTplMJl69evVBGI3eX1WlUlG2bFnq1atH3bp1KViwYKK6Ki6EEOLzfHx8GDx4MGvXrqV48eKMGjWKXLlyWbqs/zR79mxWrFhB77t9ccrmHON2NtRfz4P99zl25FisQvaoUaM4deoUd+7ckQuyX0iCZxJjNBqZNWsWo0aNImfOnEybNo3s2bNbuiwhkqSQkBD27t2LXq9HqVSiUCjMISr6z6lTp6ZYsWJfte1KchQZGcndu3e5fPkyx44d49SpU4SEhODm5kbdunWpX78+Hh4eMd6cWwghRPwzGAz88ssvjBo1CpVKxaBBg2jYsGGSuID44sULqteoTvW5NSnZL+Yj/3yvveDXgoupVbMWM2fOjHE7Pj4+1K9fn6FDhzJp0qQYt5OSSPBMojw9PWndujUPHz5k6NChNG/ePEm8aAiRmIwcOZLt27d/9jhrKzU9evahW7duCVBV0hAREcG5c+c4evQoR48e5cGDB2i1WipXrky9evWoX78+OXLksHSZQggh3jp79iw9e/bE09OTpk2b8t133+Hk5GTpsr5KxUoVcSrmTJv97WLVzvaOW7m+7hp///U3qVKlinE7c+fOZe3atdy5c4cMGTLEqqaUQIJnEhYSEsKgQYNYvHgxVapUYcKECbi4uFi6LCGSjOHDh7Nr1y6WtIWy2cEERL8iRn9//xXM2g/H7kCa1KmYNn2GzLH+iAcPHphD6Llz54iIiKBUqVJ06tSJli1bymuTEEJYyJs3bxgxYgSLFy8md+7cjBkzhsKFC1u6rBgZOHAgBw4dYKDfEKzsrWLczpv7b1iU62dKlyzNkiVLYtxOYGAg9erVo3HjxixbtizG7aQUEjyTge3bt9OlSxcUCgUTJ06kQoUKli5JiCTh0KFDDB38HWERBgpnhO88oFVJsP7X1GmTCTach4F/wPMAcE2XHoVCEbXXpl6P0WigUeMmDBo06L3HGY1G/P39cXR0TFGrw4aEhHDkyBG2b9/O8ePHUalUfPPNN3Ts2JFatWrJ3HQhhEgAJpOJNWvWMHjwYEJCQujTpw+tWrWK8WquicG5c+fo3LkzTTY2I2/TfLFqa9+AvXj+fI5tW7eRJUuWGLezZs0aZsyYwaVLl3B3d49VTcmdBM9kwsfHh44dO7Jv3z7atWvHd999h5VVzK8ECZFS6PV6li5dyvp1a3jl9wYnG+hZCXpVhsz/6qQLDocZ+2D3VbDSgI0mKqSeuw/PA8HGSoPBaMBgNGE0mDC8fXUtVKgQ48aNIywsjLCwMEJDQ0mTJg358+f/4joPHDhAcHAw9erVey/EGo1GwsLC8Pf3JzAwkICAAAICAggKCiIoKIiQkBAA2rVrZ5F5qi9fvmTnzp1s376dmzdvkjZtWtq2bUvHjh2T7BV3IYRI7G7cuEGvXr04fPgwtWrVYujQobi6ulq6rDhRonQJ8rTIS/0VsdtDM/hFMD9n/Ym8OfKybt26GLcTGRlJ48aNyZcvH7t3745VTcmdBM9kxGg0smDBAoYNG0bmzJmZNm0auXPntnRZQiQZp06dYtasWdy+5YXRBI0KQ/+qUDk3/NcU6j3XYKMn2GrB1ur9r78cgQuPPnyMAjh46NBn55YEBQXRvn077tzxBsBKo0QBGEwmjEYTBuOXPbcBAwZYfI6ql5cXW7duZdeuXfj5+VGoUCE6depEmzZtks0HIiGEsKSQkBAmTZrErFmzSJ8+PaNGjaJcuXKWLitOtWnThtuPb/Od72CUqtiNJjoy7hDHJx9jzeo1FCxYMMbt7Nu3j8GDB7N//36qVYvZPqMpgQTPZOjKlSu0bt2a27dvM2jQINq0aSMLDwnxFV6+fMmMGTM4dGA/oeGR5EsHAzygXemoQPk17r8Ez0egVYFGBVo1nH8AQzfB77///p+9npcuXaJnj26EhYYxvxVkcITDt6N6Wm00YKMFnfb9P9to3rlPCwYjuE+M6vEcPnx4LH8zcSMyMpLjx4+zbds2Dh8+jMFgoH79+vTq1YsaNWqkqGHJQggRF0wmE7///jtDhgzB19eXbt260aVLl2Q5+u33339n8uTJdDzZGbcyGWPVVnhAOAuy/ER653Rs3/b5xQY/xWQy0aFDBwDOnz8v72OfIMEzmQoLC2P48OH89NNPVKhQgUmTJpE6dWpLlyVEkmI0Gvntt99Y9b/fePnyFXbW8G0F6FMZsqeJebsHb4LHHPj1118pU6bMR49ZsmQJvyycj6s9bO4JJbPG7FwRerDqC02aNGHChAkxLzqe+Pv7s2vXLjZu3MitW7fImjUr3bt3p0uXLtILKoQQX+DChQv079+fY8eO4eHhwZAhQ8iUKZOly4o3ERERlCxTkjJDy1Jlikes2zsz5xT7B//FiuUrKFGiRIzbuXjxIu3bt2fVqlW0axe7VXeTK4njyZS1tTXz5s1j9+7d3L59myZNmnDw4EFLlyVEkqJUKuncuTMHDh5ixcqVZMnpzk8HIecYqLsA9l4D4xcOdX2XThv1NTAw8IOf6fV6unTpwvz58/HIA5dGxzx0QlQvq1IRdTEqMXJ0dKR169Zs3LiRVatWUahQISZMmEDGjBlp3rw5f//9N8aY/JKFECKZ8/X1pXv37hQvXpxnz56xePFi5s2bl6RD5+LFi+nYsSMvX7785DFarZYMrhm4teVWnJyzaI/iaO20/PDDD7Fqp0iRIlSvXp2RI0cm2vdcS5PgmczVrl2bK1euUL58efr378+kSZMIDQ21dFlCJDnFixdn3bp1HDh4hAbffMPhO1pqz4fc42D+QQj4iv9WNm8Xdf138Hzw4AHVPKpw7uxZJjaA3X0hlV3s6lYooob3Jvb/9wqFgiJFijBlyhT+/vtvhgwZwoULF6hevTq5c+fmxx9/xNfX19JlCiGExUVGRjJv3jxy587N+vXrGTZsGH/88UeSn8sZEhLCol8X4enpSY1aNVi6dOknj61SpQovr/vi/+BNrM+r0WkoN6I8t+/c5ty5c7Fqa8CAATx79oyffvop1nUlRxI8U4A0adKwbds2Fi1axPbt22nVqhU3btywdFlCJEnOzs5MmTKFk6fPMmLECEJUrgz4HdINg95rYdeVqNVv/0t0j2dwcLD5vq1bt9Kk8TcYw/zZ0x/G1IO4miJirU68PZ4f4+joSNu2bdm8eTMrV64kb968jB49mowZM9K6dWuOHTtm6RKFEMIi/vrrLwoXLszAgQOpXr06O3bsoF27dslim6pZs2ahD9fTdHNz3CpkZN68eXTq1Am9Xv/BsR06dAAF3Nl5J07OXaJ/KXSpdYwcNTJW7WTNmpVmzZrxww8/8OrVqzipLTmR4JlCKBQKevTogaenJw4ODrRt25YVK1bIEDYhYkipVNKmTRv2/bWfdevXk9e9KMtPKqn3MzgNhMqzYOoeOPfgw+G4/+7xvHz5MmPGjKZQBiOXxkDNL99l5YtYayA8/DNpOBFSKBQUL16cadOm8ffff9O/f39OnTpFxYoV8fDwkAAqhEgxvL29adiwITVr1sTGxobff/+dcePG4eLi8vkHJwFGo5FtO7aRvWZ28jTKS5u/2lF5chXOnz9P3Xp1zVuDRUufPj2Ojo7c3nozTs6vtdVSeUpVnj19xq5du2LVVq9evTAYDEyaNClOaktOJHimMHnz5uX06dN89913zJkzh+7du+Pj42PpsoRI0goUKMD//vc/zpy7wMKFC6niUZ3rr10Ysw1KTgWXwdDyV1h2HB75RQ19hX/C4L179zCZ4Nd2H+4dGhdstEkzeL7LycmJjh07sm3bNubNm8fTp0+pWLEiNWvW5NSpU5YuTwgh4kVQUBAjRowgf/78nDlzhhkzZrBy5Ury5ctn6dLi1JIlSwgLCaPsiPIAKJQKyo+qSOM/muLz4jn1G9T/IHwWK1qM+wfuExEUESc1FO5cBOfszkz/cXqs2nFxcaFLly4sXLgQb2/vOKktuZDgmQJptVpmzJjB/v37efz4Mc2aNeOvv/6ydFlCJHlKpZKKFSsyZ84cDh48zImTpxkxYgTZ8xZll5c1366CzCOhxNSo46MXFbe2tgaiVqCNDzaaqFUAkwOFQoGHhwcbNmxg1qxZ3L17l7Jly1K3bt1Yz80RQojEwmQysXr1avLkycOcOXPo3LkzW7dupU6dOslyi7z/rf4f6YqnJ3PlLO/dn69Zfpptbs5Lv1c0atzovfeyNm3aYNQbuffX3TipQalWUmlKFfxe+rFmzZpYtdWuXTtcXFwYOTJ2Q3eTGwmeKZiHhweXL1+mWrVqDBo0iLFjx35wNUkIEXM6nY42bdrwv//9j5OnzrJz1y46dOiAyjEbOhsrihQpYj4O4PaLmK2S+zk22uQTPKMplUpq1qzJn3/+yfTp07lx4wYlS5akYcOGXLx40dLlCSFEjJ07d45y5crRvn17ChQowNatW+nbt6/5vSK52bRpE4H+gZQbWf6joTpX/dw0Xt+EZ8+e0b59e/P9ZcqUwcrGipubvHh18yX3D96PdS35WxTAtWg6flrw00fnln4pGxsb+vTpw4YNGzh9+nSs60ouZB9PgclkYuXKlfTr149UqVIxbdo0ChYsaOmyhEgxnjx5Qr26tTEYIZUt1C4QNc+zRj5I7xj79ivPgttB6dm3b1/sG0uk9Ho9u3fvZtGiRTx8+NC8b6m7u7ulSxNCiC/y/PlzRowYwcqVK8mZMyfff/89pUqVsnRZ8c6jugd6Oz09bvZGqfp0n9jZn87w14C9tGvXjuHDhwPQuXPn90a7DHozFGtH61jV8+DQfdZUXUXHjh0ZMmRIjNsxGAy0aNGCNGnScPTo0WTZU/21pMdToFAo6Ny5MxcvXsTV1ZX27duzePFiDAaDpUsTIkVwc3Pj2PGTDB06FLcchdhyxYqOKyHDcMg3HgZvjNozNCSGnZY6bdTy+8mZWq2mQYMGbNmyhYkTJ3L69GkKFSpE586defLkiaXLE0KITzIYDMyePZvcuXOzadMmRowYwYYNG1JE6Dx69Ci+z30p+335/wydACX6lSRfy/ysXb+Wy5cvA9CiRQsA1JqoxRPiYthtlipZyVI1K+s3rI/VaCGVSsWgQYM4fvw427Zti3VdyYH0eIr3REZGMnHiRH744QeKFCnC1KlTyZAhg6XLEiLFuXv3Lr///jvHjh3D5+ljIvRG1EqomAtqv+0NLZzxy7ZcaboYDt535MiRlLMKbGRkJBs3buSXX34hLCyMIUOGMGzYMOzsYrkxqhBCxKGQkBBat27Njh07aN68OX379sXJycnSZSWY+vXr4xvqS9+HA1BbqT97fNibMBbnXYiN0YaDBw4CsHDhQjp06ECValXI0yIv9Vd8E+u6np55wsrSy2nevDljx46NVVvdu3fn9evXXLt2DWVc7ZOWREnwFB917Ngx2rZti5+fH2PGjKFu3bqWLkmIFMtoNHL06FE2b97MxQuevHn9GoMJnHVvh+Xmiwqibs4ff3y75bDjhi3HTqS81V8DAwNZunQpq1evxsXFhUmTJtG5c2dUKpWlSxNCpHAvXrygfv36XLt2jR9//JFKlSpZuqQEde3aNVq1boXH9GqUGVruix93fcM1trTcxKBBg+jcubP5/tatW+P9zJvvXgxGoYz9sNYNDdbzYP99Thw9YV4EMCYuXrxI+/bt2bx5M40aNYp1XUmZBE/xSf7+/vTu3Zu1a9dSr149Ro0ahb29vaXLEiLFCwoKYvPmzezbt487t7wICgkDILcr1H07P7RSLrC1ijr+21Xw+0VrTpw6a8GqLevJkyfMmzeP3bt3U6BAAWbNmkWtWrUsXZYQIoW6efMmderUISgoiPnz51OgQAFLl5TgWrVqxe0Ht+n35DusHKy++HEmk4mVJZcReDuQk8dPmu9fvXo106dPp/PZrqQvEfvRej4XnrG82FIaN27MxIkTY9VW586dUSqVnDp1KkXP9UzZ/b3iPzk6OrJmzRpWr17N0aNHady4MX///belyxIixbOzs6N9+/asWrWKk6fPsm3bNtq1a0e4dRZ+Oaqk7gJwGghVZsG0PfAiEIzGlD1n283NjRkzZrB27VqsrKyoXbs2tWrV4urVq5YuTQiRwhw/fpxy5cqhVCpZvXp1igydV65c4YbXDYr3LfFVoROi1iapMK4SQQFB/P777+b7mzRpglKt5M7O23FSY7qi6cn1TW527NpBWFhYrNrq0qULZ86c4ciRI3FSW1IlPZ7iizx8+JDevXuzc+dOPDw8GDFiBOnSpbN0WUKIfzEajRw/fpxNmzZx8YInr1/7YTCCjZWGM+c8LV1eomAymfj777+ZM2cOjx8/plu3bkyfPj1FzasSQljGzp07adasGe7u7sydOxdHxzhYujwJCQkJYejQoRw9fhRrZxu+vdIDu3RfP/feZDSxMMcC7PV27N6923x/9RrVUWRQ0uV8tzip9/lFH5YV/TXWcz1NJhPNmzcna9as7NmzJ05qS4qkx1N8kcyZM7N9+3b++OMPrl+/TqNGjVizZo2sfCtEIqNUKqlYsSJz5szh4KHDnDh5muHDhzN67HhLl5ZoKBQKqlevzpYtWxgyZAhr1qwhX758bN261dKlCSGSsXXr1tGoUSPKlSvHokWLUlzoXLp0KRUrV+TI0SMU712Cnrd6xyh0AiiUCgp1LsyTZ08ICAgw31++XHl8Ljwj6HlQnNTsWiQduRrkZuv2rbFa4TZ6B4m9e/dy6dKlOKktKZLgKb6YQqGgWbNmeHl50aFDB6ZPn07btm25ceOGpUsTQnyCTqejXbt2fPNN7Ff5S240Gg3t27dny5Yt5M6dm0aNGtGqVSt8fX0tXZoQKcb//vc/pk2bZuky4t3ChQtp27Yt9erVY9asWVhZfd3w0qTs/PnzeFT3YN68eaQt4Uq3i92p+VNtbJxtYtVu3mb5MBlMrF692nxfx44dwQTeu+7Etmyz8mMqEhEWwYIFC2LVTq1atXBzc2P69OlxVFnSI8FTfDVHR0cWLlzI8ePHUSgUtG7dmpkzZxISEmLp0oQQ4qulS5eOBQsWMHXqVPbt20e+fPlYu3YtMhNFiPj1119/0blz5/eCQ3ITEBDAwIED6dOnD23btmXixImo1Z/fNiQ5ePPmDV26dKFzl86EEELDtY1pf6QjaQu5xkn7qfOlxj6jAwcOHDDflz17dmztbbmzI27meQJkKJmBjOUz8fsfv2M0GmPcjlqtpmPHjvz+++/cvRv7/UaTIgmeIsbKli2Lp6cnU6ZMYcOGDTRu3DjFT5oWQiRNCoWC+vXrs3nzZkqUKEHbtm1p0KABT548sXRpQiRLt2/fpmXLligUimR54Vqv17N48WJy5szJokWLzHsJp4R9HI1GI3PmzMGjugfnLpynzLCy9LrTlwKt3eN0RVeFQkGOOjm49/Dee/e753fn7h5vDBFxNx2s/OgKhASFsGLFili106hRI5ycnJg5c2YcVZa0JP9//SJeaTQahg8fztWrVylQoAB9+vRh8ODBMlRNCJEkpUqVipkzZzJ37lzOnj1L/vz5+fXXX6X3U4g45O/vT4MGDXB0dKRdu3bJLnju3buXwoUL07NnT8qWLcv27dvp2LFjithG4+jRo1SuUpnly5eTqXImul/rSdWp1dDaaePlfNlr5SAiNIJz586Z72vUqBGRIZE8Ov4oTs/jWjQdy1csj1U7NjY2tG3blhUrVvD8+fM4qi7pkOAp4kT27NnZu3cva9eu5eLFi3zzzTf8/nvshiQIIYSlVKtWjU2bNlGtWjW6d+9O7dq1pfdTiDhgMBho3bo1T58+5aeffiJNmjSEhoZauqw4ce3aNWrXrk3t2rXR6XSsX7+eKVOmpIhdAF68eEHr1q3p3ac3Rnsjzba2oOWeNqTKnSpez5utZnaUaiWrVq0y31e7dm1UWlWczvNUKBSUH1WBAP8Atm3bFqu2WrZsiUql4qeffoqj6pIOCZ4izkTP97xx4watWrVi8uTJdOjQgVu3blm6NCGE+GqOjo5MnDiRhQsXcuHCBdzd3Vm9erX0fgoRCyNGjGDv3r38+OOPZMuWDWtr6yTf4/nixQt69uxJoUKFuHHjBnPnzmX58uUpZn/Oc+fOUbN2TW7cukGlCZXp4dWb3N/kSZAeXit7K7JVz8bZ82fN96nVajKmz8jtbXH7+TN3ozw4ZnFk3k/zYtWOo6MjzZo1Y+HChe+tyJsSSPAUcc7FxYVff/2VI0eOEB4eTsuWLZk7d26sN98VQghLqFixIps3b6Z8+fK0b9+eJk2a8OLFC0uXJUSSs2rVKn788UcGDx5M+fLlAbC2tkav1xMZGWnh6r6ev78/kydPJmfOnKxbt47BgwezZcsWqlWrliKG1UbbvXs3hkgD3a70oMKYSmhsNAl6/txN8hIYEMiDBw/M91WqVAm/W6/wf/Amzs6jVCkpN7ICL56/4MSJE7Fqq3379gQHB7Ns2bI4qi5pkOAp4k3FihW5ePEiY8eOZfXq1TRp0iTW/1GFEMISHB0dmTZtGrNnz+bw4cMUKFCAP//809JlCZFknD59mm+//ZaGDRvSvn178/3W1tYASarX89WrV4wZM4YsWbIwadIkGjZsyM6dO+nQoQMaTcKGrsTAxiZqWxRdap1Fzp+zXi4wwW+//Wa+r23btqCA23G4ui1AwQ6FsEllw4wZM2LVjqurK9WqVWPJkiUpahSNBE8Rr6ysrBgzZgyXL18mR44c9OjRg+HDh/Pq1StLlyaEEF+tRo0abN68mcKFC9OsWTPatm3L69evLV2WEInakydPaNSoEfny5WPs2LHv9QZGh5akEDyfP3/O0KFDyZIlC7NmzaJRo0bs2bOH4cOH4+TkZOnyLEaniwqc+lDL9FrbZ7DHtWg6jhz9Z2cFNzc3HBwd8Y7j4Km2VlOsV3Hu3b8X65EvTZo0wcvLi5MnT8ZRdYmfBE+RIHLnzs2BAwdYuXIlp0+fpmHDhuzZs8fSZQkhxFdLlSoVc+bMYerUqezYsYMCBQqwe/duS5clRKIUGhpKw4YNAZgzZw5a7furm0b3eAYHByd4bV/q8ePH9O/fn6xZs7Jo0SLatGnD3r17GTJkCGnSpLF0eRZnZ2cHgD5Mb7Ea8jTOg6+v73sXMAoWcOf+oQfow+O2rqLdi2EymZg6dWqs2ildujQZM2bk119/jaPKEj8JniLBKBQKOnbsiJeXF7Vr12bo0KFMnjyZiIgIS5cmhBBfJXrfz02bNpEjRw7q1q1Ly5YtefjwoaVLEyLRMJlMdOvWjWvXrjFv3jxSp079wTGOjo4AiXIk1L179+jRowc5cuTgf//7H126dGHPnj30798fZ2dnS5eXaNja2gIQGWq54Jnrm9wYDUZWr15tvu+bb77BEKbnyam4XZHcIZMjeZvm48ixI+j1MX/OSqWSxo0bs2HDhhSzyJAET5HgUqdOzbp16/jll1/YvHkzHTp04PHjx5YuSwghvpqrqysLFy5kypQpHDx4kDx58jB+/PgkMWxQiPg2Y8YM1q5dy6RJk8ifP/9Hj0mVKmq7jcS0YFdERARDhw4lV65cbNy4kd69e7N371569eplDsriH/8MtbVc8ExbyBXHrE7vbXVSs2ZNlGolDw/dj/PzlehfkoiwCFauXBmrdho2bEhYWBjr1q2Lm8ISOQmewiIUCgU9e/bk5MmTBAcH07JlSw4ePGjpsoQQ4qspFAq++eYbtm/fTrt27Zg6dSq5c+dm/fr1KWrRCCHetW3bNkaMGMG3335L7dq1P3lc9NzIxBI87969S/ny5Zk7dy59+vRhz549dO3a1dyrJz6UGIbaKhQKCrQpwKMnj8y7KKjValycXbh/4H6cny9j+UykypOKdetjFxhdXV2pWLFiihluK8FTWFSxYsW4cOECVatWpX///syePTtJLqkuhBC2trYMGDCALVu2kDdvXlq3bk2FChU4f/68pUsTIkFdvnyZNm3a4OHhQd++ff/zWLVajbOzc6IInuvXr6dIkSI8f/6cVatW8e2335oXPxKf5uDgAFhucaFoub7Jg1FvZNOmTeb78ufLz9PTTzBEGuL0XAqFgmK9S/DixYtY71fftGlTzp8/z8WLF+OmuERMgqewOCcnJzZv3sysWbNYtWoV3bp14/nz55YuSwghYiRTpkzMnTuXX3/9FV9fX0qWLEmXLl3w8fGxdGlCxLsXL15Qv359MmXKxA8//IBS+fmPmi4uLhYNnsHBwXTt2tV8sWjDhg24u7tbrJ6kxt7eHrDsUFuADCUzoEutY+vWreb7PDw8MIQbeH4h7l9/3dsVRKlWMnfu3Fi1U7FiRdKmTZsiej0leIpEQaFQMGjQIA4dOsTz589p3ry57PkphEjSypQpw4YNGxg5ciSbN28md+7c/Pjjj4SHh1u6NCHiRXh4OI0bNyY0NJT58+eb5/59jiWD55UrVyhRogTr1q1j4sSJTJ8+3Tx0VHyZ6N9XpIV7PBVKBXma5eOW9y2MRiMAtWrVQqFU8OR03C4wBGDjYkPeZvk4ffa0+XwxoVar+eabb1izZg2hoaFxWGHiI8FTJCrly5fnwoULlChRgp49e7Jw4UIMhrgdHiGEEAlFrVbTqlUrduzYQYMGDRgxYgQFChRg+/btMv9TJCsmk4mePXty7tw55s6dS7p06b74sS4uLgk+0slkMvHLL79QsmRJTCYT69evp3Hjxu/tMSq+TPRQW0OY5T+v5W2SF324nr/++guICsU2tjY8O/s0Xs5XqGNhIsIi2LFjR6zaadKkCf7+/vz5559xVFniJMFTJDpp0qRh9+7dTJgwgUWLFtGzZ89Eucy6EEJ8KUdHR0aMGMHGjRtJkyYN33zzDYULF2bZsmXJ/gq3SBlmzZrFypUrmTBhAoULF/6qxyZ0j+eDBw9o1KgRvXv3plGjRqxevZrs2bMn2PmTG2tra1BYvscTIHPlLKht1O/N83RL58aTk3Hf4wmQtVo2dKl1/Pbbb7FqJ1OmTJQuXZolS5bEUWWJkwRPkSipVCrGjBnDX3/9xd27d2nRogWenp6WLksIIWIlZ86cLFmyhCVLluDi4sK3335LxowZGTFihOwBKpKsHTt2MGzYMLp160b9+vW/+vEJFTyDgoIYM2YMefPm5eTJk8ydO5fRo0dHBScRK0ql0uJzPAFUWhWZKmTG66aX+b4CBQrw2tuP8MC4n+agVCsp1Lkwd+7eISgoKFZtNWnShKNHj8Z6saLETIKnSNSqVavGhQsXyJ07N126dGHFihUyPE0IkaQpFArKli3LggUL2LlzJ/Xq1WPBggVkz56dZs2aceTIEXmdE0nGn3/+ScuWLalatSr9+vWLURsuLi68fPkyVvPk/ovRaOS3334jV65czJgxg/bt27Njxw6qVasWL+dLiRRKhcVXtY2WsUIm3vi/ISIiAoBy5cqBCXyvxM/FjUKdC2PUG/nll19i1U61atVwcnJi2bJlcVRZ4iPBUyR6GTJk4ODBgwwePJjZs2czYMAA/P39LV2WEELEWqZMmRg2bBh///0333//PRcuXKBy5coUKVKEZcuW8fr1a0uXKMRHGQwGRo0aRbNmzahYsSLTpk37ohVsPyZVqlQYDIZ4+fd+/PhxSpUqRadOnShcuDDbtm2jf//+X7zwkfgySpXSovt4vitThUwY9UZOnjwJQOXKlVEoFTw7/yxezpc6Xxpci7iyZ8+eWLVjZWVF/fr1WblyJXp94vhdxjUJniJJUKvVTJ8+ne3bt3Px4kVatmzJtWvXLF2WEELECZ1OR6tWrdi8eTOLFy/G2dmZb7/9llSpUlGsWDGGDBnCzp07CQgIsHSpQvDmzRsaNGjA1KlT+e677/jxxx9jtd9l9KqocXlR+cGDB7Rq1YoKFSoQFhbGb7/9xsyZM3Fzc4uzc4h/JJahtgCuRaIWtooOnjqdDhtd/C0wBFFbq/i+9OXZs9iF21q1avHixYtkO71MgqdIUurXr4+npyfp06enQ4cOrF+/XoakCSGSDaVSSbly5ViwYAF79+5lwoQJuLm5sXr1aurXr4+LiwulS5dmxIgR7Nu3j+DgYEuXLFKY69evU7JkSY4fP84vv/xC165dY70SrK2tLQCBgYGxru/deZwHDhxg0qRJrF27lmLFisW6bfFpSqWSyEQSPG1cbLBJZYOX1z/zPNOmTovP+fjbSzlfi/yYjCYWLVoUq3YKFCiATqfj0KFDcVNYIqO2dAFCfK2sWbNy7NgxBg8ezJQpU/D09GTcuHHmNy4hhEgO0qdPT+PGjWncuDEmk4mHDx9y5swZzpw5w6+//sq0adPQaDSULFmS8uXL4+7ujru7O/ny5YtV75MQn7Jlyxbat29PunTpWLduHZkzZ46TdqOHvcYmeJpMJjZu3MjAgQPx9fWlY8eOdO3aVT4bxIN58+axd+9e9Ho9er0eg8FAWEhYopnjCZAqb2qeeP+zkm2ePHnYt38f+nA9aqu4jz8OmRzJVDEzfx/8mwkTJsS4HY1GQ9GiRTl48CDDhg2LwwoTBwmeIkmysrJiwYIFVKxYkW7dutG6dWtmzpxJ7ty5LV2aEELEOYVCQZYsWciSJQvNmzfHZDJx9+5dzpw5w9mzZ1m3bh2PHz8GonoesmfPTsGCBc1h1N3dnVy5cqHRaCz8TERSZDQaGT9+PJMmTaJGjRpMnjw5TudIxrbH8+bNm/Tt25f9+/fj4eHB0qVLyZgxY5zVJ9638c+NhCnCcS2UFhsrG9TWatJqXcnfqoClSzNzyu7E/Sv3zH8uXbo0e/fu5eU1X9IVSx8v5yzUqTA7u27n2rVrFCgQ899FiRIlWLZsGXq9HrU6eUW15PVsRIrTsmVLihYtStOmTWnbti2jR4+mYcOGli5LCCHilUKhIEeOHOTIkYPWrVsDEBwcjLe3N3fu3OH27dvcuXOHY8eO4evrC0RdSc+dOzcFCxakQIECFChQgPz585MjR45k9+FGxJ1nz57RvXt3du7cSf/+/enWrVush9b+W0x7PENCQpg8eTIzZ87E1dWVn3/+mUqVKsVpbeJDRpORbDWy0fj3ppYu5ZN0qXVERv7TA1u1alUmTpzIi8sv4i145mmal909d7Jo0SLmz58f43ZKlizJvHnz8PT0pFSpUnFYoeXJO41I8nLnzs3p06fp06cPo0ePxtPTkxEjRsi+XEKIFMXW1pZChQpRqFCh9+5//fo1d+7cMd9u3brF7t27zQu5aLVacuXKhbu7O/nz5zffcubMiVartcRTEYmAn58f06dPZ/78+eZRRvEV6qKD55fug2gymdi2bRsDBgzg2bNndO3ala5du8r7fgIJCw/DIZODpcv4T2obNUbTP9vzpE6dGq21lheXn8fbOa0drclVPzen952OVTv58+c3z/OU4ClEIqTT6VixYgWVKlWiT58+XLt2jZkzZ5I1a1ZLlyaEEBbl7OxMyZIlKVmypPk+k8nEq1ev8Pb2xtvbm7t373L37l327dtn3tJCrVaTM2fODwJp7ty5sbKystTTEfEsKCiIefPm8eOPPxIZGUn79u3p1KkT9vb28XZOlUqFjY3NF/V43r17l379+rFr1y4qVKjAwoUL42yuqfi8iIgIIsMjccjiaOlS/pMx0vhBz7yDnQMvLsfPXp7R3NsX5OZmLw4dOkSVKlVi1EZynucpwVMkK507d6ZEiRI0bdqU1q1bM378eGrVqmXpsoQQIlFRKBSkTp2a1KlTU7p06fd+5ufnZw6j0V8PHz5sHrKrUqnInDkz2bNn/+jN2dk5zodiivgXHh7O4sWLmTJlCm/evKFFixZ069aNVKlSJcj5bW1t/zN4hoWFMWPGDKZOnYqzszNz5syhWrVq8m8tgV25cgWT0YRLThdLl/KfwvzDUavejzmurq48uvUoXs+bo25OtA5WLFu2LMbBE5LvPM/k80yEeKtgwYKcP3+eb7/9liFDhuDp6cmQIUNkUY1EYtu2beh0OooUKULq1KktXY4Q4l9cXFxwcXF5r4cUovZujA6jjx494vHjxxw/fpz169e/FxgcHR3Jli0bOXLk+CCUZs6cWYbvJjIGg4FVq1Yxbtw4Hj9+TMOGDenZsycZMmRI0Dp0Ot0ng+fevXvp06cPDx8+pEOHDnTv3j1OFzcSX+7ChQsAuORO3MEz+FkQVtr3R2ZkyZKF63uvY9QbUarjZ0dJtZWafM3ycX3tdYxGI0plzM5TokSJZDnPU4KnSJbs7e1Zt24dFStWZODAgVy5coWZM2cm+BupeN+VK1cYNWqU+c8qjQorayvsdHakSZ0GNzc3cuTIQd68eSlUqNBHg+nevXtZu3YtGTNmJE+ePOZFUmRujxDxy8nJiWLFin10P0R/f38eP378we3MmTM8ffoUg8EARK24mzFjRrJnz06OHDnImjUrmTNnJlOmTGTOnJmMGTPKMN4EEhYWxtatWxk/fjxeXl7UqFGD+fPnkz17dovU87Eez8ePH/Pdd9/x559/Urp0aWbPnm2x+kQUb29vUIBD5sQ91Pb5RR/cXN3euy9Xrlzs2rWLgEf+OGVzjrdz52uZn0vLL7Jr1y7q168fozYKFCiAVqvl1KlTEjyFSAoUCgV9+vShVKlSNG/enBYtWvDDDz/IincWpFKpAKjyQ1UcMjvif/8N/vf9eX33NY+9H3PjwA327dv3z/EaFVZWb4NpmqhgevPmTR48fIDnBU/Y9vZABWisNOisdTg7OZMhQwayZs1Knjx5KFKkCFmzZo3xVUchxOc5Ojri6Oj40S0E9Ho9Pj4+H4TS06dPs2nTJvOc0mhp0qQhc+bM5lt0KM2UKROZMmUiXbp05tcS8XWCgoLYvXs3f/75Jzt37iQoKIhy5cqxfv36WG3/EBfe7fGMjIzkp59+Yty4ceh0OqZPn06dOnVkWG0i8OTJE3RpbFFpEu//wYDHAQQ8CqBh+/d3OciXLx8Ab+69idfgmdUjG9bO1qxZsybGwVOj0ZAlSxZu3rwZx9VZlgRPkeyVLFkST09POnToQJ8+fejatSt9+/ZNVmPmk4rouUJO2Z3J3/LDDzkmo4mgZ4G8ue//NpS+H0yv77+ORq3BPr09ve72xf+BP/733vD67mv8773hzb03+N3y49zVc5w4ccLcrkKlwMrKClud7Xs9q/ny5aNo0aIJNodJiJRIrVaTMWPGT+6rGBoayvPnz/Hx8cHHx4dnz57x/Plznj17xuXLl3n27BkhISHvtZchQwZzME2fPr35liFDBvP39vb2ElSI6o3esWMHGzduZM+ePYSFhZEvXz46d+5M9erVE00Pok6nIygoiGPHjtGzZ09u3LhB69at6dOnT7wubCS+zqtXr3BM5Cva3tpyExSYt5qKVrBgQQD8H/jH6/mVaiX5WuTn6sorsRpumzVrVry8vOK4OsuST94iRXBxcWHbtm3MmjWLESNGcOnSJWbMmEGaNGksXVqKEh3wIgIjPvpzhVKBvZsD9m4OZCqf6YOfb2z0O3d33SUiOAK1lZpUuVORKvfHQ2PYmzDe3HvNm3tv3obTN7y5+5rnt59z68gt/v77b/OxKo0Ka2trHOwccHV1pWTJkvTv3z8OnrEQ4nNsbGzImjXrJ1chN5lMBAQEmIPpuwH11q1bnDhxAl9fX4KDg997nE6nI126dLi4uKDVarG2tkar1aLVarGysnrve51Oh4ODg7nnNvr76K9OTk44OTklmWHAr169YuvWrWzcuJG///6biIgIChUqRO/evalevTqZMn34+mpptra27Nu3jy1btlCwYEHWrVtH/vz5LV2W+Bf/AH/Sl0q805ZMJhOei86TJk2aD/6dOzg4oNKqCHgUEO915G6YhwuLPTl27FiMR9plzZqVnTt3xnFlliXBU6QYSqWSoUOHUqZMGVq2bEnz5s2ZPn36Bys6ivijVqtRqBREBIbH6PFWjlHzOCNDIj9zJFg7WZOuaHrSFf1wo2iT0USQTxBv7kWF0ehw6nfbj5teN7l48SIdOnTAyckpRnUKIeKOQqEwB8I8efJ88riQkBB8fX158eIFL1++xNfXF19fXwIDA4mMjDTfQkND3/tz9H1BQUEEBgYSGhr6yXPY2NiYg6izs/N7Nzc3N3PPbsaMGXFzc8PGxiY+fiXviYiI4Nq1a5w/f57z589z9uxZLl68iNFopFixYgwcOJDq1auTLl26eK8lNjJkyIBGo2Hs2LE0bdpUpkckUqHhoTgm4q1Unp17xstrvvTp0+ejP9doNAQ/D/7oz+JS5spZUGlVbNiwIcbBM1u2bDx9+pTAwMBk0+svwVOkOBUrVuTixYu0adOG7t2706tXL7p37y5vcglEqVIS/okez8/ROlhhwoQx0oghwoBKG7M5JgqlAvsM9thnsP+gZ/XuXm/W117LgwcPJHgKkYTodDqyZMlClixZYtWOXq8nODiYwMBA89d3bwEBAQQEBJi/9/X15c2bN7x48QJ///eH8KVKlQo3NzcyZ86Mm5sbzs7O5hD97i26Z9XW1hY7OzusrKzMw4RNJhPh4eEEBgbi7+/PkydPuHXrFufPn+fcuXNcuXKFiIgIlEqleQpB3bp18fDwSFIrh/fr148+ffrIqseJmF6vj9rDMxEvLHRp+UXUWjVdunT56M+tNFYEPw+K9zo0Og1Zq2fD87hnjNuIHgVy69YtihcvHkeVWZYET5EipU2blr179zJhwgQmT57MxYsXzXuDifilUquICIhhj6eDFpPJBEBEUAQ2LnHfm6BLG7VE/4MHDyhcuHCcty+ESNzUarU5EH6tkJAQXrx4wfPnz803Hx8fnj9/zpEjRwgMDDT3rEav9PsxKpUKW1tblEolQUFB6PX6937+bsisUaMG+fPnJ0+ePEl6ixFZdyHxu3nzJiajyfw+mdhEhkZyfd1V8uXJ98kLGFZaK8JehyVIPTnq5MR79x1evnwZo4tA0cHTy8tLgqcQSZ1KpWLixIlUqFCBtm3b0qJFC3788UeKFCli6dKSNbVSHeMeTysHK0zGt8EzMDxegqdtWlsgahl/IYT4Gjqd7j/nq0YzmUzm4b3v3kJDQwkJCTF/NRgM2NraYmtri06nw84uaoXv9OnTJ8gwXiHeNW7cOJRqJadmnCRfs/zxthdmTF1ecYnwgPD/XKPBzs6OIN/47/EEyFYjG5hgw4YN9O7d+6sfb29vT5o0aZLVyrYSPEWKV7NmTS5cuEDLli3p3Lkz3333HR06dJDVEOOJWq2O8RxPrf0/wTOm4fVzdGmigqePj0+8tC+EEAqFAp1Oh06nI23atJYuR4gvEh2Anl/wwXPReUr0LWnhiv4RHhjOkbGHcHNzo0yZMp88ztraGr8gvwSpySV3KmzT2XHkyJEYBU9IfivbJq5LFUJYSMaMGTl06BADBgxg5syZDBw4kICA+F/1LCXSqrWE+8c0eP4zdOZTK+PGllKtRKVV4evrGy/tCyGEEEnN3bt3zd+rNWoOjTxAkE/C9Bx+iVMzThD2Oozp06b/53G2trZEBMXP54d/UygUZCyXkUePH8W4jWzZsiWr4Ck9nkK8pdFomDlzJhUrVqRjx460atWKmTNnynLuccza2prwNzGbX/Fu8Ly71xv/B28whBnQh+nRh0ZGfTXfou43vHtfiJ7I0Ej0oR8ebwg3YIgwYNQbAciRI0ecPF8hhBAiqVu3bh0AVX6oyqGRB1EYFez6dgfNt7W0+Agxv9uvODH1OMWKFvvs2gxWVlbow/T/eUxcylDajVtbbxIRERGjhbMyZcrErl27MJlMFv89xwUJnkL8S8OGDfH09KR58+a0b9+e4cOH07x582TxHz4xsLGx4fXr1zF6rJVD1B56CoWCYxOO/PMDRdRKtUql0vzVfFMoUSlVqFQq1Cp11H5+aiu0Wnu0tlq0zlH7+EXfbGxscHFxYeDAgXHxdIUQQogkb99f+0hfIj1lh5fn1pZbPL/4nDs7bnPjj+vkb1HAYnWZTCZOTj+ByWBi0qRJnz3eysoKQ7ghwYJcuqLpMBlMeHp6/ucQ4E/JkCEDQUFB+Pn5mfdCT8okeArxEdmzZ+f48eMMHDiQSZMm4enpydixY5P0ioGJhY2NDc9fP4/RY7X2UcFzxIgRlClTBnt7e+zs7LC2to7LEoUQQgjx1uXLl/Hz86PO1HoolArqLqvPsqK/otao2dt7N1k9sqFLnfCfj4x6I3v67ObSsovUqlWLTJkyffYxJUqU4O+//+bGhuvkbxl3gdlkNBERHEFkUAQRQRFEBEZ9fXXzFQBXr16NUfBMnz5qL/JHjx5J8BQiObO2tuaXX36hYsWKdO/eHS8vL2bNmiVDMGNJp9MR8SiG+3i+HWprMpnIli1bXJYlhBBCiI+YNm0aGp2GAq3dAUjrnpaK4ypxZOwhDG8M7O65kyZ/NEvQkWFhb8LY2noT3nu9ady4MRMnTvyix7Vp04bf/vcbW1pv4traq9iltyMyOBJDhAG1tRq1jRq1tRqbVDYolIqoEBkUSWRQBOGB4VGBMiCc8IC3ATMogsiQSAyfGb5rZ2cXo+eZLl06ICp4JoddFyR4CvEZbdq0oVixYjRt2pTWrVszZswYGjRoYOmykiw7u6gX+Ziwehs837x5E4cVCSGEEOJjnjx5wrXr1yg1qDRau3/mKJb7vjw3N3nx8povN//04urqKxRsXyhBavLxfMafTf4g8EkgA/oPoFu3bl/8WKVSydYtWxk1ahRnjpzhsT4StUqNUqHEYDRgNBoxGA3oI/WgAKUqarqOWqWOmq6jiZqe42zthLWjNbr0OvOWR/b29jg6OmJnZ4eNjQ2pUqXC1dWVNGnSxHj16lSpUqHRaHj48GGMHp/YSPAU4gvkzZuXM2fO0Lt3b0aOHImnpyfff/89VlZWli4tybGzszMv4vO1e4Bp387xlBWHhRBCiPj33XffYTQYKdHv/a1TlGoljdY3YVmRJag1anb32Ilb2Yy45HT56nOYjCYiQyKjbsERRARHEhkc9X1kcGTUENa39725+5pzP5/D2sqKZb8uo0SJEl99Pp1Ox5w5c4CoC9menp4EBgYSEBBAYGAgQUFBBAcHExwcTGhoqPkWHh5OeHg4ERERBIcEozfoMRgMGIwGDIao0GoymDAaohYpTOuWlt3bdsdoUaFoSqUSV1dXHj2K+cq4iYkETyG+kK2tLStXrqRSpUr07duXq1evMnv27C+aUyD+4eDgAETtuWXj/HUboGtto168/f3947wukXAOHz7M4cOHyZcvH0WLFiV79uwolbK7lxBCJDYPH0f1tC3O+wtZq2cjT+O8pC+VAUOYnsiQSPI2y8fVVVdQGpVsbPg7hbsUeRsc/wmLUXMfI/8ZmhoUFS71IZFEhkZiCDd8US0KpQKlSknB/O4sXrw4xsNXAW7dusXkyZO5dOWSeTX7f1Oqlais1Whs1Gh0GjQ6DVonLVZ21tjbO0TdZ6v511ctGp2G29tu4b37Dm/evIn1Xr3p06eX4ClESqRQKOjatSslSpSgadOmtGjRgkmTJlG9enVLl5ZkODo6AlH7cH5t8FQoFaht1AQFJZ69w8TXmz59+ntvogqVAitrK+xt7XFN60rmzJnJmzcvhQoVomDBgrG6WiyEECLmDv59kB07dvDXX39x5eAVbm+79dHjypQrw+lTpzn4/YGoFeVVUSvLq1Qq1Eo1GrUGrVaLjdY6aphqWmtsbGzQ6XTodDrs7Oyws7PD3t4eBwcH7OzscHZ2xsnJyXx7971Ar9fz+vVr3rx5Q2BgIP7+/ubeyhIlSpA9e/ZPPqcJEybw56Y/UVmpKNa7BHmb5cPKQWsOjdEhUqVRxfj3ZpfBLs6Cp62tbbIZ6SXBU4gYKFy4MOfPn6dLly4MHDiQdu3aMWjQIDQajaVLS/ScnJwAiAgMj9HjtXZagoOD47AikdAKFy7Mo0eP6HC8E0E+Qbzxfs1r79f43fbj4a2HXLt+jV27dkUdrACttRZbG1tSp0qNm5sbOXPmJH/+/BQrVixZrPInhBCJlU6no0WLFrRo0QKA69evc/HiRRwcHHBwcMDJyQlnZ2dcXV2/+CLhlStXWLp0KSEhIbx69YonT54QHh5OZGQkERERhIWHEeAfgL2jAyeOHQdg9+7dzJo9i5evXpqHtH6KQqlg9arVFCr08Tmnly5dwt7Nnq6Xun/1BfAvpXk7QisuAmN4eDhp0qSJdTuJgQRPIWLI0dGRjRs3Mn/+fIYMGcKVK1eYOXOmeQUy8XHOzs5AVI9nTGjtrSR4WpBer8ff3998i54XExQU9N68mODgYKpVq4aHh8cHbVSpUoUdO3ZgMkHeJvk++Lkh0oD/A39zIH3t/ZrXd/zwu/mKu6fucejQIfOxKq0KG2sbHO0dyZIlC4ULF6ZChQq4u7vL8F0hhACMRiNhYWHm12l/f38CAgLMr+GBgYGEhITwzTffkDNnTo4ePcqaNWsIDw8nLCzMPK8xIiICvV6PXq/HysqKESNGfDLc/ZeBAwfy/PlzrF1s/hnG6qhFH67n1Y2X5uOCAgK5du0avfv1xs/XD/sM9pQeUgZdGh0aOy1aWw3qt0NgVVZqHhy8z7mfzhD2Ooz9+/d/sjYrKysUkcp4C50AGl1UR4SPj0+s2woPD8fGJv5qTUgSPIWIBYVCQf/+/SldujTNmjWjRYsWTJkyhYoVK1q6tETLxSVq4YGYBk8rBytC3oTEZUlJktFoJCQkBH9/f4KDg83hL/oDRFBQECEhIeZFEUJCQggLCzN/iHj3g0T0h4nKlStTr149+vbvi96gx2g0Rt0MRox6I/ny5+PG9RtfXOPRY0c/GjxLlSoFwJu7r8lU/sM50iqNCpecLh9dpMJkMhH8PJjX3q95czcqlL7xfs2rm684e/EsJ06c4JdffkGpVmJra0u6tOnInTs31apVo1q1ahJGhRCJTlhYmPliXkBAgHmRm+iho0FBQeTIkYPGjRuzZMkSLly48N7r+Xuv5QY9BqMBk8nE4l8WM2/ePM6ePftFdZw4cYKNGzcyc+ZM7t2/hy61LirY2ajROETNX7TR6dDoNDy/4MOA7wbgXsCdBQsWfPHok5cvX/LC9wXVZlan9OCyALz29uPMnNNcXHYRhVJBRreM6HQ67ty9Q6vWrVCpVWSunJnWf7X7YPhrRHAEV/53mTOzTvHa+zW29rZ07tyZ/v37AxASEvLBHuw2NjboX8Rsdf0vZeX4z77jSqWSunXrxritiIiIZLNfuQRPIeJA6dKluXjxIu3bt6d37958++239O7dG7Va/ov9W+rUqYGoxYViwsrRivDnMXtsfInuBQwKCjJ/YAgODn6vB/DdEBgaGvrBh4bor3q9nsjISPOHh+jl3d+9mQwmTMZPDzP6KAWorNSorVRv9yrToLZWo3GJulrsf8effX/tI126dAQFBFGwYyFsXW1R20R96Dj4/QFu3ryJtbM1tX6ug/qdxRY0Oo35w0n01ec9vXZxf+u9j5bi7OyMQqkg7HXYV/+uFQoFdunssEtn90FoNZlMBDz058XlF7y4/JwXV17gc96H27t3sXPnTjTWGvLkzEOLFi1o2LChhFAhRLwbO3YsR48eJVIfGbVNh8GA0Wigbau2ODo6Mm/ePEymz7+eK9VKGjduzMJFC0EJ1o7W5tdbjZMGra0WWzs71DZqFAoFNzZc5/jx4zx79gynbE5UGFcJlZUKjc0/r9nv9hhubLiBEN+oi7oqlYpUeVLR/XqvT9ajD9dz6seTHJ90lOo1q9OpQycGDBjw2eexa9cuTEYT2Wvn4PGJR5yaeZJbW26iUqvIlSMX4eHh3H94H4VSQcEOhbjyv8s42Dnw6Ngjgp4F4Zg5ap2IgEf+nFtwFs9fzhMRFIFrWldGjx5Ny5YtAfDy8mLq1Kl4enpirbOmXJlypEuXjpCQEO7du0dkePwGz9T5UlNzfm329dvDnTt3YtWW9HgKIT6QKlUqduzYwfTp0xk9ejQXL15kxowZ5qAlokTPU4gMilmPp7WTFUHhgR/9WfRwoncXGogOgNE9gCEhIe8tkR4WFkZoaGjUvJKwMPNV4+i5Jnr9PwHw3yHQZDSZb19DqVaiig6AVlEbVmtsNKhd1Ghtteh0tlEfDKI3s7bRoLZWmUNgdGiM+pnaHCQ15u/fHvP2zxobDUqN8j83997RZRt3//Q2L/5UZlg50uT/Z06J/wN/Lv56EaVaad5E/L9o7bQYjF+2UmFcUSgUOGZxwjGLE7ka5DbfHxkaycPDD7i5yQuvjTcYO3YsE6dMJGe2nDRt2pRmzZrJRSIhRLzY//d+THaQsWpGtLZaNLYarq65yunTp8mTJw8mk4k6S+ph5Wj1z4U8Gw0a3T8X966uucrB7/+OalAB5UZWoOLYSp88Z6hfKDc2XCcsLAy1Wo21kzWFOhb+zzqtHK0Ifxp1MdDKyorIIP1/Hq+2UlNhdEUKtHFnb+/dLF26lM1bNjNr5iyKFy/+ycfduBE1amZ7+634XPBBa62lSOEiPHv2DK+bXljZW1FuRHmK9y2JnWvUqrVXfruMQqHg9KxTuLd15/TsU3j9cQOFUkG+PPkYPnw4RYsWBWDHjh38/PPPPH7yGLW1GoVKQVhIGAcOHDDXoNKqSFc8/X8+v9hSKBRkrZ4N4IMe168VGhoa6zYSC3mnFSIOKZVKRowYQdmyZWndujUtWrRg+vTplCxZ8vMPTiF0Oh0KpYLwWMzxDAkLoVSZUuYgaDKaPrvYwKeorFSotKr3w5tj1Ju+rc4Ote6dYPjvYGf9zs/eBsN3f6b5VzCM/l6pSnw9bWprNQajwbz4078vDGjttGAyERn63x9Goml0GvNeZv/25s0bTEYT1k4Jsw+uxkZDjto5yVE7J7V/qcujow/x+tOLG79fY8qUKUz7cRrZMmejYcOGtGrVKtkMaRJCWJ5KpcK1bDqabGhmvu/RsUeEBYaZtwPJ1zw/1k6fft3RpdaBKWrIpVKpxBD+mVBoHfXxPjQ0FI1GQ2Do51eC1+g0BEVGXdS1srJ6byiqIcJARFAE+tBIIkP16EMj0YcZor6G6snfqgBBzwJ5cfkFnTt3Zvr06dSpU+ej54m+ABp8N5jixYtz686tqMWKMjlQ86faFOpc2Lx1GkCpgaW5tOwiadKk4dz8M5z76QwaKw3VPKoxatQoUqdOTUREBHPmzOGPP/8g0D8Qezd7qs2sQeEuRbBytOLUzJMcHPY3nU53IV3x9An2HhwREDU6K3qKUUyFhIRga2sbFyVZnARPIeJBlSpVuHDhAq1bt6Zbt27069ePLl26yNC+txQqRSwWF9KiN+jRR+gpM6SseY8t9b+CocZG84nev3+CocpK9Z+9gCmJ2lqN0WQ0L/707wsDGjstJkyf/cBjbk+nwWj6ePC8dStqOX47N4dYVBwzSpWSLFWykqVKVmrOq8WTU4/x+tOL6+uvMWvWLOb+NJdMbpmoX78+7du3TzZXmYUQlqFRa4gMfn9Yp9ZOS/jLcHPwjAyO+M/gqbaJ+rgeEBCAQqnAEPH+a6s+XE9EYAThAeFEBIYT7h8VeMLCwtBqtby69pLLKy8R+TYo/hMg9ebw+PzSc1TGqM8oNjY2BPsG86P9dPShkV90UVdtpcbJ2Yl0runIlSvXJ48bPXo0Tk5ObNi4gfPnz5OueHqqf1+TPI3zfhAII0MieXT0EWobNeHh4WTPlh0PDw969eqFVqvl2bNn9O/fn2MnjhEZHknGshmpMaQWub/Jg1L9T1vOOaLe10xGU4Je+A18EhXks2XLFqt2QkNDJXgKIf5bunTp2L9/P2PHjuWHH37A09OTH374wdyjlJKpVKqYb6di//ZKqAkqT6kaq322xD/U1mqMRqP5yuzHejxNRhMmgwmj3vjem/rH/FePp5eXFwCOmRM+eL5LoVSQsVwmMpbLRLWZ1fE5/wyvjTe4/vt1FixYwMJFC8mQPgN1atehU6dOODhYtl4hRNKj0WgIex1G4NNAIkMiiQyOwPeaL/ZqO/NrytU1V6MuqoZEEhkSFQQj334fGRLJtbVXAQgMDESpUHLxV0+ur71KRFAEkSGRGPUff611dHTEzc2Na9eusaPzNlBEve69u89m9E2lVFO+fHkAevXqRUREBFZWVtjZ2WFra4utra35+3f33bSzsyNjxoxfvLiQTqfj9u3bhIeG03x7S3LWy/XBBeCgZ4Gc+/kc5xecJTwgnLRp0zJs6DBq1aplPmbevHksX7EckwLyt8xPqe9Kk75Eho+fM21UaIvpBe+Yip4zG5vgaTQaJXgKIb6MSqViypQpVKhQgfbt29OyZUt+/PHHGC0/npxEBc+YD7U1GqPeZCOCIuJ1OfSURGWtxmQ0meckR3x0qG3U95GhkVjZ//cwWY1OY/57+rd796IWHbLPmHiCnEKhIH2JDKQvkYEqUz3wvfICrz9vcH39dX799VeWLl9KOtd01Khegy5dusj+oQkges72u6t9Ri/iFb2Cc/R87eDgYMLDw+nSpUuKf30ViYutrS23T99mvtvc9+5v0qYxefPmBQUcHP43KKJGZEQHQ5VK9TYQqnBycqZtmzZky5aNxt805vz58+h0OnRZowLgu3tqOjk54eLigqura1T7wKtXr7C1tf3iaQSFChVi2bJl791nNBrx8/Pj5cuXvHr1ilevXvH8+XNev35t/j8avaCeyWQic+bMlC9fnpo1a36wv+fAgQNp2aolr2/7vRc6fa+94PSsU1xddQWTyUSeXHkYOnuoeSX0d507dw6tg5Zvr/bEPoP9f/8dpIkaufLv97X45n//DSqNKlYdDg8ePAAgU6YPV4BPiiR4CpEA6tSpw4ULF2jevDmdOnVi0KBBtG3bNsUO89SoNTw88pAw/zCsHb9uPp3WTmse9hMpwTPORPd4Rl+B/zB4aszfR4Z8PniqbdRRvaNG4wdDzB89eoS1szUaG80nHm1ZCoWCtIVcSVvIlUoTqvDyhu/bOaHX+d///seq1atInz49W7dslfmg/3Lo0CGuXLliXsQrejXnj23lExkZGbXSp8GAwWTAYDC8t3Kz0Wg0X+z4HIVSgclo4tWrV6xatSp+n6QQX2HatGls2bIFOzs77O3tsbOzw9HRkQoVKqDVajl+7DhqtfqLh/WPHDnyq2v41IUyo9HI3bt38fT0xMvLizx58tCyZUu8vLzo3LWz+f9n9JZa/0WpVqKx1URdpAQuX73Cjh07GD12NHly5WHkyJEULhy1wFH+/PnJkCEDZ+acpkT/Ujw88pDTM07gvccbtVZN+bLlGTVqFG5ubuj1ehYuXMj6DevJmiUr//vtf8A/C/Z8LnQC2KaLGtKc0MHz9d3XWFnFbi2Dc+fOoVQqzb3RSZ0ETyESSKZMmThy5AjDhw9n+vTpeHp6MmHCBOztP/+imdwMHDCQyT9MZlXF32i9ry12b98UvoTWXmteRTamCxSJD6nf9ngqlUqUauUHPdIau3+uWOtDPr8MffTm2QEBAR9c7X3+/Ll5SfykIHW+NFQYnYYKoyvy2tuPs/POcG7+WW7evMmFCxf47X+/ERAUgD5Sj0KpQKvVksE1A2XKlKFly5axnt+TlPQf0P+9VZ4VKsX7C3fZaNA6R+0HaG1n8/52PP/akidqdU/1B1v2vHt89M9VWhVL8i8iNDTUgs9eiA/lzp2bYcOGffLnsR3Cf/36dS5cuEBoaCiBgYG8efPGvP9no0aNqFu3LleuXOH8+fN4eXnx4MEDXrx4QUBwAOFh4e/N39TaaGnZsiWXLl0yb6vlmMURKwcrtPZWaO21aO21b//89quDFVYOVqit3o8U+jA9T88+5eYmLy6vuEj7Du0pXKgwv/zyC3Z2dvTu1ZvRo0fzS84F+N/3x1pnTfPmzRkyZAg6nY5Xr14xZMgQDh4+SERYBLautly8eJG7d++SPXt2dDodkV/wXgRvp+goEj54vrrxEge72P39nj9/nqJFiyabz4oSPIVIQFqtljlz5lCxYkU6d+5Mq1atmDVrlnk4TErRvHlznJ2dGTJ8CCtLL6ftgXY45/iyVd/MczyJ+ZYs4kNq63d6KFXKjw+1fetL3uyjg6e/v/8HwdM/wJ80RdPGvmgLcM7hQt7m+Tk3/yyrV69mz549uBZxpYhHMWzT6tCH6fG/78+jow9Zs2YNa9aswd7RnnZt2tG7d29Llx//FFBxQmVKfVcatY06Qedga3Qawv0+Pnc8JCQEpVIpPdTCIiIiIj4YbhqbtjZs2MCuXbu47X2bsJAP90NWapSYjCa8vb159eoVM2bMePsDBQ4Z7HF2d8EtV0acc7rgnMMZ5xzO3PjjOsenHKNipYqEhUe1WaJfKdLHcNsRtbWazBUzk7liZipPqsKpH09w4ofjeFT3YNmvy2jYsCHzf55PeEA4AwYMMC/AePnyZaZOncp1r+sYDUbyNslHqUGlccmTivkZ5jJ9+nQWL16MnZ0dhvCo3tjPLRgUPac1Ied4GvVGXt54SbnS5WLchslk4vz587Rt2zYOK7MsCZ5CWECTJk0oVKgQzZo1o23btowcOZImTZqkqKG31atXZ8XSFXTr3o2VpZfT+q+2pCv6+Te4d4d4So9n3FFbRwWEsLAwVCrVx7dTeSsy5PMr274bPP8tJCwEh0xJp8fz3zRvV5g8cOgA2Wtlp+WuNiiUH/7fDXwaiPeuO+zrt4fVa1ZTu3ZtmrdsjrWVNfny5qNQoUKUKFGCYsWKJZtAFD1HzcohYbbKeZfGVkOEz8dfEypXrUx4WDjWOmvSpkpLrly5KFmyJFWrViV9+k+/7kRERLBnzx4KFiyYonquRczduXOH/fv3c+7cOby9vXkT+AZ9hJ4/NvwRo4vMR44cYf78+dy6c5u2rdvg7e3NiRMnsHGxIWfTXOSok5OtbTYD0MWzG2kKpEWlVbGpxUae/fWU169fA9DxVBfSFU2HSvvxi0FGg5HnF56jtlGjtddim9aW1PnjZh9yrZ2WCmMrgULBsQlH6NK1C2fPnGX/vv3mY7Zu3cqCnxfg4+ODRqeh5IBSlOhXEscsTuZjCrR158yaM4SFhZl7ACODI7/o9UahUiRoj+dLr5cYI41UqFAhxm08efIEHx8fKlX69J6tSY0ETyEsJGfOnJw8eZIBAwYwfvx4PD09GTVqVIravqFo0aJs3LCRVm1asarCbzTf0YqsVbP+52OkxzN+qN7u+xYUFIRKqfrodirR9KGf7/FUvw2egYGB791vNBqJCI+w+Iq2sRG9tUFEeATZ6+T8aOiEqLlHRboV5enZp9xa54W3tzcRYRGggQvXL3D69Gl+/fVXFEoFNjobcmbPSY0aNWjWrJl5m4Wk5mMXLeKDIdLw3sqf+tBIXnm9RGf6+OunwWggY/lMOOdwxuf8Mw4cPsD+/fuZOnUqGmsNjvaOZMuajYIFC1KpUiWKFi2KUqlk1apVzJ07F4jaLsLRzpHMmTNTqFAhlEol586dI0+ePJQsWZIKFSrIyscpTFBQENu2bePQoUPcvnObNwFv0L/dckprryVdsfTYGHU8OvqQe/fufXHwDAkJYf78+Wzbvo0A/wCsna1RKKNWBNfr9di62tL/6UDza0908FSqleZgae1oTaQh0vyZwi6d7SdDJ0C6oulpsaNVjH8XnxIRHMHllZc4PfMU/vffoLXS0qN7D/PPz5w5w7Dhw3j18hWOWRypMa8WhToV/ug6Ajnr5+Lyikv88ssv761H8CXBU6lSJuhnhueez4Coi+wxde7cORQKBRUrVoyrsixOgqcQFmRjY8OSJUuoVKkSPXr04Pr168yaNYvs2bNburQEky1bNnZu30njJo1ZX3MNDdc1Jl+z/J88/t3gGdMtWcSHojccDwgIQK1Wf6bH8+vmeL7r2bNnmAwmHJLQHM9/U79dFEmhVGKM/O8FNyDqd2EwGMxX6Jtva0nmylkIfBLIq5uveHndlycnH3N7720uz7rM7LmzGTdmHE2bNo3X5xEfVCoVwS+C8X/oHxUIQ97uFxgSSWTo25AYoo/aT/Ddn727hURoJJHBkUQGRRAZoicyOOKfTetD9ejD9Z/cV7B+y/ofvV+pVJK+RHqqz64JRAXXV14veXHpOc8vPsfngg9XPK9w9uxZli9fjlKtxNbWFpUi6oN6zfm1CXwSwHNPH7w8o+b2Rrty5QobN24EoubIOTk4kSVzFtzd3SlTpgwlSpSIs2GWwnL0ej2XLl3i+PHjnDt3jjt37xAUGITJaEJjqyFDyQykckkdtQiPyUTwixCenn6CPkwPCkib9vPTC06dOsXs2bO5efsmRr2RjOUyUrWPB3ma5mNxnoWEhobi4OCAIdzw3gUvpVqJUW9EH/rPaBSNbdTrTvRFrIjgL5sPGVcigiI4t+AsJ6efINw/jHTp0jN44iAaN24MRC0yd+jQIXbt2sWrl6+ov/Ib3NsV/GDYrMlk4tGxR5z+8SS3d9xCoVawfft2unXrZj7Pl1CoE7bH89l5H7Q2WlxdXWPcxvnz53F3dzfvr50cSPAUIhFo164dxYoVo2nTprRu3Zpx48ZRt25dS5eVYFKnTs3ePXtp1LgRm1v8SejCUIr1LP7RY98NQBFBCftGmpxFB8/g4GDUKvWHczztvzZ4RrX37x7Pq1ej9qNzyJR0e4aih9oqlYov/l0YjUZz8NSH6lEoFDhkdMAhowPZqmWjZL9SmIwmXlx5zo6O25j+4wwaN278wYrAiZ1GreHyiktcXnHpP49TKBXmbSMUSsXbrSNUqJRK1Co1apUarVaLTmuDlZUTVqmssLKywsbGBmtra3Q63Xv7Cdra2mJvb0+1atU+ej6lUknkOx/KVRoVaQu6kragK+7tou4zmUwEPQvi+UWffwLp+WfoNDoKdij0Xq9KkE8QLy49xyaVDS65U/Hyui8vrrzA98oLXlx+wZVLUSF2xYoVKJQKrHXWpHZOTfbs2SlSpAgVKlQgd+7cSe7vN6kyGo34+/vz+vVr/Pz8ePPmjXl7nuiFeAIDAwkJCTHfgoODCQoKIiQshIjICCLCI8wXPKydrclUOTM2LjYYDUbC/MJ4dOwh4f5RF0O11lpSOaeiXKlylC5dmho1anxySHdERASLFy/mj41/8NrvddQK43ojNqls6HC8s/k4jU5DeHg4NjY2RP5r1ImVoxWhr0Lf+zeusY3a0srRMeoiX2QCBc+QVyFcXOLJqR9PEvYmjBzZczDup3EULVoUiOrFmzp1Krdu3QKiph5dvXoVlVb1Xug06o14/XmDUzNO4OPpg1KtRKFSYO1kjd6gN68d8KXzNj+2aF58enb2KamcYrft1vnz52nUqFHcFJRISPAUIpHInz8/Z8+epWfPngwfPhxPT0+GDh0a66W4kwqdTseunbto1aoVe3rtIvh5EBXGVvpg3qv27fAbhVIhPZ5xSP3OUFsrrZX5A1Q0re3XrWob3Sv47+Dp7e0NJK49PL/WPz2eii/+XRiNRnPPgz7s43NkFUoFroXTUXFiZTY23MChQ4fw8PCIu8ITwJxZczh+/Lh52whbW1scHBzMW0goFArCw8MJCQkxr77p5uZGmjRp3gsA0VuxRAeEoKAg87Ysfn5+PH36lIiICMLDw6O2ZYmMJCQ0hAmTJmAwvt2WxWDEZDLx24rfUClVnx0irlAosM9gj30Ge3LWzfWfx9qls3tvNW63MhlxK5PR/GeTyUTw82B8r74No1de8OLic46dPs7hw4eZN28eKo0KnU5HurTpyJUrF8WLF6dy5cqx6iFJ6kJCQt4Lh9H7QwYGBppv0f82QkJCCAsLIzQ09J8tevSR6I36qC16DO/8O/hED/m/qayjVknW6jRYOVmjy6wjVbrU2Ka1BcBkNGGIMBDsE8yDw/cJfxOOQqnA3t4e95zulC1blrp16/6fvbMOk6NKu/ivtH18MlEihCRICBogkLCLs4suENwWd3fdZbGgu/AtixNgcU1wdwjEQ9yIjlu7VFd9f9yu6h6fTBJI2D7PU1Pd1SW3pHvuued9z7tONRePO+44Fi9eTMWOvTnkvsMYMHYAjwz9Pydk14bm00nUJPB6vaQTaSzTclRPT5lXEM9Illjpfh0znUs8Ny7pqplTzdR//cScZ2djGiaDBw3m7w/93SmhMnnyZP710L+oqqrCXeRmz5vGMv3fU/nq668oLCpkyr0/sM1x25IMJZn15Aym3PcDoTUh4RQe0Nntyj3Y+fxd+OTyj1jx9i9Z4tlNFVPRe14/fF1hpk2qZ1axxy579HgfVVVVrFq16neV3wl54plHHpsU/H4/zz33HGPHjuXiiy9mzpw53Hvvvb+bwsFdQVVVXnnlFc4991y+vvUrIjVRDvjXgS1GQR3l7TewRv89wyZT4XAYl8tFPNjSKVHRFSRVwjKsdTIXikQiLZavXr0aJPBV+DZQy3992DmekiR1K3xN82qY6WyN1K4I0JADtkRxKbz55pubHfF88803+ezLzzHT6RYE0O128/KLL3PY4Ye1qc1phwl2BUVXUFxKy/IsxaKsSrrBJLo4SuGgIoYctCW6T8NIpPnpwSnMnj07Qzy7fm43FCRJcsjp4P2yqROWadG4rLEFIa2eUc2SD5bw3nvvcdttt6G6NAr8Afr368+IESMYPXo0e+6552ab99sawWCQr776iq+//pply5axdOlSUqnuq3GyKjtldGRdwVviQfPrFAQK0QO6WG7/z8iMW7YYwJSAzPvcxZYlBtWSkSSpcIpkKEGkOkLd3FpiDTHnGbXDsLcasBU7HbYTI0eOJBqN0tDQQENDA4888ohDlm2SnE6nuffee9l+++3bnE8oFGLAXgM4+evTAIg3i9/edDLdYj09oJNYHcfnE7+dqVjKGRD09fLRsLC+haqpZYinTdA2huJpWRa/fLKc7+/+jl8+XY6iK4zeeTTXXnstQ4YMwTRNHn30UZ59/lmCTUEKtijkwP87mO1PHYXm1SgeUsw7p0/i4IMP5v3332fSSW+x6K2FQtG1oGBgIWNvHse2J2znDI5qPh3DNCgpEU743c3bVF0qiWBbB+CNgYbFDRgxg9GjR/d4H9OnTwfIE8888shj40KSJM455xx23XVXjj76aI499lj+8Y9/bHYd0J5ClmUee+wxrr/+eiY/MplodYTD/nuEUyNMdasiTE+SftWwmd87bFfbaDSKx+OhORRss47u1UWnbB1yPFsTz/r6elwB169aZmNDQ9EV0Xm1utfp0bwaVtrK6TB2ToBUt4qvwk9dXR3hcJgXXniBpqYmEfaXUXoGDRrUaW3A3wrff/89arHK8CO3deptVk2rZPGkRdTX14MFu1w8mq0OG4bm1Zj52HRmT5zFsCOGs9O5Owti6dT8VEXtQL8gEx2ZOAHMeXYW75w+md479+bAhw4ChKvwTw9OIRQKoShdK56/BiRZomRoCSVDSxh+RNZoxogb1C+sc8ho7Zwals1YxuzZs3nllVdAApfHRXFhMQO3GMjIkSPZc8892WmnnTbZcF3TNFm0aBFffvklM2bMYOnSpVRVV7UZeMhF6dZleEo8ROuimMk0RjKNmUpjpkzMtIVlWViGiRE3xMCjBWbKRK6POTnFRsLo9BgOJHE/ZEkWr8Ufsa0l/hdbltiRoigoqvjNMtMmoWCIn3/+2UkdaA3Np+EKuHAVutD7uKj8qZL333+/XeKpaRrpnFxxm0yaKaHY26RZ92uEUkHHLCgVzRJPb6/sMhuugAvLzP7uJDeg4mkaJvNfncd3d35L7ZwaPD4Pxx9/PJdeeiler5doNMrf/vY3Jr/3DolonN479WHf6/Zn+JEjWgwkb3vCdnx+zaf8PPdnVJfKvFfmYRkm/fbsz143jmXIgVu2jXrKhBDbxLO7A9CqRyMR/HX6DFXThLFQR6H/3YFtXNad3ODNCXnimUcemyh22mknpk+fzumnn84ll1zCqaeeyiWXXIKmab91034V3HHHHZSXl/P0M0/z8kEvcPTbx+IqcCFJkjBNiKXziucGhD2abBPPZDv1EDWf5riIdrk/T3Z/uYhEIi3yRTdHSJKE4lLBzKoTncHOdzUMQ4TndkN50zwqyWSSCRMm8OabbzrLZVUGGb759ptNknhKkkTZNmXs/+CBzrJpj0xl0dsL8Xg8AAw5cAiD9xWlSWrn1DD7mVkE+gcYcuCWPT6u6tGwTKtF6HPu4IemaqQiv57iua5Q3SoVo3pTMao3yXBSqHpulXhTnNq5tdlw3VnVzJgzkylTpvDEE08gq7ITrjts2DBGjx7N3nvvTVnZhimD0V1Eo1G+/vprpkyZwty5c1m9djXBpraDV7nw9vJSPrIXW4zdguJhpUw68U3q59c5hNBd7MFV6MLd24+72C2mIjeuQjeuQkHqptz7PZE1EUpKSnArLiy/hek1MU0zG26LhUV2mTD/ASyw0hZpWiqLuaRRHNeDq8iFq9CNO3NcV6Ebd2aZK2eZq9CFK+BqMUiSTqW5W7+Dl155iVffeFW0IS0iAY44/Ah0XaexoZGaOdXCTMt+hi2Y9dRM0gnxv27x5MX4A/4WZUQoF6vufuUelG1TzpYHD3WO6yoUaSmJhPgtT20AT4RUNMWsp2by/V3fEloToqi4iCuuuIJTTjkFWZaprq7m6quv5tvvv8VIGmx58FB2v2YMW4zbot1ycYquMPry3fj8us8YMngIaxrWcNIXp1AxqneHbdD8Oul0ep1DbTWfRrwh1qPzXldUTa9Cc2vrFa02ffr09SKumyryxDOPPDZhFBUV8cYbb/DAAw9wzTXXMHv2bCZMmEDv3h3/KP+ecNlll1FeXs49997D8+MmcuyHJ+Cv8KP7dGLRWF7x3ICwiWcsFsPr9bZLLnW/Tqw+1i3lSFZkZE1uo3gmEgmHEGzOUF0KVsxqkwvb7rqZ821sbMwQz+6Vo0k2Zp/vS2oux13kRtEUZj09k3f/Opl4PL7J1f9UVZV0qxxWzauBhdPW3I6iPUDRncGM1rAskW+XiqacvNnc0OfcwQ9N03p0jN8Cj23zCKE1IYqHFtNnl770GlVBxagKhh05HH+FH8uyCK0JUTunhupZ1dTMFiZI73/wPu+99x4g3HULA4UMGjiIkSNHMnbsWHbYYQdUdf27fcuWLePzzz93VMy6hjri0c4HYHx9/JRvW84We2/B8CO3pnzb8hafR+ujYMH48eP59NNPKRpdzLHvHd9lW375eDmLVy6itrYWEL9jrgIX7iI3vhJBWNsjja5CV4bEdk4aNwRkVSiq/i0CbHXIMPSAju7X+WHCdyxevBhN02hY0MAT2z/WZtv3znwHJByV8JZbbqG5SdRGDq8NUTSoCGibYwzgKhLft4aGBiRFIhHsuSdCrDHGtP+byo/3/0C8KU6fPn24acJNHHzwwQDMmzePf/zjH8ydPxcL2O7E7dj9qj0o37Zrta7X9hVgga7rqLraKemEjOKZNtF1fZ1qc2p+jeCqzgdDNhQqf1pLcWHPnWjr6+tZunQpf//73zdgqzYN5IlnHnls4pAkicsvv5zdd9+d8ePHM378eO666y7GjBnzWzftV8FJJ51EeXk511x3DRN3f5oTPj0JPaATq80Tzw0JJUfxDAQCpKKpFmFekDV26k6OJ4hOYDzetkNqdc/nY5OG6lZFuF83Qrdsot3c3IwkS12G2trbpGpSjrqhaIoTnqz5xP7q6+vp169fT09ho8DlcrX5XtoKt2GI885VfO0Bj7VT1vDZ1Z+0KK+SiqZIhpOkwkmSkWzJFSNuYMRTpBPpNiGVuaqOoitIskQ0GkXXdcLRloMgmyqSoSRFhUWUmCWseOcX5r081zHH8ZR66L1jbyp26k3FqAq2OnQrdr9yD2RVxogbwl13doaQzqxi9ozZ2RIxiozH56V3eQXDhw9nyy23ZPDgwQwYMACXy0UsFiMajTomTvZ8yZIl/Pzzz6xes5pgKEg6le6w7bIq463wCZL5h4GM+MsISod3rb7abuWmaeLxeIg3dk+ZOvyFIwlXhhx1dFMM4ZckCVmVqRhVwf4PHOAs//m52cRjcR555BFeeuklkQ+dUWUty8IwDMc4KRaLEYvFmD9vPrvssgsA8cbOyb6nWBDP6upqFEXpEfEMrQny44M/Mu3/fiKdSLPlkC257t7rnNzFzz77jHvvu5dVq1aheTVGX7Ybu14ymkC/7pvH2TWiFUUh2Z00Dp/IXYV1q83p8rtIRTd+n8EyLaqmV7LrqF17vI/fa34n5IlnHnlsNhgzZgwzZ87kxBNP5Nxzz+Wcc87h3HPPRVE2vX+0GxoHHnggxcXFnHv+uUzc/SmkzOhvd9SmPLqHXMXT7/eLELRk2smtBXAV6CKvsRudg2hdFFmVHeL58ccf8/XXX1NVVUXa37WRzKYO1aNhNBnd6szZxLOpqQlZlrvlhKv7NcKpcDYvNJrCnVEw7E56U1PTJkc8/X4/q2pXt1jmKhTtbm5uBqkV8cyokvUL62lY3ChGJSzRebPMzkcoJFVGUWVkXUaSZRJN8RbPpgiJVojFYui6TiratIHOcuNCcSlsOWBLnn76aUAQ9unTp/PNN98wa9YsVs5cyaqvV2MkxLnKmkzZ1uX03rm3o45uddgwPCUep0RMzexqamZVUzO7xlFHu7q+XbbTreAt81K2bTkD/zCI4UeNoHSrnpWPUF0qsioTCoXwer3UNdR3azvdr1PSw2P+mlB0hXS8lVmQXyfRlODxxx/n1Vdf7XBb20zJNExmzpnJ0UcfDdDG9bY13BniWVtbi6Iq6/T/snZuDVPu/YE5z89BAkZuO5Kbb76ZYcOGYZomzz33HE88+QQN9Q34KnzsM2E/djhrR9yF6x6B4QpkiafRzoBna2g+EUERjUaRZbnbiqdeoGNEjS73v75oXNZIKpJi1117TjynTp3K4MGD6d+/f9crb2bIE8888tiMUFZWxvvvv88dd9zBLbfcwsyZM7nrrrsoLd30//GuL0aPHs0rL73CCSedQDQSFiO4+XIqGww28YzH4477ajKUbEE89QIXlmV1y5b/hX2fI94Yd3KSb7jpBmKRGJIiMWjXwRvhDH5daB6VhCR161rYxNM2uemO4ql6NVJGyjESyQ3PtYlnQ0NDT5q+UeFyuTDrW3awc/OHW4ca227KBQMKGX3ZbrgCLlSvcCxVPcKxVvOoqN7M68ykuJQWncea2dU8MeqxNoqG6laJxWK4XC6M0OYRaqu6VScvD0T48ujRo9s4ZK5atYovv/ySadOmsXjxYpa8upg5E2c7hNLX20/vHSvotX0F5SN7seXBQxl92e6CBCXThNaGCFeGidVHMVMmiktBcakouoLqUlB0hQ8v/IDV365Cdat4yr2Ub9eLgX8cyIijt6Z48IYtaq96VcLhMH6/nzVr1mzQff/WUF1qm9qbmk8nmozQ0NCArMoc+8EJuAI6ml/ntcNfoXFJA8e+fzxbHiTyNr+/+1u+vOkLx924dUh7a7iLRU51fX09mqoRWtN5mKllWfzy2S9Mued7ln24FFVXGTtmL26++WYqKiqIx+PcfffdvP7mG8QiUcq2KePQBw5nm2O3FYZrPYSteEqSJMrVJNLOb0Z7sH//6uvrhUrazdxVd5Fb5IHHDTTPxkv3+Pn5OQDrZQg5ffp09t577w3VpE0KeeKZRx6bGWRZ5sYbb2TMmDEcf/zxHHPMMdxzzz3svPPOv3XTNjqGDh3K5LcnM/648UTDUZLrkbOSR0vIqowkS8RiMWeUNRlO4i3zOuvofl2EgHWDOKUTafr168dDDz0EiE7FDmfvxJ8e/fPGOYFfGTaZ7E6JAlvVi0QiQvHsRo6n5tUw0kaWeOZ0Mu2OV3Nz8zq3e2NDUZQ2pVE0T7ZGrCzLLYvcZz7zlnvZ9aKelx6ww49bh4FrHi1LPBMdh4j2FJZlYaZMUrFMGHAsRSpmUDS4qMedW9Wtdqu8yIABA9B1nT/96U+MHTsWt9tNPB5nypQpfPfddyI89sfVrPxsJamMOirJEoH+BZRvW0bJ8DIK+gfwVfjwVfjxlHmQVVn8FihiXjCgAD2gc2Xwmh6dy7pA9+lEIhEKCwt/d2kUilsRoeE58JR6aEw2iMGatMmgfQY5gym+3j4alzS0+N5rfh0rbTnh910RLt2vI8kSVVVVbD18a6a/NZ1ofRRvqbfFeqZhMv+1eXx/57fUzK7B7XVz9NFHc9VVV+H1eqmvr+fyyy/ni6++IJVIMfCPg9jjmjEMPmDIBlEO7d8zG8lwslPiaX/Xm5qaUBW126G2dsRIMpzcaMTTsix+mPAdkiwxZMiQrjdoB83NzSxcuJBrrtn437nfAnnimUcemyn22WcfZsyYwXHHHccZZ5zBxRdfzGmnnbbJWutvKFRUVPDl51/y17/+lbm/zP2tm/O7gSRJyLpCIpHo0C1Q82dCbbtTu9KnoyU0dD0zmi1LmJ3khm1uUH1at0m4TVLD4TCKrHS7HI1pmo66YeSE6WmbMPH0eDykWikxas75t1U8M+ZC61ljUMuUlcjtqEdqI6RiKdLpNMXFxcQbYsx98WcC/QKkItk8UrsMRzKSapFf6qwTtms7ilxTI5bNNU0n0+2GrI48ZXsOnXh4j85FdausWbaGG264gUQi0WLy+Xw8/PDDyLJMZWUlt912GyC+X16fcLfddttt2XPPPbnkkkucgYs1a9bw9ddfM3PmTJYvX071T9Ws/mI1yWTSyR/tCJIiUTmtkj479+nR+XQXesBFPB5nwIABpKIpzLTZovTG5gx3kZt4U8ucTFeBi1Q6JcLpLVooff7e4nuf+73Q/TqWaZFMJpEUqcvfEUmWGLzfYL7+5hteeO6/HH3M0cx8bDpjrttL7DuWYvbTs/j+7u8IrmymqLiISy+9lNNPPx1ZlmlsbOSmm27i008+Im3CNsdty+5X7UHvnTbsc9Da5TwZSrQY8GwN+7ve2NiIqqjdDrV1ZUKPk6EkvvKNU0e6+ZcmjJjBWWed1eN9zJgxA8uyfpf5nZAnnnnksVmjb9++fPbZZ9x4443cfffdzJgxg3/84x8UFhb+1k3b6PB6vRulIPb/MlRdIR6PO89PG5OYDOHpzj96zauRrMquJ0tym4LomzNsMplOCOLRmRNmLvFUZbWb5Wg00mY6SzzbCbXdVIlna0XXPv9gMIisyCRDSeKZfMzQmhAgjFJ++fwXh9TZRkJibpNBI+d1CiNqOEqjHf2QaIpzX9EEQQoTaRRN4bzzzmP77bfnp6k/8fYJb9IeJEUSTsyyjKIoYi4rqIqGpqrouk7AFcDlzpTZcLtxu914PB68Xq8z9/l8PPfcc1ROrezxNSweWkL1rGomTZqErMkougiBxbKIN8aZOnUqo0ePdsLY97hmDL7efqqmVbL2xzVMemcSkyZNEiVJvG4qyirYZptt2H333bn++uudUHobjY2NLF26lMrKSlKpFKlUCsMwSKVSfPTRR8yZM4c3x7/O+Usv7PE5dQeugE6sLpYd+AolHZVqc4enzEvzipbfV7ssSFbBzCp9rqKMkVsu8QxkQ0wlSSLdRY4nwJgb9uL5vZ/lo48+YuDAgfz04I9s/9cdmPnYdH58cArxxji9e/fhujuv5ZBDDgFgxYoV/O1vf2Pm9KkYpoVlga5LHPbfIzbKwLbm0UDCqZma7OL/uu7LmrVpqkaymyk3vl6ZeqYbMUWnelY1APvtt1+P9zF16lT69evH4MGbf0pKe8gTzzzy2Myhqip33XUXY8eO5eSTT2b8+PHcd999bLfddr910zYqfD4fqVjXRgR5dB+KSyiexcUid6t1CJPjTtodxc6vEU9lnSllSW4TarY5I7ckTCqaahMulgs7jzESiaCqavcUY69KOp12zIVahNxljt26RuqmAK/Xi5W2mDjmaUc1DFeFAWE8pMgKP/3zR376548ttguvDfHCPs+12Z+kSMiy7JBCmxAKUqiiaRqqqlLgKqBgywI8Hg8FBQUOETzmmGPYcccdAfjwgw/56KOPhOmQomBZlhOGa7u4RqNRotEo4XDYcRK13V3j8TjJZJJQKOQQtFTKwDDSpNMmZWWlvPjif/n666/XKxrjyFeOIp1MO668NtZMWcPE3Z8iGBS5ejaBLBlWyqi/7uCsl4wkqZlVTdX0qgwZXeuUWrn5lptxe92Ul5QzfPhwdt99d8aNG+c4pbbGaaedxtlnn830edN7fD7dhV7oIr46RklJCQDxpvjvhnjqfr0NUdT9oiyIk1Ofk9qg+11tSoXYvzF1dXXCzbUb0RYDxm5Bv9378/wLz/OPv/+Dyy67jIf6PQgWDBk8hOvvud7JHZ42bRp33HEHS5csQpXhnLFw+X6wtBb2/6fF1Id+YvQlu22Iy9ECkiyhusXvHbT9v9MadsRHMBjE5XKR6IazOIjQZug6RHl9UDOrGkVTGDFiRI/3Yed3/l77NXnimUcevxP8+c9/ZsaMGRxzzDGccsopXHnllRx//PG/2x+vgoICx2FV93Xc6c+j+7BNTezi862VTTvHs6sRaRB5OCkjp7SFovyuFE/Vo2Jhj9AnOyWeuURR1/VuEk8Ny8zmc9XNq0P366RiBonmuLO/TQ2HH344P/zwA9Iq8Os+XC4X7q0GsuOOO3LCCSdQVFTETz/95KiDgUCA/v3709DQgGmaKIowDTJNE8MwHBIYiUSIRCJtyn0kEgmSySTRaJRkKkldXR1pK03KSGGkDCa/M5nttt2OK664gnPOO4dkfF1zBxVEV0lrNbmBQOa1CiisWTODc8+9gJKSwhYDBesKuyPeGppXLLOJp66LHL7WCrru0+k/ZgD9x2SL1xtxg5o5NVRNr6RqehWVP67lsy8/45NPPhFnqSm43C4KfAVUVFSwxRZbMGzYMEaOHInP5yMZTm70QT5XoYtgqpnyclHj8/Ft/8OldVdsVCOYXwuaT8NMtcp99umkzXSOwptV4nS/Ju5tpC3xbG5u7rY7tiRJbHPCtnx88YfU19ez0847YZkWN998M0OHCtOi9957j38++ACVlVUUeuCmP8EFf4AyEWzBoFIYWg4/3PntRiGekLkWGeLZVUSNrXiGQiHcbjfNwe5FfvgqfgXFc2Y1fp+/x8pwJBJh3rx5XHjhxo0u+C2RJ5555PE7wsCBA/nmm2+44ooruPPOO5kxYwa33nqro5r8npAbnpQnnhsGqkdj2bJljkty63/Qur/75VQ0r+Z0JAAUWelWaNimBssSLoiJYIJkMEEylCQRTBCtjWJJgng2Lmkk0ZwQ+YE54aG5IaMgiKKmadQsruWjSz5sETaaiqRIhZOkIiLXsHFJA5IsiXshwceXfNimbQcddNBGO2/TNAmHwzQ3NxMKhQiHwwSDQSKRSAsiaCuC0WiUeDxONBp1yGMsFiMUCmGkDZYtX0ZZWRnpdJpJ70zCtEzMtNmtsim5kDUZ1a2ieTTH6VYv0tD9LkILw4TrQvTboz99d+uHp9TDorcWsmrpKp588knSZppDnz0c1a2iuBQmnfx2pg7rWdjksSXJVIF1IVoW8+fPYd99/7hexLMj5IZs22iPeLYH1a3Sd9e+9N21r7MsnUxTN6+WugV1NC1tpGlZE41LGli6eCmzZs9qUyO1K7fR9YXu1zHSBgceeCD3338/DQ0NhNaEKBlastGO+WvBVeAi3YZ4alhpy4kwyU1t0Hw6SFnFM1wZ4scHpwDCYNCluzoN57ZMi6UfLGHKfT+w4rNf0FwapaWlTHxmorPO119/zQ3XX0tjU5ABxfDQcXD6GPC2+ncqSXDJPnDxyxGWfriELQ8cul7Xoj3oft2p89sV8bRzPIPBIG63m9pQbbeO4bVDbbuZE9oTVE2rpH/fnpdAmTlzJul0+neb3wl54plHHr876LrOQw89xLhx4zjjjDM47rjjuO+++xg2bNhv3bQNitw8ICp+27b8XuDv66dubUwYkkhtQ5J0v+j4divU1qthmFniqapqC4Oc3woNi+tZ+eVKEsFEGzKZaI4Tb0qQaM58FkqQiqQ6JEZ+v58UKZ7b65kujyspwuXQ6/Xy9jtv8/OTs3PyCFU0VUPTNLy6l2KXi4E7bMFFF11EaWkp//fw/1FfX4/f7ycQCFBQUEBpaSkVFRVEo1Hq6+tpamqiqamJ5uZmhySGw2GHJNohpLZaaCuFyVQSI204RevvufserrnuGsLBcJfnlAtZlVHcKkY8hWVYlA4vxdfbj8fjRXWrLP9oGR988AFlZWWkkin2unmsKJni1Zjx6HRq59Sw/z8PpHREqSij4lFRPaowqfJq4rVX69Rs5t0zJzPryZmMPHV7djpHuHxHayL8PH8OPp8PM22y3UkjHdWuaEgRNTNjQN8O97luqMCyZuPxeDBTZpe5v+uK9oinrHTPJbk9KLpCxQ69qdihd5vPjIRBcGWzIKPLGpEVeaOSThDmQinDwO12c/3113PllVcSb4h1veFGgDPg1Cx+F8Q8QTzndZvljXHijXHiTXGGHLglB+c4eOsBF1Zrt+dWUQ25xFMM8lnU/lzLW8e/wfxX5jkRFosWLeKwQw7jv//9L1XTK1uY/Rhxg9kTZ/H9Xd/R/EsTHp+H448/nssvvxy3u2XY8tNPP00kHOSlM+GoHUHtpCLKSbvBRS/DvBfmbhTi6fLrGGFBPLsMtfVlvwder5dkVfeef2+5N7P/jRNqmwgmCK4Ksu0x2/Z4H1OnTqVXr14MHz58A7Zs00KeeOaRx+8UxxxzDKNGjeLoo4/mxBNP5IYbbuCII474rZu1wWAb4HTXSj2PrtF7pz6s/W4Nd999N5Ikte9qC21cS9uDcGVtSTw3BcXz0ys+YfHkRSJnUBVGMoqioikquqbjdrsp9ZTg7eXFP0QQPbfbLVx/M+FTpik6kHvssQdr1qzBMAz8fr9DDP1+PwUFBRQWFlJYWIiqqgSDQRoaGmhqamLMmDEEg0GCwaCjJobD4Tbk8IEHHuDJJ5/kww8/5LMvPiNtpkmn05imUAvNtNlGlWoPduimTd40n47eS8cVcBMIFKD5NDSPxqynZvL1118Ti8UYdvgwhh+1tdjGI4hfLiFUM8vsz2xC+OoRL7P47UXse9/+DP3zVk4bHt36ERKJBB6PB8uy2OvmcQ4BXPnFCmp/rqHPLn1ahIeuK3Lzbm34KnykUilKSkqw0pa496qMaaQzvx1BYAEQz0wAuyLUz3VuAWA6Hfzg6iBYlmOMZLvnpqJtHXXbXSeSygyAJEmGE06HORKJOEeUZblbiue6QnWplGxVSslWv16NaFdAd34z+vYVgwGx+p4RTyNuEG+Kd480NmUIY4Y0JoMJkuFkm7JAuZAUCUVVUFQxcKQrOi6XC6/HS6g2xNL3l7RYX9FkxzzHhk2gVFV0xe3fW8u0WPnlCsy0ydL3liBLYFmgyrDL7rvz3HPPMWnSJF557RW+v/s7jnz5KOJNcaY/MpUp9/1ArD5Gr4pe3HzzzRx11FHIssyPP/7IPfdMoH///jzwwIOASFeRgGPbT+9tgSKvOH6kJtL1yj2AXqCTbEiCBIkuSukomoKsykQiEeH10I1aygC6V4SmbyzFs2a2MBbabbeehyNPmzaNcePG/W5TpCBPPPPI43eNYcOGMWXKFC688EJuuukmpk2bxvXXX4/H4/mtm7besMOTuvonlUf3UdA/gJE0eOGFF0CCQF9/i8/tHKOuCpeDyIG0CRqIHM9k4rdzIbYsiwWvzWfx5EWUlJTw0ksv0adPH+bOnctbb73FvHnzqKyspDnYTF1jHel0huQZZoeK56R3JjF0yFCamppIJoVyaJhGG3LY3VBSxaWgeUT4qKRIBFcF+eKLL/j222+RCmS2PmwEuk+ohLpPEEjNp4nJJpT2+8xnuk9HcSlddmQsy2LOs7NpbBTqVvn2FYw8eft1vs4ue3CiHWfbRENCqOmtSkeoHlWEjHbDLKUz2MRzyTuLCK0OYhomyz5YSjqVpqmpCYDXDn+lnS1famfZ7j1pAZAlEv838F9dbiHJkpgkQEKEY1oyYkSh/efGzvEEm3j+9gM6GwK2yyvg1BKumVODu8RDokmQwnhTPOd1QrxujBGrF/N4hlS2zqfMhUMaFQVVzZJGv9dHr4JyAv0CBAIBCgsLKS4upqSkhLKyMsrKyujVqxelpaWEQiHWrl1LVVUVtbW11NXVEQqF+Otf/8rtt9/O1DlT25xb67I1roBwrrV/J6N1UTH48/evCK1oRkIQvkv3he37wZH/wSlz8vHHH7PPH/bhw1c/xFPqYfYzs0gn0gwaOIhr77iWMWPGADBp0iQefuhfVFVVo8iwaOFCFi1axLBhwygpKSFhQCwJnkx47S91MHsNHDaq7XVza+tf9qgjuArdNCebhGlSN4ih6lGJRqMUFRVhxI1uRxe0NmzakKieVQ0SjB07tkfbx2Ixfv75Z/76179u4JZtWsgTzzzy+J3D4/Hw5JNPMm7cOM477zzmzZvHfffdx6BBg37rpq0XOnJezaPn2PXS3dj2xJEdhjXaxNM0TEzDRFY7Dnu061A67zWNcHzdwjc3FKpnVfHhee+z+vvVADQ0NHDAAQcgK7JQDRF188p36EWv8gr0AheugC7mmUkP6GKeef3zc7P54Z7vWbBgARU7VFC6RZlD9LR2yF/usvbIoebVWnSc6hfV8+jwf9PU1ISmafi3CnDQ/x280a6RJEnoAZ1gMIgkS90aXGgPWgfOx5pPlNex60qmoimHeNrnbmRcqtOJtJMn6+8bcEizaZoYUYNofZREY5xYQ8xRqRLBBGt/XIMswZJ3lrDknaziJEk4pQkeP0mYpWgK/O1d+HwhnPTVqeh+jXTSZOLuTwGhHp27TTyzz72ambL1G8t8MLAUAm4IuMDrsvDpFl4dPBr8+0uIJIuBnTL70zOTnXP6XAtTKUVWuhX6vjlAD+iOylhYWIikSHx+zadt1pM12SGNmqrh0lx4vV6KfUX4h4hog+LiYoqKiigtLW1BGisqKpxcQq/Xy48//sj06dOdaITm5maCwSDV1dVO5EEyncQwDGcgyv7NaA/19fUUFhYKM6YcMqT79Tbb2S7h0WgUJPjgvPeQAdOCAcVw7YEi59Ijom7ZZSBM/WkK++67LxMnTmTixIl88tknzHx8BiO3HcnNN9/MsGHDME2TRx99lOefnUhTMMQWJfB/x8Pho2DoTXDbbbfx3HPP0atXLwCqgzBjFTzyFXw8X7RtzV3Qt6jluXm07uX39wR6QCdlpES5pW6W64rFYgwYMED4DsS6ZzIoKfJGMxeqmVWN2+t2fuPWFXPmzCGVSv2u8zshTzzzyON/Bqeeeio77bQTRx99NMcddxy33nrrRjUn2diwnVcTG9Gh7n8NkiQ5tc7ag5bj3JqKpZwR+3bX9WotOlqapv2q5VSS4STLP17G9EemsfzjZc5yfx8/fXfrx6K3FtJvTD92On8XBu83pNOC5e0h0K/AUTLH3ro3ww7fsDk5Nsm3ieevMcDiKnARCoWQpZalGhwyGEs5BkqtjZRsE6W5//0ZJNqocLpfJ5hTXueJHR7DMkzMZJpEYwzLglcPe7mNyKcpgASmCZ309x2oslCILv4j6Cq8OQMueAnHYG2PIbBtJqVzlzmCePbZuQ+aV8vpVPf0WgvimXW0vAAoBqYDkwA4eyzcfkTHe3h5KkSSAWDPDtYQJY+cd4rSRl3eHDH3hTks/2gZlmmRTCbRdZ1/Pfgv1q5dS3l5ORUVFfTu3dv53a+rq6Oqqorq6mp23HFHysrKqK6u5q677qKxsZGVK1c6YeuJVEJEIxgGaSONlbbQdI3p06Zz1jlnOWRXkiVngMld7ME9wE2gpAB3oRtXoSszuZ2BKHeRG3eJB3exG3exmye2e5TGxkb69esnziOcxFUgfiP1jOJpmqbzfNi/H42NjeiKTNIw2boPXHeQCH/NzbmUJLjmADjm8TRz586lsrKS2tpaXnnpFYqKiujVqxfRaJS///3vvDv5LaLxFDttAdcdC0fuCPYY4gHbwFfLFgLZdJWRt0E4AZ6ARunWhdTPryPSzr9VrwsiG+lZ03wahmmIyJhu5GBqPp1YLLbOJoOyIm20cipV06ooLynv8fZTp06luLj4d18KL08888jjfwgjR45k6tSpnH322Vx11VVMnz6dK6+8El3f/Fxhs4rn5t/p2lyQWzIkFe2ceKperUVHS1VV0qGNSzyXf7KMORNnUzevjprZ1S1ytAbuM4gx1+3JoH0GbxDDF82rOSRpY4Ru2dc6FAqh6zrBWDa80rLzBiMpUpEkyYwjbjLzXixPkYwkMaKpzPIUyVCucZIwVEqFk4JQxgyS4ST+vn4kS2LGo9OY9dRM0ikDK9V1qLAsiUmSQJHaKiN2eR1bvdxKCtKrGHwuWOiChdVC6fHogjC6VRHaN6AY9t9aLPfakybeezSxvUcTy1/8Cf7xnjj+wExqYp/C7DUDiOc0qzCTcZCKpYSBkWOe09PBLLF9lnjaB8vmngbjdAqXBp0TX5V4PLsTVVY3e8XTsiwmnyqIeUlZiXP9Jk2axKxZs0gkE6I8TloQx7SRbjFA0b9/f95//32hAH7yCd5ePjwlHjxbuCkuKckQQw/uIhfuIje1P9cy66mZxONxJFlm96t2Y69bxgnlfT1y69zFbiKRiOMKHm+MOcRTcQkWGW+IE14b4ru7vmXeS3ORJViwYAFFxSXU1NYx88a2Jj9pE96aCRM+Fu+DwSC33347w4YNQ5IkampquOiii/jum69IGiYHbSvU0nFbie9jLnYZCO/PFc/P999/jyyBtkUxh9+6N9ueMJIZj03j/XPeo710fJ8OzRvBrRmy5VQE8ez691T368TD8ZZeD90wGexuKO+6wkyb1M6tYdyYnquV06ZNY+zYsT0uxbK5IE8888jjfwyBQIAXXniBsWPHctlllzFnzhzuvfde+vXr91s3bZ3gdrs3qlFAHm2RSzy76uzm1q70+/0bXfGsmV3NK4e8hCqpFBUWOaSzfNtyDvz3wWwxbuAGPZ6aqako3H+7fgZtl8xUJEUynCRpl04JJzNlVJIkw5n3kaRTFD0YDKIoCrXzarnHfxdGIt3GHTMXuQQQBJmzrEzGYDdSTWOxmAj3S5lOnpyEIEQee8oQQF+GJJZ4xTK3BvMqYeoKWP3dKmRNFk6fzXEWvDYf3a0zZMgQAJ46BUZlqg7c/p4Ie733KKFW9hQVBeIcG3PKm/oyYyN2+GusHeJpl7uRZAlFV0gne0rkWiue9nOR/d4Eu/DKcan2dlHAyJlSmTktFE9VVTeagvNrQZIkVI/Krtvvyn/+8x9n+ZfffImnl4e+f+wn1MUiN64ioS7a73+493sapjYAmdrOwBnTzyTQr6DD4y18awGznppJZWUlqqpgpq0NUpLLU+olsiyCJElIisR/RjwCpiXyvNMWSPDMnk/RtKgRVQYZ8Qy++84kLr/iKm688UbemAHjM2Y/0SQ8/R1M+AhWNkBhgZ+zzjqe8847D03TWLBgAbfddhvzfp4NwAmj4ar9YbtO/pX7XZDOKK1FRUWYFpz983koumC7dvhve/zS72KjlAkC4ZieTqfRVb1bxFD3ayTqEw7x7G4/QNa6F8q7rmhY3EA6kWannXbq0fbJZJJZs2Zx7LHHbuCWbXrIE8888vgfhCRJnH/++YwePZqjjz6aY489lttvv5299977t27aOkHeiPkaebRFa8WzM6ge8e+lsbERv9+PruukUxuHeAZXNfPSQS+gKRonn3gyzzz7DIquMO7vezP68t1RtJ44lHYOm1jLiszPz85m9XerhJLYnCDenCAZTJIIJzAiKdLJNOlkulsOtILm2ZNMv379WLRoEZjC2EOWsmqfVxfEyqeLnEG/S7y3SWGBG4q9IlxVU8Rn9jq2WhhwiW3dGpzyDHy/BlxuN3sNivHiGWJfutpWOekID3wC01bAwtcXsPD1BS0+ky3TCY2L5fT9vLogyPH15E8uFQwLnvgWhpRD0hChtJAla7nHKM5EV+eWI1HcajvE0wLStCWBqZzJAOoAclSzH4F5wFJnT89OgcYYRJKiLdEkNESgOQYJw25fHTChw/PMzSHTdb3brp6bMlwFrhZlYkCQ6kH7DebPTxza4XaL3l5I9Q9VAFmlsSneKfF0FwnX4erqalRVJdHUhQzdCVKxFLH6GLH6KJZlEYvH6du3L1baQvUrlI/sBabF6u9WgwWhJY1oCpy9F1yxP3y3FE56uplAIEBxUQG3vRdkv61Fru/9n0BTFHr36c3tt1/EYYcdBsBXX33FPRMmsHLlCtyaGKy5ZB/oX9x1e326UFANw2hxvewUCz0TxdKeD1zAzUaLWtF8OqZpZgZSukE8C1yEksFsWbVuDr4oukIiuOH7DDWzhKPtH//4xx5tP3fuXBKJxO8+vxPyxDOPPP6nscsuuzBjxgxOPfVULrzwQk4//XQuvvhix5VxU0d3jQjy2DBQXIrgQ1bbHL7WsI2JbCMPj8ez3q6l7aHplyaeHzeRSHWEgkABjz/+OFuM24I/PXHIRi0FYRNPBYvKH1aLjmW3oQMuxL9gmayBjG0i48pMPzF79myKiopwa9B0/7qRwHVFgRvSRhLdJaTA4o7TfTuEWxPtO2k3uOlPoqPrc8HNk+Gp7yVHlcpVHr26yOGMredX2aUKxVOS4JbJ4lE1LZHfZofavjINvlsGjRH4YbnY7uWDX6RgQIFQnIMJ4BfgHrJkM003Rw0A2H777VFVHdOchySJMhqmKSNh4na7+G61D1XThDmOplHQ10//ggLcbjfJZJJEIsE222yD3+/H7RZmJYFAAJ/Ph9frZYcddsies8tFKNJTM6RNB+4idxvi6dLcxOqiHWyR2a7Y7fzG2GY58cbOiaRNPGtqatAVneDqIFUzKok3JYg3xkg0JbIOus3COTfRHCdaF8246iacUPbW7rk+v89x5E00J1j9zaoWnx+5A/xzfNa4p18RXPYa/PPBBznr7HOZMGECpVeIqIXBQ7bk3uuvZ/To0QC8/PLLPPqfR6itq6c8AHceAeeMyyr33YE/U8qzubm5XeJphwa3F2obcCEG0DYCbPMll+4i0dQ1MdT9woxoXU0GVY9KonnjEE9VVxk4sGeRNVOnTiUQCLT4bv9esXn0LvPII4+NhuLiYt5++23uvfderrvuOmbPns2ECROcf+KbMhRFaVF0O4+NC0mSUF2qCBntQvG0/7lXVIjEm8LCQtJxg3Qy7YR1rS8qp67l1UNfJlwlOqyxdIw/PX4Io87YYaPXQbOJpwTsM1wUYLcVSFtxPPt5WFCtAxcj/t1qCKLZ3bbNIR6PEwgEkCU7/2/jwaeDmTZQVbXH6qM708YCNwzLyblyqWCk0w7xnF8pFJRYEr5ZKmjd9JUw8Xuh+sUyamA4IebRJEQyr8NJCMeFahhLiX0kDBxDFMuC1uL6HnvswWP/eYQnv7VQFQlVVZBlFVlOkK5Nk4gkcLlcjpGNrouarrmT1+vF4/Hg9Xpb1G0tKCggEAhQVFREQUEBuq4zduxYGhsbiUQiLWq21tXVkUgkCAaDLFiwgL59+zruqQsWLEDXRWmPL7/8kkQiQSqVIpk0HFfVdNpkwID+TJr0lriuLhcN0Yae3axNCO5iD7FlLeOQfV4vkZquiKdH5HwCffr0AWDeS3NZ/d1qUWqlOUG8KUasLkasXrggN/3SCCBCO3Wd5R8tY/lHy9rsW5FBlkGyxABGJxHueOyU73RKOK0CF+4tTIL6F4vBjhOfEqGwuW6xugrH7wIPf7GccePG8fbbb+HxeLnpppsYNmwYhmHwwAMP8OrLLxKKxBhWAXefAifs2rPfAzuiuL6+nvJyYYSTS9RtV+r2iKdXB8nYWIqn8ATQdZ14N6KYNJ+GkTYc4tndAWjVo22UKKmqGVUU+DtW2bvCtGnTGDNmzGYz6L8++P2fYR555NElJEniqquuYo899uDYY4/lmGOO4a677mKPPfb4rZvWKVSle2E5eWw4qB5BPLvK8Yw3xZFkyQkL7N27NwDR2kinYXDdgWVZTP/PND48/31n2fC/jODAhw/C3yewXvvuLpxQW1liu74W57QTISUUQxfgb/tht6ATj8fp1atXux3BDQ2vLjrjqqpSHYIP5gpSF82ZbEJov04aIkw0klm2qlF00if+AK9OF9vHUiK8r9CvO53dC15qe+x3fhYT2J1+CUWWUGQFWVGQFRVF1VBVDV3X0V3CgdSn6w5RVBSFoqIifD4fPp8Pv9/PTjvtxKhRo/j62+9Yu3YtwWCQcDhMKBQiFAoRjUaJRCJEIhFisRiRSIR4PE48HicUCpFMJqmuriadTuN2u0mlBBE0DDNbs9U0sSwTsDjppBN54YUXMc2uO+nTpk1DU4SZUighI7plBWRLqfhyXmvAWlasWOlsr+s6qcbNO8cTwFPqpnZeXYtlgUCAqhoRRmsbasXqokTrYsTqosTqo1RNr8Q0TOLxuFAaJZjx76mOi6tpdeyGvGTJEsftWFPEYEmhR3xvS30id7nYK2ppFnlazoszr4t9Yq4qcOPbcN8nBkVFRcgSDOsNe20ljjVfnIaTO2lZ8P7PcPdH8NVicOsKqVSK11573WnfM888w/899E/iSYMxW8J1B8KfthNkuKfwZ3Kem5qaHKIeb8wSfnehkETbNRdygZXJlbYsi2QkSaQ6Qqw2SrQuKkKOMyWObNKfDCZE3nrGDM2IpTASaTEImRJluay06dT11TSNpqZmmlY0Ea2JEK2NinvdECPWkFGgm+PM/e/PaLruqLbd7QdoXo1oTaSnl69DVE2vYtgWW/VoW8MwmDlzJjfeeOMGbtWmiTzxzCOPPBzstddezJgxgxNOOIFzzjmH8847j3POOWeTdVlTFTWveP7K0Hw68cZ4lyUcEs1xlBx7xi233BKA5pXB9SKetXNreP+sd52anO4iN39+6lCGHzmix/vsCVRb8ZRkQvH2e7bCKGZ9FAKNRCKB2+0WeVlpEUYa64AItjcljIxamMxslxLKYCRHQYylhNtqbQhMTAoLC/n551Uc/FDL1iiSIINiksUkSSiaC1XT0DQdzaNR0Vt0vm2F0OPx4PP5OOWUUygtLeWf//wndXV1LVTD3r17k0gksCzLUQRtlTASiRAOh4lGo848Ho8Ti8VEuYxEgnA4TDKZJJVKkUomSadTmGmhEoLEo48/yYUXnEdTc9dhqZoi7p07x0wpHATDdBMK9UIQQHtSW82/YuHChRnSuRMwjJZ1OF2Z16uAV/j6iiw5cV8ICWMk0HFOI3yJZVU77zwej2OOtKnAsixSkVS74arxpkRmLpaL0NU4lVMrUYyWkRBlZWXM/3o+/+x9P/HGeLthnpIsISsigsDr9SIDOw6A3QdniWKhR6jrdk5zRQFs8zeora0lEAjQrwhW37X+513kASPDclVFojGaDc8uyIS4NkbgyW/hvo8FGfX73JxyynguueSSNu7yr7zyCsUegzcuhd2HrH/7IGu21djYyI477gjA93d/x9wXf86kUIjf9bp2Si7Xh8FIS9yl3S5KZXUj+lzWZFSPhuZVRQ1jv463l0+UpPFn6hj7dX75dDl18+qor6+nubGJfw96qNP9SrJEQSCA1+tFkiTijXFCa4NEa6JE66M5CnfMedbMtIWVNklGNuxATbQuSrQmwnYH9KwMyoIFC4hEIv8T+Z2QJ5555JFHK/Tq1YsPP/yQf/zjH/ztb39j5syZ3HnnnZSUlPzWTWsDXdNJBvPE89eE7hOEqyvFM9GcaEE8d955ZwAalzTQf4/+PTp2tD7K49s96rzf8Zyd+ONd+zo5W78mbMUTScaO3DLSgtCF4yJENGmAMJ5ZhHAqtU1o7NxB25gmkZnSgJn5LAY0kEy68fuFYuq5qPNwv9aQJaGOKA5RVJBlBUVVUVU9QxY1dLeOv8hNryFedtttN04++WRmzJhBQUEBRUVFBAIB/H4/zc3N1NfX09TUJDqIzc00NTU5BHHx4sVsv/32NDU10djYSCqVoqGhgXg8TjKZxOVyccMNN3D/vfdQV1tN2kxjmhamadHNfqy45OQQQ9toyQXlOni9QtWxnXdNU6ivU6ZMIZVM8OeRcOk+WXOmXKMmm2Qq7YyzbXEdrGrsDZzcRet+ypQ7kYEyoKMBEaG05Ub9aYpFwuiKRKpOvipkcqc3ktNoLoKrg6z4bLnIg8yQRbtDH6uPEW/IdvCT4aRTo7INpEz+dyba3Epbzrqt89tsp/VheoTew6B3QJTHGVACQ8pgy3L4YpHFmc8JV1C3242qSByxg8WNf+r8fArcIs8xEAiwrOfeQi1Q5BXfT8Mw0FSV1Y0pltWKQZ2f14p1jnpMKLBlZSVcd905HHfccciyzNSpU7nnnnvYc889ufjiiwERRl3g23CkE7KKZzAYpKysDEmRWPnFCgBkVUZSxY35dimcuVfLbUNxUXv0D3fu45BGmzgKUqmJuU9D8+voPh1Z7d6g9fJPl/Pifs9TXl5OMBLksP8ege7XnUnsT+w30RTn/wY/xKiRowCQFIlPLvuITy77qFvHso3vNhSqM8ZCY8aM6dH206ZNw+PxsMsuu2zIZm2yyBPPPPLIow0UReGWW25hzJgxnHjiiYwfP5577rnHGSHdVOByuUg0b6BeQx7dgm2335W5ULwpjqpk/8WUlZWhulTq5tb2+Nj180UonuJSOP6jEzd4iZR1gd15kWSF16aD6wJo33cjBbzQaplwrZUkOVPjVEHXtQw5lFBVNaMWDuWkk05ijz32oLKyEkmSHIMZO88wEAg4xLCwsJDCwkIKCgooLCzEMAyampocchgMBlmxYgWhUAi3200ikWDJkiWkUik0TSMSibB27VrmzZvHVVdeRjqVIm2mMdJWh+GKrTF37lxAkN7yQLb2ZnMQ3p38NjfccANr1q5m98EwdmhObc4cApj72pOp2+ks1wXp7G4Kb11YEM9IJIIsy/QugP227t62uciWOekKKslkEkmSsKzOviPi+cktr6IpFtnan51t15J4phMGlmWtU15zG1WyxbwluYw3xR2HYkkWpULETmhX+ZIQ98/vyoSueqEsIMJXS71Q7DMdJbLIK0jgvg/i5EbasB2Q379IqJTtYVFG/F2xYgUjR45EVRSauvhtAijwQDgcZsCAAUQSYoAiN7AnkYKmmHCVbYy2/7opJhTMhijUR2CNSB2lvr4eTXfx+DcpHv8mu09ZggFbDOKGG25g9913F+f2/vs8+MD9VFZWYQFLFi/g7LPPdvKKQ41dnso6wc7xbG5uRpZlAv4A/Q8ewOEvHOk8PxO8dxJpJ9bWmyGtu1+1xwbPobcHNH0+H6ZhMuKorTs8hivgYsTRW/Pl219iGAayIjP4gCGMPGX79olwhiR/cvnHzHx8OkbcwDKtDVLPGYSxkCRL7Lbbbj3afurUqeyxxx6bZT31niBPPPPII48Osf/++zNjxgzGjx/P6aefzmWXXcYpp5yy0Y1buouKigqmTZ/Glzd/wZ7X75VTAD6PjQW9wIWkSN0yF9LUlu4XRYEi1v60tsfH7j9mAMdMOpbB+w/5ze+1rXiOHLk9kiS1azZj15gbPHgwZWVlFBYW4nYLF87q6mrq6upobm4mHA4TDAaJxWIkk0mampqIx+NEo1ESiQS9evVC13V++eUXEokEyWTSCSs1jRTptEE0Fse0hMJs5xquixOrWxMd4ylToLy8nFA4xjljhcLkd2XLtdjz1svWNMG2f4NnT4PTJkLfQliVE754/gvw3E+CmSuSxH4jLG45ZAPciC7gyTyC0WgUWVF77JwrTJO6E6InwqOFm20DwiE3t+SKrXA3AaL0yxeLIZqw64/+AjxJViHP3TbrrmuaJrIs43a7wYJlHywlFU1lw1ebc0JZmxPEG0T+XaI5Icr+hDpWJSVJqF+KApoM8YQoc9GnTx9Wr1rF3w+xWuQ52mGsxV4o8Ylw1nXNzvDpggzlwi6VEYp3TDzLMyndq1atEsRT02iMGpimyD22iWJzTOwnGBfla1Y3gqskQklJCaYFu98tytw0R8U6HeVUS4h8TlXJRA5oLnSXMJ6qGORn9MCBVFRUcPc99zFlyhSKi4spKSmhtLSU4cOHU1ZWhmEY/Pvf/+alF/9LY1OQfkXwr2NFiPBe95r861//4uqrr8bj8VC1gX1wbFfbUEiEnCuyQjqZbvE/XXWrxFJtL4BPF4qnaZgbvESVlinVpaoqlinyeTurrbrNcdsy/+V5LFq0CFVVKdmqhG2O3bbTYwzYawDT/z0VEGVwNkTtVoDqmVV4fJ4eEUfTNJk+fTpXXHHFBmnL5oB8Ly2PPPLoFP369eOLL77g+uuv595772X69Oncdtttjjvlb4l//vOfXHTRRXz7j6+Z9+LP/Pmpw9hi7Ba/dbN+13AFdCSpa+IZb4zjdrUMgd1qq62Y+sNUTMPsdghWLiRZYqtDh63zdhsDucQ3HA5TW1vrkMJUKpUxoLHNZ9IZ8xmbDHafEH788acccsghvPfee5T7oXehCCn1+QTps9XB56dIpM0iYDta5hy2nt7Fpdaw+O8WLhVOfgZmrYKqe+CFH4Xzph3Ked1BMLCbFWmaM8qdWxMd1NVNsN3fhAqcTENdCJJp8czIikx0I5VlaI3WxLM2DHPWZHJbc3JkbXdc53Wq5ToiVLKRtqTQJoT2BOFwGYoiY5ozgZnttCpbp/WTBRafLJAQIdYA0czUOerq6ujVqxeVlZUAvPynF7N7VyQURUKVM/mqsolXg7JMvdfifoIglvmgVwH0LhADBf2LYYsSoUJm2wN73gMr4hXsttturFq1iiv33/AOy4VeEf6ZCzu949JX4ODtYNs+gkg2REW+YUMUfqkX665dKwa0VM3FxO9jPP1dx98yWRLkcfjw4RxyyCF88sknVJkqvhIfgwYGnIiB0tJSysrKHKfj3r17U1ZW1i3PgzFjxjBmzBjHpKqoqIhoNMott9zCe+9OIp4w2KE//PsoOHon0R4QUQBvvfEaV199NT6fj+gGziSxuZZDPBWlTY6w6ml/gMbOD00n0huceNqKp6KI/SZDyU6JoadE1JBpbGwU7vbdMBcasFdWUU+GO9//uqBqWhUVZRVdr9gOFi9eTDAY/J/J74Q88cwjjzy6AU3TuOeeexg7diynnnoqxx57LPfeey/bbtv5COPGRkFBARMnTuSzzz7j+huv5/lxE9nh7J3Y5+7fJu/vfwGaTweZrolnQwyvx9ti2b777sv3339P1fRK+o7utzGbudEhSRKKW2XhwoU0NTUD5QjjmABtjWdsV1IVUMjW6nTTkhQqmck2o/mSdPoH3G43EvD3w+DcDvon786xqA6VAH/oouX9kaVaBpSIbnmZLxsibOd/pVLi3rYuSdIZvJk+XCoNJ+wGXywUqleBJkpGpE2ozRiWyLLSgnhaVssSKm3cc9sxU+po3WhShEGGE2KyO9CRSASQ+Hg+bH9bx+chS2LKDerIplQaCFOgzpFOpznwwP155513gFKydVvt+2o/G/Z7HViMLK/GNA3gGMCbWWcSfmo5D/FkLAdeJks8R40axXfffccrZ8JOA6FfIbj1dRvc6AplPljYGHMcRBujYgCkM1iWMK/KDUttb54buuqLtHQb3W+//XjnnXf4fNpPvPtzS2KhKqApCqqmUVbmc8IcL7/iSj744AOKioooLi6mtLSU8vJyevXqRUlJCb17924zaPrRR9ncwOrqalavXs2OO+6ILMvE43EqKyuprKxk3rx5jBs3jmXLlvHMM8+0yG+ORqPEYjFSyThpI4lhpDHSphOi/uSTT3LtNVdRW9fAn0cKh9oxW7YNGb9iPzjiPzHeeOMNfD4fsWS2Nu2GgB3ZEMlca1UVLuUtrq1ba7ecks3TjLiB7t+wYaFaKxKYCCbw9+7YDdw+fkNDAy7NRdOypi6PUTCgEH8fP+HKsDAl7BlXbIF0Mk3Donp23nenHm0/depUdF3vcZju5og88cwjjzy6jcMOO4zp06dz9NFHc8opp3DNNddwzDHH/Oaht/vssw/ffPUN1157LR8/+TGL3lzAgf8+uNM8kTx6Bs2nISF1q5xKeaC8xbLDDz+cO+66gyXvLdnsiSeA5qieOnD+RjiC7oRUynK2TmW7a3Y7B1FrYVBU4BGmSNCWeCY7CDe0rGzdzFBckIflmUoYPyyHo3aAw0YKEppKQyINT30LqxstTNNEkmQe+xqe/k4cO5leN2MhWc6QQ7s9iLIZZicGReFwmIqKCpoaGyklS/fCCB1zvwMOwO/34/V6cblcTo6d/fqpp57il19+AU4g60zbenBBBV7DNCud8kHwV2wjoc4RwzTtMilbIAYwAHzI1Dp7sIfTmpqaAJxw7lEDhNnOhoI9GNAUE2ZLqWTMKYXz7y/F89YYFWGrTVHxPNRFhBLZFBO5qx0ZYQnFUcqEq2qououCEjdHHXVUi/Xcbjf/+c9/APj6668BEYHTt29fEWLcDg4//HAOP/zwFstM06S5uZnVq1czZcoUqqurqa2tpVevXpx44oncfPPNmQGkJtauXYssiXNurxTLjTfeyHvvvcf06dNxa9k81QFeKCkWJVhyy64YabjmTZg3bx7xWIyTdoPnTm/b7kQKnpsCf3tXvJ84cSK77LILFmJgxbuBeJ4kCfLZgni2cijXvCrxYNttA5lLnt4ItZ00X0sJPRnsPMbYDs1tbm5mzzF7MvmdyQRXByno33kkVt/d+7HozYU0LK6nZGjPDRONhCFKvdRHMQ2TXXfdtUf7mTZtGrvuuisej6fHbdnckCeeeeSRxzph8ODBfPfdd1x22WXcdtttTJs2jVtuucWp1/hbQVVV7r33XubMmcPFl1zMm8e8ztBDtmLf+/andFg34wXz6BL2yHSqixIOiWACf5+WI9Zut5uKXhUsfG0+427de6O18deC6lGF+Wy3cv96Ah075FGRJaLJjumZyEG0O2sWIuyzdW5hCliDkbaoDorw2LVNomP79kxYmiGPdsjjnhNE59tIiw64aUHayg3CbIuHPhdTe5CBeDxOnz79WLx0aRszJmG0JGr56Tm1OXPNlHw+Hx6Pp11y6Ha7+fTTT/nqq684BKFBa8BzCKdRr9eLD7gw55g/Ae8CEyZMcML82sP777+fIZ5bkKV/7UHFMNJOfcjuPxu5He/c75be6p2AnQ9pG/C0F5IZT3WtMuYuq4+IuZ0Pmat4B3wwcuRIJAn+8Z4gjpqaJY6KouDxeAn0CrBVJr/Zzm0sLy+nvLyc3r17t6s4dgd77bUXsViMYDDIqlWrHKUxFAoRDAYZOHAge+21F4sWLeL8884jFguTTqVIGQZG2qK9dFYJOOqoo3jzzTcp94tQ47WI57xPAC7fL1Or0ytMrQ5+CJYuXYqu6xR7oeH+rtvdEBHEs6GhAVXTiadiLT5visL/fQETPhIKvd3O5uZm5zpFEhuOeILYVzQqwrk1TSMab9km1aOxdClM/D77LIQSot4o0EYh3RDQPFp2JAlIdFEmzVY8g8Egl1xyCe+89w5znp3Nntfv1el2O5+/C4veXEjz8qb1au93d37LN3/7ioMeEfbJf/zjH9d5H5ZlMW3aNM4777z1asvmhjzxzCOPPNYZLpeLf//734wbN44zzzyT448/nvvuu4+hQ4f+1k1j5MiRfP7Z59x///08+/yzPPrOv+m7Wz+2P30U2xy7bT4Edz2h+TQsy+pU8bQsi0Qw4agxuTj4oIN56qmnqJ1Xi+pWmfXEDHa7ag88xZvfiK/m1bBiFtkSKO39SzXJ5gSaQDyzrk0GbYKYyKyXzlm+BrCIx+PIsszrM9IsrBZEMRwXjq3hhKjNWdkMUAXcntl35xpi76tbvj/iP9l+n01qiuJQiCA7uUGhWjvLnkLQsfHY2YvZuYzIdJyGqLVYV1+PPRSUDAT4+OOPcblcqOr6d0kaGxv56quv6APYmroLSCQSeL3eNlVV7f58fX09vXr16nC/LldGDqarTrdGOp12SuCsG/G071nuNjppJGJYpMkOLdiDAzY5Oft5QVps4hiMdxwq3ZlBjq/CR0XGIKuoqIjS0lJKS0vZfffdGThwID/++BO6rve4tnNjYyMLFgiH3EAgQHFxMV6vF9M0icfjLQYwI5EIp59+OpWVlYRCoUxN1nbOR5IoLS3l888/59tvv6W6poYjd4CBJSKX1TY9yp1/vQTOfE7U8lRluOoAQTTV84Uie/EfxXsbliXyZWtra/H7/Z1GH+Si0JM9b013EcyYsK9sgAkfwmNfi7tumCKH9dqD4M2Z8NmypPMMhRNZE6UNAZ8LYjFBNnVdJxhrKW96SjxUxoRJWGtoPg29wNX2g/WEJEuoLhXTFMNaXSmetrt6c3MzFRUV9Ovbj5mPz2DMdXt2GuVUOqIMgFhdrMN1uoVM/P13d3yD5tY6/e3oCMuXL6ehoeF/Kr8T8sQzjzzyWA8cd9xx7LDDDhx99NGccMIJ3HTTTRx6aGfFz389XH755fz1r3/lkUce4f0P3+eD897jo4s+YNjhw9n+tFEMPmDIBjdI+F+A7tOw0laHOZ6JYIIFr8/HSltOaF4uzjrrLJ559hke3zbDdCxwFbnZ4+qe1UD7LaH5dJL1dgfpEbL1OXMNZ9ah8KZjOtN6GViWxLy1wuSm45DS3HIcEtmcUZXcnEKLtfQD9spZYy4wPbOlXdZib2BwN1tenDlCRyUHV5O5GqaJpuv4ESQqls5VB9cf9mBH7tOpIYhnYWFhm7thd6FXrlzZaecxGwpXBXhoX01OAbWk02YO8ZwL+Mk+F8lWr1OIwYg6snfV7vGLdRLA3a3aY3eud9ppJ/r07s2KmInH48HXy0d5DnEsKSmhrKyMsrIyevXqRd++fSkpKekxcTRNk0WLFlFdXe04M9slexobGwkGg0QiEaLRKJaZ5t777uejjz7i1VdeJN2J8kjm7IsLAzz1zLMMHTqUUCjE/PnzOeywwxg1apRTOsiebPfo119/nXvvvRfLsigrE8Tipj/Bjp34zK3KlCmprKxEVWUaoyaKLPIYYykcgpi93oK0NjQ0MGjQIGGaZdgh7h3D3mdjYyMul4ultXD0o/DGDPGZYcK+I+CaA0SZH0mCzxdC2jAcV9/whna2dZGpNSsUTyPcktAf9MjBVM+qxlXgQg+40AM6roCOHnCh+TRRh3UjQPVqzuBCMtQF8fRlFc9JkybhcXtYvXg1q75Z1anBoE1Y403rV4Zt0L6D+ebvXxNcFaS8V89i3KdOnYqiKD2u/7m5Ik8888gjj/XCiBEj+PHHHzn//PO5/vrrmTZtGtdee22HOTi/JoqKirjuuuu47rrrWLBgAQ8//DA/vDeFBa/Nx1PmYb/79me7k7fP54GuAzSfLuzuI9mu/Q/3fCf+4e89kM+u/gQrbeEv8POnP7Wt4u73+7nogov46aef6NWrF998+w2znpq5UWrDbWxoPo24aY+c13e0FlnjIFsnrMQD9CZrOyNshiw0LNTMshpEKGgwGERVVEpSBrtk9vYJ0IAPkXPYXr5hx4MqMrcyAsgtZxkjSzxtV9t1CSB2Z/bR2ecgFApd10llWmkrHOuLVCpFIpFwVNO1mf3bOnMymRSdbGABWcq3KLP9xIkT+fTTT4nFYsTj8Tbz1atXZ9Z8vpNWCI23oqJvTvTHFzmfSciykqnfKqGqCpqm4nLpeL1FDB68I9Fo1GmrruuOG/JWW22Fx+PB4/FQWlrq5DEWFBTw0ccfr/P1Mk2Tb775hrVr11JbW0tjYyONjY00NzcTCoWIRCLEYlGMZBLDSOLz+Zn0znuM2WO3dmu6ylI237HEA8PL4aN58O6777J8+XJUyeL+8ZkSLF6hziZSQq23w4Ql4Ia3Q1x04QW8/8GHjpo7ZswY/vznP3d4LgUFBc79r6gQjjFNXQhaxRlhtaqqClVRaco0osAjiGdzO9v3Cojn1yaEPy4Xx1nZAGuboSoItaFsyHIwDvGkyH+dP38+hYWFLF4lnHhlGY7bFa7cH0b1b3kcnwvMtOEMomxo4hlwQXOGeKqqSqI5zoLX5xNriDlld4qHljD8iBEb9sBdQPdqIr9cgkSw81BbSRbmbtFolLfeeovFixej6iqzn5rZOfH0bRji6cpRfQcNHNSjfUydOpWddtopZ5DqfwN54plHHnmsN7xeL8888wzjxo3jggsuYO7cudx3331sscWmU9pkxIgRPPzww5imyYcffsiDDz7I5FMnsejtRRz86J/xlv22OaqbC2wTiFz7+s+u/hSAxZNETbWJ/53I9ttv3+E+zjzzTM4880wAHn30UR5++GGqZ1TRe6c+G7HlGx4t7fiPIksl7SBUhbYKJsA/2AKD47vY/wKyxFPRNArjcUZlPpsCNGCRDShdF0ikWmmmuZ0BmwyuC/H0AKF2llu0DPytrKxE0zQaEb6tqVSK119/3Sk7kUgknNcdLWvvvWFkQ2Al4KNW7Sj3eCgvLycNvNTqM5css2DBAlauXOmQO7fbjc/no6ysDI/Hw6677kpDQwO77LJLi5qtgUCAoqIiAoFAm1Dh77//HlVVezwIF4/HMU2zTf78jz/+yNtvv43L5eKAAw7gjTfeYM2aNY7y2JI8xjCScQwjhZHJd/zDH/dhwIABTJzYMpZSVwR5LPRCXx8UV4gcx+oQfL4wQTQaJW3CRX+E8Ttn8x8LPYIstR430i8QSl9hYSFpEy7sRhrciga47xNRIsbj8aCqapsyK61hE9RgMJglnlERDRlN5tTzzKnpuahabFtTU0Pagke+EoSxslk8Pw9/IUJeYymR42yYgkCq2mph9iXB2PvatsWjCWW01Adb94ZyP7zwkxjMGTFiBIsXL+ayfeGSfUROaXvw6ZA20w7B/WGZaHM4DpGkyLc+dpdseZN1RYEbkhHx+7127VrijXHeOPo1QNRvtctd7XhWz5xaewrNr4u0AkXuUvEEEX0TDoed74fH5WHeS3PZ/18H4gq0f3FkVUZxKSS6COXtClbW6ppRo0Z1smbH20+fPp1TTjllvdqxOSJPPPPII48Nhr/+9a/svPPOHH300YwfP57bbruN/fff/7duVgvIsszBBx/MwQcfzIQJE/jvi/9l1dcrOfTZw9nyoN8+R3VTh008cxXPvqP7EpwX5IknnmDrrbdepzC+U089lUcefYQ5z87e7Iin5tdyFLtihJ1Nt7Yk2WWuYPYfdCQSQdW0NuGjXecbdgQJoxPiaZO4JYiKknbQsNHJ62WIDvu/aBmE2rqFuq5TVlbG8uXLaQJIp7n11lsdgyCXy9XCPMie+3w+SktL212ntclQKpWisbGRQCCA3++noKCArbfeGlVVOeKII/B6vRQVFVFUVNTjvFLDMBxjm7Vr1zqELxAI0L9/fz788EMaGhqcsNNQKEQikSAWi5FMJh3SHA2HURUlU+/VFJNltaj2+vbbbzNkyBCSySQnHH88CxctctoxZcoU3njjDee9SxWqXbEXtvBBaU6eY7EXnvkeFi1aRP/+Qmb74RqRC1nktQ2q2uKd2SL8s66uDlWG/kWwVzd+KgvcQiG0Q1NjSWHS0xkGlULSsEgmk+i6TkFBgZNz3OFxcoinfV7HPiEIY2eZzoosyL2RtlBleC0j+dvbrGmCQ7cXTrXFXnjwMyjwetlrr7144oknuP4gER5b5hdE84tFsKBKkETbtGllg9hXfX0dlZWV9ArAPUe115os/C5Ipy2HUF3+WvvrjN+l8/10uH83pFKCeG2//fZ8+vmnnD3/PAoHFKK6Vd49czKzn5nVs52vB3S/TqI2gaJ2ry6n5tOJRqNOeHUsEcNIGix4dT6j/rpDx9t5tfUmnskc86O99urc0Kg9rF69murq6v+5/E7IE8888shjA2PUqFFMmzaNM844g8svv5yTTjqJyy+/HE3bwBXHNwCuvvpqDjnkEM4971xePvhFdjp/F/a9Zz8076bX1k0FtsqXyoyYW5aFkUjj9Xp7VNfV7XYzfKvhzHl2Nvvcs99mlXerelRMJ2twXSq9a6Q6DUzN7D8zDwaD6LpOHIiQq0SmgBW0zDHMzR1sLwdRBJ9+B8zPvEvSUt0cNGgQqqIwI51mBtnMU0mSkCVJzGUZSZYzpV5kXKkUFRUV+AMBhxi63W48Ho/jSLvbbrsxdOhQHnvsMebPn08gEKC0tBSfz9fjnMPOEA6HHVLY1NREr169WL16NZWVlY5DaigUIhwOOwQxV0VNp1Occ855DBo0iMsuuZB0Oo1lWY7Db0coLy+ntrY2Z0lubVZ7cgFNQIptU6kWQdIS8AMidHm77bZj0KBBrFy5kr8ceSSJZJIDEcMcL5E1gvrhGtihP7i6+OmavhK+WR118q/L/V3X5CzJpOCuXbsWVVWoj3Re4DVtCqUx4IZQKOQQgzdmCFJn1/e0y7HUR4RRVm1IhKuCUMYHDhxIQUFBl4qn7ewbCoVwu92ocktjJTWTS+lSYatemXBgL0yaLc5JURTimRJC4jkXKu4fh8NrZ2eV3GV18O0aycmBHtlPrGPjr8+KeZFHEGy/S1wDrwampODz+bplSuRzgWla9O3bFxDhuON3FvVUS31QeHnXocSdwe+CtCHOt6ioCCttUbhFIapL/OJofh0rbZFOpX/V32M9oJNYLYzUkl242gK4CnRisZgTqmokDdweN7OemNE58fRppMLr50QeWpuN79hxxx3XefupU6ciSVKPSOvmjjzxzCOPPDY4CgoKeOWVV3j44Ye54oormD17Nvfeey99+mx6itY222zDF59/wVVXXcXH//mY5R8u5fAXjvxd1JncGHAUz4y50MwnZlAzq5pjjjmmx/s866yzuOyyy3jjqNc49LnDcRf+9vnB3YHm1XJCrlp3lOySJrHMlEsALcLAnMxWNvGzfW2tzNzubofDYdxuN78A97Q5xtPttEzkGtoEUVEUVFVxcgqjURFO2rt3b4cYut1uVqxYwX777ccpp5zCcccdBwiFckNDVVW23HJLkskktbW1LFy4kPr6eurr62loaMCyLFKpFKFQiGg0SjAYZPXq1bjdbuLxOKlUimQyiWEYpFIGhmG2UAwty8xcmyw79Hu9PPff/3LNNde0uVIeXZSYsIlCbzcUFMGXi0WO4j777EMsYXDJPtCnENyqWN/ezqOJ+fwqOO8FMgYp/YGTEVSyI1L9IioLOSxnSQhBKOPAMcccw80338y0adM488yzMYwkBcBuiPqjkA35c6ldk04QJNJIxpyQ1IZo+4ZQiZT4LFe1q6qqQtM0vlyc5pbJgkDWh2F1M1Q25bgsJ7NXvpfS4Pzun/S0fcWzdkKKZGHimIQ6mDNnTreJZ67iCaAoMueMNbn1UEEgv18Ge98HQ3vBnJuz2/kvFvVQt9xySxpWzeVfx8LxT8LoQfDZZW2vZ7EXUsm4Q6TbywMd1gtOHwO1YUGqGyLi+tVHJXw+H7GUONfO0tl9uihbZCueQ8th10HZzxV5/fI+fTqYmagGO5w30ZxA7SUogR2mmoqkUIp+XeIZSop89u4RTxfxxrhz/73lXojC6u9XU7+ovsMyarrf5YQa9xT1C0RO/9/+9rceDZpNnTqV7bff3rn+/0vIE8888shjo0CSJC666CJGjx7NMcccw/jx47n99ts3ydASWZa57777+Oqrr7jy6iuZuMfT7HXLOPa8fi9kdeM4+G0uMA2T6plVrPpmFdGaCI1LhR2kETOoW1DHRxd+wJAhQ7j55pu72FPH2G+//Tj77LN54qkneGqnx9nrpnEMO2L4Bi19YxomqViKVDSFEc3MYwapmCHexzLvo7nz7Hr2slQss33EoH5BHWkn1PZ94AOy5VLSdBbo1wS8nnktAbIkocgysqKIcheahqpp9Ha72XnnnbnjjjuYNGmSKHnh8+Hz+YhGowwbNsxx9+xJ+GgymeS5555j0aJFjBo1CsMweP/99znwwANZtGiRE0IaDAYJh8PEYjGi0SjRaJSmpiYnj9BWCZPJJMlkkkTGmKdXRQWVa9YgSxIWcPT48YwcOZIbbrihmy2UEOTNQKiEhZn3BbRvqpT7/n1UYASwMJ41E/nPCXD4KEEyPZowemkP2/1dlJ1wSpbsBdv07bildqiq6IiaZD1zO4Lu5L9KCEOkF4CYJPG3v/2NI488kjfeeINbb/07luUH9iDIl7wGDlm1iWeoHa8U0xQGNw0Zs5vGqCBEqVTKUdNumSzaXRWE6iA0RkTNxvZKsdTV1SEpGlOWJ5myHFqT+5ZQgDSpVConR7UvQqt1I7KCvyFtwf5ASWZpGmHftHbtWkC4FK8r8dRUFdNKEk/BgkoRAgvi/Fts5xEqqWEYVIfgmMdhy3KYfEH7JL7IC0YqJerNKm1VR8uCOWvbhsbKEqiKQUFBAWYm77Sz/Ez7s1Ao1C7JVOX273d34XeJHFKAkpISQLiR+3oJadtVkIlqiaZ+1fJjul/HSBuCeHYj1FYvcBFMZOudbj1+G6Y/Mg1JkZjzzCz+cMc+HWynE29Yv3IqVdMqKSwq5C9/+UuPtp8+fTpHHdVFzPXvFHnimUceeWxU7LbbbsycOZOTTz6ZCy64gDPPPJMLLrhgg9Ts29AYN24cX3z2Beeddx5f3/oli95ayKHPHk6v7da9RtfmimQkSc3sGlZ/u4oVn//Cyi9XkIqkRJ01XUWRFVRNJRVN8e7pk1FkhWeeeWa9j3vRRRex6667cuVVV/LO6ZOQz5IZctCWDD9yBIpLySGAKVIxg1SkJYlMRVOkwkmS4czrSFJ8Hjcw4gZmqvvuqZIsIcl2OKmEoihCOZQVVFlFVVU0TaOXvxej9xnNggULaG5udgxnbAXR6/VSXFzsKIt2iYtQKERhYSF9+/aluLi4WyPmvXr1oqioiKamJkKhkBMiumLFCidU1CaFNhG0J5sMCqUwYzJjpDEMQQxM00DGcoKGJeCdd97hq6++6rRNMlmqZ1sqqYAPQS8WIsxl0pbFdpbFYkSHK1tm5yAEkXTR0t9Xytm7mnn/D2AXYN/u3UQAFuFhKRXAfNOktFQoIH5X1+GlIHIUV8Xj3XYX9bQgnt0J5RMdfANxrd4EdLeb/z7zDNtuuy133XUX//3vCwgDqeMRVzbBPH7ALu5iO0Ff+JIgkE05dTxjHTRBkdMMHjwYCZn3fu6MPEqIe+IBGqmsrMTr9SH43Q6Z5e1NwqMZ3iQeX0Jxse2isx8t9dUpQIqhQEVmic2nGhqEzFpQUEBVVVWHVxBEjVVd1x3iGU8a/OcrYRiUi9JWVXuKvSKaYNiwYSxcuJCnT4E/bZcNLW6NYm82/9my4No34f++ENc5aUAyDSeOFvU/izwZoyYPXP0GPPqN4hCkcKIL4pkJMmhsbESRJcKJlvdHkdaPePpckM5YE9v3JtGcfbjtOp0dlczaWNB8GkbawOv2disH01XgImWknO/nsCNHMP0/0/C6vcx6cibjbvtDu6VfXAUuwmvas0LrHkzDZOWXKxg1Yt1NhUBEDqxevXqTHIT/NbDp9fzyyCOP3x1KSkqYPHkyEyZM4IYbbmDWrFncfffd7dZ5/K3h9XqZOHEir7/+OnfcdQdP7vg4Y28Zxx7XjNks8g8tyyKdSGfJWGty5iwznNfJUIL6hfVU/VRJ04omsAT5KigsYOeRO7Pffvtx6KGHOsrFlVdeyYcffsiaH1ZzxRVX5HQs1w+7774733z9DXPnzuWxxx5jypdTWPLOYudzhxAqMrIiiKA9qYrqlKDwu3y4Ai48vTxOvqGtEvp8PseV1J4KCwtb1AVcn/DS+vp6GhsbicVi1NTUYBgGkUiEuro6li1bRjgcdohiJBIhFAqxYsUKiouLaW5uxjJNrHQ6GzpqWVx1zTXMmDGDDz/8sJutUFtNufTQ32pZGFgIksxwy2QIQrttamrCDRybs6W9Ve5eO4KBoIrFxcVEIhF2BGoR9TSz5QO2pPuGTBLrlkcL4CKFhAuLNNkan+1F2VmWcC0NxQVpC8ZErmI8Hnee7wc+FYQimBCf246psZTYZzAjoggyGEHU8LTDq+1AajurNgH8DMBjiGvTt08fXn7lFQoKCjjttNOZNm0qMBKhb9pXe3/gF6ZRDVj0798fSVKYvaajvEsJQQZdCB/hBGmzHr/fnxlq2BoYhiCLbuAtoBlx50eQDYu9g+bm5oxDr0T3BgDcGIbhhPVmaaUNF5AiV4jUM3tvamoCBPFclGOm1BFyQ3I9Xh8DfCFuPkTkctrmSq0HG0p8sCwade7vX3YUKmhHKPaCkRYkUJHF87FNn2yJmCIPnLYHDO/dcruAG9Km6YRVhuJQUdDxcfwZkbGpqQlFkYkkWt7b9Q219WdySAGndm2iOXtv3IV2qO36haOuKzSfTtpM43a7SXaDeOoBoZDa11Xzagw7fDjL3l1KqibF8o+WseXBbV2wXIUujETnecqdYcWXK0iGkhxxxBE92n7q1KkAjB07tsdt2JyRJ5555JHHrwJZlrn22mvZY489OO644xg/fjwTJkxg1113/a2b1i6OOuoo9t9/f84//3y+uvkL5r8yj0MnHkbvHX+bPFXLtJxQ0R/v+4GVX65sqezFDFJxg3TC6NzKMQe5RM7j9tC3d1/+cPgfGD16NPvuu2+bMg42DjvsMKZPn86IESM47bTTNtxJZrDtttvyz3/+ExDlDnRdx+/3d1slj8fjBINBRx0Mh8POvLm5mU8//ZR0Ok3fvn2dEFF7ylUHU6kUaVOQwLQliGA6ncYyLZFLaFoUFBbwzVffAHD99dfz/jvvYDgJaxId3wybuilAFCUaJYygCNuQJXtfAzNmzMh0xHXgGNqGlLamhOtSD3U5sBBL1tDTCQKZpYoiBlkGr8OeWp+dTMvSLBoitNdWftpXBdOIMFU709VWPyVEBmQdnfvsGsBKRF7tPOLAN5k9h8NhZAnueB8e+VKESoYTIlcxkYL2NPGCgkTGTEbipanQ8n62f39FjmcEeDVnPTln3ZYqYy1w0EEHcffddyPLMocccigrVvwC7AOMJXs/zczZVDnL1qxZk/lsODAKQS49iLqyryCI/fk5rZsFvEk4HEY8e32BXHOUAIJ42gHANlyEQiF8Ph8iULw1okAj2ZzmKDCFVIqc3P7W4Y0eINyCeNo6t22a1B1XWxAGQzbxLC4uxieHOLYL19cyH8yrjznhpo3RromnaYmQXo/Xx/itIzx3epdNy4S2ms7AR1dqZUvFUyHciiSpyvqH2hqm+G7ag7+5CqMrE16bivbUNbtn0HzCJdzlcnUZXg3Z0Fx74CAVTrLDmTuy8I0FqC6VWU/ObJd46n4d0+g58Zz15Aw0t8YhhxzSo+2nTp3K1ltvvUkOvP8ayBPPPPLI41fF3nvvzcyZMznhhBM488wzufDCCznjjDM2iqvl+qKgoIDnn3+eyZMn8/d//J2nd32SMdftyZ43jnUcAHNhWRZNy5tIhZOdKoztqZBGNCXCRCNJUuEUSXudmAgXTbczQjtkyBBcrhKh7mXqD+a6iNrqXq6aV1hYSFFREQUFBT1W9saNG8dnn33Wo227g2+//ZZnn322BSnMJYNG2sBIG4IImjmmMqaFmTa7TbznLZiH4lJQXCqaW0X1qGgeDbVUw+V14fP60bwaqkdF9WhoHhU1s57q0Vj97SqWvrcEgOeee47JkyezBTAG0aV/GTDYEqFS6bSs8Wl36uuAhxmFoBRe4NCcNv4ARKPRTIffBLZan0vbDsRzbEmyQw5BEM+Oup02vbP9c3N1vFwvXZOscjUNQWeUZNLpgAtt1YUgKSGyJkwdYV5m6i7cgE4QCwjR2NiIhcKKhjQrGlqvm0siVcR9SiJJSsaR2wK2BwYirlIN4o4VA+PJmgj9q9UAiYTIR80dTtAQV+lLwOCEE47nuuuuc7ZYsWIlsCeQG4qXAt5AeBED7AFMJxwOi3tllGSO0RqtyZ4gFVVVVSiKQjrdmsHYcaZt1cls6Yql7Rzn/xBkuyUURc/8zsjttEUMbP2IMNoKI54EA5jy/feA+A0OhboOi8xdz+fz0Vjf5SaZnM24QwAaozCwfT8aQBBPEPmnmuamMdr2fNtDwA3pdFZx76pEpR2GGwwGkVWVcCKrPBqGMCbqRpnLTtsDgtjaanRuqK2nJEM8f2XFU/frmGkTj8dDbbCu6/UDOul02hk4SIaTbHXYMHy9/VhBk0VvLyRaF21To1sQz+6nXuQiXB1m/qvzGbfn2B73WaZPn85BBx3Uo21/D8gTzzzyyONXR0VFBR999BG33nort99+OzNmzOCOO+7YZB3eDj30UPbdd18uvPBCvr39Gxa8toAdz9kJI5YiGRG5halIinkvz+3cjU9ChInKsjNXZEW4jsrZUNGAHsDlduEpblmSIjdc9M9//vNGcQk2TdOpTVhVVUVNTQ21tbU0NDTQ2NhIc3MzRx11VI9He7uD++67jyVLl+At92aIn4ZerKH7dLxeH5pPEwTRJoTeHGLo0XLIoupsr3lUVK9G07JGXvnzS5z6/en0273/erVz2iNTWTx5EaZp8txzz1EGnIognQA6FgYa0LvjnWTy/OxhhdYaoIYwuBHfDZvOddThSdN+CRWjg3kKoVIB6QSrENmEIMJFDeBhWpLLddUJotEYILMg896fSGYUCglY1c29SIjuSglC1Wut8OYGAtcgwkVPAwZltl8AvJRRIkGEr+6IIL0uRIblJwjidjTZ6/s6hvFLzu/SQGDnzOtlZLXU3O9h63szAJGfmSul1QD/RZJMbrnlFo466ijef/993njjDR599FEkScaycgeFmhBet9WZ/f8F2A6YSzQaRVU1DKM1UbRNYdpfXlVVhaqq7RBPT+YY0TbLI5EIQ4cORYQJt34O0xQWFnLLLbcwYsQI+vbtyzvvvMONN95IKpXKkNz2ieeKzJ4CCBofAlLpNE1NTRQUFBCLxUilUp2W5AoEAhkVF/x+P8u78WgVecAwUg75am0+1Bo28ayqqsLt8dDQPd6J3yWGLewIknA3Fc9gMIii6kSS2YaNu1+YRcnrEtTQCjbxrK+vZ9iwYUiy1IJ4ZhXPXz/H00ybeL1eUpXdMBcKuDDT2dztZDiJrMjscNaOfH/Ht5hpk7kv/MyuF49utZ0oF2NZFulkmlQkRTKcJBVJZgd/nWUpkpGk8A+IpGha1ohlmBxwwAE9Ose6ujqWL1/+P5vfCXnimUceefxGUBSF2267jT333JOTTjqJ8ePHc8899zBqVM8S9jc2vF4vTz31FB9++CG3/O0WPr3i42yuoaIgywpuxU1xRTHnn3++oy4WFBRQWFhIYWFhh6Gr7cEwDJqbmx2yFwqFnHkwGOSll17i5JNPdqz9bZimSTQapb6+nqamJqqqqqitraW2tpbGxkbHpTQUCglDmnicpJHIGM4IEx6rdW2DDBSXQjqRZurUqQwbNoxhw4at1zXtCKqqUjSkmPMWX7DB9y1lemzrk+NjwxXQIRN6J8syXrKkE+x/sLnShElbMig6y7ZAEwTuQiKNhIkgesuXL8+51o/Rljza4andhZQzl0SrLYsIMp9iAqrzrHatO7SuUWmH+lYh6MS2OctXYBihDEGwEKpev8zndcCHwFCE5murw5MRKl9WEey6PdAyH1QQpFgshqKoGIaPliY39Zn2pGhJptykUqmMginRcljAXq91B1nN5EGCIIdH0LKrtQR4GU2TefLJp9lxxx25//77efrpZwCLZcuWIcsy6XR15rzXIkx47OOcQ3YgQ9QxVFWFtkMWtnuNqNuaba9YXldXh8ulkUi0p4hKtKdOxmK1GXXJDoPOdTz1oCgS+++/v7PEDqlubm5GVbV2SK4LkLkYk6KcFi5CuPuuWbOmRY1OW9lqD4FAgPp68S0qLCx08m07Q7EPDCPtDOB1RTyLMj/fNTU1+Hw+GrqOAAYE8QQchay7imcoFELT9BZhteE4DCqFZ0/r3rHbQ6ZaCnV1dQwbNgxZlVuE2npKxImmIr8u8dR9OpZp4fV6SeYc2yaIyXCWAKbCSSp/WotpmOL7JsHiSYsIrmymYVG9o5x+d8c3LPtgKYlggmQoSTKUIFITwUyZ3KXejtVZQd4Mcn0FZFkmUBhgu+2269E5Tps2DSBPPPPII488fiscdNBBzJgxg/Hjx3Paaadx+eWXc9JJJzlujZsaDjzwQA488MB12uaBBx7gm2++QVVVTjjhBA4//HDns4suuogfp/6YDRlNi6k7/xCfeuopPH5P1ogms21noaaqW8VV6MJd7MEz2EN5WRnuYg/uYndmavW6yOUsU10qK79eyfPjJlJdXb3RiKfL5cJo3jidHs0rVBNjA4zm2+6PdXV1qKpKJVlamEQoN/ALcAdZtbJ9ZO1TZOJsTVbJm4umaU7OpSB0XcFWCJXM5CZLED1kA2o9meVyzvF+Bmro18/WPo9GGBLZhCSXZHZUo7IJeDCzz9ySAR9iGNNzcjwHkQ0dtkmMhHC6taGxbqTaVhZzCaE431AolAlLbX3vOyKRmuP+KVTIXMZgK5KtQ4M1olGbwfyZbDfLQgROf0hRUQmvvfYK5eXlnHnmmUyZMgXh61pNQ0NDpsZjblix/Vvop6V67iIej+PxuAiHWzOtXNvUXJIo5g0NDbjdLoLB9sigRXshurFYwlGXhJLaknjG400ttsgtc9IxybXpZ+6eBNauXZs15OkG8fzll18AUZsymYZ4Klvipj0UeYRZUHeJp6141tXV4ff7WV3Z+fpO2zKXyVbcu8rPtIlqOBwW+Y4563t0YWjVWS5qV7C3tZ2DFUVpoXi6i3uueFqWJQzrbJKYcRpPhhKZZeIz+30ynHLWa1gkBg40TSMdN3iw131OuauO/h8W9xL5nR6vl4VvLmDxpEUiokiVue6663jggQdo/qkJl8tFwO3H7S3D3EIMzm6zzTb4fD4CgQB+v7/FIHFRURGlpaUUFhYiyzKNjY2OY/n6YNq0aWy55ZZOOaP/ReSJZx555PGbY8CAAXz11Vdcc801TJgwgZ9++omrr76a/v3XLxRyU8GLL71ILCo6XTfdfBMrV67koosuAmDGzBno5TrDjhiO6tXQfVpmrqN5VTSfjubVRHhpzvzHB38kUh1G8+nofh3Np7Wd+/QMybRJpBtFXz9nXrvGm91p2Rhwu92kOqoFsZ7QfKInuiHCyPRAlnhalkUSoU+1hK0O2ZARZC/XG1ZHhB0uQ5CMY3LWr0TTJOJOHcqjEB12FXgXodYdgAhBzSWRPR240YCPHKVJhJcGOlm/o31AW1LmIp1O5+w797rYRK61HGTXxAzRMny4o8m+Tq8DMzP7Ww2QCddUSSRaE0ybpLU+tuaYIwnimfvMdEQ89ZyIAfuzNOJeTWe77bZj4sSJRKNRDjzwIKqqKoE/IJTeJ6ivr0fTNAxDQRgD2U7EM2mrauokk2F8Ph+1ta3jPu36p7ajbkviWVdXl+lEt2ZbbsQz2zZE1zCMHPLX+nMvqVRLfdzOaVyyZEkm/3Ie8BwiFzSKnRPaOvbA5lTV1dVsscUWAF3meQYCAWcd22ymOdY58bTNgpLJJIosaph2hlziWVBQ0C1VFbJEsrm5GaUbNTjtsjyhUAi3200onP3M52rflXldYCuedv61qqoEVzZTPbMqQwaTSLLEis9+wYgZzrJUOClUw8zcVhETwYQTnpqKpbrMsW/hTK7kpJqoGttssw1nn302tbW1aJqGx+Nx/AoCgYDjV1BYWEhxcTFbbbUVqqry4w9T2j3WkUce2e7yVatWcc011xAKhYSHQCqJYRik0gZmxlCu9SCwL+Djh+9+6NE1tzFt2jT23nvv9drH5o488cwjjzw2CWiaxv3338/YsWM5//zzOeywwxg/fjxnn312pyPdmwOef+55jj/xeNAl3MVuHnv8MWpqajjooIMIhUOMOnYH9rt/3XJG9r1nv43U2s5hk62NSTw9Hg9GfOM4Kqoe8W9vQxBPV0H2Wgh1x4cghrk5h7mTQueE8B+0zePUSCajOWRtS+zcOOFIWk+2VMaGgAJYOQZAHfVyTbKZn61zSmM56/xI1o7oWyzLyFHNvkUQqiRgP0+rgDvJXgfbXfW+brRdzhBEGb/fh8fTjK7ruFyD2X///Rk7diwul0a4TS0Km3i2JaSWJYinLMuYZu4zaRNP21XXHtBRHbIqziGKcJf9hSOOOILbbruNJUuWcMIJJxGLxRGDDNtiBzU3NDSgaSqxWD+EaREIgjaT9khuMpmid+8Cstev9XnZ96MwZ5nkqHZtHWo7I57pHCfO3M8tQM3JoRWwFc+FCxdmrqOEcFG22+EDgm3OyiaetbW1znPflcup3+93iKf9fDVFOy9bYofOVlVVoSkyjdHOlXWPDpoiTHkKCwuJpcBIC5fZTtuWQ/QUWSKU6JyZyTK4VWEq5na7ac7E4afSooZnJAkLqrKOzOFE29ctprgI7w3GBeltyow12NfUrbuZ/8o85r+SNe6SFIm5L/zMvJfmdkgSbRWxl78cT3m2ZFWukZ2tHpaUlFBSUkJxcTFerxfDMGhoaKCuro7GxkYnDWT06NH079+frbfemurqasLhMA0NDcRiMRKJBPF4nJQhDObSZhoJiXcmveOUheku3n33XebMmUOgf4DirUrwFfjR/dlBXM2vo2fmqlvl/bPfpSDQycPUDTQ3N7No0SJuuOGG9drP5o488cwjjzw2KRx55JEccMABPPjgg0yYMIG3336bU089lVNOOWW9w1x+KwwbNowX//siJ596MpHKMAUDCnnrrbeYNHkSRUOK+ONd3amJt2nAJlv2aPnGgMfjIZ1IY1nWBg+5tt2IN4zimVV/XS7RoW+ZO7iukEHWgAmIzrwJlklz0JVxtQVBjuzvga1s2gTNzvWMIwhPkqziGibrRWvnhUYz6yZzpmwZC4GXMusmM/u299Hd8Nf3yJYTMTn4YFEPdtCgwTQ2NqJpSTRNQ9OK6dNnG0zTdJQOVRUkLh6PM2TIEEzTdMrd2E7G6XSaeDxONBolHo8Ti8VobGzM5EmmiUSiNDUFef75F9hzzz0zIcutNTZbFmtNSHVAHEdVFQwjl5jmdp/+A5wNaEhSOQ0NtvPsd8BiIMjVV1/NySefzFdffcXFF19KOq0BfyVr5ySepebmZnTdVipp8Vl77TYMW0FuL3HQ3i5X1ZQAF83NzZl7bBNI+7mxZb9GYHZmWQz4EsvCUSDhmTZHM1s9EvYzlDVnsgcRzEx7/wL8twP7I/G96i7xLCgoIBwOY1mWk/fe1IUimWsWpKgqjdGupcRCj7hHosSOUFVL/R2vb6RF6RLIKJ6KzPfL0jz8uSCFoXhLkhiKQ3MckmmRk+zz+ZjTAPoFgnja2PrW9o8nS6AoEoosZYiiiqJqqJoLl8uFy+2isNjDgK0LOOooEQb/6H8e5ccff6SoqIiioiKKi4spLCykrKyszf/cZcuWsXjxYscrwPYLsGsTNzU1sWTJEocoJpJJjHROWaqMkmil2yffZeVl3HD9Dbz44osAeMq86D4NPaDjqnBRUFCIq8CF7tdJhpPMf2UeP//8M/vss0+X9y4X9rN5/EcnUrZ152VNZjw+HYDLLr1snY7RGnZ+Z17xzCOPPPLYxODz+bjhhhs455xzuP322/n3v//Nyy+/zNlnn83RRx/dqbvhpophw4bxwXsf8Jej/kL96np6jaogGUxw7PvH4y5yd72DTQQ22dqYxNPr9YrSKClzvUODW0OSJCRF2iA5nrkkXBDP9lTa3OIjHU22amiBaauGWSiqmlGnACZm1o1n1lOAdzJTdyAhSaIupqqq6LqWM+l4PH0ZOnQof/zjH9lqq61IJpNOmR5VVXG5XLjdbmcuy7IzQGBP9nvLsgiHw8RiMaLRKLFYjPr6agzDYMCA/oTDIQwjTTyewDBMampqSSZTGcWwZa3Lr7/+upNzksnmnqoI0mQPAuiZ1/P44osvMsSz9X2yu0KtiUeWDLY18MktKVKLrVBb1vbAPPz+AOHwT2iai8cee5JddtmFRx99lIcf/jfCu/VkoChnH4LwiXxIFy0VRXuAwcq0XXWWp9PpTCc6mfO5XUsz41jMIoQybi+PEw6HM6kMcbJKey4aEeVbbAMqGUmSnDDW7sB+TmRZZrfddmPKlJ+Am7FzXaEs58yzsLORGxoa8Pl8yLLcrVDbdDpNLBZzcjabWkcRt0JRRlqtrq5G1fQ2xNM0hVLYHINgTMzrwlCWk2964ySxbnNM5Ig2RMS8KSZIZLzVz4zfX8AHcxv5aB4oMsiyhCzJokaKJGNa4jvkcktUVlZSUlLC2LFjcbvd+P1+53rOmjWLaDTqhHVbluUQO8MwSBsGZtrANA1SyQSJeIxQs4llmpiWhWnB/vvvx9lnn8OFF17IiBEjABxjusbGRpYvX96CYM6ZM4fvM2VuWkPWZDSv5qiGei8XnkIvhQVF4n3AhSugCyXRr6MH2s6/+fvXrP1sDX/4wx9weVz0GdOXEz45qcP7VzOnmvmvzKOxsbHDdTqCcGeG0OpQp8QzUhPhs6s/pW+/vhx88MHrfJxcTJs2jQEDBjBw4MD12s/mjjzxzCOPPDZZlJWV8cADD3DppZdy8803c+edd/L8889z4YUXcuCBB26StT87uvqnKwABAABJREFUQ3FxMR9+8CGHHnoolT9X8pfXjqZ4y80rjFh1qcia3K0C3z2FPcqeiqY2OPEEkBR5vRRPM20Sb4wTqRLJV8FgMONkmgAeyswTOGRyXSCryIoqQtoUBU1TGLvnGA444ABeffVVJElyarMWFhZSUVFBdXW1YzCVTqdxuURdV1mWHXXQ5XKRTCaFk3EsRiQSaVMnNRQK09DQRG1tPalUilAoTDAYEh3ZtHA7FqTQJobdhR12LAFRpk+fzjfffIdlpcExUmoGVmbW3xNR+9I2SLIVUxciENNDS6KZm9MaRSjGGoLckbkP8wiFQpmakm2daLPrtV0eDAZzCKsJfA7YRHh74DCyobbDgcGEw8s59thjOfvssykrK+OSSy7J1L7dimyero2FiHBcid69e2eIZxWizqk9EGFf7yfJVlBtxjR9mUEJC/g7be+LhCB6kA3fFoMFWRJpn7eeaZcbcY3diGuuAwksaw5r167N7GdnYFey92Mqgky2REFBAcFgMGOmYivmnkw7BbEPt9lKHLm5uRlZlvH7/W1+b5LJJMFg0HH5njVrFiDuVe/ewoBpUQ30LcohjvEsgQzGYV7GHKi+vh5Nd/P+3DAjboXmqCCN0WT7T/nYsWMB0BSJp78DSZaxkLCcQZ3s5dZ1EwlRuuOR//sX6YzSZ1pgpoG0HamQha6A3w3NNb/wyy+/MHLkSGKxGPF4XHxn41HCkSiFbmEU5NZEaK5bB69X5Ii6cyaXmvk8970GV70OS5cu5euvvuL8Cy5AlkS7OoPu0zj+05PbEEdFW//f6YIBBaxMr0BVVU48/kSeeuopFk9exFaHtm9ip/t7Pgi6/fbbI8kSNXNqGLx/+1EqZtpk8ilvkwqneOjJh9b5GK0xbdo0/vCHP6z3fjZ35IlnHnnksclj4MCBTJw4kSuvvJJrr72Wq6++mmeeeYZLL72UPfbY47du3jpB13XefvttDj3sUN445nWOfuOYDv+xbqrQ/TqRSDeL2PUAdlipEUvBRlCDZVlaZ+KZiqV4dszTNC5tJBlu2SNduXIlp5xyCsuWLcPtdreot2qTQF3XcbvdmKZJIiHK1+i6MKIJBoMUFRWRSqVoaGigqamJeDzuhJW6XC68Xi/NzUHC4QiGkXZCTUXu3Lq4vuY62OZOBdgEIxJZxldffZUxvhmMUKZ0slqUncOqIohJbh6rTTL/jSDf55Ctc7kaeCITcqhgGLshjJFAFJK5P/N6B4SxTk9gh5faZC1TLgZIJBKZQY3W6lmu82yUrCFPNWC7i+qZ7V5EhM8CHAjsTpb0poBJwHJGjx7N9ddfTzKZ5NBDD2PlyhXAGGA/sgTQBL7MTELRXLhwYeazOPBTTrtstLRSTaWSOQ7EkCWKXgTBs8m6F5FT6QZ+oqamJqcO8JmIe9QZeVgBzKGmpgZZVjBNF5CbVydyQ2tra3PyQLPEM5vXm2t0FEWireJJpsW2yjl06FCefvpp3n77bUKhkGMI0xpDhw4lEAjg8XiQJLj45fbPRM0ojZIkUVLkZebMmaiqSrOhsayejIpooalgWZkBFytLyh5//PFWexTE0aOJfM4CtwjJLfKIPNICd2bydP064AY98zg+PwVOfhrmzJnD1r2hlw8KCuHrJWBZ8OQp8Jcd2z/H7uC6t8S53nLrrQD861jR/haTW8zfmAFXvwHj/vEH+u3Wr9P99hR6gYu0Ia7lJZdcwptvv8m7Z0zmzDnn4K9oG8+sZYhnTwZB3W43Pr+PVV+vZLfLd2/zuWVZfHL5xyz7aCkXXXjReju4h8Nh5s+fz6WXXrpe+/k9IE8888gjj80GI0eO5N133+Wrr77i6quv5uyzz2b33Xfn0ksvZdttt/2tm9dtuN1u3n5LkM/X//IqR789nqF/2qrrDTcR6AGXU6x9Y8AOK91YBcwlRSIVXTfzokRzguqZ1QwuhV13gjK/6JBN+AiGDx/OPffcS3V1bYYQWhlS2FOF0CZzKhBn2bIVHHvssdTW1iDCR8toSfZalzhpb7LJYlfKxCpgGYZhX5+dEfUo1xUuBMnIvYciRF7UPVVoqTzm5vKua41VuwZnbpiyHXZqK60yiUQioyY2AP+kpTmSjQmt9i1TVFSU2W4V2ZIyJyIcf200Ay8DlZx44olce+21GIbBn/70Z2praxH1PHfIWT8GvIpwMgZxnWcQjUYpLS1l2bJVwEG0vL86Le/1VCzrmxz37zOA7jiBLyceX5lTA9h2Wu4MgizabqNtS6OIuNUlS5ZQVFTkEMQ1a9bQ3NycQ46/AdZkXtcgIfEZFmHE0xLPTI2AlClLc9999/Hkk0+KGoo5zqb2a/t9UVERL730Eo8+8jCaIpE2Lcx2qksZJhkWaZFqDjN76rf43TCkNEMWPYI4dpcsFnjEb4GyEQNwPr88a5T0/s/wp4chtp7uthI40RC7D4YL/tD+ek1RuOtDKNyigNGXtiVpGwquApdjUCXLMo/95zGOO+E4XjvsFU78/GSnFJazfsborqsw7I6w3TbbMfXDqaSiqRb7NtMmH1/6EdMe/omDDz6Ys88+u4dnlMWMGTMwTfN/Pr8T8sQzjzzy2Awxbtw4vv/+e95++22uvfZajjvuOA466CAuuuiiHPOLTRter5fJkyZzyKGH8NoRr3D8Rycy8A+DfutmdYnFkxeRThg5tQo3PGzFc+MSz3Xbt12G5ZDthTIAIhRvwkciL2rJkqUI1chWjuySKbnEwc5FtENF7XqbXgT5eRGhsv0VUdcR4D0kaXaOy+zOCBfUjQWhImSJ57reA9uIyCYyuaROXMNIJIKqKiSTbUmpwLdkHVmTCErS2kE3N0e2I8XXJp4AquMSKtBZXpgd1hsDTKZMmZIxubFrjJ6KyNO0sRJ4EVlOcdtt/+Cwww4DYMqUKZnBgn1pSTrrgWcRCqoMHALsBPxMNBrNMXTZuZM2AviwLDOHQLZnMNQe3CSTKSoq7Gestb2PbUhlU8Fops2ilIjLpZNIrEDkFdtlUYQp1fnnn5/z7Ajst99+mWPJwJSM87AEvOpo0jNUFVXT0HQdl9tNH7fbMb8pKyvjmmuuabHPVCrlhNqGQiGWLVtGIpHgu+++Ix5PcPl+3SOOXh020ZLRLRCKZ4lnQeYRXt+KU5IklD23202wkwCWWyaL8ORTXj+m45U2AFwFLkxDRHLIssyIESO45aZbuOVvt/DywS9wzOTjnLx6AMWlIMlSjwdBzzrrLH444wcWvD6fkSdvD0C4Ksykk97il8+W86c//Ym77757g5zbtGnTqKiocHJL/5eRJ5555JHHZglJkjjiiCM45JBDmDhxIrfccguHH344Rx11FOeee25OZ2zThdfr5Z3J73DAgQfw2pGvcsb0Myka3H3zjt8Crx/1KpZpMWT79XFv7Ry24mnENk5JFVmR19lcyB4Rb84Rejya6DSLEXcJka+413q0rABBPHN7gaKWZJZ4ru81sd1pbWJhq4Q2oRMEQoRSSwhn07U5n+c64LauodkeCWxLLoVZj9rqMxeCmFjAXFqbC3UO2wDHDiG2O6K5yqlKPB7POKxKwElkcxhdZGuj2vsDQU7/SXNzc0bxBDiXlvmZs4C38fm8PP/8C05YKMAuu+ySWad3zvpLEMpoGjHgcDy5zraxWCxjXGPnB3fGinTAynEgbk0g02QNhXKnegzDyAmJ/RBx7W29MUFHZL6srIyttx7OTz9NQ1Xn4nbr+HxefL4yTLOEI488kuLi4hZK5FZbbYUsy8yZI/Iww+Ewd911F+l0Gl3XKS8vJx6PO2HmoVCIaDTqdNKnTJnC/fffTzwed4hmtq5tS+ywww5YwF1Hbh6EsrvIrf0ZyDx+3TDh7RSyJBRPt9tNsIPqWLNXw0NfwJCDh9Jnl77rd8AuYNeIbm5udvKPjzzySBKJBHfefSfPjH6Sv7xxDOXbiOdWkiRUr9Zj4jl69GiKS4r5YcJ3bH3MNsx4fDpf3vA56XiaSy+5lDPOOGPDnBjZ/M4N7dK+OSJPPPPII4/NGqqqcsYZZ3DCCSfw0EMPceeddzJ58mROPvlkTjvttBw30E0TXq+XF194kcOPPJxXDnmJ06ac4ZgmbIqQFZlxY8dx//33d71yD2GXUNhoiqe67uZCsiKj6ArBWJbMSBK4NKF4CvVgXXItWyNNtp5kbhijhmmmc1Sw9bkmlcCj3VpT1GmVEWreiszS7pBBW+nNdVm1IZ7rUCiEprV2iZVyttsOUaPUVotzQ49tgqjQMq80t0N3N+IaJgD7+y9jGEbm2bIQYbJddYHE55FIBI/HVqlt0mkCnwLfssUWW/Dyyy/jdrs544wz+PHHH3G7vUya9FZmXftafA98lFk2ADgys8+GzDoS8Xg8o/hbCMMe20jIJvq5CnBTq/Z+hsgXtQlmR4MUEkVFpQwYMIBBgwYRjUbxeHS83iInZLWkpISioiJKS0vp378/ffv2paKiAq/Xy1FHHUU8HqeyspLq6mpqamqoqamhoaGBefPmUVlZyY477kgwGCQYDFJSUsJ1113HnnuOzQxodPY9sa9xiGeeeYaxY8dy5plnAnDyySe3G2ZrT3/+859FGR1TuMl6Nt2f0XVGKEfMthXPyAYItbUsC6/XS1U7+7IsOPcF0HSZI18+av0O1g3oGTWztra2hXvycccdR58+fbj8yst5cofHGHP9Xux+5R7C3MirrVf0zbnnnMudd97JBM+dAAwcNJCH/vUQgwcPXr+TyUEsFuPnn3/mrLPO2mD73JyRJ5555JHH7wIej4err76as846izvvvJOHHnqIV155hbPPPpvx48dn3Cw3TQwYMIB7J9zLpZdfyuRT3+Yvrx29yY6Mqm6VWKyLAnnrCVvd21iKp6LJwiBoHaF6VcKJlvmHXv3/2TvvMCfq9Yt/MunZXmDpCCIIKnoVsQAigu3a6UWxgCg2FEXsBUVQBK8NC2ADlSqCCKIioICCIGIBpUgv29N7Jr8/vjPJJLvLLuyC1/vLeZ55sjuZ+U7LZufMed9zUG58dMAu4sY0EeK9h1rSoP7sJ04MZKruazQTjcqKg7OO2hHPrYd5T0/c0VTEKQgy3RI4mYq9olryp5Iy1YEW4EOECU/FUlun06lEIiVfX7VJrg1H11eq3Y6PZLU1EAholOMgh78FiquNXq9XIYMBYCfiWv0I/MmFF17Iyy+/DMDFF19CUZEwJPL7/Zqb5y8Q/Zxa7AVeqbjnxqa0adNG+e1z1PgbSZKQJD1GowGjUcTgWCw26tc/gRNPPJGTT25LeXmZ4njcKJbJmJubS0FBAQ0aNIgRSO3DuOnTp7N161b2799PYWEhxcXF2O12duzYEctn9Hp9+P0BQqEwI0eOYNeuXcycOfMw5w727dtH/fr1CQaDfPXVV4wcORKPxw20Viatc676s6p6A7wSU7Jyc3MZNGhQtb126enpivuwqEz4nyKeGsUzU4mBqW2PJ0qpbVpaGp5KqrQ/Wgff/wXdnu92XB6GmjXEM9nMp2vXriz7ahl33nknq5/5jh9fXseZt5+Jp8iDN+/oiefAgQP5fPHnyLLM7bfdfkx6MDdt2kQ4HE71dypIEc8UUkjhfwo5OTm88MIL3HPPPTz55JNMmDCBGTNmcOedd8aeiP834qKLLmLIzUOYOnUq61/9kbPv6fh371KlMFgNVZa51RXU0sFjpXjqzQaCriO/azPajHiS3DRtJvFEW6+XCId3kagOqq9HGKmSQJiEW6hQVSWi0dqckwuADsRJo5YoavEUgLK9AqrvNawMam+l9jxLgBGPx6MQz+RjUfelJuZCUc2klgH7lXXV/tK9CLfcEFBOIJCrKUv9VFnWT7z8WFUSIyQbQsUNfN6PHcttt4kMRBDlox6PB5NJOKo+//zzWCwWzjvvPIqKirBam9O5c2d+/fVXrFYrNpuN9PR00tLSyMzMJCsri5ycHM4991wsFgtXXXUVJpMJg0H8vTmdTux2O3a7PZazaLfbYw/U3nzzDdavX09RURHFxcWUl5dTVlbGX3/9FYvQEQQySCgUwmazsmLFN1xwQddKlHodcTJoRfSzWoHNrFy5Uik7loDexMljoXJOBUwmE16vl7179wJCQRefp0bU7PNkjRHPzMzMGhnIaImn0w8NsqpZ4R+EY1lqm5aWhj8kckvVf49OH9w7BzIbZ3Deg+fXbkM1hEo8S0tLK30/OzubDz/8kI0bN/Lcc8+x9kURE3TaaafVarsfzviwVutXhw0bNpCXl0fbtm2P6Xb+KUgRzxRSSOF/Ek2aNGHatGk88MADPPzwwzzyyCO8//77jBgxgs6dO/9XKoojRoxg1apVLLv/K5p2bkqDMxtWv9JxhsFqrDTKoC4RVzyPDfE0WAxHRWqNNiOeJLE3zSzULZHfl4NwnTUi+vfSifcfmomTPbWnUHVcVU2IViBcP7Ubifc9iRv32qjAOkSkRs2g1+uR5aO5BjLxYytHZFJGUAmi2+1WCFMxQjlU+0PV4/4R2EJcIY4Qz0ZVx6nJefg84bfhw4dz4okn8v77HyDLhZjNIuLGYslGr9djs9lIS0uLEb7MzEwKCgq44YYbsFgsdO7cGVmWsdlsNG3aNKEc0GAwsGDBpzgcDpxOZ6x/8oYbbmD9+vW4XC727NmD0WjE5XJRXFwcy2YUWapBQqEwHTqcxahRD3DttT0Vk57qH1y0a9eO++9/ALc7mZypn0P181aAElSCy7WdYDCokM5zEOZHagSLeu2SsReHw6EosjJCmVZJvpa8Gjh06FAsSkgl5Xq9gXC4pg+trPh8ot84IyOjRsRTLdEHQTz/l6A9HqNeZH3WlniqVzg9PZ0owqwoTan2f/pzKPPADV/3rt1GjgCmaoinin/961/MmTMHt9vNwoUL6d69+/HYvaPGhg0buOCCC/5rH3ofb6SIZwoppPA/jbZt2/Lpp5+yZs0aRo8ezR133MHZZ5/NvffeS/v27f/u3auAd999l4t6XMS8XnMZumlYgovffwOMNgMB37ElnvE4lWNTamuw6I9K8TSlm/AlRcalm6EkEMBg0BMKNQOuqM2eUbGcVtzYu1wuJEk6SiKoqoJqma9K4FTFT1X6gmgNavR6iVBoF7BYWSasGUdVBcPE3U/Dmvkox7JGmeK46qqrWL58OTt27ECQQwmdTodOZyAnJwefz4HJ5MFkMiok0IzNloPNZsNkMmG1WrFarRgMBnQ6HWazmfT0dPLy8jCbzRgMBrKzs7FarTFTIFmWadOmDenp6UybNjVWSup0OikqKuLSSy9l48aN7N69G7fbTWlpKTt37sTr9dKgQQOuueYaOnZMrEIYN24cH388i2i0MoVWmOncffcIIhH1mqmZotqyZTPiYUAucIh1635k3759hMNBhDJYn8QSZzWn04RQc6dTWFiILEeBkxARLGrZalURKVuA7RQVFSnLpBPPWj0cBImMZ3LuV9b3IQyoxN/u999/H1tjz549XHHFFdjtdozGIyGeFvz+otiYNSWeIr4o0QTsnw69lKh4giCItS0I0SmKp0rY3QEx7u8H4D/LoPnFLWl8Tk3ieeoGpgzxkM1ut9do+fT0dK699tqYuj5mzBgKCwvx+/1Eo1GMRmPs4VHr1q0577zzaN68efUD1yGCwSCbNm1i4MCBx3W7/81IEc8UUkjh/wXOP/98vv32WxYvXszo0aMZNGgQPXr04J577qlTI4HaIj09nddeeY1bh93Kkts+55qPrvuvUmeNaSaCjto2Fx0ekiQdVeRJTWG0GvEUHnlfkCnDhD9plzLMsN8ZwGg04DvipqsoWiUwruKVIcpEwwhzH1GqKJ6Y70eoosk9o1q32QCJvaWqclYddOh0eiTJRIcOHdi5cxdbtvyJwSBcaPV6PUajHpPJpPQcShgMFmy2XNLT07FarVgslthrfn5+bH5aWho2m43s7GxOO+00evbsid/vR5ZlSktLY6Wk4XCYQ4cOxdxLvV6v0mfojU1lZWX4/X6CwaCiFIYIhyOEwxEikYiSBRgl2cQmP78ec+fOicV0aPHxxzM150jtd7UC5cycOZNrrrkmtmw4HOaWW25h48aNQDvgBBIzU/cAq5VSUR3QEbiYiiZIyficSORXDbFrjVAVq4JQUFwuFxaLCa9XAvIOs7wKUat58OBB9Ho9kUh1LE0tYxZl0nHl5p0K+5Nchh8jNW43ZrMJn6/mxDMQCMXGqGmprUpanP9TxFOHy5/495thqZtS22g07orsCQhDoeEfgcEocd2cY28opIX6kPVw1zocDjN9+nTmz5/P/kP7CfqCNG7SmDvvuJPPPxcVDlknZKPTQdgfJrAtQMgT/9I2mA3Uy63H2WefzY033lihl7Su8euvvxIMBlP9nRqkiGcKKaTw/wY6nY4rrriCyy67jA8//JDHHnuM6667jmuvvZbhw4drcu3+XnTs2JH+/frz0UcfcUKPFpwx5F9/9y7FYLQZCYSOfR2bJEnHrtTWZjyqsU0ZZpzJxNMCodIgJpMRUVL6FYLwaeNHtERQna9OyYRQAn5WJgG93kzz5s3Jy8vl4ME9wD50Ogm9XkKv12MwxA1nzGYzZnNWjACq5aNpaWkx98+cnBzMZjM2m438/HwKCgqwWq2UlJTEttmyZUuuu+46iouLcTgcuFwuQqEQwWCQXbt2xQihx+PB6/Xi9/vx+/04HA48Hg9+v5/09HQKCwsJhSKEQmEikQiyLHP11VeQl5fH1KlTj+Dsq4ZGaiaqqvqlkaggGoE/EOT9Xwj3WiOwAY+nMBZQDxcpy3+hnmXgGkRGqkqs/MDkBDOt0tJS+vTpq+Rz9gA6UZFMimtaVFSkELsoiTmlVcFEJKLN5ayOWQiFyOl0Kvmkh2NbsnI8PkR+qNg/o9FAJLITcR68yvte4tEqat+sgNttoVu3brz99ttcdNFFnHbaaTRv3pwWLVpw3XXXEQ7LhEIhpYc3Xr3gcrmwWi3Y7TV94GON5YFmZGRw8ODBatfIyMhQVNz/LcUT4Lvt8O02QahdAdhTBqc1rn69w0HN8VRbG9wBmLVebKvrsxdgUe1zjxMMZgOSUcLhcFT6/quvvsr7098n4AuQ1TyLU29uz0+T11NWXqa4TsON399M43MTVdqgJ4h9p53i34o4tP4gu1fsZuFnC1m4cCGZWZlccvEljBgxQolaqlts2LCBrKys/8rqqr8LKeKZQgop/L+DXq9n8ODB9O3bl8mTJzN27Fg+//xzBg0axC233KIxIPn78PDDD7Pm+zUsvXMJjc9tTL1T6v/duwSAKd2IO1S9+lBbSPojjzypKYw2I2H/kZfxmtJNBGQdWrKYZoJIKES7U0+jtHQtkrQOvV6vEEJ9rFzUaDRisaRjNBpjpaIZGRlYLJaYGpiVlUV2djayLGM2m2NKd7169WjYsCGvvvoyy5YtIxQKKSpfGJ/Ph9PpJBgMKgYyXhwOB263O0b2dDrx0CUQCBEKhYlGowoJ1J6DRAL82GOPMW/ePLZs2VKDM6OWkKq9qmagBEHgZIRDrUoOt7B+/XqlT1CHIHsqaVwJ7EOUK59Eoop4JKq/E0E8myN6FwF2EYkc1Pxt/4VwIdYhehsHI3ogVRwCPgbc9Op1OyDcKW+5ZSjBYBjoj3D7VRFFlJu6Eao0lJSUYDAYCAarI5BqybIOWZaVHE+U8cxUVLbVV8Gu3G634rq7H1EWrRJGr7I/HipzQy4qKkKSdAhjoEIq7yeViJP8MJFINNbb2q9fP84/P248Y7FYYlmb6jGon3eXy6VEAtX0u8OCLAvCeyQ9nn6/H0n3v9XjGYlE+eZP+ObPxPkPXlK7cdU4FfV6HnTAiNmQ0TCNTo92qd3gRwljJbmcJSUlDBw0kIMHDtL8ohPo8uQFNO3STHw/RqNsmb45Rhorcys3pZmof2p96p9an1P6C7dsb4mXbQu38tv0X5g7dy7zF8znzDPO5JlnnqFx41oyeg3Wr19Pp06dYqZXKaSIZwoppPD/GBaLhZEjRzJkyBAmTJjApEmTmDt3Lvfddx89e/b820tcP3j/Ay6+9GI+6TOPIT/disHy939lG6xGwpE4YZk6dSq///47AwYMqNADVxsIxfPY9Hga0wXxjEajR3SNjWlGwtFE4pluhkgkxCOPPMKMGTM0DqLeWDloOBwmGAwqZDCELMuEQmFNaaiMLMtEo7Ji9lKxLPbBBx9k+vTph1F+tIpfOqJMVFUJ1bJTC/HIChOCcKnHb1LWAZjFvHnzFOUhH6HsGYkrgZkkqo+VueP+DHwGnIvoO1RRQiDgVhS6KHC6Zt2NxIlgdhXHWROo6mJilIssR5TtgiCdAPWAQYDWAnUbMBuz2cDkyVPo2LEjTqeTG264kWg0DbgJaKBZPggsAn7RzNNRVFSEyWTA692JILGq+q2dtD2xEI1Kmuic75VJC/U9UCNfvF6vso4fkf1ZlSFRJuIa64EDOJ1OMjMz8HqNQGfiLrZ/Icq5TwRu0Ky/DFn+IaZiJhMEm80WU73j5FkQQofDoaxXXMl+VQYLIMeyV51OZ7VrpKen43Q6Meh1OP2VHf9/L6JRsHvhkBMKnbDfAV/8Jt6LRGHtaMi2iQqLTItw067tvydV8VSJ5+j5UOKGQUuOn6GQY48D9wEXIW+IkCeETtIpWa8CO3fupN+AfoTkENd8eC3tBpya8J1tSjcRiURinzdtWe3hYMu3cfotZ3D6LWdQtr2MH19ex4+v/chV11zFT+t/qpNjC4VCbNq0iSeffLJOxvtfwd9/F5NCCimk8DcjKyuLZ599ljvvvJNHHnmEp556ijVr1vDkk0/+repnTk4O458bz8j7R7LikW/oMamWj7jrAEabkYgcL7175dVXiMpRVq5aybfLv03ICKwNJOnYKZ6mdBNEIRKIHBGZN6aZiCTdz6aZQY5EePzxx5WeP0hU/wya383Eb/zV91QCZ0AQk7UId9wLNOvOZufOncrYTYABCAIiIfrsCoE7qR1Z06IBfr9fMeaRSVT2agoT8RxTLcwEAvHSOEEOVaJo0MxLRrJBknZKVgNLEeRMO45BMeARsQyiF/BkoCdqyarYxnLgW7Kzc5k3bw7164tKg9LSUsVE6FISSWcpMJO4wttQGW8nW7ZswWIxI65P9cRJQGvMFEUQfxOqeY94/wzEZ8kMfIvD4aB+/fr8+ec24GrEZ0x1qH1LOSf3IAyM1DHGUF5erqiQUUTMjgpv0qsKoUJmZmZiMBgoKytLeDc9PZ2ysrIKJDErKwuHw6H0e9a8xxOgsLCQ9PT0CiS3MmRkZOB2u9HrJRy+mkTyHF+s+BM27YOlm2G/Hcq9QpkNhIh9r0SjICsTgEEvEY7ItGsI6bWsfPX4YVeZKNPdVw5FLsj3emOkbdM+aH7RCTTt1Kx2G6ohAs4Ak1u+SjTpSzXndEGEy8vL6T+wP1jgxmU3U3B6gwpjGBXiqZLnkOfIG19zW+Vy4dhubHjtR/Lz8qtfoYbYsmULXq831d+ZhBTxTCGFFFJQ0LBhQ959912uuOIKbr31Vvr06cPzzz/PGWec8bftU48ePbiw64Usf2k5J15xEi26/71GSAarIYF46iQdp95wGr9N/5VRo0bxxhtv1Ml29JL+2BJPRE7okRBPU7qRSFLkYZoJInJEKaVKAx7gyMpCtShHEE8Toj8RVFVLZESaEORKG4mi3o3WpTpsJhAIKA9dKuu3SiZ+qtutapTkR5gjRanYp2giFAorhEcdS0s8dYg+2WWaccMkGwVVDjW6Rv1Z+/kxxPIqXS4vQs3rS1zFDSMU2k2cf/75vP7664TDYa6//noOHDjIwoULNPur4k9gjnLcaQh1sED5/RmcTqfS6xgFLieuSu8ENiCI4y3ElePNwGK8Xi96vYFI5GyEKRHAT8BCoC1wrWYf1uH1epW+UBlorzkmEJ8PlZSrEHmqdrtducbJKqR6bZJJohWQ8fl8ZGZmViCY2dnZ7Nmzh2HDhhGNRgmFwshyVFH4Q5x66qnEM1Ori5YQn+tDhw6RkZGhGEkFYi7FlSEjIwOv10u6zYzT/99FPNfuhG4vJc47rTGcWA/y0yHHBllWMWVaICzD/XPhzLM6sG7dupjjrMsvSOvuUthnhwMOKHIKElnmgXKf6AN1B0Q8SliGcES8Jn93gSB32ligXvP6HNPzkIxoJErXrl0ZPHgw2dnZZGdnxx723HjTjQRCAW5cWTnpBPFdLkfkWJ9q0H10/zO+e/pbdHodL0548egOpBJs2LCBtLQ0zjzzzDob838BKeKZQgoppJCE3r17c/bZZzNw4EBuuukmhg8fztChQ/+2Po1JkyZxYbcLWThoPsO2DMeaY612nWMFo80YiywA0YuZ3SKbDnefzerXVrN582batWtX6+3oJf0xK7VV3RND3hDW3JqfS2OaKaZEqEgzQ0SOKiWcok/v6FFZiagOkAgGg8o2kpUfcyXrqD2D2ilCPC7FS7zMU6sUoswrJxAwKduzA5OIEwY1hqWmSCae5iTimVgOK1CVuqVVkrUGQ+qkzi9EKISJimc0Ki6eJElEInnEyY8TmAUcYMCAATzyyCP89ddfXH/9YFwuB6DTKPl7lX3YrEwgrtEFxHtExfdE/JrVQ2RlqrAhiGcGiX2l4oFCeXm5YkqkjS1SVdlkMmjE7/drSlsDxEum1W25qGg8ZMXhcCi9cZURzMq2Ja7ZgQMHKlUh1XPk9RoQ6rtaursbh8OpIThB4g9MqoJ4v6ioKFZ14nK5Dks81e3rDUacxzjy6Uix3y5e33nnHW655RZ6nAwNssDth1I3/FUiSKU7INxl1R7VQCCAXoITHhXEMSJXXkStQ3wX5djE1DwP8tIgN038nmuDnDTl1SbmD34PynSG2N/iGcPOxJJ9/AyFjGni7z0nJ6dCm8Znn33Gzr92cunkyw+bZ21MM8YUU51ed1SKZ+mfJfz48lrOOfucOjUBWr9+Peedd17MaCsFgRTxTCGFFFKoBM2bN2flypWMGTOGsWPHsnbtWp577jkaNKj8yeuxhMFg4I3Jb3D94Ov54vbFXDvz7+s/NVgNGmfQeElsl6e68usHv3D//fezZMmS2m9Hbzhmiqc5K048jwSmNCNROUpEFtl6IBRPWY4qpaNVjaeWifpJdLbV5mKqPX9UMo6eQCBQxTZMiNvOD4grjjUlhjoSVcL4/ubknKgorDLVl4mqpcMq+VPNhdTjStxfWY5UQTxVxbMjIsNSSya1PabVYRVwkMRzZURVTUXmqvreXuBjdLogTz31FD179mTRokU8+ujjyLIRoTxvxOv1otNJRKMbEKRR3Zc0BKlLfkhiwul0KkSpouorkHydxA2q3W5Hr5eonJQnn09BPONOuH4Siaeqjlckni6XS8k1DJGoQqrrJ28rXv6alpaWYPhTXFysIaIXkFi6+ymBwHYNOfZRU+JZXFzMKaecAgjiGT/OilCjWyS9Eft/mautTbnkkiSh18F3OyUkiwHJpEeyGDFaDRhzjRjTTORnmGhgNbB1vnATishwfUc4qb6GSCa9Zlnj30k1hVEPclgW0UgGidxWOdWvVIeQ9BJ6k77SMurt27cDcHLvthXei8pRgp4gQVcQ137xGXQ4HEh6qVJzocMhGo3yxe2LMRgMTJw48SiOonJEIhE2btzIqFGj6mzM/xWkiGcKKaSQQhUwGAyMGTOG7t27M2jQIPr06cPTTz/NRRdddNz35bTTTmNg/4HMmDGDVleexGk3/D327EabIF8qBPEMY8m20O357iwZ9jlz5syhT5/alWwZjcYaG0UcKUxKTED4CImnMc1ENBLFE4BM5d5cKJ4oypYMTCaxD/FIFUKoSC6N+Hw+hawl31ipjq+VxVQYNJOeOEE0I9Qr1ahGza20KL+vZN++/dSvX09Ztz+CLKmKv7qs2pta2UOQYuB1KieeclKprXZ/o8q+1SbaqLJeUTHP6/ViMKj9tD8DC7HZbEyfPovWrVszbtw4PvroY0QfZ3+Ey+5GysvLsVis+HxRRB+nBVHWuhP4kYqlwDbKy8sVMpR8DlTVrvL5paWlyj4mE+fK1jHh9/s12Z9VqZcViafP59NESHkQCqwYU0BGnCf19zjxLCwsZMuWP5k79xMSzYxUkyMtLIRCoVgJZc36PMV+l5aWxghldc626nImk4kap7YcAWQZghGwHIWApRJPv9+PLOno8nwPOo44p8rlo9Eo4w1jY7/fcxGcWYetl/vLYcMeuPJKUQaqk3RHTNpqgmg0SiQQIeAKEHIHCbqDBN0hgq4AQXeQSDCC11vxYgmXZpj975lEAmECLrFuyB2s4EhuMBvQ6XTo9foj/p/x2/Rf2L1iN3fddVed+jls3boVl8uV6u+sBCnimUIKKaRQDbp27cqmTZsYMmQII0aMoF+/fjzwwAMah8zjg9GjR7Ni5Qq+uH0xTTs3JbvF8X1CDcLVVltqK0pixT/70285g59eX8+EiRPo1auXJmj+8PD7/bzwwguUlZXh8/kIBAKUlZWR7647owctLNlHp3iqpWHF7kTiCWjOSVHSWiqxk4iXiaoEUHWZ1ZaLrqUy9cwbMwFJvjlUyeA1QCvNNi0c/b94JzrdVs3nu9Vhl64cVZWGmohGZU3pakVyeHjlWC0P1pYIJxsMHVCW3Qt8rcwXxk9utxuTyYBwrv2dZs2aM2vWTCwWCzfeeCM//fQTglBehbhG4nra7XaFDDYBtA9V9iqvydfFisfjUSokkq+nylwqPmAAod6YzUZcrkAN1jERDAaTFM/E/RCfvcqIp11DPP3Eiaf2e81H/FqKD31xcbHyeZeBrsp8C7AOEUOTvC0L4XDkCImn+MMqKyurMfFUP1NG45Epnt4ALPgFdpUI052DTih2QalH5IG6AxCKiAdMchR+fRxOrrr6s1KoxNPpdIqoqGoIkk6nw2AzxpT5cB23rM5YJ74l7r77bt58800iwQgNzmxIOBCOEbyAK6ghi0JhPNx7AWeAoDMoSKUrSNAbIuwNJTyorAyDBw+uMO/iiy9m3rx5cAAyrBnUT7diqy8yidPT08nMzCQzM5OsrCzOPvtssrOzkSSJ4BEQT0+Rhy/vWUrDRg257bbbjvQUHhYbNmzAbDbXqdP7/wpSxDOFFFJIoQbIy8tj/vz5vPnmm9x333389NNPvPDCC7RqdTQ35UeP9959j8uvuJxPB8xn8KqbkAxHWF9VSxhtoqdGlkWJll6KP2WW9BLnP9qZ+X3nsXXrVk4+uWZuqC+//DJz5sxBb9ZjMBswWAxYG1pp3v2EY3IMlhxxY320xLPEJUxBQJTaAhqX1lsQ/W1q6emR9gWvp6JCqiccVvsi1Rt+9bqrimM6ceJQW0jIsb7VI73jVVUy9dwGEKqhmkX5JxDV9OqtRRCXICo5FLEkO6lILsNU3uFW+THodEXodCXo9Xp0OonrrhtAfn6+cq1K6datG//5z38oLy/nqquupqSkGBEd04m4iituk8rKyjAa9VROIqNUVpbq83mVbanmSJJmHTTHo420EQRLnHstQauKeFoIBEpjOYaVuQiL8ZOZWBo+X0DTd6ndfwnxuY0o+6BGzcRVyLy8POx2E9BNs54fQforEs9oNJJEcquD6Oe12+1HrHgajUYcRxA13Ost+EJp11V7JbOskG2FVvXj5j++IHz0I2zafxTEU7l8LpdLEM8aqItGazwDNlSHxNMfgnfWQIMGDWnYsCHvvf8eAPN6zangMFsBOvE9L+mleF6x3oBRb8RsNmMzW8mz5mLNtsZIYlpaGhkZGWRnZ8eyivPy8sjNzSUvL09T/RBHixYtWLp0aY2OR5ZlysvLkXQ1O68qvrznC0KeEJPfm1zjdWqK9evX07Fjx+P+cPqfgBTxTCGFFFKoIXQ6HcOHD6dz587069ePAQMG8OCDD9K7d+/j1nNZUFDA6FGjeXbss6wZt4rOj19wXLarwmAV/zbcbjeZmZno9XHFE8CaJ25Oa5K7p+Krr7+i4IwChmwcVrc7WwVUQ6Ej7/EUxKBE05KkKp7x65+OyEs8WugRZCtCnLSKckrRc4nynpZ4qjmdh4NqLqSqhhEE2VCJndaVtgxZlhVyGAWWavZJa1IUpKLqmHyH7AWmJsypV68RJ598MkajGVn+QzzA0OvR6zNo3rwZZWWlmEwGzOY0zGYzVqsVm82G1WrFYBBldTqdDkmSiEajSJKETqfD5/NRWFhI/fr18fv9sUxVv99PIBDg++9XU1hYyDPPPMPBgwe56qqr2Lp1K4MGXY/fH0LE1LTR7KkPWALoaNmypWISUlmps9rDq4V4MBMv3wsRL7FVSaR6LQwJ8+MmOlr2VJnxFICJQEBbxlpZX2aUimTQTCQS0fSs+5WxfYhrpl7H3xAPAXyohNFutyuEejcwRVlefb+ysm+xDyoxPJJIFafTic1mQ5KkIyKeJTXdBPEok/JJwlG2qkKNX/cL4uk8grFVqIqny+VCL+k5tPEQm975WaMYBgi6QzEVMeAMEHAECNnEd1Qy8YxGhRJb5lEmb/zncm/ivGK3+M4qUxTcgPIRGjGiLwADBwxky5YtMYKYmZlJRkYGWVlZ5OTkkJOTEyOJ6enpNa5kqSn8fj/z5s2jpKQEp9OJ2+3G7XYnZCIHAgECwQDhSJhwOExEFhnIckRGDmtzcGv2YGrz7N/ZMmszffv2rfOHx16vl7Vr16b6O6tAinimkEIKKRwhTjvtNNavX8/IkSMZM2YMa9as4amnnopZuh9r9OvXj8WLF/PdU9/S4pITaXxO4+OyXRCKJ4hywMzMTIwGIyFv/GbYYBXv15R4lpeXU1RcRNcR3apfuI5wtMRTVTxL4/nmMcUzTjyPtC9VJS2q4ZBKNnchyGUIOIDfX1/z9Pw75TUCrFZ+Xq78HEKQD7W/9EjiSOLIz29Mhw4dWLhwETrdTwoxlJAkHSaTUDfMZjMWSxYmkwmr1YrVasVoNColqcJgQ5KkGFlUy5Gzs7Np2LAhN9wwiJ9++gmv1xsrsT5w4AChUJhQyIEsy0QiESUG5cj2X+t7ayQeGLJmzRp69eoFCOfMxx57Alk2A0MQvZsqSoCPEBE3UTZu3KgQ/+Trq95GJe+fnkgkovlOCBAnntpbryCVEU9xrbU5meoyFQ2JIpGIxrjHo0x+ZTqo7Nt+YKVm/kYiEWjWTG0c/LiSsUF81rQGVBI2m01RqcLKuFpEEQ9ftBCfW5H7WVkPaFWw4Ha7kSSpgplRZTAajVgsFgwGA56gIGc1eR5YP0McXcZhSCeA8tWG8yiMi1Ti6fF4SLel89cXO/jrix3oJF2CgmjQG4SCaDDSsH5DevTowbRp07j0VTi9iXDAtfsEgawsHgWEaZDBoMdgMGE0mbFYbaRnptOsSSbZ2dnk5OTQrFmzWInrPffcU+V+y7KM2+2mtLSUHTt2UF5ejsPhwOFw4HK5YpPX640RRfVvORAIEAwFBVmMhGN/z3JYZtDAQTz44IPIssw1117Dgf0HYtvUSaLM2JRmxJRuwpRtxpxlJj0zHWO6CVOGGXOGCWO6KfZqSjdhyjDR+Nwm1V4L5z4ni4csokHDBjz66KM1v4g1QCAQ4N5770WWZQYOHFinY/+vIEU8U0ghhRSOAjabjTfffJNLLrmEIUOG0KdPH8aNG8dZZ511XLb/xhtv0K17Nz7t/wm3/npbLJvyWENVPFViaTKZEizsjcr7Ho+n4sqVYNq0aUQj0UrdC48VrHmitCvsTVaPDg/1HJdpBB1V8YxjDeJGW9uLmKwsJudgVobpCb/dfPPN1KtXD51OD6xGp5MUMqcnJycHvT6C0RjGZBJkUCV/qpqo3pSHQiGCwSCyLKPX65FlOVbOFwwG8fl8yLJMo0aNOOecczj99NNwOp0x1TAQCOL3B/F4fEQiMrIsI8sRjpQYdurUiXff/YBoVI8oETYjCItKFS0IEm5WJm1vrGqQpJobqdN24DPuJ150HAG+RVCuzPR0Lr30UoAkE6EBJKrU2xDxKrIyboiysjJFhUx+oKIqzhWVRr+/VKN4BoirxNprvk05jiCqKhknnuXAfGV5VWaXgZfR9rSazXmKCiUBXyiTFjpExExxhbmqiYtKOiXEmbciLJ6sQBpRLESxAD8Af/75J02bNlXW60e8x1N9Tf4uEg96Dh06hCRJyHJNiac19j2SmZlZqftpMlRFTo6KWJL0GlQ6NsgUV9DuhTwNZ5Zl8ATjMSd/For5P+2BWevF+G7NpP3d5QeX+qr8DIKYLFm8JJafWV0pZjAYxONy8OVXyzgUSSejfgatMzNjSqTVasViscRU/0gkEiOGTqczRgq9Xi+XX345Q4cOjY39wgsvsGLFCgKBAKFQiFAkFFcSZVmoiVXltyRBbzFgVMiiMc2EucCEJcNKVmY2RoVAGiwGNr71E+igc+fOAAwbNowD+w9w5XtXc+K/W8WWO1YVRKqLbSQQ4Z3Z79SpehsKhbj//vvZuHEjixcvPu5tOP8UpIhnCimkkEIt0LNnTzp06MCgQYO45ZZbuP3227n11ltjN/7HCjabjZdfeplhtw/jqxFLuWLaVcd0eypUxVNVH4xGI15PnIkZlPdrcpMI8OVXX1LvtPrktsqtfuE6gsFqQCfpjlrxLNcST+UeOyMjA4PBhCxvRq/XI0miFNRoNGI2C4XQaBSlokajUagbBgN6vT4hXzIajRIOh2OkMBKJEA6HsdvtdO/enUaNGuB2ewiFRMkZiIcAOp0UW/7I+jJ1xHtRjQiSEGD//h+ZP3++YraTp8zPpGJmpkH53UzcPEm7jClpuVLgbYqLi9HpdESjZyP6KusCgm66lZ8cwFyE/U/Hjh154403MJlMvPnmm3z00UckmgiBuMP+AVFarENEkXQDPtNEo7gRJjpqmfEGZd1diIcFaoRMEQZDvqZ/7fUq9nl+0u862rZti9/vR9DAX0l0jQVBSOPwuh0AGA0SobCMjviVFFOUdMCGHJtXqhxFaWkpEiIApTPV3xRuQTxUiiusLYkruVUhHo2i1xuQ5aokQ7XUV1Vl9+P1CsU4PT2dbdu2sWzZslgpprYsU1XenE4njRuLChCnXxBPWRY/l3sFuSxPmn5W/KE6TRBlqCp59Ffx9fDRj2ICkHSgl3To9Tr0kh5Jb0BvMGIwGpWHQBYsmRZyrFZOy8pi8ODBWCwWsrKyOHjwIMXFxRQVFVFeXk5ZWRkOhwO73Y7L5cLtdseqAaI6HeWOcorLiolEIkTCkYQS08qgEj5zlgV3uYtZs2bFiOd3333H9BnTyT85n4yTMzGmK+piugljmglTulF5NcXG0f5sTDMKpTHNhNFmRCdVTxSX3rWEcCDMyPtGcv755/PCCy+wdu1aujzdlfY3nl7t+nWBXz/4he2fb2Po0KGahye1Rzgc5sEHH+T7779n4cKFdOt2/Cp4/mlIEc8UUkghhVqiWbNmLF++nLFjxzJmzBjWrl3L+PHjj3nm57nnnstVV1zFwncWcuIVrTi557FXDdVSWodD3OyazWYcXkfs/SNRPL1eL4cKD9Fl+PG1nNfpdEdJPAXL1DpmahXPE09sgcPhwO8PEAyGiUTC+Hx+3G6PxgW0puY4ECeDYbZv30nTpk3Zv38/4mZfJYElwF8Icngy8agTbdyJGqWiUhL1PXX85JvGTcB8jQHQAKCuHIYFCSkvL1fI9dFE5sgklier/aXic7gF+B0RchKRJB59+GH69++PLMuEw2H+/PNPZZxDwBtJY6g381EEyfwMEMq+Xq9H9C8uRpspqsNAFC+wI2Evo3KYE088scqjUK+GlqoXEeW7775TeihlzkRcQTPxq6ZNNt0C/KLk6lrMZtqEw/Si8oAbLXYgiOf+/fvRSxIRWa7RDaEV8Xcbd9H1EieeqhmRL2kSzsDFxcWYTEZCoa0I8qztDQ1Q2QOTf//7CkD0tq9YsYIVK1aIY7VYSE9Pjzmcqq9XXXUVmZmZbNq0ibOeE4qlJyDcaCuDQUIhjeC3NCMjI4P6trh7qtrrmJkpylSNRiNpaWlEIhH8fj9Op5OysjLsdjt2uz1Ghj0eD16vF5fLRUlZKeFIiHA4zKX/vhQ5LB/W6VUySoIwZpoxZ1mwNLGQkZup/G7GlGnGkiXeM2WasGRZMCnvWZT3zRnmGBmUIzJTTn0L7PFtPPr4o2Q2yeTmDUMxqjXExxDrX/uRDa+v58orr+Tmm29m4cKFzPhwBu36n0Lnx7sc8+0DOHbbWXrnEpo2bcqIESPqbNxIJMIjjzzCihUr+OSTT2JVFSlUjhTxTCGFFFKoAxgMBp588kkuuugiBg4cSO/evXnqqafo0aOu1JzK8cwzz/DD2h/4/JbPaHxOYzIa110WWWVIJpZms5lQSZw8qIpnTYinLMtEI1Gymh+f3lgtdJIuoUS4JlAVT4eWeCqK58GDBxVCUx+RQalV+ipT/rQUQi0b1QOfIsxcBgEnKVv5kkhkvaZf8EJA7c37GUE86wMXH9HxVA3RZxo3MzoScqiNPFF7TbXutEIpdzqdIncvtAf4MmlZLaEMaOap0+EV3W8RtLBR48a8PWUKTZs25e233+b1116jU+fOXHvttXzzzTfIcnL0TdWYN2+eKE8G6gEmZE0QjlxB490G7HO7Y32tlyMeF2iXq+wG7AXA5/PFcjmv4PC+yCXEDVVMFgtBj6da0gnxdM9Dhw5hMBrxBUQtqFo0rFJGL3H90Y8oNI4k5H9OR1wfP4f7nJjNafTs2ZOtW7fy/fc/YLGUY7VaSUurX8HIJi8vj/r161NQUEDbtuJh2oQJEygpKYmRQWH0VDnsdjs//vgjOp2OExXCmJubGyttFQ8QBGGw2+243W4GDx5M48aNWbp0KZMnT2bv3r34A35C4VDM0CbZyKYCdKIixJyhkMNGFjKyM8jPtmDOssSIY+w19rMlYb7BXLe35pum/UzpHyU89thjAEyePJny0nJ6TelzXEjnji+28+U9X9CuXTvGjRvHr7/+yhNPPUGDsxpy5btXHxdjPjkis2Dgp0RDUaZNm1Z348oyTz31FF9++SWzZs3iqquOT+XRPxkp4plCCimkUIfo0qULmzZtYujQodx333307duXUaNGHTNbdUmSeGfaO1zT8xoWDl7AwK+ur1HZ09EimVhaLJaEQG+1FLeyUPBkqGWIR6o81gWE4nlkPZ6xMmMN8VRNQ9SyV1Gw2L4We6aWZmr3TWSnxqMvggnvCdTlORRqXvzm/jsEVdGSP7VvVe1b1JLC6lVdg8GA1WrG7y9EKGJRKpaUHj2++PJLGjZsiN/vZ9CgQfzyyy8A7N27F6NRnM+7776bgoICbBqFKz09nffee4/58+dzO+IRwoeAIxTCYrHQEBh6mO2q8AG7ZTkWc5KJIKzVwYQgnmompdZuqqrlZcQNsMViwYc4g2rBb7L2qBJIu7L+oUOHMJlM/BwI8JuyTlVXQA/o9XpOb92abt26xdRcLXHMz88nPz+fgoICGjVqRKNGjTRRLzBp0qQanAXhdHrw4EHC4TAGgwGbzUZ+fj4HDhzgjz/+oLi4mJKSEsrKyigvL8fpdOJ0OmNKo8/vixvbhMPVlqbu27eP119/nRkzZrBrzy6adGpKRk5mtSQxQX1MNx3T796jgbfUyzcPfk2Tpk3o168fbrebae9Oo3m3E2h9bZvqB6glin4r4pNec8nLy2P69OmUlpYy5NYh2OrZ6PNZPwyW40NDvn9+Dfu+38vDDz1Mw4ZHmIVTBaLRKGPHjmXBggXMmDEjZlqWwuGRIp4ppJBCCnWM3Nxc5s2bx9tvv829994by/w86aSTql/5KNC8eXPuGn4XL7/8Mj++vJaO9517TLYDccVT7fG0Wq2EfRpXW0vNS20lSUKn1xH+O4in/shLbSW9hN6kx+WPK26SBGYDsaD32hNAtWxRO46JaFTW3MC7EcphhHjkhhtRQKmNOFHLHtUYFdVBVyWLOs18p7JsALXctFWrVuj1RiKRPzT7cnTkUC34lZStuFwuiMrYkGlGvEu0sldVC9YR148NJGrJvwGfAzq9nieffpqGDRvyyy+/cNutt+LxeukBbEWYu6gPga644opYP6AW6jxZs81wOIzFYtFWKx4WRgQZVJXL6rR1tXjYSCLx3EI8YCegmdSrXKqs73a7sdlsbAfGUPUVkgC9YjiVbbPRsWNHdDodX3/9daycNCcnh3r16lFQUEDDhg1p1KgR9erVq9C3/umnn1Z9PLJMSUkJe/bsYf369QSDQS655BJ+/fVXZs6cicvlivUyer1e/F4voWBQicqQYwXPp7Vty0ezZ1NYWMjFl1xcZYmqMU0ojeZsC9ZmFnJycrHkKCQx2yLIYbYFS7aYp76asy28e+ZU7HY7IB6GGW1Grl8xuMpj+ydh5aPLCblDvPzOywCMGjWKUDDEJa9eesyVRk+Rh9mXf4wePbNnzUaSJPr07UNIDjFoyQ2kFyS7Hx8b7F29l28fX8EZp59RZ06z0WiUF154gdmzZ/POO++kHGyPACnimUIKKaRwDKDT6bjtttsSMj9HjRpF3759j8k//KFDh/Lll1/yzYPLOKF7C+q3L6h+paOAIUnRtNlsCYqnTqdDb9bj89Usc0DSS3+b4nk0hNdgNeAOJpZ6Wo0oZjA6RN/gnyQqgGrJqBpropaeajMw1Untl9Xum3BOFX1/IMpxE45G2e70pPkS8W4/7Q17ZcqPjmS6EgwGKSioh+PAAdoQ7zXcjCA8Dz74IFlZWaSlpcXiVKxWq2KkZGT8+PGsXLmSB4gHbMgIYuR0OjEYjeQC/SvZm5rChzgbvyN6rd9//33y8/N59dVXmfr226QBNyMKk/cA7kAgdh7FNasIVVlWr4ABiCjEUyWA2iuondT565VjVQnkOuW8BahIIMMknvmMaDSmJi7QzJeIuxQbDAYxmUycXK8e6enp3HfffUyfPp3s7OzYpBLI5s2bU69ePY3ZURzt27dnyJAhlZ4Lr9fLrl272LBhAx6Ph6KiIrp3705WVhZjxoyhrKwspjIGfD6CwaAwv6kkT7GoqIgFCxawfft2TMTNj9IRheLJ3rg/Ar9u2RLbj6gc5bzR59Py8lYJRNKUYULSH707qSXXEntQlp2dTdAdJBqNHrds5mOFg+sPsPHtn+h+UXdat27NH3/8wervV3PWXWdT75T61Q9QC4T9YeZcPQtPoYf3332f+vXrM2jQIIqLi+mzoN8x+/+UDG+pl/l95pKWnsaUKVPqZMxoNMp//vMfZsyYwRtvvMHNN99cJ+P+f0GKeKaQQgopHEOccsop/PjjjzzwwAM8++yzrFmzhjFjxhyTzM+pU6dyUY+LmN//E4b8dOsxKWNSe4K0xDMqR4mEIuiNoijQYDHUqNQWxI3030I8j0LxBFFu6w0mRmfYzEKl0ukkotEfEbfMCVvTTCoOV1qqI5F4GoGoRvFUnWhVLVCN0tDezqvLqHEk2uX1ys865edNwC/cg7gpKAJmIK6xyWQiE7haszcFCLfYzp0706JFi0rPExBT+7RHIinbcLvdmM3mapXAw2G3sh8e4Prrr2f06NF4vV769e3L5i1baAX8WznCYgRZDvr9McVz+fLlrF27FofDgc/nw+/34/P5+P333wGRbJmJKEs1RSJkZGTgAMbVcP/OOfNMAJo3a0ZZaSk+kwmTWTidZlutsRJfLWm3WCxcffXVtGrVipNPPhlZlsnIyCA3N7fa6IcuXbrQpUtFoxan08muXbvYtm0bpaWlFBUVxQxxbrnlFsrKyhg7diw+j4dgIEAoHEaORKq0wnr77bcZOHAga9asIRtBHHNRo1fi5FElkHrgA2DPnj2YTCZygJpYu4RRUkhlOdZTmtsmj+Zdm9dg7ZrDkmPFWyq+r7KysojKUULeECa1gfsfiKgc5YvhSzBbzIwfPx6AkSNHYsow0+WpC47ttqNRPh/yGQd/PMBTTz7F6aefzpgxY/jll1/o/mIPTrqq9THdvgo5IrNgwHy8xV6mvz+9ztpd3nzzTd555x1eeuklbr/99joZ8/8TUsQzhRRSSOEYw2q18vrrr3PJJZdwyy230Lt3b5577jnOPvvsOt1OZmYm458bz8j7R7L8oWVc/J+6d9fTm/WgSySeIPo09VmCeBqtxirVpGQI4nlkvZZ1AclwdITXmGbCmxTlmGYS6pkkSUQiRiCHePZkcvSItkg0uWjUjCga3UhyqS0IJ1hB3boDnY5436uGA/iFbGV09fAikQhmszlWzJu4N8IR9XDEU1UOk8mlEXG+1LG1iafJk7bENKyZ50HYKqWlpfHeG2/wr3/9C4Crr76awkIRuLgdeCVp213+9S9yc3PR6XS8/PLLGAwGsrOzY6TParWSkZEhyGF2NmlpadS3WunRowcXXXQR77zzTmyZZFOc7Oxs8vLysNlsCSRx0eefV3mOtAgGg9jt9phKunv3bvbu3UswGCQUCsUiN+x2Ow6HA7fbTTAYRKfT8cwzz7B8+XIWL15M0O8nFAoRCYeJRKOHLYwuLS0lPz+fgwcP0o44WbQQ/0SmKZMREU3zGfH+38EI0lkdJMTnNyMjg501OhtxA6SysjLy8/PR6XUEHMl5qbWHNddCcUB8ytWHOwG7/x9NPDe9+zMH1x/goYcewmKx8Mknn7B3714ue/PfWHOs1Q9QC6weu4rfP/qN66+/np49e7Jw4ULmzJlDTqscOo48dm0gyfj2yZXs/PovRtwzgvbta9N3H8fUqVOZPHky48eP5957762TMf+/IUU8U0ghhRSOE6655ho2bdrEoEGDGDp0KMOGDeO2226r08zPHj16cFG3i1j28jJOvLwVLS+tOs7haCBKaQ2xUlo1gD7kCWHJEk+UDVZDjYmnXq//WxRPvUlP0HXkepsp3Yi/NHFeuhnK/X4MBgORyCnAlbXYs4PKa7LiKW7cRfZlXRN18fnzI6yNVF02HA5jtVpjFkIRRGmrmtC6Zs0aAoFALGtQVQz9CvFRoy8WIYiH6k3rR/RaWq1WdgDP1HAvdYCk0yHpdOj1es487TTefOutBCXj2muv5ddff8VmsyXEYahT165dsdlsfPPNNxiNRjIzM4+opPKZZ8TeyrKMw+GgtLQUu91OcXExO3bswOl0xvIkZVlm1KhRzJ49m88++wy/34/f7xeqYjBIOBQiEokgyzJyNBorgM7KzOTFiRO58847Kz0HZuKKogmh/C5YsIDVq1cTKCujFYklq1bEdbUkrfsWQnlWHx70pPqbQrWOQf3OqllBvdi20+kkKysLLXXUBrDoEY9stOsAHDhwgPz8fPR6PQFHzb5XjgTmbAuhsPh7U1V6vyNARsX2338EfGU+vhn1NY0aN2LQoEGEw2Gen/A89U6rzxlD/3VMt7159u98+/gKOnbsyOjRowGYOXMmAK2vaXPcypf//PQP1oxdRadOnWLZpbXF9OnTefnll3nyySdjx5bCkSNFPFNIIYUUjiOaNGnCN998w7hx43jqqadimZ+NGjWqk/Hfe+89li1bRnpmOgtv+JRbf7+dtHppdTK2CqMlTixVdSbs00SqWI0EAjVTJvSS/m8xF9KbDQTdR0E8M0z4knY3wwKH3AElpqG2x6L+W9b2kSr5oXa7Us4bRtyqq9mg6q17iHj5rqoZavVEWfk9TLy70I/aVzpD2Zp6ViJKeakLGF/Jnk6fPp3p00VfqU6nw2KxYLPZsFgsGI1GLBYLGenp+MxmjCYTJpMJq8FAvtnM0KFDyc3N5f3331diNeKushkZGWRmZsbyEwsKCsjKyqq21BTgrrvuqjBPlmX8fj+lpaUx9+E///yT33//HYfDgdfrxePxxCaVSAcCAQKBAKFAgHPPP5/777+fq6+8UkRrVLsnAm3atGHqlCl47HbyEGQqi0QDJVX7tiC6g3d5PESUbM7rgKYI4q7meCafhTHETZNsyjo1gRVhAqaW/Qep/qZQtb5SiacX8YnSOuj6SXTQ9SvLuVwumjdvjgxMIK5ia/EQccKpvh48eJD27dujN+jx2+te8bRUQjyPhbJ6vLDy8RUEnUFeflsYCo0ZMwav20vP1/vUqhe2OhxYt5/PblhAo0aNYv2UGzZsYPMfm2nSuSkXPnfRMdu2FiVbilk46FPqF9Rn8uTJdTLmrFmzeOGFFxg9ejRPPvlknYz5/xUp4plCCimkcJyh1+t57LHH6NatGwMGDKBPnz48+eSTXHLJJbUeWy19dTvd6PQ6Fg9ZRO8FdWtoZLDEFc9Y7IOGPJrSjDVWEw2S4W9RPA0WPSHPkW/XlGHGnnS3nGGBUFkQo9GAuOUup2L2ZDhpnvqz1mAojOhGBPgFkecZ0swTpcmy/B0i4qSmEP2l4jOgQ5KEYihJesxmI9GojMetR65fTxgDAfVsNi644AJOP/10GjRogNFoxGazkZeXR2ZmJlarlVNOOSXBUOhoPmMvvvhiwu+yLOP1eiktLaWsrIwDBw6wefNm8vPzueCCCxKWmzJlChdffDElJSU8+eST+Hw+goEAEUVJjEQiCUoigNlo5NtVqyr0ZulITFVViWEGUAj88P337N+/n2A4zFlAQ6iQ3al9DQKvIspEDUYjTYEbanA+3MBfmugcK9WXsuoRxNNms9XYdVcdu9jvJzNTZP+6iJPGAHHCGFBefcTLsPfu3QvARyT2gRokkKNiElCJThSXyxXr1aze8zpealtaKkoMDHrDsVE8s8wxol+/vjDdORbbOR44tPEgP72xngu7XsjJJ59MYWEhCxctpG2/djTr0qz6AY4Sjj0OZl3xMRazhTlz5iBJEoWFhdw2/DbSG6XTe35f9KbDBQPVDXzlPuZcNQspKvHxRx/X6GFVdZg/fz7PPvssI0aMYNy4cf9406m/GynimUIKKaTwN6FTp05s2rSJYcOGcf/999OrVy9Gjx6tcS89cvTt2zdWDnhiixPZ9tlWfp66kX/demZd7TYGW1zRzMjIAEiIVDGmGQmEaqYYGAwGQp7aWMwcHQwWI77SmhYKxmFKNxGOJjrAppkgHA5itabhcGxFBHdUB5UESkiSHr1euJXq9Xpk2UpmZjomkxGTKQ2zuYDu3btz7rnnMnr0A/zyyy9kZWVhMpkwGo3k5ubSoEEDsrKyMBgM6HS6WN+hSVEajwSyLMdu2DIzM3niiScS3vd6vRQWFrJv375YdqLb7cbj8ZCTk0OfPn0A2Lp1Kw8++CBms5lQKCTKTINB4XwaChEOh4lGo3To2JHffvtN6WGt+mz9tHEjBoOBvXv3ctONN1JUXMyGDRsoKChg3759nERcFVQnlUAagL+A30KhmFp3MXAm8U7bqrAQ+CMUIj8/H4CTgJOrOYfqJ8vhcGCxWKipfmYCItFobFs1+cvQI/pDbTYbBxGPPZLVRu3PXgTxKwT0Pl9M5Xuj0tG1plhxM6wVK1Yg6SDHBrlpkJ8O9TNgbzn8sg/kaHMSLYa+x+fzx0rzRTl6BnEVfi4o+5iseJaUlABgNBiPiRJpybYQCQvi2aBBA+CfqXhGo3FDoeeffx6AESNGgF5H9wk9jtl2A64As//9MWFXmNkzZ5OZmYnf76dPvz5E9VH6LRmALb+im3JdQw7LzO87D8duB2+98VbsIUJt8Pnnn/Pkk09y++2389JLL6VIZx0gRTxTSCGFFP5G5OTkMHv2bKZNm8Y999zDzz//zPPPP0+bNkcf7v3www8zbtw4tm/fTkGDAr68ZynNujYnr3Veneyz0WYg4BM3ZmqZnla1NKabcIfcla6bDJPJhKfQw741ewn5woR9ISKBCM27nYA199iZYBishoTyYBXRaJSwP0zAGSDoDBBwBgk4AwQcfoLOAM49DgIRHdN/iBKMQCAEszeAzeLnrbensmzZsphbqVo2arVayczMjBnS2Gy2I+rrDYfDBIOCglx99dW0bNkSr9eL2+2O9VRu3749ZkKjmtR4vV78fn+s1NTlcuHz+USpqCwTDkcIhcKxdSTkBJ/d4cOH065dOx647z7CinpYkwTPXr168cQTT7BwwYLY8vUQpMqGcEHdoczXAd999x1nnXUWGzZsIBvopiz7J8JAyKjX8+jjj2MwGJg1axbjn3sOSZYxIkpF1YcfvYiTlcoQROjIJpMp5udbk0+YCVF2nJubGxunJuuA6KG0WCyxkBy1CLqqtNUDyjKqCrkBYZLk0yyfrJn7EWZNFosFN/Byhb3RaV4Ti4T1wXDsuOKBOWonqLo3bSFWKGxB2AvBuS1h9ajELb39Hdz2IQh9V/sZd+P379LEuSyqsJcox5mt/KyW9aoPJCxmC/7yY9PjGY1E8Xq9McLvt//zFM/fZvzKgbX7uf/++7HZbKxevZrfN//OBWMuJLNp3buoQ9w5tvSPUia+OJFWrVoBcMMNN2Avt9P/i4Hkt613TLatRTQa5csRS9m1bCcj7xvJuefW3sToyy+/5NFHH+XGG2/k9ddfT5HOOkKKeKaQQgop/M3Q6XQMHTqUTp060a9fPwYOHMjYsWO57LLLjmq8/v37M/GliQT9QVwuFzoZPrthAYPX3FTrHp9oNIrRZiToELffaqntwfUHkcMyIU+I7Yu2kZGZUaPxMjIy+OPnP/ig03sJ8zs/eQEXPNW1Vvt6OBjTjHiLvbx//rsEHH4CjgBBd5CQJ4QcrrqDTyeJm4/Byu7qdEr4STiC1WqlWbNm+Hw+3G43hYWFOJ1O3G53zFhG7Ru86aabcDqdTJkyRVEBw4TDYaVEVEaWZaJRNdBC0LfHHnuM9957n3379tbwKFXHXJ3yqrWbkYjHqgD8QjOgjTJnMbB582YAAuEwZyJiVNQwFtWoxoiIX/lMGeXyyy9nwIAB/Ll5M1FlnSLgWqAxQmWbQzw1NMopgOi1zM/Lo7S0lAwEQfwZaNigAe9/8AF5eXkMHTqUtWvX0gTojUgtDQaDsYcfAQ5PPNUj9Xq9SDodwUqyJpMRVc6cLMsxMngIQaBV8picyammtoLoa7RarWwDnqViT2NlkHRCCdfrJPZExcOA6npKy8rKaNmypfKbShBVWyEHUIZIM1W9a63Ab0Sjv8fKeqEfQs9VMRFRgHsOoI0w+RydTkd5JYlJOTFh603U7k+9FELShQj4YNeuXQD0OBkuPxWa5UCLfLjmDdhvTzQrMiI+a3a7XRyVxYKv7MirFKqDOUtQ3OLiYpo3b45kkP5ximfAGWDZ/V/RoGEDbrrpJgAeefQRMhtncs79x85JdvnoZWxfvI0777iTHj2Eqjp69Gj++OMPLn39clpc3LKaEeoGayf+wE+T13PVVVfVSa7mihUrGD16NH379mXq1Kl1UrKbgkCKeKaQQgop/Jegbdu2rFu3jptvvpknn3ySdu3a0azZkfflSJJE7569+eijj/B6vNSvX58D6/az7IGvaXxuY0LeECFPSLxqfg4rvwfdQULuECFPUPyszA/7woT9YaJylCZNmgCIni0dfDPq64R96Nuvb4329c0332T16tWkp6eTmZlJZmYmA64feMxv/DqO6Ihzj5OQJ4g520JGk0xMGSbMmWbMmWYsORbMOVZseRasuVZs9dKw1rNhybYQlaMYrAYMJgM6Scfca2dR8l0JvXr1JRSqSinREyeCPt566y2MRiMHDxYDLYl3GGonA3E7mc/ZunUrwWAA0WF4ufK+XvOqThKV29BUBS/wC42B85Q5yxDZpCrZOh/IT1pLBtYoy9osFu68+25effV1xXjqRGAHpyBKP+cg6MvXynpRLEBf5dgz2L79B4YNG8a0KVP4IBpFB/Ts2ZMnn3ySLVu20LtnT5xuNxcAXZWjVGNZYhEYJEaxqOWlqlK4X9nv0tJSJEnij0iEcuLkUbustvM2CkhK+bGkHPMazXlITmiFuGpst9vj5ehVnn/1OpuAKHLUwa5du5CRiNIIQQbVDNbFyjp9NOvMJBwOx0pm4X5UJ2SB1Yir1ADoqJlfiCxHYn2XVCgIthLv/NRC9PM6K+GAKvHMTy+hZb4owc1LE+W4ry6Hv/76C4BbO0PfDvH16mdUJJ5iS6JcGcRDrnJ71eXYRwtLtnhcUVhYSPPmzdEbjo177rHEqjHf4ivzMeUDYeozefJkykrL6PVJn1j2cl1j45SfWDvxB3r06BHrmZ42bRqLlyymw91nc9YdHaoZoW7w+8e/8c2orznjjDN47rnnaj3e6tWrhZHY1VfzwQcfKKZxKdQVUsQzhRRSSKGGGD58OFOnTSU7OxudToekk5QevcRJGLck/qz273Xp0oWXXnqpym1YLBamTJnCunXreOSRR3jvvfeOKm7lvvvuY868OYQCISJEOOOMM/jxP2v5UXlfJ+nQSTokvYSkl9BL+thkNBgxGo2YTCYyLZmYc8xYGgrHUnW69tprAeEC+cH7H+BwOGLRFdnZ2ZryvcMjLy+Pq6++mq1btzJ58mS8Xi/hULjSMti6RKsrWtPqiroJMjdYjUTkCNFoFGgPdCGROBpJpCZT8fv9CiGxAgOr2UIU+Byv16vp16xLoxDx+QomzdGSuuTyUjfwCaJv8tRTT6Vt27ZMmDAR0bc3CKFvvsUKDtIZYYW0NLb2CQjNMl35vQfwF2+/PRWIYjKZmDRpEl27duW1115jyltvYQVuBLSpoUYgFApRr54o5auJf6UOodobjEYORSIcgoTy4qogI1TPqM4A0TDx0lMbUbYRRUI8EDiReHfpNwSDQeUcSojUS/U9O0KztQLaaIa/gA+w2+2YTEYCgQIE1UbZS5V4nowgogD1keWQRrkMkEg81aLVymidHFOMKxJP82Hmh3FVws1U4nlxW2ieCyVuKHbD1iIxXzUKSlZL66VXvocWhGoMgngG9h+bHk8glv8qYlv+OYpnyZZi1v1nLed2PJf27dvj9XqZ9u40mnc7gdbXHn3LxuGw65udfDF8Ma1atWLixIkAfPvtt7zy2iu06NGSHpNqb5RXE/z15Q4WDl5A06ZNef/992s93rp16xgxYgQXX3wxH3/8cZ1GnaUgkDqjKaSQQgo1hMPhIBwKU1JcQutrWiNHoshhmWjsVSYUDhOIBImGZeRIlGhERg5HiQZkQr4QG/6zgWXLlvHDDz9o+p0SkZ6ezowZM+jcuTNTp06t4MBZE1gsFmZ8MIM///yTDh060LRpU3bv3h3rN9RmH4IgGd988w12uz1mFONyuWLREj6fD7vdzqFDhzCZTAk9qI8/8ThFJUXIskwkEiEqC3OUr7/8Onm3qsQrr7zCypUrsWRbsNWz0uCshkd8zH8XjDYjYTmMXi8RDpsQHY2Hg4lg0KNcg5oQbFEq6/P5MJvNxJM06wriVkDdkyiKM2swGCMzvwOlyjI+hIYW0OkYOmQIX375FXPmzAFOA64gXvB6E1Em8G2C1tcNQcy1aqwPlQRdeOGFTJo0iUAgQO9evfhz61ZaA9cA2lAgjzKZIhEN4dJCQtBFA/EAE4hygO3bt2MyW/D50xH2QurDgX3Aj8r+30JcUfwDWIhfyWoNhc5CKM4qZgLbEA8DtJER6/D5fIqhjqy8rx53LC01ab/Fg4Xy8vJK4nnUkmnVDVntUJWQNU64QqFM16xnIR67o4VYv7i4WNmv5PfV65hMwixEoy7cAYhGRbm5CpV4fvwjWI1gNoDZCBlm4XTrdrsx6MGexDDz0kGvA1/SEwAbYh0QZflHE4FUHdRSW5UUG/VGAs5/BvGMRqMsvesLDAYDkyZNAuCBBx4gFAxxyauXHpO+xNKtpcy9djZZmVl8/LFwjt29ezf3jryX7BNzuG5OLyTDsS9N3fPdHuZcM5ucrGzmzp1b63LYjRs3ctddd3HBBRcwd+7cIzZlS6FmSBHPFFJIIYUaYsaMGaxavYqDxQfpOrYb9U45cte8jW//xBd3LKZp86b8tP4nmjdvXuly5513Ho888gjjxo2jc+fOnHrqqUe8rXbt2tGuXbvY71VtC4T74Zo1ayq+oQOD2YDBZsRoFY6pzn1O1qxZw0UXiZvsPXv20PjcxjQ6twlGm4G9q/Zy8IcDR7SvJpMJySgxsnxU9Qv/l8FgMyLL8hHkeJoIBu3Kg4eaKrta4lmC6NnTFoaGET11auehSjQ8mnkRzXytPY3Yh02I3kq1n9DqdsdKqldr9kQH5GRnc9vNN/Pqq68TDssIangGcUIVAb4lGiNW6cAAhBKqxVbgE3S6EPfdN5Kbb76Z1atXc9+IEQQCAS5HFIdqb593IUp3vUAjpZJAoBeiS9WIsCaaiSjlVRXlImAyJSUlmM0mZZ/O14ysRxDPMKD9244TE4NBTyiUTH5UZTB5vgW/3x/rgxbnWV1WVSQrJ54Oh0NRW5LfNyrzAsSJp5lAwBEr6a34mVIJZHJTptiXkpISJaYnmWyp5drJx2VFlh3IUfCFwKa5P89Vng7cexG8lFRtf/oz4NHrMej1lHsjCe/l2kAvgT9xNjagxCv2Ozs7GzkkE/aHMVjq7vbVrCieZWVlgHDPDf5DiOef8/9g9ze7GD58OOnp6fzxxx+sWrOKs+7scFT/n6qDt9TLzEs/QhfWMXvebCwWC16vlwGDBqC36em/ZACWrMN1WtcNDq4/wKzLPiLNYmPBpwuqfIhbU/z666/ccccddOzYkU8//bTCg9kU6g4p4plCCimkUENIksS6teto0aoFc66ezS0/DT3if7L/GnYmOSflMu/a2Zzc7mS+/vJrOnXqVOmyTzzxBEuWLOHhhx9m9uzZtYpZqQ6hUAhztpmbfrgFo82IwWbElGZCb9YnPDUv/PkQ0/41JaZCAEh6iVZXteb8h8RxrB77HftX7zui7aenpyOHZCKhCHrjP6unxmg1EIlEsBjNJHYHqqQwufvQTTAYUq5nBPiSuD2N1vM0oPnZj9PpVG6IHMArNdw7icQ4DPU1ubhUR5RE11pV7U7GJ/PnM2XKFF566T8IdbcviR2gJYhCXPXhw2mI+AyzZpkQouNzLbm5ebz77ju0bNmS1157jbffeoscRGGqVveWESW7y4mbEzkcLo3SpzVLUomdljSJeS6XSyHwVRHIsLI1VUERY6rlrz5fsjJoVPamojLo85VpCGFAsw11P6OIz4A+Yb7dbsdsVhNBtVBv27TzTQQC2lLbqo6rckWzpKQEvd5QCfE0I850xfmyEtTp9CUSz0yLWKPQSQVkWqHE68NgMFDujeAPiZLbMg94Q+JMJO+hDQgon0H1+Px2P+kN0qkrqN/hqnuu2WzGX/7fTzxD3hBf3bOU3Pxc7rjjDgBGjhyJKcNMl6fr3pgtEowwr+ccXPtcTHnrbRo2bIgsy/Tt1xePx8Og5TeQ3aKy6oO6RfHvRXzUYwYmvYkF8xfE2gGOFn/88Qe33347p512GosWLao1iU3h8EgRzxRSSCGFI0CDBg1YtGARl1x2CYsGL6DXp32PuJzphG4ncOPaW5h5yYdceNGFfPzhx/Tu3bvCckajkQ8//JB//etfTJw4kccee6yuDqMCrFYrcjhKXptkC5lEGKzi34badwWiX1Qbp2KwCgXwSKDenAfdQaw5x45gHwuoiqf4GGwBxla7TjSappRyScBa4mSw6m7DQCAQM/sRUENB1H5SrTGRWiaq/b2q1y3AL9xBNDZnIVAYicTiJQBMBgP3jhzJkCG3UlZWAnQALiWxn/BnRFSGrOzbVcDpJGqWZcBsoJAePXowceLEWJncnDlzKCBe6KrCBcxDqJ0AUaWXMhTarTHV0ZIFde2KxFONOBG2R1SyjjqWNWF+WVmZopRWRtDkSuabCAbDmh7KUMJ7cWjLZsV8j8eD0VgZ8TQjzkYgYV6iuVBFAixQ0SQItEpuVetVnK/+fTt80ECT1CFJkG6BEg8VkG2FQHmAQDDM29+J6BUt9BL8irBA8inTfiCkRAmpfeMBR90ST8kgYbAYYiZGVqsVt6OSA/gvw5rxq3EfdDN1ylQAPvnkE/bu3ctlb/67zr9DRUboYvat2ssjDz9Cx47CpOruu+9m967dXPX+1TTtXJd955XDvrOcj7rPgBDMmTsn4fvpaLBt2zaGDRtG69atWbJkiaY6IYVjhRTxTCGFFFI4QnTv3p2HHnyI5557jh9eWMN5oytXLA+H/JPzuenHIcy85CP6DejH5NLJ3HbbbRWWa9OmDS+++CJ33nknXbt2pUuXLnVxCBVgsViI+KsPezDaxM27xxO/MZP0EmFffF2D1UBUrkniYxwqoQr9A4mn0WpAlmUkJWrFqBe9bRajMFcRaEI8I3EXubk5ioIjI/oe0xHE6EdEr2E3oDVxgvg+0WhUo3o/hiB2ddHDVQxULC6NhELk5uZiNBho0LAhV111FRMmTCQaNQL9EQY3KgLA54hiXRBBKn1IVEKjyvuL0OvhuefG8+9//5ulS5fyyiuv8Pbbb2MymbCSSMt2AHPR0p+LEf67XxEO/6UoFDoSiVVlxjhinsPhUBTP5M+79paoIhl0OBzK+U8mcOreViSeoVBYo8hoSaQOxR4paVtxVVaU2ibVnsbIoHbf9ciyTIMGDZTfK+/lrMo8SCXUFZVcK5UruRrFsxKDoWyrUDGTkWGBcCiA1ZZGyBlG5IOmIQypSonIPxBBfIpUnT4KyBFxDlSScSyMf8yZZpxOIdPabDZK7aV1vo26RPmOMr4fv5ozTj+Djh07Eg6HeX7C89Q7rT5nDP1XnW9v7cQf2PTOz/Tp04f+/fsDoi//22+/5fyHO3Ha4NPrfJvJcOy2M6PrBwTtQWZ+NJOmTZvWarydO3cybNgwmjVrxpdffql5QJTCsUSKeKaQQgopHAXGjh3L8uXLWf7wNzQ6twnNu1bdP1kV0gvSueG7G5l95UyG3zmc8vJyHnrooQrLDR8+nIULF/Lkk08yb968KsxUagebzYYclpEj8mGzPg3WSoinTkpQPI1WI0RFqWZNy5bUf/rHwjzkWMNgMxKNRElPT+ekrHLWai6hNFwYsMBNxP/lfkAo5NE8XW8PqNd0B0LnyURoPyosBAIBTe9RXZFOELf5iQ8KjEA4EkGSJNZ8/z3XXXstb0yeTJR6wA3K/qnYj+i6dCi/n48w2dHeYvgRSuhvNGrUmA8+eJ969erxwAMPsHTpl0CUr776CrPZTBnwA4KO7VQmQUJMyrbVG04TsixIiU6nJxrVsqDKyKDINHW73QrxTO6F1JYCJ5aygiCe4vNcmLReZWW9YrxwOKxxeE4mTFrimbiPHo9H2VZx0jrq9dcSUj3RaDTJ1bayddTeX/XvWxyv3W7HYjFTeSluZUqumUhEKJ6VEc/cNKGEJiPDApFwiHoFjXA6fQg1XMWfwA80adKERYsWxfp2P/74Y1544QWi0Sj164tHI8eEeGZbYt9p6enpBA78d38PfXnPUiSdxMsvvwzAmDFj8Lq99Hy9T62zmpOxdcGffPOgiCt54oknAFiyZAlT35lK6+va0PXZbnW6vcrg2u9k+gUf4Cvy8d4779G6de0cyffu3cvQoUOpX78+X3311TH5n5pC5UgloqaQQgopHCW++eYbsnOymd9nLu6DrupXqATmTDP9lw6kRY+WPPLoIzz99NMVltHpdLz77rvIssyYMWOU2I66hUoQtcplZVAVT683blSi1+sT4k/Ucly1dK0mUP/xB93HNkblWMBoNRKVRQyIO+me2BxrV00sswwGg1WYwVRFYowEg0ENkT/cdZKJ+8+6EH60B4E9wHZgM6Ikdh0ikXIXIJSm+Ygi2N80o11x+eXs379foZE+4o6patflVMCJIGiDgEtIJJ17EWEnmxkwYABLl35BMBike/ceLF26FBDGWS6Xi6ysLByI6JUVypqg0uIM4qQTwEQ0KgiQKNXVnsfKDH+EyuhyuRTlMvkcVkU8xfzy8vIqCGvViqcsy5ry6JqYEol9jJcD12QdQcDFOajMDMhA/CFFRULtdDqr6Hk9XImuOO9PL4IrX4PzX4B2T0Hj0bBpH0iVPBPJMEMkElY+w5Wrsn6/PyE3MSMjg3A4TCAQiBFPv73uMzYtOYnEM/Rf/ABs22db2bF4O4MGDCInJ4fCwkIWLlpI237taNalbstdD208yPz+n1BQUMC7774LiJ7IRx57hPqn1ufq6deiq+xi1yG8JV4+vGg63kMepk2ZRvv27Ws13oEDBxg6dChZWVksW7YsFseUwvFBSvFMIYUUUjhKWCwWVi5fyVlnn8UnvecyaMXgozLGMVqN9F7Ql/l95vH0mKfR6XSxJ8sqGjZsyNtvv02vXr1YsGBBLEezrqASmpA3hCm9aht51U1Sq3jqJUMCYVXJqcvlomHDmsWixInnf+8NX1Uw2sQ5MZlMuJPaBs1GEBXMieWUib1/FclAZWpcMGjXlNq+gyATWvMi1RTnSPpr4/Eev5mMSHo9BqORTLOZIUOGAKIHsDPQCJiFG9FteTnxJE+A5ghX2QzN2DKC2C7DarUxefI0OnTowKxZs3juufHIsh7oh3Ce/RWPx6MQIIjSiCgmZEwIsrmMisTIBMiKo7BEJKIlkiqBDyFoqy62jtfrVco2D6d4ViSxdntVTsSV9ZOKfZBlWdOHVvH9quZ7PB7lWidvS73+iWXFcQKur8QkSN2WalSlEspkIp4c03M44hnFaNCzZkeESNRGvF/YDBzE6Y9w58dC+VQNhH7YCToiChHfUelxab9XIF6C73K5YgThWCie1lwrjh322DYjwQiRYAS96b/L6CzkC7H0ziVk52QzcuRIAO69916QoPuEHnW6LdcBF7Mu/xiT3sic2XMwGAzY7XZuvPlGzDlm+n7eH1PasY0cCTgDfHzJhzh2Onj91df5179qV0ZcWFjIrbfeitlsZvny5Zry9BSOF1LEM4UUUkihFmjfvj2v/OcV7rjzDpY/9A09Jl58VOMYzAZ6zu3NvF5zeOrpp7BarYwalRgt0rNnT2666Saef/55OnToEIu6qAuIrEESlMvKoJN06E36BLdTg6RPMhcS/1rUnqmaQO2D+29WGqqCWn5sMBjwJu2+zaSWHSYqnuFwRKN4hhPeE0i+DoKsXnrppSxZsgRJkrFYLFgsGZhMJgwGEXUjSRI6nQ5ZlmPKuNlsplGjRuh0OvR6fczIJxKJxDJafT4fRqOR559/HoPBwMiRI1m4cCGDBg1C0unQRaO0RUSbrON3RLKnSlp7IMprtUVUToR+upP27dszZcoUDAYDQ4cOZe3atQgy2QvIRi0bjRvqmIFhmrH8COJZWbSIUN+FSqY9+SphiCrrqSTPjNfrVcpfZWUd9Zxrb4m0518ohk6nU/k7OZwbrhaCGMdNf6paryIB9vl8ynpBEomzlYpOs4IIqmph5cRTVTQrls26XK4q1NXDmxK1bXcKv/yyA0iOQJpIqcfFlFUSsmwgElVJaTlR1L/1gDKuT3ktEVvy+4lGozHDNvVvxOl0Uq9ePSS9RMBR94qnOdtMUInJUb+LAq4Atrz/LofT78evxrXfxZtvvIkkSaxevZrffv+NC57uSmbTuutRDHlDzL5yJoHyAB9/+DE5OTmEw2H69O1DIBRg8Oc3k9E4s/qBarkPs674mOJfixj/3Pgq3d9ripKSEm699VZkWebbb7+lcePkaKcUjgdSxDOFFFJIoZYYPnw4S5cuZcGkBTS7oBmtr2lzVOPoTXp6zu3N3KtnMfrh0WRlZTFs2LCEZV5++WWWL1/Oo48+yjvvvJNQllYbtGrVCoB93+8jq3n2YZc1WAyJxNNgIORJdLUFEiJXqoN6c/7PVDyrJp7WmOFrortqJBI54lLbcDiCTqfDbncSDkcUlavm6qbRaMRqtSZMer2e334ThbUdOnRgy5Yt3HPXXTjd7hjVkSSJkCwTItnqJg2hWCY/APkTmJ+Qzbl582aGDh2Gy+UALgC6EieHekDSEMjkY1JvVSrPvCwvL1diTrSkSqesF0acS/W86gmFQkn9kCrxlJR9iZB4TYQy6PV6lc+pTGIESlUE0gRECYfDxNVpLSozQBLr+Xw+Re1Tt6WeA7VstqJKXl5ejtFoIBSqjJipbrgV+zjdbrdSmVA5waxcaUZRSX0kEmMZyCAUcSFyXVVyKUrzmzRpohC7KDC+wl5Go9GEXmZVAQ8ExDmSDHr8x0DxtGRbCEXE9VOJZ9B57IhnJBQh4Ajgt/vjr5qf1fkBh5+AM0DALuYX/VLI6e1P5/zzRf7sI48+QmbjTM554Lw627eoHGXh4E8p+qWQ58c9z8knCxOxoUOHcujgIXrO603Ds2pWyXK0CPvDzLl6Fvu/38djjzzG5ZdfXqvxysvLGTZsGH6/n2+//fawmdYpHFukiGcKKaSQQh3gk08+oWmzpiy8/lOG/HwrOSfmVr9SJTCYDfT6tC8fX/whw+8cTr169bjuuuti72dmZjJ9+nS6du3Ku+++y9ChQ+tk/y+88EKyc7JZNeY72vU95bB9OwaLIXYjCKLENNFc6MgVz3+0uZByvHq9Hn9ImAmpCTsZserNRMVTliNVuJ0aiStxJMyPRCJs375dib3oAOQqy/+O6NPsDJyIIFA/IGJSdNSvX4/XXnuNtm3bJoy4efNmHnnkEYxGI/fccw+RSISbb7qJNOBM4CfEwwO9Xk9hOMwUtFY3pyDMYbQ5tiHgK2Adubl5vPfeu7Ro0YLXXnuNt96aglDrbgRaaNaJIvpNZfx+v6b3UktmVIKX7PAqyKSar1mZSizGSjz3gUBAQ/or64dMJp5iLL/fn9Rjm0w8kwmpIGilpaXodBLRaGXEs3JCGggENGXVYeK3a/pK1tHGvRjxeisjZsklurLysx+fz6f8/YUQV92vTKqJkgfRx+tBkGRBIuOuzM8px61esyjiM/gzyRFBPp+Pdu3aib02mbDZbKSlpZGZmUl+fj6nnHKKxkArXomhluAaDHoCx6LHM9uiPCDQxLY4qya40WiUkDdEQCGJ/nJliv3sS/jdV+LFVybmBxwBwodxENfpdegNegwGAwa9AaPBiNlkxmqxcPZZZzNx4kQAJk+eTFlpGT3n9RaGbnWElU+s4M95fzB06NAY4XvuuefYsGEDFz7XjZN7tq1mhNohEorwSe+57F6+iwfuf4C+ffvWajyHw8Ftt92Gw+Fg5cqVsYesKfw9SBHPFFJIIYU6gCRJfL/me05qcxKf9JrLjT/cEuuHPFIYrUb6ft6f6Z3fp2//vqz6dhXnnHNO7P0uXbrw4IMPMmnSJDp16lSBUBwt7rrzLp599lm2zNlMu36nVLmcwSZuwlWYTCbcXk/C+3BkiqfBYECn1/0jS21VxVOv1yNHwR8CqyKiZcUEk2QzGFmjutVE8RTrxB1ST0P0VYJwk90F5CH+rX+KMBSCQYMGcv/99yslrALhcJhp06bx5ptv0qpVK9566y0+/PBDli1bhoSgKK0QFGTfvn0YjEb+CgQUSqEHrgD+RaKrbhHC2baEiy++mBdffBGv10ufPn34448/gJOAaxEqqYoAsABhdiT6+ETPVVQ5J6oSqeaVRkh0ZY0TT3F8lSmOvqT5Vny+8sMY/liV/apI/H0+X4wIiTHNsffiqBiN4nQ6kSSJSKQy5bAq4unWuB6HiBN8PeL8+BBEMITqJlxWVqaQtiLgWxLVRtWmaQ7iHMaJT716J2k+V58Rv65aE7N9JOOUU07hiy+WKuchD7Ap+2lVXi1Jvy8nHC6LmQR99tlnNGrUqMK4WqjXSX2IZdAb6qTHMypHCThVxdFP4aZCQn7xOVHPxeqxqzClmeJksswnlncGCLqCVUZG6SRBHPUGPUaDEZPRhNViJdeWQ0bjZmS1yyIrK4vc3Fzy8vLIz88nPz+fgoICCgoKEoh3VfB6vUx7dxrNL2xOm+tOrnb5muLX6b+wZuwqunTpwogRIwCYN28eM2fN5NQbTuO8h2pX7lod5IjMwus/Zfvibdx5x50MHjy4VuO53W6GDx9OYWEhK1asiKm3Kfx9SBHPFFJIIYU6QrNmzXjvnfcYdP0gvhyxlH+/dcVRj2XJstB/6UDeO2ca3bp3Y+sfWxN6OseMGcMXX3zBww8/zMyZM2t0s1Id+vXrx1tvv8WS2xfTpFNTMptU3sNjtFaieHrsCe9DovNtTSDppX+84gngCWqIZ+yyJCtUUY16Vpm5UMXojWhUSzwruq4K0iADOjIzM3nxxRc577zEErydO3fy2GOP8dtvvzFkyBCaNm3KvffeiyRJ/Oc//2HPnj28NGkSa5TlDxw4QFZODk63myg24BYqZnNuAJZgNBoYN24Cl156KW63m27duisPKC4DziGRqB4EPkKQJwMg4ff7kwih1rjESFyJVI837jYrPv/J4ZGV9cuaCIVCGrU5maxaqIoMer3eKgirdj/9JOd/2u129Ho9kYgbKCNu8rNHWW43cTfaILAVny9N8/l4H0EUAwgiCeKxwE8Je1hQUKBkfx4CVhJXG7UEKTm6Jcr27X9xwQUXKPNaIaJy0pTjULNnTcrvNmWMNwmHw0qMTQfgQqpHNn7/oVh1g91ur5Z4quRbJZ4mo4mAw0/QE0woVQ04tOWpAaFEqq8a4hhwBAi4AgmtAbHz16QAgHbt2mFNs7Jr8c6Y4mgxW8iwZVCQXUBG0wyysrLIzs4mOzs7RhobNmxIgwYNNJ+RI4Msy5SXl7Nt2zYOHTpEUVERJSUl2O12evfuzSmnxB8Gjho1ilAwxMWvXhbrh60t9q7aw+e3fEbzE5rz2muvAbBx40aeGfsMjTo25t9TrqyzbVWGcCDMopsWsmXOZm65+RZuv/32Wo3n9Xq544472LNnD9988w2nnXZaHe1pCrVBinimkEIKKdQhBgwYwJIlS5j+9nSad23GKQOP/p9dRqMM+n8xkPfOeYczzzqTPbv3xAimyWTiww8/5KyzzuLll19m9OjRdbL/777zLtf2vJZPB3zC9SsGV5oJZ0wzESyO38BaLBbCxRV7PJMdKquDXq//RxJPVfFUb8q0fZ7ZKgephHgKUpYcA1K14lk18TyovIreyOuuu5aHHnooIUM1Go3y3nvv8corr9CoUSMmT57MwoULmTJlCldccQWjR4+OKbCffvopf/0l3GoPHToUU7d1eIniIk48vcBC4A9atjyR9957NzbG999/j9/vBfoC7TT7GgXWIgJTogiv3N7AHE1fY2XHb0SQugBx4imIvs/nU461rJJ1kscyEQqFK9mOGkGj9mOWIhJEQ8pUiM/XUGMUtACh8oUQvZMqXkOQ13g+Z9zQ6Wdl0kIH/IHoi9Upk0RWVrbG/KSEmuDHH38kKyuLvXsPIRRplSjaSFQhzcqrEVhIMPiH5nPVAyioZkuC/JaXl4sopXBNS1+tCaQ/OW4pFArhcDgqTAaDAZdLnGOb1cbWBVt5Mf35KrciGaSEUlVVccyz5ZHWMI3MNplkZmaSnZ2doDqeeqqI9cnLy2PdD+tqeEyJCIfDHDx4kAMHDlBYWEhRURFlZWWUlpZit9txOp24XC68Xi9+v59gOEgoHCISjhAJR5LjdGP47bffmDNnDgBbt25l1epVnHVnB+qfWv+o9jMZ5X+VM+fqWaSlpTF71mwkSaKoqIhbb7uVtAZp9F7YF4P52FEGv8PP3Gtms/e7Pdw4+Ebuu+++2o3n93P33Xezbds2vv76a84888w62tMUaosU8UwhhRRSqGO89957fPvdt3w+dBENOjQir3Ve9StVgXqn1Kf3/L7MvOwjOnXqxIYNG2LvnXLKKTz//PPce++9XHDBBRXUraNB8+bNeWDkA4x/fjyrn/2OLk92rbCM0SYyJVWYzeaEniWViB0V8XT984inSrRV4unW3IdnVko8xfLl5eWV9P5VrXhCNIkw+YEvgc3odAYsFiPjxo2je/fuCWsWFRXxxBNPsHr1am6++WZOP/10nnjiCXw+H+PGjePKK69MWP7+++/nrjvvBOCkk07CbDZTX9mai4+IMgpBducCHm6++WZGjhzJH3/8wVVXXYPBYODFF19QRtMq8V5lnZ2IO+zOQDcEgRTZpvHjq0lkiZgXd2W1I8p2VYJ6QFnue2CTsu7v+P16DdGaSeURNBUVRZvNSps2bdBLIOl2A7uJRsWRRGKrR0hWXnft2oXRqEfwd4Oy32r0iFZRVKe9lJSUYjKpx9wNaECcRIJQJI0IouoHXqCoqEjpXdUD/6ZmEL2NcUJdExIZV5pNJiPhsE/zXoREt1r1VfSManubX3jhBQwGQ4xgVlUhkZ6eHlNGH3vsMRYsWBBTG/Py8qhXrx716tWjoKCA/Pz8mGvz0cDpdHLo0CGKi4spKSmhuLiY8vLyBNLo8XjweDz4fL4YcQyHw0TCEeRw1WZfxjQj5iwLlmwL1sZW6uVlYs2xYsmxYM62YMmxYM2xYNHMM2eaea/jtITv23vvuxdjhokuT1f8bj4a+B1+Zl3+ERFvhLnz5mKz2QgGg/Tp1wdZkum3ZABp9dKqH+goUbKlmLnXzcG+o5yHRj/EwIEDazVeMBjkvvvu47fffuOLL75IaFNJ4e9HinimkEIKKdQxJElizeo1tGzVkk96zuGmdUNiZOxo0OLilvT4zyV8dc9Shg8fzhtvvBF77+677+azzz7j8ccfZ968eZpsyKPHoEGDWLZsGd89/S3Nu51AswsSHQCN6Ua8ofjNtcViIeQN4Sn2EPKECLoEadI639YEBsnwj1Y8VXg0h2AzgSAHieWeIHryRO9fzRRPQLkB1QFbga9RjV7OPfdsnnnmGQoK4mpVNBpl0aJFjB8/HpPJxKRJk1i9ejX33nsvnTp14qmnnkrIsXO73bz00kvMnj07Nm/Dhg2YzWaCCO1yGiHgVcBFWloGb789nfbt2/Paa6/x9ttTiUYjSJJRU8rqU6a9iN5TlVx0BhoDvynnZjeBQJ7m8/uNcsyq4liqzJ9G3MRHPOyImxIBxPddVQ9hO1opKS0tTZMvm2zyosMgRQnL0DIfMi2QYYGDDti7ZxehUIiIDP9uD6c0hGAEJn0NcDLQFqEsqgQyAkyjsLAQq9WKy1UfuIHqsYxIpFiT/9kI0SNbFeK5uYJ4JkewHA4WZDmi2VaAeEmvljyqP6sxKCJexmo14/VuRuRy+ql4PrXQk5WVhc1mo3///tjtdrKysg47ZWZmJvQnd+rUqcpYDa/Xy65duygqKqK4uJjS0lLKy8spKyuLkUa3243X68Xr9RIMBQlFFNIYEaSxqr5NECX15gwz5iwzloZWMnOzsGRbMGcp8xTCKAhkIqG0ZFmQDEdOiDdO+QnXfhcPPP0AAPPnz2fvnr1c9sa/seZYq1m7eshhmU96z8X+l53XXnkt5vZ6ww03UFZaRr/FA6h3St2oqsmIRqP8/tFvLL51EVJU4tWXX9WUfB8dQqEQDzzwAOvXr+fzzz+nS5cudbS3KdQVUsQzhRRSSOEYoFGjRnw4/UP69O3D0ruWcOU7V9dqvA53nc2h9Qd56+23uPDCC+nXrx8gSO57773HaaedxrPPPssLL7xQJ304b775JhdedCHz+83j1t9uT4gVMNqMhCPxG8zMzEzCvjAv15+UMMaRKrAGwz+TeKo9nmpuplsjVqaZoSLxjJviiN6/msWpgFqeKAFbkCQDkqRn1KhRDBgwIOG62+12Hn30Ub799lsuv/xyrrzySsaPH09JSQlPPPEEvXv3Tlh+06ZN3H333TjKy9EBnYBVyjgWi4UyIB2RulmOizPPPJMpU6ZQXl7OxRdfzKFDhxDkKJdodL2GyMzRHIP2c7mqwnnMzMyiWbNmynLbqNifCPEexzhWrlzJiSeeqPxWn3hZqZoJqiqKBmAXXu9upRcSRLTLGcoyRsBIRH6B687w8Ymmxez5pfDYQjl2XDecA33OgmKXSjzzgNOT9kxcQ7fbrcSC1NQURxhJJZLBw0FErHg8HoW4y4gcVS2BTCaSXuW1hGg0onlg8dFhtiMhSXr0ej0mUzoXXHABHTp0YNGiRTFnWrV8NT8/n/r169OwYUMaNWpEbm5ughI5dOhQfvjhhxhB3L9/P3v27KFBgwbs3LmTsrIy5EiEUCBAOBymy4UX8tJLL7Fu3ToeffRRfH6fII2RuNJ4ONKotxgwZ5gwZ5qx5FtIz8vQkEbxqiWR5iwLFvVnRXmsrO3gWMLv8LP8oW9o3KQxPXv2JBwO8/wLz1Pv1Pqcceu/aj1+NBrlyxFL2bVsJyPvGxkjaQ8//DCbN2/m4lcu5cTLjo0DrOuAiy+GL2bbwq00atyID2d8qPm8Hx3C4TAPPfQQq1evZsGCBVx00UV1tLcp1CVSxDOFFFJI4RihV69e3HTjTbz77ruccNEJnHp9+6MeS6fTcdmb/6bw50IG3zSY8847T7lJF9l4b775Jv379+fCCy/kiiuO3tRIhclk4q033uL6wdez6MaFXP7Wvwl5Q4S8IQp/LiQUipOlkSNHkpOTg9lsJi0tjYyMDJo0aRLLmqvxNo0mgs5/HvHUG/Xo9LoY8fRoeEJ6LE6lYomow+FAr6+qxzOEIAZB5eeS2DonndSSbdu2ceKJLRg/fjytW7dO2J8vv/ySsWPHIssy//nPf9i0aRN33XUX7du354033kjIsAuFQrz++utMmzYNHZCBSOZsiKCGDocDq9VKOTAZkCWJ24cN484772TFihWMvPdeQpEIcAIwEPiBaFROMlgxKJMetaxWTNqew30cOlSILMsIstkBkQ9qVCZTJT+bgHeS3GaHkmj2kwwdsvyXQoIkZduJ0UdRjDj9iQQ3wyzKadUeVpdSkZoWu76VkUNxi+V2uzWlwFqoGaPqpLrplgOypvz1V0Rpc0izrE9ZXjVciuLz+TTX9qVKj12SRO+j2WzCarVgs+XSuvW55Ofnc8MNN1BaWppQwtqwYcPYZDKZCIfDFBcXc+jQIdq0aYMkSRiNxlgvo8PhYO/evbjd7lg5qs8XJBQKKjm0UWbNmsmQIbfidCafD53IlI0KG6tmiE/HTuDnjRsB+OKLLzh06BCtr2uDLc+WQBQPRxr1xrrJOz6eWPPcKgIOPxNemwDAM888g8ft4brXe9UJCV7/2o/8NHk9V1xxBTfffDMAH3zwAYs+X8SZd3Sgw11n13obyQh6gqx/9Ue+e3olclDmlFNOYebMmbUeNxKJ8Nhjj/HNN98wd+5cLrvssjrY2xSOBVLEM4UUUkjhGGLq1KmsXLmSxcM+p+HZjchrc/RPdY1WI73m92Fq+7c4v9P57Nm9J6Yi9OvXj4ULFzJu3Dg6d+5cq5Jbu91OeXk5LVq04NYht/LWW2/xapOXE5a5sPuFsZ9feeUVNm7cSCAQIBgMEo6ECUfCRIny8YyPadmyZY22azabCR4mO+94wH3QxYKB82ncqSkXPtutxusZzIYY8fxuuzAY8gThve/VJXYhDHUCqGZAasyGyOHcjiAQ6vH7gIoGKgaDgdLSUi699FLGjRuXUIbocrl47rnnWLRoEd27d6dfv35MmDCBnTt3cu+993LjjTfGnHcBtm3bxqhRo9ixYwcg1POALJOJoGQmZR+bNm3K+vXryWvQgGnvvEPjxo0ZPXo0SxYvJhfhgbqLXUTZj9qLKqAj7mhbHRYTifyiydc8AZEVWh0MhEIhzec92Q03GSZARpblKrI1xZhODe+UZTAZRD5rOBxGL8GmfbD0d626fQBYQ5xAqiRRKJGiFHgHMJH4w4Sq+wElSRgSpadn4vHsQJJ2IkkSBoMeo9GA1WrBYrGg11swm83YbC249dZbadGiBYWFhWRlZdGoUSMaNWpEgwYNaNKkScxsKhKJ4HK5KC8vZ926dbGy6FNPPZVFixZx4MABPB4PXq8Xn8+H3x8kFAoTDoeJRuOVDieeeCJnnXVWQmm2uK1UzYusCCVY/TlKNLqOn376SflbORG4kvjDhw8xGkXsS0EwSD9lxLnAHqXHUSX+l73xb9IL1LiZ/z2U7yhj3aS1dDizA6eddhpFRUUs+GwBbfu2q9D6cDTYsWQ7X41YSrt27Rg/fjwAq1evZuJLEznhohZc8vKldepgG3AF2DR1I6vHrsJXGv/j2rZtW63HlmWZMWPGsGTJEmbOnMk111xT6zFTOHZIEc8UUkghhWMISZJYvXo1J7Q8gU96z+OmdbfUKuw7p2UOV753NfP7zGPw4MHMmDEj9t7EiRNjTqUPPPDAUY1/0003xQyMjBYjQ28eytixYzlw4ADp6elkZIgogY4dO8bW+fyLz9Gn6anXrh5pGekY042Y0kxseudnRo8eHXNjrA4Wi4VSV7Iz6fFF2bYydq/Yze4Vu4+MeFoNRCIRdMALX4JOJ2iXqP6TgUJlipePCjKiuqkmm+noiCt8RgQVLOPXX3/FZrPRuHHjBNK5du1annjiCZxOJ8888wx79+7ljjvuoGXLlsycOZM2bdrElpVlmenTp/PSSy8hyzIFBQWMGzcOk8nE4OuvZwEwCEHR3G43r7/+OnfeeScFBQXs3r2bHt27U1xSQgfgEoRu9zrgZTpRhNIg4i+S1dzDwUgkImsIZM3XC4W0brh+5XwFiSuKat+ieh1EP6BwZP0TkYOpVR0dbNwL9R4Q6rU/FKfSXq8Xg17PK8sjvLJcux8HgEPodBJ6vRRTFi2WHC677DJsNhuTJ09WSKItVhmg9jGq8Rw5OTnk5eVRUFCAJEmsXLmcgwcPUlJSQnl5OU6nMza5XC6cTmeMJG7evJmCggKKiorYunVrzATH4XDSrFlTvF4vbrcbp9MZe0iiomnTpjz77HO4XA6E7q2q0fnES5fNiIJrG/BDzNVWYATiEcThlEUXsI7S0lLMZiMulwTkaN63EInIWK0WPFrzMiCs/K6qwAG7/3+aeC4b9TWSJDFx4kQAkakpQfcXe9R67KLfivik91zy8vKYPn06AHv37uXue+8hq0U2Pef2Pqp+1GRE5Sj71uzltw9/47cPfiHkDWEwJtKOlStX1m4b0SjPPfcc8+fP54MPPqBPnz61Gi+FY48U8UwhhRRSOMZo0KABMz6YQd9+fflm1Ndc+trltRqvbe927By2k4+mfcSgQYO4/PLLY9sZPXo0zzzzDP369aNp06ZHPHZJSQlZJ2TRbXx31r20ljfefIMrr7iScePGVblOui2djFMzGLQ8Mez74PqDleblVQWr1Uqw9O8ttT1aEyiDxUAwGESng5MLoG1D+GQjNM6G/XYQ5ZynAcsRhawH2b59OzabDY8nB7iWxDLS5H/PDuAlvF4vGRkZuN1uQBChSZMmMWvWLDp27Mitt97KhAkT+Ouvvxg6dCjDhg1LIKj79+/n4YcfZqNSutirVy8efPDBmBp22b//zeLFi/kMcEMscL2goIDp06czacIE9NEo/RBWOih73Bt4nwDCFEiU6EqSDllOvv5R4uWhWnXQiyxHNBEwu4mTclUhrOznA4TDVk1/2GuHv1Cg9MZKtGrVkh07dmIwuDAaRfmpKCfNJxKJ0LJlS9LS0mL9i61atSI/P5+Zs+eyf//+mLNqTk4O6enpyLKMw+Fg3759FBcX4/V6sdvtNG3alDZt2rBmzRoKCwvxeDwcOnSIHTt24Pf7CQSCBIOqyY1MJBJBknQsWDCfQYNuoLy89DBHo0N8ViJs27ad0tJSVq1ahaBrOcp7fkKhEJdddhnp6emx/f7yyy9ZsmQJAH/99ZdS9n0y0L/acwjb8PvtGhMptYz6cBAOx/Hc1WQHXTOyLL4HPG43XuJ2ReGwUFpjxNPx91ZGHEvsXrGLrfP/pH///uTk5LBmzRp++/03ujzVlcymtTOP8xR5mHXZR+jRM3fOXEwmE16vlwEDByBZJPovGYAl++gzof0OPwfW7uevL3awZdZmXAdcIt5G0oMOwiFxHbOys1j8+eJYTuvRIBqNMmHCBGbNmsXUqVO5/vrrj3qsFI4fUsQzhRRSSOE4oHfv3vTu1Zs5r8/hhB4taHPtybUar8eki9n19U769O9LSWFxLN9z5MiRvPHGG7zyyitMmDDhiMe1Wq3Y/Xba9TuFk3u35ct7lrJo8iL27dvH+++/X2lUwXnnnsfniz/HfdBFesOM2HyjzUCwvOZE0mazEdrz9xJP1SjoSGG0GfH7/eglGHwuPHgJGO6Ac06AT34G6IIwn1mByLU8iNfrVUihRDwbs8otAHHnUpfLxdq1a3nyyScpKyvj4YcfRpZl7r77bho0aFCpyjl37lyef/55wuEwGRkZjB8/voKL5KhRo1i6dCk/RSLUr1ePm266iWAwyJBbbuHnTZswAOchrGu+I04B4/RS5C0K4ikhy2uBjcQVyKpdTyXJiCRJ6PUGIpGNynrCmVaSJOU9Cb1elJuaTEbM5jwuu+wyLrvsMn766SdCoRBpaWmkp6djsVhiCmJubi4Wi4X69euTmZmJJEm8//57/PLLL5SXlyeoiar7qcfjobS0lH379uH3+1m9ejXdunXjzTffZOXKVYTDYWRZJhoVU1UhjBkZWTz22CN8/PHHJPa4qiWpaST2sAaJRDbx888/4/P5gRYIF2ATif2t6s86YD7B4F+aUuVhiDLXEuA1OnfuzL333puwX36/P0Y8Dx06hMVyJAZIFkKh5BiWTOUcqM632kmlkYJ4CrV/GzAfET/jBUqJRiN4vV48wAuarUmRCEDMhdlvr2l26D8LckTmy7uXkpaRxsMPPwzAw48+TEbjDM4dVbu4rLA/zJyrZuEt8vLBex+Qn5+PLMv0H9Afp9vFoGXXk3NibrXjBN1BSjYX4z7oxn3QjWOXnZItJRRtKsSxW2SzSgYJg2RAb9ITCUbIbZdL/dML2Dzzd7Kzs/l80edJfeBHhmg0yiuvvML06dN57bXXGDJkyFGPlcLxRYp4ppBCCikcJ8ycOZNVa1bz2eAFFPzSgOwTso96LFOaiWs+uo73z3uHq666iq+++goQ5G3s2LHcfPPNXH/99Zx+erLT5uFx3nnn8e6771K6tZS81nlc+tplZJ+QxTcPLuOKK65g3rx5GlVK4M4772TxF4vZ8MYGuo65MDbfYDMSCNX8BjEtLY2Qt+YK6bHA0SqexjQjQWcQvSThCcpIEpiNokxTQL2hlxBKnuj9E1mNNbnZF/vldrvJyMjgu+++Y8mSJXTo0IFnn32WN998k7Vr1zJgwADuvffehGu0d+9eHn300ZjK2bVrV5566qkKLpKqKVFGRga9e/cW5X3AggUL+HnTJkDQxhXEg0pUQijp9dh0OgLBINffcAPt27end++e/Pzzz5jNZqxWKzabLfZqsVjIzMwkJycnVmraqpVw0Fy16jtcLhfRaBS32x3rOXY4HDFiqOYper1eOnfujN/v548//sButxMMCgVRqIgREZUhy8hyBLXU+cUXX+TVV19l9+7dVZxvPYkETw8Usnz5cr7/fi1+vxGhYFdmfmRDkEoD8D2BwDaN+dFgoLoevRJgE06nU8n/TEP0Qx4OxiryOMUDqaKiogpraE2mSkpKFOddbWSK+qo64mp/30U4HKZevXrKCO9wuAcLeh1IkuiTLS8vVx646BD5qolo3bo1zZs3p7y8nHr16nHo0CF+/PFHAOrXF9Ee/6vE85d3N1H8WxFPPfUUkiTx5ptvUlZSRs+5vWvVohGNRll0y0IOrj/A0089Tfv2wuju3nvvZedfO+n+Yg+yW2RT8kcJYW+IoCdEyBMk5BGGciFPiIM/HuDgjwco3lxMNKI8ZNGB0WwkzZZOw/oNOf/y81myZAlyWCZIkIYdGnLxy5eik3R8eNF0srKy+GzhZ7UinQBvvfUWU6dOZeLEidypZA6n8M9AinimkEIKKRwnSJLE6u9W0aZtGz7tO48bVt9UK7fFxuc05vyHOvH1+K9ZuHAhV18tIltuuOEGXnrpJSZOnMj7779/RCYRt9xyC+998B5/zN1Cp0c6o9PpOHfU+WQ0zeSzGxZw8aWXMHf2HE0OougPa9umLWtf/J4zbz+LjEZCdTHajLhDrhpvOz09nUggghyRj3t0gQqD5uYuGo3W+NwZbUaCJUEkScLulSlxC9K53wGCohURz5PcC0TxeDyKUn0I+IxE19LkSTDYUChEgwYNiEQiPPTQQ0iSxB133EFWVhZvv/12hQibZcuWMXr0aMLhMBaLhWeeeYZLL000DnE6nTz33HN8/vnn9OjRg0cffZT8/PyY8cw111xD27ZtMRgMsXxFNY7E6/VSXl6O3W7HbrfjcDjweDw4nU5uvfVW3njjDex2e2y5gwcPKuWlAYUgCtOacFiQwrfeeoMXXpjAli2ba3DWdUCUFStWMGnSJH799VegHqIP0URFhVAlO1+zefNmJRO1PnANcQVSXTb579IO/AeHw4HJZEAEy9TEOTOTSERL0GrykCHuemw0GqgYrVP5OpGINo/ThXDHFSXZv//+O/Pnz4/1hTqdTiUCRyAUCikPQXYDVZXV6zRTlGg0qsmB1RJBPaKcWuCyUyDHBtlW+HCdyKIVJboScXMhK1AGLIz1KauYP38+q1atIhKJxM7j/2KpbcAZYPlDy2jUuBG9evXC6/UyZdoUmnVtTpuetauQWf3sd2z++Heuv/56rrvuOgDWrVvH8uWiUXnZA1+z7IGvDzuGJc1Cvdx6XH7p5XTp0oXmzZuzf/9+/vzzT3799Ve2btvKn9v+jC1vzbcx8JsbcO5x8MH572ExWvhs4Wea8uyjwzvvvMPrr7/O2LFjGTlyZK3GSuH4I0U8U0ghhRSOI1q0aMHk1yZz67Bb+e6plVw4tnZZY52fuIAtszdz/eDrKSspw2AwoNfrmThxIhdffDHLli2jR4+aG1JkZ2dTUK+ALbN+p9MjnWPzT+l/KhmNMph95UyuuuYq3pn6TuypOcCkSZP495VX8NWIpVw3uxc6nU4Y7siRyjZTKdQywaA7iCXr6PuMagOt4imHZPSmmj0YMKaZ8ATd6A1GXlsR5rUVYv6mfeoS65VJhyCgcNttt/Hll1+yfftO9PrNGAyGWAmpyWTCYsnEYhHupVarlczMTIYPH05WVhbXXnstEyZMYN26dfTp04eRI0cm9Es5nU6effbZWCnl2WefzbPPPkujRo0S9nv58uU8++yzeL1exo0bxxVXXEEoFGL48OGsWrWKRo0aMWfOHG679VY8Hg+RcJhwJIKskI+qsHLlSnJycvj0008RBEOrDKqTTfMzwM98//33lJQUIwjkBVReWqr+bAB+AeZr+lgvBw7nohwGvsblcimk3wc0PszyKsQ+2u12haDVtCTcFDNwEtASJpm4AZK2f1WQxXj+ZxGwjIoPI1RVUhgiybKexo3VY0mMqNi/fz9PPPEE4lqo1y7uquv1epP67QzECbgLQcpPIW46VAL8oolVOgfRuxxVjmExANNvhus1psard4Db41E+h1FEhqr6EERk03g8noR9V9V7j8cjyqQNEoH/QcVzzXOr8Jf7mTBdtEiMGjWKUDDEJa/WzmF286zf+faJlZxzzjmMHj06Nv/UU0+NmfGkp6fHJrUCISsri5ycHHJycti+fTuLFy9m06ZNrPl+DV998xWhQNx1y5RhotE5jWl/4ek0u/AEGp3dCL1JT+mfJcy48AN0YR1zP5kbcyU+Wnz44Ye89NJLPPbYYzzyyCO1GiuFvwcp4plCCimkcJwxdOhQZs+ezVfjvqL5RS1o0b3FUY9lsBi44t2rmXHB+9x44418+OGHAPTo0YPLLruMl156ia5duyYYzFSHiy++mOnTp1O+oyyh56fZBc25ae0tfHzJh9x4842Mf248l156KQCNGzdmYP8BzJgxg/Wv/cjZd3fEaDUeEfFUy69CfyPx1PZ4hnyhIyCeRkLhEE8+/Qxr164lIyMDv98fIwLNmjUjJycnFnNhMKgGN61o0qQJdrsdt9sdi7BQHUh9Ph9Op5OSkhLC4TC///47WVlZ3H333WRmZlaqcv7www+MHj2a8vJyTCYTjz/+ONdcc03CzavL5WLcuHF89tlndOnShccff5yGDRuyadMm7rj9dpxuN1bA6/Gwe/duSsrKOAnRiSoSYhoA/6JirqYR+Jjy8vJYWSQ8QvW3GwHgZw3ZAlHKWh0EIYy7q1anhBkACafTqfQZOmqwjfh2XC6Xsn8OYA8VVWmVEPqU3wuJRmXNDfcihLodQasKVoY4OT4ErCZOGOPOyImIakqsVVdklSimIRIxZaCTZr4FWMKePXsU9VICHlNeUY7xHWX9qzXb+gP4JaZ8w9oKeyPpoNybOC/HBsVu1YVYJd7qd5O47l5v4koqIfZ6vWRmZqI36PH/jyme5TvKWDvxB8468yzat2/P1q1bWbV6FWfe0YH6pxVUP0AV2L92P58NXkCjxo14++23E96z2WzKw4iKCAaDfPDBByxevJjde3cT9IsHLRlNMml4QUNyW+eS3TKH/Hb55LbOI61+GjopkRyX7yhjRtcPkD0ys2fO1jwUOTrMnj2b8ePH88ADDzBmzJhajZXC34cU8UwhhRRS+BuwePFiChoUsGDgfIb9fju2fFv1K1WBZl2a0f6m05k5YyaPPfYYbdsKv9EJEyZw+umnM3v2bAYNGlTj8YYMGcKMD2fwx9wtnDe6U8J7+W3rcfO6Icy6/GMeHP0ge/fuZejQoQCMHj2an376ia9GLCW9QTp6i4gYqSlU4hl0/30GQ9rS57A3BDUkwEabkUgkwt69e1m3bh2BQIBQMEgkHMLt8RCRJXQ6XYIRTefOncnNzWXhwoWVjGigIqk7xKxZs2jdujVWq5V58+ZpzGSEWcykSZMUExto06YNL730UgV34w0bNvDwww/jcrkYO3YsV111FTqdjscee4zPFizABtwI/AzsCIVipXFnIDSvdf/H3nmHN1W3YfjOXm26y95TKhtRWaIgKgqiKCCCgAIiIEsBUZGhiGwFZYlsBGSq7D0FAWVJGTJkFrqbJs1Ovj9OkyadaQFRv3NfV680Z/7OyTrPed/3eQEnIUBDckeNxWLxGZuNgi83smpYBWEXaIq2IAhdLk/0zoYg6HJzwvUIRN/emkZgG7lHEn1dd4XIXnJycub5uIIgyLLjiSgKLXXcbjdKpRKJREJ0eDDxyXkfl0YBWiUEqeBairAvYYxyoBaCMFMiRM6NQEugWOb0WNzuIz6ivS3CjQEPLsBzsf4EWVFmgAMYjcbM43Jlnj+P8PS8/3M60AKZqcdKypQpQ+XKlZHJZDidTrZu3YpMCsn+wUvCdWBJNPukW9rILjytVn9R6amP9URCZTLZfy7i6WmfMnXqVAAGDR6EIlhJszFPFHmbadfS+OGF5ahValb9sCpXY7jsHD9+nEmTJhF7PhanzUlwqWBiuj1MhacrUvaJcgH/ThluGFj6xGJsaXZWLAu8l3Ne/Pjjj3z66af079+fiRMn3tMeoyJ/L6LwFBEREXkAyOVytm/bTsPHGrKh+0+8+nPHu/oxfXJiC86vPcdLL73EuXPnACGV6s0332T27Nm0adMmYEOHiIgIoqKiiF0Zm0N4AgSVCKbrge6sfWU1X03/imvXrnnvQC9btoznX3iedR3XEF45HJfblWP9vPBcjNqMD9ZgyIPdnLcDa3Y8acXff/891vQE6pWFYDXolLD2uASHW4/bHUOWiDzMtWvXfCJhg8gypJGTlX7oyzRMJpM3AuQrOmNjYxk8eDBxcXFIpVLeeecd3nrrLb9It81mY8aMGSxatIi6deuyYMECSpUqRVxcHN17dOfWzVuEAH0QKu7Ogp9hjedVUQDOfFNNlVit1mzCM/sFqycl01cYSsnIyMgUW3HAb+Ssey2F4A6ctS/AJ+VzPYJTav64XK7MlFkHQrQuv0hiFiaTibZt23L8+HH0ej0ajcYvTTEoKAitVuv9u3btGlu3bsVisZCcZqRWKfi6kyAug9XCY5BKEJy+uqDYUCHCJ0QwlUAbn1FcQhCepYHymdPuAK7MCKmEnEJRSpaxlQ1/4anGbDZnMyZSeOcJZH+9hekeU6JLly5x6dIlvyXcbpixGxYdAqMVMuxSzDaQSAw+73srQjQVPMLTZvPfl2+qLYBCpvhPmQt52qd06NCBsLAw1q1bx/Vr13l2Vms04ZoibdOabuWH1stxpDv4YcUPBX7379mzh/Hjx3Mr7hbKICV1etej5hu1KNGgRKF/l4x3jCxrvhhzgplFCxZRtWrVIh2Dh82bN/PJJ5/Qq1cvpk+fLorOfzmi8BQRERF5QNSrV48Rw0fw2WefedNTi4ouSkezz5qzfcBWVq5cSceOHQEYO3Ysy5cvZ968eYUyYni65dMsW7aM1L9Sc3XfVQYp6bChE1ve2cS6eeu4ceMGX3/9NVarldmzZjNkyBAu/nkRhTrwFN8s4flgW6p4KIzDrkIrpBUrFAqqloLdPqe6xDA3tw0RQAufNc5js5l9xJkGz4V33qgwm4U0RU8fT7vdzpw5c5g7dy5ut5sKFSowadIkv1YqAH/++ScffPABV65cYfDgwbzxxhvIZDKWLVvG5KmTkSiEizkLQjKoHbgASGw274V/MnALjyQ2AGfIPaKYjsWiJSTE03NwLoLw9I1C5i7qExMTMy8szcBG/NNLydynr/AU3l8Gg8FnWRAub2R4Um89PTmDgoI4deoUFStWJDk5GUGItSQrmuhrMuQ7TQlMx2azecX/wYMHcz0GX3bt2sXWrVsxm82olAqaVrbRtEqBqxGsFoSnYKaTu+jzTysW3jspKSlIJDLc7tyEmRLhFc65PYslPZvw9H1fgvB6uclej5mUlIRer6dUqVJ89NFHlChRgqioKJYsWcLkyVNIMrlJ8kY9M29CuWU+EU/fYxDSoN1uFzabLfPGQFbE0/OeVyqV/xlzIZfTxbYBW9EFCe1THA4HEyZOIOrhaOr0qlvwBvLY5vpOa0k6l8S0qdO8btG5cfr0aYYNG8aNGzcILhnM0189Q+0366DUKfNcJz8ykjL4/qklGK4bmDtrrp8PQFHYuXMnI0aMoGvXrsyePVsUnf8BROEpIiIi8gD59NNP+fnnn9n53nbKNS93V/U89frU57cZR3mn7zu8+uqrSKVSSpQowbBhwxg3bhwdO3YMuM7mzTff5Pvl33Nu9Vkeez/3/nFSuZTn5j5PSIVQ9n60m0cffdRvvlwp59EGj+a6bm6Ehwv1pLb0f8ZFpaOQEU+XS4g4GbMNXyWHnLWHSmy2tGxRwYKFp8ViISgoCIvFQmxsLB9++CGXLl1CKpXSr18/3nrrLZ+6O0GYzp8/n7lz51K2bFmWL19OtWrVMBgMvP322/zxxx+Ufrw0Ly5/iW/Kz8AOXA5VIVXJUFmc2E2C+JYC+zL/BBKBVT5j83c8TU21+xjqZGT+ZUdCVoRXAaTjcrkyBZAbIVXUk2psRUjyzX4zQLhANhgMmYKrEfAkWQLpK8LDJfz444/eNTp37ozZbPZpcfIoWRG+/FBisVjQaDRkZGQE5HrsEe0ZGRnI5HKM1sBuqgSrITUjI3OMdvxFnzLzf99tZQlBuVyGPdc2Rh7hmf0cqrFabT7C0+wzTxCDWZFSlXcdEIRuyZIliYyMpE6dOt61hAibcBNAIpEhlUqQy2Xec5a1r5yfC7AwatQoMjIySEtLIzU1FYBbt24Je1apsSSb+S9wauFJEk4L7VPkcjmjRo3CZDTx0jfti+zsvWvYTi5tvsi7/d/lqadyN6/LyMhg8ODBHPr1EOpQNc/MfI46b9UNuKY9N6wGK8tbLiPlzxS+mvYVDRsW/UYqwL59+xg6dCivvvoq3333XUCpwiL/fEThKSIiIvKA2bNnDyVLl2Rdh7W8+XvPIvdrkylktPyyFT88v4JRo0Z5WxK89957zJo1i6+++oqJEycWsBWB6OhoIiMiOTX/BOpQNRHVIyjTpGyO5SQSCY0/bEKZJmVIvZKKTCFFqpBhz7CzoftPnDhxgnPnzlG9esHtAMqWFbZvumMqYMn7i0Qmwe104zAXLuLpFZ7p4HRBhg1MVhCuH00IRi2eyKAJu93hExXcm/loJisqZSYrSugA7KSnh3jFaqdOnQAoUaIEM2bMyBHlvHz5Mh988AEXLlygW7duvPPOO6jVan7++WfGfDoGu93Ok188xaPvP+69yHUBg1OGAXB0+hF2DN4GgFsiA7cMoVWJHEF4GBFadpRCSPn0pBFfwOWK8xHArRF6V3qcbRcCScBbCOmiHmZgtVp9DHLakCW2PMIze81wlumPTCbD4bDjn6asxG73F71arRaTyeTTCuQXBJHkqeu0AWFA9ot2IeKs1WpxuVxeEZof/sJTkeOmRF6EqOGO0erjNGsnKz1WRV7C886dO8hkMp/UY3IskzMNV43dbs/DeVeC8FpnZK7n2UZWhDUkJCQz4pyF5z1au3Ztli5d6p2+YsUKJkyYkC266mvIJIxtw4YNgNCCyvM+SksTjKC0Wi0pKSm5HN+/C2/7lJJC+5Q7d+7w488/8lCHGpRtVlCv19w5Pvd3jkw9zNNPP03v3r1zXWbv3r0MHT4Ui9nCI4MepemoZqj0Bd30yh+bycbK1stJ+COeiV9M5Iknil6bCvDLL78wePBgnn/+eRYvXuxjHibyb0cUniIiIiIPmNDQUJYtWUb7V9qza9hOnpkRSH/A3Kn0XGXKNivL5GmTGTlyJEqlEp1Ox7hx43jrrbfo2rUrNWsG4hYKbdq0Yf78+WzqtQGFVkH/GwPRhOV+oV22WbkcF0tSmYRtA7bSsVNHmjZpysSJE31ERU60Wi0KlYLE2MTAD/g+oA7TYE7MKFSqrVyjwOV0IZfLiU0Aed/sSySR3YzGalVl1rpJEAxjCsbpdFGqVKlMoyI3L7zwAh999JFfKwyXy8XPP//M+PHjKVasGMuWLSMmJoaMjAzefPNNjh47SnTNaNoubZdvhF0ZpMTtcmOz2ZDJ5Dgc9YBnfJY4AOxGEJ2+LXsycDpveCPYQs9L3/14UkWzn9+siG7WfI/Y8tyMEfp9ZolLYXqW8MwZQUtPv0PDhg1xOh2CuZPLSUhoBK+99lrmdvbgidYK59UT6faNnHrGZ/TWzfbo0QObzebtS+pyuZgwYYJfpMcjTM1mMwqFirQASxNDNGBNsvrcmPCty/REPHNPtVUo5FgsuUVWPZ9dC1lC0gzcxG63+rTaySlMs6LWnvHIADmpqalERET49QSFLKMwT7TSd7rD4fCJ9K8kt7raRYsWUa9ePe/zBg0aZNavCqm3cXficjm+fxe/fH4AS3JW+5RBgwaBFFpMDrz9lS9Xdl5hS99NVKlaxWtSlJ2xY8eyes1qwiqH0WlZZ0o0KJnrcoXBnmHnh9bLuXn4BiM/Gul1Oi8qR48eZeDAgbRs2ZKVK1cWypFd5J+PGLcWERER+Qfw0ksv8fJLL/Pb10c5Of9EkbcjkUh4cmJLLCYLgwcP9k7v1q0bDz/8MFOmTMHtzt9AxcPgwYPZt28fq1atwmFx8NvXRws1loe71OKdS/2p+0599u7bS9Pmzfjuu+/yXScqMorLWy4Waj/3Gk2EcIFemFRbhVaB2+X21qUBKGSg9zPFDQPKAJWBcFwuT2TIDTQCXgY6IbS7ACEaOAAYitCSRIbL5UYmE1IWx44dy/jx4/1EZ3x8PO+88w4ff/wxzZo14/vvvycmJoZdu3bR/KnmHPvtGE1GNqXH0Z55ik6nXYgqKoKEC76kpCRkMik5haKCLJMg/+n+LUSyiyBVHtMFU6IsIxTf7UoRxA7414dmueEqFPJctilEyeqVNPNUFTsv1nRSJgwsZlNmaribNWtWc/r0SU6fPsGpU8d9UsZzCmObzc758+cBOHPmDNev/Ikx6ToKWzyJiYnMnDnTbw3fiKdKpcIQoPAMVoPTbvWKLVgKfANMBQ5nTvM91ux9RnMLrXq2tRyYCEwHvgVuAAqf1yv7IDV5TFeRnp5OSEiINxrpwfMapqf7u/h6BKcnRbl48WI8+uijtG7dmh49elCiRAmAHG7YOp3O22YlODgYa/o/ow68qKRcTuHXKYepV7cetWrV4uDBg/xx5g8af9QEfZmQgjeQjaTziax56QdCQ0L5ftn3OebbbDZeeeUVVq1axcNda/LW8d73RHQ6rA5WvbiS6wev8+EHH3r7ghaVEydO0L9/fxo3bsyaNWv8vk9F/huIEU8RERGRfwg//PADNWvWZGPPn5HIJNTqVrvglXKh1KOlqNS6Mt8t+I4pU6agVquRyWRMmTKFZ555hl27dtGiRYuCNwTeBuJVKlfh1ymHaTj4MZRBgV8MaMI0PDPjWer0rMPWvlv48ssvuXDhAhMmTMh1+TYvtGHOnDnc/PUmpR69u75vRUUboSGZwpkLyTVy3C43er0ehQysXwvtNABiRkPsbRCEpae1yXbc7qM+F/sVAI/rjOeiWwqEI9RTLgecuFwub7sJ39Rat9vNjz/+yKRJk1AqlcyePZvGjRtjs9no168f+w/sDzjKYUu3oQnXoAoWBGJiYiJyuRxrjvpETyQip4AsqvC02Qw+QtpGluOpZ3+eVimefUsBOSaTCaVSTk6xKKFZFdj7XtaUPstg2W9ZPS/NZv96wdyjjJ7x2b3taY5/BHV8OtUo+2Y5r3rwjXiq1WrSjQREsBqcTofXEVStTkWlUqFUqrDbQ0lNTcNfXArjTEtLQ6XKS3hqAQmhoSHUqVOHqKgoSpUqxe3bt1mxYkVmeq6UwMyMhOkmkylf4ekxBMo+3WAwoNVq6dq1K2+88YZ3/tmzZ4mLi2PRokWcPn0ag8FAWloaycnJXhGr1+uxm2y4Xe4cvSP/LezKbJ8yZcoUAD78+EP0pfQ8mkc9fX5kJGWw4tnlSBwSVq5Z6XOzQsBoNPJiuxeJj4/nmW+eo9479e+JSY/T7mRdhzVc3fUXQwYP8ab+F5UzZ87wzjvvUL9+fX788cccxyHy30AUniIiIiL/EKRSKSdPnqRmzZps6PETMpWMmE4PF2lbzcY2Z0GDebz33nt88803ALRq1Yqnn36aadOm0axZs0KlMH3yySd06dqFE9/+TsPBjxV6PMVqF6frgW58V2cuZ86cyXO53r17s3DxQnYO2UaXfd2KbLBxNxQ14gmgUCiwO8HlBlnmtV2wNzvZVxQpfYx0wP9iX+Gz/C/ATjzpiDabPUdfQ4PBwEcffcSePXto06YNw4YNIzQ0lCNHjjBw8ECM6UYeHfIYzT5tnm/9cLNPm3Nh3XkUusxjybzB4EnfzClIPBHP3KcLZiDZaxEhf+HpyNbj0X9+Vk2gryCVeyOKOcWRPEeUMUgFLqfTex49kTSHw0FqaqrPRbkVoaY1a/8Oh8MrqE3ZdqVVZW3LO80n4qnVakkOMIs8WCUIz3LlhPT1WbNm0aBBA0C4QfXpp+PILdXWI+gE1+HsCLWhPXv2pFu3bt6pu3fvZsWKFd50ZaczuxGUhtxbtAg1ryEhIaSnp3P48GHS09NJT08nPj4eECJtQ4cOJTk52fvnOR8ajSaH6PekZ+8/uJ/9v+zH7XTjdgnv/VOnTgGZ7tduwf36bmsT/w5cDheWNAvWNCvWVAvxp+I5v/Ycr776KhEREcycOZPkxGReXv1Koev7nTYna15aRfqNdL77dp43YuzBYDDwQpsXSEtP45X1HajatloeWyr8Mf3UZT1//nyBd/q8Q/fu3e9qe+fPn+ftt98mJiaGjRs3+hh/ifzXEIWniIiIyD8IuVzO6dOnqVatGj91WY9Kr6Jy6wD6L2SjRP0SVHymEvMXzuerr77yGnRMnjyZOnXqsGrVKjp37hzw9mrXrk3ZMmU59MUv1OvbALmq8D8fEokETbgGW0LeaXJKpZJBAwYxYeIENr75M8/Pb/O3i09FZiuBwkY8Aa+Yz7AJUSsQjGIE/MWlf1TQX5QKxGX+AVQConG5jvhFjfbu3cvYsWMxm83MmDGD5s2b43K5GDZsGFu2bSG4lJ4uG7pRtmlOY6jsNPm4KU0+bup9rgr2Td9UkLujrJucrVGy1pNIpLjduQnP3ASpIOxyT7XN2m5u0/MWngrScxGeTqczc3nyNGHJLdXW4XB6xVF2oyCdEq5fu0b9ujXBLfSxdCFEvrdu3Sr09QzQXChILdTzZhfH4Gkvkl3wKwAJBoMhM8qakMtWVTm25XA4vG6hRqMRuVyB03kLOEpWDegfCK/XgczpHhOgDEJCaqHVanG73fTq1ctnX57UaDdbtmwlex2n2+0mKCgoR0R0wIABbN66mZByIVR7uTqHJx0CIAK8daSez4wl1XLfhafb5caabhVEY5oFS6rwaE2zYkm15JyeasGcbMGSYhGep9twWnLewAoOCebDDz8kIyOD7xZ8R7nm5aj2csEGbH5jc7vZ3GcjNw5e56MPP/LemPBgsVh4sd2LGIwGOm7uTIUWFe7qXHj363KzocdPnF0Vy5s93qRv3xwF7YXi0qVL9OrVi0qVKrFlyxa//sQi/z1E4SkiIiLyD8MjPitVrsSal1fRZc8blHqsdMErZuOx4Y34fusSPv/8cz755BMAatWqRffu3Zk9ezZt2rQp1I/8Bx98QN++fTm9+BR1e9UreIVcUAQpMdr9LzZdLhedO3fm8ccfZ+DAgXTp0oVr166xfMlyXA4XbRa9iFT+94lPT/SysK62gNd90WTNEp7BXuHpuz0F4PJJJ8tZr5dlbPMc8AiCq6vL+5pNnz6dy5cv06RJEz755BNKlCjBqVOn6Ne/H6kpqdR9ux4tJj9dqNRov2MKKqhu0LPd3KcnJydnCs/cBKSE3ASkw+HMlurqS96RUovFknku03PMyx6ZDFKB0+X2nseKFStSrlw5goKCCA4O5vLlyxw+fBi4iZDa63G7TcHlyrpZkJugdQOv1IVyEaBVCn8f/QhWq1WoUwywNDE4c4yetGPfFN4s4ZndfVaO0WjMnG9GqAW1ZC5nBk4AMH/+fBYsWIDdbsfhyBJFqamphIQEEx9/BbhClg2Ipz9qznDt9etXfRxmVQgmUtrM/9UIr/HvvPMEtHoIEtKh9zK4efMmOp0uh/AsVaoUSrmS0EphPDWxJWnX0ri66iwRLjdJFuGEe4S/Nc1CltlRTtxuNw6zQxCGmdFGa5pViD5mCkXf6dY0C+YkM5YUiyAq023YTbbcvI+8p0UmlyGTy5DL5CjkCpQKJRqNhhBtNEHlgtDr9ej1esLCwggPDyciIoKoqChq1aqFXC5nzZo12Cw2nhj3ZKHTX3+dfIhTC07y6quvevs2+9L59c4kJSXx6s8d753odLvZ0ncTfyw7zeudX/fzESgKf/31F7169aJ06dJs27bNJ9tB5L+KKDxFRERE/oFotVrO/HGGCpUqsOK55XT/9U0iqkYUvGImKZeS2TN8J4C3JtDDp59+ysqVK5k3b16hLhyaNm1KseLF+GXcAWr3qFMkMajQKfwudj2cOXOGM2fOsHHTRpYuWcqHH36IXC5nydIluBwu2i5th0zx91jqe6KXhXW1hSzh6Ssw9LlGPAVxJlx4+xr3mIAdnq0CPclyhBUijE6nk+DgYG7fvs3YsWNp164dbrebMWPGsHb9WjQRGjpt6UzFZyoFPP7cUAZn1Q0Koi57+mbeNZ4gCBmZTIrLlfv83EyJXC5XAam2uU+3Wq2ZgiTnPFO2SUFqcLiEGzwymYzXX3+dDh06eOf//vvvmcLzZ3IiJSoqCoDs7WY9Nxh6NoHapYWIqNEC760WajyLFSuG1SG02ikoiB+sFpZTKBRIJJJchCcIpkBLEISlFbBjNBp56KGHOPLrYaSSLUgkQuTV6crSTxZL9pRZGeDk0KFDmSnV8HpDCNe5CNVAmBZCtZmPGgjTCY8f/wQ//mH1eb26AdnrhxOA33m4JLSrA5cyA7EpKSkEBwfnMB8CIevBeEuYHlxKj0MmQeVyY8/8HouOjgbg0IRfUAarsBkyo4+pWZFHW7oNm8mG25m3kZpULkUmE4SjQqZAoVCgUWsI04ahK6YjuHKwVzSGhYV5RWN0dDTFihUjJCSkUL0lXS4XBoOB+Ph4EhISMBqN3u8Lz2ctUM6vP8eu4TupW7eu96aiL4MHD+bPC3/y7OzWRcqYyQ23283O93dwfM7vvPTSS3zwwQd3tb3r16/Ts2dPIiIi2LFjh0/Zgch/GVF4ioiIiPxDCQ8P5/djvxNTM4aVz35P9yNvoY3Mux0JCBcHJ+efYNu7W5C4pcyZMydHKmGpUqV4//33+eKLL+jYsaNPG4WCGTJ4CMOHD+fsqlhiXit8/alCq8Dh8heeUqkUiUxCifolSPgjgdYvtGbp4qUMGzYMuVzOgoULcNqdvLSi/V01OC/MGKFoNZ6eqIWv2AlSQ84In7B8SkqKTzpqHIKJkCcK1Br/NiRZNZdff/01xYoVo1SpUly4cIE+7/QhIT6Bh7vU5Onpz+TZ9qYweCKlRqMxMy01L3OhnOmtnnHm3lNSSAvNTST6170Gmmqrwmo1Z9Y25txm9ihjpmcS6enpaLXaHHWZ9erVo3///qSnp3vFRrFixfjjjz+YOnWqIFil8PlmmLlXiHymZsCdTA311DRyUK9ePe95MFlBX8DL4xGxycnJ3r6jHipXrkyJEiWIj09AJruOXC5HoVBgMsmJiYkhNTUVlxt6NIJwbZZQDMsUj38lQZ/vAdoDNRBSpcdn1vIqqFsGlr6Z//gAooPB6XAQGRmZOSW3POJMg6rMc+O5CZOf8NRqtRhuG0iITSD5QhI2uwsDeM9fTEwMKo2KP9deQCYTRKNSoUStVhOmCUUXpUNXXoderyckJISQkBDCw8O9ojE6OpqoqCifPrOBYbFYSEhIIC4ujsuXL5OUlERiYiKpqamkpaVhMBgwmUyYTCbMZjNWmxW7U4gqOx1OXE6Xt17VQ/v27YHCfdfc/j2O9a+to1ixYsyfPz/H/JkzZ7Jj5w4aDnmMem/XL9Qx5sfekXs4MvUwrVq1YuzYsXe1rbi4OHr16kVQUBC7du3y3kwQ+e8jCk8RERGRfzCVKlViy6YttGzVkjUvr6Lzzi55Rv4SzsSzY8h2rmy7TNWqVb2OibkxdOhQ5syZw/Tp0/niiy8CHk/r1q35YuIXHPh0PzU6xhTaVVKhU+ZolQCC+KzyYjWen9+G71su5fWur7N29VqGDBmCXC7n23nfsn3wNp795rlC7a8oeCOehRKewjpe4elzDS6UjGYXnllRQalUitP5G0INnRshRbEDQn9MD7cQTIakqNVqb4/DyZMns2TZUpR6BS+veYXqLz8U8JgL4vq+a4BwwS/UDQZecwlCpFS4uM8tcpmbKZEScOWRauvKfMxdsNpsaZlpqdnHosbpgrXHwWIXXpfDV4Q5uYk6EGo+Dx36lfDwUPbu3eud7kkpzcjIQK3RcikxgwA7E5GUlETp0kK6fLolAOGZKY4TExNziOOwsDC2bduWY53XXnuNjIwMb43stFd907yz+OOm5z8rQrRTMIFKSxPOYdKtwI4pVAMOp9NHeHoiqc7M/y14ouQbTkO0HuIzdWZaWhrBwcEcPnyYLl26YDabycjIwGw2k5ycjNvt5tuY2QBIpBKuutwUz3xfREZGcuxIYH1vfbFYLMTFxXH16lWOHj1KQkICSUlJpKSkkJqaSnp6OkajkYyMDCxWC3aHHbvDjtPpxOlw5hs9lalkqIJVqEJUqCPV6CKCiAiNRBWqRh2iQhWqRhWiQq6Ss6XvJnALBj2eVONAsyvSbxpY2Xo5KrmSVT+syiGet27dyuy5s6nyQlWemhiYc3kgHJ58iF/GHaBZs2b5/q4EQnx8PD179kQul7N79+4chkgi/21E4SkiIiLyD6d58+ZM/3I6/fr3Z9uArTw3q7V3XvLFZM7+EEvs8jMk/BGPQq2gZcuWnD13ljZt2yCRSNi1cxfNmzf322ZQUBCffvopvXv3pmvXrsTExAQ8nr59+jJu3Dj+3HCh0C6JCq0cl8uVY7pUJsWeYScqJpqu+7uz8NHveL3L6+zauYsBAwZw5coVdszcQY2ONSjbrFyh9llYPGmzjiKk2nrwjXjqvP4nufdddDptPvMqIUSiPJFtB7Af2IdKpWbq1BmEhYV509Ru3bpF1XbVeG7O8+ii760TpDlZcBzt1q0bEydOJPc+nr7H5cxcRlDdBoMhs8VJChCbOc8GHEcQnteBjZnTLIDQHzPr/bEF2Jy5Xc9Ff17C055Zs+lCOGeeyxshetp+TtbSUomQ6pqWlpZrxDM2NhZwkZzs3yLE16FW4nZSvyz0bOyJHsLM1+CZGkJd569XoN1sGDNmDNu2bcNkMmW1GAnAYMgjGJOSkvx6WOaHZzlP9Mhgzl14hnqTJjyRdQmgzKzxDOHqn0KqeIoJUs1CNDclQ/g/JUN4nmqGg5fA4XT7RKtWIrwncn5ujl6FY1dBLgONSk7NmjUpWbKkIOLVarRaLRqNxtt+5urVq9SuXZsyZcoQERFBdHS0t7YTYMeOHZw6dYqUlBSvk67JZBJEo8WCRCLh0UcfZfPWzTicjvyFo0SI7qtC1GjC1KhLqQkOCxZEY6ZgFB7VqEJVqEL8xaQ6RB1wJsbBcftx2bO+/zw34QKJeNpMNn54fgXWFCvLly33MSYTiI2N5YMPPyC6ZjFeXP7SPTNlO73kFLuG7qBOnTpeh/SikpSURK9evXA6nezfv9/bmkjk/wdReIqIiIj8C+jbty+HDx9myewlaKO0KHUKYpef4c7JO0KaavESxMTEcPmvy+zYsYPwKuE8/dUzHJ74Cy+1f4mEOwk57o736NGDL7/8kilTpvDdd98FbG7RqVMnpn89nYNj91OlTdVCmWIodEpczlyEp1SK3SRcsIZXDqf92g4se3IxgwYNYubMmUyaNIkmzZqwuc8mep16+76aDXnSZu1FMBdyZ4bAfCOeWm/5lu/2hNciLS0NvV6PwZAOPA08Tpap0G1gHXCHRx55hK+//hqtVsvs2bOZ/e1sZEoZbZe8SMzrNe9JX74cx5TZVsXl8rir2oAFZJnteFphWIAxZHdh0ev1mefjJhJ+QCoVRJ8HtzsRpzvRL2ookQjCLkSnJM1HvXtWc+NG6G3qN1LsdqePUZaNrMubxgiGQ4eZ9Rq8sxxc7vLg/guz2Zyr8Myrab2vw6xUKqNGCXi7Gcw7KIiqCB1UFMo/KZWpCVJSUtBqtRiNRm8tZGGEZ2pqaq5R2dzwHItnPzdSwebMEoppZv+UYDgLJAMZgB2TyUS5cuVIt4JuQO77kEhAIZMgk8mQyxVUrFiSoKAgWrZsSWJiInq9ntDQUCIiIoiMjCQqKoqIiAjKlStHWFgYCQkJxMfHExMTQ0JCAnq9nqSkJG7cuJElHs0Z2Ow2tu3ahtMuRBtxC1kanp6fQ94b4k1blalkKIOUKINVqENUSCOk3Dp2i61bt+KWu3n0vcfRhKm9QlIdlvWoClWjClb9Lf1A02+lc+Cz/UjlUqq1r87ZH2K9wrOgiKfb5eanruuJ/yOeCeMnUL26vwNuUlIS3d/sjjpCTYeNnVDqimYolp1za87yc7cfKVe+HIsWLbqrbaWmptK7d28yMjLYt28fFSrcG8MjkX8XovAUERER+ZewePFijhw5wsFP9yORSihZsiSdOnXiwoULnDh1glu3blHpuco0HPQo5VtWQCKREFkjiuUtl9K1a1eWL1/utz25XM7kyZNp3bo1e/bs4cknnwx4LN3f6M6MGTO4uvsvyj8V+AWEQqvINeIpk8n8Lr7KPVGORwY+yoEZBzh37hzVq1fn4w8/ZsSIEZxecoraPeoEvM/C4k21NRW+nYpXePpGPL3XgMcQnEUdeFqQaDSazBsCVYBGmcu5gEPAThQKJZ99NoHWrVsTHx9Pp9c6ceXyFSo8XZHn57dBX1rP/cJT45mcnEzVqlXZvHkzcDWPpd1IZBLkGjkKtYKMxAzOnTtHcHAwjowUWj4kRH51ytwftUo4GwdjNgptM8xWO50fga86CsuoFdD6a9hyJgjI/n4T0rez2rDYyIoYAwgRuUaVQCaR4nQLx5WUlJSrqPNE3bLSewU8EU+TyYRUJsOaGaQqlql3ffuFemoZ79y5g06n486dO7m2YXG7weoQ0m+N1qzH83eE+R7h+ccff/DNN994U0E9tYS+/yckJFCnTh1vBPKxCbm/UjIJyKXgIgmNJgONRoVOV4bOnTtTv3593G43Op2OsLAwPzMdj7C9ffs2d+7cISEhgWLFhBrkChUqYDKZSExM5OrVq2RkCOLR5rBl1Tg6ss5n/fr1CQ4OZs+ePUKaql6FOkyDpqyGqOgoNBFaQSBmisNt/bewYMECr/CUSCU0/rgJjT9qmiPaaLhh4OsyXwEgVcp4Ymzz3E/E38yeEbtwWp3IlDJaTGrJnz9e8BqtFeSgvffj3VxYd56ePXvy3HP+5QY2m41XXn0Fu8tO581dCC55b9qRXN56iXWd1lKiRAnWrllbKCOl7BgMBvr06UNKSgp79uyhSpV7Y3gk8u9DFJ4iIiIi/yKOHTvG9OnTcTgczJk7hxUrV6DUKWkwoCH1+9YnrJJ/NKhCiwrU6V2Pld+tpF+/fjRp0sRv/rPPPkuLFi2YNm0aTZo08fahLIiePXsyb/48Dn52oHDCU6fINeIpk8pypLY2HdWMk98dZ+TIkaxatYoXXniBiZMmcnzO7/dVeHqil55U04DWyUy19UQwfA1tiutBEDGeNNAsLl++nCk8PdNTEaKcV3n44Yf57rvv0Gq1LF26lKlfTsUtdfPs7NbU7V3vvkQ5ffH0M01LSyM5ORmAditeJqhkMEqdAoVOgUKnRKFToNQpvQLA7XbzhXwcJpMJrVaLOhTW9il4f7vPAxsFsaWQy4kIshMZlDU/SAUSiSOXukrBlMhfePoiCEmTTUixdTqFC+iUlBR0Oh1ms//rnCU8BQdhp9OJyWTyGuFkZGQglclJt8CtVCGCKJPCb9cg7HchHfWs0HKSgwcPCs6pMplXeL72nSA4TVbIsAuOs3kRGhpKTEwMq1evZu3ateh0Or+/sLAwtFqt9/mjjz5KzZo16dGjh9eoyZOqGhQUhMPhIC0tjTt37pCYmEhSUpK3vjEkJIQrV65w4sQJTCYTNocNu0Mwx3E5chrjACCBF198kW/nfYtCo0BXTIcmUkNwZDDqMI2QihqmRh2m8UYa9368xytaJVIJwzJGFBhxvHnoBtc2ZN30kMllOCyOXFNcNeFZBbTWNAtut/u+f1YK4sr2y5xefAqJVEKjEY3RlwlBrpF7DZPsGXmn2p5adJJfxh+kWbNmDBw4MMf8rl27kpiYSIcNnShWu/g9Ge+1/ddY9eJKwkPDWL9ufZ5ZAIFgMpno27cvcXFx7N69mxo1atyTMYr8OxGFp4iIiMi/iKCgIM6dO8eSJUsIrxLOszNb83CXmvn2amwxqSUXN1zgxXYvkhCf4HfnWiKRMHnyZOrVq8eaNWvo1KlTQOOQSqV0eKUDixYt4uavNyn1aKmA1lNoFeAWLt49ESQAuUyOLVvfC3Womkffe4wDn+7n+vXrlClThhZPtWD16tVkJGWgjcjf4beoeKKXGQkF19V5kMoFZ16LxYJU4p9q27Y2XBgj1Ld5Inw2B4S/J4g64aLOBvwGbEEqdTNo0BB69OhBamoq3bp149y5c5RuXIY2i18krGJYHqO4tyiDBDGdmprqFXVlmpQhuFT+UVaJRIh8Go1G1Go18QGkloIgLEE4J1KZnAyb/40InRJkEicOP/3jAqR+/TWFFh5OhHNqRzBmElxobU6Ac4AgPDUaDb/++ivvvvuu1+Dm/Pnz3q3Xr18/VzMspVLFpj+gVGZHCbkUZu8T/kAQogqZYMwUERFBo0aNqFy5Mg0aNMBoNKLRaNDpdAQHB3v7h3pad3hSVYsXL+6NKL766qvcuXOHlJQUr1hMS0sjLS2N27dve6OeZ86cYdq0aezctZOk5CQcTkf+whGQyCS43UJku2LFily/fp0qbapkiUVf4ejz//V919jyziaSkpKQyqQ0+aQpjw9vXODrfPK746T/nk5UVBRulxtLmqVAF2ZNuAa7I+v9IJcrsKTm/saSa+RIFcJ3nMvuwmF2eG8mPQicdidb3tmEVCZFG63lsaGNMsepEOpRZZI80/qv7b/Gxp4bKFe+HDNmzMgxf/jw4cTGxtJqxrP3rG1K3G9xrHz2e3QaHT/9+JPf93RhycjIoF+/fly5coVdu3ZRq1atezJGkX8vovAUERER+Zfx2GOPsWTJEp6Z1TqgxuAqvYoX5rdlxbPf88Ybb7B06VK/+XXq1KFbt27MnDmT559/3qdWLn8GDRrE8pXL+WXcAV79KWcD89zwXAB6jF280+WKXFNb6/VtwMFxB5gwYQJff/01L7zwAqtXr+bWrzfv2YVWXmM0JwUe8QSQq+VYLBZkUv9UW4kEqhTzX1aZ+etrNBozo8xXgBuUK1eOb7/9VkhvW7uWcePH4XA6aDn1aR4Z+OjfUovmwTfi6XlP2AJMP1ZoFd4aSpNVMKox2QRBntfj5US8+5PJ5ew8B6/OzUpBPXcbBCE5OfPRjq/bbalSnpsfq3KMRyYRnFU9QlWj0VCvXj2qVatGQkICLpeL8PBwSpcuTYUKFbh48SJVqlTh4Ycf9kYUNRoNISEh1KhRg3HjxrF161YiIiIIDQ3FZrOh1+vR6XTI5XJMJhMGgwGDwYDRaKRZs2ZIpVJiYmI4c+YMFouF27dvc/XqVaxWqxBZdDqECKvLicvlwuV08UaXN0hJSWH9+vW5n2iJcK6VOiUul5uzZ8+SnJzMtavXKPtEOYrXL4E6VEhjVYcKhjie/9WhQhqrQqtgSZOFmK6ZvPWhLy5/ucA6wfQbgmPtrVu3kCvkAWcIqELVJNqTvKL6xsHrqELUWNMsWNOsWA1WwiqG+fWhVYdr/Pr/KuUKLCm5708ikaAOUQulqwiZCw9SeP428xgplwRH5BZTnvaORaEVhKdUKs3VyCzlUjKr2q4kWBfEDyt/yJHqOnfuXDZt3kSDAQ1p0P+RezLW+D/i+b7lUpRSJevWrPPJIig8FouFgQMHcu7cObZv3079+veutYvIvxdReIqIiIj8y+jTpw/vDX2P47N/C0h4AlR8phK136zD8kXL6devH48//rjf/E8//ZSVK1cyf/78XNO5ckMul/NC6xdYu3Yt8X/EE/1wwb3YPIY1KSkpfjb6SqUSuzF7iiToonRUe7k6v274FYBq1QQXXXNi4NHIwuJxqM3rwjbP9dRyzGYzMqkUkzWf/EmEaJhcKkQEypYty9Wr13n77V707duXjIwMunbtyomTJyhetwRtl75I5ENRRT6eouKJoqenp3uNQH6feQxViAq7yY7NaBMeTTbsRju2dCs2ow2b0Y45yYy1hJXIyEjSzEJ0Nz8kCFFClUJGxYoVafjo4xz+5QA7LstRKFQolUpkOjt6lYPy5ct7HVCDgoLQ6XTUqlWLGjVq8Nlnn2E0Gv16OIaGhqLVarHZbKSmppKamorRaPReCGdkZJCSkoLBYCA9Pd0bpbx+/ToXLlwQejJarVhtVlwuF8OHDefKlSusWb9G6M2YT5sND0uWLOH48eMsXroYhUaBJlKLMkiBMlKJLigIRVBW6rJSp0CuVXBs+hFOnjyJXq9HqpTScVNnVHoVymAlKr0KlV6FQqvw3ow48/1pfnx9vbfOOOb1h6nbq15Ar7U6XEPy+SRvOrA11VKg8FRnRinj4+ORy+VYki3YM+xYUi1YUi1YUy1YUize55YUYdqdE3ewO2xUqiQIy1VtVuZ8P8gkjHB87H2uCdfgtGdFnlVKVb43htRhaiwZQtGtJcV8X2uh88NmsrFj0DZkChnF6hWnRqcs93ClToHVYEUileRo3WRJtbDyueW4LS5WrF2RI+q4fft2vpn5DZWeq0zLqU/fk7EmxCaw9InFSGywavWqu+qtabPZGDJkCCdPnmTz5s05fm9E/n8RhaeIiIjIvwypVMpzzzzH+nXrMcalE1QisAhli6lPc2nTRdq82Ib42/F+d9BLly7Ne++9x6RJk+jQoUPAvdWGDx/OTxt+4tD4g7y47KUCl/fc7TcYDH7TlUolxjycO6u2rcrZlbFcuXKFChUqIJFKsKbnFKn3CoXHKMjpZt+oPdgzHNhNNuwZduHP6CO6jMJ0h9mOOdmMo5wDqUzK4Ssupu0QonWeiJ7RmvloEyJ4TheYzWa/FgVbt27l408+xmq10mxscxp90Pi+Ovjmex4ybxKkp6cTExODVC7lt2+OIZVJkUqlgrOpVI5cJkehUKBUKtGr9Kh1ajT1KvP2229ToUIFQkJCUCqVBAcHe1NKPYIwLCyMsLAw1Gq1txVGdHQ0lStXZt++fX5RQ6PR6DXUMZvNpKSkCILQauXUqVM8++yzzF8wn7g7cULU0Ony/pGLNnz88cdRKpV+vTpBMK5R6BRCJDFIiTJMhSpYRVBIEJc2X2Lr1q04nU7cbjctJrX01rl61vGIR8/zPzf8yZY+m7w9Wx8f0ZjGHzbJOaBsnFn2B1arFZ1Oh8vuovxT5fOtVVQEZUWoJVJJwL0hQRBqNruNiAih/Ywl1VJgSrU6VMiNTkxMRC6Vc2LecU7MO577whIhHR0J4BKiktlbaWijtIRWCCW4jJ6SDf1T99VhatwuNwaDAb1ej1arzVd4aiK1WG4KwtOcbMlzufvN4UmHACHdttWMZ/1eP0WQEluiDZnMv77daXey9pXVpF5JZebXM3OcpwsXLjDsg2FE1IjkpZXt70nblPhTd1j65BLcFherVqy6qzYndrudoUOHcuTIETZs2MATTzxx1+MT+e8gCk8RERGRfyHTpk1j/U/rOTHvOE1GNgtoHXWImufnt2Fl6+W8+eabLFy40G/+sGHDmDt3LjNmzODzzz8PaJtarZanmj/FtuXbaDb2iRzmRtnxTd/0G5taTYoxJdd1ImOEaN/vv/9OhQoVkMqkZMSbsBqsOCwOHGY7drPw6DA7/P53mO2CcMwUjY4MO7eP38ZutOG0OrGZhEidJcUiNHhXy7EasmrHDk84hEwmE4SWVIZcJggtpVKJWqkiVB2CKkLl7UXYq1cvhgwayM5zCey5ADKpJFOkSZFK5cjkCuQKBUqligoV1XTu3BkQ0tIGDBjAocOHiHwokteXdaVYnXtjFFJUFBoFSIR04IoVK3L8t+OkpaWRkpJCSkqKt8YwPT3dKw5NJpPXeTU5OZkSJUrw22+/CWY1tkyzGpeQUupyuQQB53T7GU7169ePDRs2cPVqTgddqVwqOOdqBGGnDFJilzi4cfoGaWlp/HX1L4rXL0GZJmWyzI8yBaTCxxDp5zd+JDk5maioKBRaBb3O9BFEZpASmUqWp8D7MnoKJpOJ8PBw3C43DQc/VuB59LiMJicnI5VLc43s53r+tQpvCi9uoeVGflFIXxdiqUwaUG9ID+pQNQ6nw5v+mlf9pN86mRHPxMRElAph3xK5BKVWiTJYKbjURmrQRetQR2jQhGtQh6o58e3vpFxKISQkBIA2i9pS843a+e7LYxh069Yt9Ho9QUFBJKfk/n0BgohNQTDEshTCJOxeYriexi/jDyCVS4np/DAlHynpN18ZpCDdli60ksp8rdxuN9sGbOWvXVd4b8h7NG7sXzObkpJC125dUYWp6LjptXxr+wMl/vQdljRbhNQhZfUPayhXruh9kh0OByNGjGD//v2sW7eOli1b3vX4RP5biMJTRERE5F9IuXLlqFa1GsdmHOXx4Y0DbmBe6bnK1OxWiyVLl9CvXz8eeSSrNig4OJixY8fSp08funTpErD74MiRI9m5eyeHJh6i9Zzn811WoRV+drJHPFUqFelX0vnx9XXYzXbsJjt2ow17hoOkC0nCupmOu2GhYRwYu58DY/cHND4AqUww/5FKhWid3WqnTOky6JQ6FMEK4oxxhKvDUalUqMuoCasVxueff14kY42t23cI+8yn/YDD4SApKYmkpCTWrl3LhEkTyDBl0OiDxjQZ1Qy56sH/PEukEuQquTfK2KhxI6GnYgFIFVLcLjcXL16kffv2/Pnnn5RoUJKQqFAUQUI9oq8IVPq4427o/hNXrlzB6XQSXTOal1a/4jdfpsj5Pj+//hxrXlpFSkoKUpmUCq0qFthCQxupxZJkQafT4bQ5CS0fGtA5UQYpMZvN6HQ63E43TpuzwM+eJxKZmpqKTCrzi9a7nC5s6TasBqG20ZpmxZb5f2JsArpy2qz62nRbQMIzJSWl0BFPVagah8NB8eLCzY5A0sxVIULEMzk5WagNjZbQ+0zB9sUKrYKt/TcTFBSERCrBnFJwRFIdJvSnuXXrFtWrV0ev12O9lvd6mnANEplw86Aw7tT3kl3Dd+Kyu5Cr5TQf/1SO+QqdEofD4efofWzGUY7P/o02bdrQvXt3v+UdDgevvPoKVoeNbpu635P04aQLSSx7aglSh5Q1q9bcVaTT5XLxySefsGPHDlatWsXzz+f/WyDy/8mD/2UTERERESkSE76YwIsvvkjsyjPU7Bq4W2DLaa24vOUSz7/wPLfjbvsJpLfeeosvv/ySyZMn89133wXUhiA0NJTHH32cX+b/QtNPmuaboueJeBqNRr/pTz31FBcuXuD6pmvIpDIUcgUKhQKtUkNY2Uo0adKEtm3bArB0yVK+++47lEolWq0WrVbrrfXT6XQEBQWh1+sJDg4mJCQErVZ7Vz3oXC4XaWlpJCQkkJqa6hfx800F9fRUNJvNWCwWLBYLdrsQ4bM7HLhcQmuO3OoCQyuG8srSjpR+vHSRx3k/UOgUZGRkeHsx1u1Tn4rPVMxbPGoVSOVSVr/0Awn7ErzCvd2KlwqMhgNs7r0Ri8WCSqXC6rYSUTWiwHU8YiwlJUXoBxuAAZIyWIktzkZwcDAuhysgASmsp8JsMvuYLdnQKPN3ZFX61DVLpVJOLTjB2RVnsBltOCz5RyU9KckAtnQrFA/Kc1nfVNu8DGvyQhOmxulw+gjPgsWgTCFDrpaTmppKUFAQd+LvBLQvVYgK3B4TKVlA+1IG+WdKhIaGYjPacLvcuRpuqcOECK5ULn0gEc8bh24Qu/wMSKDxx01z7a2p0CpwOAXhaTfbubjpT7YP2kqNGjVyzTjp2rUr8fHxvLK+A8XrBVYKkR+pV1JY9sRinEYnq1beXXqty+Vi7NixbNy4ke+//56XXiq47ELk/xNReIqIiIj8S2nbti2RUZEcnniIh7vUDLhXnSZMQ+t5L7CqzUr69+/PzJkzvfPkcjmTJ0/mhRdeYN++fQHX54wePZpWz7bi16m/0nJK3mYXnhpPT09ED927d89xhz83kpKSuHnzJo8//jipqakYDAZMJhNlypThmWeeCWis2enfvz+XL1/GYrFgs9uwO+3e/o0uhyvXvqPZkSllmfV9CpTBKlTFlGj1OlQhKiGFM1hI41T4/O+dHqyiRIMS3l6g/yQUOiUZGRkEBQmCp3jd4lRrV73A9eQaOU6XwyvQspun5LlepjOwWq0m0ZgU4BiznJJlUhl2U8GprCq9EpPNmCUgjTa//o95oQxWYk22Zh2Xye5tBeJ2u1nSeCFI4I2DPXIdH04hZdZPHEsAtyCugksHowpVo43QEv9HPH9d/cubkmorIEXXI84MBoMgwAuRaqsKVeN2upHL5UINdVpgPXBUISpv3aXlz8BqKT2R0jt37gimRAEIT0U24RkWFobb5cZmtKHSq3Is73HBlcllf3vE0+1ys63/FqRyKUElgnh0SO7p2HKtAqfLiVKhJOF0POteXUNERARLlizJseyIESP4448/aDmtFVXbVrvrMaZdS2Nps8VYU6x8v/R7KlasWORtud1uvvjiC9auXcvChQvp2DEwh3OR/09E4SkiIiLyL+aD4R/w/vvvc2X7ZSq2qlTwCplUeaEq1ds/xNzv5vLhhx9SunRWpK1169Y8+eSTTJ06lcaNGyOXF/xTUaxYMerUqsNvM4/R6MPGefbY9AhPUx5GQidPnqT/u/2x2W04XA5cThcSJGzZtIXo6GhaPN3Cz90yO4UVny6Xi7179xJaIZTweuGoQtRe51Cl3l80ekSiIBoV3v/zSgH9L6DUZbV8kMgCT99UaBQ4nU6vYA00+uZxBtZqtdjjA6yF1GX1G5XJZNgCMJ5SBCmxO+zedhGBCk+VXoXBmuYnWH25cegGAC6Hy2sK5SsItVotkQ2jaPf9SyiChAjx4UmH2DNiF3V61/O7abOx589c/OFPb3/Sgo7LT3hKZYWKeKpDhVTWO3fuCFHCAB2d1aFqjEYjpUuXxm6y43K6CjS7UYUI+8pywy14X9kjnpGRkYCQEpyb8PS44Cp1qr/dXOiPZae5/XscIGSXyNW5f38qdYLwVCgU3I69jVqrZvWq1Zl9fbP47rvv2LBxA/X6NuCRgQ3venyG62ksaboIc4KZhfMXUr16wTeS8sLtdjNlyhSWL1/OnDlzeOONN+56fCL/bUThKSIiIvIvZvDgwYwaM4qDn+4vlPAEaDntaf7c+Ccvvvgiv/32m3e6RCJhypQp1K9fn7Vr19KhQ4eAtjd69GhefOlFjk0/QrMxzXNdxiM8s6faejh48CCpKak83KUmumI6zElmTi08yZ9//kl0dDRut5u6b9ejVo86QnRRp8BmtDGv1lzOnz9faOHpEVT1+z+SZ2Ti/xllsApLnHDhLpVKAxaeMrUcp8tV+IinRo7VKkQUA92Xr+CSyxQBRTyVQUocLkfA0UTvesFKbHabV7D6Ri59Mw5sJpvQS5Ks9PL09HQUCgVul9vPidrzmcg+BrlGECYep9lAhWd6ero3fTNQVD7CUy6XB2QuBKCJ0GC6avKKY2uatUABrw7JcsNVKVUBRSQ9wjgpSYiCe913UyyE5OKFow5XgxukSP7WVFub0cbO97cjlUsp9Xhpqr2ct6iTaxW4XC6CgoKQyqXMmzvPK6g97Nmzh+lfT6fi0xVp9dUzAWe15IUQ6VxExm0T8+fNp1atwEs0cuPrr79m0aJFTJ8+nd69e9/VtkT+P3gwHu0iIiIiIvcEqVTKgP4DuH7gOtcPXCvUuvoyITQd1Yzfj//OunXr/ObVrVuXrl27MnPmzDyjk9mpUKEC1atW58i0X7Gm537hKpFKkCllZGTk3odToxEuWpuNfYIWk5+mXt8GQJYZkVQmpK+VerQU0Q9HE1ohjJByoQB5brMgpDJpQGLl/xFlsBKbTTg3UlngwlOhVeByOb3CLuCIp0ZwctXpdDjMDm9Pynz35SPslAoFtvSC96XQKXE6nd76yUCdZpVBShxOh1do5fW+8RWkvm1plEoljmw1qJ6IWPYxKLRyXC5X1r4KOIcypQypQpopwOWFrvEEnyhkoBHPcA1mi9kvAlkQSn2WKZFWk39bFA+eVPaUTCdbj/tuXsZEnvRn3Pe35292Dk04SEZCBi6ni1bT8xeKCq0Cl9PFt99+y/q166ld29/Z98KFCwx5fwjhVcN5adUrd91WKfVKCksaLyTjdgbz5s6jbt26d7W9uXPnMnfuXCZNmsS77757V9sS+f9BFJ4iIiIi/3LGjh2LRqfhwJjAXV49PDrkMcIqhvHmW2/icvnXMn722WeYTCbmz58f8PZGjRqFzWjj+Ozf8lxGrhHSKXPDY0bjsArptB4XXE+EVCaTYTPmfuGe1zYLQiqVBmRI8/+IUi9E+KBw50kZJERzvJHBAKNvnhYiOp0Ot8uN01qwi65H2BmNRsGUyFBwtE6hEy76vWmsgbY4CVLidPkI1jzOh68glauFukmTyYRSqcwhIGUqIU3bkuovojzCxCPeAxH9Cq0Co9GIQqHI8TnJD0/EMyEhAaVcmWMseeHp/xkdHZ15DAWfe0+NZ0pKCjqdLiDh6RljamoqgLfPsDWPcap9oq4Zf5PwTLuayqEJvyCRSqjTs26+7ZDcbjcpF5Nxu91ERkZSoUIFv/mpqam80f0NFHoFHTd3zjWduDAknU9kUaMFWBItLJy/kPr169/V9hYtWsSMGTMYO3Ys77///l1tS+T/C1F4ioiIiPzLkcvl9Ondhys7LnP94PVCrStTynhm5nOkpqQycOBAv3llypRh8ODBLF68mNu3bwe0vZiYGMqXL8+hCb/4iQ2n3YnVYMV4x4hMKctTJHoins5Mt8/sqbkyqSyHSJAqpEikEiyWotVyyWSyQrWe+H9CqRMifCCc+8Kkv/qKpkCjb8ogBXa73c81tiB8DatUKlVAIlKpE4RxeLjgtBtIXSgIEWDfSGle+/KdLpFIkKuFtjQqlSrHOfTcOMlI8BdIisxUTK1WC5LAhKcySInJZEKhUBTqZopvKqtGrQlYDKrDBBMfr/AMpA1LpohKTU0VTIkCjK5qIzReUzKP8MzLmEgTLhyPy+UK+Fjull3DduJyCO1TnvjsyTyXc7vcbB+0jVMLTvLkEzmXczgcvNrhVSw2Cx03vUZI2ZC7Glf86TssbrwQR5qDZUuW3XV67fLly5k8eTIjRoxg5MiRd7Utkf8/7mmNp9PpxG4Xf7xFRB4UCoUCmey/aXIikj8TJ05k9tzZ7P1oN132FM7goWKrSpR7qjwLFi/gq6++8ms98sEHHzBv3jy+/vprPvvss4C299GHH9GrVy++KjZVaFVhdeJ2ZUuZzMO53xvxzBSe8uzCUybLkZIokUjyFbMFITihir9duaEI8hGessANaxQ6QXh6Xs+Aazy1Ckx2k9eUyG6yQwEdVaQyqTd9W61WY08LLNXW5fQRnoVItfWNlGZ/36jD1FhSLDmmy7VCWxqNRoP9jv88T8TWdNu/7lmuUXg/N0I6eMHHpdKryMjIQKVSkWZKC+iYPOshEaKQWq2WtKTA1lWHqnA4HQUKQV+kMilyjRyDwUBISAhWgxW3211g/aImUovprJD2r1arkcgkeYpWTaTwvjObzWh1he/FW1iuH7jG2R9iQQJNRz+BLlqX63L2DDs/dV3P+bXneO6555g4cWKOZd544w1u375N+zWvUqJBybsa17X911j1wgqkTik/rPjhrtxrAdasWcPnn3/O4MGDGTdu3F1tS+T/k3siPN1uN7dv3/amQIiIiDw4QkNDKV68+F2bEIj8u5DL5QwaMIjx48dzZecVKrSoUPBKPjw+rBErnv2eBQsW8NZbb3mn6/V6xowZQ79+/ejSpUtADoiPPfYYXbp04eLFi6jVajQaDWq12ttzU6vV0q5du1zX9bqgZgrPSxsvAtCoUSMAFHJFriJBrhZMaYqCTCYPKLL2/4gnMgggL8R5UgYpwC3U3RbKDTezt2GWeU+AKbC+wi4usMig2+VGrRYiY4UVnlqtFolUkuN8aKN1uQpPhVaB2WymWLFiOVKB1Zn1iOm30nOs43a5va7CAUU8M82ggoODC9VORSKVoNApSUtLK1RPTnWYBqfD6a25DDRFVxWsIj09nSpVquCyu3CYHd7IdV5oo7SkHk/xPpfJZXnuTx2ipsOGTtgz7ETXig5oTEXF7XKzNbN9ir6snkcG5O48m3IpmdUvrSIxNoGePXvmyDABGDhwIKdPn6bl1Kep9lLR3WYBzq6O5cfO6wjSBbF69WrvzYGi8vPPPzNmzBjeeecdpkyZIl5jiBSJeyI8PaIzOjpa+DIW34wiIn87brebjIwM4uPjAe76R0bk38dnn33GjG9msOu97bz5e69cG6vnRfmWFQgqHsSUKVP8hCdAz549+eqrr5gyZQpz584N6Dt++PDhhR7//v372bhxI5AlPCUyYV8PPfQQAEqFMtcaPplKVuRUW4VMLkY880ARJKSWgiD67QHWDXr6LiYlJSGVSnEUwtXW6XT6tDkJXLCazWZCQ0MDisp6oowGgwGpXFoocyGPGJRIJTneN0HFg0g+n5RDyCqDlFgsFkJCQnBanThtTmRKITvFU/PodvpnBcgz65sNBkOBBlhutxuH2YE13YrGrUatVuNILtx7Wp3ZkzMkJATLxcBu4qgz+3/abLbMNiyB9/I0mUw+7rTmAoWnOkyNzeFTO1tAD9DKz1cJaCx3y+nFp4g/KQj1p798xvu6+nJmxR9s6rkBHDBpwqRc3beHDx/Orl27eGzY4zQcXHSHbbfbzZFpv7Lz/e2UKFGCtWvWem/oFZUtW7bw8ccf06NHD77++mvxOl+kyNy18HQ6nV7R6fkCEREReTB46uPi4+OJjo4W027/z5BKpYwfN553332X2B/OENPp4cDXlUmp9WZtDk88RHJysjcFEYQU7kmTJtG2bVv2799Ps2bN7sfwGTBoAA6bA6lC6k1VU/oIGK1WK9TwGXKJeGoURY54KpXKgGv8/t9QZprwgOc8BXaOlZlOs6mpqYJAK4SrrcPPvCfwXp5ms5mSJUvisDgKTN307f0plUkLYS4krJeUlJTrerpiulzHrQxWYrlpyUohzrB7BUpegssz3ePofHrxKS5vuYTNaMdhtuOwOHBYnThtDtyOLNH6yCOPCDWehaxbVoWoMZlMlC1bFrvRFlhPzlBBNN++fRuZXJan2U921KFqTLd9hGeqheBS+vzXCVPjcGTdwFDIFQEL3fuFNd3KzqFC+5SyT5Sj8gv+Ytd428jW/ps5v+YcxUuUYPGiRbneFH7vvffYtm0bDd59hCe/aFHk8bicLrYP2sZvXx+lZs2aLF68OKA+zPmxc+dOPvjgAzp37szcuXP9SjFERArLXQtPT02np45DRETkweKtqbLbReH5f0j//v0Z9/k4dg3dQbV21fNsXp4btbrX5pfPDzJy5Ei++eYbv3kvvPACTZs25ZtvvqFp06b35Y630+HkqYktaDjkMe8Fr69AKFOmDGq1GmN6zh6gCo3c2/ajsCiVSqyi8MwVRWZqqcvlQqVSYTCmF7wSWRHPlJQUIeIZaKqtRo4rANfY7CiDlFiSLX5uuPm99z0tWJKTkwUBmfn6u11ubCYbdqMNm9GGLT3zMfP/m4dveteTyWRcWH+OlIvJWNOs2Aw2Es8mALC1/xa2DdjqjW66XW7CwsLQ6TKFaYbda+ijzaxHDCoZ7D9GbdZ73+0C020Tptu5tzaSKqTIVDIcZgdxcXE89NBDherjCaAJ15BxOcN708lqsGa1JckDT5pwXFwccrk8z/YmOdYLV5P+V7qfG67L4cKSasGSYsaSYhH+UjMfU8zcPHQTpyPL5VilUBH3WxwOqwO56sG0pT/0xUHMSWYkEglPf9XK+73otDn5beYx9o3cg8Pi4PXXX+eDDz7IdRtLlixh27ZtPDKwIS2ntSryd2tGYgbrOq7h6u6/aN26NRMmTCjycXnYt28fQ4cO5eWXX2bBggXiNYXIXXPPPqli2F1E5J+B+FkUmf/dfFo/35pfpx6m8YdNAl4vvEoEpRuVZsUPK3IIT4lEwpgxY3jqqafuS9QzIyMDt8uNNlrnF2XxCARP/z6NRoMtIadIVGgV2BKLJh61Wi2GNEOR1v2vo9Rl1Wqq1WoSjUmBrZcZGUxLSxNa4AQqIDNdY71tTgKtKdWrsN22eSOKB8buw+V0YTfZsaXbsBqs3j9buo3k88JxOJ1OJG4JR786wtHpR3DZXXnuQyKVCH8yiXDT3Q0JpxNIOJ2QY9ncIuhOl9Mv4ulBpVcx1PRBjtR4pY94V6oUlG1RlubjnkSukSPXKFBoFcL/arn3M7Piue9J/81AUFAQLrsLp92JTBGYWFCHq0mKTfRGIa2ploKFZ2bEMz4+HoVciemOkbSrqYJgTLViSTFjTbVmPhdEpDXVwuWtlwkJDfFG/5Y9uTjfcy9TyJDJZd5aUoCQkBAunb/ErqE7aDX92YCO8V6ScimZQxOF9in1+tQnKiYal8PFmeV/sG/kHtKupVGxQkWmT59OuXLl8tzOzZvCzYxmY5sX+ff7zsnbrHphJcbbJt4b8h7du3cv0nZ8OXz4MIMHD+a5555j2bJldx05FRGBe+xqezcYbt7k8vbtWA0GVHo9FZ9+Gn2pUg96WCIiIiL/Op577jnq1a3Hwc/2U7NrTfRlArfjr/1WXTa+9TOHDh3i8ccf95vXvHlzGjduzKxZswoV9Tx+/DgqlYrSpUt7a/ey88cffwDk6FfnK2BAEIm5RcHkGkWRXdX1ej1X/vyrSOv+1/GNXGo0GhwJeZ9jl9MlRAjTrRjjhKh0WloaSrmSpLOJnFt7FrvJjj3D7n10ZNixmezYjTYsaRbOrzkHkKdrbF4og5UYbemEhoYilUs5PPkQuIV2GuStZ0hLS0MqkeJyZC0k18hRaBUog5UodUpUISpUIWpBFNucXFh/nhs3bqBUKgl/OIIG7z6CQqdAqVOi0CmEP60CReZzpU7Jxrd+5upPf2WZZ5lzmg/ldkwgRDxlUjnKYGW+vSE969gdPu1ojLYCxaMHdahQQxkVFQVA3LE4DDfSM8WjBbMnCpkZgbQkW4g/LXgKOBwOtGoNF9ad58K68zm2LZFJkMllyOVy5DI54eHhrFixghIlSvD8889jMpkIDQ0lPDycyMhI0tPTWblyJTaHDYfTgdPpxGF3EHczjiFDhjB16lRmzJhB6+dbe99rfzfbB27D7XKjDFLRcMhjHPnqV45M/RXDtTQioyKZNnUaLVu2LHA73ii42V6kfp2nFp1k89sbUcqVLF64iNq1axd6G9k5duwY7777Lk8++SQ//PADCkX+9bciIoHywIXnrWPH2P/551z4+WdcPrn7Urmcqm3a0PTDDynZoMEDHKGIiIjIv49169ZRsXJFdr6/g5dWtg94veqvPsSWfpv5+OOP2blzp988iUTCqFGjaNWqFQcPHqRJk4KjqZs3b2bYsGF+0xo81oAF3y7g8OHD9B/QH5fbJTiMhqkp2dC/fYAn4umpc9NqtbnWril0ciz2orVTCQ0NxZpmCailw/8bSp+UVLVaTUZSBosbLxCih+k2HCY7dosdp8XpJ9482Gw2JEi4tvcq1/Zd9UYNkQBucLvB7XSBj6+OVC4V2mXkYt6TFwqdAofTiU6nw+VwUffteoRWCPWKP39hqMSaamF5q2XExcWhVqkp2bwUL/3QHoVGka8pV/LFZC6sP09aWhpymZzgkkHU7FpwX0SFToHD5fAKQntGwWZLnohnWloaMqnU29u2oHXsTrv3Bs+d47eF+mdPCmtmFNKSas0Uk4KANCebiT95B32Y3hudW9dhjd+2JVIJMoUMuUyOXC5HpVCh1wTzzKutePXVV6lcuTLbtm0jIiKCqKgooqKiKF68OMWLF89RjmU0Grl9+zYHDx7k8ccfJzExkeTkZBITE7l8+TJnzpwhOTmZqi9VQ1csCE2YGnWYmqNfHeHy5cuA0Gc4KjIqV7Ox+83FTX9yceOfANgMVubGzMZpFdrKfDThQ1q3bh3Qdmw2W1YLqUK4EIMQNd/abzOnFp6kXPlyLFm8xHvD5m44efIk/fv3p1GjRqxbtw6VqvBiWEQkLx6o8Dy7bh1rXnsNZy6GEC6Hg3Pr1vHnpk28smIF1fOw3he5e8qXL8+gQYMYNGhQQMsvXLiQQYMG3ZP2OfdyW7789ddfVKhQgePHj1OnTp17um0RkX8DZcuW5c3ub/Ltt99Sp3e9gNurqIJV1OhYgwMrD+BwOHKkV7Vs2ZJHH32U2bNn07hx4wKFmscR1Zdjh48B8O6gd9GW0lL1xWqkXUujycimOQxGfC++QYiEOW1OLKkWb40cCH0SPf0mC0tkZKTgzJluK1LE4b+MJ+KZmppKSEgIbqebG7/cyHN5iVTiTQHNSMggNTWV8PBwTA4TlZ6tnBkJ9I0GZv6fOf36/mscm3EUl8slpLQGmmqrU+L0acNS+626lHwk7x6IpnihVtJoNKJQKHDZXF6Rne9+fN6PSkXgtcEKnRKXy+UjPHMX1C6n0FrEZrRhzKznNBgMgqNwgGLV6XR6U1i/b7HUfwFJVtqqXKZAKVegVqkJ1gVRvFYxRo0aRdWqVRk7dix2u51ixYpRokQJSpYs6Y3WWiwWEhISuHPnDgkJCSQmJvLVV1+RmppKamoqFy5cwGg0YjKZMJvNWG1W7E67ELl0CDcocvT19UGukaMKVlG6UWleWtneL1X40qaLmM5l1bkKZmN/j/B0u9wkXUji+v5r7Bu5B6lUisvlQqFSUL9Ofd57772A2k3duXOHOXPmsGv3LpKSkqhVU7hxkT0Knh8JsQmsbb+a5AtJvPzyy4wZM6bIx+XLmTNneOedd6hTpw4//fST17BQRORe8cCE561jx/IUnb44rVZWd+rEmwcO3PPI5+3btxk3bhwbN27k5s2bREdHU6dOHQYNGkSLFkV3Ffu3cfToUW+qx73C92JUq9VSsmRJGjduzLvvvkv9+vW98zp27BjwncHCiNQyZcoQFxdHZGRkQNvu3r07qamprF+/PqDlRUT+DcyePZsfVv/A5t4b6X2mT8BGQ7V61OH0olN88803OXrNSSQSRo8ezXPPPcfhw4dzpONm54UXXiAmJobr16+TlJREUlISZcqUAcBqsdKkb9N8Wwd4zIWMRiGdrl27dqxatYoL689Tq3tWSplCo8hV5AaC53vCnGwWhWc2PKnOHuEJ8OLyl9BF61AGKVEEKYVHnQJlkBKZUoZEIsFhdTBRPR6z2YxWq0UXpePlVa8UuD+nzQkzjgotRKTSgGtDFToFTpfTO8aCBKvv+0qlUuUbNXO73TgsghjMSMgSrEqlEsPVNE58dzwrfdhk80sntpls2NPtXNlxGYlU4hXGP7+xHqlchtPiyHSndQgmRM6cgkwmkwljTCvYuEehEz4HL7zwAgaDAblcTnR0NMWKFaNkyZLodDpSUlK4c+cOd+7cISkpieTkZFJSUkhJSWHKlCmkp6d7RaPFahHSXR1CuqvTkfsYvWNVylAGK1GFqFFHqNGG6QgPi0Adqs5MWRbSltWhwqMqROUzT41Kr0Iqz9s1VR2uIc2a5n2u0WhISUvJc/mC8Ly2Vk8NamYk2JJqwZpmxZxsJu1KKolnErhz6o43Aq/T6xg6dChRUVE8/fTTBTq9xsfH8+2337Jj5w6SkpJwu9yUbFgKRYbCe1MtkBsLbrebE/OOs23AVuQSGV9O+5KnnnqqyMfvy/nz5+nTpw8PPfQQmzZtuufXhSIi8ACF5/7PPy9QdHpwWq0cGD+eDmvWFLxwgPz11180btyY0NBQJk6cSK1atbDb7WzdupV+/fpx7ty5e7avB4ndbi8wN99Tz3GvWbBgAc8++ywWi4ULFy4wd+5cHn30UebPn88bb7wBCD8a9/qOms1mQ6lUUrx4/rUwIiL/daRSKYsXLubFdu048Nl+mn/2ZEDrlW1WlpCyIcyYMSPXJufPPPMMDRo0YPbs2Tz22GMFRj0rVKhAhQr+EVebzYbb6UYVos5jLQFPFCo9XXBTrVWrFiFhIUK7gG61vPuWq+U4XUUTnp7vCkuyGcqHFmkb/1V8U509F6LlnixPULH8+wLKlDKQgNlsFgyh7gQYGdT6mBJJZQGl2jptTpx2Fw6Hw+vIeuvILRwWh1cM2kz+wtDTBsVoNKJWq7l67io/tFkhuNOmW7EaMp1tM+tQs0fopFIpJUuW5Ndff2VTzw1e0yGpVIpMJkMmlSGTyZBL5SgUCkqXLs2YMWOoXr069erV8wpXVYQKlUr4U6vVqFQqNBoNwcHBhIeH43K5iI2NFdyB3QW3sVAGq3A5XRiNRpYsWUK6KR27w+4VjbmlQ3uQyCQog5So9CrUYRrUpdUEh+tzEY1Zz/1EY4jqvrvLqkPV2B1Z7wmNRsOd5Du5Lut2C1kMt47c5Pr+aySeTRTqU5PMmFMsmUZT1rxNjSRC2rdKpSI0OJRHaj/CI488Qps2bfxMjvIiPj6eefPmsWPnDhITE3G73JR4pCRPfdCS6q88REjZEGZWnOEV8vYM4YZFXu11jHHpbOq1kYsb/6R8hfIsXrT4nqTWAly+fJnevXtToUIFtm7dmmctvojI3fJAhKfh5k3O//RTodY5/9NPGG7evGeGQ3379kUikXDkyBG/uzoxMTG8+eab3ufXrl3j3XffZefOnUilUp599llmzJjh/dIZPXo069evZ8CAAYwePZrk5GS6du3K119/zZQpU5g6dSoul4uBAwfy0UcfebcrkUiYPXs2P//8M7t27aJcuXLMnz+fqKgoevbsydGjR6lVqxZLly6lUqVK3vV+/vlnRo8ezZkzZyhZsiTdunXjo48+8qbDSSQSZs2axebNm9mxYwfvv/8+Y8aM4aeffmLs2LH88ccfBAUF0axZM9auXQvkTLWdOnUqCxYs4PLly4SHh9OmTRsmTpxY6AbEoaGh3gu68uXL06pVK7p160b//v1p06YNYWFhOaKYJ0+eZNCgQRw7dgyJREKVKlWYM2cORqORHj16eI8RYNSoUYwePZry5cvTs2dPLl68yLp162jXrh1jxozJkWp75swZhg0bxv79+3G73dSpU4eFCxeyZMkSFi1a5Lft3bt307x580Idr4jIP5G2bdvS/Ikn2PvFXmp0qEF0rYIvmCQSCbV71mH/mH3cunWLkiVL5pg/atQo2rRpw9GjR2nYsGGhx3XnjnCxqNTnn94olUuRKqSYTFnpdZ07dWbWrFn8tfMKFVpWBIT0PJc7HxeZfPCkJZqTilYj+l/GN7XUmyYagBiUSCTIlDIsFkuOuly3243T5sRhduAw27FnPjrMDuJ+iwMEoSuVSbn48wVSr6RgN9qxpVszW5tkCsjM9TwX7vqIEOG3WQK7h+/MNiChV61UKs18lFGqbClGjhzJkiVLWLJ0CSmHk1EpVWhVWiI0EWjDtV4RGBQUhF6vR6/XExkZyXPPPYdUKiU5ORm9Xo9SWXCargfP700gdOnShZMnT6IMVlK+XvkCl1cGC+1vYmNjuXXrFuVbViC8akSeEUbf6Qqt4h9Z4+x2u7Gb7JiTzTjtTr9enlqtFuNZI8ueXIwlzYot07U4+w0DqUJGkE6HWqlGq9USoYsguHgwer2e0NBQIiIivLWpxYoVo3jx4oSFhRW6Z2VcXByLFi1i+/btJCQm4Ha5KV6/BE8NayGIzXKhfsvLNQqcacINs3UdVuO0OXn7XF90UVnXpW63mzPLTrO13xYcZge9evViwIABRTybObl27Ro9e/akRIkSbN++3dvKSETkfvBAhOfl7dtxFzIlyuVwcHnHDup063bX+09OTmbLli2MGzcu11QCz4fO7XbTrl07dDode/fuxeFw0LdvXzp27MiePXu8y1+6dInNmzezZcsWLl26xCuvvMKVK1eoWrUqe/fu5ZdffuHNN9+kRYsWPPZYVkrZp59+ytSpU5k6dSrDhw+nc+fOVKxYkREjRgj1WW++Sf/+/dm8eTMAW7dupUuXLkyfPp2mTZty6dIlevfuDQgizMOoUaMYP34806ZNQyaTsXHjRl5++WU++ugjlixZgs1mY+PGjXmeH6lUyvTp0ylfvjxXrlyhb9++DBs2jJkzZ97NaQdg8ODBLF68mO3bt9OhQ4cc819//XXq1q3LrFmzkMlknDhxAoVCQaNGjfjyyy/55JNPOH9ecMzzFcKTJk1i5MiRfPzxx7nu9+bNmzRr1ozmzZuza9cu9Ho9Bw8exOFw8P7773P27FkMBgMLFiwA8N41FxH5L/Dzzz8TVSyKn7v9RI+jb+WbyuahZrfa7Bu1l48++sj7ufDl+eefp27dusyePbtIwjM+XnDDVBcQ8QQhjTYjI8P7vE+fPixeupgdg7fz1vFeSOVS4QKuiBHP0qVLA0KqrYg/vimpHoF+ff81ks8nYU23YjfaMx99el5mPjqtTlwuF0FBQdhNdqaETsRhdeLK7GuZ5z7VCiIjI6lftz4nT54kITEehUKBUqlEr9Kj1qnRRArZMlqtFp1OR1BQEB06dCAsLIxlS5eRmppKaGgoYWFhhIWFodVq8xQRb7/9Nm+//XaRzk+g5RxFRSaToSumY+DtIQEtrwpW4Xa5vZlEjwxsSJUXqt7PIRaIy+HCkia0UbGmWbGkWbGmCf9bs/1vSbNgTRFSXC3elFeLX3qvUp0l8l988UUuXryI7bwNjUpNuCYMbZms94Rer+fxxx+nWbNm960dyPHjx5k7dy6/Hf8Ns0n4DilerwRPDhXEZmg+WRQKrQJ3qhsk4DQ4sdls7PlgF89/1waA1CspbHlnM5e3XqJkqZLM/X5uvq1ZCsvNmzfp2bMnYWFh7Ny509tKR0TkfvFAhKfVULR+aUVdLzsXL17E7XYXWAS+Y8cOTp06xZUrV7w1SUuWLCEmJoajR4/yyCOPAIJd+/z58wkODqZGjRo8+eSTnD9/nk2bNiGVSqlWrRoTJkxgz549fsKzR48eXvE1fPhwHn/8cUaOHMkzzzwDwMCBA71RPoBx48bxwQcf0C1TfFesWJFPP/2UYcOG+QnPzp07+0VtX3vtNTp16uRXfJ6f3bavyVCFChX49NNPeeedd+6J8PSc87/++ivX+deuXWPo0KHe5apUqeKdFxISgkQiyTWF9qmnnuL999/3Ps++/W+++YaQkBBWrFjhTT2uWjXrx1ij0WC1WsX0XJH/JEFBQcz8eiZvvvUmv4w/QJORBffgDCkbQvknK7B2/dpchacn6tmuXTuOHTtGg0LW4CcmJgKgCim4plKpV3H23FmvmJBKpQwfOpxPPvmELX03Ue3l6rhdblzOokU8o6KihLRQMeKZA0/aX3p6uvd3cEN3/4wliVQiRKalUuRyOTKpDIVMQUxMDNOnT+fixYskJiYik8lQq9Wo1ULUyVc4eoRCUFAQlSpVokSJEsyaOatIY65Vq2CX2X8LGo0GeyFMZzwtWNxuQah5UoqLisvhEhyM03xEY6YYtHiFozDfkmoRenZmikaP83F+brye945gdiRHIVOgUgrpxmHaUPRV9N42K2FhYZQsWZJ69ep512/YsCH169cnKSmJ9PR00tPTuRN/B5vdht1hx+V00bRp03suOs+dO8ecOXM49OshTOkmZCoZVdpUpUqbKpRpWpbQCoGlwCp0CuxuG0uXLGXhwoXs3L2Tm4du4LA6+HXyIQ58uh+JW0Lv3r1599137+kx3L59m549e6LRaNi1a1dA6cMiInfLAxGeqiLmjhd1vex4vpALSik5e/YsZcqU8f7YAtSoUYPQ0FDOnj3rFZ7ly5f3piABFCtWDJlM5nd3tVixYt47/B58fxw9H/iaNWv6TbNYLBgMBvR6Pb/99htHjx5l3Lhx3mWcTicWi4WMjAyvJXf2C8ATJ07Qq1evfI/Vl927d/P5558TGxuLwWDA4XBgsVgwmUx3XWxe0LkfMmQIPXv2ZMmSJbRs2ZJXX33VL9U4Lwq66D1x4gRNmzYVe1GJ/N/So0cP5n33HfvH7KNS6yqUqF+iwHVqv1WbH3etZ/v27Tz99NM55rdt25ZatWoxZ86cQgvPpKQkQBCVBfHk+CfZ8ObPPP3M08ydPZe6devSokULVqxYwYlvj3Pi2+PoigflG0XLD6lUikwhEyOeuSCVSZGpZGRkZNCwYUPmzJmDw+EgIiKC8PBwIiIiCkwzrVy5MvPmzfubRvzfQqvVFqrNhkd4enramm6bSLua6jXL8YpEP8GYKSxTLViSs4x1rAYrjkBEo0zoz+krGkO1oQSVDyI4WEhn9USeIyIiiIyMzLPNii9tX2zL5dOXcTqE+lSA5d8v97smW7BgAVu3bkUTqUUTpkZTWkN4RATqcDXKICW/z/qNw4cP06xZwW8P3WkAAHFNSURBVDfbCuLKlSvMnj2bAwcPYEgzIJVLqfhsJWI6P0yVNlW9aemFQaFVYLIZGfL+EOJvx1Ondz3KPVmOuTVmkXollZgaMXz99df3PLKekJBAz549kUql7N69m1L3qIxNRKQgHojwrPj000jlcr++nQUhlcupGEAj3kCoUqUKEomEs2fP0i6fNi159XTLPj27mJFIJLlOc7n878b7LuPZXm7TPOu5XC7GjBnDyy+/nGNManVWulp2cVgY856rV6/SunVr+vTpw6effkp4eDgHDhzgrbfeKnJzdl/Onj0LkMNoxMPo0aPp3LkzGzduZPPmzYwaNYoVK1bw0ksv5bvdggSxaAkuIgJbt2yhWMli/Nh5LW+d6I1Ck/+NmKovVUcZpGT06NG5Ck9P1LN9+/YcP36cunXrBjyW5ORkANQBRDwf7lKLqJrRrGqzkm7dhYwPj8isW7cup/44hem20a/tQmGRy+WCuZBIDhRahbfGtlGjRg94NP9f6HQ6XHYXsSvPCGnMBmumKY5NiCgaMgVkpiNr0jkhk0AqlSKRSdkxeBs7Bm/LuWEJyOQyb6RRLlegUihRq9VE66LRldERHBxMaGgoISEhhIWFERkZSWRkpLcGsrC+D4Xl6vWrlHykJBVaVcRpc/LL5wf5448/iImJ8S7juenf61RvgkoE+63vdrn5ffZv3u+aohAXF8fs2bPZvWc3KSkpSCQSyj1Vnqadn6Bqu2powop+beG0OzFcT8PhdJCanspDHWtgijPy42vr0Ifo+erLr+6ZY60vycnJ9OrVC4fDwd69eylbtuw934eISF48EOGpL1WKqm3acG7duoDXqda27T0zFgoPD+eZZ57hm2++YcCAATlEiyedq0aNGly7do3r169777DFxsaSlpbGQw89dE/GUhjq1avH+fPnqVy5cqHWq1WrFjt37vRL282LY8eO4XA4mDJlijdi+8MPPxRpvLnx5ZdfotfraZnPTYSqVatStWpVBg8ezGuvvcaCBQt46aWXUCqVRW6XUKtWLRYtWpSny+/dbFtE5N9CUFAQy5cup91L7dj53naenZl/KyOFRkHM6w9zZMERr1t0dtq1a0dMTAxz5sxh9uzZAY8lJUVogVCQq62HYrWL8+bvvTi14ARSpQxNmJqLmy5y/IfjvPrKq8TExATUQy8vFHKFmGqbBwqtf42tyN9H+fLlAVjfSTADlMgkgmCUCX8KmQJlpmAMV4dRpk5pWrVqRf369floxIdcu3bNL9JYrFgxoqOj0ev1hTbO+buRy+WUblKGpqOeIP1WOr98fpCEhAS/ZTw1ieYUSw7hKZEKLr2F7RNuMBiYO3cumzZvEtxo3W5KNypDw9cfo3r7h9BF332bkZu/3mRTzw0knEnguedas2PXds6ujEWulNOlSxeGDh16X16ftLQ0evfujdFoZO/evQFllImI3EseWDuVph9+yJ+bNgXUUkWuVtNkxIh7uv+ZM2fSqFEjGjZsyNixY6lVqxYOh4Pt27cza9Yszp49S8uWLalVqxavv/46X375pddc6Iknnih0Wtm94JNPPuGFF16gTJkyvPrqq0ilUk6dOsXp06f57LPP8lxv1KhRtGjRgkqVKtGpUyccDgebN29m2LBhOZatVKkSDoeDGTNm0KZNGw4ePFioi0lfUlNTuX37NlarlQsXLjBnzhzWr1/P4sWLc3VNM5vNDB06lFdeeYUKFSpw48YNjh49Svv27QHhB9hoNLJz505q166NVqvNN03Hl/79+zNjxgw6derEiBEjCAkJ4fDhwzRs2JBq1apRvnx5tm7dyvnz54mIiCAkJERMyxX5T9K2bVs6dujIilkrKN+yAtVfzv8mWq0edTg+53cmTZrk58ztQSqV8sknn9CxY0dOnToVcH1deno6EqkEuSbwnyFtpJbHhmZF3GI618SaYmH12jU0aNDALxJSWFQKlSg880AZpMRiKbiHpMi956233uLJJ59Ep9MRFhZWKPfcjh073seR3X9kMjmWVOEaUR0m3KDKHr30lClZUnJ/f6pD1RgC8AdxOBysWLGClStXcu3GNVwOF8XqFuep4S15qEMN9KXvTamX1WBlz0e7+e2bo2i0GsZ9No62bdvSq3cybpebSZMm3bMWKdlJT0/n7bffJikpiT179lCtWrX7sh8Rkfx4YMKzZIMGvLJiBas7dcpXfMpUKtovX07Jeyz0KlSowO+//864ceN47733iIuLIyoqivr16zNrlmBoIJFIWL9+Pe+++y7NmjXza6fyIHjmmWfYsGEDY8eOZeLEiSgUCqpXr07Pnj3zXa958+asWrWKTz/9lC+++AK9Xp9nvUOdOnWYOnUqEyZMYMSIETRr1ozx48d7+24WBk+EVa1WU6pUKZo0acKRI0f8jAF8kclkJCUl8cYbb3Dnzh0iIyN5+eWXvaZIjRo1ok+fPnTs2JGkpCRvO5VAiIiIYNeuXQwdOpQnnngCmUxGnTp1aNy4MQC9evViz549NGjQAKPRKLZTEflPs2zZMvYf2M+Gbj9RvG7xfI0wSjYsSXjVcObMnZOr8ARo37491atXZ/bs2QGbkKWlpaEMUt5V+wapXMpLq19hWfMljPhoBBKJhOeee65I29JqtWQk/Pejek6b09u/0JZuw5qemb6Z+dyTwin8LzwabxsJCr2/aZUieVOxYsUHPYR7isvlwmAwcOvWLeLj47lz5w5JSUkkJSWRkpKCwWAgKioKpVyBJUW4GaTQKJAqpN5MCQ/R0dEAWFNzF56acA2mdFOu8wB27drFd999R+y5WBw2B/rSeh7/oBExr9cksvq9q6t0u92cXRXLtne3YE4y82TzJ5k4caK3TOrbud/es33lhslkom/fvty8eZPdu3ff1U06EZG7QeL2uL0UEYvFwpUrV6hQoYJfnWGg3Dp2jAPjx3P+p5/8aj6lcjnV2ralyYgR91x0ioj8l7nbz6TI/wdXr16lSvUqRNSIpNuhHsiUeddHHp58iN3Dd3Lp4qU867OXL19O586dWbFiRUAXNa+//jpXkv/i3esDi3wMHszJZr5vsZSE0/EMHjSY7t27F3obHTt25JYljr6X+t/1eO4lbrcbh9mRq1i0pQuuobmJRb92FZnT7Rl2XPb8nX8lMolgKOSTyqmQK+jUqRN9+vT5m45a5J+OxWLhzp07xMXFER8fT0JCglc4pqamkp6ejtFoJCMjA4vFgs1hw+F04HA4cNld5HXpKZULZlZ2kx2ZTEaZ5mXpvKMLANMiJ/NwxYeZP3++d/nU1FSaNm1K2yUv8nAXIdvC5XRxff81Ylec4fic33nmmWeYPHmyd53Y2FhmzpzJr8d+xWKyoNSriOn8MA93qUnpRqXveS/T5D+T2NpvC1e2XyYqOoovp335tzovm81m+vXrx7lz59i5c6fXGFNE5EHwwCKeHko2aECHNWsw3LzJ5R07sBoMqPR6KrZsec9qOkVERERE/ClXrhzz5syjW/dubB+8jWe/yTtS+HCXmuwevpMRI0awYsWKXJfp0KEDo0aNYvbs2QFlhWRkZKAKwNE2EDThGrrsfYM17VcxZeoUjh8/zrRp0wpVI6XX64k9G0vS+UQiqt1dpMPtcmf2s8wuDK05RKLV4CMWPe6iHvMYow1Hhr1At16pXOojFuUoZHKUCiUqlYowTRjaCC26skLLkpCQEPR6vddp1ONOGxkZGZBDrch/A5fLRWJiIrdv3+bOnTvEx8d7o45paWmkpaVhNBoxmUxkmDO87UkcDgcuhyvv1kUSITVbpVehCdOgLqVBHxmCOlSNKlSNOkyN2ucx+zS5Wk7yhSRWvfgDCo2Cym2yWqqpQ9UYjUa/3en1eiQSCeZkCzd+uU7sijPELj9DRmIGcqWcSpUq0bFjR+Lj4/nmm2/YuXsnaSlpSBVSqratxsNda1Lpucr53ngrKnaznUNfHOSX8QeRSqT3pSVKQVitVgYOHEhsbCxbt24VRafIA+eBRzxFRETuLeJnUqQwvP7663z//fe8sKAttbrn3d/3hxdWELfvFkaDMc9llixZwhtvvMEPP/xQoAFbx44duXj9Iv2uDrgrZ0hfXA4Xu4bv5MjUw4RHhDNr5ixq1KgR0LorVqxg/ITxuBwuQiuGUaZpGSKqRaCJ1CKRSrCb7H7i0esqmmr19jm0pduwmWwFt7+Q4G1DIZNl9S9UKgWTGN/+lp52FHq90M8wNDTUaxQTERFBWFhYkU1IXC4XKSkpJCUlkZqa6o1WpaWlYTAYvFErk8lE48aN6dq1a5H2I/JgWLlyJUuXLsVsNmOxWrA7BeHobU+Sx9WfTC1HFaxEHaZBE65GHa5BE67xF4mZ/6tC1WgyH9WhalR6FRLpvY0YeljwyHfI78jYtGmT3/Q69evgdrtx2V3IlHJKFiuBSqXiVtwtMkxC+rxEKsHtclOmWVlqvlGL6u0fQh16/34fL278ky3vbMJww0Cd2nWYPn36favdzAu73c6gQYM4cuQImzdvFsuHRP4RiMJTROQ/hviZFCkMLpeLhx56iItXLvLGge6UbJh7psn5dedY8/Iq1q5dm2d7I4fDQfXq1SlfvjxffvllvvuNjY3ltc6vUbtnHZ6b/fzdHoYff264wMY3f8aSbKFpk6Z88cUXAbV+SE1NZcGCBezdu5e4+DgsFos3NdW3Z6GvWFSpVF6xqNPpchWLHoEYGRlJdHR0kdpQ2Gw2r0hMSUkhJSXFG5nyCERvhCojQxAbFgtWq9UbrXK6nDidwp/L6cLtLPjnX6qQgluo1f/10K+FHrfIg6N169bEJcVR+vEyqMPVqMM0qENVmY9qVKFCVNJXUKpCVMhVDzwZLldWPPc9hmNp7N692296t27duHnzJgqFgvikeGxmGzKVjIrPVsJpdZJ6OYXab9ahxmsPE1I25L6OMfliMtsHbuXSpouEhoXyxfgvvF4Sfyd2u52hQ4eyf/9+fv75Z1q1avW3j0FEJDdE4Ski8h9D/EyKFBaDwUDpsqVxqly8dbwXwSWDcyzjtDn5qvhUYirGcOzYsTy3tXDhQnr06MHq1asLdE0cPHgwO3bs4I2D3SndqEy+yxYWc4qZvR/v4fdZx5Ar5DzR9AkGDx5MuXLlCrUdl8uFy+VCLg/8YjwjI4OkpCSSk5P9RKInipieno7JZMJkMmE2mzGbzVitVqxWK3aHHbvT7icQXU5Xgem2IESqFFoFSp0CZZASZbAKlV6FUq9EGaREEaREGZQ5z/s8+/+KrOc6JTKljJ3vb+fErOMc+zXv113kn8fLL79MiiKV3mf+3bW5adfSiF1xhiNTDyO3yjmw/4B3Xv/+/fnl11+wW+wogpRUbVuVau2rU/GZSih1f1/auM1k49AXBzn0xS9IpVI6d+rMe++990Ba1jidTkaMGMH27dtZu3Ytbdq0+dvHICKSF6LwFBH5jyF+JkWKwqlTp6j/SH0iHoqg68HuuV60bR+8jd+/OYYh1ZBnKyO73U7VqlWpUqUKU6dOzXefNpuNpk80RVVcRc9Tb6PQ3PsWRonnEjkwZh9nV8XidrnRh+ipVqUadevWJSYmhvLly1O+fHkMBgMHDhzAYDB4/3wNUjwmKR6RaLNlmqW4HDlEYl4pjF4kgkunQqtAEaREFaQUxKFehTI4Swx6BWBu07IJSIVWgVR2fy5y943ey+EvfuH4seP3Zfsi94fOnTtzNe0a/a8OeNBDKTQZiRmcXRXLmSWnuXHoBhKphOjoaJ579jkefvhhnnnmGQBq1a6F2+Wm05bOlHuy/H2p1cwPt9tN7PI/2DFkOxkJGdSuVfuBpNV6cLlcjBw5ko0bN7Jy5UpvOzoRkX8K/8x8ChERERGRv5VatWqxdPFSXnv9NX58bR3t172aQ8jU7lGbo1/+ymeffcbnn3+e63YUCgUfffQRvXr14s8//6RKlSq5LgegVCqZMH4CAwYOYP/ovTw1oeU9PSaAyOqRtFv+Mk9/9Qzn1pzl4sY/Obn/FEePHvUu07JlS/7880+uXr2aY32ZUoZcI0epEwSeKlKJSq8mOFife6RQJ/zviRh6RKNvdFGukd9z58z7iTJImbeZjMg/Fo1Ggz3O/qCHUSiSLiRxblUsez/eA0BYRBiNGzfGbDZz9vxZFi5ciFQm9QrPIF0QJrOJyBqRf7vojPstjm3vbuHmoRtEFYvi6+++fiA93j243W4+++wzfv75Z5YuXSqKTpF/JGLEU0TkP4b4mRS5G0aPHs2YsWNo8G5Dnv6yVQ6B9F2duUjiJcTdistzGzabjSpVqhATE8PEiRML3Ofbb7/NL4d+4Y2DPSj9eOm7PoaCcLvdpN9MJ+2vVJY0XUT16tUxm83cSoqjycim1OgUgypEhVKnRCr/+1Pl/mn8Pvs3tvTdxMkTJx9I6qBI0RgwYAD7D+9nWMaIBz2UfEk8l8i5VbHELj9D4tlEJDIJIcEhhIaGEncnDqvZilwjp0qbqqhC1ZyY+zt79uwhIiKC48eP81avt1CFq+i0tTPRNYvd9/Ea49LZPWIXpxedQqVR0e+dft6+5Q8Kt9vNxIkTWbp0KfPnz3/g4xERyYt/zi+I6SacXwinpwuPppsPekQiIiIi/3eMHj2a1zq9xrHpRzg86VCO+bV71uX27ducOXMmz20olUo+/PBDtmzZwuXLlwvc51dffYVWp+XHzmuxGW13Nf5AkEgk6EvrKdOkLMXrlcBsNjNkyBCUEgW7h+9k6ROL+WPJaRyWAtxp/09QBinATY5WFiL/bHQ6HQ6LI8+emQ+ShNgE9o/dx5yHZjH3oVnsH7MPabKU6OhoZHIZqamp3Eq8RZVXqvLK+g4MTnqfl1a2p3r76gD89ddfANStW5eli5fiSHOw6LEF/Lnhwn0bsz3DzsFx+5lZ6WvOLPuD5557jgP7Djxwked2u5k2bRpLly5l1qxZD3w8IiL58eCFZ8Ix2PoyfF8e9vSAXwYKj9+XF6YniGYGIiIiIn8n33//PY2bNGH38J2cWnjSb15M54eRyqR8+OGH+W6je/fulCxZkrlz5xa4P7VazbQp0zBcN7C1/+a7GnthUYWosFqt7N27F7PZQsmSJZEZpGwbsIXpJaexa/gODDcMAW3L7XZjM9kw3jGScimZhNiE/0SKqiJIqPdNTEx8wCMRKQxBQUHgpuD2Pn8TCWfi2Td6L7OrzeTbmNkcHLsfVbqSt956iw6vdCApJYkkQzIPv1GTjpteY3DS+7Rd3I6qL1bz1n/ronUAXLt2zbvdGjVqsHnjZkJ0Iaxqu5K9n+zB5bh3nzu3y80fS08xq9LX7B25h6oVqrJpwyYmTpz4j8gqmjVrFgsWLGDatGn06fPvNpIS+e/zYGs8r6yDna+B05pznssBf62D65ugxQqo0O5vH96/hfLlyzNo0CAGDRr0n9jPvdj3woULGTRoEKmpqXe973u5LV/++usvKlSowPHjx6lTp8493baIyN2yb+9eYmJi2PjWz6hCVVRrJ0QaNOEaqrarxrZN23C5XHmmXqpUKj788EPeffdd+vTpQ/ny5fPdX6NGjWjXth3rFq2jwtMVefj1mvf6kHIfZ4gKg81AYmIiLoeTW7dueeeVLVaWI1N/5dfJh6nStirqMDU2o9DD02awYjXYhOdGG3aTTYiQZgsutVn8IjW71vpbjuV+4TGaSklJecAjESkMwcGCO7XNaEOhvffGXQXhdrtJ+COec6vPErv8DMl/JiOVSylRrAS9evWie/funDt3jveHvU9KUgpV2lSl1dfP5tvyRFdMEJ43b/pnxUVGRrJ923beeecdDn62n792XKHt0naEVbw7k5+/dl1h55Dt3Dl5h+hi0Xw17ysaNmx4V9u8l8ybN49Zs2bxxRdfPJBrMxGRwvLgIp4Jx/IWnb44rbCz032JfN6+fZuBAwdSuXJl1Go1xYoVo0mTJsyePZuMjIx7vr8HxejRo/9xwuavv/5CIpFw4sSJgJY/evQovXv3vqdjkEgk3j+dTkeVKlXo3r07v/32m99yHTt25MKFwNJ3Fi5cSGhoaEDLlilThri4OB5++OGAlu/evTvt2rULaFkRkbtFKpVy8uRJSpUqxbpX13B52yXvvNpv1sGSYWHJkiX5buPNN9+kePHifPvttwHtc/To0ZQqXYpNvTaQeO7via4pg5Q4nIITb3ZWrlzJ9q3bafFUC27uvMGlNRe5vSOO9N8McA305mDKBJWmZsWHafpYU154/gVee+013n77bYYNG4ZEJiEj3vS3HMf9RKETREtaWtoDHolIYfAVnn8Xbreb+FN32DtyN7OrfMO8WnP55fODaC1a3n77bQ7uP8iWLVvo2bMn77//Pj179cSusvPK+g68+lPHAvtsaqN0SBVSLl26lGOeXC7n22+/5aMPPyL+9zt8GzObI9MOFyn6mXAmnpWtl/N9i6WkX0pnxIgR7Nyx8x8lOhcvXsxXX33FqFGjGD58+IMejohIQDy4iOfvnxcsOj04rXB8PLRac892f/nyZRo3bkxoaCiff/45NWvWxOFwcOHCBebPn0/JkiVp27btPdtfYXG73TidzkL1jvsvYrPZUCqVREVF3ZftL1iwgGeffRaLxcKFCxeYO3cujz76KPPnz+eNN94ABGdAjUZzT/frOa7ixYvf0+2KiNxLlEolZ2PPUqlyJVa1WUnHza9R/qkKVGhVEV0xHZMmTaJbt255rq9Wqxk+fDhDhgzh7bffpmzZsvnuTyqVsnTJUp5p/SyrX1zJm7/1Qhl0f3vxKYOVOJwOBg4cSIcOHdBoNOj1em8kNzo6mmnTpuW6rsVi4auvviI5ORmLxUJycjK3b9+mdOnS9O/fn6lfTsVm+ne5iuaG5zUQhee/i5AQQcTZTfdXeLrdbuJP3uHsqlhiV8SSejkFqVxKqRKl6PROJ9544w0h7TeTZcuWMfWraditNh597zGafNIs4M+5VC4lqkYU58+fz3OZTp068dRTT9GnTx92DNnOiXnHefqrZ6jQsmKB2zdcT2PfqL2cWngShVJB165def/99/9xplorVqxg0qRJDB8+nFGjRj3o4YiIBMyD+SSZbsLVnwq3ztWf7qnhUN++fZHL5Rw7dowOHTrw0EMPUbNmTdq3b8/GjRv9Gu6mpaXRu3dvoqOj0ev1PPXUU5w8mVX35IkoLlmyhPLlyxMSEkKnTp1IT0/3LuNxHKtYsSIajYbatWuzevVq7/w9e/YgkUjYunUrDRo0QKVSsX//fi5dusSLL75IsWLFCAoK4pFHHmHHjh13deyeyNnkyZMpUaIEERER9OvXD7s96wIpPj6eNm3aoNFoqFChAsuWLfPbRm4Ry9TUVCQSCXv27AGEtKzXX3+dqKgoNBoNVapUYcGCBQBUqFABEIwBJBIJzZs39xvb+PHjKVmypDcKUb58eb788kvvvqZOnUrNmjXR6XSUKVOGvn37Fsn4IjQ0lOLFi1O+fHlatWrF6tWref311+nfv783rSx7FPPkyZM8+eSTBAcHo9frqV+/PseOHWPPnj306NGDtLQ0byR19OjR3vF/9tlndO/enZCQEHr16pXrOTxz5gzPP/88er2e4OBgmjZtyqVLlxg9ejSLFi3ixx9/9G7bc55FRO4nQUFBnD93nrCQMFY+v4Kre68ilUmp1aM2Z8+fLTAFvVevXkRFRTFv3ryA9hcZGcm0yVNJuZTCT2+sv+/GKMogJZYMC02aNuGtnm/5iU5fLl68yObNm/2yYTZt2sTSpUvZtGkTu3bt4rczv3Hs2DHWr18vpCHLpDgy/v3C01Pj6fubJvLPx/O7ZTPe+/egy+Hi2v5r7By6g5kVv+a7ut9yeOIh9I5g+vXrx6GDh9i0aRN9+/b1is4rV67Q+vnWfPHFF0TWjOSt4714amLLQt9cKvFoKRKSEvJdJjo6mrVr1zJu3DjM180sf3oZ37dcyo1frue6vDnZzK5hO5hZ6Wv+WPIHLVu05MC+AwwbNuwfJzrXrVvHuHHjGDhwIOPHj/9XtWYSEXkw4bQb28HtLNw6Lgfc2AHV8r67HihJSUls27aNzz//HJ1Ol+syng+y2+3m+eefJzw8nE2bNhESEsKcOXNo0aIFFy5cIDw8HIBLly6xfv16NmzYQEpKCh06dOCLL75g3LhxAHz88cesXbuWWbNmUaVKFfbt20eXLl2IioriiSee8O532LBhTJ48mYoVKxIaGsqNGzdo3bo1n332GWq1mkWLFtGmTRvOnz9fYPQgP3bv3k2JEiXYvXs3Fy9epGPHjtSpU4devXoBggC8fv06u3btQqlUMmDAAOLj4wu1j5EjRxIbG8vmzZuJjIzk4sWLmM1mAI4cOULDhg3ZsWMHMTExKJVZPzw7d+5Er9ezffv2PC86pVIp06dPp3z58ly5coW+ffsybNgwZs6cWcQzksXgwYNZvHgx27dvp0OHDjnmv/7669StW5dZs2Yhk8k4ceIECoWCRo0a8eWXX/LJJ59478b63uWdNGkSI0eO5OOPP851vzdv3qRZs2Y0b96cXbt2odfrOXjwIA6Hg/fff5+zZ89iMBi84t3z3hMRud+EhoZyNvYs1R6qxopWy+iwsRO1etTh0Be/8MknnzB9+vQ819VoNAwbNoxhw4bRu3dvSpcuuF1Ks2bNeKvHW8ybN4+Dn+2nychm9/Jw/KjarhrJfyZjjDNy/dfrNGjYACRCI3aX0wVu+OzTz5gxYwa3b99GIpWg0WooUawEJUqUAAlCXacEyreoQGjFMH6dfAiDwYBUKhUjniIPjHsZ8XS73aRcTOavXX/x1/YrXN52CVu6DZlCRplSZejavwtdu3ZFq9XmWNfhcDBmzBh+2vATCq2C5+Y+T5236iKRFk0wlahfghPf/k5qamqBpS1t27blhRde4Msvv+T7Fd+zuPFCSjcqQ8P3HqVq22o4LA6OfPkrh744iMPioF6dekyYMIFixe5/W5aisGHDBkaNGkWfPn2YNm2aKDpF/nU8GOFpC8whMAf2Iq6XjYsXL+J2u6lWrZrf9MjISCwWCwD9+vVjwoQJ7N69m9OnTxMfH49KpQJg8uTJrF+/ntWrV3vrDl0uFwsXLvTWVHTt2pWdO3cybtw4TCYTU6dOZdeuXTz++OMAVKxYkQMHDjBnzhw/4Tl27Fiefvpp7/OIiAhq167tff7ZZ5+xbt06fvrpJ/r371/kcxAWFsbXX3+NTCajevXqPP/88+zcuZNevXpx4cIFNm/ezOHDh3n00UcB+O6773jooYcKtY9r165Rt25db0NlX3MRT+psREREjnRTnU7HvHnz/MRodnyL6CtUqMCnn37KO++8c0+EZ/Xq/nbt2bl27RpDhw71LlelShXvvJCQECQSSa4ptE899RTvv/++93n27X/zzTeEhISwYsUKFAqhpsq37kyj0WC1WsX0XJEHQmRkJJf+vETValVZ2Xo5r6zvQKnHSvH9iu/zFZ4g9On84osvmDdvnjcLoCAGDhzIyZMn2ffJXiKqR/LQqzXuwVHkpHSjMryyrgwXfjrP6hd/IKh0ENVfeQhFkBKZUsbu4Tu9rWOiHo6iTu963D4Wx83DN7h0IKvOLEQfwqWNF3FYBQdRs9mMTCq772mOfwce4Wkw3JvfYJG/B8/NyaLUeNoz7Nw5cZtbv97kxi83uLb3KhkJGSABXZCOWtVq0aZNG9q2bZtvSdCOHTsYOWokRoORh7vUpMWUp73OtEWleL3i4BZuUrdv377A5aVSKUOGDGHAgAHMmDGDH1b/wNr2q1GHa3A5nNiNdqpVrcb48eOpXLnyXY3tfrJ161Y++ugjunXrxjfffCOKTpF/JQ9GeCr1RVtPUcT18iD7h/bIkSO4XC5ef/11rFah/vS3337DaDQSERHht6zZbPYrbi9fvrxXdAKUKFHCGyGMjY3FYrH4CUoQ6vzq1q3rN80j0jyYTCbGjBnDhg0buHXrFg6HA7PZ7GclXhRiYmKQyWR+4z19+jQAZ8+eRS6X+42levXqAZvmeHjnnXdo3749v//+O61ataJdu3Y0atSowPVq1qyZr+gEIWL7+eefExsbi8FgwOFwYLFYMJlMeUaxA8UTZc3rS33IkCH07NmTJUuW0LJlS1599VUqVapU4Hazv7bZOXHiBE2bNvWKThGRfxqhoaFc/PMi1R+qzqoXf6D8k+W5mXCTX3/91XuTKje0Wi3vv/8+H330Eb1796ZkyZIB7W/evHn/a+/e43K+3weOv+5Dd4e7czohOqmIkBw2VCaLTY5zNmIMY8YYw2bGd2PmbD82zGlGNsM2Z5mKOR/CFBrKUBKdz933/fvj1q1IipLD+/lYj3Xf9+fw/tx1677u63pfb9p3aM8f/bdiUtOUmq89Plv6pPTudW6t2cqBNrPaAtplFPZP3Ed6ejr6+vrk6efR9MP7zUXmW88lOykLuyb23L10h4K8AmxtbalWrRq2trboyfWI+eMSV/dewTHA6YV9oyg3lINEBJ4vmsL3LfmlBJ55mXmkXEnm7qW7JEUnkXT+Ngkn47l7ORnUGiRSCUpjJc61nGn9Tmu6d+9epmzgnTt3+PDDDzn3zzksXCzou/VdHNs4Vsh1WTewQSKVcPz48TIFnoXkcjljx45l7Nix7Ny5kxUrVqBWq5n63dSH3os9b/bv38+nn35K7969WbFixXNX/isIZVU1gWfNdiCVa8tny0oqh5oBFXJ6V1dXJBIJFy5cKHa/s7N24nnRRjJqtRp7e/sS59MVDcQeDBYkEglqtVp3DIDt27dTo0aNYtsVZlELPRg0ffLJJ+zevZs5c+bg6uqKoaEh77zzDnl5T/cpemnjfVzgBej+0StaClt0jihAhw4diIuLY/v27YSGhtK2bVtGjhzJnDlzSh3b4wLHuLg43nrrLYYPH86MGTOwtLTk4MGDvPfeew+N4UlER0cD9+ehPmjatGn07duX7du3s3PnTr744gtCQkLo2rVrqcd93HVVdAMjQagMpqam/BvzL/Ub1Ofq3iuAdirB3r17S91vxIgRzJ49m5UrVz6y3PxBUqmUTb9uIrBDIL+8vYGBRwZj5Wb1+B2fQGFWL/PW/S60EqkEmYGczMxMDA0NSU8rPsfR2N6Y7KQsfKf74dCqFgdnHODo3MPk5OVQUFDA2DFjmTV7Fhve/Jlq9arRbGwL6vXx1C1P8qKQSCToGeq9VN3eXwWFUz0OTj/AnYt3yE3LI+duNpmJmaRfTyf9Rhq5qfebPEr1pCgNldja2NLy7Za0atUKf3//EstnS7NkyRKW/7gctUaN75d+tJjwOnL9inu7KdeXo2+q/1TL+3To0IEOHTpU2Jgq099//8348ePp1KkTa9asKZY0EIQXTdUEnsoaUCtIu05nWdXupN2vAlhZWdGuXTu+++47Pvzww1IDAm9vbxISEpDL5Y9dh+5R6tWrh76+PteuXStWVlsWBw4cIDg4WBfUZGRkPLIEtKLUrVuXgoICTpw4oWsdfvHixWJNRApLZePj43WfFJa0NIq1tTXBwcEEBwfTunVrPvnkE+bMmaPLaKpU5ZzrC5w4cYKCggLmzp2rC4B/+eWXch/nURYsWICpqSkBAY/+oMPNzQ03NzfGjh1Lnz59WLVqFV27dkWhUDzRNQF4eXmxZs0a8vPzS8x6Ps2xBaEiGRkZceniJZo2bUpkZCShoaGlrukJ2jfB48aNY+rUqXTv3r3Mpfumpqb8EvILXbp1YX3bnwg++h4m1U0ev2M5KYy1r7mM+OJNyvSMtAGXoaEheQnFP/AzqWHC7XOJ5KXnoW+qT9tvA7Byt2LH0G3s27ePLVu2kJOTg62tLdk3stkxdBt7x+ymwQAvvIIbYt+0+guTBdUz0iMz88VfGuZVIpVKMTY1JvlKCke/PYJcJkcul2Oob4iZqSmudV1wcHDAw8MDb29vnJ2dnyqTdu7cOcaMHUPirUQcA5xov/QtLF0rpxeBvpn+S5+Bz8vLY926dSxZsoR27dqxYcOGV36lA+HFV3W/wd6T4b8dZVtSRWYAjSdV6OmXLFlCy5Yt8fHxYdq0aXh5eSGVSjl+/DgXLlygSZMmAAQEBPDaa6/RpUsXvvnmG9zd3bl58yY7duygS5cujy2fBO1aWuPHj2fs2LGo1WpatWpFWloahw4dwtjYuNTlCFxdXdm8eTNBQUFIJBI+//xzXWaysri7u9O+fXuGDh3KsmXLkMvljBkzplhGztDQkBYtWjBr1iwcHR1JSkp6KIsxdepUmjRpgqenJ7m5uWzbtk33ZtPGxgZDQ0N27dpFzZo1MTAw0DVCeBwXFxcKCgpYvHgxQUFB/P3333z//fdPdK0pKSkkJCSQm5vLpUuX+OGHH9i6dStr164tsbQ4OzubTz75hHfeeQcnJyeuX79erNzH0dGRjIwM9u3bR8OGDTEyMirzp8WjRo1i8eLF9O7dm0mTJmFmZsaRI0do1qwZ7u7uODo6snv3bi5evIiVlRVmZmaiLFeoMnK5nJMnT+Ls7ExcXBwJCQmPLaEdO3YsISEhTJw4kZCQkDK/NhwcHFj14yoGDhrI+rbrePfgQIysypeFeZzCUtvMhOKBp0KpDTxNTEzIf6BRkLGdNqOUl34/IK3ZUlsOnJycTHJyMnpGeqTkpJCbmouRsRFGBkac+TGSU0tPYu5kTr2+9fHo7oFtI7vnOgjVM1aIjOcL6PDfhyv9HDk5OUyYMIGwiDAMLQ3pEtKNuj3rVervs4GFIZmpL+cHIRqNhn379jFv3jxu3rzJiBEj+Pbbbx87BUkQXgRVVyRu7QNtQ0CmX/p2Mn1ou0G7fQVycXHh9OnTBAQEMGnSJBo2bIiPjw+LFy9m/PjxzJgxA9CWGO3YsQNfX18GDx6Mm5sbvXv3JjY2tlxdz2bMmMHUqVOZOXMmdevWJTAwkD///POR5ZyF5s+fj4WFBa+//jpBQUEEBgbi7e39VNdeFqtWrcLBwQE/Pz+6deumW06mqJUrV5Kfn4+Pjw8fffQR//vf/4o9rlAomDRpEl5eXvj6+iKTyQgJCQG0b1oXLVrEDz/8QPXq1encuXOZx9aoUSPmzZvHN998Q/369fn555+ZOXPmE13noEGDsLe3x8PDgxEjRmBsbMyxY8fo27dvidvLZDLu3LnDgAEDcHNzo2fPnnTo0IEvv/wSgNdff53hw4fTq1cvrK2tmT17dpnHYmVlxV9//UVGRgZ+fn40adKE5cuX64LLoUOH4u7ujo+PD9bW1vz9999PdM2CUFGkUimxsbHk5+eXad6mvr4+ISEh3Lp1q9yvWS8vLxbOX0jK5WQ2BPxMTkrOkw67RCWV2oI2IM3JycHY2Jj8B5ZGUZhq/37lFWkgVLQRj76+PiY1TPko4WM6/dQZSy8rkpKSUKvU2NraoshUcOSbQ6z0XsHiGgvYMWw7F36LJuvO8xfg6RsrdM33BKHQ5s2bae3Xmv1h+/Ee4cPwmJHU6+VZ6R+iGFga6Lrkv0yio6N57733GDt2LJ6enpw7d47FixdjYGBQ1UMThAoh0TzlImk5OTlcvXoVJyenJ3th3D4Bp2dq1+ksOudTKteW1zaeVOFBpyC8zJ76NSkIlWzNmjUEBwczffr0x86NftDOnTv5dPKn2Da2o8/efhiYVczveH52Pt8azQJgsuZz3f2rm/2ILEFG8+bN2bRpExNzJyNTaOdYhX22n0NfHcR3hj+tPmsNQE5KDvMsvuXdd9/l9OnT/Jf+HyNjR+uOdzvqNpHLTnFmZSR56XkYKA2wsbKhoKCApLtJ5OVog1grj2rU8qtFzZYO2DetjpWb1RMvP1ER1rZcBde0z78g3Lhxg5GjRnL538vYeNny1oqOVG9atqZhFWFzj00k7IvnQMSBZ3bOypSUlMSiRYvYunUr7u7uzJs374WZgyoI5VH1xeLWPvDmb5B5Q7tOZ36atnttzYAKm9MpCIIgPD8GDhxIREQEX331FfXq1XtoaavSdOjQgfz8fD7/4nM2tF1Hn9D+GJg/ffApNyj5z6G+uQGZcRm6ruW56bm6Mt/C7GbRrqGF96Wnp2NoaEj+reJZUut61rRbEIj/zDeI+eMSF36N5t8dMRRkF6Cnr0eNGjVQKpVkZWQRtfY8p384pR2foRybBjbYeNli5WGFhasl5k7mmNYyQ99Mv0IyTBqNhoKcAvIz88nPzCMvM1/3/a3IW5grzZ/6HMKLTa1WM3PmTH797VekelLaLQykyQc+SOXProAu83Ym2XeyySt48dfIzc3N5aeffmLFihXo6+uzePFi3n//fTGNRnhpVX3gWUhZA9wfPddREARBeHl89913nDhxgnHjxhESEqLrwFkWnTp1QiqVMuXzKazzW0Of0P4orZ9uGaVHBW76Zvok59/F1FS7nFd+Rh4UBp4m2iCz6DqJUrkUmUJGRkYG+vr6FOSU3L1dz1CPer08qdfLk4KcAuL2x/Lv9hj+3f4vNy7dAMBQaYizszPm5uaoVCqSbiRxMeoCOdk5aFT3i5VkBnKMrAwxslZiZG2EgaUBCmMFMn25Nkuq0aDKU+mCyNy0PPLS88jLyCM/M4/8rHzys/JR5arQqB9dBGVgJSooXlXnzp1j3bp1RByMICMtA/fuHry5MBCTGhW7zN2jaNQaYv+6SuSy01zccgGNWvPcL4FSGo1Gw549e5g3bx6JiYmMGjWKqVOnYmFhUdVDE4RK9fwEnoIgCMIrw9DQkE2bNtGkSRO++OIL5syZU66sXceOHdHT02PipImsfW0Vff96F7NaZWtQVh76pvrkF+Trmp/lFmkkpH8v8My+U3yumZ5Sj4wMbZb0UYFnUXIDOS4dXHHp4Ergd5ByNZm4/XHEhcUStz+OK6e0y9bIFDLMTMxwc3GjevXqGBsbI5FIuH37NklJSaSnpJNxI507+XdQqQpQqdWANpCUSqXIpDLkMjkKPQUKhQJjAwsUpgrklnJkMhkymUz3Myi6xFZBQQEqlYqmTZsC2oXs169fj5WVFdWrV8fBwQEnJyfq1Kkj3ji/JG7cuMGWLVuIiIjgStwVcrNykUgl1GzlwFsTO+L6Vp1nMo70m+mcW3OGU9+fIu1aKgpDBX6t/Rg7duxje2Q8r86fP8/s2bM5deoUHTt2ZO7cubi5uVX1sAThmRCBpyAIglAl6tSpw6pVq3jnnXdYv349/fr1K9f+gYGBmJmZ8cGoD1jd/Ef6hvbH2tPm8TuWg55SD5VapQuoipbV6t0rq31wCRaFUtsB1sbGBnW+Go1aU675meZOFpg7WdBwcKN7x0/nxpEbxJ+4SfzxeC6djuHMmTP3x2Ggh5GBEeZm5tSuXRuNRkN2djbZ2dnk5uaSm5tLvqoAlaqA7IJsMrIyUKvUqFXqUjOchWT62jmtURejGTNmDCtWrODCxQtIZVLUBcW7rEvlUuR6cvQV+igNlZibm2NlZYW9vT01atSgdu3auLi4UKtWLbE0xHMiIyODsLAwDh8+zLlz54i/FU9OlraRlNLOGPeeHji3d8HpTWcMLSp/vWlVnoqYPy9x5sdIruy+DECN6jX4cMooevbs+VRLvlSlxMREFi5cyB9//IGnpyd79uyhXbt2VT0sQXimxL/6giAIQpXp3r07o0ePZu7cuTRo0AAvL69y7d+iRQvWr1vPuwPfZU2LVbzzRy8c2zg+1ZiKBooKY+36uYWBZ16xjKe2q21GfHqx/RUmCrKzs3VrROdn56NQPvlSCMb2Jrh39cC9q4fuvqw7WdyJTuLOhTvcjblD8uVkUi6nEBUXhUQqQWGsQGGmj8JEgYWpEoWJAoWxAj2lHnrG2u8Lv/SM9e49pkBx73vt/Qr0jPSQyqQcmvU3B6aGa69bXx+T6iaMuvYR2cnZZMRnkJmQcf//CZlkxKeTcTODW9dvceXsFfL+Lr4GKhKQK+QoFAoMFAaYmphiaWmJtbU1NWrUoGbNmjg7O1OnTh1dmbPwZNRqNUlJSVy4cIFLly5x5coV4uLiiE+IJy0jjdzsXG1iXApWdaxw7+NBzdcdqOVXC3Nni2eyzI9GoyH++E3OrT3LP+vO6ZYf6tC+A6NHj6ZGjRe350d2djZr1qxh5cqVKJVKli5dypAhQ8QHL8IrSfzWC4IgCFXq22+/5ejRo3zyySds3LixxDV0S+Ph4cG2P7bxTo932PDmz7y17G0aDmr0xOMpGijqKfVQq9RUq1YNKD6f85EZT1N9cm7m6NY+LsgueKrAsyRGVkYYtaqFQ6taFXrcRyl8HgCMjIzIz8xHIpVox2FlBPVLzzTnZ+eTeSuzWJCakXDv+5sZpN9I5+L1i5w+c7rY/FXQZlH1FHq6LKqFhQVWVlbY2dnpsqh16tTBwcHhhc2GPSgnJ4c7d+5w9+5dkpOTSUlJITU1lZSUFNLT00lPTyczM5PMzEyysrLIyckhJydHm90uyCcrOwuVSoVEKkGV/8DcXYl2DVqr+tWoVbc21vVtsGloi42XTYX/nj7O3Zg7/PPzP/yz9iwpV1OQKWTUdavLsP8Nw9/f/5mOpaJpNBp27tzJggULSEpKYvTo0Xz22Wfl/vdNEF4mIvAUBEEQqpRCoeCXX36hcePGTJ48me+++67cAYStrS27d+2md5/ebB/8J7fPJfLG7IAn6raZl5FXJPBUoFarsbKy0j6Wnnt/3IXNhdKLZ/P0zfTJuJKu64T74PqfLyKFsQKNWkNeXp428Mwu3zXpGeph7miOuaN5qdtp1Bqy7mSVkEHVfp9+PZ2bN+OJORVDfuYDYyiSRTXUN9RlUW1tbbGzs6N27do4OTnh4uJSoVnUrKysYkHi3bt3SU1NJS0tjbS0NDIyMnQBYklBYoFKO4dWpVaVqwRabijXZqmV97LU1vqYmJqgMNHn+qH/yLiZQcvPWmFYzQiljRLj6iaYOphiWtNUtyRQVUiNSyH6lyjOr/+HW5G3kMqk1Kheg4EfDWTAgAEoFM82+K0MZ8+e5dtvvyUyMpLOnTszZ84cXF1dq3pYglDlnpvAM5U0LnOZHHIxQB8XXDBDlNcIgiC8CmrVqsW6det46623WLlyJUOGDCn3MYyMjNi6ZSvjxo0jdEEotyJv0fWX7hhVMyrT/oaWhmTfzdbO47TV3qcwVqBRaWjl1wqAghzV/fNZa4+rtCneUVffTJ+7+XcwMtI+/jIEnnpK7fIOycnJGBkZocrVBklSWcVmGCVSCUprJUprJTYNbEvdNj8r/37WtGgGNV77lX49nVvXbnHq9KmHArn7WVQDjI20c1GrVauGoaGhLkB8eJ5skSBRdT9I5DExokQq0QaJRvcCRBN9FHYKzEzNUZgqipU2l1QCXdLjekZ6pc4b3txjE5d+v4jfjDZlfu4rU0psChd/iyZqYxTxx28ikUqwtrZmwIABDB069KXJAiYkJLBgwQK2b9+Ol5cXf/31F23aPB8/A0F4HlR54HmdG4QTwQUuouZ+kwIpUjxwxw9favLi1vYLgiAIZdOhQwcmT57MrFmzaNiwoa6LanlIpVLmz5/P2rVrmbdgHiu8fqD75h7UaFHzsfu2/tKPc6vPYFgkUHUNqkPLz1sjU8jQN9XHvfv9eZbGtsYMv/QBBg80XDEwNyBfla9bIqagnNnB51Hh+qR37twplsktnOdaFfSM9LBwtsDCufROuhq1hqyke1nUopnUe8Fqxo0Mbt64SczJGApyCrQBovJeoGeuQN9EH6WZsnjgV/i9yYPB4v15soXbyA3kz2SeZFEKEwXqfHWlfDhQFhqNhqToJC5tucCFTdHciryFRCrBysqKnj17MmzYMGxsKrYRWFXKyspi9erVrFq1ClNTU5YvX86gQYOQyaousywIz6MqDTzPE8UvbKKAh9vNq1ETRTSXiKEXPahH3SoY4csvODiYlJQUtm7d+tyfOzY2FicnJ06fPk2jRo2e6twVeawHOTo6MmbMGMaMGVOhxxWEV8H06dM5dOgQEyZM4Ndff9XNrSyvAQMG0LhxY4YOG8raVqt545u2NBvbotQskc+opviMKh7sGtsa4zfd/5H7WNaxeug+A3MDstKzmDJlCsBDcxZfRHr3So9TUlJ0AXV+ZtUGnmUlkUpQ2ihR2iix8So9i/qyUNz7uTzLDwfUBWquH75OzO8Xubj5AilXU5DIJNhY29CvXz8GDx78UgWboG3ctH37dhYuXEhycjJjxoxhypQpoiGWIDxClc3Cv86NRwadRRVQwEZ+5To3KvT8wcHBdOnS5aH7w8LCkEgkpKSkVOj5KtrzOk5HR0cWLFhQpm0XLlzI6tWrK/T8/v7+SCQSJBIJ+vr61KhRg6CgIDZv3lxsOwcHB+Lj46lfv/5jjxkbG4tEIiEyMrJMYzh+/Djvv/9+mbZdvXr1S1NiJAgVQSaTsWHDBuRyORMmTKCg4PHrYD5KgwYN+Cv0L+p51GPf+FA2dlj/UAfayuAzuhmB/9eBDsvepsefvbBtbFfp56xshRnPlJQUXcazaKMl4fmib3Yv8HxwHmwFy7ydyT8/n+P3fluYZzWHdb5rOLHoOGZqM4YNG8bBiIOE7g3l008/femCzsjISPr378/kyZNp1aoV0dHRfPPNNyLoFIRSVFnGM5yIxwadhQooIIID9KV3JY9KeBZUKhUSiUS3IHtFGzp0KNOnTyc/P1+3CHbv3r0JDg5m2bJlgPbNrZ1dxb4ZzMvLQ6FQYG1tXaHHFYRXjZ2dHSEhIbzxxhssWbKE0aNHP/GxjIyMCAkJ4YcffmDpD0tZ5vk9b63oiEe3yquiMa1pSpMPfCrt+FWhcI5nWlqa7t/u/EwReD6vDHSBZ8X+jFT5Km4evcGVPVe4vONfEk7FgwYMlYZ4utWjS5cudOrU6aVeKiQ+Pp758+ezc+dOGjVqRHh4OL6+vlU9LEF4IVRJxjOVNC5wsVz7RHOBVNIqaUSlO3ToEL6+vhgaGuLg4MDo0aPJzMzUPb5u3Tp8fHwwMTHBzs6Ovn37kpiYCGjLMGrWrMn3339f7JinTp1CIpFw5coVBg8eTMeOHYs9XlBQgJ2dHStXrizTGAszZ7t376Zu3boYGxvTvn174uPjdduoVCo+/vhj3YLeEyZMQKMpXgJWUsayUaNGTJs2TXd72rRp1KpVC319fapXr657U+jv709cXBxjx47VZR2Ljm3btm3Uq1cPfX194uLiHso679q1i1atWunG17FjRy5fvlym6y/KyMgIOzs7HBwcaNGiBd988w0//PADy5cvJzQ0FHg4i5mcnEy/fv2wtrbG0NBQt7A9gJOTEwCNGzdGIpHoWrwXjn/mzJlUr14dNze3Ep/DlJQU3n//fWxtbTEwMKB+/fps27aNsLAwBg0aRGpqqu75Kvo8C8KrzM/Pj6+++orly5cTERHx1McbNmwYmzdtxkRhwubum9jaZzNZSVkVMNJXg+Je4Jmamlok8Hzx566+rPTNDYCnb2yl0Wi4fT6R44uO8UtQCPPMv+Wn1ms4PPNvuKGhS+cu/Prrrxw7coy1a9fSrVu3lzbozMrKYvHixQQFBXH69GlWrlzJiRMnRNApCOVQJYHnZS4XayRUFmrUXKb8QcjTOnfuHIGBgXTr1o2zZ8+yceNGDh48yKhRo3Tb5OXlMWPGDM6cOcPWrVu5evUqwcHBgLbRRe/evfn555+LHXf9+vW89tprODs7M2TIEHbt2lUsSNyxYwcZGRn07NmzzGPNyspizpw5/PTTT0RERHDt2jXGjx+ve3zu3LmsXLmSH3/8kYMHD3L37l22bNlSrudj06ZNzJ8/nx9++IGYmBi2bt1KgwYNANi8eTM1a9Zk+vTpxMfHF7uerKwsZs6cyYoVKzh//nyJJTeZmZl8/PHHHD9+nH379iGVSunatStqdfl+V0oycOBALCwsHiq5LfT5558TFRXFzp07iY6OZunSpbq5ZceOHQMgNDSU+Pj4YsfYt28f0dHR7N27l23btj10XLVaTYcOHTh06BDr1q0jKiqKWbNmIZPJeP3111mwYAGmpqa656voz0sQXnUTJkzg7bffZsqUKcX+PXlSzs7OhO4JpUePHlzYFM33bv/H+fXnHvoATnhY4Zql6enpWFhom/mIUtvnl4HFvcCznB8OaNTaQPPkkhNs6fkbC6znsrz+D4SO3cOtAwk08WrC9OnTOX70OHv37mXGjBl4eHg8/sAvMLVaze+//05QUBBr1qzh448/5tKlS6J5kCA8gSr5WCqH3MdvVILcJ9zvUbZt26ZrklBIpVIVu/3tt9/St29fXaOYOnXqsGjRIvz8/Fi6dCkGBgYMHjxYt72zszOLFi2iWbNmZGRkYGxsTL9+/Zg3bx5xcXHUrl0btVpNSEgIkydPBuD111/H3d2dn376iQkTJgCwatUqevTo8dD4SpOfn8/333+Pi4sLAKNGjWL69Om6xxcsWMCkSZPo3r07AN9//z27d+8u8/EBrl27hp2dHQEBAejp6VGrVi2aNWsGgKWlJTKZTJf5fXBsS5YsoWHDho88duG4Cv3444/Y2NgQFRVVprmYpZFKpbi5uREbG/vI62rcuDE+PtryOEdHR91jhaWzhQuWF6VUKlmxYsUj1x0LDQ3l2LFjREdH6zKizs7OusfNzMyQSCQVXvYrCC8DqVTK2rVr8fb2Zvz48axevRo9Pb2nPubUqVPp3bs3H4z8gN/7beXMyjO0X9qhxEZBglbhuqbp6em6eeki4/n8MrTUdlrOe8zPqCC3gPgT8Vz/+z+u//0f18LjyE3NBQkojZXUcapDm/5t6N69u+4Dh1fJqVOnmD17NufPn6dXr17MmjWr2PsDQRDKp0oyngY8WYc1/Sfc71HatGlDZGRksa8VK1YU2+bkyZOsXr0aY2Nj3VdgYCBqtZqrV68CcPr0aTp37kzt2rUxMTHRlWJeu3YN0JZoenh4sGHDBgDCw8NJTEwsls0cMmSIrrQzMTGR7du3Fwtoy8LIyEgXdALY29vrSn5TU1OJj4/ntdde0z0ul8t1gVZZ9ejRg+zsbJydnRk6dChbtmwpU/MPhUKBl5dXqdtcvnyZvn374uzsjKmpqa7EtfB5fFoajeaRLe1HjBhBSEgIjRo1YsKECRw6dKhMx2zQoEGpi11HRkZSs2ZNXdApCEL5WFpa8uuvv3LhwgXmzp1bYcd1c3MjdG8oI0aM4MbB6yyr9z37J+0jN71iP+B8WcgUMqRyKZmZmboARMzxfH4ZVtMGngVFSm01Gg3Jl+9yfsM/7B2zm1XNfmSO8Tf81Go1YZP/4lbELTxdPBkzZgxh+8M4cugIP//8M0OGDHnlgs7r168zbtw4Bg4ciIGBAQcOHCAkJEQEnYLwlKok4+mCC1Kk5Sq3lSLFBZfHb1gOSqUSV1fXYvddv3692G21Ws2wYcNKbG5Rq1YtMjMzefPNN3nzzTdZt24d1tbWXLt2jcDAQPLy7v9R7tevH+vXr+fTTz9l/fr1BAYGFlsmYMCAAXz66accPnyYw4cP4+joSOvWrct1PQ9mAiQSSblLyKRS6UP75Off/8Pl4ODAxYsX2bt3L6GhoXzwwQd8++23hIeHl5qJMDQ0fOw6ZkFBQTg4OLB8+XKqV6+OWq2mfv36xZ7HJ6VSqYiJiXnkuoAdOnQgLi6O7du3ExoaStu2bRk5ciRz5swp9bhKpbLUxw0NDUt9XBCEx2vatCnz5s1j1KhRNG7cmMDAwAo79gcffEDfvn0ZO3YsR2YfJnLFaVp+1prG73ujZ/h02dWXjdxQTlZWlq5rZ16GyHg+rwwttWvRnlt7lhuHr3Pz2E3ij98kN037wYqegR7Wlta84f8Gbdq0ISAgACMjo9IO+UrIzMxkxYoVrF27lmrVqrFmzRr69++PVFpli0AIwkulSgJPM0zxwJ0oosu8T108MOPZt6j29vbm/PnzDwWohc6dO0dSUhKzZs3CwcEBgBMnTjy0Xd++ffnss884efIkmzZtYunSpcUet7KyokuXLqxatYrDhw8zaNCgCr0OMzMz7O3tOXLkiG4ifEFBASdPnsTb21u3nbW1dbG5VGlpabrMbiFDQ0M6depEp06dGDlyJB4eHpw7dw5vb28UCsVD5cplcefOHaKjo/nhhx90AffBgwef5FJLtGbNGpKTkx8q5y3K2tqa4OBggoODad26NZ988glz5szRZTSf5Lq8vLy4fv06ly5dKjHr+aTPlyC8aj744AMOHjzItGnTcHd3r9DMg7m5OatWreLMmTNMmjSJ0LF7OPS/g7w+uSWN3vfWlZm+6vSM9MjKykIqlSKRSUTG8zmRfTebxHOJ3D6XSOK5RBIjE7h19hYAFzZFI1fIMTM1w8vdi2bNmhEYGKirKBK0VCoVv//+O4sXLyYjI4NPP/2UCRMmPPbDZUEQyqfKWo/54cslYsq0pIocOb6UL/tXUSZOnEiLFi0YOXIkQ4cORalU6prJLF68mFq1aqFQKFi8eDHDhw/nn3/+YcaMGQ8dx8nJiddff5333nuPgoICOnfu/NA2Q4YMoWPHjqhUKgYOHFjh1/LRRx8xa9Ys6tSpQ926dZk3b95D64C+8cYbrF69mqCgICwsLPj888+LTZ5fvXo1KpWK5s2bY2RkxE8//YShoSG1a9cGtHMjIyIi6N27N/r6+mVe/N3CwgIrKyuWLVuGvb09165d49NPP32i68zKyiIhIYGCggJu3LjB5s2bmT9/PiNGjKBNmzYl7jN16lSaNGmCp6cnubm5bNu2jbp1tcst2NjYYGhoyK5du6hZsyYGBgZlXgrGz88PX19funfvzrx583B1deXChQtIJBLat2+Po6MjGRkZ7Nu3j4YNG2JkZCQ+dRaEEkgkEpYtW4aPjw/jx49n3bp1GBgYVOg5GjZsyI4dOzh27BjTp08ndNxeDs44QPPxr9FkpA8GZhV7vheNnlJBdnY2oK2OKch58jVWhfIryC3gTnTS/SDz7C0STt8iK/Fel30JGBgZYGVuxWs+r2FjY0PPnj3x9PSs2oE/544fP87s2bO5cOECffv2ZebMmdSqVauqhyUIL6UqCzxrUoNe9GAjv5YafMqR04se1KTGMxzdfV5eXoSHhzNlyhRat26NRqPBxcWFXr16Ados2erVq5k8eTKLFi3C29ubOXPm0KlTp4eO1a9fP0aOHMmAAQNKLMEMCAjA3t4eT09PqlevXuHXMm7cOOLj4wkODkYqlTJ48GC6du1KamqqbptJkyZx5coVOnbsiJmZGTNmzCiW8TQ3N2fWrFl8/PHHqFQqGjRowJ9//omVlbYpx/Tp0xk2bBguLi7k5uaWudRXKpUSEhLC6NGjqV+/Pu7u7ixatEg3X7Y8li9fzvLly1EoFFhZWdGkSRM2btxI165dH7mPQqFg0qRJxMbGYmhoSOvWrQkJCQG0c2EXLVrE9OnTmTp1Kq1btyYsLKzM4/ntt98YP348ffr0ITMzE1dXV2bNmgVoG0sNHz6cXr16cefOHb744guxpIogPIKJiQm//fYbzZo14+uvvy7WPK0iNWvWjG3btnHixAn+97//EfF5GIe+PojPh01pOqY5xrZlb/r2MlEYK8jJyAFAKhOBZ2XRqDWkxqUUy2LeOp1A8r/JaNTav6l6+nqYmpjiVrMO9QLq0bx5c1q0aCE+uCyH//77j3nz5hEaGkqzZs04dOhQsT4YgiBUPInmKfvI5+TkcPXqVZycnJ7o0+fr3CCCA0RzodicTylS6uKBL62rLOh81rKysqhevTorV66kW7duVT0c4QX1tK9JQXjerVmzhuDgYGbMmFFsLeDKEhUVxfTp04m6EIVEKsFrUCOaj2uBldur1QV3bavVaGLV7Nq1iybNm9B4hDdt57Sr6mG90Eoqk008l0hBtjaol8qlKJVK7GzsqFOnDk2aNMHPzw9bW9sqHvmLKz09neXLl/Pzzz9jbW3N7Nmz6d27t5jHKQjPQJWv8luTGvSlN6mkcZnL5JKLPvq44FIlczqrglqtJiEhgblz52JmZlZitlQQBEHQGjhwIBEREXz11VfUq1ev0rtG16tXj5CQEP777z9mzJjBsVXHiFx+CrfO7jQf14KaLR0e2zztZaAwUZCWq62QkUpk5GeLjGdZlVQmeyvyFpm3Hi6TbdGkBQ0bNqRly5Z4enqKgKiCZGVlsW3bNv7v//6PnJwcpkyZwvjx40WWWBCeoSoPPAuZYYo3jat6GFXi2rVrODk5UbNmTVavXo1c/tz8WARBEJ5L3333HSdOnGDcuHFs2LChXGsePykHBweWLVtGWloaM2fOZM+uPVzaehE7b3uaj2+Bxzt1kem9vAvKK0wUFKi0waZMJkUlSm0fUp4y2To161CvrSiTrUwJCQlEREQQFhbG0aNHycvLY8CAAXz99dfUqPFqVNMJwvOkykttBUGoWOI1KbwqYmJi8PHxwcXFhYULFz7ztQYLCgpYtmwZ6zesJzUlFaWtEp8Pm9JoqDdKm5evG+a2QX9wZfNlDv99mJatWlLzLQe6rH+1poVoNBqybmdpg/DsgofKZG//c5v8e2tnSuVSjIyU2NuKMtlnRa1WExUVRVhYGBEREURHRyOTyWjVqpWuG/+jVikQBKHyicBTEF4y4jUpvEqOHDlCp06dMDQ0ZMmSJboO289aaGgoCxYu4Nq1a0ikEur19qTJSB+qN6/x0pTh7v5wF+dXneP4keP4+vmSfDeZSerPXprrA21gmX0nm9TYFFJiU0i9mkJKbCqpV1NI/vcuqf+lPZzpLVIm6+TkJMpkn7GsrCyOHj1KeHg4ERER3L59GwsLC9q3b09QUBDt27d/5h9KCYJQMlHTKQiCILywWrRowZEjR3jrrbfo378/CxYsoEmTJs98HAEBAQQEBBAXF8esWbM4+utR/ll3DisPKzz71sejRz2qeZRteannlUKph1qtbQJoZGhEMsnkZ+ajMH5+1zlVF6jJSc0hNyWHnJRcclJyyEnOJrfI9znJOaRdS9MGltdSdY19ACQyCfoG+igNlVhbWdPwtYZkZ2eTm5tL3bp1RZlsFSlaQnvs2DFyc3OpU6cOAwYMICgoiJYtW4ppS4LwHBKvSkEQBOGF5uzszOHDh+nWrRvvv/8+X375JR07dqySsdSuXZulS5dSUFDAihUr2LJlCwe+jCBiajhWHtXw6FEXGy8brNyssHC1RM9Ir0rG+ST0jBWoVCoAAgMDWblyJQW5BZUaeGo0GvLS8+4HiSm594LIHHKStf/PLfJ99p1scu5qg8nctFxd2WtJpHIpMrkMmUyGkYERVpZW1G9WH0dHR9zd3fHy8sLBwUFkLZ8DhSW04eHhhIeHFyuh/frrr+nYsWOlNxkTBOHpicBTEARBeOFZWFiwe/duhg0bxqRJk7h+/TrDhg2rsjJQuVzO8OHDGT58OCkpKaxatYpdu3ZxeObfqAvuLx2mtFVi5VENKw8rLN2ssKxjiaWbJeZOFsgUz1ejIoVSD7XqXsbzXobvcQ2GNBoNBTkFxYPERwSNOSk55Ny9FzwmZ5ObmkteRp6uKc+DJDIJMrkMuVyOXCZHoafA0MAQCyMLjO2NMXU3xcLCAgsLCywtLbGxscHGxgZbW1tsbW1RKJ7fTK0A2dnZHDlyhPDwcA4cOEBiYiLm5uZ06NCBzz//XJTQCsIL6LkJPG+kwd7LkJYLpvrQzgVqvBqrqQiCIAgVQKFQsHLlSlxdXfnss8/477//mDZtGnp6VZtVNDc3Z+zYsYwdOxaAuLg4jh07xj///MOVK1eIvxBP9IkocnNy0ai0QZZEKsGkpinV6lXTBqRultosaR1LTB1MkcqefRZOT6lAo9KgVqt1gefBGQcwsDDQZSKz72aTcyeb7HvBZF56Lup8dckHlIBMT6YNHmV6KOR6GOgbYGykxNbcBtNappiZmWFhYUG1atWwsrLCxsYGe3t77OzsRHnrS6iwhDY8PJyjR4/qSmjfffddUUIrCC+BKm8udOIGfB0Bf16EIh8CI5dCkDtM9gUf0fFaEMpMNBcSBNiwYQPBwcE0bNiQ+fPnY2ZmVtVDeiy1Wk10dDQnT54kKiqKq1evcivxFhlZGeRm58K9v9ZSPSnmjuZUq2eNpbsllnW0gallHUuUdsaVluU9v/4cv/fbSkREBGq1mrc7vU12VrYu66gnV6Cvp8DIyAilUomJiQmmptqso5WVFVZWVlhbW2NnZ4e9vT1mZmaijPUVV/g7HxYW9lAJbadOnUQJrSC8ZKo08NwSBX02QW4plTr6cgjpAV3qPs0oy08ikbBlyxa6dOnyxMcIDg4mJSWFrVu3Vti4Cvn7+9OoUSMWLFhQ4ccuj4p4np4H06ZNY+vWrURGRlboccPCwmjTpg3JycmYm5tX6LEfRQSegqB18OBBunTpgqmpKf/3f/+Hg4NDVQ/pieXk5HD27FlOnz7NhQsXiIuL43bSbTKzM8nPuT+PUW4ox7KOFW8uDqSWb8V2+L30+0U2dfmFLVu2iCUphCdWWgmt6EIrCC+3Kgs8T9yAVj+WHnQW0pfDwfcqNvP5uKAwISEBCwsL9PX1n/gcqampaDQaXcBRkcHi4461evVqBg0apLutVCpxd3dnypQpdOtWceuuVcTzVFliY2NxcnLS3TY2NqZWrVr4+/szZswY6tSpo3ssIyOD3NxcrKysHnvc8gSpeXl53L17F1tb2zJlISrid0QEnoJwX0xMDG+99RZ3795l4cKFNGrUqKqHVOHS0tI4deoUp0+fJiYmhoOHDmLnbY+9jz1qlQbUGtQqNRq1RlsqW+R77f+1t9UFGjRq9b3/a1AXqIs9lpOSQ/K/d1m9enWVdA4WXly3bt3SNQYqWkLbqVMnUUIrCK+QKnuVfx1RtqATtNvNPAC/9a7cMRVlZ2f31Meo6tIuU1NTLl68CEB6ejqrVq2iZ8+enD9/Hnd39wo5R0U8T5UtNDQUT09PsrKyOHfuHAsXLqRhw4b8+eeftG3bFtAGpcbGxhV63vz8fBQKxQvxHAnCy6pOnTocOXKEzp0789577/HVV1/Rvn37qh5WhTI1NcXf3x9/f38AevTsQWxUHCnRyUgkEiRI0P4n0d6+d19J30ul0of+L5VIkUul6EsU1GxQA09Pz6q9YOG5V1oJ7VdffUVQUJAooRWEV1CVTK64kQZ/XCzfPn9c0O73rEgkEl02NDY2FolEwi+//ELr1q0xNDSkadOmXLp0iePHj+Pj44OxsTHt27fn9u3bumMEBwfrSlCDg4MJDw9n4cKFuj/wsbGxAERFRfHWW29hbGyMra0t7777LklJSbrjZGZmMmDAAIyNjbG3t2fu3LllvgY7Ozvs7OyoU6cO//vf/5BKpZw9e7bE6yxkbm7O6tWrAW3GbtSoUdjb22NgYICjoyMzZ84s9XnavHkzbdq0wcjIiIYNG3L48OFixz906BC+vr4YGhri4ODA6NGjyczM1D2+ZMkS6tSpg4GBAba2trzzzju6xzZt2kSDBg0wNDTEysqKgICAYvuWxMrKCjs7O5ydnencuTOhoaE0b96c9957T7c0wLRp04plQsLCwmjWrBlKpRJzc3NatmxJXFwcq1ev5ssvv+TMmTO6n2PhcyWRSPj+++/p3LkzSqWS//3vf4SFhSGRSEhJSdEd+++//8bPzw8jIyMsLCwIDAwkOTm51N8RQRCenJWVFfv27eOdd97hk08+YcWKFTxlsc9z7ddffuX4kWMcO3yMo4eOcuTQEY78fYTDfx/m0MFD/H3gbw4eOMiBiANEhGsbuYSFhbF//3727dtHaGgoe/bsYffu3ezcuZMdO3awbds2/vzzT9avXy8qKYQSZWdnExYWxrRp02jXrh29e/cmJCSERo0asX79em7fvk1YWBjjxo0TQacgvKKqJPDcexlUj2hy9ygFagi9XDnjKasvvviCzz77jFOnTiGXy+nTpw8TJkxg4cKFHDhwgMuXLzN16tQS9124cCGvvfYaQ4cOJT4+nvj4eBwcHIiPj8fPz49GjRpx4sQJdu3axa1bt+jZs6du308++YT9+/ezZcsW9uzZQ1hYGCdPnizX2FUqFWvWrAHA29u7zPstWrSIP/74g19++YWLFy+ybt06HB0dS91nypQpjB8/nsjISNzc3OjTpw8FBdr09rlz5wgMDKRbt26cPXuWjRs3cvDgQUaNGgXAiRMnGD16NNOnT+fixYvs2rULX19fAOLj4+nTpw+DBw/WfZLarVu3cr+BlEqlfPTRR8TFxZX4PBYUFNClSxf8/Pw4e/Yshw8f5v3330cikdCrVy/GjRuHp6en7ufYq1cv3b5ffPEFnTt35ty5cwwePPihY0dGRtK2bVs8PT05fPgwBw8eJCgoCJVK9cjfEUEQnp6+vj7r1q3j888/Z+HChXz55Zfk5z96jUdBEB7v1q1b/PLLL4wcOZLWrVvz4YcfcvbsWd599132799PYmIi69evp0+fPmLepiAIVVNqm5b7bPerKOPHjycwMBCAjz76iD59+rBv3z5atmwJwHvvvafLfj3IzMwMhULb7a9o6eXSpUvx9vbm66+/1t23cuVKHBwcuHTpEtWrV+fHH39k7dq1tGvXDoA1a9ZQs2bNx443NTVVVz6anZ2Nnp4ey5Ytw8XFpczXfO3aNerUqUOrVq2QSCTUrv34ZhXjx4/n7bffBuDLL7/E09OTf//9Fw8PD7799lv69u3LmDFjAG0Z3KJFi/Dz82Pp0qVcu3YNpVJJx44dMTExoXbt2jRu3BjQBp4FBQV069ZNN44GDRqU+VqK8vDwALRZ2mbNmhV7LC0tjdTUVDp27Kh7rurWvd/dytjYGLlcXmIJbd++fYsFnFevXi32+OzZs/Hx8WHJkiW6+4qWrZX0OyIIQsWQSCRMnz4dV1dXhgwZwo0bN5g7dy6mpmLtLkEoi8IS2sL5mlFRUchkMlq2bClKaAVBeKwqCTxNn7APzZPuV1G8vLx039va2gLFAx9bW1sSExPLdcyTJ0+yf//+EucXXr58mezsbPLy8njttdd091taWpZpjqaJiQmnTp0CICsri9DQUIYNG4aVlRVBQUFlGl9wcDDt2rXD3d2d9u3b07FjR958881S9yn6PNnb2wOQmJiIh4cHJ0+e5N9//+Xnn3/WbaPRaNeFu3r1Ku3ataN27do4OzvTvn172rdvT9euXXVlu23btqVBgwYEBgby5ptv8s477zzRp6iFWdKSGv5YWloSHBxMYGAg7dq1IyAggJ49e+qupTQ+Pj6lPh4ZGUmPHj3KPV5BECrOgAEDqFWrFl27diUwMJAuXbrQt29fUWEgCCXIzs7m6NGjhIWFPdSF9rPPPhNdaAVBKLMqKbVt56Jdp7M85FIIKHuirlIUXYS8MGB58D61unw1xGq1mqCgICIjI4t9xcTE4Ovr+1TzkKRSKa6urri6uuLl5cXHH39MmzZt+Oabb4qN+cFzFC0/8/b25urVq8yYMYPs7Gx69uxZbM5lSUp6ngqfF7VazbBhw4pd65kzZ4iJicHFxUUXLG/YsAF7e3umTp1Kw4YNSUlJQSaTsXfvXnbu3Em9evVYvHgx7u7uD2UVyyI6OhqgWNfbolatWsXhw4d5/fXX2bhxI25ubhw5cuSxx1UqlaU+bmhoWO6xCoJQ8fz9/fnnn38YNWoUO3bs4O2332b06NEcPXr0pZ7/KQhlUVhCO2rUKF0J7ZkzZ+jfv78ooRUE4YlVSeBZwxSCytlUtZOHdr8XmUKh0DWzKeTt7c358+dxdHTUBYmFX0qlEldXV/T09IoFPcnJyVy6dOmJxiCTycjOztbdtra2Jj4+Xnc7JiaGrKysYvuYmprSq1cvli9fzsaNG/ntt9+4e/fuE52/8HofvFZXV1cUCgUAcrmcgIAAZs+ezdmzZ4mNjeWvv/4CtIFsy5Yt+fLLLzl9+jQKhYItW7aUawxqtZpFixbh5OSkK+MtSePGjZk0aRKHDh2ifv36rF+/Hij551hWXl5e7Nu375GPP82xBUEonxo1ajBz5kz+++8/fvjhBxITExkyZAjdu3dn06ZNxf6tFISXmUaj4fz58yxZsoRevXoREBDA119/jUaj4auvvuLixYvExMQwd+5c/P39i33ALAiCUFZVtpzKZF/YEVO2JVUM5DCpdcWPITU19aG1GC0tLalVq1bFnwxwdHTk6NGjxMbGYmxsjKWlJSNHjmT58uX06dOHTz75hGrVqvHvv/8SEhLC8uXLMTY25r333uOTTz7BysoKW1tbpkyZglT6+M8MNBoNCQkJgLZUZu/evezevbtYA6Q33niD7777jhYtWqBWq5k4cWKxPyjz58/H3t6eRo0aIZVK+fXXX7Gzs9OtTVpeEydOpEWLFowcOZKhQ4eiVCqJjo5m7969LF68mG3btnHlyhV8fX2xsLBgx44dqNVq3N3dOXr0KPv27ePNN9/ExsaGo0ePcvv27WLzL0ty584dEhISyMrK4p9//mHBggUcO3aM7du3I5PJHtr+6tWrLFu2jE6dOlG9enUuXrzIpUuXGDBgAKD9OV69epXIyEhq1qyJiYlJmdcxnTRpEg0aNOCDDz5g+PDhKBQK9u/fT48ePahWrVqJvyNl+VkLgvDkjIyMGDp0KEOGDGH//v0sWLCA6dOns3DhQrp3707v3r3FvGvhpVNSCa2ZmRkdOnRgypQptG/fHktLy6oepiAIL5EqCzx9akBID+j9a+nBp74cNvTQbl/RwsLCHsp4DRw48JENgp7W+PHjGThwIPXq1SM7O5urV6/i6OjI33//zcSJEwkMDCQ3N5fatWvTvn17XcDx7bffkpGRQadOnTAxMWHcuHGkpqY+9nxpaWm6eYn6+vrUrl2b6dOnM3HiRN02c+fOZdCgQfj6+lK9enUWLlxYrNOrsbEx33zzDTExMchkMpo2bcqOHTueOBjy8vIiPDycKVOm0Lp1azQaDS4uLrrOsObm5mzevJlp06aRk5NDnTp12LBhA56enkRHRxMREcGCBQtIS0ujdu3azJ07lw4dOpR6zoCAAED75rJ27dq0adOGZcuW4erqWuL2RkZGXLhwgTVr1nDnzh3s7e0ZNWoUw4YNA6B79+66JWNSUlJYtWoVwcHBZbp+Nzc39uzZw+TJk2nWrBmGhoY0b96cPn36AI/+HREEofJJJBLeeOMN3njjDa5cucLixYtZuXIlq1evpm3btvTv359GjRqVODdcEF4Et27dIiJCu4TOkSNHyM3NxdXVlf79+xMUFETLli1FNlMQhEoj0TzlZJacnByuXr2Kk5PTE63tdeIGzDygXaezoMj0SLlUW147qXXlBJ2C8LJ62tekIAj3paens2bNGhYtWkRMTAyenp707duX9u3b66YHCMLzSqPREBUVVWIX2k6dOtGxY8cyNSsUBEGoCFUeeBa6kaZdpzMtV9u9NsDlxZ/TKQhVQQSeglDx1Go1u3fvZsGCBezZs4dq1arRo0cPevbsSbVq1ap6eIKgU1hCGx4eTkRERLES2qCgIFFCKwhClXluAk9BECqGeE0KQuW6cOECixYtYu3ateTl5REYGEj//v2LrckrCM9SYmKiLqt59OhRcnJycHV1pVOnTqKEVhCE54YIPAXhJSNek4LwbKSkpLBy5UoWLVpEXFwcjRo1ol+/frRt21a8yRcqVdES2oiICM6fP49MJuP111/XBZuihFYQhOeNCDwF4SUjXpOC8GypVCr+/PNPFixYQHh4OHZ2dvTq1Yvu3buLNQ6FCpOTk6PrQitKaAVBeBGJwFMQXjLiNSkIVefs2bMsXLiQ9evXo9FoePvtt+nbty9ubm6iG65QbiWV0Lq4uOiymq1atRLZdUEQXhgi8BSEl4x4TQpC1UtKSmL58uV899133Lx5ExsbG7y9vWnSpAne3t64urqKNXqFYgoKCkhOTubmzZscOnSI8PBwUUIrCMJLRQSegvCSEa9JQXh+5Ofns3fvXt1cvBMnTlBQUICZmRmNGzfWBaN169YVmauXkEajISsri9u3b5OUlFTi1507d0hKSuLu3buo1dp15QpLaDt27EiHDh1ECa0gCC+F5ybwvHEjjb17r5CWloupqT7t2jlTQ6ynIgjlJgJPQXh+ZWVlcfToUSIiIjhw4ACHDx8mKysLQ0NDGjZsqAtEGzRogKGhYVUPV3iEgoIC7t69y+3bt3WBY1JS0kO3k5KSyM7OLravgYEBdnZ22NnZYW9vj729/UO3vby8xAcRgiC8dKo88Dxx4iZff32AP/+8REGBWne/XC4lKMiNyZNb4+NT/WmG+MoLCwujTZs2JCcnY25uzurVqxkzZgwpKSmVfu7Y2FicnJw4ffo0jRo1qtBjOzo6MmbMGMaMGVOhx33RicBTEF4c+fn5nDp1ioiICCIiIjh48CApKSno6enh6elJ48aN8fHxoVGjRpiaig9jK5NKpSIzM/OhwPHB7OTt27dJTk7mwbdP1apVeyiYfDCotLOzw9TUVMz3FQThlVSlgeeWLdH06fMbubmqR26jry8jJOQdunTxeJphFvO4f/AHDhzI6tWrK+x8ZTl/y5YtOXjwYKWcLy8vj7t372Jra4tEIqmQwNPf35/w8HAAFAoF1apVw9vbm0GDBtGtWzfddiqVitu3b1OtWjXkcnmpxyxvkHr79m2USiVGRkaP3fZZBttVTQSegvDiUqvVnD9/XpcRDQ8PJyEhAYlEgpubG97e3rqsqLW1dVUPt0ppNBqys7PJzMzUfWVlZZGVlVXs9oOPl/T/zMzMhzKTAIaGhqVmJwu/t7GxERlKQRCExyg9EqhEJ07cfGzQCZCbq6J3700cPDi4wjKf8fHxuu83btzI1KlTuXjxou6+Z1HetGrVKtq3b6+7rVAoKu1cCoUCOzu7Cj/u0KFDmT59Ovn5+dy4cYMtW7bQu3dvgoODWbZsGQAymazCz52Xl4dCoXjl33QJgvDykUqlNGjQgAYNGjBy5Eg0Gg1XrlzRBaEHDhxgw4YNANSoUQMzMzOUSqXuQ7jC74t+GRkZYWxsXGybwtvPMljSaDTk5uaWGhAWDQIfFVAW3ba0z84lEgnGxsYYGxtjYmKCiYkJpqammJub4+DgoLvvwS9bW1tdUGliYiKyk4IgCBWkygLPr78+8Nigs1BuroqZMw/y2289K+TcRQMhMzMzJBIJdnZ2aDQabGxs+P777+nevTsAjRo14ubNmyQmJgJw+PBhfH19SU5OxtjYmGvXrvHhhx+yb98+pFIp7du3Z/Hixdja2pY6BnNz84cCsjt37jBq1CgOHDjA3bt3cXFxYfLkyfTp00e3jb+/Pw0aNEAmk7FmzRoUCgUzZsygX79+jBo1ik2bNmFjY8N3331Hhw4dgIdLbYuKjY3F2dmZY8eO4ePjo7t/8eLFzJkzh9jY2Ef+0TUyMtJdg4ODAy1atMDDw4PBgwfTs2dPAgICHspiJicnM2rUKPbs2UNGRgY1a9Zk8uTJDBo0CCcnJwAaN24MgJ+fH2FhYQQHB5OSkkLz5s1ZvHgxCoWC2NjYh0ptU1JSmDBhAr///jupqam4uroya9YsjI2NGTRoEHA/2/zFF18wbdq0Un9GgiAIVU0ikeDi4oKLiwvBwcGA9sPTAwcOcPLkSVJTU0lPTyc9PZ20tDTi4+N1t9PT08nKyir1+AqF4qEgtWhwWlIwa2RkRH5+/mMzig9mHzMzM1GpSv+7XxgkFw0ETU1NdUFgWb8KA2sRNAqCIDw/qiTwvHEjjT/+uPj4DYv444+L3LiRVqkNhyQSCb6+voSFhdG9e3eSk5OJiopCqVQSFRVFvXr1CAsLo0mTJhgbG6PRaOjSpQtKpZLw8HAKCgr44IMP6NWrF2FhYeU+f05ODk2aNGHixImYmpqyfft23n33XZydnWnevLluuzVr1jBhwgSOHTvGxo0bGTFiBFu3bqVr165MnjyZ+fPn8+6773Lt2rXHlqE6OjoSEBDAqlWrigWeq1atIjg4uNx/tAcOHMi4cePYvHkzAQEBDz3++eefExUVxc6dO6lWrRr//vuvrrzp2LFjNGvWjNDQUDw9PYtlgfft24epqSl79+4t8RNutVpNhw4dSE9PZ926dbi4uBAVFaVrQ79gwYJimW1jY+NyXZcgCMLzwt7enp49e9Kz5+M/jFWpVGRkZOgC06JBadHbJT3233//FbsvPT39oX9/DQwMSswqWllZlStQNDExQalUIpPJKutpEwRBEKpYlQSee/deQaUq39TSggI1oaFXGDiwUeUM6h5/f39dmWhERAQNGzakVq1ahIWF6QJPf39/AEJDQzl79ixXr17FwcEBgJ9++glPT0+OHz9O06ZNH3mePn36FPsDu27dOrp06cL48eN193344Yfs2rWLX3/9tVjg2bBhQz777DMAJk2axKxZs6hWrRpDhw4FYOrUqSxdupSzZ8/SokWLx17zkCFDGD58OPPmzUNfX58zZ84QGRnJ5s2by/is3SeVSnFzcyM2NrbEx69du6ZrlgHawLdQYemslZXVQ9lgpVLJihUrHlmSHBoayrFjx4iOjsbNzQ0AZ2dn3eNFM9uCIAivCplMhpmZGWZmZk99rMKlQdLT09HX18fY2FjMaxQEQRDKrEpWr05Ly32m+5WHv78/58+fJykpifDwcPz9/XWNdAoKCjh06BB+fn4AREdH4+DgoAs6AerVq4e5uTnR0dGlnmf+/PlERkbqvtq1a4dKpeKrr77Cy8sLKysrjI2N2bNnD9euXSu2r5eXl+57mUyGlZUVDRo00N1XWOZbWB78OF26dEEul7NlyxYAVq5cSZs2bYoFheWh0WgemSkdMWIEISEhNGrUiAkTJnDo0KEyHbNBgwalzoONjIykZs2auqBTEARBqFgSiQSlUomdnR0WFhYi6BQEQRDKpUoCT1NT/We6X3nUr18fKysrwsPDdYGnn58f4eHhHD9+nOzsbFq1agU8OsAqLfAqZGdnh6urq+5LqVQyd+5c5s+fz4QJE/jrr7+IjIwkMDCQvLy8Yvs++MdeIpEUu6/w3IULUT+OQqHg3XffZdWqVeTl5bF+/XoGDx5cpn0fpFKpiImJ0c3XfFCHDh2Ii4tjzJgx3Lx5k7Zt2xbL8j6KUqks9XGx3p0gCIIgCIIgPL+qJPBs184Zubx8p5bLpQQEOD9+w6dUOM/z999/559//qF169Y0aNCA/Px8vv/+e7y9vTExMQG02c1r167x33//6faPiooiNTWVunXrlvvcBw4coHPnzvTv35+GDRvi7OxMTExMhV1baYYMGUJoaChLliwhPz+/2JIo5bFmzRqSk5N1zZlKYm1tTXBwMOvWrWPBggW60ubCjObjmk+UxMvLi+vXr3Pp0qUSH1coFE90XEEQBEEQBEEQnl6VBJ41apgSFFS+kshOndwrtbFQUf7+/qxfvx4vLy/dQs++vr78/PPPuvmdAAEBAXh5edGvXz9OnTrFsWPHGDBgAH5+fsUa9ZSVq6sre/fu5dChQ0RHRzNs2DASEhIq8MoerW7durRo0YKJEyfSp0+fMmUQs7KySEhI4Pr16xw9epSJEycyfPhwRowYQZs2bUrcZ+rUqfz+++/8+++/nD9/nm3btumCdBsbGwwNDdm1axe3bt0iNTW1zOP38/PD19eX7t27s3fvXq5evcrOnTvZtWsXoJ1LmpGRwb59+0hKSnpsp0dBEARBEARBECpOlQSeAJMnt0Zfv2zd6wwM5Eya1KqSR3RfmzZtUKlUxYJMPz8/VCqVbn4naLOjW7duxcLCAl9fXwICAnB2dmbjxo1PdN7PP/8cb29vAgMD8ff3x87Oji5dujzl1ZTde++9R15eXpnLbJcvX469vT0uLi507dqVqKgoNm7cyJIlSx65j0KhYNKkSXh5eeHr64tMJiMkJAQAuVzOokWL+OGHH6hevTqdO3cu1/h/++03mjZtSp8+fahXrx4TJkzQZTlff/11hg8fTq9evbC2tmb27NnlOrYgCIIgCIIgCE9Ooilt9eUyyMnJ4erVqzg5OWFgYFCufbduvUDv3ptKXc9TX19GSMg7dOni8TTDFMrgq6++IiQkhHPnzlX1UISn8DSvSUEQBEEQBEGoDFWW8QTo0sWDgwcH061b3YfmfMrlUrp1q8vBg4NF0FnJMjIyOH78OIsXL2b06NFVPRxBEARBEARBEF4yVbKOZ1E+PtX57bee3LiRRmjoFdLScjE11ScgwPmZzel81Y0aNYoNGzbQpUuXJ+5mKwiCIAiCIAiC8ChVWmorCELFE69JQRAEQRAE4XlTpaW2giAIgiAIgiAIwsuvwgLPp0ycCoJQQcRrURAEQRAEQXjePHXgqaenByDWRRSE50Tha7HwtSkIgiAIgiAIVe2pmwvJZDLMzc1JTEwEwMjICIlE8tQDEwShfDQaDVlZWSQmJmJubo5MVrZ1cgVBEARBEAShsj11cyHQvuFNSEggJSWlAoYkCMLTMDc3x87OTnwAJAiCIAiCIDw3KiTwLKRSqcjPz6+owwmCUE56enoi0ykIgiAIgiA8dyo08BQEQRAEQRAEQRCEB4nlVARBEARBEARBEIRKJQJPQRAEQRAEQRAEoVKJwFMQBEEQBEEQBEGoVCLwFARBEARBEARBECqVCDwFQRAEQRAEQRCESiUCT0EQBEEQBEEQBKFSicBTEARBEARBEARBqFQi8BQEQRAEQRAEQRAqlQg8BUEQBEEQBEEQhEolAk9BEARBEARBEAShUonAUxAEQRAEQRAEQahUIvAUBEEQBEEQBEEQKpUIPAVBEARBEARBEIRKJQJPQRAEQRAEQRAEoVL9P9XWlnqHdn/1AAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 1400x800 with 1 Axes>"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -280,9 +592,20 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(14,8))\n",
-    "kc_zones.plot(ax=ax, column='ZONENAME',categorical=True, legend=True, \n",
-    "              legend_kwds=dict(ncols=4, loc=(-0.1,-0.3)), cmap='jet_r')\n",
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "kc_zones.plot(ax=ax, ec='k', fc='None')\n",
+    "kc_zones[~kc_zones['ZONENAME'].str.contains('Planned')].plot(ax=ax, \n",
+    "                                                             column='ZONENAME',\n",
+    "                                                             categorical=True, \n",
+    "                                                             ec='k',\n",
+    "                                                             legend=True, \n",
+    "                                                             legend_kwds=dict(ncols=1, \n",
+    "                                                                              loc='lower left'), \n",
+    "                                                             cmap='jet_r')\n",
+    "armourdale.dissolve(\"CITY\").plot(ax=ax, fc='lightgray', ec='k', zorder=-1)\n",
+    "# ax.minorticks_on()\n",
+    "# ax.grid(color='k')\n",
+    "plt.tight_layout()\n",
     "ax.set_axis_off()"
    ]
   },

From be79c906a02aafdc14c12d459d5878ae26130b88 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 17 Jul 2024 15:49:14 -0500
Subject: [PATCH 02/52] adds snakefile and the steps required for pulling
 residential data

---
 Snakefile                           | 40 ++++++++++++++
 config.yml                          | 30 +++++++++++
 scripts/calculate_res_structures.py | 23 ++++++++
 scripts/retrieve_armourdale.py      | 17 ++++++
 scripts/retrieve_census_data.py     | 83 +++++++++++++++++++++++++++++
 scripts/retrieve_lut.py             | 19 +++++++
 scripts/retrieve_res_load.py        | 58 ++++++++++++++++++++
 utils/__init__.py                   |  0
 utils/api_functions.py              | 43 +++++++++++++++
 9 files changed, 313 insertions(+)
 create mode 100644 Snakefile
 create mode 100644 config.yml
 create mode 100644 scripts/calculate_res_structures.py
 create mode 100644 scripts/retrieve_armourdale.py
 create mode 100644 scripts/retrieve_census_data.py
 create mode 100644 scripts/retrieve_lut.py
 create mode 100644 scripts/retrieve_res_load.py
 create mode 100644 utils/__init__.py
 create mode 100644 utils/api_functions.py

diff --git a/Snakefile b/Snakefile
new file mode 100644
index 0000000..5d16e2d
--- /dev/null
+++ b/Snakefile
@@ -0,0 +1,40 @@
+configfile: "config.yml"
+
+from pathlib import Path
+env_file = Path("./.env").resolve()
+from dotenv import load_dotenv
+load_dotenv(str(env_file))
+
+
+rule retrieve_spatial_lut:
+    output: 
+        spatial_lut = "data/spatial_data/spatial_lut.csv"
+    script: "scripts/retrieve_lut.py"
+
+rule retrieve_census_data:
+    output:
+        census_data = "data/spatial_data/county_census_data.gpkg"
+    script: "scripts/retrieve_census_data.py"
+
+# a bespoke step to make this analysis specific to armourdale
+rule retrieve_armourdale_shape:
+    output: 
+        armourdale = "data/spatial_data/armourdale_shape.gpkg"
+    script: "scripts/retrieve_armourdale.py"
+
+rule calculate_res_structures:
+    input: 
+        census_data = "data/spatial_data/county_census_data.gpkg",
+        armourdale = "data/spatial_data/armourdale_shape.gpkg"
+    output: 
+        res_structures = "data/residential_buildings.csv"
+    script: "scripts/calculate_res_structures.py"
+
+rule retrieve_res_load:
+    input:
+        spatial_lut = "data/spatial_data/spatial_lut.csv",
+        res_structures = "data/residential_buildings.csv"
+    output: 
+        sfa = "data/timeseries/single-family_attached_load.csv"
+    script: "scripts/retrieve_res_load.py"
+    
\ No newline at end of file
diff --git a/config.yml b/config.yml
new file mode 100644
index 0000000..775df55
--- /dev/null
+++ b/config.yml
@@ -0,0 +1,30 @@
+# geographic data
+state: 'Kansas'
+county: 'Wyandotte'
+
+# historical data
+census_year: 2020
+census_level: 'tract'
+
+# building data options
+building_data_options:
+  resstock_year: 2021  # DO NOT CHANGE
+  comstock_year: 2021  # DO NOT CHANGE
+  weather_version: "tmy3"  # or "amy2018"
+  release_version: 1
+  building_types:
+    - multi-family_with_2_-_4_units
+    - multi-family_with_5plus_units
+    - single-family_attached
+    - single-family_detached
+    - mobile_home
+
+energy_sectors:
+  - residential
+  # - commercial  # pending implementation
+  # - industrial  # pending data availability
+
+
+# geographic options
+geographic_crs: 4326  # for using lat/lon; EPSG code
+projected_crs: 5070  # for doing calculations; EPSG code
diff --git a/scripts/calculate_res_structures.py b/scripts/calculate_res_structures.py
new file mode 100644
index 0000000..b055ef5
--- /dev/null
+++ b/scripts/calculate_res_structures.py
@@ -0,0 +1,23 @@
+import geopandas as gpd
+import pandas as pd
+
+if __name__ == "__main__":
+    
+    census_data = gpd.read_file(snakemake.input.census_data)
+    building_opts = snakemake.config['building_data_options']
+    
+    
+    # specific to armourdale; selecting only block groups within armourdale
+    armourdale = gpd.read_file(snakemake.input.armourdale)
+    armourdale_bg = census_data.sjoin(armourdale, how='inner', predicate='within')
+    
+    # replace `armourdale_bg` with `census_data` for a non-armourdale analysis.
+    building_data = armourdale_bg[building_opts['building_types']]\
+                    .sum()\
+                    .to_frame()\
+                    .rename(columns={0:'n_units'})
+
+    building_data.to_csv(snakemake.output.res_structures)
+    
+    
+    
\ No newline at end of file
diff --git a/scripts/retrieve_armourdale.py b/scripts/retrieve_armourdale.py
new file mode 100644
index 0000000..6c68ed4
--- /dev/null
+++ b/scripts/retrieve_armourdale.py
@@ -0,0 +1,17 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import geopandas as gpd
+from census import Census
+from us import states
+
+if __name__ == "__main__":
+    kc_wards_url = "https://maps.wycokck.org/gisdata/shp/ward_prec_py.zip"
+    kck_wards = gpd.read_file(kc_wards_url).to_crs(epsg=int(snakemake.config['geographic_crs']))
+    
+    armourdale_ward = '06'
+    armourdale = kck_wards[kck_wards['WARD'] == armourdale_ward].dissolve("CITY").reset_index(drop=False)
+
+    armourdale = armourdale[['CITY','geometry','WARD']]
+    
+    armourdale.to_file(snakemake.output.armourdale, driver="GPKG")
\ No newline at end of file
diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
new file mode 100644
index 0000000..1104d05
--- /dev/null
+++ b/scripts/retrieve_census_data.py
@@ -0,0 +1,83 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import geopandas as gpd
+from census import Census
+from us import states
+import os
+import sys
+import yaml
+
+sys.path.append("utils/")
+
+from api_functions import *
+
+
+column_names = {
+    "B01003_001E":"total_population",
+    "B25024_002E":"single-family_detached",
+    "B25024_003E":"single-family_attached",
+    "B25024_004E":"2 units",
+    "B25024_005E":"3-4_units",
+    "B25024_006E":"5-9_units",
+    "B25024_007E":"10-19_units",
+    "B25024_008E":"20-49_units",
+    "B25024_009E":"50plus_units",
+    "B25024_010E":"mobile_home",
+}
+
+
+if __name__ == "__main__":
+    # gather config options
+    state_name = snakemake.config['state']
+    state = states.lookup(state_name)
+    county = snakemake.config['county']
+    counties = pd.read_html((f"https://en.wikipedia.org/wiki/"
+                             f"List_of_counties_in_{state_name.capitalize()}"))[1].set_index('County')
+    county_fips = counties.at[county.capitalize() + ' County','FIPS code[3]']
+    census_year = int(snakemake.config['census_year'])
+    
+    
+    # get census data
+    api_key = os.environ.get('CENSUS_API_KEY')
+    c = Census(api_key)
+    county_census = c.acs5.state_county_blockgroup(fields=tuple(column_names.keys()),
+                               state_fips=state.fips,
+                               county_fips=str(county_fips),
+                               blockgroup="*",
+                               year=census_year)
+    county_df = pd.DataFrame(county_census)
+    county_df.rename(columns=column_names, inplace=True)
+    
+    county_df['GEOID'] = county_df['state'] + county_df['county'] + county_df['tract'] + county_df['block group']
+    county_df.drop(columns=['state','county','tract','block group'], inplace=True)
+    
+    
+    # get the map of state level block groups
+    state_map = get_tiger_files(year=census_year,
+                         state_abbr = state.abbr,
+                         feature='blockgroup')
+    state_map = state_map.to_crs(epsg=int(snakemake.config['geographic_crs']))
+    
+    county_bg = state_map[state_map['COUNTYFP'] == str(county_fips)]
+    county_bg = county_bg.drop(columns = ['STATEFP','COUNTYFP', 'TRACTCE',
+                                'BLKGRPCE', 'NAMELSAD', 'MTFCC',
+                                'FUNCSTAT', 'ALAND','AWATER',
+                                'INTPTLAT', 'INTPTLON'])
+    
+    county_merge = county_bg.merge(county_df, on='GEOID')
+    
+    
+    # combine structure types by unit; harmonize with NREL resstock
+    multi_family = ['2 units','3-4_units']
+    many_family = ['5-9_units', '10-19_units', '20-49_units','50plus_units']
+    
+    county_merge['multi-family_with_2_-_4_units'] = county_merge[multi_family].sum(axis=1)
+    county_merge['multi-family_with_5plus_units'] = county_merge[many_family].sum(axis=1)
+    county_merge = county_merge.drop(columns=multi_family+many_family)
+    
+    county_merge.to_file(snakemake.output.census_data, driver="GPKG")
+
+
+    
+    
\ No newline at end of file
diff --git a/scripts/retrieve_lut.py b/scripts/retrieve_lut.py
new file mode 100644
index 0000000..ec7d7ba
--- /dev/null
+++ b/scripts/retrieve_lut.py
@@ -0,0 +1,19 @@
+import pandas as pd
+
+resstock_opts = snakemake.config['building_data_options']
+
+BASE_URL = (f"https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
+            f"/end-use-load-profiles-for-us-building-stock/")
+
+URL = BASE_URL+(
+       f"{resstock_opts['resstock_year']}"
+       f"/resstock_{resstock_opts['weather_version']}_release_{resstock_opts['release_version']}"
+       f"/geographic_information"
+       f"/spatial_tract_lookup_table.csv")
+
+
+
+if __name__ == "__main__":
+    
+    df = pd.read_csv(URL)
+    df.to_csv(snakemake.output.spatial_lut)
\ No newline at end of file
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
new file mode 100644
index 0000000..bc0cba1
--- /dev/null
+++ b/scripts/retrieve_res_load.py
@@ -0,0 +1,58 @@
+import pandas as pd
+import geopandas as gpd
+from us import states
+
+def create_resstock_url(state_abbr, 
+                        puma_id, 
+                        building_type,
+                        year=2021,
+                        product='resstock', 
+                        weather_version='tmy3', 
+                        release=1, 
+                        ):
+    
+    BASE_URL = ("https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
+                "/end-use-load-profiles-for-us-building-stock")
+    
+    data_route = (f"/{year}"
+                  f"/{product}_{weather_version}_release_{release}"
+                   "/timeseries_aggregates/by_puma"
+                  f"/state={state_abbr}/")
+
+    file = f"{puma_id.lower()}-{building_type}.csv"
+    
+    return BASE_URL+data_route+file
+
+
+if __name__ == "__main__":
+    
+    columns = ['timestamp', 'out.electricity.total.energy_consumption']
+    
+    
+    # gather config options
+    state_name = snakemake.config['state']
+    state = states.lookup(state_name)
+    county = snakemake.config['county']
+    building_opts = snakemake.config['building_data_options']
+    
+    
+    # load spatial lut
+    lut = pd.read_csv(snakemake.input.spatial_lut)
+    
+    
+    # get the PUMA ID
+    # this method is unstable, since some counties might contain multiple PUMAs
+    county_and_puma = lut[((lut['state_abbreviation']==state.abbr)\
+        & (lut['resstock_county_id'] == f"{state.abbr}, {county.capitalize()} County"))]['nhgis_puma_gisjoin'].unique()[0]
+    
+    # puma_id = county_and_puma.split(',')[-1].replace(' ','')
+    
+    # for bldg_type in building_opts['building_types']:
+    for bldg_type in ["single-family_attached"]:
+        bldg_url = create_resstock_url(state_abbr=state.abbr, 
+                                       puma_id=county_and_puma, 
+                                       building_type=bldg_type)
+        bldg_df = pd.read_csv(bldg_url)
+        
+        bldg_df[columns].to_csv(f"data/timeseries/{bldg_type}_load.csv")
+    
\ No newline at end of file
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/utils/api_functions.py b/utils/api_functions.py
new file mode 100644
index 0000000..7b243cc
--- /dev/null
+++ b/utils/api_functions.py
@@ -0,0 +1,43 @@
+import requests
+import json
+import geopandas as gpd
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import zipfile
+import io
+import glob
+from us import states
+
+_TIGER_URL = "https://www2.census.gov/geo/tiger/"
+
+def get_tiger_files(year, state_abbr, feature='tract'):
+    """
+    This function retrievs a TIGER shapefile from the United States Census
+    website.
+
+    Parameters
+    ----------
+    year : int
+        The shapefile year of interest.
+    state_abbr : str
+        The abbreviation for the state of interest.
+    feature : str, optional
+        Indicates which TIGER file data feature to extract, by default 'tract'.
+    """
+    
+    
+    try:
+        state = states.lookup(state_abbr)
+        assert state, f"{state_abbr} is not a state in the U.S."
+    except AssertionError as error:
+        raise error
+
+    _FEATURE_FILE = {'tract':f'TRACT/tl_{year}_{state.fips}_tract.zip',
+                     'blockgroup':f'BG/tl_{year}_{state.fips}_bg.zip',
+                     'county':f"COUNTY/tl_{year}_us_county.zip"}
+    data_route = f"TIGER{year}/{_FEATURE_FILE[feature]}"
+    
+    geo_df = gpd.read_file(_TIGER_URL+data_route)
+    
+    return geo_df
\ No newline at end of file

From 5d48685051a1afb1d4f6805a8f8371096826e352 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Thu, 18 Jul 2024 10:13:33 -0500
Subject: [PATCH 03/52] adds rule to build the dag file

---
 Snakefile |  12 +++++++++++-
 dag.png   | Bin 0 -> 17184 bytes
 2 files changed, 11 insertions(+), 1 deletion(-)
 create mode 100644 dag.png

diff --git a/Snakefile b/Snakefile
index 5d16e2d..e95626d 100644
--- a/Snakefile
+++ b/Snakefile
@@ -5,6 +5,10 @@ env_file = Path("./.env").resolve()
 from dotenv import load_dotenv
 load_dotenv(str(env_file))
 
+rule targets:
+    input: 
+        sfa = "data/timeseries/single-family_attached_load.csv",
+        res_structures = "data/residential_buildings.csv"
 
 rule retrieve_spatial_lut:
     output: 
@@ -37,4 +41,10 @@ rule retrieve_res_load:
     output: 
         sfa = "data/timeseries/single-family_attached_load.csv"
     script: "scripts/retrieve_res_load.py"
-    
\ No newline at end of file
+    
+rule build_dag:
+    input: "Snakefile"
+    output:
+        "dag.png"
+    shell:
+        "snakemake --dag | dot -Tpng > {output}"
\ No newline at end of file
diff --git a/dag.png b/dag.png
new file mode 100644
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literal 0
HcmV?d00001


From bc173027736927eb5984b58404eb7ea39432133e Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Tue, 23 Jul 2024 11:53:49 -0500
Subject: [PATCH 04/52] starts adding utility access feature

---
 scripts/retrieve_usrdb.py |  7 +++++++
 utils/api_functions.py    | 42 ++++++++++++++++++++++++++++++++++++++-
 2 files changed, 48 insertions(+), 1 deletion(-)
 create mode 100644 scripts/retrieve_usrdb.py

diff --git a/scripts/retrieve_usrdb.py b/scripts/retrieve_usrdb.py
new file mode 100644
index 0000000..ff61990
--- /dev/null
+++ b/scripts/retrieve_usrdb.py
@@ -0,0 +1,7 @@
+import pandas as pd
+
+
+if __name__ == "__main__":
+    
+    URL = "https://apps.openei.org/USURDB/download/usurdb.csv.gz"
+    usrdb = pd.read_csv(URL, low_memory=False, parse_dates=True)
\ No newline at end of file
diff --git a/utils/api_functions.py b/utils/api_functions.py
index 7b243cc..7b13504 100644
--- a/utils/api_functions.py
+++ b/utils/api_functions.py
@@ -10,6 +10,28 @@
 from us import states
 
 _TIGER_URL = "https://www2.census.gov/geo/tiger/"
+_RETAIL_SERVICE_URL = ("https://services1.arcgis.com/Hp6G80Pky0om7QvQ/"
+                      "arcgis/rest/services/Retail_Service_Territories/"
+                      "FeatureServer/0/query?")
+RETAIL_SERVICE_COLUMNS = ["CNTRL_AREA",
+                          "PLAN_AREA",
+                          "HOLDING_CO",
+                          "NET_GEN",
+                          "PURCHASED",
+                          "RETAIL_MWH",
+                          "WSALE_MWH",
+                          "TOTAL_MWH",
+                          "TRANS_MWH",
+                          "CUSTOMERS",
+                          "YEAR",
+                          "NET_EX",
+                          "NAME",
+                          "REGULATED",
+                          "STATE",
+                          "ID",
+                          "NAICS_CODE",
+                          "NAICS_DESC"]
+
 
 def get_tiger_files(year, state_abbr, feature='tract'):
     """
@@ -40,4 +62,22 @@ def get_tiger_files(year, state_abbr, feature='tract'):
     
     geo_df = gpd.read_file(_TIGER_URL+data_route)
     
-    return geo_df
\ No newline at end of file
+    return geo_df
+
+
+
+
+
+def get_retail_service_area(state_name=None,
+                            crs=4326,
+                            columns=RETAIL_SERVICE_COLUMNS):
+    
+    try:
+        state = states.lookup(state_name)
+        assert state_name, f"{state_name} is not a state in the U.S."
+    except AssertionError as error:
+        raise error
+    
+    state_field = f"where=STATE%20%3D%20'{state.abbr}'" if state_name else ""
+    crs_field = f"outSR={crs}"
+    format_field = f"f=json"
\ No newline at end of file

From d3383d39df5b00e6f4c85a3984ee3c5d1affd69f Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Tue, 23 Jul 2024 15:48:18 -0500
Subject: [PATCH 05/52] adds steps to get relevant utility rates (no
 processing)

---
 Snakefile                            |  18 +-
 config.yml                           |   2 +
 dag.png                              | Bin 17184 -> 25845 bytes
 notebooks/01-energy-utility.ipynb    | 267 +++++++--------------------
 scripts/retrieve_electric_utility.py |  23 +++
 scripts/retrieve_usrdb.py            |  33 +++-
 utils/api_functions.py               |  50 ++++-
 7 files changed, 186 insertions(+), 207 deletions(-)
 create mode 100644 scripts/retrieve_electric_utility.py

diff --git a/Snakefile b/Snakefile
index e95626d..d053a09 100644
--- a/Snakefile
+++ b/Snakefile
@@ -8,7 +8,9 @@ load_dotenv(str(env_file))
 rule targets:
     input: 
         sfa = "data/timeseries/single-family_attached_load.csv",
-        res_structures = "data/residential_buildings.csv"
+        res_structures = "data/residential_buildings.csv",
+        rates = "data/usrdb_rates.csv",
+        dag = "dag.png"
 
 rule retrieve_spatial_lut:
     output: 
@@ -26,6 +28,20 @@ rule retrieve_armourdale_shape:
         armourdale = "data/spatial_data/armourdale_shape.gpkg"
     script: "scripts/retrieve_armourdale.py"
 
+rule retrieve_electric_utility:
+    input: 
+      cutout="data/spatial_data/armourdale_shape.gpkg"
+    output:
+      utility="data/spatial_data/electric_utility.gpkg"
+    script: "scripts/retrieve_electric_utility.py"
+
+rule retrieve_usrdb:
+    input: 
+        utility="data/spatial_data/electric_utility.gpkg"
+    output: 
+        rates="data/usrdb_rates.csv"
+    script: "scripts/retrieve_usrdb.py"
+
 rule calculate_res_structures:
     input: 
         census_data = "data/spatial_data/county_census_data.gpkg",
diff --git a/config.yml b/config.yml
index 775df55..42cb03c 100644
--- a/config.yml
+++ b/config.yml
@@ -5,6 +5,8 @@ county: 'Wyandotte'
 # historical data
 census_year: 2020
 census_level: 'tract'
+usrdb_start_date: "2024-07-23"  # today?
+usrdb_future_date: "2099-01-01"  # some date in the future, replaces NaT values
 
 # building data options
 building_data_options:
diff --git a/dag.png b/dag.png
index 45db361ea25ee3499050e4cd32b33c2932d8fd74..74f5c653244be30ca11deb082c08e2ad9ac5138d 100644
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diff --git a/notebooks/01-energy-utility.ipynb b/notebooks/01-energy-utility.ipynb
index fd3938f..e93d5e9 100644
--- a/notebooks/01-energy-utility.ipynb
+++ b/notebooks/01-energy-utility.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,8 +22,12 @@
     "import census\n",
     "from us import states\n",
     "from pathlib import Path\n",
+    "import sys\n",
     "\n",
-    "from gridstatus import EIA"
+    "from gridstatus import EIA\n",
+    "\n",
+    "sys.path.append(\"../utils\")\n",
+    "from api_functions import get_retail_service_area"
    ]
   },
   {
@@ -55,15 +59,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 234 ms\n",
-      "Wall time: 2.04 s\n"
+      "CPU times: total: 828 ms\n",
+      "Wall time: 2.44 s\n"
      ]
     }
    ],
@@ -98,217 +102,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = gpd.read_file(\"../data/spatial_data/armourdale_shape.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sgdf = service_gdf.sjoin(armourdale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CNTRL_AREA</th>\n",
-       "      <th>PLAN_AREA</th>\n",
-       "      <th>HOLDING_CO</th>\n",
-       "      <th>NET_GEN</th>\n",
-       "      <th>PURCHASED</th>\n",
-       "      <th>RETAIL_MWH</th>\n",
-       "      <th>WSALE_MWH</th>\n",
-       "      <th>TOTAL_MWH</th>\n",
-       "      <th>TRANS_MWH</th>\n",
-       "      <th>CUSTOMERS</th>\n",
-       "      <th>YEAR</th>\n",
-       "      <th>NET_EX</th>\n",
-       "      <th>NAME</th>\n",
-       "      <th>REGULATED</th>\n",
-       "      <th>STATE</th>\n",
-       "      <th>ID</th>\n",
-       "      <th>NAICS_CODE</th>\n",
-       "      <th>NAICS_DESC</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>SOUTHWEST POWER POOL</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>KANSAS CITY POWER &amp; LIGHT CO</td>\n",
-       "      <td>14911882.0</td>\n",
-       "      <td>5624474.0</td>\n",
-       "      <td>14223892.0</td>\n",
-       "      <td>5957441.0</td>\n",
-       "      <td>20536356.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>562180.0</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>EVERGY METRO</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>KS</td>\n",
-       "      <td>10000</td>\n",
-       "      <td>2211</td>\n",
-       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
-       "      <td>MULTIPOLYGON (((-94.66607 38.27027, -94.66941 ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>SOUTHWEST POWER POOL</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>WESTAR ENERGY</td>\n",
-       "      <td>9324681.0</td>\n",
-       "      <td>1566609.0</td>\n",
-       "      <td>9181326.0</td>\n",
-       "      <td>1628176.0</td>\n",
-       "      <td>10891290.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>334500.0</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>EVERGY KANSAS SOUTH, INC</td>\n",
-       "      <td>REGULATED</td>\n",
-       "      <td>KS</td>\n",
-       "      <td>10005</td>\n",
-       "      <td>2211</td>\n",
-       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
-       "      <td>POLYGON ((-95.50819 38.42458, -95.50818 38.415...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>SOUTHWEST POWER POOL</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>FREESTATE ELECTRIC COOP INC</td>\n",
-       "      <td>214.0</td>\n",
-       "      <td>287728.0</td>\n",
-       "      <td>270021.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>287942.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18451.0</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>FREESTATE ELECTRIC COOP</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>KS</td>\n",
-       "      <td>10019</td>\n",
-       "      <td>2211</td>\n",
-       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
-       "      <td>MULTIPOLYGON (((-95.61781 38.76753, -95.62164 ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ASSOCIATED ELECTRIC COOPERATIVE, INC.</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>CITY OF LAMAR - (MO)</td>\n",
-       "      <td>50225.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>CITY OF LAMAR - (MO)</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>MO</td>\n",
-       "      <td>10057</td>\n",
-       "      <td>2211</td>\n",
-       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
-       "      <td>POLYGON ((-94.28309 37.49345, -94.28324 37.493...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>SOUTHWEST POWER POOL</td>\n",
-       "      <td>SOUTHWEST POWER POOL (SPP)</td>\n",
-       "      <td>CITY OF KENNETT - (MO)</td>\n",
-       "      <td>111178.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>-999999.0</td>\n",
-       "      <td>CITY OF KENNETT - (MO)</td>\n",
-       "      <td>NOT AVAILABLE</td>\n",
-       "      <td>MO</td>\n",
-       "      <td>10152</td>\n",
-       "      <td>2211</td>\n",
-       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
-       "      <td>MULTIPOLYGON (((-90.02360 36.26603, -90.02347 ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                              CNTRL_AREA                   PLAN_AREA  \\\n",
-       "0                   SOUTHWEST POWER POOL               NOT AVAILABLE   \n",
-       "1                   SOUTHWEST POWER POOL               NOT AVAILABLE   \n",
-       "2                   SOUTHWEST POWER POOL               NOT AVAILABLE   \n",
-       "3  ASSOCIATED ELECTRIC COOPERATIVE, INC.               NOT AVAILABLE   \n",
-       "4                   SOUTHWEST POWER POOL  SOUTHWEST POWER POOL (SPP)   \n",
-       "\n",
-       "                     HOLDING_CO     NET_GEN  PURCHASED  RETAIL_MWH  WSALE_MWH  \\\n",
-       "0  KANSAS CITY POWER & LIGHT CO  14911882.0  5624474.0  14223892.0  5957441.0   \n",
-       "1                 WESTAR ENERGY   9324681.0  1566609.0   9181326.0  1628176.0   \n",
-       "2   FREESTATE ELECTRIC COOP INC       214.0   287728.0    270021.0  -999999.0   \n",
-       "3          CITY OF LAMAR - (MO)     50225.0  -999999.0   -999999.0  -999999.0   \n",
-       "4        CITY OF KENNETT - (MO)    111178.0  -999999.0   -999999.0  -999999.0   \n",
-       "\n",
-       "    TOTAL_MWH  TRANS_MWH  CUSTOMERS  YEAR    NET_EX                      NAME  \\\n",
-       "0  20536356.0        0.0   562180.0  2020       0.0              EVERGY METRO   \n",
-       "1  10891290.0        0.0   334500.0  2020       0.0  EVERGY KANSAS SOUTH, INC   \n",
-       "2    287942.0        0.0    18451.0  2020       0.0   FREESTATE ELECTRIC COOP   \n",
-       "3   -999999.0  -999999.0  -999999.0  2020 -999999.0      CITY OF LAMAR - (MO)   \n",
-       "4   -999999.0  -999999.0  -999999.0  2020 -999999.0    CITY OF KENNETT - (MO)   \n",
-       "\n",
-       "       REGULATED STATE     ID NAICS_CODE  \\\n",
-       "0  NOT AVAILABLE    KS  10000       2211   \n",
-       "1      REGULATED    KS  10005       2211   \n",
-       "2  NOT AVAILABLE    KS  10019       2211   \n",
-       "3  NOT AVAILABLE    MO  10057       2211   \n",
-       "4  NOT AVAILABLE    MO  10152       2211   \n",
-       "\n",
-       "                                          NAICS_DESC  \\\n",
-       "0  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
-       "1  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
-       "2  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
-       "3  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
-       "4  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  MULTIPOLYGON (((-94.66607 38.27027, -94.66941 ...  \n",
-       "1  POLYGON ((-95.50819 38.42458, -95.50818 38.415...  \n",
-       "2  MULTIPOLYGON (((-95.61781 38.76753, -95.62164 ...  \n",
-       "3  POLYGON ((-94.28309 37.49345, -94.28324 37.493...  \n",
-       "4  MULTIPOLYGON (((-90.02360 36.26603, -90.02347 ...  "
+       "'9996'"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "service_gdf.head()"
+    "sgdf.loc[:, 'ID'].values[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "service_gdf.sjoin(armourdale).plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, ec='k')"
    ]
   },
   {
diff --git a/scripts/retrieve_electric_utility.py b/scripts/retrieve_electric_utility.py
new file mode 100644
index 0000000..b914c1e
--- /dev/null
+++ b/scripts/retrieve_electric_utility.py
@@ -0,0 +1,23 @@
+import pandas as pd
+import geopandas as gpd
+import sys
+
+sys.path.append("utils")
+
+from api_functions import get_retail_service_area
+
+
+
+
+if __name__ == "__main__":
+    state_name = snakemake.config['state']
+    
+    cutout = gpd.read_file(snakemake.input.cutout)
+    
+    url = get_retail_service_area(state_name=state_name)
+    
+    service_gdf = gpd.read_file(url)
+    
+    service_gdf = service_gdf.sjoin(cutout)
+    
+    service_gdf.to_file(snakemake.output.utility)
\ No newline at end of file
diff --git a/scripts/retrieve_usrdb.py b/scripts/retrieve_usrdb.py
index ff61990..9e348a5 100644
--- a/scripts/retrieve_usrdb.py
+++ b/scripts/retrieve_usrdb.py
@@ -1,7 +1,38 @@
 import pandas as pd
+import geopandas as gpd
+import datetime as dt
 
 
 if __name__ == "__main__":
     
+    if snakemake.config['usrdb_start_date'].lower() == 'today':
+        start_date = pd.to_datetime(dt.date.today())
+    else:
+        start_date = pd.to_datetime(snakemake.config['usrdb_start_date'])
+        
+    future_date = pd.to_datetime("2099-01-01")
+    
     URL = "https://apps.openei.org/USURDB/download/usurdb.csv.gz"
-    usrdb = pd.read_csv(URL, low_memory=False, parse_dates=True)
\ No newline at end of file
+    usrdb = pd.read_csv(URL, low_memory=False, parse_dates=True)
+    
+    # filter by date
+    usrdb.loc[:,'enddate'] = pd.to_datetime(usrdb['enddate'].fillna(future_date))
+    usrdb.loc[:,'startdate'] = pd.to_datetime(usrdb['startdate'])
+    usrdb = usrdb[(usrdb['enddate'] > start_date)]
+    
+    # get utility info
+    utility_service = gpd.read_file(snakemake.input.utility)
+    utility_ids = utility_service.loc[:, 'ID'].values.astype(int)
+
+    sectors = [sector.capitalize() 
+               for sector 
+               in snakemake.config['energy_sectors']]
+    
+    # filter by utility and sector
+    usrdb = usrdb.loc[usrdb['eiaid'].isin(utility_ids)]
+    usrdb = usrdb.loc[usrdb['sector'].isin(sectors)]
+    
+    # filter: is default?
+    usrdb = usrdb[usrdb['is_default']].dropna(how='all',axis=1)
+    
+    usrdb.to_csv(snakemake.output.rates)
\ No newline at end of file
diff --git a/utils/api_functions.py b/utils/api_functions.py
index 7b13504..3219f78 100644
--- a/utils/api_functions.py
+++ b/utils/api_functions.py
@@ -13,6 +13,43 @@
 _RETAIL_SERVICE_URL = ("https://services1.arcgis.com/Hp6G80Pky0om7QvQ/"
                       "arcgis/rest/services/Retail_Service_Territories/"
                       "FeatureServer/0/query?")
+AVAILABLE_COLUMNS = [
+"ID",
+"NAME",
+"ADDRESS",
+"CITY",
+"STATE",
+"ZIP",
+"TELEPHONE",
+"TYPE",
+"COUNTRY",
+"NAICS_CODE",
+"NAICS_DESC",
+"SOURCE",
+"SOURCEDATE",
+"VAL_METHOD",
+"VAL_DATE",
+"WEBSITE",
+"REGULATED",
+"CNTRL_AREA",
+"PLAN_AREA",
+"HOLDING_CO",
+"SUMMR_PEAK",
+"WINTR_PEAK",
+"SUMMER_CAP",
+"WINTER_CAP",
+"NET_GEN",
+"PURCHASED",
+"NET_EX",
+"RETAIL_MWH",
+"WSALE_MWH",
+"TOTAL_MWH",
+"TRANS_MWH",
+"CUSTOMERS",
+"YEAR",
+"Shape__Area",
+"Shape__Length"]
+
 RETAIL_SERVICE_COLUMNS = ["CNTRL_AREA",
                           "PLAN_AREA",
                           "HOLDING_CO",
@@ -78,6 +115,17 @@ def get_retail_service_area(state_name=None,
     except AssertionError as error:
         raise error
     
+    if columns != RETAIL_SERVICE_COLUMNS:
+        for col in columns:
+            if col not in AVAILABLE_COLUMNS:
+                print(f"{col} not in available columns. Must be one of:\n {AVAILABLE_COLUMNS}")
+                raise KeyError
+    
     state_field = f"where=STATE%20%3D%20'{state.abbr}'" if state_name else ""
     crs_field = f"outSR={crs}"
-    format_field = f"f=json"
\ No newline at end of file
+    format_field = f"f=json"
+    return_fields = f"outFields={','.join(columns)}"
+    
+    params = "&".join([state_field, return_fields, crs_field, format_field])
+    
+    return _RETAIL_SERVICE_URL+params
\ No newline at end of file

From 52cce013ea7143faa0688c74f342580444de9520 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Tue, 23 Jul 2024 16:13:14 -0500
Subject: [PATCH 06/52] updates the residential load retrieval

---
 Snakefile                         |   4 +-
 config.yml                        |  11 +--
 notebooks/01-energy-utility.ipynb | 121 +++++++++++++++++++++++++++++-
 scripts/retrieve_res_load.py      |  28 +++++--
 4 files changed, 146 insertions(+), 18 deletions(-)

diff --git a/Snakefile b/Snakefile
index d053a09..e228a67 100644
--- a/Snakefile
+++ b/Snakefile
@@ -7,7 +7,7 @@ load_dotenv(str(env_file))
 
 rule targets:
     input: 
-        sfa = "data/timeseries/single-family_attached_load.csv",
+        sfa = "data/timeseries/residential_load.csv",
         res_structures = "data/residential_buildings.csv",
         rates = "data/usrdb_rates.csv",
         dag = "dag.png"
@@ -55,7 +55,7 @@ rule retrieve_res_load:
         spatial_lut = "data/spatial_data/spatial_lut.csv",
         res_structures = "data/residential_buildings.csv"
     output: 
-        sfa = "data/timeseries/single-family_attached_load.csv"
+        sfa = "data/timeseries/residential_load.csv"
     script: "scripts/retrieve_res_load.py"
     
 rule build_dag:
diff --git a/config.yml b/config.yml
index 42cb03c..efc92a8 100644
--- a/config.yml
+++ b/config.yml
@@ -15,11 +15,12 @@ building_data_options:
   weather_version: "tmy3"  # or "amy2018"
   release_version: 1
   building_types:
-    - multi-family_with_2_-_4_units
-    - multi-family_with_5plus_units
-    - single-family_attached
-    - single-family_detached
-    - mobile_home
+    residential:
+      - multi-family_with_2_-_4_units
+      - multi-family_with_5plus_units
+      - single-family_attached
+      - single-family_detached
+      - mobile_home
 
 energy_sectors:
   - residential
diff --git a/notebooks/01-energy-utility.ipynb b/notebooks/01-energy-utility.ipynb
index e93d5e9..bb9217e 100644
--- a/notebooks/01-energy-utility.ipynb
+++ b/notebooks/01-energy-utility.ipynb
@@ -120,22 +120,135 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'9996'"
+       "array([9996])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sgdf.loc[:, 'ID'].values[0]"
+    "sgdf.loc[:, 'ID'].values.astype('int')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CNTRL_AREA</th>\n",
+       "      <th>PLAN_AREA</th>\n",
+       "      <th>HOLDING_CO</th>\n",
+       "      <th>NET_GEN</th>\n",
+       "      <th>PURCHASED</th>\n",
+       "      <th>RETAIL_MWH</th>\n",
+       "      <th>WSALE_MWH</th>\n",
+       "      <th>TOTAL_MWH</th>\n",
+       "      <th>TRANS_MWH</th>\n",
+       "      <th>CUSTOMERS</th>\n",
+       "      <th>...</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>REGULATED</th>\n",
+       "      <th>STATE</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>NAICS_CODE</th>\n",
+       "      <th>NAICS_DESC</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>index_right</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>WARD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>272</th>\n",
+       "      <td>SOUTHWEST POWER POOL</td>\n",
+       "      <td>KANSAS CITY BOARD OF PUBLIC UTILITIES &amp; WYANDO...</td>\n",
+       "      <td>CITY OF KANSAS CITY - (KS)</td>\n",
+       "      <td>1078879.0</td>\n",
+       "      <td>465370.0</td>\n",
+       "      <td>2065050.0</td>\n",
+       "      <td>482228.0</td>\n",
+       "      <td>2738716.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66249.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>CITY OF KANSAS CITY - (KS)</td>\n",
+       "      <td>REGULATED</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>9996</td>\n",
+       "      <td>2211</td>\n",
+       "      <td>ELECTRIC POWER GENERATION, TRANSMISSION AND DI...</td>\n",
+       "      <td>MULTIPOLYGON (((-94.15155 39.01439, -94.14789 ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               CNTRL_AREA                                          PLAN_AREA  \\\n",
+       "272  SOUTHWEST POWER POOL  KANSAS CITY BOARD OF PUBLIC UTILITIES & WYANDO...   \n",
+       "\n",
+       "                     HOLDING_CO    NET_GEN  PURCHASED  RETAIL_MWH  WSALE_MWH  \\\n",
+       "272  CITY OF KANSAS CITY - (KS)  1078879.0   465370.0   2065050.0   482228.0   \n",
+       "\n",
+       "     TOTAL_MWH  TRANS_MWH  CUSTOMERS  ...                        NAME  \\\n",
+       "272  2738716.0        0.0    66249.0  ...  CITY OF KANSAS CITY - (KS)   \n",
+       "\n",
+       "     REGULATED STATE    ID NAICS_CODE  \\\n",
+       "272  REGULATED    KS  9996       2211   \n",
+       "\n",
+       "                                            NAICS_DESC  \\\n",
+       "272  ELECTRIC POWER GENERATION, TRANSMISSION AND DI...   \n",
+       "\n",
+       "                                              geometry index_right  \\\n",
+       "272  MULTIPOLYGON (((-94.15155 39.01439, -94.14789 ...           0   \n",
+       "\n",
+       "            CITY  WARD  \n",
+       "272  Kansas City    06  \n",
+       "\n",
+       "[1 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgdf"
    ]
   },
   {
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index bc0cba1..1fe09c9 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -34,6 +34,7 @@ def create_resstock_url(state_abbr,
     state = states.lookup(state_name)
     county = snakemake.config['county']
     building_opts = snakemake.config['building_data_options']
+    sector_buildings = building_opts['building_types']
     
     
     # load spatial lut
@@ -47,12 +48,25 @@ def create_resstock_url(state_abbr,
     
     # puma_id = county_and_puma.split(',')[-1].replace(' ','')
     
-    # for bldg_type in building_opts['building_types']:
-    for bldg_type in ["single-family_attached"]:
-        bldg_url = create_resstock_url(state_abbr=state.abbr, 
-                                       puma_id=county_and_puma, 
-                                       building_type=bldg_type)
-        bldg_df = pd.read_csv(bldg_url)
+    # for sector in list(sectors_buildings.keys()):
+    for sector in ['residential']:
+        building_types = sector_buildings[sector]
+        sector_frames = []
+        for bldg_type in building_types:
+        # for bldg_type in ["single-family_attached"]:
+            bldg_url = create_resstock_url(state_abbr=state.abbr, 
+                                        puma_id=county_and_puma, 
+                                        building_type=bldg_type)
+            bldg_df = pd.read_csv(bldg_url, 
+                                  parse_dates=True, 
+                                  index_col='timestamp',
+                                  usecols=columns)
+            
+            bldg_df.rename(columns={'out.electricity.total.energy_consumption':bldg_type},
+                           inplace=True)
+            
+            sector_frames.append(bldg_df)
         
-        bldg_df[columns].to_csv(f"data/timeseries/{bldg_type}_load.csv")
+        sector_ts = pd.concat(sector_frames, axis=1)
+        sector_ts.to_csv(f"data/timeseries/{sector}_load.csv")
     
\ No newline at end of file

From 9ec5bfbdcaf8c982f6a125bd68e398c0b083b258 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 24 Jul 2024 17:05:18 -0500
Subject: [PATCH 07/52] adds rule to download project sunroof data

---
 dag.png                             | Bin 25845 -> 31072 bytes
 scripts/retrieve_project_sunroof.py |  20 ++++++++++++++
 utils/api_functions.py              |  41 +++++++++++++++++++++++++---
 3 files changed, 57 insertions(+), 4 deletions(-)
 create mode 100644 scripts/retrieve_project_sunroof.py

diff --git a/dag.png b/dag.png
index 74f5c653244be30ca11deb082c08e2ad9ac5138d..826523153049f69283e74d5eed462d6ee6df7a6f 100644
GIT binary patch
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z|L364to4<X9^X74z@s>xY&mj{BtV6}{Z5^&-1XR$;>_0=D}||F6kD%Y!%H4BghglQ
zgmYB8IWXrp`Y<C>wd}Q}j%~3V$j&I-Ce@fBd7McuC#w7FRG`0v5|Qe@e!mwz1W(g;
zN;zdi#+7_*>5(-^3(h{Iw7Z~~NgmE4h_h>4byYiIs17!wh8}F)iAxT^$B}@a*VZ;T
znQJun-PA+u`(l)wa7I_KTsb_mn7cZKU);yjwb>__x~Wy!s=Z<&c?HFt^c$(h>xJ7W
zHXeSvzMo8voQaak_KoM#oERMM_TJi)8}GfCb?>@)h}0{r;!EQ_WP@~6@Aj>boT>1_
z>P!3|4r&I|s8%lG&NQz}5y|`Kr-ZT9Mik8Ur`sRqR~&_p>JwkC*SfN-?e|w|X<Nqs
zJIs!i3F{Mmvn3uoQKCsmMMvl8%V>D#=oxDDSL;}JCDr@t^X&1*J0-f<jPZA!Uj@+2
zp7dU;OL7X)Fi$!zUvHW1m6LCrGZ>IUYZjv@FA+Vv#jGf{5^~7)D0Ee}P)~O;wO>!p
zXzIMlmZetm^5#QDG#}1up-@LbTv_m?Oc3YPsQMbT@I6eH*}#3~?w`msj6Wazb?D0n
Q|HlNOte}A^ls60dKLG@~g#Z8m

diff --git a/scripts/retrieve_project_sunroof.py b/scripts/retrieve_project_sunroof.py
new file mode 100644
index 0000000..34bd3bf
--- /dev/null
+++ b/scripts/retrieve_project_sunroof.py
@@ -0,0 +1,20 @@
+import pandas as pd
+import geopandas as gpd
+
+URL = "https://storage.googleapis.com/project-sunroof/csv/latest/project-sunroof-census_tract.csv"
+
+if __name__ == "__main__":
+    # download project sunroof data
+    df = pd.read_csv(URL)
+    df.to_csv(snakemake.output.project_sunroof)
+    
+    # load the blockgroup shapefile
+    print("processing local data")
+    census_tract = gpd.read_file(snakemake.input.blockgroups)
+    census_tract['region_name'] = (census_tract['STATEFP'] + census_tract['COUNTYFP'] + census_tract['TRACTCE']).astype("int64")
+    census_tract = census_tract.dissolve('region_name').reset_index()[['region_name','geometry']]
+    
+    # merge dataframes
+    solar_gdf = census_tract.merge(df, on='region_name')
+    solar_gdf.to_file(snakemake.output.local_potential, driver='GPKG')
+    
\ No newline at end of file
diff --git a/utils/api_functions.py b/utils/api_functions.py
index 3219f78..b6361bb 100644
--- a/utils/api_functions.py
+++ b/utils/api_functions.py
@@ -102,9 +102,6 @@ def get_tiger_files(year, state_abbr, feature='tract'):
     return geo_df
 
 
-
-
-
 def get_retail_service_area(state_name=None,
                             crs=4326,
                             columns=RETAIL_SERVICE_COLUMNS):
@@ -128,4 +125,40 @@ def get_retail_service_area(state_name=None,
     
     params = "&".join([state_field, return_fields, crs_field, format_field])
     
-    return _RETAIL_SERVICE_URL+params
\ No newline at end of file
+    return _RETAIL_SERVICE_URL+params
+
+
+def get_county_fips(state_name, county_name):
+    """
+    This function retrieves the FIPS code for a county given
+    the name of the county. The `county_name` parameter must
+    be the name only. It should not have "county" at the end.
+    
+    Example:
+
+    Parameters
+    ----------
+    state_name : str
+        The name of the state. E.g., "Kansas" or "Idaho"
+    county_name : str
+        The name of the county. E.g., "Cook", "Wyandotte."
+        Should not include "county." So, "Cook County" would be
+        incorrect.
+
+    Raises
+    ------
+    error
+        Assertion error if the state name cannot be found.
+    """
+    try:
+        state = states.lookup(state_name)
+        assert state_name, f"{state_name} is not a state in the U.S."
+    except AssertionError as error:
+        raise error
+    
+    counties = pd.read_html((f"https://en.wikipedia.org/wiki/"
+                             f"List_of_counties_in_{state_name.capitalize()}"))[1].set_index('County')
+    county_fips = counties.at[county_name.capitalize() + ' County',
+                              counties.columns[0]]
+    
+    return county_fips
\ No newline at end of file

From 0c51a31711983d4f7c9a58fc3b56dfa0a537e7f8 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 24 Jul 2024 17:05:54 -0500
Subject: [PATCH 08/52] propagates update to affected rules

---
 Snakefile                           | 13 ++++++++++++-
 scripts/calculate_res_structures.py |  3 ++-
 scripts/retrieve_census_data.py     |  9 +++++----
 3 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/Snakefile b/Snakefile
index e228a67..b718f70 100644
--- a/Snakefile
+++ b/Snakefile
@@ -10,6 +10,7 @@ rule targets:
         sfa = "data/timeseries/residential_load.csv",
         res_structures = "data/residential_buildings.csv",
         rates = "data/usrdb_rates.csv",
+        project_sunroof = f"data/spatial_data/project-sunroof-census_tract.csv",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -19,9 +20,19 @@ rule retrieve_spatial_lut:
 
 rule retrieve_census_data:
     output:
-        census_data = "data/spatial_data/county_census_data.gpkg"
+        census_data = "data/spatial_data/county_census_data.gpkg",
+        state_blockgroups = f"data/spatial_data/{config['state'].lower()}_blockgroups.gpkg",
+        county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg"
     script: "scripts/retrieve_census_data.py"
 
+rule retrieve_project_sunroof:
+    input: 
+        blockgroups = f"data/spatial_data/{config['state'].lower()}_blockgroups.gpkg"
+    output: 
+        project_sunroof = "data/spatial_data/project-sunroof-census_tract.csv",
+        local_potential = f"data/spatial_data/{config['state'].lower()}_rooftop_potential.gpkg"
+    script: "scripts/retrieve_project_sunroof.py"
+
 # a bespoke step to make this analysis specific to armourdale
 rule retrieve_armourdale_shape:
     output: 
diff --git a/scripts/calculate_res_structures.py b/scripts/calculate_res_structures.py
index b055ef5..99530c7 100644
--- a/scripts/calculate_res_structures.py
+++ b/scripts/calculate_res_structures.py
@@ -12,7 +12,8 @@
     armourdale_bg = census_data.sjoin(armourdale, how='inner', predicate='within')
     
     # replace `armourdale_bg` with `census_data` for a non-armourdale analysis.
-    building_data = armourdale_bg[building_opts['building_types']]\
+    building_types = building_opts['building_types']['residential']
+    building_data = armourdale_bg[building_types]\
                     .sum()\
                     .to_frame()\
                     .rename(columns={0:'n_units'})
diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
index 1104d05..2658dee 100644
--- a/scripts/retrieve_census_data.py
+++ b/scripts/retrieve_census_data.py
@@ -10,7 +10,7 @@
 
 sys.path.append("utils/")
 
-from api_functions import *
+from api_functions import get_tiger_files, get_county_fips
 
 
 column_names = {
@@ -32,9 +32,7 @@
     state_name = snakemake.config['state']
     state = states.lookup(state_name)
     county = snakemake.config['county']
-    counties = pd.read_html((f"https://en.wikipedia.org/wiki/"
-                             f"List_of_counties_in_{state_name.capitalize()}"))[1].set_index('County')
-    county_fips = counties.at[county.capitalize() + ' County','FIPS code[3]']
+    county_fips = get_county_fips(state_name, county)
     census_year = int(snakemake.config['census_year'])
     
     
@@ -77,6 +75,9 @@
     county_merge = county_merge.drop(columns=multi_family+many_family)
     
     county_merge.to_file(snakemake.output.census_data, driver="GPKG")
+    
+    state_map.to_file(snakemake.output.state_blockgroups, driver="GPKG")
+    county_bg.to_file(snakemake.output.county_blockgroups, driver="GPKG")
 
 
     

From 9a962ff5e6662f2e784bbb6af341ed890e54a561 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 29 Jul 2024 10:10:54 -0500
Subject: [PATCH 09/52] commits local updates

---
 Snakefile                             |  6 ++---
 notebooks/01-energy-utility.ipynb     |  6 +++--
 scripts/calculate_residential_load.py | 15 +++++++++++
 scripts/retrieve_cejst_data.py        | 25 +++++++++++++++++
 scripts/retrieve_kck_shapefiles.py    |  7 +++++
 scripts/retrieve_pums_data.py         | 21 +++++++++++++++
 scripts/retrieve_res_load.py          | 39 ++++++++++++++++++---------
 7 files changed, 101 insertions(+), 18 deletions(-)
 create mode 100644 scripts/calculate_residential_load.py
 create mode 100644 scripts/retrieve_cejst_data.py
 create mode 100644 scripts/retrieve_kck_shapefiles.py
 create mode 100644 scripts/retrieve_pums_data.py

diff --git a/Snakefile b/Snakefile
index b718f70..864e91f 100644
--- a/Snakefile
+++ b/Snakefile
@@ -63,10 +63,10 @@ rule calculate_res_structures:
 
 rule retrieve_res_load:
     input:
-        spatial_lut = "data/spatial_data/spatial_lut.csv",
-        res_structures = "data/residential_buildings.csv"
+        spatial_lut = "data/spatial_data/spatial_lut.csv"
     output: 
-        sfa = "data/timeseries/residential_load.csv"
+        elec_load = "data/timeseries/residential_elec_load.csv",
+        heat_load = "data/timeseries/residential_heat_load.csv"
     script: "scripts/retrieve_res_load.py"
     
 rule build_dag:
diff --git a/notebooks/01-energy-utility.ipynb b/notebooks/01-energy-utility.ipynb
index bb9217e..6f7a906 100644
--- a/notebooks/01-energy-utility.ipynb
+++ b/notebooks/01-energy-utility.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1662,7 +1662,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "res_structures = pd.read_csv()"
+   ]
   }
  ],
  "metadata": {
diff --git a/scripts/calculate_residential_load.py b/scripts/calculate_residential_load.py
new file mode 100644
index 0000000..1d3c271
--- /dev/null
+++ b/scripts/calculate_residential_load.py
@@ -0,0 +1,15 @@
+import pandas as pd
+import numpy as np
+
+
+if __name__ == "__main__":
+    
+    # load the number of residential structures
+    res_structures = pd.read_csv(snakemake.input.res_structures)
+    res_elec_load = pd.read_csv(snakemake.input.res_elec_load)
+    res_heat_load = pd.read_csv(snakemake.input.res_heat_load)
+    
+    total_elec_load = res_elec_load*res_structures.T.loc['n_units']
+    total_heat_load = res_heat_load*res_structures.T.loc['n_units']
+    
+    
\ No newline at end of file
diff --git a/scripts/retrieve_cejst_data.py b/scripts/retrieve_cejst_data.py
new file mode 100644
index 0000000..5ab0842
--- /dev/null
+++ b/scripts/retrieve_cejst_data.py
@@ -0,0 +1,25 @@
+import geopandas as gpd
+import os
+import time
+import zipfile
+import requests
+
+
+if __name__ == "__main__":
+
+    if not os.path.exists("..\\..\\cjest-data\\"):
+        print('Downloading file from internet')
+        r = requests.get(cjest_url)
+        z = zipfile.ZipFile(io.BytesIO(r.content))
+        z.extractall("..\\..\\cjest-data\\")
+
+        start = time.perf_counter()
+        cjest_df = gpd.read_file("..\\..\\cjest-data\\usa.zip")
+        end = time.perf_counter()
+        print(f"It took {(end-start)/60:3f} minutes to load the CJEST data")
+    else:
+        print("Loading from previously saved file...")
+        start = time.perf_counter()
+        cjest_df = gpd.read_file("..\\..\\cjest-data\\usa.zip")
+        end = time.perf_counter()
+        print(f"It took {(end-start)/60:3f} minutes to load the CJEST data")
\ No newline at end of file
diff --git a/scripts/retrieve_kck_shapefiles.py b/scripts/retrieve_kck_shapefiles.py
new file mode 100644
index 0000000..67b7065
--- /dev/null
+++ b/scripts/retrieve_kck_shapefiles.py
@@ -0,0 +1,7 @@
+import geopandas as gpd
+import pandas as pd
+
+
+URLS = {
+    
+}
\ No newline at end of file
diff --git a/scripts/retrieve_pums_data.py b/scripts/retrieve_pums_data.py
new file mode 100644
index 0000000..4c861e6
--- /dev/null
+++ b/scripts/retrieve_pums_data.py
@@ -0,0 +1,21 @@
+import requests
+import json
+import pandas as pd
+import numpy as np
+
+if __name__ == "__main__":
+    
+        
+    # gather config options
+    state_name = snakemake.config['state']
+    state = states.lookup(state_name)
+    county = snakemake.config['county']
+    building_opts = snakemake.config['building_data_options']
+    
+    lut = pd.read_csv(snakemake.input.spatial_lut)
+    
+    
+    # get the PUMA ID
+    # this method is unstable, since some counties might contain multiple PUMAs
+    county_and_puma = lut[((lut['state_abbreviation']==state.abbr)\
+                          & (lut['resstock_county_id'] == f"{state.abbr}, {county.capitalize()} County"))]['nhgis_puma_gisjoin'].unique()[0]
\ No newline at end of file
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index 1fe09c9..e9f7614 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -26,7 +26,9 @@ def create_resstock_url(state_abbr,
 
 if __name__ == "__main__":
     
-    columns = ['timestamp', 'out.electricity.total.energy_consumption']
+    time_col = 'timestamp'
+    elec_col = 'out.electricity.total.energy_consumption'
+    heat_col = 'out.natural_gas.heating.energy_consumption'
     
     
     # gather config options
@@ -46,27 +48,38 @@ def create_resstock_url(state_abbr,
     county_and_puma = lut[((lut['state_abbreviation']==state.abbr)\
         & (lut['resstock_county_id'] == f"{state.abbr}, {county.capitalize()} County"))]['nhgis_puma_gisjoin'].unique()[0]
     
-    # puma_id = county_and_puma.split(',')[-1].replace(' ','')
-    
-    # for sector in list(sectors_buildings.keys()):
     for sector in ['residential']:
         building_types = sector_buildings[sector]
-        sector_frames = []
+        elec_frames = []
+        heat_frames = []
         for bldg_type in building_types:
-        # for bldg_type in ["single-family_attached"]:
             bldg_url = create_resstock_url(state_abbr=state.abbr, 
                                         puma_id=county_and_puma, 
                                         building_type=bldg_type)
             bldg_df = pd.read_csv(bldg_url, 
-                                  parse_dates=True, 
-                                  index_col='timestamp',
-                                  usecols=columns)
+                                  parse_dates=True,
+                                  index_col=time_col,
+                                  usecols=[time_col,
+                                           elec_col,
+                                           heat_col])
+            
+            print(f"Accessing {bldg_url}")
+            
+            heat_df = bldg_df[[heat_col]]
+            elec_df = bldg_df[[elec_col]]
             
-            bldg_df.rename(columns={'out.electricity.total.energy_consumption':bldg_type},
+            elec_df.rename(columns={elec_col:bldg_type},
                            inplace=True)
             
-            sector_frames.append(bldg_df)
+            heat_df.rename(columns={heat_col:bldg_type},
+                           inplace=True)
+            
+            elec_frames.append(elec_df)
+            heat_frames.append(heat_df)
+        
+        elec_ts = pd.concat(elec_frames, axis=1)
+        elec_ts.to_csv(f"data/timeseries/{sector}_elec_load.csv")
         
-        sector_ts = pd.concat(sector_frames, axis=1)
-        sector_ts.to_csv(f"data/timeseries/{sector}_load.csv")
+        heat_ts = pd.concat(heat_frames, axis=1)
+        heat_ts.to_csv(f"data/timeseries/{sector}_heat_load.csv")
     
\ No newline at end of file

From b772eb6df939c3a8c1b4bd6f1fe93ad2a071e507 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Tue, 3 Sep 2024 11:26:36 -0400
Subject: [PATCH 10/52] pep8 fixes for census data retrieval

---
 scripts/retrieve_census_data.py | 95 +++++++++++++++++----------------
 1 file changed, 49 insertions(+), 46 deletions(-)

diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
index 2658dee..fef1052 100644
--- a/scripts/retrieve_census_data.py
+++ b/scripts/retrieve_census_data.py
@@ -1,3 +1,4 @@
+from api_functions import get_tiger_files, get_county_fips
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
@@ -10,20 +11,18 @@
 
 sys.path.append("utils/")
 
-from api_functions import get_tiger_files, get_county_fips
-
 
 column_names = {
-    "B01003_001E":"total_population",
-    "B25024_002E":"single-family_detached",
-    "B25024_003E":"single-family_attached",
-    "B25024_004E":"2 units",
-    "B25024_005E":"3-4_units",
-    "B25024_006E":"5-9_units",
-    "B25024_007E":"10-19_units",
-    "B25024_008E":"20-49_units",
-    "B25024_009E":"50plus_units",
-    "B25024_010E":"mobile_home",
+    "B01003_001E": "total_population",
+    "B25024_002E": "single-family_detached",
+    "B25024_003E": "single-family_attached",
+    "B25024_004E": "2 units",
+    "B25024_005E": "3-4_units",
+    "B25024_006E": "5-9_units",
+    "B25024_007E": "10-19_units",
+    "B25024_008E": "20-49_units",
+    "B25024_009E": "50plus_units",
+    "B25024_010E": "mobile_home",
 }
 
 
@@ -34,51 +33,55 @@
     county = snakemake.config['county']
     county_fips = get_county_fips(state_name, county)
     census_year = int(snakemake.config['census_year'])
-    
-    
+
     # get census data
     api_key = os.environ.get('CENSUS_API_KEY')
     c = Census(api_key)
-    county_census = c.acs5.state_county_blockgroup(fields=tuple(column_names.keys()),
-                               state_fips=state.fips,
-                               county_fips=str(county_fips),
-                               blockgroup="*",
-                               year=census_year)
+    county_census = c.acs5.state_county_blockgroup(
+        fields=tuple(
+            column_names.keys()),
+        state_fips=state.fips,
+        county_fips=str(county_fips),
+        blockgroup="*",
+        year=census_year)
     county_df = pd.DataFrame(county_census)
     county_df.rename(columns=column_names, inplace=True)
-    
-    county_df['GEOID'] = county_df['state'] + county_df['county'] + county_df['tract'] + county_df['block group']
-    county_df.drop(columns=['state','county','tract','block group'], inplace=True)
-    
-    
+
+    county_df['GEOID'] = county_df['state'] + county_df['county'] + \
+        county_df['tract'] + county_df['block group']
+    county_df.drop(
+        columns=[
+            'state',
+            'county',
+            'tract',
+            'block group'],
+        inplace=True)
+
     # get the map of state level block groups
     state_map = get_tiger_files(year=census_year,
-                         state_abbr = state.abbr,
-                         feature='blockgroup')
+                                state_abbr=state.abbr,
+                                feature='blockgroup')
     state_map = state_map.to_crs(epsg=int(snakemake.config['geographic_crs']))
-    
+
     county_bg = state_map[state_map['COUNTYFP'] == str(county_fips)]
-    county_bg = county_bg.drop(columns = ['STATEFP','COUNTYFP', 'TRACTCE',
-                                'BLKGRPCE', 'NAMELSAD', 'MTFCC',
-                                'FUNCSTAT', 'ALAND','AWATER',
-                                'INTPTLAT', 'INTPTLON'])
-    
+    county_bg = county_bg.drop(columns=['STATEFP', 'COUNTYFP', 'TRACTCE',
+                                        'BLKGRPCE', 'NAMELSAD', 'MTFCC',
+                                        'FUNCSTAT', 'ALAND', 'AWATER',
+                                        'INTPTLAT', 'INTPTLON'])
+
     county_merge = county_bg.merge(county_df, on='GEOID')
-    
-    
+
     # combine structure types by unit; harmonize with NREL resstock
-    multi_family = ['2 units','3-4_units']
-    many_family = ['5-9_units', '10-19_units', '20-49_units','50plus_units']
-    
-    county_merge['multi-family_with_2_-_4_units'] = county_merge[multi_family].sum(axis=1)
-    county_merge['multi-family_with_5plus_units'] = county_merge[many_family].sum(axis=1)
-    county_merge = county_merge.drop(columns=multi_family+many_family)
-    
+    multi_family = ['2 units', '3-4_units']
+    many_family = ['5-9_units', '10-19_units', '20-49_units', '50plus_units']
+
+    county_merge['multi-family_with_2_-_4_units'] = county_merge[multi_family].sum(
+        axis=1)
+    county_merge['multi-family_with_5plus_units'] = county_merge[many_family].sum(
+        axis=1)
+    county_merge = county_merge.drop(columns=multi_family + many_family)
+
     county_merge.to_file(snakemake.output.census_data, driver="GPKG")
-    
+
     state_map.to_file(snakemake.output.state_blockgroups, driver="GPKG")
     county_bg.to_file(snakemake.output.county_blockgroups, driver="GPKG")
-
-
-    
-    
\ No newline at end of file

From 30b63f8e68bc2b2b8cc928360190f0ded33391e5 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Tue, 3 Sep 2024 13:52:38 -0400
Subject: [PATCH 11/52] pep8 fixes

---
 scripts/calculate_res_structures.py  |  19 ++---
 scripts/retrieve_armourdale.py       |  14 ++--
 scripts/retrieve_electric_utility.py |  17 ++--
 scripts/retrieve_lut.py              |  15 ++--
 scripts/retrieve_project_sunroof.py  |  13 +--
 scripts/retrieve_res_load.py         |  70 ++++++++--------
 scripts/retrieve_usrdb.py            |  29 +++----
 utils/api_functions.py               | 117 ++++++++++++++-------------
 8 files changed, 147 insertions(+), 147 deletions(-)

diff --git a/scripts/calculate_res_structures.py b/scripts/calculate_res_structures.py
index 99530c7..5ada7b1 100644
--- a/scripts/calculate_res_structures.py
+++ b/scripts/calculate_res_structures.py
@@ -2,23 +2,20 @@
 import pandas as pd
 
 if __name__ == "__main__":
-    
+
     census_data = gpd.read_file(snakemake.input.census_data)
     building_opts = snakemake.config['building_data_options']
-    
-    
+
     # specific to armourdale; selecting only block groups within armourdale
     armourdale = gpd.read_file(snakemake.input.armourdale)
-    armourdale_bg = census_data.sjoin(armourdale, how='inner', predicate='within')
-    
+    armourdale_bg = census_data.sjoin(
+        armourdale, how='inner', predicate='within')
+
     # replace `armourdale_bg` with `census_data` for a non-armourdale analysis.
     building_types = building_opts['building_types']['residential']
     building_data = armourdale_bg[building_types]\
-                    .sum()\
-                    .to_frame()\
-                    .rename(columns={0:'n_units'})
+        .sum()\
+        .to_frame()\
+        .rename(columns={0: 'n_units'})
 
     building_data.to_csv(snakemake.output.res_structures)
-    
-    
-    
\ No newline at end of file
diff --git a/scripts/retrieve_armourdale.py b/scripts/retrieve_armourdale.py
index 6c68ed4..8436168 100644
--- a/scripts/retrieve_armourdale.py
+++ b/scripts/retrieve_armourdale.py
@@ -7,11 +7,13 @@
 
 if __name__ == "__main__":
     kc_wards_url = "https://maps.wycokck.org/gisdata/shp/ward_prec_py.zip"
-    kck_wards = gpd.read_file(kc_wards_url).to_crs(epsg=int(snakemake.config['geographic_crs']))
-    
+    kck_wards = gpd.read_file(kc_wards_url).to_crs(
+        epsg=int(snakemake.config['geographic_crs']))
+
     armourdale_ward = '06'
-    armourdale = kck_wards[kck_wards['WARD'] == armourdale_ward].dissolve("CITY").reset_index(drop=False)
+    armourdale = kck_wards[kck_wards['WARD'] == armourdale_ward].dissolve(
+        "CITY").reset_index(drop=False)
+
+    armourdale = armourdale[['CITY', 'geometry', 'WARD']]
 
-    armourdale = armourdale[['CITY','geometry','WARD']]
-    
-    armourdale.to_file(snakemake.output.armourdale, driver="GPKG")
\ No newline at end of file
+    armourdale.to_file(snakemake.output.armourdale, driver="GPKG")
diff --git a/scripts/retrieve_electric_utility.py b/scripts/retrieve_electric_utility.py
index b914c1e..2852abf 100644
--- a/scripts/retrieve_electric_utility.py
+++ b/scripts/retrieve_electric_utility.py
@@ -1,23 +1,20 @@
+from api_functions import get_retail_service_area
 import pandas as pd
 import geopandas as gpd
 import sys
 
 sys.path.append("utils")
 
-from api_functions import get_retail_service_area
-
-
-
 
 if __name__ == "__main__":
     state_name = snakemake.config['state']
-    
+
     cutout = gpd.read_file(snakemake.input.cutout)
-    
+
     url = get_retail_service_area(state_name=state_name)
-    
+
     service_gdf = gpd.read_file(url)
-    
+
     service_gdf = service_gdf.sjoin(cutout)
-    
-    service_gdf.to_file(snakemake.output.utility)
\ No newline at end of file
+
+    service_gdf.to_file(snakemake.output.utility)
diff --git a/scripts/retrieve_lut.py b/scripts/retrieve_lut.py
index ec7d7ba..8e34466 100644
--- a/scripts/retrieve_lut.py
+++ b/scripts/retrieve_lut.py
@@ -5,15 +5,14 @@
 BASE_URL = (f"https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
             f"/end-use-load-profiles-for-us-building-stock/")
 
-URL = BASE_URL+(
-       f"{resstock_opts['resstock_year']}"
-       f"/resstock_{resstock_opts['weather_version']}_release_{resstock_opts['release_version']}"
-       f"/geographic_information"
-       f"/spatial_tract_lookup_table.csv")
-
+URL = BASE_URL + (
+    f"{resstock_opts['resstock_year']}"
+    f"/resstock_{resstock_opts['weather_version']}_release_{resstock_opts['release_version']}"
+    f"/geographic_information"
+    f"/spatial_tract_lookup_table.csv")
 
 
 if __name__ == "__main__":
-    
+
     df = pd.read_csv(URL)
-    df.to_csv(snakemake.output.spatial_lut)
\ No newline at end of file
+    df.to_csv(snakemake.output.spatial_lut)
diff --git a/scripts/retrieve_project_sunroof.py b/scripts/retrieve_project_sunroof.py
index 34bd3bf..06cc56c 100644
--- a/scripts/retrieve_project_sunroof.py
+++ b/scripts/retrieve_project_sunroof.py
@@ -7,14 +7,17 @@
     # download project sunroof data
     df = pd.read_csv(URL)
     df.to_csv(snakemake.output.project_sunroof)
-    
+
     # load the blockgroup shapefile
     print("processing local data")
     census_tract = gpd.read_file(snakemake.input.blockgroups)
-    census_tract['region_name'] = (census_tract['STATEFP'] + census_tract['COUNTYFP'] + census_tract['TRACTCE']).astype("int64")
-    census_tract = census_tract.dissolve('region_name').reset_index()[['region_name','geometry']]
-    
+    census_tract['region_name'] = (
+        census_tract['STATEFP'] +
+        census_tract['COUNTYFP'] +
+        census_tract['TRACTCE']).astype("int64")
+    census_tract = census_tract.dissolve('region_name').reset_index()[
+        ['region_name', 'geometry']]
+
     # merge dataframes
     solar_gdf = census_tract.merge(df, on='region_name')
     solar_gdf.to_file(snakemake.output.local_potential, driver='GPKG')
-    
\ No newline at end of file
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index 1fe09c9..0efc542 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -2,71 +2,71 @@
 import geopandas as gpd
 from us import states
 
-def create_resstock_url(state_abbr, 
-                        puma_id, 
+
+def create_resstock_url(state_abbr,
+                        puma_id,
                         building_type,
                         year=2021,
-                        product='resstock', 
-                        weather_version='tmy3', 
-                        release=1, 
+                        product='resstock',
+                        weather_version='tmy3',
+                        release=1,
                         ):
-    
-    BASE_URL = ("https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
-                "/end-use-load-profiles-for-us-building-stock")
-    
+
+    BASE_URL = (
+        "https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
+        "/end-use-load-profiles-for-us-building-stock")
+
     data_route = (f"/{year}"
                   f"/{product}_{weather_version}_release_{release}"
-                   "/timeseries_aggregates/by_puma"
+                  "/timeseries_aggregates/by_puma"
                   f"/state={state_abbr}/")
 
     file = f"{puma_id.lower()}-{building_type}.csv"
-    
-    return BASE_URL+data_route+file
+
+    return BASE_URL + data_route + file
 
 
 if __name__ == "__main__":
-    
+
     columns = ['timestamp', 'out.electricity.total.energy_consumption']
-    
-    
+
     # gather config options
     state_name = snakemake.config['state']
     state = states.lookup(state_name)
     county = snakemake.config['county']
     building_opts = snakemake.config['building_data_options']
     sector_buildings = building_opts['building_types']
-    
-    
+
     # load spatial lut
     lut = pd.read_csv(snakemake.input.spatial_lut)
-    
-    
+
     # get the PUMA ID
     # this method is unstable, since some counties might contain multiple PUMAs
-    county_and_puma = lut[((lut['state_abbreviation']==state.abbr)\
-        & (lut['resstock_county_id'] == f"{state.abbr}, {county.capitalize()} County"))]['nhgis_puma_gisjoin'].unique()[0]
-    
+    county_and_puma = lut[((lut['state_abbreviation'] == state.abbr) & (
+        lut['resstock_county_id'] == f"{state.abbr}, {county.capitalize()} County"))]['nhgis_puma_gisjoin'].unique()[0]
+
     # puma_id = county_and_puma.split(',')[-1].replace(' ','')
-    
+
     # for sector in list(sectors_buildings.keys()):
     for sector in ['residential']:
         building_types = sector_buildings[sector]
         sector_frames = []
         for bldg_type in building_types:
-        # for bldg_type in ["single-family_attached"]:
-            bldg_url = create_resstock_url(state_abbr=state.abbr, 
-                                        puma_id=county_and_puma, 
-                                        building_type=bldg_type)
-            bldg_df = pd.read_csv(bldg_url, 
-                                  parse_dates=True, 
+            # for bldg_type in ["single-family_attached"]:
+            bldg_url = create_resstock_url(state_abbr=state.abbr,
+                                           puma_id=county_and_puma,
+                                           building_type=bldg_type)
+            bldg_df = pd.read_csv(bldg_url,
+                                  parse_dates=True,
                                   index_col='timestamp',
                                   usecols=columns)
-            
-            bldg_df.rename(columns={'out.electricity.total.energy_consumption':bldg_type},
-                           inplace=True)
-            
+
+            bldg_df.rename(
+                columns={
+                    'out.electricity.total.energy_consumption': bldg_type},
+                inplace=True)
+
             sector_frames.append(bldg_df)
-        
+
         sector_ts = pd.concat(sector_frames, axis=1)
         sector_ts.to_csv(f"data/timeseries/{sector}_load.csv")
-    
\ No newline at end of file
diff --git a/scripts/retrieve_usrdb.py b/scripts/retrieve_usrdb.py
index 9e348a5..31c7da4 100644
--- a/scripts/retrieve_usrdb.py
+++ b/scripts/retrieve_usrdb.py
@@ -4,35 +4,36 @@
 
 
 if __name__ == "__main__":
-    
+
     if snakemake.config['usrdb_start_date'].lower() == 'today':
         start_date = pd.to_datetime(dt.date.today())
     else:
         start_date = pd.to_datetime(snakemake.config['usrdb_start_date'])
-        
+
     future_date = pd.to_datetime("2099-01-01")
-    
+
     URL = "https://apps.openei.org/USURDB/download/usurdb.csv.gz"
     usrdb = pd.read_csv(URL, low_memory=False, parse_dates=True)
-    
+
     # filter by date
-    usrdb.loc[:,'enddate'] = pd.to_datetime(usrdb['enddate'].fillna(future_date))
-    usrdb.loc[:,'startdate'] = pd.to_datetime(usrdb['startdate'])
+    usrdb.loc[:, 'enddate'] = pd.to_datetime(
+        usrdb['enddate'].fillna(future_date))
+    usrdb.loc[:, 'startdate'] = pd.to_datetime(usrdb['startdate'])
     usrdb = usrdb[(usrdb['enddate'] > start_date)]
-    
+
     # get utility info
     utility_service = gpd.read_file(snakemake.input.utility)
     utility_ids = utility_service.loc[:, 'ID'].values.astype(int)
 
-    sectors = [sector.capitalize() 
-               for sector 
+    sectors = [sector.capitalize()
+               for sector
                in snakemake.config['energy_sectors']]
-    
+
     # filter by utility and sector
     usrdb = usrdb.loc[usrdb['eiaid'].isin(utility_ids)]
     usrdb = usrdb.loc[usrdb['sector'].isin(sectors)]
-    
+
     # filter: is default?
-    usrdb = usrdb[usrdb['is_default']].dropna(how='all',axis=1)
-    
-    usrdb.to_csv(snakemake.output.rates)
\ No newline at end of file
+    usrdb = usrdb[usrdb['is_default']].dropna(how='all', axis=1)
+
+    usrdb.to_csv(snakemake.output.rates)
diff --git a/utils/api_functions.py b/utils/api_functions.py
index b6361bb..2b9641d 100644
--- a/utils/api_functions.py
+++ b/utils/api_functions.py
@@ -11,44 +11,44 @@
 
 _TIGER_URL = "https://www2.census.gov/geo/tiger/"
 _RETAIL_SERVICE_URL = ("https://services1.arcgis.com/Hp6G80Pky0om7QvQ/"
-                      "arcgis/rest/services/Retail_Service_Territories/"
-                      "FeatureServer/0/query?")
+                       "arcgis/rest/services/Retail_Service_Territories/"
+                       "FeatureServer/0/query?")
 AVAILABLE_COLUMNS = [
-"ID",
-"NAME",
-"ADDRESS",
-"CITY",
-"STATE",
-"ZIP",
-"TELEPHONE",
-"TYPE",
-"COUNTRY",
-"NAICS_CODE",
-"NAICS_DESC",
-"SOURCE",
-"SOURCEDATE",
-"VAL_METHOD",
-"VAL_DATE",
-"WEBSITE",
-"REGULATED",
-"CNTRL_AREA",
-"PLAN_AREA",
-"HOLDING_CO",
-"SUMMR_PEAK",
-"WINTR_PEAK",
-"SUMMER_CAP",
-"WINTER_CAP",
-"NET_GEN",
-"PURCHASED",
-"NET_EX",
-"RETAIL_MWH",
-"WSALE_MWH",
-"TOTAL_MWH",
-"TRANS_MWH",
-"CUSTOMERS",
-"YEAR",
-"Shape__Area",
-"Shape__Length"]
+    "ID",
+    "NAME",
+    "ADDRESS",
+    "CITY",
+    "STATE",
+    "ZIP",
+    "TELEPHONE",
+    "TYPE",
+    "COUNTRY",
+    "NAICS_CODE",
+    "NAICS_DESC",
+    "SOURCE",
+    "SOURCEDATE",
+    "VAL_METHOD",
+    "VAL_DATE",
+    "WEBSITE",
+    "REGULATED",
+    "CNTRL_AREA",
+    "PLAN_AREA",
+    "HOLDING_CO",
+    "SUMMR_PEAK",
+    "WINTR_PEAK",
+    "SUMMER_CAP",
+    "WINTER_CAP",
+    "NET_GEN",
+    "PURCHASED",
+    "NET_EX",
+    "RETAIL_MWH",
+    "WSALE_MWH",
+    "TOTAL_MWH",
+    "TRANS_MWH",
+    "CUSTOMERS",
+    "YEAR",
+    "Shape__Area",
+    "Shape__Length"]
 
 RETAIL_SERVICE_COLUMNS = ["CNTRL_AREA",
                           "PLAN_AREA",
@@ -84,48 +84,48 @@ def get_tiger_files(year, state_abbr, feature='tract'):
     feature : str, optional
         Indicates which TIGER file data feature to extract, by default 'tract'.
     """
-    
-    
+
     try:
         state = states.lookup(state_abbr)
         assert state, f"{state_abbr} is not a state in the U.S."
     except AssertionError as error:
         raise error
 
-    _FEATURE_FILE = {'tract':f'TRACT/tl_{year}_{state.fips}_tract.zip',
-                     'blockgroup':f'BG/tl_{year}_{state.fips}_bg.zip',
-                     'county':f"COUNTY/tl_{year}_us_county.zip"}
+    _FEATURE_FILE = {'tract': f'TRACT/tl_{year}_{state.fips}_tract.zip',
+                     'blockgroup': f'BG/tl_{year}_{state.fips}_bg.zip',
+                     'county': f"COUNTY/tl_{year}_us_county.zip"}
     data_route = f"TIGER{year}/{_FEATURE_FILE[feature]}"
-    
-    geo_df = gpd.read_file(_TIGER_URL+data_route)
-    
+
+    geo_df = gpd.read_file(_TIGER_URL + data_route)
+
     return geo_df
 
 
 def get_retail_service_area(state_name=None,
                             crs=4326,
                             columns=RETAIL_SERVICE_COLUMNS):
-    
+
     try:
         state = states.lookup(state_name)
         assert state_name, f"{state_name} is not a state in the U.S."
     except AssertionError as error:
         raise error
-    
+
     if columns != RETAIL_SERVICE_COLUMNS:
         for col in columns:
             if col not in AVAILABLE_COLUMNS:
-                print(f"{col} not in available columns. Must be one of:\n {AVAILABLE_COLUMNS}")
+                print(
+                    f"{col} not in available columns. Must be one of:\n {AVAILABLE_COLUMNS}")
                 raise KeyError
-    
+
     state_field = f"where=STATE%20%3D%20'{state.abbr}'" if state_name else ""
     crs_field = f"outSR={crs}"
     format_field = f"f=json"
     return_fields = f"outFields={','.join(columns)}"
-    
+
     params = "&".join([state_field, return_fields, crs_field, format_field])
-    
-    return _RETAIL_SERVICE_URL+params
+
+    return _RETAIL_SERVICE_URL + params
 
 
 def get_county_fips(state_name, county_name):
@@ -133,7 +133,7 @@ def get_county_fips(state_name, county_name):
     This function retrieves the FIPS code for a county given
     the name of the county. The `county_name` parameter must
     be the name only. It should not have "county" at the end.
-    
+
     Example:
 
     Parameters
@@ -155,10 +155,11 @@ def get_county_fips(state_name, county_name):
         assert state_name, f"{state_name} is not a state in the U.S."
     except AssertionError as error:
         raise error
-    
-    counties = pd.read_html((f"https://en.wikipedia.org/wiki/"
-                             f"List_of_counties_in_{state_name.capitalize()}"))[1].set_index('County')
+
+    counties = pd.read_html(
+        (f"https://en.wikipedia.org/wiki/"
+         f"List_of_counties_in_{state_name.capitalize()}"))[1].set_index('County')
     county_fips = counties.at[county_name.capitalize() + ' County',
                               counties.columns[0]]
-    
-    return county_fips
\ No newline at end of file
+
+    return county_fips

From 63a3aea556f06104d0411016272a0dd32504e635 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 11 Sep 2024 14:02:04 -0400
Subject: [PATCH 12/52] merge conf

---
 Snakefile                       |  1 +
 environment.yml                 |  4 +-
 scripts/retrieve_census_data.py |  1 +
 scripts/retrieve_res_load.py    | 77 ++++++++++++++++++++++++++++-----
 4 files changed, 70 insertions(+), 13 deletions(-)

diff --git a/Snakefile b/Snakefile
index 864e91f..e5c219e 100644
--- a/Snakefile
+++ b/Snakefile
@@ -67,6 +67,7 @@ rule retrieve_res_load:
     output: 
         elec_load = "data/timeseries/residential_elec_load.csv",
         heat_load = "data/timeseries/residential_heat_load.csv"
+        weather = "data/timeseries/weather_year.csv",
     script: "scripts/retrieve_res_load.py"
     
 rule build_dag:
diff --git a/environment.yml b/environment.yml
index c37a978..d97a657 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ channels:
     - bioconda
 dependencies:
     # Requirements for core model functionality
-    - python==3.10.13
+    - python>=3.9
     - pip
     - ipython
     - matplotlib
@@ -25,7 +25,7 @@ dependencies:
     - momepy
     - pysal
     - osmnx
-    - spyder-kernels=2.5
+    - spyder-kernels>=2.5
     - unyt
     - cartopy
     - descartes
diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
index 2658dee..d0c4f40 100644
--- a/scripts/retrieve_census_data.py
+++ b/scripts/retrieve_census_data.py
@@ -9,6 +9,7 @@
 import yaml
 
 sys.path.append("utils/")
+from api_functions import get_tiger_files, get_county_fips
 
 from api_functions import get_tiger_files, get_county_fips
 
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index e9f7614..8c4c9e8 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -2,18 +2,20 @@
 import geopandas as gpd
 from us import states
 
-def create_resstock_url(state_abbr, 
-                        puma_id, 
+
+BASE_URL = (
+    "https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
+    "/end-use-load-profiles-for-us-building-stock")
+
+
+def create_resstock_url(state_abbr,
+                        puma_id,
                         building_type,
                         year=2021,
                         product='resstock', 
                         weather_version='tmy3', 
                         release=1, 
                         ):
-    
-    BASE_URL = ("https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
-                "/end-use-load-profiles-for-us-building-stock")
-    
     data_route = (f"/{year}"
                   f"/{product}_{weather_version}_release_{release}"
                    "/timeseries_aggregates/by_puma"
@@ -24,6 +26,51 @@ def create_resstock_url(state_abbr,
     return BASE_URL+data_route+file
 
 
+def create_weather_url(puma_id,
+                        year=2021,
+                        product='resstock',
+                        weather_version='tmy3',
+                        release=1,
+                        ):
+
+    data_route = (f"/{year}"
+                  f"/{product}_{weather_version}_release_{release}"
+                  "/weather/tmy3/")
+
+    file = f"{puma_id.upper()}.csv"
+
+    return BASE_URL + data_route + file
+
+
+def process_weather_timeseries(data_url, tmy=True, weather_year=2018):
+    """
+    This function processes timeseries weather data from NREL's
+    ResStock and ComStock. If the data are from the typical
+    meteorological year (TMY), then artificial timestamps will
+    be applied.
+    """
+    
+    df = pd.read_csv(data_url)
+    df.columns = ['date_time', 
+                'temp_db', 
+                'rel_humidity',
+                'wind_speed', 
+                'wind_direction',
+                'ghi', 
+                'dni',
+                'dhi']
+    if tmy:
+        timestamps = pd.date_range(start=f'{weather_year}-01-01', 
+                                   freq='h', 
+                                   periods=8760)
+        df.index = timestamps
+    else:
+        msg = "Processing non-TMY weather data has not been implemented."
+        raise NotImplementedError(msg)
+    
+    return df
+
+
 if __name__ == "__main__":
     
     time_col = 'timestamp'
@@ -41,13 +88,21 @@ def create_resstock_url(state_abbr,
     
     # load spatial lut
     lut = pd.read_csv(snakemake.input.spatial_lut)
+
+    # get the PUMA ID this method is unstable, since some counties might contain
+    # multiple PUMAs
+    region = lut.loc[((lut['state_abbreviation']==state.abbr) 
+                                & (lut['resstock_county_id']==f"{state.abbr}, {county.capitalize()} County"))]
+    county_and_puma = region.loc[:,'nhgis_puma_gisjoin'].unique()[0]
+    county_gis_join = region.loc[:,'nhgis_county_gisjoin'].unique()[0]
+    # puma_id = county_and_puma.split(',')[-1].replace(' ','')
+
+    weather_url = create_weather_url(puma_id=county_gis_join)
+    weather_data = process_weather_timeseries(weather_url)
     
+    weather_data.to_csv(snakemake.output.weather)
     
-    # get the PUMA ID
-    # this method is unstable, since some counties might contain multiple PUMAs
-    county_and_puma = lut[((lut['state_abbreviation']==state.abbr)\
-        & (lut['resstock_county_id'] == f"{state.abbr}, {county.capitalize()} County"))]['nhgis_puma_gisjoin'].unique()[0]
-    
+    # for sector in list(sectors_buildings.keys()):
     for sector in ['residential']:
         building_types = sector_buildings[sector]
         elec_frames = []

From 3420aa61a3298580d817af713504c7da32852927 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 11 Sep 2024 14:55:28 -0400
Subject: [PATCH 13/52] adds info about rules to README

---
 README.md | 40 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 39 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 46bfdb5..7baff8f 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,40 @@
 # 2024 Kansas City Analysis
-This repository holds analysis for the energy system in Kansas City, Kansas. Located in Wyandotte County, Kansas.
\ No newline at end of file
+This repository holds analysis for the energy system in Kansas City, Kansas. Located in Wyandotte County, Kansas.
+
+## Workflow
+
+The flow of data through the modeling process is shown in the graph below.
+
+![DAG](dag.png)
+
+There are a few categories of steps:
+* **Retrieve**: In a `retrieve` step, data are primarily downloaded and lightly processed (e.g., ensuring good formatting and data types).
+* **Calculate**: In a `calculate` step, data are transformed through some calculation.
+* *place holder for future additions*
+
+
+## Steps
+
+### `retrieve_census_data`
+In this step, data from the U.S. Census Bureau are queried. The datasets gathered, here, are:
+* Total population and
+* the number and types of residential building units.
+
+### `retrieve_armourdale_shape`
+In this step, the "shape" of the community of interest is retrieved. This shape can be used as a cut-out
+to subset other geospatial data later.
+
+> [!NOTE]
+> This data is specific to the particular community of Armourdale in Kansas City, Kansas. If you 
+> wish to model a different community, should omit this step or replace it with a different shape.
+> For example, by specifying a few census tracts.
+
+### `retrieve_spatial_lut`
+This step downloads the spatial lookup table (LUT) for NREL's ResStock datasets. The spatial LUT 
+cross references census tracts, counties, and states with public use microdata areas (PUMAs). As
+well as how the data are stored within NREL's models.
+
+### `retreive_res_load`
+Simulated building load data is collected from NREL's ResStock database in this step. Currently, 
+the data collected are aggregated building data for the building types defined in the `config.yml` file.
+Future versions may include an option to specify individual buildings.
\ No newline at end of file

From a468aefd99f8bb080efdb91b6eec23d3586af429 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 11 Sep 2024 14:57:14 -0400
Subject: [PATCH 14/52] downloads weather data along with load data

---
 Snakefile                    |  4 ++--
 scripts/retrieve_res_load.py | 27 +++++++++++++++------------
 2 files changed, 17 insertions(+), 14 deletions(-)

diff --git a/Snakefile b/Snakefile
index e5c219e..c1e70d3 100644
--- a/Snakefile
+++ b/Snakefile
@@ -66,8 +66,8 @@ rule retrieve_res_load:
         spatial_lut = "data/spatial_data/spatial_lut.csv"
     output: 
         elec_load = "data/timeseries/residential_elec_load.csv",
-        heat_load = "data/timeseries/residential_heat_load.csv"
-        weather = "data/timeseries/weather_year.csv",
+        heat_load = "data/timeseries/residential_heat_load.csv",
+        weather = "data/timeseries/weather_year.csv"
     script: "scripts/retrieve_res_load.py"
     
 rule build_dag:
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index 8c4c9e8..2cc699c 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -1,7 +1,7 @@
 import pandas as pd
 import geopandas as gpd
 from us import states
-
+from tqdm import tqdm
 
 BASE_URL = (
     "https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock"
@@ -37,7 +37,7 @@ def create_weather_url(puma_id,
                   f"/{product}_{weather_version}_release_{release}"
                   "/weather/tmy3/")
 
-    file = f"{puma_id.upper()}.csv"
+    file = f"{puma_id.upper()}_{weather_version}.csv"
 
     return BASE_URL + data_route + file
 
@@ -97,17 +97,25 @@ def process_weather_timeseries(data_url, tmy=True, weather_year=2018):
     county_gis_join = region.loc[:,'nhgis_county_gisjoin'].unique()[0]
     # puma_id = county_and_puma.split(',')[-1].replace(' ','')
 
+
+    print("Retrieving weather data...")
     weather_url = create_weather_url(puma_id=county_gis_join)
     weather_data = process_weather_timeseries(weather_url)
     
     weather_data.to_csv(snakemake.output.weather)
+    print("Weather data retrieved.")
     
-    # for sector in list(sectors_buildings.keys()):
-    for sector in ['residential']:
+    outer_pbar = tqdm(list(sector_buildings.keys()), position=0, leave=True)
+    all_frames = []
+    for sector in outer_pbar:
+        outer_pbar.set_description(f"{sector.upper()}")
         building_types = sector_buildings[sector]
+        inner_pbar = tqdm(building_types, position=1, leave=True)
+    
         elec_frames = []
         heat_frames = []
-        for bldg_type in building_types:
+        for bldg_type in inner_pbar:
+            inner_pbar.set_description(f"Retrieving {bldg_type}")
             bldg_url = create_resstock_url(state_abbr=state.abbr, 
                                         puma_id=county_and_puma, 
                                         building_type=bldg_type)
@@ -117,17 +125,12 @@ def process_weather_timeseries(data_url, tmy=True, weather_year=2018):
                                   usecols=[time_col,
                                            elec_col,
                                            heat_col])
-            
-            print(f"Accessing {bldg_url}")
-            
             heat_df = bldg_df[[heat_col]]
             elec_df = bldg_df[[elec_col]]
             
-            elec_df.rename(columns={elec_col:bldg_type},
-                           inplace=True)
+            elec_df = elec_df.rename(columns={elec_col:bldg_type})
             
-            heat_df.rename(columns={heat_col:bldg_type},
-                           inplace=True)
+            heat_df = heat_df.rename(columns={heat_col:bldg_type})
             
             elec_frames.append(elec_df)
             heat_frames.append(heat_df)

From a06a68765082e4ee7baa2833a50a0c4ce6aadf6e Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 11 Sep 2024 15:35:56 -0400
Subject: [PATCH 15/52] downloads total building data

---
 scripts/retrieve_res_load.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index 2cc699c..b9dc228 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -121,10 +121,9 @@ def process_weather_timeseries(data_url, tmy=True, weather_year=2018):
                                         building_type=bldg_type)
             bldg_df = pd.read_csv(bldg_url, 
                                   parse_dates=True,
-                                  index_col=time_col,
-                                  usecols=[time_col,
-                                           elec_col,
-                                           heat_col])
+                                  index_col=time_col)
+            bldg_df.to_csv(f"data/timeseries/{sector}_{bldg_type}.csv")
+            
             heat_df = bldg_df[[heat_col]]
             elec_df = bldg_df[[elec_col]]
             

From 92fb838c678b74e6fc4c7802d70da06d31109d8d Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Thu, 12 Sep 2024 14:51:23 -0400
Subject: [PATCH 16/52] adds rule to retrieve lead data

---
 Snakefile                             |  32 ++++++++++++++++++----
 dag.png                               | Bin 31072 -> 50892 bytes
 scripts/calculate_residential_load.py |   6 ++--
 scripts/retrieve_lead_data.py         |  38 ++++++++++++++++++++++++++
 scripts/retrieve_res_load.py          |   5 ++--
 5 files changed, 71 insertions(+), 10 deletions(-)
 create mode 100644 scripts/retrieve_lead_data.py

diff --git a/Snakefile b/Snakefile
index c1e70d3..169d478 100644
--- a/Snakefile
+++ b/Snakefile
@@ -1,16 +1,29 @@
 configfile: "config.yml"
 
+from us import states
 from pathlib import Path
-env_file = Path("./.env").resolve()
 from dotenv import load_dotenv
+
+state = config['state']
+state_abbr = states.lookup(state).abbr
+
+env_file = Path("./.env").resolve()
 load_dotenv(str(env_file))
 
 rule targets:
     input: 
-        sfa = "data/timeseries/residential_load.csv",
+        armourdale = "data/spatial_data/armourdale_shape.gpkg",
+        census_data = "data/spatial_data/county_census_data.gpkg",
+        state_blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg",
+        county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg",
+        elec_load = "data/timeseries/residential_elec_load.csv",
+        heat_load = "data/timeseries/residential_heat_load.csv",
+        weather = "data/timeseries/weather_year.csv",
         res_structures = "data/residential_buildings.csv",
         rates = "data/usrdb_rates.csv",
         project_sunroof = f"data/spatial_data/project-sunroof-census_tract.csv",
+        utility="data/spatial_data/electric_utility.gpkg",
+        lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -21,16 +34,16 @@ rule retrieve_spatial_lut:
 rule retrieve_census_data:
     output:
         census_data = "data/spatial_data/county_census_data.gpkg",
-        state_blockgroups = f"data/spatial_data/{config['state'].lower()}_blockgroups.gpkg",
+        state_blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg",
         county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg"
     script: "scripts/retrieve_census_data.py"
 
 rule retrieve_project_sunroof:
     input: 
-        blockgroups = f"data/spatial_data/{config['state'].lower()}_blockgroups.gpkg"
+        blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg"
     output: 
         project_sunroof = "data/spatial_data/project-sunroof-census_tract.csv",
-        local_potential = f"data/spatial_data/{config['state'].lower()}_rooftop_potential.gpkg"
+        local_potential = f"data/spatial_data/{state.lower()}_rooftop_potential.gpkg"
     script: "scripts/retrieve_project_sunroof.py"
 
 # a bespoke step to make this analysis specific to armourdale
@@ -69,6 +82,15 @@ rule retrieve_res_load:
         heat_load = "data/timeseries/residential_heat_load.csv",
         weather = "data/timeseries/weather_year.csv"
     script: "scripts/retrieve_res_load.py"
+
+rule retrieve_lead_data:
+    input: 
+        community = "data/spatial_data/armourdale_shape.gpkg",
+        county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg"
+    output: 
+        lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
+        lead_community = "data/spatial_data/armourdale_lead.csv"
+    script: "scripts/retrieve_lead_data.py"
     
 rule build_dag:
     input: "Snakefile"
diff --git a/dag.png b/dag.png
index 826523153049f69283e74d5eed462d6ee6df7a6f..ba5c897d6e5427a7405a15c6a0955417ce89d26d 100644
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diff --git a/scripts/calculate_residential_load.py b/scripts/calculate_residential_load.py
index 1d3c271..55bfb4c 100644
--- a/scripts/calculate_residential_load.py
+++ b/scripts/calculate_residential_load.py
@@ -5,9 +5,9 @@
 if __name__ == "__main__":
     
     # load the number of residential structures
-    res_structures = pd.read_csv(snakemake.input.res_structures)
-    res_elec_load = pd.read_csv(snakemake.input.res_elec_load)
-    res_heat_load = pd.read_csv(snakemake.input.res_heat_load)
+    res_structures = pd.read_csv(snakemake.input.res_structures, index_col=0)
+    res_elec_load = pd.read_csv(snakemake.input.res_elec_load, parse_dates=True, index_col='timestamp')
+    res_heat_load = pd.read_csv(snakemake.input.res_heat_load, parse_dates=True, index_col='timestamp')
     
     total_elec_load = res_elec_load*res_structures.T.loc['n_units']
     total_heat_load = res_heat_load*res_structures.T.loc['n_units']
diff --git a/scripts/retrieve_lead_data.py b/scripts/retrieve_lead_data.py
new file mode 100644
index 0000000..2012716
--- /dev/null
+++ b/scripts/retrieve_lead_data.py
@@ -0,0 +1,38 @@
+import geopandas as gpd
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from us import states
+
+from pathlib import Path
+import zipfile
+import io
+import requests
+
+
+if __name__ == "__main__":
+    state_name = snakemake.config['state']
+    state = states.lookup(state_name)
+    state_abbr = state.abbr
+
+    # download the LEAD data
+    url = f"https://data.openei.org/files/573/{state_abbr}-2018-LEAD-data.zip"
+    r = requests.get(url)
+
+    z = zipfile.ZipFile(io.BytesIO(r.content))
+    z.extractall(f"data/spatial_data/{state_abbr}-2018-LEAD-data/")
+
+
+    # cutout data for local community
+    dataset = snakemake.output.lead_data
+    
+    lead_df = pd.read_csv(dataset)
+    
+    county_bg = gpd.read_file(snakemake.input.county_blockgroups)
+    community = gpd.read_file(snakemake.input.community)
+    
+    bg = int(county_bg.sjoin(community, predicate='within').GEOID.values[0][:11])
+    
+    community_lead = lead_df[lead_df['FIP']==bg]
+    
+    community_lead.to_csv(snakemake.output.lead_community)
\ No newline at end of file
diff --git a/scripts/retrieve_res_load.py b/scripts/retrieve_res_load.py
index b9dc228..eeb08ed 100644
--- a/scripts/retrieve_res_load.py
+++ b/scripts/retrieve_res_load.py
@@ -124,8 +124,9 @@ def process_weather_timeseries(data_url, tmy=True, weather_year=2018):
                                   index_col=time_col)
             bldg_df.to_csv(f"data/timeseries/{sector}_{bldg_type}.csv")
             
-            heat_df = bldg_df[[heat_col]]
-            elec_df = bldg_df[[elec_col]]
+            num_units = bldg_df['units_represented'].unique()[0]
+            heat_df = bldg_df[[heat_col]] / num_units
+            elec_df = bldg_df[[elec_col]] / num_units
             
             elec_df = elec_df.rename(columns={elec_col:bldg_type})
             

From 966fe2c3f82d577aaf3cdf7fbe5a9452868b76ef Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Thu, 12 Sep 2024 15:48:12 -0400
Subject: [PATCH 17/52] adds rule to calculate the weighted average energy
 expenses by building type

---
 Snakefile                                |   8 ++++++++
 dag.png                                  | Bin 50892 -> 52391 bytes
 scripts/pre_calculate_energy_expenses.py |  21 +++++++++++++++++++++
 3 files changed, 29 insertions(+)
 create mode 100644 scripts/pre_calculate_energy_expenses.py

diff --git a/Snakefile b/Snakefile
index 169d478..c10abeb 100644
--- a/Snakefile
+++ b/Snakefile
@@ -24,6 +24,7 @@ rule targets:
         project_sunroof = f"data/spatial_data/project-sunroof-census_tract.csv",
         utility="data/spatial_data/electric_utility.gpkg",
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
+        res_energy_expenses = "data/armourdale_energy_expenses.csv",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -91,6 +92,13 @@ rule retrieve_lead_data:
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
         lead_community = "data/spatial_data/armourdale_lead.csv"
     script: "scripts/retrieve_lead_data.py"
+
+rule pre_calculate_energy_expenses:
+    input:
+        lead_community = "data/spatial_data/armourdale_lead.csv" 
+    output: 
+        res_energy_expenses = "data/armourdale_energy_expenses.csv"
+    script: "scripts/pre_calculate_energy_expenses.py"
     
 rule build_dag:
     input: "Snakefile"
diff --git a/dag.png b/dag.png
index ba5c897d6e5427a7405a15c6a0955417ce89d26d..8dc67eb08a69f4cee02c542d655a284e1625f43f 100644
GIT binary patch
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diff --git a/scripts/pre_calculate_energy_expenses.py b/scripts/pre_calculate_energy_expenses.py
new file mode 100644
index 0000000..3743ad9
--- /dev/null
+++ b/scripts/pre_calculate_energy_expenses.py
@@ -0,0 +1,21 @@
+import pandas as pd
+
+
+if __name__ == "__main__":
+    
+    lead_df = pd.read_csv(snakemake.input.lead_community)
+    
+    bldg_types = {'1 ATTACHED':"single-family_attached", 
+                '1 DETACHED':"single-family_detached",
+                '2 UNIT':"multi-family_with_2_-_4_units",
+                '3-4 UNIT':"multi-family_with_2_-_4_units",
+                '50+ UNIT':"multi-family_with_5plus_units",
+                'MOBILE_TRAILER':"mobile_home"}
+    
+    lead_df = lead_df.replace(bldg_types)
+    
+    by_unit = lead_df[['BLD','ELEP*UNITS','GASP*UNITS','HINCP*UNITS','UNITS']].groupby(['BLD']).sum()
+    
+    by_unit = by_unit.div(by_unit['UNITS'], axis=0).drop(columns=['UNITS'])
+    
+    by_unit.to_csv(snakemake.output.res_energy_expenses)
\ No newline at end of file

From d22b6d2c36e6123ddcd9a884b81b1e5229549a60 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 13 Sep 2024 11:00:03 -0400
Subject: [PATCH 18/52] adds model options to config

---
 config.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/config.yml b/config.yml
index efc92a8..597d701 100644
--- a/config.yml
+++ b/config.yml
@@ -8,6 +8,9 @@ census_level: 'tract'
 usrdb_start_date: "2024-07-23"  # today?
 usrdb_future_date: "2099-01-01"  # some date in the future, replaces NaT values
 
+# model options
+topology: "sectoral"  # or building type?
+
 # building data options
 building_data_options:
   resstock_year: 2021  # DO NOT CHANGE
@@ -21,11 +24,12 @@ building_data_options:
       - single-family_attached
       - single-family_detached
       - mobile_home
+    # commercial: # pending implementation
+    #   -
 
 energy_sectors:
   - residential
   # - commercial  # pending implementation
-  # - industrial  # pending data availability
 
 
 # geographic options

From c46986022c622ffb42fc56bd48d6e3260e4a4303 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 13 Sep 2024 11:01:14 -0400
Subject: [PATCH 19/52] adds hplib to environment file

---
 environment.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/environment.yml b/environment.yml
index d97a657..6ed07e0 100644
--- a/environment.yml
+++ b/environment.yml
@@ -48,3 +48,4 @@ dependencies:
       - census
       - streamlit
       - vresutils
+      - hplib

From 08ce7801958b720c21c83939d3065550e09a0ca1 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 16 Sep 2024 14:23:10 -0400
Subject: [PATCH 20/52] adds rule to download several files from wykck gis
 database

---
 Snakefile                          |   8 ++++++++
 dag.png                            | Bin 52391 -> 58341 bytes
 environment.yml                    |   2 +-
 scripts/retrieve_census_data.py    |   1 +
 scripts/retrieve_kck_shapefiles.py |   7 -------
 scripts/retrieve_shapefiles.py     |  28 ++++++++++++++++++++++++++++
 6 files changed, 38 insertions(+), 8 deletions(-)
 delete mode 100644 scripts/retrieve_kck_shapefiles.py
 create mode 100644 scripts/retrieve_shapefiles.py

diff --git a/Snakefile b/Snakefile
index c10abeb..7d8844a 100644
--- a/Snakefile
+++ b/Snakefile
@@ -25,6 +25,7 @@ rule targets:
         utility="data/spatial_data/electric_utility.gpkg",
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
         res_energy_expenses = "data/armourdale_energy_expenses.csv",
+        zoning_data = f"data/spatial_data/armourdale/zoning.gpkg",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -99,6 +100,13 @@ rule pre_calculate_energy_expenses:
     output: 
         res_energy_expenses = "data/armourdale_energy_expenses.csv"
     script: "scripts/pre_calculate_energy_expenses.py"
+
+rule retrieve_community_spatial_data:
+    input: 
+        community_cutout = "data/spatial_data/armourdale_shape.gpkg"
+    output: 
+        zoning_data = f"data/spatial_data/armourdale/zoning.gpkg"
+    script: "scripts/retrieve_shapefiles.py"
     
 rule build_dag:
     input: "Snakefile"
diff --git a/dag.png b/dag.png
index 8dc67eb08a69f4cee02c542d655a284e1625f43f..935fb8c18e03bd3622542c2bae80a95e8657243d 100644
GIT binary patch
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Perc-dsJ>FU`RM-u{H*<X

diff --git a/environment.yml b/environment.yml
index 6ed07e0..5c5fb97 100644
--- a/environment.yml
+++ b/environment.yml
@@ -48,4 +48,4 @@ dependencies:
       - census
       - streamlit
       - vresutils
-      - hplib
+      - git+https://github.com/FZJ-IEK3-VSA/hplib.git
diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
index d0c4f40..3b13c74 100644
--- a/scripts/retrieve_census_data.py
+++ b/scripts/retrieve_census_data.py
@@ -71,6 +71,7 @@
     multi_family = ['2 units','3-4_units']
     many_family = ['5-9_units', '10-19_units', '20-49_units','50plus_units']
     
+    breakpoint()
     county_merge['multi-family_with_2_-_4_units'] = county_merge[multi_family].sum(axis=1)
     county_merge['multi-family_with_5plus_units'] = county_merge[many_family].sum(axis=1)
     county_merge = county_merge.drop(columns=multi_family+many_family)
diff --git a/scripts/retrieve_kck_shapefiles.py b/scripts/retrieve_kck_shapefiles.py
deleted file mode 100644
index 67b7065..0000000
--- a/scripts/retrieve_kck_shapefiles.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import geopandas as gpd
-import pandas as pd
-
-
-URLS = {
-    
-}
\ No newline at end of file
diff --git a/scripts/retrieve_shapefiles.py b/scripts/retrieve_shapefiles.py
new file mode 100644
index 0000000..a3171c0
--- /dev/null
+++ b/scripts/retrieve_shapefiles.py
@@ -0,0 +1,28 @@
+import geopandas as gpd
+import pandas as pd
+from pathlib import Path
+
+
+URLS = {
+    'building_footprints':"https://gisapp.wycokck.org/gisdata/shp/bldg_ftpt_py.zip",
+    'zoning':"https://gisapp.wycokck.org/gisdata/shp/zoning_py.zip",
+    'vacant_parcels':"https://gisapp.wycokck.org/gisdata/shp/vacant_py.zip",
+    'parcels':"https://gisapp.wycokck.org/gisdata/shp/parcel_py.zip",
+    'impervious_land_cover':"https://gisapp.wycokck.org/gisdata/shp/impervious_landcover_py.zip",
+    'land_bank_parcels':'https://gisapp.wycokck.org/gisdata/shp/landbank_py.zip',
+    
+}
+
+if __name__ == "__main__":
+    
+    community_cutout = gpd.read_file(snakemake.input.community_cutout)
+    
+    for name, url in URLS.items():
+        gdf = gpd.read_file(url)
+        gdf = gdf.to_crs(snakemake.config['geographic_crs'])
+        
+        gdf_cutout = gdf.sjoin(community_cutout, predicate='within')
+        print(f"data/spatial_data/armourdale/{name}.gpkg")
+        
+        gdf.to_file(f"data/spatial_data/armourdale/{name}.gpkg", driver="GPKG")
+        
\ No newline at end of file

From 7e1d1e20a02abb57fec549f3d9e0ef767e191cee Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 16 Sep 2024 14:40:44 -0400
Subject: [PATCH 21/52] generalizes the community 'name'

---
 Snakefile                                     | 32 ++++++++++---------
 config.yml                                    |  1 +
 ...urdale.py => retrieve_community_cutout.py} |  4 +--
 3 files changed, 20 insertions(+), 17 deletions(-)
 rename scripts/{retrieve_armourdale.py => retrieve_community_cutout.py} (79%)

diff --git a/Snakefile b/Snakefile
index 7d8844a..0de91e6 100644
--- a/Snakefile
+++ b/Snakefile
@@ -7,12 +7,14 @@ from dotenv import load_dotenv
 state = config['state']
 state_abbr = states.lookup(state).abbr
 
+community_name = config['community_name']
+
 env_file = Path("./.env").resolve()
 load_dotenv(str(env_file))
 
 rule targets:
     input: 
-        armourdale = "data/spatial_data/armourdale_shape.gpkg",
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg",
         census_data = "data/spatial_data/county_census_data.gpkg",
         state_blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg",
         county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg",
@@ -24,8 +26,8 @@ rule targets:
         project_sunroof = f"data/spatial_data/project-sunroof-census_tract.csv",
         utility="data/spatial_data/electric_utility.gpkg",
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
-        res_energy_expenses = "data/armourdale_energy_expenses.csv",
-        zoning_data = f"data/spatial_data/armourdale/zoning.gpkg",
+        res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv",
+        zoning_data = f"data/spatial_data/{community_name.lower()}/zoning.gpkg",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -48,15 +50,15 @@ rule retrieve_project_sunroof:
         local_potential = f"data/spatial_data/{state.lower()}_rooftop_potential.gpkg"
     script: "scripts/retrieve_project_sunroof.py"
 
-# a bespoke step to make this analysis specific to armourdale
-rule retrieve_armourdale_shape:
+# a bespoke step to make this analysis specific to community
+rule retrieve_community_shape:
     output: 
-        armourdale = "data/spatial_data/armourdale_shape.gpkg"
-    script: "scripts/retrieve_armourdale.py"
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
+    script: "scripts/retrieve_community_cutout.py"
 
 rule retrieve_electric_utility:
     input: 
-      cutout="data/spatial_data/armourdale_shape.gpkg"
+      cutout=f"data/spatial_data/{community_name.lower()}_shape.gpkg""
     output:
       utility="data/spatial_data/electric_utility.gpkg"
     script: "scripts/retrieve_electric_utility.py"
@@ -71,7 +73,7 @@ rule retrieve_usrdb:
 rule calculate_res_structures:
     input: 
         census_data = "data/spatial_data/county_census_data.gpkg",
-        armourdale = "data/spatial_data/armourdale_shape.gpkg"
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
     output: 
         res_structures = "data/residential_buildings.csv"
     script: "scripts/calculate_res_structures.py"
@@ -87,25 +89,25 @@ rule retrieve_res_load:
 
 rule retrieve_lead_data:
     input: 
-        community = "data/spatial_data/armourdale_shape.gpkg",
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
         county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg"
     output: 
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
-        lead_community = "data/spatial_data/armourdale_lead.csv"
+        lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv"
     script: "scripts/retrieve_lead_data.py"
 
 rule pre_calculate_energy_expenses:
     input:
-        lead_community = "data/spatial_data/armourdale_lead.csv" 
+        lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv" 
     output: 
-        res_energy_expenses = "data/armourdale_energy_expenses.csv"
+        res_energy_expenses = "data/community_energy_expenses.csv"
     script: "scripts/pre_calculate_energy_expenses.py"
 
 rule retrieve_community_spatial_data:
     input: 
-        community_cutout = "data/spatial_data/armourdale_shape.gpkg"
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
     output: 
-        zoning_data = f"data/spatial_data/armourdale/zoning.gpkg"
+        zoning_data = f"data/spatial_data/{community_name.lower()}/zoning.gpkg"
     script: "scripts/retrieve_shapefiles.py"
     
 rule build_dag:
diff --git a/config.yml b/config.yml
index 597d701..3fc7f5d 100644
--- a/config.yml
+++ b/config.yml
@@ -1,6 +1,7 @@
 # geographic data
 state: 'Kansas'
 county: 'Wyandotte'
+community_name: 'Armourdale'
 
 # historical data
 census_year: 2020
diff --git a/scripts/retrieve_armourdale.py b/scripts/retrieve_community_cutout.py
similarity index 79%
rename from scripts/retrieve_armourdale.py
rename to scripts/retrieve_community_cutout.py
index 6c68ed4..c152572 100644
--- a/scripts/retrieve_armourdale.py
+++ b/scripts/retrieve_community_cutout.py
@@ -12,6 +12,6 @@
     armourdale_ward = '06'
     armourdale = kck_wards[kck_wards['WARD'] == armourdale_ward].dissolve("CITY").reset_index(drop=False)
 
-    armourdale = armourdale[['CITY','geometry','WARD']]
+    cutout = armourdale[['CITY','geometry','WARD']]
     
-    armourdale.to_file(snakemake.output.armourdale, driver="GPKG")
\ No newline at end of file
+    cutout.to_file(snakemake.output.community, driver="GPKG")
\ No newline at end of file

From 88845c66e1b23b484874ef70899e7d3cd6f5d481 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 18 Sep 2024 10:40:03 -0400
Subject: [PATCH 22/52] propagates name change

---
 Snakefile                           |  11 ++++++-----
 dag.png                             | Bin 58341 -> 65251 bytes
 scripts/calculate_res_structures.py |  10 +++++-----
 scripts/retrieve_project_sunroof.py |   4 ++++
 scripts/retrieve_shapefiles.py      |   2 +-
 5 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/Snakefile b/Snakefile
index 0de91e6..5f66dd0 100644
--- a/Snakefile
+++ b/Snakefile
@@ -44,10 +44,11 @@ rule retrieve_census_data:
 
 rule retrieve_project_sunroof:
     input: 
-        blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg"
+        blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg",
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
     output: 
         project_sunroof = "data/spatial_data/project-sunroof-census_tract.csv",
-        local_potential = f"data/spatial_data/{state.lower()}_rooftop_potential.gpkg"
+        local_potential = f"data/spatial_data/{community_name.lower()}_rooftop_potential.gpkg"
     script: "scripts/retrieve_project_sunroof.py"
 
 # a bespoke step to make this analysis specific to community
@@ -58,7 +59,7 @@ rule retrieve_community_shape:
 
 rule retrieve_electric_utility:
     input: 
-      cutout=f"data/spatial_data/{community_name.lower()}_shape.gpkg""
+      cutout=f"data/spatial_data/{community_name.lower()}_shape.gpkg"
     output:
       utility="data/spatial_data/electric_utility.gpkg"
     script: "scripts/retrieve_electric_utility.py"
@@ -89,7 +90,7 @@ rule retrieve_res_load:
 
 rule retrieve_lead_data:
     input: 
-        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg",
         county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg"
     output: 
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
@@ -100,7 +101,7 @@ rule pre_calculate_energy_expenses:
     input:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv" 
     output: 
-        res_energy_expenses = "data/community_energy_expenses.csv"
+        res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv"
     script: "scripts/pre_calculate_energy_expenses.py"
 
 rule retrieve_community_spatial_data:
diff --git a/dag.png b/dag.png
index 935fb8c18e03bd3622542c2bae80a95e8657243d..e73bf05550299b98b9fbb95a5c6c14d8ad699096 100644
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diff --git a/scripts/calculate_res_structures.py b/scripts/calculate_res_structures.py
index 99530c7..db6460c 100644
--- a/scripts/calculate_res_structures.py
+++ b/scripts/calculate_res_structures.py
@@ -7,13 +7,13 @@
     building_opts = snakemake.config['building_data_options']
     
     
-    # specific to armourdale; selecting only block groups within armourdale
-    armourdale = gpd.read_file(snakemake.input.armourdale)
-    armourdale_bg = census_data.sjoin(armourdale, how='inner', predicate='within')
+    # specific to armourdale; selecting only block groups within the community
+    community = gpd.read_file(snakemake.input.community)
+    community_bg = census_data.sjoin(community, how='inner', predicate='within')
     
-    # replace `armourdale_bg` with `census_data` for a non-armourdale analysis.
+    # replace `community_bg` with `census_data` for a non-localized analysis.
     building_types = building_opts['building_types']['residential']
-    building_data = armourdale_bg[building_types]\
+    building_data = community_bg[building_types]\
                     .sum()\
                     .to_frame()\
                     .rename(columns={0:'n_units'})
diff --git a/scripts/retrieve_project_sunroof.py b/scripts/retrieve_project_sunroof.py
index 34bd3bf..e1a91d7 100644
--- a/scripts/retrieve_project_sunroof.py
+++ b/scripts/retrieve_project_sunroof.py
@@ -17,4 +17,8 @@
     # merge dataframes
     solar_gdf = census_tract.merge(df, on='region_name')
     solar_gdf.to_file(snakemake.output.local_potential, driver='GPKG')
+    
+    #get cutout
+    community_cutout = gpd.read_file(snakemake.input.community)
+    combined = solar_gdf.sjoin(community_cutout, predicate='within')
     
\ No newline at end of file
diff --git a/scripts/retrieve_shapefiles.py b/scripts/retrieve_shapefiles.py
index a3171c0..3c322c8 100644
--- a/scripts/retrieve_shapefiles.py
+++ b/scripts/retrieve_shapefiles.py
@@ -15,7 +15,7 @@
 
 if __name__ == "__main__":
     
-    community_cutout = gpd.read_file(snakemake.input.community_cutout)
+    community_cutout = gpd.read_file(snakemake.input.community)
     
     for name, url in URLS.items():
         gdf = gpd.read_file(url)

From 49d43f427b2bb73d3306d3c2c0cf77a8fcae5d50 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 18 Sep 2024 11:12:34 -0400
Subject: [PATCH 23/52] adds retail prices to config

---
 config.yml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/config.yml b/config.yml
index 3fc7f5d..55961dd 100644
--- a/config.yml
+++ b/config.yml
@@ -9,6 +9,11 @@ census_level: 'tract'
 usrdb_start_date: "2024-07-23"  # today?
 usrdb_future_date: "2099-01-01"  # some date in the future, replaces NaT values
 
+# price data
+retail_price_elec: 0.1129  # from google, $/kWh
+# https://www.kansasgasservice.com//media/KGS/Tariffs/20-RSS.pdf
+retail_price_gas: 2.3485 # $/Mcf, 0.0080126123 $/kWh
+
 # model options
 topology: "sectoral"  # or building type?
 

From c5fc2e2db0be4061a74d0b19cfd12f581981b011 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 18 Sep 2024 11:16:45 -0400
Subject: [PATCH 24/52] adds rescaling rule to snakefile

---
 Snakefile | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/Snakefile b/Snakefile
index 5f66dd0..93406cf 100644
--- a/Snakefile
+++ b/Snakefile
@@ -28,6 +28,7 @@ rule targets:
         lead_data = f"data/spatial_data/{state_abbr}-2018-LEAD-data/{state_abbr} AMI Census Tracts 2018.csv",
         res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv",
         zoning_data = f"data/spatial_data/{community_name.lower()}/zoning.gpkg",
+        rescaled_elec_load = "data/timeseries/residential_elec_load_rescaled.csv",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -97,7 +98,7 @@ rule retrieve_lead_data:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv"
     script: "scripts/retrieve_lead_data.py"
 
-rule pre_calculate_energy_expenses:
+rule calculate_historical_expenses:
     input:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv" 
     output: 
@@ -110,6 +111,15 @@ rule retrieve_community_spatial_data:
     output: 
         zoning_data = f"data/spatial_data/{community_name.lower()}/zoning.gpkg"
     script: "scripts/retrieve_shapefiles.py"
+
+rule calculate_rescaled_load:
+    input: 
+        res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv",
+        elec_load = "data/timeseries/residential_elec_load.csv",
+        res_structures = "data/residential_buildings.csv"
+    output: 
+        rescaled_elec_load = "data/timeseries/residential_elec_load_rescaled.csv"
+    script: "scripts/calculate_residential_load.py"
     
 rule build_dag:
     input: "Snakefile"

From e1e730dc98252df9080f3952f4118af3b572c4ba Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 18 Sep 2024 11:33:33 -0400
Subject: [PATCH 25/52] adds rule to rescale load from resstock based on LEAD
 data

---
 Snakefile                                     |   6 ++--
 dag.png                                       | Bin 65251 -> 85676 bytes
 ...es.py => calculate_historical_expenses.py} |   0
 scripts/calculate_residential_load.py         |  29 +++++++++++++++---
 4 files changed, 29 insertions(+), 6 deletions(-)
 rename scripts/{pre_calculate_energy_expenses.py => calculate_historical_expenses.py} (100%)

diff --git a/Snakefile b/Snakefile
index 93406cf..f9557af 100644
--- a/Snakefile
+++ b/Snakefile
@@ -103,7 +103,7 @@ rule calculate_historical_expenses:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv" 
     output: 
         res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv"
-    script: "scripts/pre_calculate_energy_expenses.py"
+    script: "scripts/calculate_historical_expenses.py"
 
 rule retrieve_community_spatial_data:
     input: 
@@ -116,9 +116,11 @@ rule calculate_rescaled_load:
     input: 
         res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv",
         elec_load = "data/timeseries/residential_elec_load.csv",
+        heat_load = "data/timeseries/residential_elec_load.csv",
         res_structures = "data/residential_buildings.csv"
     output: 
-        rescaled_elec_load = "data/timeseries/residential_elec_load_rescaled.csv"
+        rescaled_elec_load = "data/timeseries/residential_elec_load_rescaled.csv",
+        rescaled_heat_load = "data/timeseries/residential_heat_load_rescaled.csv",
     script: "scripts/calculate_residential_load.py"
     
 rule build_dag:
diff --git a/dag.png b/dag.png
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diff --git a/scripts/pre_calculate_energy_expenses.py b/scripts/calculate_historical_expenses.py
similarity index 100%
rename from scripts/pre_calculate_energy_expenses.py
rename to scripts/calculate_historical_expenses.py
diff --git a/scripts/calculate_residential_load.py b/scripts/calculate_residential_load.py
index 55bfb4c..8e1b71f 100644
--- a/scripts/calculate_residential_load.py
+++ b/scripts/calculate_residential_load.py
@@ -4,12 +4,33 @@
 
 if __name__ == "__main__":
     
+    # load price data
+    electricity_price = float(snakemake.config['retail_price_elec'])
+    gas_price = float(snakemake.config['retail_price_gas'])  # price in $/Mcf
+    mcf_to_kwh = 1/297.59333 
+    gas_price = gas_price*mcf_to_kwh  # price in kWh
+    
     # load the number of residential structures
     res_structures = pd.read_csv(snakemake.input.res_structures, index_col=0)
-    res_elec_load = pd.read_csv(snakemake.input.res_elec_load, parse_dates=True, index_col='timestamp')
-    res_heat_load = pd.read_csv(snakemake.input.res_heat_load, parse_dates=True, index_col='timestamp')
+    res_elec_load = pd.read_csv(snakemake.input.elec_load, parse_dates=True, index_col='timestamp')
+    res_heat_load = pd.read_csv(snakemake.input.heat_load, parse_dates=True, index_col='timestamp')
+    
+    # load energy expenses from the LEAD data
+    expenses = pd.read_csv(snakemake.input.res_energy_expenses, index_col='BLD')
+    
+    
+    rescaled_elec_load = (res_elec_load.div(res_elec_load.sum(),axis=1)*
+                         (res_elec_load.columns.map(expenses['ELEP*UNITS'])/electricity_price))
+    
+    rescaled_heat_load = (res_heat_load.div(res_heat_load.sum(),axis=1)*
+                         (res_heat_load.columns.map(expenses['GASP*UNITS'])/gas_price))
+    
+    total_elec_load = rescaled_elec_load*res_structures.T.loc['n_units']
+    total_heat_load = rescaled_heat_load*res_structures.T.loc['n_units']
+    
+    total_elec_load.to_csv(snakemake.output.rescaled_elec_load)
+    total_heat_load.to_csv(snakemake.output.rescaled_heat_load)
+    
     
-    total_elec_load = res_elec_load*res_structures.T.loc['n_units']
-    total_heat_load = res_heat_load*res_structures.T.loc['n_units']
     
     
\ No newline at end of file

From 73e8161da7883d863e367b1f7b1512fdbeca7067 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Thu, 19 Sep 2024 10:55:20 -0400
Subject: [PATCH 26/52] adds rule to retrieve nrel costs

---
 Snakefile                      |  5 +++++
 config.yml                     | 15 ++++++++++++---
 scripts/retrieve_nrel_costs.py | 25 +++++++++++++++++++++++++
 3 files changed, 42 insertions(+), 3 deletions(-)
 create mode 100644 scripts/retrieve_nrel_costs.py

diff --git a/Snakefile b/Snakefile
index f9557af..0710424 100644
--- a/Snakefile
+++ b/Snakefile
@@ -98,6 +98,11 @@ rule retrieve_lead_data:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv"
     script: "scripts/retrieve_lead_data.py"
 
+rule retrieve_nrel_costs:
+    output: 
+        costs = "data/technology_costs.csv"
+    script: "scripts/retrieve_nrel_costs"
+
 rule calculate_historical_expenses:
     input:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv" 
diff --git a/config.yml b/config.yml
index 55961dd..68cf9a9 100644
--- a/config.yml
+++ b/config.yml
@@ -14,8 +14,18 @@ retail_price_elec: 0.1129  # from google, $/kWh
 # https://www.kansasgasservice.com//media/KGS/Tariffs/20-RSS.pdf
 retail_price_gas: 2.3485 # $/Mcf, 0.0080126123 $/kWh
 
+# ATB cost options
+atb_params:
+    atb_year: 2023  # the ATB publication year // DO NOT CHANGE
+    case: 'Market'  # 'R&D'
+    scenario: 'Moderate'  # 'Conservative', 'Advanced'
+    scale: 'Residential'  # 'Utility', 'Commercial'
+    maturity: 'Y'  # 'N'
+    crp: 30  # '20'
+    cost_year: 2025  # Any year 2020-2050
+
 # model options
-topology: "sectoral"  # or building type?
+topology: "sectoral"  # or building type // NOT IMPLEMENTED
 
 # building data options
 building_data_options:
@@ -37,7 +47,6 @@ energy_sectors:
   - residential
   # - commercial  # pending implementation
 
-
 # geographic options
 geographic_crs: 4326  # for using lat/lon; EPSG code
-projected_crs: 5070  # for doing calculations; EPSG code
+projected_crs: 5070  # for doing calculations; EPSG code
\ No newline at end of file
diff --git a/scripts/retrieve_nrel_costs.py b/scripts/retrieve_nrel_costs.py
new file mode 100644
index 0000000..155fc67
--- /dev/null
+++ b/scripts/retrieve_nrel_costs.py
@@ -0,0 +1,25 @@
+import numpy as np
+import pandas as pd
+from nrelpy.atb import ATBe
+
+if __name__ == "__main__":
+    atb_params = snakemake.config['atb_params']
+
+    atb = ATBe(atb_params['atb_year'])
+    df = atb.raw_dataframe
+    new_selection = df[
+                        (df['core_metric_case']==atb_params['case'])
+                        &(df['scale']==atb_params['scale'])
+                        &(df['maturity']==atb_params['maturity'])
+                        &(df['scenario']==atb_params['scenario'])
+                        &(df['core_metric_variable']==atb_params['cost_year'])
+                        &(df['default']==1)
+                        &(df['crpyears']==atb_params['crp'])
+                        &(df['core_metric_parameter'].isin(['Fixed O&M', 'OCC']))
+                        ]
+
+    pivot_table = new_selection.pivot_table(index=['technology'],
+                                            columns=['core_metric_parameter'],
+                                            values='value')
+    
+    pivot_table.to_csv(snakemake.output.costs)
\ No newline at end of file

From 34a134a9195ba135dd8b81c649af69ee0dffe4bd Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Thu, 19 Sep 2024 10:59:36 -0400
Subject: [PATCH 27/52] adds costs to targets

---
 Snakefile |   3 ++-
 dag.png   | Bin 85676 -> 89526 bytes
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/Snakefile b/Snakefile
index 0710424..bf37b1b 100644
--- a/Snakefile
+++ b/Snakefile
@@ -29,6 +29,7 @@ rule targets:
         res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv",
         zoning_data = f"data/spatial_data/{community_name.lower()}/zoning.gpkg",
         rescaled_elec_load = "data/timeseries/residential_elec_load_rescaled.csv",
+        costs = "data/technology_costs.csv",
         dag = "dag.png"
 
 rule retrieve_spatial_lut:
@@ -101,7 +102,7 @@ rule retrieve_lead_data:
 rule retrieve_nrel_costs:
     output: 
         costs = "data/technology_costs.csv"
-    script: "scripts/retrieve_nrel_costs"
+    script: "scripts/retrieve_nrel_costs.py"
 
 rule calculate_historical_expenses:
     input:
diff --git a/dag.png b/dag.png
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GIT binary patch
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From 7aba5cba58d87d59acbc904d39b3a0216d27f812 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Thu, 19 Sep 2024 15:47:18 -0400
Subject: [PATCH 28/52] updates readme and env template

---
 .env.template | 18 +---------------
 README.md     | 57 ++++++++++++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 57 insertions(+), 18 deletions(-)

diff --git a/.env.template b/.env.template
index a60f391..8163d37 100644
--- a/.env.template
+++ b/.env.template
@@ -1,24 +1,8 @@
-# This template comes from https://www.github.omc/kmax12/gridstatus
-
 # Register at https://www.eia.gov/opendata/register.php for an API key
 EIA_API_KEY=
 
-# Register at https://apiportal.pjm.com/ for an API key
-PJM_API_KEY=
-
-# Register at https://apiexplorer.ercot.com/ for username/password
-# and follow instructions at
-# https://developer.ercot.com/applications/pubapi/ERCOT%20Public%20API%20Registration%20and%20Authentication/
-# to get the subscription key
-ERCOT_API_USERNAME=
-ERCOT_API_PASSWORD=
-ERCOT_API_SUBSCRIPTION_KEY=
-
-# Request access at https://www.ncdc.noaa.gov/cdo-web/token
-NOAA_API_KEY=
-
 # Request access at https://api.census.gov/data/key_signup.html
-CENCUS_API_KEY=
+CENSUS_API_KEY=
 
 # Request access at https://www.epa.gov/power-sector/cam-api-portal#/api-key-signup 
 CEMS_API_KEY=
diff --git a/README.md b/README.md
index 46bfdb5..bd0bb66 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,57 @@
 # 2024 Kansas City Analysis
-This repository holds analysis for the energy system in Kansas City, Kansas. Located in Wyandotte County, Kansas.
\ No newline at end of file
+This repository holds analysis for the energy system in Kansas City, Kansas. Located in Wyandotte County, Kansas.
+
+
+# Installation
+
+## Requirements
+
+* `git` - version control software
+    * [Windows Installation instructions](https://git-scm.com/download/win)
+    * [MacOS Installation instructions](https://git-scm.com/download/mac)
+    * [Linux Installation instructions](https://git-scm.com/download/linux)
+* Python installed with either `conda` or `mamba`(recommended)
+    * Download `mamba` installer [here](https://github.com/conda-forge/miniforge).
+    * 'anaconda' ('conda') installation instructions [here](https://docs.anaconda.com/anaconda/install/windows/).
+
+> [!NOTE]
+> Make sure you add Python to PATH during installation.
+
+## Installation Steps
+0. Open command prompt or terminal window. Copy and paste the following commands.
+
+1. Clone the repository
+
+```bash
+git clone https://github.com/ucsusa/2024-kansas-city-analysis.git
+```
+
+2. Set up the environment
+
+```bash
+cd 2024-kansas-city-analysis
+mamba env create  # mamba and conda may be used interchangeably, here
+mamba activate kansas-city
+```
+
+3. Creating the `.env` file
+
+Users should copy the `.env.template` file into a new file simply called `.env`.
+This file contains "secret" information, such as API keys, emails, and other data
+that should remain local. In order to run the current model, users must have API keys
+from the following organizations:
+
+* [U.S. Census API](https://api.census.gov/data/key_signup.html)
+
+These keys may be added directly to the `.env` file.    
+
+## Running the model 
+
+This project uses the workflow management tool, `snakemake`, to create a reproducible data pipeline.
+Running the command
+
+```bash
+snakemake --cores=1
+```
+
+will run the workflow illustrated in the directed acyclic graph (DAG) shown below.
\ No newline at end of file

From 99674aff26ea8509ed68ab412e428c5a2af5060a Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 10:16:53 -0400
Subject: [PATCH 29/52] updates readme

---
 README.md | 76 +++++++++++++++++++++++++++----------------------------
 1 file changed, 38 insertions(+), 38 deletions(-)

diff --git a/README.md b/README.md
index 5d295e0..b4759fc 100644
--- a/README.md
+++ b/README.md
@@ -1,44 +1,6 @@
 # 2024 Kansas City Analysis
 This repository holds analysis for the energy system in Kansas City, Kansas. Located in Wyandotte County, Kansas.
 
-## Workflow
-
-The flow of data through the modeling process is shown in the graph below.
-
-![DAG](dag.png)
-
-There are a few categories of steps:
-* **Retrieve**: In a `retrieve` step, data are primarily downloaded and lightly processed (e.g., ensuring good formatting and data types).
-* **Calculate**: In a `calculate` step, data are transformed through some calculation.
-* *place holder for future additions*
-
-
-## Steps
-
-### `retrieve_census_data`
-In this step, data from the U.S. Census Bureau are queried. The datasets gathered, here, are:
-* Total population and
-* the number and types of residential building units.
-
-### `retrieve_armourdale_shape`
-In this step, the "shape" of the community of interest is retrieved. This shape can be used as a cut-out
-to subset other geospatial data later.
-
-> [!NOTE]
-> This data is specific to the particular community of Armourdale in Kansas City, Kansas. If you 
-> wish to model a different community, should omit this step or replace it with a different shape.
-> For example, by specifying a few census tracts.
-
-### `retrieve_spatial_lut`
-This step downloads the spatial lookup table (LUT) for NREL's ResStock datasets. The spatial LUT 
-cross references census tracts, counties, and states with public use microdata areas (PUMAs). As
-well as how the data are stored within NREL's models.
-
-### `retreive_res_load`
-Simulated building load data is collected from NREL's ResStock database in this step. Currently, 
-the data collected are aggregated building data for the building types defined in the `config.yml` file.
-Future versions may include an option to specify individual buildings.
-
 # Installation
 
 ## Requirements
@@ -92,3 +54,41 @@ snakemake --cores=1
 ```
 
 will run the workflow illustrated in the directed acyclic graph (DAG) shown below.
+
+# Workflow
+
+The flow of data through the modeling process is shown in the graph below.
+
+![DAG](dag.png)
+
+There are a few categories of steps:
+* **Retrieve**: In a `retrieve` step, data are primarily downloaded and lightly processed (e.g., ensuring good formatting and data types).
+* **Calculate**: In a `calculate` step, data are transformed through some calculation.
+* *place holder for future additions*
+
+
+## Steps
+
+### `retrieve_census_data`
+In this step, data from the U.S. Census Bureau are queried. The datasets gathered, here, are:
+* Total population and
+* the number and types of residential building units.
+
+### `retrieve_armourdale_shape`
+In this step, the "shape" of the community of interest is retrieved. This shape can be used as a cut-out
+to subset other geospatial data later.
+
+> [!NOTE]
+> This data is specific to the particular community of Armourdale in Kansas City, Kansas. If you 
+> wish to model a different community, should omit this step or replace it with a different shape.
+> For example, by specifying a few census tracts.
+
+### `retrieve_spatial_lut`
+This step downloads the spatial lookup table (LUT) for NREL's ResStock datasets. The spatial LUT 
+cross references census tracts, counties, and states with public use microdata areas (PUMAs). As
+well as how the data are stored within NREL's models.
+
+### `retreive_res_load`
+Simulated building load data is collected from NREL's ResStock database in this step. Currently, 
+the data collected are aggregated building data for the building types defined in the `config.yml` file.
+Future versions may include an option to specify individual buildings.
\ No newline at end of file

From 340812e04d8b38519ee49feef8a80bca58685e25 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 10:50:45 -0400
Subject: [PATCH 30/52] adds CI workflow

---
 .github/workflows/CI.yml | 47 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 .github/workflows/CI.yml

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
new file mode 100644
index 0000000..5da615c
--- /dev/null
+++ b/.github/workflows/CI.yml
@@ -0,0 +1,47 @@
+name: Workflow Test
+
+on: 
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs: 
+  build:
+    name: Build environment
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: 
+        - "3.9"
+        - "3.10"
+        - "3.11"
+        - "3.12"
+        os:
+        - macos-latest
+        - windows-latest
+
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up conda
+      uses: conda-incubator/setup-miniconda@v2
+      with:
+        miniforge-variant: Mambaforge # mamba is faster than base conda
+        miniforge-version: latest
+        activate-environment: kansas-city
+        use-mamba: true
+        use-only-tar-bz2: true
+    - run: |
+        conda config --env --set pip_interop_enabled True
+
+    - name: Update environment
+      run: conda env update -n kansas-city -f environment.yaml
+
+    - name: Run Snakemake Workflow
+      run: |
+        snakemake --cores=1
\ No newline at end of file

From 1abc59ef6536d2c534293dd653d6f111e5aea9fe Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:05:19 -0400
Subject: [PATCH 31/52] adds env cache to ci

---
 .github/workflows/CI.yml | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 5da615c..840751d 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -39,6 +39,13 @@ jobs:
     - run: |
         conda config --env --set pip_interop_enabled True
 
+    - name: Cache Conda env
+      uses: actions/cache@v4
+      with:
+        path: ${{ env.CONDA }}/envs
+        key: conda-${{ runner.os }}--${{ runner.arch }}--${{ env.today }}-${{ hashFiles('environment.yaml') }}
+      id: cache-env
+
     - name: Update environment
       run: conda env update -n kansas-city -f environment.yaml
 

From 6a4b537674aae66b9f9ac7a2921a5a4ef7ff4bef Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:08:43 -0400
Subject: [PATCH 32/52] fixes name of environment file

---
 .github/workflows/CI.yml | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 840751d..70bef57 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -39,15 +39,15 @@ jobs:
     - run: |
         conda config --env --set pip_interop_enabled True
 
-    - name: Cache Conda env
-      uses: actions/cache@v4
-      with:
-        path: ${{ env.CONDA }}/envs
-        key: conda-${{ runner.os }}--${{ runner.arch }}--${{ env.today }}-${{ hashFiles('environment.yaml') }}
-      id: cache-env
+    # - name: Cache Conda env
+    #   uses: actions/cache@v4
+    #   with:
+    #     path: ${{ env.CONDA }}/envs
+    #     key: conda-${{ runner.os }}--${{ runner.arch }}--${{ env.today }}-${{ hashFiles('environment.yml') }}
+    #   id: cache-env
 
     - name: Update environment
-      run: conda env update -n kansas-city -f environment.yaml
+      run: conda env update -n kansas-city -f environment.yml
 
     - name: Run Snakemake Workflow
       run: |

From 523a40899494cc1afa85773e3061731b2772faa4 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:15:30 -0400
Subject: [PATCH 33/52] sets targz to false

---
 .github/workflows/CI.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 70bef57..a684b4b 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -35,7 +35,7 @@ jobs:
         miniforge-version: latest
         activate-environment: kansas-city
         use-mamba: true
-        use-only-tar-bz2: true
+        use-only-tar-bz2: false
     - run: |
         conda config --env --set pip_interop_enabled True
 

From 7e565e1a6f4f92586bf122c96cfe5eedf627e4f8 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:23:55 -0400
Subject: [PATCH 34/52] fixes api functions call

---
 scripts/retrieve_census_data.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
index 690667f..8c61fd0 100644
--- a/scripts/retrieve_census_data.py
+++ b/scripts/retrieve_census_data.py
@@ -1,4 +1,3 @@
-from api_functions import get_tiger_files, get_county_fips
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np

From a6afdd54329e0b8ab3b30c930c980ee0bcd192ca Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:25:13 -0400
Subject: [PATCH 35/52] fixes api functions call, again

---
 scripts/retrieve_electric_utility.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/scripts/retrieve_electric_utility.py b/scripts/retrieve_electric_utility.py
index 2852abf..05c74c4 100644
--- a/scripts/retrieve_electric_utility.py
+++ b/scripts/retrieve_electric_utility.py
@@ -1,10 +1,9 @@
-from api_functions import get_retail_service_area
 import pandas as pd
 import geopandas as gpd
 import sys
 
 sys.path.append("utils")
-
+from api_functions import get_retail_service_area
 
 if __name__ == "__main__":
     state_name = snakemake.config['state']

From e7e30ee880eb5110712d3d5a2e64a25f26dc292a Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:33:14 -0400
Subject: [PATCH 36/52] removes breakpoint

---
 scripts/retrieve_census_data.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/scripts/retrieve_census_data.py b/scripts/retrieve_census_data.py
index 8c61fd0..88d7179 100644
--- a/scripts/retrieve_census_data.py
+++ b/scripts/retrieve_census_data.py
@@ -74,8 +74,7 @@
     # combine structure types by unit; harmonize with NREL resstock
     multi_family = ['2 units','3-4_units']
     many_family = ['5-9_units', '10-19_units', '20-49_units','50plus_units']
-    
-    breakpoint()
+
     county_merge['multi-family_with_2_-_4_units'] = county_merge[multi_family].sum(axis=1)
     county_merge['multi-family_with_5plus_units'] = county_merge[many_family].sum(axis=1)
     county_merge = county_merge.drop(columns=multi_family+many_family)

From aed7248e3afc0562a3eab5dc6a8fe00666c2841f Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:42:22 -0400
Subject: [PATCH 37/52] adds step to create env file with secret to workflow

---
 .github/workflows/CI.yml | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index a684b4b..cc4581a 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -39,16 +39,13 @@ jobs:
     - run: |
         conda config --env --set pip_interop_enabled True
 
-    # - name: Cache Conda env
-    #   uses: actions/cache@v4
-    #   with:
-    #     path: ${{ env.CONDA }}/envs
-    #     key: conda-${{ runner.os }}--${{ runner.arch }}--${{ env.today }}-${{ hashFiles('environment.yml') }}
-    #   id: cache-env
-
     - name: Update environment
       run: conda env update -n kansas-city -f environment.yml
 
+    - name: Create .env file
+      run: |
+        echo ${{ secrets.ENV_FILE }} > .env
+
     - name: Run Snakemake Workflow
       run: |
         snakemake --cores=1
\ No newline at end of file

From 1d754c226c5989b642f8b03cfa00100b464c0397 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 11:55:01 -0400
Subject: [PATCH 38/52] reduces the workflow matrix to a single run

---
 .github/workflows/CI.yml | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index cc4581a..e2d9150 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -14,12 +14,8 @@ jobs:
       fail-fast: false
       matrix:
         python-version: 
-        - "3.9"
-        - "3.10"
         - "3.11"
-        - "3.12"
         os:
-        - macos-latest
         - windows-latest
 
     defaults:

From dcd51289047a42bb53b9fa6f4bfe02657bb2543c Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 20 Sep 2024 14:55:09 -0400
Subject: [PATCH 39/52] updates environment and wycokck data access

---
 .github/workflows/CI.yml       |   2 +-
 dag.png                        | Bin 89526 -> 92314 bytes
 environment.yml                |   4 ----
 scripts/retrieve_shapefiles.py |  25 ++++++++++++++-----------
 4 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index e2d9150..d6d5bd7 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -36,7 +36,7 @@ jobs:
         conda config --env --set pip_interop_enabled True
 
     - name: Update environment
-      run: conda env update -n kansas-city -f environment.yml
+      run: mamba env update -n kansas-city -f environment.yml
 
     - name: Create .env file
       run: |
diff --git a/dag.png b/dag.png
index d415462f7c56978804d87ea2fbae0534295b582b..9da3ae695276fa1471b0a2eedc86ca99f98571ac 100644
GIT binary patch
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diff --git a/environment.yml b/environment.yml
index 5c5fb97..443d604 100644
--- a/environment.yml
+++ b/environment.yml
@@ -40,12 +40,8 @@ dependencies:
     - pytz
     - camelot-py
     - pip:
-      - git+https://github.com/kmax12/gridstatus.git
       - python-dotenv
       - highspy
       - nrelpy
       - us
       - census
-      - streamlit
-      - vresutils
-      - git+https://github.com/FZJ-IEK3-VSA/hplib.git
diff --git a/scripts/retrieve_shapefiles.py b/scripts/retrieve_shapefiles.py
index 3c322c8..f82e6c6 100644
--- a/scripts/retrieve_shapefiles.py
+++ b/scripts/retrieve_shapefiles.py
@@ -1,6 +1,6 @@
 import geopandas as gpd
-import pandas as pd
-from pathlib import Path
+from pyogrio.errors import DataSourceError
+from tqdm import tqdm
 
 
 URLS = {
@@ -17,12 +17,15 @@
     
     community_cutout = gpd.read_file(snakemake.input.community)
     
-    for name, url in URLS.items():
-        gdf = gpd.read_file(url)
-        gdf = gdf.to_crs(snakemake.config['geographic_crs'])
-        
-        gdf_cutout = gdf.sjoin(community_cutout, predicate='within')
-        print(f"data/spatial_data/armourdale/{name}.gpkg")
-        
-        gdf.to_file(f"data/spatial_data/armourdale/{name}.gpkg", driver="GPKG")
-        
\ No newline at end of file
+    pbar = tqdm(URLS.items())
+    for name, url in pbar:
+        pbar.set_description(f"{name}")
+        try:
+            gdf = gpd.read_file(url)
+            gdf = gdf.to_crs(snakemake.config['geographic_crs'])
+            
+            gdf_cutout = gdf.sjoin(community_cutout, predicate='within')
+            
+            gdf.to_file(f"data/spatial_data/armourdale/{name}.gpkg", driver="GPKG")
+        except DataSourceError:
+            print(f"Failed to download {name} from {url}")
\ No newline at end of file

From 627ec397613f7b3c8c92d3ed9789b282a5f55396 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 23 Sep 2024 11:03:55 -0400
Subject: [PATCH 40/52] saves the project sunroof cutout

---
 scripts/retrieve_project_sunroof.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/scripts/retrieve_project_sunroof.py b/scripts/retrieve_project_sunroof.py
index 89d2906..5526684 100644
--- a/scripts/retrieve_project_sunroof.py
+++ b/scripts/retrieve_project_sunroof.py
@@ -20,9 +20,12 @@
 
     # merge dataframes
     solar_gdf = census_tract.merge(df, on='region_name')
-    solar_gdf.to_file(snakemake.output.local_potential, driver='GPKG')
+    
     
     #get cutout
     community_cutout = gpd.read_file(snakemake.input.community)
     combined = solar_gdf.sjoin(community_cutout, predicate='within')
     
+    
+    solar_gdf.to_file(snakemake.output.local_potential, driver='GPKG')
+    

From d8e4ee05d38171f71b7af2bb9550e5559c552e56 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Tue, 24 Sep 2024 12:38:35 -0400
Subject: [PATCH 41/52] adds pep8speaks config

---
 .pep8speaks.yml | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 .pep8speaks.yml

diff --git a/.pep8speaks.yml b/.pep8speaks.yml
new file mode 100644
index 0000000..70e9db5
--- /dev/null
+++ b/.pep8speaks.yml
@@ -0,0 +1,30 @@
+# File : .pep8speaks.yml
+
+scanner:
+  diff_only: True # If False, the entire file touched by the Pull Request is scanned for errors. If True, only the diff is scanned.
+  linter: pycodestyle # Other option is flake8
+
+pycodestyle: # Same as scanner.linter value. Other option is flake8
+  max-line-length: 100 # Default is 79 in PEP 8
+  ignore: # Errors and warnings to ignore
+    - W504 # line break after binary operator
+    - E402 # module level import not at top of file
+    - E731 # do not assign a lambda expression, use a def
+    - C406 # Unnecessary list literal - rewrite as a dict literal.
+    - E741 # ambiguous variable name
+
+no_blank_comment: True # If True, no comment is made on PR without any errors.
+descending_issues_order: False # If True, PEP 8 issues in message will be displayed in descending order of line numbers in the file
+
+message: # Customize the comment made by the bot
+  opened: # Messages when a new PR is submitted
+    header:
+      "Hello @{name}! Thanks for opening this PR. "
+      # The keyword {name} is converted into the author's username
+    footer:
+      "Do see the [Hitchhiker's guide to code style](https://goo.gl/hqbW4r)"
+      # The messages can be written as they would over GitHub
+  updated: # Messages when new commits are added to the PR
+    header: "Hello @{name}! Thanks for updating this PR. "
+    footer: "" # Why to comment the link to the style guide everytime? :)
+  no_errors: "There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: "
\ No newline at end of file

From c8a269576f8a052ee95cd1e426f9eca176ec058e Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Wed, 25 Sep 2024 16:18:38 -0400
Subject: [PATCH 42/52] fixes some rules and adds pypsa notebook

---
 Snakefile                               |    2 +-
 dag.png                                 |  Bin 92314 -> 88507 bytes
 notebooks/09-electricity-use.ipynb      |  711 +++++++++++++
 notebooks/10-pypsa-model.ipynb          | 1226 +++++++++++++++++++++++
 notebooks/gis_notebooks/kc-zoning.ipynb |  367 +++----
 scripts/calculate_residential_load.py   |    5 +-
 scripts/retrieve_project_sunroof.py     |    4 +-
 scripts/retrieve_shapefiles.py          |    4 +-
 8 files changed, 2129 insertions(+), 190 deletions(-)
 create mode 100644 notebooks/09-electricity-use.ipynb
 create mode 100644 notebooks/10-pypsa-model.ipynb

diff --git a/Snakefile b/Snakefile
index bf37b1b..4e0bcac 100644
--- a/Snakefile
+++ b/Snakefile
@@ -122,7 +122,7 @@ rule calculate_rescaled_load:
     input: 
         res_energy_expenses = f"data/{community_name.lower()}_energy_expenses.csv",
         elec_load = "data/timeseries/residential_elec_load.csv",
-        heat_load = "data/timeseries/residential_elec_load.csv",
+        heat_load = "data/timeseries/residential_heat_load.csv",
         res_structures = "data/residential_buildings.csv"
     output: 
         rescaled_elec_load = "data/timeseries/residential_elec_load_rescaled.csv",
diff --git a/dag.png b/dag.png
index 9da3ae695276fa1471b0a2eedc86ca99f98571ac..427db4a134085a73461df4219ee25f272c3e2542 100644
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diff --git a/notebooks/09-electricity-use.ipynb b/notebooks/09-electricity-use.ipynb
new file mode 100644
index 0000000..1968262
--- /dev/null
+++ b/notebooks/09-electricity-use.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_elec = pd.read_csv('../data/timeseries/residential_elec_load_rescaled.csv', parse_dates=True, index_col='timestamp')\n",
+    "res_heat = pd.read_csv('../data/timeseries/residential_heat_load_rescaled.csv', parse_dates=True, index_col='timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <th>mobile_home</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:15:00</th>\n",
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+       "      <td>11.182543</td>\n",
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+       "      <td>1.316949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:30:00</th>\n",
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+       "      <td>1.402989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:45:00</th>\n",
+       "      <td>124.810963</td>\n",
+       "      <td>2.734115</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>130.157708</td>\n",
+       "      <td>2.679941</td>\n",
+       "      <td>11.317656</td>\n",
+       "      <td>605.812913</td>\n",
+       "      <td>1.601132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:15:00</th>\n",
+       "      <td>16.824577</td>\n",
+       "      <td>0.403772</td>\n",
+       "      <td>2.361815</td>\n",
+       "      <td>133.074632</td>\n",
+       "      <td>0.205631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:00:00</th>\n",
+       "      <td>155.102361</td>\n",
+       "      <td>2.646652</td>\n",
+       "      <td>14.567161</td>\n",
+       "      <td>695.344421</td>\n",
+       "      <td>1.082926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:15:00</th>\n",
+       "      <td>128.287742</td>\n",
+       "      <td>2.732418</td>\n",
+       "      <td>14.002470</td>\n",
+       "      <td>622.587237</td>\n",
+       "      <td>1.316956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:30:00</th>\n",
+       "      <td>138.694172</td>\n",
+       "      <td>2.711169</td>\n",
+       "      <td>13.979686</td>\n",
+       "      <td>610.486911</td>\n",
+       "      <td>1.224628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:45:00</th>\n",
+       "      <td>130.288110</td>\n",
+       "      <td>2.756182</td>\n",
+       "      <td>13.871853</td>\n",
+       "      <td>610.047892</td>\n",
+       "      <td>1.236934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-01-01 00:00:00</th>\n",
+       "      <td>134.371567</td>\n",
+       "      <td>2.746753</td>\n",
+       "      <td>13.342888</td>\n",
+       "      <td>615.014955</td>\n",
+       "      <td>1.295301</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35040 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     multi-family_with_2_-_4_units  \\\n",
+       "timestamp                                            \n",
+       "2018-01-01 00:15:00                     113.972825   \n",
+       "2018-01-01 00:30:00                     122.073324   \n",
+       "2018-01-01 00:45:00                     124.810963   \n",
+       "2018-01-01 01:00:00                     130.157708   \n",
+       "2018-01-01 01:15:00                      16.824577   \n",
+       "...                                            ...   \n",
+       "2018-12-31 23:00:00                     155.102361   \n",
+       "2018-12-31 23:15:00                     128.287742   \n",
+       "2018-12-31 23:30:00                     138.694172   \n",
+       "2018-12-31 23:45:00                     130.288110   \n",
+       "2019-01-01 00:00:00                     134.371567   \n",
+       "\n",
+       "                     multi-family_with_5plus_units  single-family_attached  \\\n",
+       "timestamp                                                                    \n",
+       "2018-01-01 00:15:00                       2.678051               11.182543   \n",
+       "2018-01-01 00:30:00                       2.689016               11.389590   \n",
+       "2018-01-01 00:45:00                       2.734115               11.064188   \n",
+       "2018-01-01 01:00:00                       2.679941               11.317656   \n",
+       "2018-01-01 01:15:00                       0.403772                2.361815   \n",
+       "...                                            ...                     ...   \n",
+       "2018-12-31 23:00:00                       2.646652               14.567161   \n",
+       "2018-12-31 23:15:00                       2.732418               14.002470   \n",
+       "2018-12-31 23:30:00                       2.711169               13.979686   \n",
+       "2018-12-31 23:45:00                       2.756182               13.871853   \n",
+       "2019-01-01 00:00:00                       2.746753               13.342888   \n",
+       "\n",
+       "                     single-family_detached  mobile_home  \n",
+       "timestamp                                                 \n",
+       "2018-01-01 00:15:00              591.611432     1.316949  \n",
+       "2018-01-01 00:30:00              603.432355     1.402989  \n",
+       "2018-01-01 00:45:00              600.966481     1.501680  \n",
+       "2018-01-01 01:00:00              605.812913     1.601132  \n",
+       "2018-01-01 01:15:00              133.074632     0.205631  \n",
+       "...                                     ...          ...  \n",
+       "2018-12-31 23:00:00              695.344421     1.082926  \n",
+       "2018-12-31 23:15:00              622.587237     1.316956  \n",
+       "2018-12-31 23:30:00              610.486911     1.224628  \n",
+       "2018-12-31 23:45:00              610.047892     1.236934  \n",
+       "2019-01-01 00:00:00              615.014955     1.295301  \n",
+       "\n",
+       "[35040 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'kWh')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "res_elec.resample('h').mean().sum(axis=1).plot(ax=ax, label='electricity')\n",
+    "res_heat.resample('h').mean().sum(axis=1).plot(ax=ax, legend=True, label='heat')\n",
+    "ax.set_xlabel('')\n",
+    "ax.set_ylabel('kWh')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_electrified_data = res_elec.resample('h').mean().sum(axis=1)+res_heat.resample('h').mean().sum(axis=1)/4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5266385.120228281"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_electrified_data.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1430.8057224551974"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec.resample('h').mean().sum(axis=1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1450.0)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "res_elec.resample('h').mean().sum(axis=1).plot(ax=ax, label='electricity')\n",
+    "# res_heat.resample('h').mean().sum(axis=1).plot(ax=ax, legend=True, label='heat')\n",
+    "res_elec.resample('h').mean().sum(axis=1).plot.area(ax=ax, lw=0, color='lightgray')\n",
+    "ax.set_xlabel('')\n",
+    "ax.set_ylabel('kWh', fontsize=16)\n",
+    "ax.set_ylim(0, 1450)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Average Day')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Rzz77rNF2+/btg4eHB5ydndGvXz989NFH8PDwAADExMRAq9Uajc3i4+ODoKAgREVF3TYQqdVqwyzSQMW4IDk5OXBzc7vjTNlERERkOkRRRGFhIXx8fCCT3aGnkISDQtaISqUSVSqVOHfuXDE2Nlb8+uuvRWtra3HNmjWGbSIiIsTff/9djIuLE7du3Sp26tRJDAwMFMvKykRRFMUffvhBVCqVVfY9ZMgQ8bnnnrvtcd977z2jUWH54osvvvjiiy/zfaWkpNwxb5h8HyKlUomwsDCjwcBeeeUVREdH48iRI7d9T1paGvz8/BAREYExY8Zgw4YNeOqpp4xafABgyJAhaNWq1W0nc/x3C1F+fj6aN2+OS5cuGfVNItOk1Wqxd+9eDBgwAAqFQupy6C54vcwHr5V54fWqmMzY398feXl5RhMf/5vJ3zLz9vZGhw4djJa1b98emzdvvuN7/Pz8kJCQAADw8vKCRqNBbm4uXFxcDNtlZmYiPDz8tvtQqVRQqVRVlru6usLNze1eToUakFarha2tLdzc3Cz2h4A54fUyH7xW5oXXC4bzvlt3F5N/7L5Xr164ePGi0bJLly7Bz8+v2vdkZ2cjJSUF3t7eAIDQ0FAoFApERkYatklLS8PZs2erDURERERkOUw+EL322ms4evQoFi5ciMuXL2PDhg349ttv8dJLLwEAioqKMGfOHBw5cgRJSUnYt28fRo4cCXd3dzzyyCMAKia7nDZtGmbPno09e/bg5MmTmDx5MoKDgw1PnREREZHlMvlbZl27dsWWLVswd+5cvP/++/D398eSJUswadIkAIBcLkdcXBzWrl2LvLw8eHt7Y8CAAdi4cSMcHBwM+/niiy9gZWWF8ePHo7S0FIMGDcLq1as5BhERERGZfiACKkaWHTFixG3X2djYYOfOnXfdh7W1NZYtW4Zly5bVdXlERERk5kz+lhkRERFRfWMgIiIiIovHQEREREQWj4GIiIiILB4DEREREVk8BiIiIiKyeAxEREREZPEYiIiIiMjiMRARERGRxWMgIiIiIovHQEREREQWj4GIiIiILB4DEREREVk8s5jtnoiIyBSUaXX4ck8CTqfmwdFaAWdbBZxslLf+VMDZRgEnWwWc/7HMVimHIAhSl053wUBERERUA9eyi/HC+licSyuo1fsUcqHa0ORkUxGqnG0VcLVTopu/K1RW8no6A7oTBiIiIqK7+PNsGv7z0xkUqsvhaqfEzMFtAAB5JVrklWiRX6pFfqmm4uvSiq/zSjTQ6kRodSJuFqlxs0h91+ME+jhizdPd4G6vqu9Ton9hICIiIqqGplyPj/+4gO8PJwIAwvxcsHxiF3g5Wd/1vaIoolSrMwSmij81hr/n3fqzoFSLvFINzl4vQPyNAoxdEYV107rD19W2vk+P/oGBiIiI6DZu5JXipQ2xOJmcBwB4rm9L/GdYWyjkNXseSRAE2CqtYKu0go+zzV23v5pVhCdWHkdSdgkevRWK2no53M8pUC3wKTMiIqJ/2XsxEw99eRAnk/PgaG2Fb58IxbwH29c4DN2Llk3ssfmFcAR42iOzUI1xX0ch5lpOvR2PjDEQERER3VKu0+O/Oy/iqVXRyC3RIripE7a/0gdDA70a5PheTtbYNL0nQv1cUFBWjkn/dwx7L2Q2yLEtHQMRERERgMzCMkxeeQzL914GADzRww8/v9CzwfvyONsqsX5ad/Rv2wRlWj2eXXsCv5683qA1WCIGIiIisnhHrmTjwaWHcPRqDuyUcnz5eAg+eDhIskfgbZRyfPdkGB7u7INyvYiZG09h1a2O3VQ/2KmaiIgsll4vYsX+K1i86yL0ItDW0wH/m9wFrZrYS10aFHIZPh/fGS52Sqw6nIQF284hp1iDWUMCONBjPWAgIiIii5RbrMFrm05h38UsAMDY0Gb4YHQQbJSmMzCiTCbg3REd4GanxH93XcKyvy4ju1iDD0YHQS5jKKpLDERERGRxYpNzMeOHWNzIL4PKSoYPHg7C+DBfqcu6LUEQMGNgGzjbKvHOb2ex4Vgy8ko0+GJCZ45qXYcYiIiIyGKIoojvDydh0Y7zKNeLaOluh68mdUF7b0epS7uryT384GKrxMyNJ7EjLh35pdH45okw2Kv4q7wusFM1ERFZhIIyLV5YH4sPfj+Hcr2Ihzp647cZvcwiDFV6qKM3Vk3tBlulHIcvZ2Pid0eRU6yRuqxGgYGIiIgavbPX8zFy2SH8GZ8OhVzA+6MDsfzxEDhYK6QurdZ6t3HHj8/2gKudEmdS8zH26yhczyuVuiyzx0BERESNliiK2HAsGWNWROFadgmaudjg5+fD8WTPFmb9pFYnX2dsmt4TPk7WuJpVjLEronA5s1DqsswaAxERETVKpRodZm06jXlb4qAp12Nwew9sf7kPOvk6S11anWjtYY/NL4ajtYc90vLLMPbrIziZnCt1WWaLgYiIiBqlBdviseXkdchlAuYOb4fvngyDk6353SK7E28nG/w0vSc6+zojr0SLSf93DAcuZUldllliICIiokbnZHIuIqJTAADfT+2K6f1amfUtsjtxsVPih2e6o08bd5RodJi2JhrbTt+Quiyzw0BERESNik4v4t3f4gEAj3Zphn4BTSSuqP7ZqaywckpXjOjoDa1OxCsRJ7HuSJLUZZkVDl5ARESNSkR0MuKu58PB2gpvDm8ndTkNRmklw9LHQuBiq8S6o9fwzm/xyCooQ0tR6srMA1uIiIio0cgt1uCznRcBALOGBKCJg0riihqWXFYxpMDMwW0AAF/uvYLNSTLo9UxFd8NAREREjcanOy8ir0SLdl4OeKKHn9TlSEIQBMwcHID3RwdCEICD6TJ8ufeK1GWZPAYiIiJqFM6k5iEiOhkA8P7oIFjJLftX3JM9W+Cj0YEAgK/2XcVvp65LXJFps+x/LURE1Cjo9SLe+S0eogg8EtIU3fxdpS7JJIwLbYpBPnoAwH9+PsNxiu6AgYiIiMzephMpOJ2SB3uVFeZaUEfqmhjRXI9B7ZpAU67Hs2tjcIPTfNwWAxEREZm1vBINPvnzAgBg5uA28HC0lrgi0yITgP+ODUY7LwfcLFJj2poTKFaXS12WyWEgIiIis/bfXReRW6JFgKc9poS3kLock2SvssL/TQmDu70S59MK8NrGU3zy7F8YiIiIyGydvZ6PH4793ZFaYeEdqe+kmYstvnkiDEorGXady8Bnuy5KXZJJMYt/OdevX8fkyZPh5uYGW1tbdO7cGTExMYb1oihi/vz58PHxgY2NDfr374/4+HijfajVarz88stwd3eHnZ0dRo0ahdTU1IY+FSIiqiMVHanPQhSBUZ180KOlm9QlmbxQPxd8+mhHAMCKfVewOYa/ByuZfCDKzc1Fr169oFAo8Mcff+DcuXNYvHgxnJ2dDdt8+umn+Pzzz7F8+XJER0fDy8sLQ4YMQWFhoWGbmTNnYsuWLYiIiMChQ4dQVFSEESNGQKfTSXBWRER0v36OTcXJ5DzYKeV466H2UpdjNh4OaYoZA1oDAOb+EocTSTkSV2QaTD4QffLJJ/D19cWqVavQrVs3tGjRAoMGDUKrVq0AVLQOLVmyBG+99RbGjBmDoKAgrFmzBiUlJdiwYQMAID8/HytXrsTixYsxePBghISEYP369YiLi8Pu3bulPD0iIroH+SVafPJHRUfqVwe3gSc7UtfKrCEBeCDQCxqdHtPXxSAlp0TqkiRn8nOZbd26FcOGDcO4ceOwf/9+NG3aFC+++CKeffZZAEBiYiLS09MxdOhQw3tUKhX69euHqKgoTJ8+HTExMdBqtUbb+Pj4ICgoCFFRURg2bFiV46rVaqjVasPXBQUFAACtVgutVltfp0t1pPIa8VqZB14v82Eq1+q/O88ju1iDVk3sMLlbM8nrMVV3ul6fjOmA5JxinEsrxLTV0dj4XDfYq0w+FtRaTf9tmPyZX716FStWrMCsWbMwb948HD9+HK+88gpUKhWefPJJpKenAwA8PT2N3ufp6Ylr164BANLT06FUKuHi4lJlm8r3/9uiRYuwYMGCKsv37t0LW1vbujg1agCRkZFSl0C1wOtlPqS8VqnFwPozcgACHmhSgMidf0pWi7mo7npN8AYW35TjUmYRJi3fjWfb6SETGri4elZSUrPWL5MPRHq9HmFhYVi4cCEAICQkBPHx8VixYgWefPJJw3aCYHwFRVGssuzf7rTN3LlzMWvWLMPXBQUF8PX1xYABA+Dmxo57pk6r1SIyMhJDhgyBQqGQuhy6C14v8yH1tRJFEY//XzRE5OHBIE/MnNCpwWswJzW5XkFd8zFxZTTO5QFn5f5484G2DVtkPau8w3M3Jh+IvL290aFDB6Nl7du3x+bNmwEAXl5eACpagby9vQ3bZGZmGlqNvLy8oNFokJuba9RKlJmZifDw8NseV6VSQaWqOkuyQqHgD2wzwutlXni9zIdU12pzTCpikvNgq5TjnZGB/PdSQ3e6XqH+7lg8vhNmbDiJlYevIcDLERO6Nm/gCutPTf+NmHyn6l69euHiReOxEi5dugQ/v4pZjP39/eHl5WXUHKjRaLB//35D2AkNDYVCoTDaJi0tDWfPnq02EBERkWkpKNNi0a2O1C8PbANvJxuJK2o8RnT0wczBbQAAb/96FkevZktcUcMz+UD02muv4ejRo1i4cCEuX76MDRs24Ntvv8VLL70EoOJW2cyZM7Fw4UJs2bIFZ8+exdSpU2Fra4uJEycCAJycnDBt2jTMnj0be/bswcmTJzF58mQEBwdj8ODBUp4eERHV0BeRl3CzSI2WTewwrbe/1OU0Oq8OaoMRHb2h1Yl4YX0MrmUXS11SgzL5W2Zdu3bFli1bMHfuXLz//vvw9/fHkiVLMGnSJMM2r7/+OkpLS/Hiiy8iNzcX3bt3x65du+Dg4GDY5osvvoCVlRXGjx+P0tJSDBo0CKtXr4ZcLpfitIiIqBYupBdg7ZGKB2UWjAqE0srk/z9vdgRBwH/HdUJKTglOp+Zj2poT+OXFcDhaW8ZtSZMPRAAwYsQIjBgxotr1giBg/vz5mD9/frXbWFtbY9myZVi2bFk9VEhERPVFFEW8+2s8dHoRw4O80KdNE6lLarSsFXJ892QYRi0/jMuZRZix4SS+nxIGKwuYEqXxnyEREZm1307dwPGkHNgo5Hh7RIe7v4Hui4ejNf5vShhsFHIcuJSFD7efl7qkBsFAREREJquwTIuPdlT8Qp4xsDWaOrMjdUMIauqEL24NabA6Kgnrj16TuKL6x0BEREQma+nuBGQVquHvbodn+rAjdUN6IMgb/xlWMSbRe1vjEXX5psQV1S8GIiIiMkkX0wuxKioJAPDeyA5QWfEhmIb2Yv9WeLizD3R6ES/8EIurWUVSl1RvGIiIiMjkiKKId387C51exNAOnujf1kPqkiySIAj4+NGOCGnujPxSLZ5ZcwL5JY1z3jgGIiIiMjlbT9/AscQcqKxkeIcdqSVlrZDj2yfC0NTZBldvFuPFDTHQ6vRSl1XnGIiIiMikFKnLsfBWR+qXBrSGrysn1JZaEwcV/m9KGGyVchy+nI35W+MhiqLUZdUpBiIiIjIpX+5JQEaBGn5utniub0upy6Fb2ns7YuljIRAE4IdjyVh/LFnqkuoUAxEREZmMhIxCfH8oEUBFR2prBTtSm5IhHTzx+rB2AIBlexIaVSsRAxEREZkEURTx3tZ4lOtFDG7vgYHtPKUuiW7j6d4tYKeUI7NQjfgbBVKXU2cYiIiIyCRsj0tD1JVsKK1keHdEoNTlUDVUVnLD9Cl7zmdKXE3dYSAiIiLJFavL8eHvFR2pX+jXCs3d2JHalA1sVzEMwl8XMiSupO4wEBERkeSW/XUZ6QVl8HW1wQv9W0ldDt1F/3YVLUSnU/ORWVgmcTV1g4GIiIgkdTG9ECsPXQUAvDsikB2pzYCHgzU6NXMCAOy7kCVxNXWDgYiIiCRTrtNjzk+nodWJGNzeE4Pbc0Rqc1HZ6f2vC42jHxEDERERSebbg1cRdz0fjtZWWPhIEARBkLokqqFBt8LrwYQsqMt1Eldz/xiIiIhIEgkZhVgSmQAAeHdkIDwcrSWuiGoj0McRHg4qFGt0OJ6YI3U5942BiIiIGpxOL+I/P5+BRqfHgLZN8GiXplKXRLUkCILhabPG8Pg9AxERETW4lYeu4lRKHhxUVlg4Jpi3yszU34/fZ5r9qNUMRERE1KCuZBXhv7suAQDeHtEe3k42EldE96pXa3corWRIzinBlaxiqcu5LwxERETUYHR6Ea//fAaacj36tHHH+DBfqUui+2CnskKPlm4AzH+QRgYiIiJqMKujkhBzLRf2Kit8/GhH3iprBAY1kn5EDERERNQgkm4W47OdFwAAcx9sh6bOvFXWGFT2IzpxLRf5JVqJq7l3DERERFTv9HoRr28+gzKtHuGt3DCxW3OpS6I64utqiwBPe+j0IvYnmO+o1QxERERU79YdvYbjiTmwVcrxCW+VNTqVo1bvNeNRqxmIiIioXiVnl+DjPypulb05vB18XTmTfWNTedts78VM6PTm+fg9AxEREdUbvV7EG5vPoFSrQ3d/V0zu7id1SVQPujR3hpONAnklWpxMzpW6nHvCQERERPVmw/FkHLmaDRuFHJ+O7QiZjLfKGiMruQz92zYBAOwx09tmDERERFQvUnNLsGjHeQDAf4a1hZ+bncQVUX0y3DZjICIiIqogiiLm/hKHYo0OXVu4YGp4C6lLonrWL6AJ5DIBF9ILkZpbInU5tcZAREREdW5jdAoOJtyEykqGT8d24q0yC+Bsq0RocxcA5tlKxEBERER16kZeKT7cXnGrbM7QtvB3560ySzGw/a1RqxmIiIjIklXeKitSlyOkuTOe7u0vdUnUgCqn8Yi6ko0STbnE1dQOAxEREdWZn2NSsf9SFpRWMnw2thPkvFVmUVp72MPX1Qaacj2iLmdLXU6tMBAREVGdSM8vw/u/nwMAvDY4AK097CWuiBqaIAgY2NY8b5sxEBER0X0TRRFvbYlDYVk5OjVzwrN9eKvMUg1sXzGNx18XMiCK5jNqNQMRERHdt19PXceeC5lQymX4bFwnWMn568VSdfd3ha1SjowCNeJvFEhdTo3xXywREd2XzIIyzN9acavslUGtEeDpIHFFJCVrhRy9W7sDAP4yo9tmDERERHTPRFHE27+eRX6pFkFNHTG9XyupSyITMOjW4/cMREREZBG2nUnDrnMZUMgFfDa2ExS8VUYABtzqWH06NQ9ZhWqJq6kZ/sslIqJ7klWoxnu/nQUAvDSgNdp7O0pcEZkKD0drBDd1gigC+y6aRyuRyQei+fPnQxAEo5eXl5dh/dSpU6us79Gjh9E+1Go1Xn75Zbi7u8POzg6jRo1CampqQ58KEVGj8t7Ws8gt0aK9tyNe7N9a6nLIxFRO9mout81MPhABQGBgINLS0gyvuLg4o/UPPPCA0fodO3YYrZ85cya2bNmCiIgIHDp0CEVFRRgxYgR0Ol1DngYRUaPxx9l07IhLh5VMwGdjO0JpZRa/TqgBVfYjOphwE5pyvcTV3J2V1AXUhJWVlVGr0L+pVKpq1+fn52PlypVYt24dBg8eDABYv349fH19sXv3bgwbNqxeaiYiaqyKtMDi3yvmKnuhfysENXWSuCIyRUE+TmjioEJWoRrRSTnodevJM1NlFpE+ISEBPj4+8Pf3x2OPPYarV68ard+3bx88PDwQEBCAZ599FpmZfzfPxcTEQKvVYujQoYZlPj4+CAoKQlRUVIOdAxFRY7E5UYacYi3aejpgxkDeKqPbk8kEDGjbBACw57zp3zYz+Rai7t27Y+3atQgICEBGRgY+/PBDhIeHIz4+Hm5ubhg+fDjGjRsHPz8/JCYm4p133sHAgQMRExMDlUqF9PR0KJVKuLi4GO3X09MT6enp1R5XrVZDrf67Z3xBQcXgUlqtFlqttn5OlupM5TXitTIPvF7mY+upVMRmyyAXBCx6pANkoh5arenfDrFUUn+2+rVxw6YTqdh9Ph1vDmsNQWj4ue1qeu4mH4iGDx9u+HtwcDB69uyJVq1aYc2aNZg1axYmTJhgWB8UFISwsDD4+flh+/btGDNmTLX7FUXxjhdm0aJFWLBgQZXle/fuha2t7T2eDTW0yMhIqUugWuD1Mm3HMgVEXJEBEDDAW4eU04eRclrqqqgmpPpslekAuSBHck4pVv/yBzxtGr6GkpKSGm1n8oHo3+zs7BAcHIyEhITbrvf29oafn59hvZeXFzQaDXJzc41aiTIzMxEeHl7tcebOnYtZs2YZvi4oKICvry8GDBgANze3Ojobqi9arRaRkZEYMmQIFAqF1OXQXfB6mTZRFPHNgURsuHIZANDVXY/FTw2ArbVK4srobkzhs7UtJwaHLmdD79UBD/Zq0eDHr7zDczdmF4jUajXOnz+PPn363HZ9dnY2UlJS4O3tDQAIDQ2FQqFAZGQkxo8fDwBIS0vD2bNn8emnn1Z7HJVKBZWq6oddoVDwB7YZ4fUyL7xepkevF/H+7+ewOioJAPBs7xYILL8MW2sVr5UZkfKzNai9Jw5dzsb+S9l4vn+bBj9+Tc/b5DtVz5kzB/v370diYiKOHTuGsWPHoqCgAFOmTEFRURHmzJmDI0eOICkpCfv27cPIkSPh7u6ORx55BADg5OSEadOmYfbs2dizZw9OnjyJyZMnIzg42PDUGRERVaUu1+HliJOGMPTOiA54fVgAJOgGQmascjyi6KQc5Jeabj9Bk28hSk1NxeOPP46bN2+iSZMm6NGjB44ePQo/Pz+UlpYiLi4Oa9euRV5eHry9vTFgwABs3LgRDg5/Ty74xRdfwMrKCuPHj0dpaSkGDRqE1atXQy6XS3hmRESmq6BMi+fWnsDRqzlQyAUsHt8Zozr5sOM71Zqfmx1ae9jjcmYRDiZkYURHH6lLui2TD0QRERHVrrOxscHOnTvvug9ra2ssW7YMy5Ytq8vSiIgapcyCMkxZFY3zaQWwV1nhmydCTX4MGTJtg9p54HJmEf46n2mygcjkb5kREVHDuZJVhEf+F4XzaQVwt1ch4rkeDEN03ypvm+27lAWdXpS4mttjICIiIgDAyeRcjF0Rhet5pWjhZotfXgjnKNRUJ0L9XOBobYWcYg1OpeRJXc5tMRARERH2XsjExO+OIbdEi07NnPDzC+Fo7sYx16huWMll6Ne2crLXDImruT0GIiIiC/fTiRQ8s/YESrU69A1ogg3P9oC7PccYoro16NZtM1OdxoOBiIjIQomiiP/tu4z//HwGOr2IMSFNsXJKGOxUJv+8DZmhfgFNIBOAC+mFuJ5XKnU5VTAQERFZIL1exIJt5/DpnxcBANP7tcTi8Z2gkPPXAtUPFzslQv0qZozYe8H0Won4L5+IyMLcbsDFucPbSzLxJlmWAe0q+xExEBERkYQKyrSY+n00tp9Jg0Iu4MvHQzCtt7/UZZGFGNTOEwBw+PJNlGp0EldjjIGIiMhCZBaUYcI3R3HkajbsVVZY/VQ3jOpkmoPkUeMU4GmPps42UJfrEXXlptTlGGEgIiKyAFeyijBmBQdcJGkJgoBB7W89bWZit80YiIiIGrnKARdTczngIkmvsh/R3guZEEXTGbWagYiIqBHbe5EDLpJp6dnSDTYKOdLyy3A+rVDqcgwYiIiIGglRFFGkLkdKTgnOXs/H6sOJeGYNB1wk02KtkBtu15rSqNUcfYuIyARpyvXIK9Egt0Rb9c9SDfKKtcgt0SCv5NafpRXrtbqqtyDGhDTFJ2M7cowhMhmD2ntg9/kM7LmQiRkD20hdDgAGIiIiyeSXahGdmIMjV7NxMb3QEHDySjQovo9HkpVWMrjYKuBiq8TITj54sX8rjjFEJmXArXnNTqXkIbtIDTcTaLlkICIiaiAFZbcC0JVsHE3MRvyNAtypT6lMAJxsKoKN862A43TrTxdbBZz/sfyff9oo5AxAZNK8nKwR6OOI+BsF2HcxC4+GNpO6JAYiIqL6UlCmxYmkHBy9WhGC4m/kQ/+vANTS3Q7dW7ohpLkzmtirDMHGxVYJB2sryGQMNtQ4DWrngfgbBfjrQiYDERFRY1JYpsWJpFwcvZqNo1ezEXe9agDyd7dDj5au6NHSDT1ausHT0VqaYokkNrC9J7786zIOXMqCplwPpZW0fdwYiIiI7lGRuhzRSTm3AlAOzl7Ph+5fCcjPzRY9b4Wf7i1d4e1kI1G1RKalY1MnuNsrcbNIgxNJOQiXeKBQBiIiohoSRRFHrmbjwKWbhhagfweg5q626NHSFT1buaG7vxt8nBmAiG5HJhMwoK0HfopJxZ4LmQxERETmoFynx3tb4/HDsWSj5b6uNujhX9EC1KOVG5oyABHV2MB2FYFo74VMvDOig6S1MBAREd1FqUaHl3+Mxe7zmRAE4OHOTdGrtTu6+7vC15WjPhPdq95t3KGQC7h6sxhXs4rQsom9ZLUwEBER3UF2kRrT1pzAqZQ8KK1kWDqhM4YHe0tdFlGj4GCtQHd/Nxy6fBN/XciUNBBx2FIiompcyy7GoyuicColD042Cmx4pjvDEFEdG3hrste/LmRKWgcDERHRbZxOycOjK6KQlF2Cps422PxCT4S1cJW6LKJGpzIQHU/MQWGZVrI6GIiIiP5l74VMPPbtUdws0qCDtyO2vBiO1h4OUpdF1Ci1cLdDyyZ2KNeLOJhwU7I6GIiIiP5hY3QynllbMUN8nzbu2Di9Bzw4eCJRvRp0q5Voz3npbpsxEBERoWKMoS8iL+GNzXHQ6UWM6dIUK6d0hYO1QurSiBq9ge08AQD7LmZWGduroTAQEZHF0+r0eHNzHJbuSQAAvDSgFRaP6yT5VAJEliKshQscrK2QXazB6dQ8SWrgp52ILFqxuhzPrj2BjSdSIBOADx8Own+GteNs8UQNSCGXoU+bipGqoy5L04+IgYiILFZWoRqPfXsU+y5mwVohwzdPhGFyDz+pyyKySD1augEAjiXmSHJ8DsxIRBbpalYRpqw6jpScUrjYKrByald0ae4idVlEFqubf8WwFjHXcqHV6aGQN2ybDVuIiMjixCbn4tEVUUjJKYWvqw02vxDOMEQksQAPBzjZKFCi0SH+RkGDH5+BiIgsSuS5DEz87ihyS7To2MwJv7zQS9LpAoiogkwmoOutwU+PJ2Y3/PEb/IhERBJZf/Qapq87gTKtHv3bNsGPz/ZAEweV1GUR0S3d/SsDUcP3I2IfIiJq9ERRxH93XcRXe68AAMaHNcNHjwQ3eB8FIrqzbv8IRHq9CJms4Z72ZCAiokZNU67Hm7+cwS+x1wEAMwe3wauD2vCxeiITFOjjCDulHAVl5biYUYj23o4Ndmz+94iIGq3CMi2mrYnGL7HXIZcJ+OTRYMwcHMAwRGSirOQyhLaQ5rYZAxERNUqZBWWY8M1RHEy4CRuFHP/3ZBgmdG0udVlEdBdS9SPiLTMianSOXMnGzI0nkVGghpudEt9P7YpOvs5Sl0VENVDZj+hYYg5EUWywFl0GIiJqNMp1eizdk4Dley9DFIGWTeywampX+LnZSV0aEdVQx2ZOUFrJcLNIjcSbxQ02LAZvmRFRo5CaW4IJ3x7Fsr8qwtD4sGbYNqM3wxCRmVFZyRFyq0W3IafxMPlANH/+fAiCYPTy8vIyrBdFEfPnz4ePjw9sbGzQv39/xMfHG+1DrVbj5Zdfhru7O+zs7DBq1CikpqY29KkQUT3ZEZeGB5ceRMy1XDiorLD0sc74dGwn2KnYCE5kjqToR2TygQgAAgMDkZaWZnjFxcUZ1n366af4/PPPsXz5ckRHR8PLywtDhgxBYWGhYZuZM2diy5YtiIiIwKFDh1BUVIQRI0ZAp9NJcTpEVEdKNTrM/SUOL/4Qi4KycnT2dcb2V/pgdOemUpdGRPehm3/FRK8NGYjM4r9PVlZWRq1ClURRxJIlS/DWW29hzJgxAIA1a9bA09MTGzZswPTp05Gfn4+VK1di3bp1GDx4MABg/fr18PX1xe7duzFs2LAGPRciqhsX0gswY8NJXM4sgiAAz/drhVlDAjjYIlEj0MXPGVYyAdfzSpGaW4JmLrb1fkyz+MmRkJAAHx8f+Pv747HHHsPVq1cBAImJiUhPT8fQoUMN26pUKvTr1w9RUVEAgJiYGGi1WqNtfHx8EBQUZNiGiMyHKIpYdyQJo5YfxuXMIjRxUGHd093xxgPtGIaIGglbpRWCmjoBaLhWIpNvIerevTvWrl2LgIAAZGRk4MMPP0R4eDji4+ORnp4OAPD09DR6j6enJ65duwYASE9Ph1KphIuLS5VtKt9/O2q1Gmq12vB1QUHFzLtarRZarbZOzo3qT+U14rUyDzW9XrklGszbEo/dF7IAAP0C3PHJmCC42Sl5rRsIP1vmxZyvV5ifM06l5OHolZsYGex59zdUo6bnbvKBaPjw4Ya/BwcHo2fPnmjVqhXWrFmDHj16AECVMQpqMm7B3bZZtGgRFixYUGX53r17YWtb/013VDciIyOlLoFq4U7X63IBsC5BjjyNALkgYpSfHv1c03Fsf/X/saH6w8+WeTHH6yXLFQDIsS8+FTuU1+55PyUlJTXazuQD0b/Z2dkhODgYCQkJePjhhwFUtAJ5e3sbtsnMzDS0Gnl5eUGj0SA3N9eolSgzMxPh4eHVHmfu3LmYNWuW4euCggL4+vpiwIABcHNzq+Ozorqm1WoRGRmJIUOGQKFQSF0O3cWdrle5To+v9l3F/85dhV4EWrjZYsn4jgj0abg5juhv/GyZF3O+Xr1Ltfhu0V5klgno2mcQmjio7mk/lXd47sbsApFarcb58+fRp08f+Pv7w8vLC5GRkQgJCQEAaDQa7N+/H5988gkAIDQ0FAqFApGRkRg/fjwAIC0tDWfPnsWnn35a7XFUKhVUqqrffIVCYXb/qCwZr5d5+ff1up5XipkRJxGdlAsAGBvaDAtGBfJxehPAz5Z5Mcfr5aZQoL2XI86lFeBkaiEe6nhvAzTW9LxN/qfKnDlzMHLkSDRv3hyZmZn48MMPUVBQgClTpkAQBMycORMLFy5EmzZt0KZNGyxcuBC2traYOHEiAMDJyQnTpk3D7Nmz4ebmBldXV8yZMwfBwcGGp86IyPT8eTYNr/98BgVl5bBXWeGjR4L4OD2Rhenm74pzaQU4npiNhzp63/0N98HkA1Fqaioef/xx3Lx5E02aNEGPHj1w9OhR+Pn5AQBef/11lJaW4sUXX0Rubi66d++OXbt2wcHBwbCPL774AlZWVhg/fjxKS0sxaNAgrF69GnK5XKrTIqJqlGl1+OD3c/jhWDIAoFMzJ3z5eAhHnCayQN39XbE6KqlBRqw2+UAUERFxx/WCIGD+/PmYP39+tdtYW1tj2bJlWLZsWR1XR0R16VJGIV77KQ6XMooA/D22kNKKj9MTWaKut0asvpBeiLwSDZxtlfV2LP6UISLJiaKIQ+kCxnx9DJcyiuBur8K6ad3w5vB2DENEFszdXoVWTSpahyv7EtaXOvlJk5OTg3nz5iEkJAROTk6Qy+XVvqysTL5RiogaUH6pFjMiTuOnRDnU5Xr0C2iCP2f2QZ82TaQujYhMwN/TeGTX63HuO50kJiaiT58+SEtLgyiKd92+JtsQkWVIySnBU6ujcTmzCHJBxOvD2uLZvq0hk915HDEishzd/V3x4/Hkeh+x+r5biN544w3cuHEDwcHB2Lp1K9LS0qDT6aDX66t9ERHFXMvFw19VTL/h6ajCzCAdnu7VgmGIiIx0u9WP6OyNAhSpy+vtOPcdiPbs2QNra2vs2rULI0aMgKen511HiSYiy/b7mRt4/LujyC7WINDHET9P747m9zbECBE1cj7ONvB1tYFOLyL2Wv31I7rvQFRWVoZ27drBw8OjLuohokZMFEV8tfcyZmw4CU25HoPbe2DT9J7wcrSWujQiMmHdWlT2I6q/22b3HYjat2+PvLy8OiiFiBozTbkeb2w+g892XgQAPNWrBb55IoyjThPRXXW/ddvMpAPRzJkzkZSUhF27dtVFPUTUCOWXaDF11XFsOpEKmQC8PzoQ740MhJz9hYioBir7EZ1KyUOZVlcvx7jvQDR58mS8+eabmDBhApYuXYrCwsK6qIuIGonk7BKMWXEYUVeyYaeUY+WUrniyZwupyyIiM+LnZgsPBxU0Oj1Op+TVyzFq1VbdsmXLatcVFxdj1qxZmDVrFtzd3WFnd/th9gVBwJUrV2pXJRGZpZhruXhu7QlkF2vg5WiN76d2RQfOUk9EtSQIArr5u+L3M2k4lpiD7i3d6vwYtQpESUlJNdouKysLWVlZt13HJ9CILMPvZ25g1qbT0JTrEejjiJVTusLLiZ2niejedL8ViOqrH1GtAlFiYmK9FEFEjYcoivjfviuGztOD23tg6WMh7DxNRPelcsTqmGu50Or0UMjrdlqfWv2EqpxhnojodjTlery1JQ4/xaQCAJ7u5Y+3HmrPztNEdN/aeNjD2VaBvBItzl7PR0hzlzrdf63jlUajqdMCiKhxyC/RYsr3x/FTzN9Pkr07sgPDEBHVCZlMQNcW9ff4fa0DkbOzM/r374+3334bu3btQlFRUZ0XRUTmpfJJsiNX+SQZEdWf+hyPqNY39cvKynDgwAEcPHgQixYtglwuR6dOndCnTx/Dy93dvc4LJSLT9M8nybydrLFyCp8kI6L60f1WP6LjSTnQ6cU6bYGudSA6dOiQ4XX48GHk5uYiJiYGsbGxWLp0KQCgbdu26Nu3ryEgNW/evM4KJiLTse30Dcz+qeJJsqCmFU+SeXIaDiKqJ+29HWCvskJhWTkuphfW6X++ah2IwsPDER4ejtdffx0AEB8fj0OHDuHgwYM4dOgQkpOTceHCBVy4cAHfffcdAMDX1xd9+vQxhKR27drV2QkQUcOr+iSZJ758vDNslXySjIjqj5VchlA/F+y/lIXjidnSBqJ/CwwMRGBgIKZPnw4ASE1NNYSjAwcO4Ny5c0hOTsaGDRuwYcMGCIKA8vLy+y6ciKShKddj3pY4/MwnyYhIAt38XSsCUVIOpvbyr7P91u1D/ACaNWuGxx9/HF999RXi4uJw9epVvPDCC1CpVBBFEaIo1vUhiaiBVD5J9vOtJ8k+4JNkRNTA/tmxui4zRZ23b6elpRluoR08eBBnz56FXq+HKIqQyWQICgqq60MSUQMoUpdj3DdRuJRRBDulHMsndcGAth5Sl0VEFia4mRNUVjLcLNLgSlYxWnvY18l+7zsQXbp0yXCL7ODBg4bRrEVRhK2tLfr06YNevXqhV69eCA8Ph6Mjnz4hMjeiKGLeL3G4lFEEDwcVVj/VjU+SEZEkVFZyhDR3xtGrOTiemCNdIIqJiTG0/hw+fBhZWVmGJitPT0888sgj6NWrF3r37o2QkBBYWbGTJZG52xidgq2nb0AuE7BicheGISKSVDd/t1uBKBsTu9fNk+y1Titdu3aFIAgQBAFt27bFqFGj0Lt3b/Tq1QutWrWqk6KIyHRcTC/Ee1vjAQBzhrZFqJ+rxBURkaWr7Ed07FY/orqYOP6em2/8/PwwcOBA9O7dG71790bTpk3vuxgiMi0lmnK8tCEW6nI9+gY0wfS+LaUuiYgIIc2dYSUTkJZfhtTcUvi62t73Pmv9lNl7772HQYMGISsrC1999RUmTpyI5s2bw9/fH0888QS++eYbxMfH33dhRCS9d3+Lx+XMin5Dn4/vBBmfJiMiE2CrtELHZk4A6m4aj1q3EL333nsAAL1ej5MnTxo6Ux8+fBg//PADfvjhBwiCAGdnZ4SHhxtakLp27QqlUlknRRNR/dsck2p4vP7Lx0Pgbq+SuiQiIoNu/m6ITc7D8cQcPBra7L73d8+3zGQyGUJDQxEaGopXX30VAHD58mWjDtfbt2/H9u3bIQgClEolwsLC0KdPHyxcuPC+Cyei+nM5swjv/HYWAPDqoAD0aOkmcUVERMa6+7vi6/1XcDypblqI6nRgxtatW+Opp57C999/j4sXLyIjIwM///wznnnmGQiCgMOHD+OTTz6py0MSUR0r0+owY0MsSjQ6hLdyw4yBraUuiYioitAWLhAEIPFmMTILyu57f/XyTHxOTo7R/GaxsbGcroPITCzYdg4X0gvhbq/EkgmdOQo1EZkkR2sFOng7Iv5GAY4n5WBER5/72l+dBKJr164ZjU594cIFw7rKMYrc3NzQu3dv9OnTpy4OSUT1YOvpG/jxeDIEAfhiQmd4cOZ6IjJh3fxdEX+jAMeuShSIzp49azQ69fXr1wHAaE6Rf89w3759+/sqlIjqV9LNYsz7JQ4A8FL/1ujTponEFRER3Vl3f1esOpxUJ0+a1ToQubm5IS8vD4BxAGrfvj369OljeDVvXjcjRxJR/VOX6zDjx1gUqcvRrYUrZg5uI3VJRER31bVFxQCNFzMKkVusgYvdvT/NXutAlJubC7lcjpCQEKMA5ObGp1CIzNWiHRdw9noBXGwVWPp4Z1jJ6/R5CyKieuFmr0JrD3tczixCdFIOhgZ63fO+ah2Idu7cifDwcNjZ2d3zQYnIdPx5Ng2ro5IAAJ+P7wxvJxtpCyIiqoVu/q64nFmE44n3F4hq/d/AIUOGGIWhlJSUWr1/69attT0kEdWTlJwS/OfnMwCA6X1bYkA7D4krIiKqncp5ze53PKL7bhevnMajJn766SeMGzfufg9JRHVAU67HjB9PorCsHCHNnTFnWFupSyIiqrVutwLR2ev5KFLf+xA/9x2ILl++jCFDhhg6Wldnw4YNmDhxolFHbCKSzmc7L+B0Sh4cra3w5WMhULDfEBGZIW8nGzR3tYVeBGKu5d7zfu77J+CMGTNw5swZPPjggyguLr7tNqtWrcKUKVMgk8kQERFxv4ckovu053wGvjuYCAD4bFynOpkpmohIKpWtRMcTs+95H/cdiL788ktMmTIFR48exejRo6HRaIzWf/3113jmmWcgl8vx888/Y8yYMfd7SCK6DzfySjH7p9MAgKnhLTDsPjohEhGZgr8D0b33I6qTNvKVK1dizJgx+OuvvzBu3DjodDoAFWHpxRdfhEqlwm+//YaRI0fWxeGI6B6V6/R45ceTyCvRIripE+Y+2E7qkoiI7ltlx+rTKfko0+ruaR91MnWHTCbDjz/+iJEjR+L333/HlClTEBQUhHnz5sHW1hZbt27FwIED6+JQRHQfvth9CSeu5cJBZYXlE0OgspJLXRIR0X1r7moLT0cVMgrUOJmch56taj82Yp31olQoFNiyZQvCw8OxYcMGvPXWW7C3t8eOHTvqLAwtWrQIgiBg5syZhmVTp06FIAhGrx49ehi9T61W4+WXX4a7uzvs7OwwatQopKam1klNRObiwKUs/G/fFQDAokeD4efGscSIqHEQBAHd/CtC0L3eNqvTx0psbGywfft2dOnSBY6Ojti1axf69u1bJ/uOjo7Gt99+i44dO1ZZ98ADDyAtLc3w2rFjh9H6mTNnYsuWLYiIiMChQ4dQVFSEESNGGG7tETV2mQVleG3jKYgiMKl78/ueBJGIyNQY+hEl3VvH6lrdMpPLa9e83qtXryrLBEFAeXntxgkoKirCpEmT8N133+HDDz+ssl6lUsHL6/YdQ/Pz87Fy5UqsW7cOgwcPBgCsX78evr6+2L17N4YNG1arWojMjU4v4tWIU8gu1qCdlwPeGdFB6pKIiOpcZT+imGu50JTrobSqXZtPrQJRXYwhdC/7eOmll/DQQw9h8ODBtw1E+/btg4eHB5ydndGvXz989NFH8PCoGHE3JiYGWq0WQ4cONWzv4+ODoKAgREVFVRuI1Go11Gq14euCggIAgFarhVarrfU5UMOqvEa8VsCyv67gyNVs2CrlWDq+I+TQQ6vVS12WEV4v88FrZV4s6Xr5OavgYqtAbokWp5KzEeLrDKDm516rQKTXN/wP0YiICMTGxiI6Ovq264cPH45x48bBz88PiYmJeOeddzBw4EDExMRApVIhPT0dSqUSLi4uRu/z9PREenp6tcddtGgRFixYUGX53r17YWvLMVvMRWRkpNQlSCohX8BX52QABIxprsGF6P24IHVRd2Dp18uc8FqZF0u5Xr7WMuSWyLD+zyNIa1rRAFNSUlKj99bJU2b1JSUlBa+++ip27doFa2vr224zYcIEw9+DgoIQFhYGPz8/bN++/Y5jHomiCEEQql0/d+5czJo1y/B1QUEBfH19MWDAALi51b73OjUsrVaLyMhIDBkyBAqFQupyJJFdpMZH/zsKEWqM7dIU7z0SKHVJ1eL1Mh+8VubF0q5XhvM1nPnjIgqsPfHgg10A/H2H525MOhDFxMQgMzMToaGhhmU6nQ4HDhzA8uXLoVarq/Rr8vb2hp+fHxISEgAAXl5e0Gg0yM3NNWolyszMRHh4eLXHVqlUUKlUVZYrFAqL+EfVWFjq9dLrRfznl1hkFqrRxsMe7z8cBIXCpD/uACz3epkjXivzYinXK7x1EwAXEZOcB5ncCnKZUOPzNunJiwYNGoS4uDicOnXK8AoLC8OkSZNw6tSp23byzs7ORkpKCry9vQEAoaGhUCgURs2FaWlpOHv27B0DEZE5Wx2VhIMJN2GtkOGrSV1gqzT9MEREdL/aezvCXmWFwrJyXEivWctQJZP+Keng4ICgoCCjZXZ2dnBzc0NQUBCKioowf/58PProo/D29kZSUhLmzZsHd3d3PPLIIwAAJycnTJs2DbNnz4abmxtcXV0xZ84cBAcHG546I2pMNOV6fHOgYryhtx5sjwBPB4krIiJqGHKZgLAWLth3MQvHE3MQ6ONU4/eadAvR3cjlcsTFxWH06NEICAjAlClTEBAQgCNHjsDB4e9fAl988QUefvhhjB8/Hr169YKtrS22bdtW62EEiMzBH2fTkFGgRhMHFSZ0bS51OUREDapyPKJjV2s3QKNJtxDdzr59+wx/t7Gxwc6dO+/6HmtrayxbtgzLli2rx8qIpCeKIlYeqpjF/okefrUeh4OIyNx1NwzQmFOroX7405KoEYlNzsWZ1HworWSY2J2tQ0RkeYKbOkNlJUNOsQZXsopq/D4GIqJG5PvDSQCAhzv7wN2+6lOSRESNndJKhi7NK54qP1aLec0YiIgaiet5pfjzbMVgo0/18pe4GiIi6RjmNWMgIrI8a48kQacXEd7KDe29HaUuh4hIMt3/0bG6pv2IGIiIGoESTTl+PJYMAHiarUNEZOFCmrtAIReQXlCG67mlNXoPAxFRI7A59joKysrh52aLge08pC6HiEhSNko5OjZzBgCcuJZbo/cwEBGZOb1exKrDFY/aTw1vAZms+jn6iIgsRWU/ophrNetHxEBEZOb2J2ThalYxHFRWGBfmK3U5REQm4e9AxBYiIovw/a2BGMd39YW9yuzGWiUiqhehfi6QCUByDvsQETV6CRmFOJhwEzKh4nYZERFVcLRWoINPzZ+4ZSAiMmOropIAAEM6eMLX1VbaYoiITEy3Fm413paBiMhM5RZr8EtsKgA+ak9EdDuV/YhqgoGIyEz9GJ2MMq0eHbwda/WhJyKyFF1buNR4WwYiIjOk1emxNuoaAODp3v4QBD5qT0T0b272Kqx5umuNtmUgIjJDf5xNR3pBGdztlRjZyVvqcoiITFaoX81a0BmIiMxQ5aP2k3v4QWUll7gaIiLzx0BEZGZik3NxKiUPSrkMk7r7SV0OEVGjwEBEZGZWHU4CAIzq7IMmDippiyEiaiQYiIjMSFp+KXbEpQEAnurVQtpiiIgaEQYiIjOy9sg16PQiuvu7ItDHSepyiIgaDQYiIjNRqtFhw7FkABWP2hMRUd1hICIyE7+cTEV+qRa+rjYY3N5T6nKIiBoVBiIiM6DXi4ZH7aeG+0Mu40CMRER1iYGIyAwcvHwTV7KKYa+ywviwZlKXQ0TU6DAQEZmBVYcrWofGhTWDg7VC4mqIiBofBiIiE3c5swj7LmZBEICp4S2kLoeIqFFiICIycaujKlqHBrXzhJ+bncTVEBE1TgxERCYsr0SDzTHXAQBP924hbTFERI0YAxGRCYuITkGpVod2Xg7o2dJN6nKIiBotBiIiE6XV6bEmKglAxUCMgsBH7YmI6gsDEZGJ2hmfjrT8MrjZKTGqk4/U5RARNWoMREQmqnJW+0k9/GCtkEtbDBFRI8dARGSCTqXkIeZaLhRyAZN7NJe6HCKiRo+BiMgEVQ7EOLKjDzwcrCWuhoio8WMgIjIx6fll2H4mDQDwVC/Oak9E1BAYiIhMzLqjSSjXi+jWwhXBzZykLoeIyCIwEBGZkFKNDhuOJQPgQIxERA2JgYjIhPx66jpyS7Ro5mKDIR28pC6HiMhiMBARmQhRFA2dqaeGt4BcxoEYiYgaCgMRkYk4fDkblzKKYKeUY3xXX6nLISKyKAxERCbi+1utQ+PCfOForZC4GiIiy8JARGQCrmYV4a8LmRAEYEp4C6nLISKyOGYViBYtWgRBEDBz5kzDMlEUMX/+fPj4+MDGxgb9+/dHfHy80fvUajVefvlluLu7w87ODqNGjUJqamoDV09UvdW3JnEd2NYD/u520hZDRGSBzCYQRUdH49tvv0XHjh2Nln/66af4/PPPsXz5ckRHR8PLywtDhgxBYWGhYZuZM2diy5YtiIiIwKFDh1BUVIQRI0ZAp9M19GkQVZFfosVPJyoC+tO9ORAjEZEUzCIQFRUVYdKkSfjuu+/g4uJiWC6KIpYsWYK33noLY8aMQVBQENasWYOSkhJs2LABAJCfn4+VK1di8eLFGDx4MEJCQrB+/XrExcVh9+7dUp0SkcHGE8ko1erQ1tMB4a3cpC6HiMgiWUldQE289NJLeOihhzB48GB8+OGHhuWJiYlIT0/H0KFDDctUKhX69euHqKgoTJ8+HTExMdBqtUbb+Pj4ICgoCFFRURg2bNhtj6lWq6FWqw1fFxQUAAC0Wi20Wm1dnyLVscprZOrXqlynx+pbs9pP6emL8vJyaQuSiLlcL+K1Mje8XjU/d5MPRBEREYiNjUV0dHSVdenp6QAAT09Po+Wenp64du2aYRulUmnUslS5TeX7b2fRokVYsGBBleV79+6Fra1trc+DpBEZGSl1CXd0NkfAjXw57KxEKG6cwY6MM1KXJClTv170N14r82LJ16ukpKRG25l0IEpJScGrr76KXbt2wdq6+hm/BcF4ADtRFKss+7e7bTN37lzMmjXL8HVBQQF8fX0xYMAAuLnxtoap02q1iIyMxJAhQ6BQmO4j7Nt+OAkgCxO6tcDDw9tKXY5kzOV6Ea+VueH1+vsOz92YdCCKiYlBZmYmQkNDDct0Oh0OHDiA5cuX4+LFiwAqWoG8vb0N22RmZhpajby8vKDRaJCbm2vUSpSZmYnw8PBqj61SqaBSqaosVygUFvuPyhyZ8vXKLCzD3ks3AQCPdfcz2TobkilfLzLGa2VeLPl61fS8TbpT9aBBgxAXF4dTp04ZXmFhYZg0aRJOnTqFli1bwsvLy6gpUKPRYP/+/YawExoaCoVCYbRNWloazp49e8dARFTftsReh04vorOvMwI8HaQuh4jIopl0C5GDgwOCgoKMltnZ2cHNzc2wfObMmVi4cCHatGmDNm3aYOHChbC1tcXEiRMBAE5OTpg2bRpmz54NNzc3uLq6Ys6cOQgODsbgwYMb/JyIgIpbtptOpAAAJnCaDiIiyZl0IKqJ119/HaWlpXjxxReRm5uL7t27Y9euXXBw+Pt/3F988QWsrKwwfvx4lJaWYtCgQVi9ejXkcrmElZMli03OxZWsYtgo5BjR0fvubyAionpldoFo3759Rl8LgoD58+dj/vz51b7H2toay5Ytw7Jly+q3OKIa2hRdMRDjg8HecOC8ZUREkjPpPkREjVGxuhy/n7kBABgf1kziaoiICGAgImpw2+PSUKzRoYWbLbr5u0pdDhERgYGIqMFtiq7oTD0uzPeu42UREVHDYCAiakBXsopw4louZAIwNpS3y4iITAUDEVEDqnzUvn9bD3g6Vj/6OhERNSwGIqIGotXpsTnmOgBgfBjHHiIiMiUMREQNZN/FLNwsUsPNTomB7TykLoeIiP6BgYiogWy81Zl6TJemUFrxo0dEZEr4U5moAWQWlmHvxUwAvF1GRGSKGIiIGsAvtyZyDWnujDacyJWIyOQwEBHVM6OJXNk6RERkkhiIiOpZzLVcXL01ketDnMiViMgkMRAR1bPK1qGHOnIiVyIiU8VARFSPitTl+P1MGgB2piYiMmUMRET1aMeZNJRodPB3t0PXFi5Sl0NERNVgICKqRxtPVE7k2owTuRIRmTAGIqJ6cjmzCDHXciGXCRjbhRO5EhGZMgYionryU+VErgFN4MGJXImITBoDEVE90Or02BybCgAY35WdqYmITB0DEVE92HshEzeLNHC350SuRETmgIGIqB5Ujj00pkszKOT8mBERmTr+pCaqY5kFZdh7MQsAMD6MnamJiMwBAxFRHdt8ayLXLs2d0dqDE7kSEZkDBiKiOiSKouHpsgnsTE1EZDYYiIjq0Ilrubh6sxi2Sjke6ugjdTlERFRDDEREdWhT9K2JXIO9Ya+ykrgaIiKqKQaiWios1UpdApmoInU5tsfdmsiVt8uIiMwKA1EtrTiQKHUJZKK2n7mBEo0OLd3tEObHiVyJiMwJA1EtbYq5jovphVKXQSZoY3TlRK6+nMiViMjMMBDVkk4v4r2tZyGKotSlkAm5nFmI2OQ8yGUCHg1tKnU5RERUSwxEtaS0kuHo1RxDXxEiANh0omLesgFtPeDhwIlciYjMDQNRLT3d0w8A8NH28yjRlEtcDZkCrU6PXyoncuXI1EREZomBqJamhDdHMxcbpOWX4X97r0hdDpmAvwwTuaowgBO5EhGZJQaiWrJWyPH2Qx0AAN8euIpr2cUSV0RSqxx76NEuTTmRKxGRmeJP73swLNATfdq4Q6PT44Pfz0tdDkkoo6AMey9mAqh4uoyIiMwTA9E9EAQB740MhJVMwO7zGdh36xciWZ7NsanQi0Conwtae9hLXQ4REd0jBqJ71NrDHk/1agEAeH/bOWjK9dIWRA2uYiLXis7UE9g6RERk1hiI7sMrg9qgiYMKV28W4/vDHMHa0kQn5SLx1kSuD3b0lrocIiK6DwxE98HBWoE3H2gHAFi2JwEZBWUSV0QNadOJis7UIzpyIlciInPHQHSfHglpipDmzijW6LBoBztYW4rCMi22n6kYnHMCJ3IlIjJ7DET3SSYT8P6oIAgC8OupG4hOypG6JGoA28+koVSrQ8smdujSnBO5EhGZOwaiOhDczAmP3WoleO+3eOj0nOessdt463bZBE7kSkTUKJh8IFqxYgU6duwIR0dHODo6omfPnvjjjz8M66dOnQpBEIxePXr0MNqHWq3Gyy+/DHd3d9jZ2WHUqFFITU2t0zrnDG0LR2srnEsrwIbjyXW6bzItCRmFOHlrItdHunAiVyKixsDkA1GzZs3w8ccf48SJEzhx4gQGDhyI0aNHIz4+3rDNAw88gLS0NMNrx44dRvuYOXMmtmzZgoiICBw6dAhFRUUYMWIEdDpdndXpZq/C7KFtAQCLd11EbrGmzvZNpqWyM/XAdpzIlYiosTD5QDRy5Eg8+OCDCAgIQEBAAD766CPY29vj6NGjhm1UKhW8vLwML1dXV8O6/Px8rFy5EosXL8bgwYMREhKC9evXIy4uDrt3767TWid1b452Xg7IK9Hiv7su1um+yTRoyvX4JfY6AGA8xx4iImo0zOpZYZ1Oh59++gnFxcXo2bOnYfm+ffvg4eEBZ2dn9OvXDx999BE8PCom2YyJiYFWq8XQoUMN2/v4+CAoKAhRUVEYNmzYbY+lVquhVqsNXxcUFAAAtFottFpttTW+/WBbTP7+BDYcT8a4Lj4I9HG8r3Ome1N5je50re5F5LkMZBdr0MReid4tnet8/5aqvq4X1T1eK/PC61XzczeLQBQXF4eePXuirKwM9vb22LJlCzp0qJhgdfjw4Rg3bhz8/PyQmJiId955BwMHDkRMTAxUKhXS09OhVCrh4mL8JJCnpyfS09OrPeaiRYuwYMGCKsv37t0LW1vbO9bbxU2G2GwZXlt/BK8G6sA+t9KJjIys0/19c14GQIaOjmXYtfPPOt031f31ovrDa2VeLPl6lZSU1Gg7swhEbdu2xalTp5CXl4fNmzdjypQp2L9/Pzp06IAJEyYYtgsKCkJYWBj8/Pywfft2jBkzptp9iqJ4x6eD5s6di1mzZhm+LigogK+vLwYMGAA3N7c71hvSqwzDlh5CYqEe5c1CMLoTRzFuaFqtFpGRkRgyZAgUCkWd7DOjoAwXjh4AALw+tg9aNrGrk/1S/Vwvqh+8VuaF1+vvOzx3YxaBSKlUonXr1gCAsLAwREdHY+nSpfjmm2+qbOvt7Q0/Pz8kJCQAALy8vKDRaJCbm2vUSpSZmYnw8PBqj6lSqaBSqaosVygUd/1H1dxdgRkD2+CznRfx6c5LeCDYhyMZS6Qm16umfjtzDXoRCPNzQVsf5zrZJxmry+tF9YvXyrxY8vWq6XmbfKfq2xFF0ah/zz9lZ2cjJSUF3t4VrTKhoaFQKBRGzYVpaWk4e/bsHQPR/Xqmjz/83GyRWajGsr8S6u041DAyC8vw3cGrAIDxHJmaiKjRMflANG/ePBw8eBBJSUmIi4vDW2+9hX379mHSpEkoKirCnDlzcOTIESQlJWHfvn0YOXIk3N3d8cgjjwAAnJycMG3aNMyePRt79uzByZMnMXnyZAQHB2Pw4MH1VrfKSo53R1T0c/r+UCKuZBXV27GofomiiLe2nEVeiRYdvB3xSAjHHiIiamxM/j5ORkYGnnjiCaSlpcHJyQkdO3bEn3/+iSFDhqC0tBRxcXFYu3Yt8vLy4O3tjQEDBmDjxo1wcHAw7OOLL76AlZUVxo8fj9LSUgwaNAirV6+GXC6v19oHtffEgLZNsPdiFt7fdg6rn+rKUY3N0K+nriPyXAYUcgGLx3eCQm7y/48gIqJaMvlAtHLlymrX2djYYOfOnXfdh7W1NZYtW4Zly5bVZWk18u7IQBy+fAD7L2Vh9/lMDOng2eA10L3LKCjDe79VDAL66qA2aO/NYRSIiBoj/le3nvm722FaH38AwAe/n0OZtu5Gx6b6JYoi5v4Sh4KycgQ3dcLz/VpJXRIREdUTBqIGMGNAa3g5WiM5pwTfHbgqdTlUQz/HpOKvC5lQymVYPL4TrHirjIio0eJP+AZgp7LC3AfbAQC+2ncZ1/NKJa6I7iYtvxTvbzsHAHhtSAACPB3u8g4iIjJnDEQNZFQnH3Tzd0WZVo+F289LXQ7dgSiKeGNzHArV5ejs64xnb93yJCKixouBqIEIgoD5IwMhE4DtcWmIunxT6pKoGhHRKThwKQtKKxn+O463yoiILAF/0jegDj6OmNzDDwAwf1s8tDq9xBXRv6XmluDD3ytulf1naFu09rCXuCIiImoIDEQNbNaQALjYKnApowjrj16Tuhz6B71exOs/n0GxRodQPxc83Zu3yoiILAUDUQNztlXiP8MqOlh/HnkJN4tuPwUJNbwfjicj6ko2rBUyfDa2I+QyDqJJRGQpGIgkMKGrL4KaOqKwrByf/XlR6nIIQHJ2CRbtqOjs/vqwdmjZhLfKiIgsCQORBOQyAQtGBQIANsWkIOZajsQVWTa9XsR/fj6NEo0O3Vq4Ymp4C6lLIiKiBsZAJJFQP1eM6dIUogg8tzYGiTeLpS7JYq09koRjiTmwUcjx2biOkPFWGRGRxWEgktCCUYEI9HFEdrEGT6w8hoyCMqlLsjhJN4vx8Z8XAADzHmwHPzc7iSsiIiIpMBBJyMFagdVPdUMLN1uk5pbiyZXHkV+ilbosi6HTi5jz02mUafUIb+WGSd39pC6JiIgkwkAksSYOKqyb1h1NHFS4mFGIaWuiUarhBLANYdXhRJy4lgs7pRyfPMpbZUREloyByAT4utpi7dPd4GBthRPXcjFjQywHbaxnV7KK8NnOiif83nqoA3xdbSWuiIiIpMRAZCLaezti5ZSuUFnJsOdCJt7cHAdRFKUuq1GqvFWmLtejTxt3PN7NV+qSiIhIYgxEJqSbvyu+mtgFcpmAzbGp+PiPC1KX1Cj938GrOJmcBweVFT55tCMEgbfKiIgsHQORiRncwRMfjwkGAHxz4Cq+2X9F4ooal4SMQiyOvAQAeGdEB/g420hcERERmQIGIhM0LswX8x6smN5j0R8X8NOJFIkrahzKdXrM+ek0NOV69G/bBOPCmkldEhERmQgGIhP1XN9WmN63JQDgzV/isPtchsQVmb9vDlzF6dR8OFhb4eMxvFVGRER/YyAyYW8Ob4exoc2g04t4aUMsjidyio97dSG9AEt2V9wqmz8yEF5O1hJXREREpoSByIQJgoCPxwRjUDsPqMv1mLYmGufTCqQuy+xob90q0+pEDG7vgTFdmkpdEhERmRgGIhNnJZdh+cQu6NrCBYVl5Xjy++NIySmRuiyzsmLfFZy9XgAnGwUWPhLMW2VERFQFA5EZsFHK8X9PdkU7LwdkFarxxMpjyCpUS12WWTiXVoAv9yQAAN4fHQgPR94qIyKiqhiIzISTrQJrnu6GZi42SMouwdRVx1FYxnnP7qRcD7zxSzzK9SKGBXpiVCcfqUsiIiITxUBkRjwdrbFuWne42ysRf6MAz62NQZmW855VZ9d1GS6kF8LFVoEPH+atMiIiqh4DkZnxd7fD6qe6wV5lhSNXszEz4hR0ek7x8W/xNwoQmVoRgD54OAhNHFQSV0RERKaMgcgMBTV1wrdPhkIpl+HP+HS8/etZznv2D0Xqcry++Sz0EDA80BMjOvJWGRER3RkDkZkKb+WOLx/vDJkA/Hg8GZ/fmo7C0u2/lIVhXxzApcwi2FuJeG9ke6lLIiIiM8BAZMYeCPLGhw9XzHu27K/LWHU4UeKKpJNfosWcn05jyvfHcT2vFM1cbPBsOx3c7JRSl0ZERGaAgcjMTezeHHOGBgAAFmw7h99OXZe4ooa3Kz4dg7/Yj59jUiEIwNO9/LF9Rk+0cJC6MiIiMhdWUhdA9++lAa1xs0iD1VFJmL3pNJxsFOjf1kPqsupddpEa87edw7bTNwAALZvY4bOxHRHq5wqtlkMSEBFRzbGFqBEQBAHvjuiAUZ18UK4X8cL6WMQm50pdVr0RRRFbT9/AkC8OYNvpG5DLBLzQvxV2vNIHoX6uUpdHRERmiC1EjYRMJuC/4zohr1SLA5ey8Ng3RzEk0BMTwnzRu7U7ZLLGMQZPZkEZ3vr1LCLPZQAA2nk54LOxnRDczEniyoiIyJwxEDUiSisZvp7cBdPXxeBgwk1sP5OG7WfS0NTZBmNDm2FcWDM0c7GVusx7Iooifo5JxQe/n0NBWTkUcgEzBrTBC/1bQWnFhk4iIro/DESNjK3SCuumdcfZ6/nYdCIFv568jut5pVi6JwFf/pWA3q3dMaGrL4Z08ITKSi51uTVyPa8Uc3+Jw4FLWQCAjs2c8OnYjmjn5ShxZURE1FgwEDVSQU2dENTUCfMebI+d8enYGJ2CqCvZOJhwEwcTbsLFVoGHQ5piQldfkw0Wer2IH44n4+Md51Gs0UFpJcOsIQF4prc/rORsFSIiorrDQNTIWSvkGN25KUZ3bork7BJsOpGCn2NSkV5QhlWHk7DqcBI6+TpjQpgvRnbyhoO1QuqSAQBJN4vxxuYzOJaYAwAI83PBJ2M7olUTe4krIyKixoiByII0d7PFnGFt8dqQABy4lIWN0SnYfT4Dp1PycDolDx/8fg4PBntjQldfdG3hIslkqDq9iFWHE/HfXRdRptXDRiHHGw+0xZM9WzSajuFERGR6GIgskFwmYEA7Dwxo54GbRWpsib2OjSdScDmzCJtjU7E5NhUt3e0wvqsvxnRpCg8H6wap63JmIf7z8xmcTM4DAIS3csPHYzqiuZt5dgQnIiLzwUBk4dztVXi2b0s808cfscm52Bidgt/PpOHqzWJ8/McFfLbzIga288CEMF/0b9ukXvruaHV6fHvgKpbuToBGp4eDygrzHmqPx7r6StJKRURElsfkA9GKFSuwYsUKJCUlAQACAwPx7rvvYvjw4QAqHsdesGABvv32W+Tm5qJ79+746quvEBgYaNiHWq3GnDlz8OOPP6K0tBSDBg3C//73PzRr1kyKUzJJgiAg1M8VoX6ueHdkILafuYGN0SmITc5D5LkMRJ7LgLu9Ck1dbKCQCbCSC1DIZbCSCZDLZFDIBVjJZYZ1hmWGdbeWyW5tJxduvVdARHQK4m8UAAAGtG2ChWOC4e1kI/F3hIiILInJB6JmzZrh448/RuvWrQEAa9aswejRo3Hy5EkEBgbi008/xeeff47Vq1cjICAAH374IYYMGYKLFy/CwaFiMquZM2di27ZtiIiIgJubG2bPno0RI0YgJiYGcrl5PHrekOxVVpjQtTkmdG2OhIxCbIxOwS8nr+NmkRo3i9T1ckxnWwXeG9kBD3duylYhIiJqcCYfiEaOHGn09UcffYQVK1bg6NGj6NChA5YsWYK33noLY8aMAVARmDw9PbFhwwZMnz4d+fn5WLlyJdatW4fBgwcDANavXw9fX1/s3r0bw4YNa/BzMidtPB3w9ogOeP2Bdoi5lotidTnK9XpodSLK9XqU60SU60WU6yqW6fQitJXLdXpo9beW6Sq3/fs9lcu8nKzx4oBWDdZXiYiI6N9MPhD9k06nw08//YTi4mL07NkTiYmJSE9Px9ChQw3bqFQq9OvXD1FRUZg+fTpiYmKg1WqNtvHx8UFQUBCioqIYiGpIaSVDz1ZuUpdBRERUL8wiEMXFxaFnz54oKyuDvb09tmzZgg4dOiAqKgoA4OnpabS9p6cnrl27BgBIT0+HUqmEi4tLlW3S09OrPaZarYZa/fftofz8fABATk5OnZwT1S+tVouSkhJkZ2dDoTCNsZWoerxe5oPXyrzwegGFhYUAKvoc34lZBKK2bdvi1KlTyMvLw+bNmzFlyhTs37/fsP7ffU5EUbxrP5S7bbNo0SIsWLCgyvKAgIBaVk9ERERSKywshJNT9ROBm0UgUiqVhk7VYWFhiI6OxtKlS/HGG28AqGgF8vb2NmyfmZlpaDXy8vKCRqNBbm6uUStRZmYmwsPDqz3m3LlzMWvWLMPXeXl58PPzQ3Jy8h2/oWQaCgoK4Ovri5SUFDg6mubUJPQ3Xi/zwWtlXni9KhpACgsL4ePjc8ftzCIQ/ZsoilCr1fD394eXlxciIyMREhICANBoNNi/fz8++eQTAEBoaCgUCgUiIyMxfvx4AEBaWhrOnj2LTz/9tNpjqFQqqFSqKsudnJws9h+VOXJ0dOT1MiO8XuaD18q8WPr1qklDhskHonnz5mH48OHw9fVFYWEhIiIisG/fPvz5558QBAEzZ87EwoUL0aZNG7Rp0wYLFy6Era0tJk6cCKDimzBt2jTMnj0bbm5ucHV1xZw5cxAcHGx46oyIiIgsm8kHooyMDDzxxBNIS0uDk5MTOnbsiD///BNDhgwBALz++usoLS3Fiy++aBiYcdeuXYYxiADgiy++gJWVFcaPH28YmHH16tUcg4iIiIgAAIJ4t27XBKDiqbNFixZh7ty5t72VRqaF18u88HqZD14r88LrVXMMRERERGTx6n6mTiIiIiIzw0BEREREFo+BiIiIiCweAxERERFZPAaiGvjf//4Hf39/WFtbIzQ0FAcPHpS6JLqN+fPnQxAEo5eXl5fUZdEtBw4cwMiRI+Hj4wNBEPDrr78arRdFEfPnz4ePjw9sbGzQv39/xMfHS1Ms3fV6TZ06tcrnrUePHtIUa+EWLVqErl27wsHBAR4eHnj44Ydx8eJFo234+bo7BqK72LhxI2bOnIm33noLJ0+eRJ8+fTB8+HAkJydLXRrdRmBgINLS0gyvuLg4qUuiW4qLi9GpUycsX778tus//fRTfP7551i+fDmio6Ph5eWFIUOGGCZmpIZ1t+sFAA888IDR523Hjh0NWCFV2r9/P1566SUcPXoUkZGRKC8vx9ChQ1FcXGzYhp+vGhDpjrp16yY+//zzRsvatWsnvvnmmxJVRNV57733xE6dOkldBtUAAHHLli2Gr/V6vejl5SV+/PHHhmVlZWWik5OT+PXXX0tQIf3Tv6+XKIrilClTxNGjR0tSD91ZZmamCEDcv3+/KIr8fNUUW4juQKPRICYmBkOHDjVaPnToUERFRUlUFd1JQkICfHx84O/vj8ceewxXr16VuiSqgcTERKSnpxt91lQqFfr168fPmgnbt28fPDw8EBAQgGeffRaZmZlSl0QA8vPzAQCurq4A+PmqKQaiO7h58yZ0Oh08PT2Nlnt6eiI9PV2iqqg63bt3x9q1a7Fz50589913SE9PR3h4OLKzs6Uuje6i8vPEz5r5GD58OH744Qf89ddfWLx4MaKjozFw4ECo1WqpS7Nooihi1qxZ6N27N4KCggDw81VTJj+XmSkQBMHoa1EUqywj6Q0fPtzw9+DgYPTs2ROtWrXCmjVrMGvWLAkro5riZ818TJgwwfD3oKAghIWFwc/PD9u3b8eYMWMkrMyyzZgxA2fOnMGhQ4eqrOPn687YQnQH7u7ukMvlVRJ0ZmZmlaRNpsfOzg7BwcFISEiQuhS6i8qnAflZM1/e3t7w8/Pj501CL7/8MrZu3Yq9e/eiWbNmhuX8fNUMA9EdKJVKhIaGIjIy0mh5ZGQkwsPDJaqKakqtVuP8+fPw9vaWuhS6C39/f3h5eRl91jQaDfbv38/PmpnIzs5GSkoKP28SEEURM2bMwC+//IK//voL/v7+Ruv5+aoZ3jK7i1mzZuGJJ55AWFgYevbsiW+//RbJycl4/vnnpS6N/mXOnDkYOXIkmjdvjszMTHz44YcoKCjAlClTpC6NABQVFeHy5cuGrxMTE3Hq1Cm4urqiefPmmDlzJhYuXIg2bdqgTZs2WLhwIWxtbTFx4kQJq7Zcd7perq6umD9/Ph599FF4e3sjKSkJ8+bNg7u7Ox555BEJq7ZML730EjZs2IDffvsNDg4OhpYgJycn2NjYQBAEfr5qQtJn3MzEV199Jfr5+YlKpVLs0qWL4VFGMi0TJkwQvb29RYVCIfr4+IhjxowR4+PjpS6Lbtm7d68IoMprypQpoihWPBr83nvviV5eXqJKpRL79u0rxsXFSVu0BbvT9SopKRGHDh0qNmnSRFQoFGLz5s3FKVOmiMnJyVKXbZFud50AiKtWrTJsw8/X3QmiKIoNH8OIiIiITAf7EBEREZHFYyAiIiIii8dARERERBaPgYiIiIgsHgMRERERWTwGIiIiIrJ4DERERERk8RiIiIju4ttvv4Wvry9kMhmWLFlSo/ckJSVBEAScOnWqXmsjorrBQEREDWLq1Kl4+OGHqyzft28fBEFAXl5eg9dUEwUFBZgxYwbeeOMNXL9+Hc8995zUJRFRPeBcZkRkEbRaLRQKRa3fl5ycDK1Wi4ceeogTlxI1YmwhIiKTs3nzZgQGBkKlUqFFixZYvHix0XpBEPDrr78aLXN2dsbq1asB/H27atOmTejfvz+sra2xfv362x4rOTkZo0ePhr29PRwdHTF+/HhkZGQAAFavXo3g4GAAQMuWLSEIApKSkm67n+PHjyMkJATW1tYICwvDyZMnjdbrdDpMmzYN/v7+sLGxQdu2bbF06VLD+gMHDkChUBgm5qw0e/Zs9O3b947fLyK6fwxERGRSYmJiMH78eDz22GOIi4vD/Pnz8c477xjCTm288cYbeOWVV3D+/HkMGzasynpRFPHwww8jJycH+/fvR2RkJK5cuYIJEyYAACZMmIDdu3cDqAg8aWlp8PX1rbKf4uJijBgxAm3btkVMTAzmz5+POXPmGG2j1+vRrFkzbNq0CefOncO7776LefPmYdOmTQCAvn37omXLlli3bp3hPeXl5Vi/fj2eeuqpWp87EdWSxJPLEpGFmDJliiiXy0U7Ozujl7W1tQhAzM3NFUVRFCdOnCgOGTLE6L3/+c9/xA4dOhi+BiBu2bLFaBsnJyfD7N6JiYkiAHHJkiV3rGnXrl2iXC43mqU9Pj5eBCAeP35cFEVRPHnypAhATExMrHY/33zzjejq6ioWFxcblq1YsUIEIJ48ebLa97344ovio48+avj6k08+Edu3b2/4+tdffxXt7e3FoqKiO54HEd0/thARUYMZMGAATp06ZfT6v//7P6Ntzp8/j169ehkt69WrFxISEqDT6Wp1vLCwsDuuP3/+PHx9fY1afTp06ABnZ2ecP3++xsc5f/48OnXqBFtbW8Oynj17Vtnu66+/RlhYGJo0aQJ7e3t89913SE5ONqyfOnUqLl++jKNHjwIAvv/+e4wfPx52dnY1roWI7g07VRNRg7Gzs0Pr1q2NlqWmphp9LYoiBEGosuyfBEGoskyr1d72eHdyu2Pdafmd9nM3mzZtwmuvvYbFixejZ8+ecHBwwGeffYZjx44ZtvHw8MDIkSOxatUqtGzZEjt27MC+fftqXAcR3TsGIiIyKR06dMChQ4eMlkVFRSEgIAByuRwA0KRJE6SlpRnWJyQkoKSk5J6OlZycjJSUFEMr0blz55Cfn4/27dvXaj/r1q1DaWkpbGxsAMDQylPp4MGDCA8Px4svvmhYduXKlSr7euaZZ/DYY4+hWbNmaNWqVZXWMiKqH7xlRkQmZfbs2dizZw8++OADXLp0CWvWrMHy5cuNOikPHDgQy5cvR2xsLE6cOIHnn3/+nh6pHzx4MDp27IhJkyYhNjYWx48fx5NPPol+/frd9XbbP02cOBEymQzTpk3DuXPnsGPHDvz3v/812qZ169Y4ceIEdu7ciUuXLuGdd95BdHR0lX0NGzYMTk5O+PDDD9mZmqgBMRARkUnp0qULNm3ahIiICAQFBeHdd9/F+++/j6lTpxq2Wbx4MXx9fdG3b19MnDgRc+bMMeq/U1OVj++7uLigb9++GDx4MFq2bImNGzfWaj/29vbYtm0bzp07h5CQELz11lv45JNPjLZ5/vnnMWbMGEyYMAHdu3dHdna2UWtRJZlMhqlTp0Kn0+HJJ5+s9TkR0b0RxJrc/CYiogbz7LPPIiMjA1u3bpW6FCKLwT5EREQmIj8/H9HR0fjhhx/w22+/SV0OkUVhICIiMhGjR4/G8ePHMX36dAwZMkTqcogsCm+ZERERkcVjp2oiIiKyeAxEREREZPEYiIiIiMjiMRARERGRxWMgIiIiIovHQEREREQWj4GIiIiILB4DEREREVk8BiIiIiKyeP8P9sapIulNrkQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "res_elec.sum(axis=1).groupby(res_elec.index.hour).mean().plot(ax=ax)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xlim(0,23)\n",
+    "ax.set_ylim(300,650)\n",
+    "ax.set_ylabel(\"kWh\", fontsize=16)\n",
+    "ax.set_xlabel('Hour of day')\n",
+    "ax.set_title('Average Day', size=18)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4315674125864762"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec.resample('h').mean().sum(axis=1).sum() / (6079*8760*0.18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather = pd.read_csv(\"../data/timeseries/weather_year.csv\", parse_dates=True, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.16817788689562815"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather.ghi.mean() / weather.ghi.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rooftop_solar_energy = (weather.ghi / weather.ghi.max() * 6079)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_elec_resampled = res_elec.loc['2018'].resample('h').mean().sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "timestamp\n",
+       "2018-01-01 00:00:00    734.275500\n",
+       "2018-01-01 01:00:00    319.253001\n",
+       "2018-01-01 02:00:00    206.265092\n",
+       "2018-01-01 03:00:00    200.158683\n",
+       "2018-01-01 04:00:00    214.710827\n",
+       "                          ...    \n",
+       "2018-12-31 19:00:00    756.661431\n",
+       "2018-12-31 20:00:00    799.025894\n",
+       "2018-12-31 21:00:00    814.918117\n",
+       "2018-12-31 22:00:00    842.407166\n",
+       "2018-12-31 23:00:00    790.741970\n",
+       "Freq: H, Length: 8760, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec_resampled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2018-01-01 00:00:00    0.0\n",
+       "2018-01-01 01:00:00    0.0\n",
+       "2018-01-01 02:00:00    0.0\n",
+       "2018-01-01 03:00:00    0.0\n",
+       "2018-01-01 04:00:00    0.0\n",
+       "                      ... \n",
+       "2018-12-31 19:00:00    0.0\n",
+       "2018-12-31 20:00:00    0.0\n",
+       "2018-12-31 21:00:00    0.0\n",
+       "2018-12-31 22:00:00    0.0\n",
+       "2018-12-31 23:00:00    0.0\n",
+       "Name: ghi, Length: 8760, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rooftop_solar_energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(res_elec_resampled - rooftop_solar_energy/2).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()).plot(ax=ax, color='gold', lw=0.3)\n",
+    "res_elec_resampled.plot(ax=ax, label='electricity', lw=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum())[24*30*3:24*32*3].plot(ax=ax, color='gold', lw=1)\n",
+    "res_elec_resampled[24*30*3:24*32*3].plot(ax=ax, label='electricity', lw=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_battery_needs(dataframe):\n",
+    "    df = dataframe.copy()\n",
+    "    # calculate max discharge power\n",
+    "    max_storage_power = df['net_load'].max()\n",
+    "    \n",
+    "    # calculate max storage duration\n",
+    "    df['grp'] = df['net_load'].gt(0).astype(int).diff().abs().cumsum().fillna(0)\n",
+    "    df_grouped = df.groupby(by='grp').sum()\n",
+    "    df_grouped['battery_duration'] = df_grouped['net_load']/max_storage_power\n",
+    "    \n",
+    "    max_storage_duration = df_grouped['battery_duration'].max()\n",
+    "    \n",
+    "    return max_storage_power, max_storage_duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()\n",
+    "rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * total_electrified_data.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3574.700141052983"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rooftop_solar_energy.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_load = rooftop_solar_energy.to_frame()\n",
+    "net_load['net_load'] = res_elec_resampled - net_load['ghi']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1331.4884367473771, 34.817758877371986)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_battery_needs(net_load)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/10-pypsa-model.ipynb b/notebooks/10-pypsa-model.ipynb
new file mode 100644
index 0000000..ab74e5a
--- /dev/null
+++ b/notebooks/10-pypsa-model.ipynb
@@ -0,0 +1,1226 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pypsa\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PyPSA Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = pypsa.Network(name='Armourdale')\n",
+    "\n",
+    "N_days=365\n",
+    "N_hours=24\n",
+    "\n",
+    "index = pd.date_range(start=\"2018-01-01\", \n",
+    "                      periods=N_days*N_hours, \n",
+    "                      freq='h')\n",
+    "\n",
+    "n.set_snapshots(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 1: Add buses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bus_name = 'Residential'\n",
+    "n.add(class_name='Bus',\n",
+    "      name=bus_name,\n",
+    "      carrier='AC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 2: Add energy carriers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(class_name=\"Carrier\", name=\"grid\")\n",
+    "n.add(class_name=\"Carrier\", name=\"solar\")\n",
+    "n.add(class_name=\"Carrier\", name=\"battery\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 3: Add load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load = pd.read_csv(\"../data/timeseries/residential_elec_load_rescaled.csv\", parse_dates=True, index_col='timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_resampled = load.loc['2018'].resample('h').mean().sum(axis=1)\n",
+    "load_resampled = load_resampled / 1e3  # kW --> MW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(\n",
+    "    class_name=\"Load\",\n",
+    "    name=f\"Load {bus_name}\",\n",
+    "    bus=bus_name,\n",
+    "    p_set=load_resampled\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 3: Add weather data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>temp_db</th>\n",
+       "      <th>rel_humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "      <th>wind_direction</th>\n",
+       "      <th>ghi</th>\n",
+       "      <th>dni</th>\n",
+       "      <th>dhi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>2005-01-01 01:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>2005-01-01 02:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>2005-01-01 03:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>2005-01-01 04:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>2005-01-01 05:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               date_time  temp_db  rel_humidity  wind_speed  \\\n",
+       "2018-01-01 00:00:00  2005-01-01 01:00:00      8.0            61         5.7   \n",
+       "2018-01-01 01:00:00  2005-01-01 02:00:00      8.0            57         5.1   \n",
+       "2018-01-01 02:00:00  2005-01-01 03:00:00      8.0            57         5.1   \n",
+       "2018-01-01 03:00:00  2005-01-01 04:00:00      7.0            56         6.2   \n",
+       "2018-01-01 04:00:00  2005-01-01 05:00:00      7.0            56         5.1   \n",
+       "\n",
+       "                     wind_direction  ghi  dni  dhi  \n",
+       "2018-01-01 00:00:00              80    0    0    0  \n",
+       "2018-01-01 01:00:00              90    0    0    0  \n",
+       "2018-01-01 02:00:00              90    0    0    0  \n",
+       "2018-01-01 03:00:00              80    0    0    0  \n",
+       "2018-01-01 04:00:00              90    0    0    0  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather = pd.read_csv(\"../data/timeseries/weather_year.csv\", parse_dates=True, index_col=0)\n",
+    "weather.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# normalize GHI\n",
+    "ghi = weather['ghi'] / weather['ghi'].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 4: Upload cost data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DistributedWind</th>\n",
+       "      <td>35912.100000</td>\n",
+       "      <td>5.678577e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ResPV</th>\n",
+       "      <td>28108.825392</td>\n",
+       "      <td>2.630889e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>78943.789878</td>\n",
+       "      <td>3.157752e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Fixed O&M           OCC\n",
+       "technology                                             \n",
+       "DistributedWind              35912.100000  5.678577e+06\n",
+       "ResPV                        28108.825392  2.630889e+06\n",
+       "Residential Battery Storage  78943.789878  3.157752e+06"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "costs = pd.read_csv(\"../data/technology_costs.csv\", index_col='technology')\n",
+    "costs *= 1e3  # convert /kW to /MW\n",
+    "costs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 5: Add generators to network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def annuity(r, n):\n",
+    "    return r / (1 - 1 / (1 + r)**n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.09439292574325567"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "annuity_adj = annuity(0.07, 20)\n",
+    "annuity_adj"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResPV\n",
+      "276446.11698458303\n",
+      "Residential Battery Storage\n",
+      "377013.20171200635\n"
+     ]
+    }
+   ],
+   "source": [
+    "for generator in costs.index:\n",
+    "    if generator == 'DistributedWind':\n",
+    "        pass\n",
+    "    else:\n",
+    "        print(generator)\n",
+    "        annualized_cost = costs.at[generator, 'OCC']*annuity_adj + costs.at[generator,'Fixed O&M']\n",
+    "        print(annualized_cost)\n",
+    "        \n",
+    "        if generator=='ResPV':\n",
+    "            n.add(class_name='Generator',\n",
+    "                    name=generator,\n",
+    "                    bus=bus_name,\n",
+    "                    carrier=\"solar\",\n",
+    "                    capital_cost=annualized_cost,  # $/kW\n",
+    "                    p_min_pu=ghi,\n",
+    "                    p_max_pu=ghi,\n",
+    "                    p_nom_extendable=True,\n",
+    "                    )\n",
+    "        elif generator=='Residential Battery Storage':\n",
+    "            n.add(class_name=\"StorageUnit\",\n",
+    "                    name=generator,\n",
+    "                    bus=bus_name,\n",
+    "                    carrier=\"battery\",\n",
+    "                    capital_cost=annualized_cost,  # $/kW\n",
+    "                    p_nom_extendable=True,\n",
+    "                    max_hours=2.5,\n",
+    "                    cyclic_state_of_charge=False,\n",
+    "                    )\n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unyt import MWh, kWh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(112.9, '1/MWh')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(0.1129/kWh).to(1/MWh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(class_name='Generator',\n",
+    "      name='Evergy Import',\n",
+    "      bus=bus_name,\n",
+    "      carrier='grid',\n",
+    "      capital_cost=0,\n",
+    "      marginal_cost=112.9,\n",
+    "      p_nom_max=2,\n",
+    "      p_nom_extendable=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 6: Run the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 31.13it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 89.55it/s]\n",
+      "INFO:linopy.io: Writing time: 0.56s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 43803 primals, 105124 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>attribute</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>control</th>\n",
+       "      <th>type</th>\n",
+       "      <th>p_nom</th>\n",
+       "      <th>p_nom_mod</th>\n",
+       "      <th>p_nom_extendable</th>\n",
+       "      <th>p_nom_min</th>\n",
+       "      <th>p_nom_max</th>\n",
+       "      <th>p_min_pu</th>\n",
+       "      <th>p_max_pu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>min_up_time</th>\n",
+       "      <th>min_down_time</th>\n",
+       "      <th>up_time_before</th>\n",
+       "      <th>down_time_before</th>\n",
+       "      <th>ramp_limit_up</th>\n",
+       "      <th>ramp_limit_down</th>\n",
+       "      <th>ramp_limit_start_up</th>\n",
+       "      <th>ramp_limit_shut_down</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>p_nom_opt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ResPV</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Evergy Import</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "attribute              bus control type  p_nom  p_nom_mod  p_nom_extendable  \\\n",
+       "Generator                                                                     \n",
+       "ResPV          Residential      PQ         0.0        0.0              True   \n",
+       "Evergy Import  Residential      PQ         0.0        0.0              True   \n",
+       "\n",
+       "attribute      p_nom_min  p_nom_max  p_min_pu  p_max_pu  ...  min_up_time  \\\n",
+       "Generator                                                ...                \n",
+       "ResPV                0.0        inf       0.0       1.0  ...            0   \n",
+       "Evergy Import        0.0        2.0       0.0       1.0  ...            0   \n",
+       "\n",
+       "attribute      min_down_time  up_time_before down_time_before  ramp_limit_up  \\\n",
+       "Generator                                                                      \n",
+       "ResPV                      0               1                0            NaN   \n",
+       "Evergy Import              0               1                0            NaN   \n",
+       "\n",
+       "attribute      ramp_limit_down  ramp_limit_start_up  ramp_limit_shut_down  \\\n",
+       "Generator                                                                   \n",
+       "ResPV                      NaN                  1.0                   1.0   \n",
+       "Evergy Import              NaN                  1.0                   1.0   \n",
+       "\n",
+       "attribute      weight  p_nom_opt  \n",
+       "Generator                         \n",
+       "ResPV             1.0       -0.0  \n",
+       "Evergy Import     1.0        2.0  \n",
+       "\n",
+       "[2 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>attribute</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>control</th>\n",
+       "      <th>type</th>\n",
+       "      <th>p_nom</th>\n",
+       "      <th>p_nom_mod</th>\n",
+       "      <th>p_nom_extendable</th>\n",
+       "      <th>p_nom_min</th>\n",
+       "      <th>p_nom_max</th>\n",
+       "      <th>p_min_pu</th>\n",
+       "      <th>p_max_pu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>state_of_charge_initial_per_period</th>\n",
+       "      <th>state_of_charge_set</th>\n",
+       "      <th>cyclic_state_of_charge</th>\n",
+       "      <th>cyclic_state_of_charge_per_period</th>\n",
+       "      <th>max_hours</th>\n",
+       "      <th>efficiency_store</th>\n",
+       "      <th>efficiency_dispatch</th>\n",
+       "      <th>standing_loss</th>\n",
+       "      <th>inflow</th>\n",
+       "      <th>p_nom_opt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>StorageUnit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "attribute                            bus control type  p_nom  p_nom_mod  \\\n",
+       "StorageUnit                                                               \n",
+       "Residential Battery Storage  Residential      PQ         0.0        0.0   \n",
+       "\n",
+       "attribute                    p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
+       "StorageUnit                                                                     \n",
+       "Residential Battery Storage              True        0.0        inf      -1.0   \n",
+       "\n",
+       "attribute                    p_max_pu  ...  \\\n",
+       "StorageUnit                            ...   \n",
+       "Residential Battery Storage       1.0  ...   \n",
+       "\n",
+       "attribute                    state_of_charge_initial_per_period  \\\n",
+       "StorageUnit                                                       \n",
+       "Residential Battery Storage                               False   \n",
+       "\n",
+       "attribute                    state_of_charge_set  cyclic_state_of_charge  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                  NaN                   False   \n",
+       "\n",
+       "attribute                   cyclic_state_of_charge_per_period  max_hours  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                              True        2.5   \n",
+       "\n",
+       "attribute                    efficiency_store  efficiency_dispatch  \\\n",
+       "StorageUnit                                                          \n",
+       "Residential Battery Storage               1.0                  1.0   \n",
+       "\n",
+       "attribute                    standing_loss  inflow  p_nom_opt  \n",
+       "StorageUnit                                                    \n",
+       "Residential Battery Storage            0.0     0.0       -0.0  \n",
+       "\n",
+       "[1 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.storage_units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.236071</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid               2.0                 0.0  4135.96535     0.00000   \n",
+       "Load      -                  0.0                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.236071          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Load\n",
+       "Load Residential    1.430806\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.loads_t.p.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>StorageUnit</th>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>snapshot</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 19:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 20:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 21:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 22:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:00:00</th>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8760 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "StorageUnit          Residential Battery Storage\n",
+       "snapshot                                        \n",
+       "2018-01-01 00:00:00                         -0.0\n",
+       "2018-01-01 01:00:00                         -0.0\n",
+       "2018-01-01 02:00:00                         -0.0\n",
+       "2018-01-01 03:00:00                         -0.0\n",
+       "2018-01-01 04:00:00                         -0.0\n",
+       "...                                          ...\n",
+       "2018-12-31 19:00:00                         -0.0\n",
+       "2018-12-31 20:00:00                         -0.0\n",
+       "2018-12-31 21:00:00                         -0.0\n",
+       "2018-12-31 22:00:00                         -0.0\n",
+       "2018-12-31 23:00:00                         -0.0\n",
+       "\n",
+       "[8760 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.storage_units_t.p_store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TECH_ORDER = ['grid',\n",
+    "              'solar',\n",
+    "              'battery'\n",
+    "              ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def power_by_carrier(n):\n",
+    "    p_by_carrier = n.generators_t.p.T.groupby(\n",
+    "        n.generators.carrier).sum().T \n",
+    "    \n",
+    "    if not n.storage_units.empty:\n",
+    "        sto = n.storage_units_t.p.T.groupby(\n",
+    "            n.storage_units.carrier).sum().T\n",
+    "        p_by_carrier = pd.concat([p_by_carrier, sto], axis=1)\n",
+    "        \n",
+    "    last_cols = [col for col in p_by_carrier.columns if col not in TECH_ORDER]\n",
+    "\n",
+    "    p_by_carrier = p_by_carrier[TECH_ORDER+last_cols]\n",
+    "\n",
+    "    return p_by_carrier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_dispatch(n, year=2025, month=7):\n",
+    "\n",
+    "    time = (year, f'{year}-0{month}')\n",
+    "    p_by_carrier = power_by_carrier(n).div(1e3)\n",
+    "\n",
+    "    # y-limits\n",
+    "    y_min = -n.storage_units_t.p_store.max().max() / 1e3\n",
+    "    y_max = n.loads_t.p_set.sum(axis=1).max() / 1e3\n",
+    "    margin = 0.1\n",
+    "    y_low = (1 + margin) * y_min\n",
+    "    y_high = (1 + margin) * y_max\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "\n",
+    "    color = p_by_carrier.columns.map(n.carriers.color)\n",
+    "\n",
+    "    display(p_by_carrier)\n",
+    "\n",
+    "    p_by_carrier.where(p_by_carrier > 0).loc[time].plot.area(\n",
+    "        ax=ax,\n",
+    "        linewidth=0,\n",
+    "        color=color,\n",
+    "        ylim=(y_low - margin, y_high + margin)\n",
+    "    )\n",
+    "\n",
+    "    charge = p_by_carrier.where(\n",
+    "        p_by_carrier < 0).dropna(\n",
+    "        how=\"all\",\n",
+    "        axis=1).loc[time]\n",
+    "\n",
+    "    if not charge.empty:\n",
+    "        charge.plot.area(\n",
+    "            ax=ax,\n",
+    "            linewidth=0,\n",
+    "            color=charge.columns.map(n.carriers.color),\n",
+    "            ylim=(y_low - margin, y_high + margin)\n",
+    "        )\n",
+    "\n",
+    "    n.loads_t.p_set.sum(axis=1).loc[time].div(1e3).plot(ax=ax, c=\"k\")\n",
+    "\n",
+    "    ax.legend(loc=(1.05, 0))\n",
+    "    ax.set_ylabel(\"GW\", fontsize=16)\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    return fig, ax"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07'\n",
+    "n.generators_t.p.loc[time].plot(ax=ax, legend=False)\n",
+    "n.storage_units_t.p_store.loc[time].plot(ax=ax, legend=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/gis_notebooks/kc-zoning.ipynb b/notebooks/gis_notebooks/kc-zoning.ipynb
index 405ce25..1562f21 100644
--- a/notebooks/gis_notebooks/kc-zoning.ipynb
+++ b/notebooks/gis_notebooks/kc-zoning.ipynb
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +170,7 @@
        "[2 rows x 28 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -303,7 +303,7 @@
        "[1 rows x 27 columns]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -324,31 +324,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wy_bg = gpd.read_file('../../data/spatial_data/wyandotte_blockgroups.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = armourdale.dissolve(\"CITY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kc_zones = kc_zones.to_crs(epsg=4326)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = armourdale.to_crs(epsg=4326)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "Index(['ZONEDIST', 'ZONENAME', 'APPRDATE', 'ORD_NO1', 'ORD_NO2', 'ORD_NO3',\n",
-       "       'PET_NO1', 'PET_NO2', 'PET_NO3', 'NOTES', 'SPLIT_ZONE', 'ICOMAPATTR',\n",
-       "       'DATE_MOD', 'DATE_ADDED', 'MOD_BY', 'ADDED_BY', 'Shape_Leng',\n",
-       "       'Shape_Area', 'geometry'],\n",
-       "      dtype='object')"
+       "<Figure size 1000x600 with 1 Axes>"
       ]
      },
-     "execution_count": 17,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "kc_zones.columns"
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "kc_zones.plot(ax=ax, ec='k', fc='None')\n",
+    "kc_zones[~kc_zones['ZONENAME'].str.contains('Planned')].plot(ax=ax, \n",
+    "                                                             column='ZONENAME',\n",
+    "                                                             categorical=True, \n",
+    "                                                             ec='k',\n",
+    "                                                             legend=True, \n",
+    "                                                             legend_kwds=dict(ncols=1, \n",
+    "                                                                              loc='lower left'), \n",
+    "                                                             cmap='jet_r')\n",
+    "armourdale.plot(ax=ax, fc='lightgray', ec='k', zorder=-1)\n",
+    "# ax.minorticks_on()\n",
+    "# ax.grid(color='k')\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "kc_zones.plot(ax=ax, ec='k', fc='None')\n",
+    "kc_zones[kc_zones['ZONENAME'].isin(['Single Family District','Two Family District'])].plot(ax=ax, column='ZONENAME',categorical=True, legend=True, \n",
+    "              legend_kwds=dict(ncols=4, loc=(0.15,-0.)), cmap='jet_r')\n",
+    "armourdale.dissolve(\"CITY\").plot(ax=ax, fc='lightgray', ec='k', zorder=-1)\n",
+    "# ax.minorticks_on()\n",
+    "# ax.grid(color='k')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -372,189 +447,148 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>ZONEDIST</th>\n",
-       "      <th>ZONENAME</th>\n",
-       "      <th>APPRDATE</th>\n",
-       "      <th>ORD_NO1</th>\n",
-       "      <th>ORD_NO2</th>\n",
-       "      <th>ORD_NO3</th>\n",
-       "      <th>PET_NO1</th>\n",
-       "      <th>PET_NO2</th>\n",
-       "      <th>PET_NO3</th>\n",
-       "      <th>NOTES</th>\n",
-       "      <th>SPLIT_ZONE</th>\n",
-       "      <th>ICOMAPATTR</th>\n",
+       "      <th>GEOID</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>index_right</th>\n",
+       "      <th>VTD</th>\n",
+       "      <th>VTD_S</th>\n",
+       "      <th>CITY_CODE</th>\n",
+       "      <th>CITY_PREF</th>\n",
+       "      <th>WARD</th>\n",
+       "      <th>PRECINCT</th>\n",
+       "      <th>BPU</th>\n",
+       "      <th>...</th>\n",
+       "      <th>BPU_Member</th>\n",
+       "      <th>BPU_At_Lg1</th>\n",
+       "      <th>BPU_At_Lg2</th>\n",
+       "      <th>BPU_At_Lg3</th>\n",
        "      <th>DATE_MOD</th>\n",
        "      <th>DATE_ADDED</th>\n",
        "      <th>MOD_BY</th>\n",
        "      <th>ADDED_BY</th>\n",
        "      <th>Shape_Leng</th>\n",
        "      <th>Shape_Area</th>\n",
-       "      <th>geometry</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>M-3</td>\n",
-       "      <td>Heavy Industrial District</td>\n",
-       "      <td>None</td>\n",
-       "      <td>32282</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>13380.173937</td>\n",
-       "      <td>2.017360e+06</td>\n",
-       "      <td>MULTIPOLYGON (((2263627.750 292663.281, 226362...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>M-3</td>\n",
-       "      <td>Heavy Industrial District</td>\n",
-       "      <td>None</td>\n",
-       "      <td>45043</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>4294.963937</td>\n",
-       "      <td>4.933658e+05</td>\n",
-       "      <td>POLYGON ((2254872.499 297255.187, 2254877.499 ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>M-3</td>\n",
-       "      <td>Heavy Industrial District</td>\n",
-       "      <td>None</td>\n",
-       "      <td>45701</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>None</td>\n",
+       "      <th>77</th>\n",
+       "      <td>202090426002</td>\n",
+       "      <td>POLYGON ((-94.64458 39.08921, -94.64242 39.089...</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>KC06-02</td>\n",
+       "      <td>600310</td>\n",
+       "      <td>1</td>\n",
+       "      <td>KC</td>\n",
+       "      <td>06</td>\n",
+       "      <td>02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>753.562992</td>\n",
-       "      <td>3.208129e+04</td>\n",
-       "      <td>POLYGON ((2271434.999 291624.125, 2271441.000 ...</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>46433.543935</td>\n",
+       "      <td>5.578922e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>M-2</td>\n",
-       "      <td>General Industrial District</td>\n",
-       "      <td>None</td>\n",
-       "      <td>56119</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>None</td>\n",
+       "      <th>110</th>\n",
+       "      <td>202090426001</td>\n",
+       "      <td>POLYGON ((-94.64455 39.08409, -94.64453 39.084...</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>KC06-02</td>\n",
+       "      <td>600310</td>\n",
+       "      <td>1</td>\n",
+       "      <td>KC</td>\n",
+       "      <td>06</td>\n",
+       "      <td>02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>429.868788</td>\n",
-       "      <td>1.148440e+04</td>\n",
-       "      <td>POLYGON ((2269504.750 290045.344, 2269498.500 ...</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>46433.543935</td>\n",
+       "      <td>5.578922e+07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>CP-0</td>\n",
-       "      <td>Planned Nonretail Business District</td>\n",
-       "      <td>None</td>\n",
-       "      <td>65831</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
-       "      <td>NO</td>\n",
-       "      <td>None</td>\n",
+       "      <th>111</th>\n",
+       "      <td>202090426003</td>\n",
+       "      <td>POLYGON ((-94.64837 39.08002, -94.64833 39.080...</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>KC06-02</td>\n",
+       "      <td>600310</td>\n",
+       "      <td>1</td>\n",
+       "      <td>KC</td>\n",
+       "      <td>06</td>\n",
+       "      <td>02</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
        "      <td>None</td>\n",
-       "      <td>568.157502</td>\n",
-       "      <td>1.925364e+04</td>\n",
-       "      <td>POLYGON ((2268886.000 290452.812, 2268862.000 ...</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>GIS_EDITOR</td>\n",
+       "      <td>46433.543935</td>\n",
+       "      <td>5.578922e+07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>3 rows × 29 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  ZONEDIST                             ZONENAME APPRDATE ORD_NO1 ORD_NO2  \\\n",
-       "0      M-3            Heavy Industrial District     None   32282    None   \n",
-       "1      M-3            Heavy Industrial District     None   45043    None   \n",
-       "2      M-3            Heavy Industrial District     None   45701    None   \n",
-       "3      M-2          General Industrial District     None   56119    None   \n",
-       "4     CP-0  Planned Nonretail Business District     None   65831    None   \n",
+       "            GEOID                                           geometry  \\\n",
+       "77   202090426002  POLYGON ((-94.64458 39.08921, -94.64242 39.089...   \n",
+       "110  202090426001  POLYGON ((-94.64455 39.08409, -94.64453 39.084...   \n",
+       "111  202090426003  POLYGON ((-94.64837 39.08002, -94.64833 39.080...   \n",
+       "\n",
+       "     index_right      VTD   VTD_S CITY_CODE CITY_PREF WARD PRECINCT BPU  ...  \\\n",
+       "77   Kansas City  KC06-02  600310         1        KC   06       02   3  ...   \n",
+       "110  Kansas City  KC06-02  600310         1        KC   06       02   3  ...   \n",
+       "111  Kansas City  KC06-02  600310         1        KC   06       02   3  ...   \n",
        "\n",
-       "  ORD_NO3 PET_NO1 PET_NO2 PET_NO3 NOTES SPLIT_ZONE ICOMAPATTR DATE_MOD  \\\n",
-       "0    None    None    None    None  None         NO       None     None   \n",
-       "1    None    None    None    None  None         NO       None     None   \n",
-       "2    None    None    None    None  None         NO       None     None   \n",
-       "3    None    None    None    None  None         NO       None     None   \n",
-       "4    None    None    None    None  None         NO       None     None   \n",
+       "    BPU_Member BPU_At_Lg1 BPU_At_Lg2 BPU_At_Lg3    DATE_MOD  DATE_ADDED  \\\n",
+       "77        None       None       None       None  2023-01-09  2023-01-09   \n",
+       "110       None       None       None       None  2023-01-09  2023-01-09   \n",
+       "111       None       None       None       None  2023-01-09  2023-01-09   \n",
        "\n",
-       "  DATE_ADDED MOD_BY ADDED_BY    Shape_Leng    Shape_Area  \\\n",
-       "0       None   None     None  13380.173937  2.017360e+06   \n",
-       "1       None   None     None   4294.963937  4.933658e+05   \n",
-       "2       None   None     None    753.562992  3.208129e+04   \n",
-       "3       None   None     None    429.868788  1.148440e+04   \n",
-       "4       None   None     None    568.157502  1.925364e+04   \n",
+       "         MOD_BY    ADDED_BY    Shape_Leng    Shape_Area  \n",
+       "77   GIS_EDITOR  GIS_EDITOR  46433.543935  5.578922e+07  \n",
+       "110  GIS_EDITOR  GIS_EDITOR  46433.543935  5.578922e+07  \n",
+       "111  GIS_EDITOR  GIS_EDITOR  46433.543935  5.578922e+07  \n",
        "\n",
-       "                                            geometry  \n",
-       "0  MULTIPOLYGON (((2263627.750 292663.281, 226362...  \n",
-       "1  POLYGON ((2254872.499 297255.187, 2254877.499 ...  \n",
-       "2  POLYGON ((2271434.999 291624.125, 2271441.000 ...  \n",
-       "3  POLYGON ((2269504.750 290045.344, 2269498.500 ...  \n",
-       "4  POLYGON ((2268886.000 290452.812, 2268862.000 ...  "
+       "[3 rows x 29 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kc_zones.head()"
+    "wy_bg.sjoin(armourdale, predicate='within')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -565,46 +599,13 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(10,6))\n",
-    "kc_zones.plot(ax=ax, ec='k', fc='None')\n",
+    "kc_zones.plot(ax=ax, ec='k', fc='None', lw=0.2)\n",
     "kc_zones[kc_zones['ZONENAME'].isin(['Single Family District','Two Family District'])].plot(ax=ax, column='ZONENAME',categorical=True, legend=True, \n",
     "              legend_kwds=dict(ncols=4, loc=(0.15,-0.)), cmap='jet_r')\n",
     "armourdale.dissolve(\"CITY\").plot(ax=ax, fc='lightgray', ec='k', zorder=-1)\n",
     "# ax.minorticks_on()\n",
     "# ax.grid(color='k')\n",
-    "plt.tight_layout()\n",
-    "ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(10,6))\n",
-    "kc_zones.plot(ax=ax, ec='k', fc='None')\n",
-    "kc_zones[~kc_zones['ZONENAME'].str.contains('Planned')].plot(ax=ax, \n",
-    "                                                             column='ZONENAME',\n",
-    "                                                             categorical=True, \n",
-    "                                                             ec='k',\n",
-    "                                                             legend=True, \n",
-    "                                                             legend_kwds=dict(ncols=1, \n",
-    "                                                                              loc='lower left'), \n",
-    "                                                             cmap='jet_r')\n",
-    "armourdale.dissolve(\"CITY\").plot(ax=ax, fc='lightgray', ec='k', zorder=-1)\n",
-    "# ax.minorticks_on()\n",
-    "# ax.grid(color='k')\n",
+    "wy_bg.sjoin(armourdale, predicate='within').plot(ax=ax, fc='None', ec='b', lw=3)\n",
     "plt.tight_layout()\n",
     "ax.set_axis_off()"
    ]
diff --git a/scripts/calculate_residential_load.py b/scripts/calculate_residential_load.py
index 8e1b71f..e21656d 100644
--- a/scripts/calculate_residential_load.py
+++ b/scripts/calculate_residential_load.py
@@ -22,8 +22,9 @@
     rescaled_elec_load = (res_elec_load.div(res_elec_load.sum(),axis=1)*
                          (res_elec_load.columns.map(expenses['ELEP*UNITS'])/electricity_price))
     
-    rescaled_heat_load = (res_heat_load.div(res_heat_load.sum(),axis=1)*
-                         (res_heat_load.columns.map(expenses['GASP*UNITS'])/gas_price))
+    rescaled_heat_load = res_heat_load.copy()
+    # rescaled_heat_load = (res_heat_load.div(res_heat_load.sum(),axis=1)*
+    #                      (res_heat_load.columns.map(expenses['GASP*UNITS'])/gas_price))
     
     total_elec_load = rescaled_elec_load*res_structures.T.loc['n_units']
     total_heat_load = rescaled_heat_load*res_structures.T.loc['n_units']
diff --git a/scripts/retrieve_project_sunroof.py b/scripts/retrieve_project_sunroof.py
index 5526684..e713ead 100644
--- a/scripts/retrieve_project_sunroof.py
+++ b/scripts/retrieve_project_sunroof.py
@@ -24,8 +24,8 @@
     
     #get cutout
     community_cutout = gpd.read_file(snakemake.input.community)
-    combined = solar_gdf.sjoin(community_cutout, predicate='within')
+    combined = solar_gdf.sjoin(community_cutout, predicate='within').drop(columns=['index_right'])
     
     
-    solar_gdf.to_file(snakemake.output.local_potential, driver='GPKG')
+    combined.to_file(snakemake.output.local_potential, driver='GPKG')
     
diff --git a/scripts/retrieve_shapefiles.py b/scripts/retrieve_shapefiles.py
index f82e6c6..17e072d 100644
--- a/scripts/retrieve_shapefiles.py
+++ b/scripts/retrieve_shapefiles.py
@@ -10,7 +10,7 @@
     'parcels':"https://gisapp.wycokck.org/gisdata/shp/parcel_py.zip",
     'impervious_land_cover':"https://gisapp.wycokck.org/gisdata/shp/impervious_landcover_py.zip",
     'land_bank_parcels':'https://gisapp.wycokck.org/gisdata/shp/landbank_py.zip',
-    
+    'parks':'https://gisapp.wycokck.org/gisdata/shp/park_py.zip'
 }
 
 if __name__ == "__main__":
@@ -26,6 +26,6 @@
             
             gdf_cutout = gdf.sjoin(community_cutout, predicate='within')
             
-            gdf.to_file(f"data/spatial_data/armourdale/{name}.gpkg", driver="GPKG")
+            gdf_cutout.to_file(f"data/spatial_data/armourdale/{name}.gpkg", driver="GPKG")
         except DataSourceError:
             print(f"Failed to download {name} from {url}")
\ No newline at end of file

From a7a212522cbf918a8e3d6a5371f6d772648265be Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 27 Sep 2024 14:38:50 -0400
Subject: [PATCH 43/52] updates pypsa model

---
 notebooks/09-electricity-use.ipynb |  340 ++++--
 notebooks/10-pypsa-model.ipynb     | 1644 ++++++++++++++++++++--------
 2 files changed, 1482 insertions(+), 502 deletions(-)

diff --git a/notebooks/09-electricity-use.ipynb b/notebooks/09-electricity-use.ipynb
index 1968262..0a4a156 100644
--- a/notebooks/09-electricity-use.ipynb
+++ b/notebooks/09-electricity-use.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -202,7 +202,7 @@
        "[35040 rows x 5 columns]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +222,7 @@
        "Text(0, 0.5, 'kWh')"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -265,7 +265,7 @@
        "5266385.120228281"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +285,7 @@
        "1430.8057224551974"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +305,7 @@
        "(0.0, 1450.0)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +348,7 @@
        "Text(0.5, 1.0, 'Average Day')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -376,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -385,7 +385,7 @@
        "0.4315674125864762"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -414,7 +414,7 @@
        "0.16817788689562815"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,113 +425,267 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "rooftop_solar_energy = (weather.ghi / weather.ghi.max() * 6079)"
+    "res_elec_resampled = res_elec.loc['2018'].resample('h').mean().sum(axis=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "res_elec_resampled = res_elec.loc['2018'].resample('h').mean().sum(axis=1)"
+    "rooftop_solar_energy = (weather.ghi / weather.ghi.sum() * res_elec_resampled.sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "timestamp\n",
-       "2018-01-01 00:00:00    734.275500\n",
-       "2018-01-01 01:00:00    319.253001\n",
-       "2018-01-01 02:00:00    206.265092\n",
-       "2018-01-01 03:00:00    200.158683\n",
-       "2018-01-01 04:00:00    214.710827\n",
-       "                          ...    \n",
-       "2018-12-31 19:00:00    756.661431\n",
-       "2018-12-31 20:00:00    799.025894\n",
-       "2018-12-31 21:00:00    814.918117\n",
-       "2018-12-31 22:00:00    842.407166\n",
-       "2018-12-31 23:00:00    790.741970\n",
-       "Freq: H, Length: 8760, dtype: float64"
+       "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Kc+bhpopC858riwDdOcBQ3branVzN+0PVXXWzvMIJCI2/3IMTfru5Hgf3r315+ONXv0DrpMDmZ0ahm7+b3CXZlAaDHsd++gb6wx+jr2631SKHv6hvQ3XsA4i96xGr7TOuxsDTRgw81Gomk3mxvzM/mXtw8vc1v9Cff2zjb74jzL05HsGdXyt1DVWlQMXZxiDjZQ4KnblasBDmYTRLELoiEFmWAThnXiagiZOLOdRE3mEOOaHxNrX4X2c4XVqFiW/vQZ3BhEWT+mDmHdFyl2TTKnUXkLX9Q7hm/Qd99Ucsz9cKNX7xvBPqgQ+hz8jJUDlZ36/GwNNGDDzUIqPBPCTV1HuTv8/6jQEwb80Q3N/ccxM5wvzRxT7X3yA7pq8xhx9Djfl+HBvYwkEuDUYTHnhvH9LyL2FEN198OGsoFAoOS7eXojMncGbHPxGS/yUiTJd/oSyDF7IDJyBg1OOIiRsKgIGnzRh4yEJfbZ6Fc2af+R6Gs6nXrsmicgbChzTOQBlunlWllmdZdSLqfO/syMZfvz8Bd40K/33uToR6sferIwiTCafS9+Di3n+iZ1kKvHH5HrjTiiiUxPwKgUN+jZje/Rh4WouBx4FVFJmnHBccMM+8KUq/dsqx1utyz03kCHNvDn/7JXJIvxTqcP87P8FgFFj2QH/8Ot7B12DqJPr6OhzbvQmm9E8QV7UXasn8c/piHeDzl4obvn/bxwYfRK1lNADnM83hpumhy7+2nUdoY7hpvAfHvzf3VSIi1DcYkfzpERiMAol9AjF1kGNMve8K1BotBox7GBj3MHTl55G27V/wPLUJIfW/tOrr2cPTiD08dqq6/HLvTcEBoPDwtcNTksI8gyp8CBA+1Bx0vCI4TZyIrvHGd8exelcOfF3V+P65O+HnppG7JIeXlfYz+gwazh4eciAmk3ntkIL95nBz9gBQnn1tO62n+Z6b8KHmWSih8eb1TIiIriM17wLe250DAFjyq34MO11EaLfWbVPCwEO2q05nvqH47EFzyDmbap56ezW/nuZgEz7UvCuzX08OTxFRm1TXN2D+50cgBDBlUCjGxwXJXRK1EQPPVVJX/wYuIb2hDewJ7/DeCIrqDY32Jpdjp3ZhMhpRdOYUSnMOo+5cJlTlxxFQk41wYz6kq1ezdXIFwuIvh5uwwZweTkS37PUtWThTXoMQTy1endRX7nLoJjDwXGXwxS3wqPkOaBwJMQoJhQp/lGnCUesWCeHTDc5BPeETEYugiJ5wUrNLs70IkwnlxQUoyj6M6oIMKEqz4FWVjVDDGYRK9Wju1kDhHQUpfOjlIaqAPuZVYomI2smuk6X4aL95csNfH+gPT2fO0LRFfGe4yoHg/4G3vhCetQUIbjgHV6kOIaIEIXUlQN0hoAxA464BBqHEWUUAyjVhqPWIhuTbDS5BPeEb0QeB4d2hVPHytkR3oRSFpw5Dd+YoUHIM7hWnEKLPgx8qcc1uPJJ5Z90CVQQuunaDwa83Psxxwf76KLx3/3jER7IHh4g6hq7GgOf/Y171d+aIKNzR3fH2C7MXfEe+yu0z/2K5y1uYTCgrOYuSvGOoLjyBhtJsaCpy4V1bgCBjIZwlPcJEEcLqioC6g0AJgCzzcfRChbPKIFzShEKv9oZR4wmh9YLk7AWVqzec3LyhcfOFs4cvXL384O7lZxdDZ8Jkgl5fB319HRr09Wgw1ONSSQEu5h6BqTgTLrpTCKo7jQBcgGczX28UEs4pQ1Dm3A31Pr2gCekLv24DERLdB92uWE7880/TUZZ2Dl+nFzLwEFGH+ePXmThfUY8YP1e8ML633OXQLWDguQ5JoYBfUAT8giIAjLd6zWQ04nxRHkrPHEN14UmI8hxoKnLhU1eAYGMR1FIDIk1nEVl7Fqht3ferFWpUSm6oUbihVumOeicPGJw8rMKS0sUbTm4+UDo5QxgNMJkaIIwNEE0fG/8MkxEmYwNgaoAwGc0L6Zkuv4am54QRUuOfJVMDYDJAYdRDMhmgMBmgMOmhEAYoTXooTQ1QCT2UogEqYYBKGOAEA1RogFo0fpSM0AC4cqDPv4XzLYY/zjtHo8arJ1RBfeET3R+hPQYgwtkVETe4VpMGhGBT2jlszijCH+7tw92JiajdbT5ahK/SC6GQgDen9YezWil3SXQLGHhukkKpRGBYNwSGdbvmNWNDAwrP5qA8/xhqS3JhrLkI1F6Cov4SnPQ6OBkqoG2ohIupEm6iCu6iBgpJwFnSwxkXANMFwATA0PnnddNaWLKmQShQIbmhSB2NKs8eQEAfeEb2R0jPgQjy8sXNznMY2d0P3i5OKKvSY9/pcozq0VKsIiJqu5KKOrzyZQYA4Okx3TEwwlvmiuhWMfB0AKVKhZCoXgiJ6tWq9iajEbqKi6i+VIaaijLUVZRDX3UBDdUXYaq5CFF3CYp6HVT1OqgNFdAaK6E0GWCSlOYHzB+FdO1HISkhFE1/VkFICgiFyrzZZdOfG9tAoQKUakhKNaBSQ1JpICnVUDhpIKnUUKg0UDhpoFSpoVRroXTSQOmkhcpJDZXaGSq1GionDZw0zlCrNXBSa6FSqeADoL0HnZyUCkzsF4yP9ufjq/RCBh4iajdCCLy4KQMXawzoG+KB393VQ+6SqB0w8HQBCqUSnt5+8PTmzXBtMbl/CD7an4/vM4vx5/vjoHVidzMR3brPUguw/XgJ1EoFlk8bALWKQ+b2gH+LZLOGRPkg2FOLyvoG7DxRKnc5RGQHavVG/Plb8+yT+Yk90SuIq7DbCwYeslkKhYRJ/UMAAN8cKZS5GiKyBztPlKCyvgFh3s6YPSpG7nKoHTHwkE2b3Bh4fsg6j6r6BpmrISJbtyWzGAAwsV8wlApuIGxPGHjIpvUN8UCMnyvqG0zYeqxY7nKIyIbVGYzYnnUeADCBe2XZHQYesmmSdHlY66t0DmsR0c3bc6oM1XojQjy1GBDuJXc51M4YeMjmTR5gDjw/nirDhWq9zNUQka36LqMIADA+LhiSxOEse8PAQzavm78b4kI90GAS2NL4A4uIqC3qG4zY2jicNbEfh7PsEQMP2YWmm5e/5mwtIroJe7PLUVnXgAB3DQZxVWW7xMBDduHe28yB52DeBRReauXmZUREjZp6hyfEBUHB2Vl2iYGH7EKIlzNuj/KBEMC3R9nLQ0StZzCakHLMPJw1Pi5Y5mqoo9hV4Fm1ahWio6Oh1WoRHx+PPXv2yF0SdaJJAzisRURtty+nHLpaA3xd1bg9ur13/qOuwm4Cz6effopnn30WL7/8MtLS0jBq1ChMmDAB+fn5cpdGneSefsFQKSRknqvA6dIqucshIhvxXaZ5OCspLoiLDdoxuwk8y5cvx6xZszB79mzExsZixYoVCA8Px7vvvit3adRJfFzVGNnDvAEre3mIqDUajCZ8/0vj7CwOZ9k1uwg8er0ehw4dQmJiotXziYmJ2Lt3b7NfU19fj4qKCqsH2b4rZ2sJIWSuhoi6ugO5F3ChWg9vFycMjeFwlj2zi8BTVlYGo9GIwMBAq+cDAwNRXNz8dgNLly6Fp6en5REeHt4ZpVIHS+wbBI1KgdOl1filkCGWiK5vS+NwVmKfIDgp7eItkVpgV3+7V6+MKYRocbXMhQsXQqfTWR4FBQWdUSJ1MDeNCnfHBgDgsBYRXZ/RJPDfzMa9s7jYoN2zi8Dj5+cHpVJ5TW9OSUnJNb0+TTQaDTw8PKweZB+ahrW+OVIIk4nDWkTUvNS8CyirqoeHVoUR3fzkLoc6mF0EHrVajfj4eGzdutXq+a1bt2LEiBEyVUVyGd0rAO4aFYp0dUg9c1Hucoioi/ou0/xL8rg+QVCr7OLtkK7Dbv6Gk5OT8Y9//AMffPABsrKy8NxzzyE/Px9z5syRuzTqZFonJZLizN3TXx85J3M1RNQVmUwC/20MPNw7yzHYTeB58MEHsWLFCixevBgDBgzA7t27sWXLFkRGRspdGsmgaVhrS0YxDEaTzNUQUVeTVnAJxRV1cNOoLMtZkH1TyV1Ae5o7dy7mzp0rdxnUBYzo5gs/NzXKqvT4MbsMY3oFyF0SEXUh3zXunTU2NgAalVLmaqgz2E0PD9GVVEoFJvYzLyL2TTpnaxHRZUIIy/07E/pxsUFHwcBDdqtpWOv7X4pRZzDKXA0RdRVHz+pw7lItXNRKJPT0l7sc6iQMPGS3BkV4I9TLGdV6I7YfL5G7HCLqIpoWGxzTOwBaJw5nOQoGHrJbCoWESU1bTXBYi4jQOJyV0Tg7i3tnORQGHrJrTcNa20+UoKLOIHM1RCS3XworkH+hBlonBUb34nCWI2HgIbsWG+yO7gFu0DeY8H1m8/uqEZHj+K5xOGt0zwC4auxqojLdAAMP2TVJkqx2UCcixyWEwJaMptlZXGzQ0TDwkN1rCjx7c8pRVlUvczVEJJcT5yuRW1YNtUqBu3pzbS5Hw8BDdi/KzxX9wzxhNAlsaVxsjIgcT1Pvzp09/OGudZK5GupsDDzkEDhbi4iaVlfm3lmOiYGHHMKk/iGQJCD1zEWcvVgjdzlE1MmySypxqqQKTkoJd8cGyl0OyYCBhxxCoIcWQ6N9AADfHOGwFpGjaVp7Z2R3P3g6czjLETHwkMOY3D8UAGdrETmiLdw7y+Ex8JDDmBAXBCelhKyiCmSXVMpdDhF1ktyyamQVVUClkJDYh8NZjoqBhxyGt6sad/Ywr6zKm5eJHEfTYoPDu/nCy0UtczUkFwYeciiTB1xehFAIIXM1RNQZmu7fmcC9sxwaAw85lLGxgdA6KZBXXoOjZ3Vyl0NEHazgQg0yzumgkIDEvhzOcmQMPORQXDUqjG2cksqbl4nsX9Nw1tBoX/i5aWSuhuTEwEMOp2mriW+PFsJo4rAWkT1rWl2Ziw0SAw85nIRe/vDQqnC+oh4Hci/IXQ4RdZBzl2qRXnAJkgQk9WXgcXQMPORwNCql5eZFDmsR2a//Nq69MyTSBwEeWpmrIbkx8JBDapqt9V1mEfQNJpmrIaKO0LR31gQOZxEYeMhBDYvxhb+7BpdqDNhzqlTucoionZ2vqEPqmYsAgPFxDDzEwEMOSqmQcE8/DmsR2avvfzEPZw2K8EKwp7PM1VBXwMBDDqtpWGvrsfOo1RtlroaI2tOWxuGsidw7ixox8JDDGhjuhXAfZ9Tojfgh67zc5RBROymtvDwDk8NZ1ISBhxyWJEmWNXk4rEVkP1KOFcMkgP5hngjzdpG7HOoiGHjIoU3uHwoA2HmiBLoag8zVEFF7sOydxeEsugIDDzm0XkHu6BXoDoNR4L+/FMldDhHdogvVeuw7XQ4AmMDhLLoCAw85vCt3UCci27b1WDGMJoE+wR6I9HWVuxzqQhh4yOFNus0cePbllKOksk7maojoVnDvLGoJAw85vAhfFwyM8IJJAJuPcliLyFbpagz4KbsMAO/foWsx8BABnK1FZAe2Zp1Hg0mgV6A7uvm7yV0OdTEMPEQA7rktGAoJSMu/hPzyGrnLIaKbwL2z6HoYeIgABLhrcUd3PwDAfw4VyFwNEbVVRZ0Be06Zh7O4ujI1h4GHqNG0weEAgM9Sz6LByB3UiWzJ9qwS6I0mdPN3RY8ADmfRtRh4iBol9g2Et4sTiivqsOskd1AnsiXfZV7eO0uSJJmroa6IgYeokUalxJRBYQCADQc5rEVkK6rrG7DzhPmXlAlxHM6i5jHwEF3hodvNw1rbj5egpIJr8hDZgh0nSlDfYEKUrwtig93lLoe6KAYeoit0D3DH4EhvGE0Cnx86K3c5RNQKV+6dxeEsagkDD9FVHhxi7uX59GABTCYhczVEdD21eiO2Hy8BAEzkcBZdBwMP0VXuuS0Y7hoV8i/UWDYhJKKuadfJEtQajAjzdkZcqIfc5VAXxsBDdBUXtQr3DTSvvMybl4m6tqa9sybEBXE4i66LgYeoGdOHRAAAvs8sxoVqvczVEFFz6gxGbMs6D4B7Z9GNMfAQNSMu1BNxoR7QG03YdJg3LxN1RXtOlaFab0SwpxYDwrzkLoe6OAYeohY82NjL8+nBAgjBm5eJuprvfzEPZyX1DYJCweEsuj4GHqIW3DcgBM5OSpwqqcLh/Ityl0NEVzCZhGVF9LGxgTJXQ7aAgYeoBR5aJ9xzm/m+gA0HePMyUVdyrKgCpZX1cFErMSTaW+5yyAbIGniioqIgSZLV48UXX7Rqk5+fj0mTJsHV1RV+fn545plnoNdb30SakZGBhIQEODs7IzQ0FIsXL+YQBLWL6Y1r8nx7tAiVdQaZqyGiJk29OyO6+UKjUspcDdkCldwFLF68GE8++aTlcze3y7vcGo1G3HPPPfD398ePP/6I8vJyzJgxA0IIrFy5EgBQUVGBcePGYcyYMTh48CBOnjyJmTNnwtXVFfPnz+/08yH7Eh/pje4BbsguqcJX6YV4ZFik3CUREYCdJ8yLDSb0CpC5ErIVsgced3d3BAUFNftaSkoKjh07hoKCAoSEmNdFefPNNzFz5kwsWbIEHh4e+Oijj1BXV4f169dDo9EgLi4OJ0+exPLly5GcnMx1GeiWSJKE6UPC8efNWfj0YAEDD1EXoKs14HD+JQDA6J7+8hZDNkP2e3j+8pe/wNfXFwMGDMCSJUushqv27duHuLg4S9gBgKSkJNTX1+PQoUOWNgkJCdBoNFZtCgsLkZeX1+L3ra+vR0VFhdWDqDlTBoVBrVQg45wOmed0cpdD5PB+PFUGo0mge4Abwn1c5C6HbISsgef3v/89NmzYgB07dmDevHlYsWIF5s6da3m9uLgYgYHWd997e3tDrVajuLi4xTZNnze1ac7SpUvh6elpeYSHh7fXaZGd8XFVI7Gv+d/UhoP5MldDRE3DWezdobZo98CzaNGia25EvvqRmpoKAHjuueeQkJCA2267DbNnz8bq1auxdu1alJdf3r+ouSEpIYTV81e3abph+XrDWQsXLoROp7M8Cgo4C4da1rTy8ldphajVG2WuhshxCXF5Ovpo3r9DbdDu9/DMmzcP06dPv26bqKioZp8fNmwYACA7Oxu+vr4ICgrC/v37rdpcvHgRBoPB0osTFBR0TU9OSYk5/V/d83MljUZjNQxGdD0juvki3McZBRdqsTmjCL+OD5O7JCKHdKyoAiWcjk43od0Dj5+fH/z8/G7qa9PS0gAAwcHmtU+GDx+OJUuWoKioyPJcSkoKNBoN4uPjLW1eeukl6PV6qNVqS5uQkJAWgxVRWykUEqYPicBfvz+BTw/mM/AQyWTnCU5Hp5sj2z08+/btw1tvvYX09HTk5ubis88+w1NPPYXJkycjIsI8fJCYmIg+ffrg0UcfRVpaGrZt24YFCxbgySefhIeHBwDg4YcfhkajwcyZM5GZmYkvvvgCr7/+OmdoUbv7dXwYlAoJB/MuIrukUu5yiBzSrsbAw+no1FayBR6NRoNPP/0Uo0ePRp8+ffDHP/4RTz75JD755BNLG6VSic2bN0Or1eKOO+7AtGnTcP/992PZsmWWNp6enti6dSvOnj2LwYMHY+7cuUhOTkZycrIcp0V2LNBDizGNP2S58jJR59PVGnCocZsX3rBMbSUJLkkMwLyAoaenJ3Q6naX3iOhq27LOY9Y/U+Hjqsa+hXexS52oE23JKMLcjw6jm78rts0fLXc51EW09v1b9nV4iGxJQk9/BHpocKFaj63HzstdDpFDsUxH53AW3QQGHqI2UCkVmDbYvGYTh7WIOo/1dHQOZ1HbMfAQtVFT4PkxuwwFF2pkrobIMWQVVeJ8RT2cnZS4PdpH7nLIBjHwELVRuI8LRvUwL73w6UH28hB1hp0nzcNZnI5ON4uBh+gmNK28/PmhAjQYTTJXQ2T/mtbf4XAW3SwGHqKbMLZPAHxc1ThfUW/5QUxEHaOizoBDZxqno/OGZbpJDDxEN0GjUmLqoFAA3FCUqKP91Lg7eoy/K3dHp5vGwEN0kx5sHNbafrwExbo6mashsl+W4aye7N2hm8fAQ3STuge4YUiUN0wC+M8h3rxM1BE4HZ3aCwMP0S1ounn509QCmExctJyovR0vrkRxRR2no9MtY+AhugUT+wXDXatCwYVa7M0pl7scIrvTNJw1vJsvtE6cjk43j4GH6BY4q5W4f4D55uVPePMyUbu7vJ0Eh7Po1jDwEN2i6bebV15O+aUYF6r1MldDZD8qr5yOzhuW6RYx8BDdor4hnugX6gmDUWDT4bNyl0NkN37KLkODSSDGzxURvpyOTreGgYeoHTT18nxyIB9C8OZlovbQdP9OAoezqB0w8BC1g8n9Q+DspEROabWlC56Ibp4Q4ortJDicRbeOgYeoHbhrnXDvbcEAgE8OcE0eolt14rx5OrrWSYGhnI5O7YCBh6idTL/dvCbP5oxC6GoNMldDZNss09FjOB2d2gcDD1E7GRThhR4BbqgzmPD1kUK5yyGyaZeno3M4i9oHAw9RO5EkydLLs+EA1+QhulmVdQak5jXtjs4blql9MPAQtaMpA0OhVirwS2EFMs/p5C6HyCb9lF2OBpNAtJ8rIn1d5S6H7AQDD1E78nZVIykuCIB5ijoRtd2uk+bhrISe7N2h9sPAQ9TOHhpiXpPnq/RC1OgbZK6GyLZYT0dn4KH2w8BD1M6Gxfgi0tcFVfUN2Hy0SO5yiGzKyfNVKNLVQaNSYFiMr9zlkB1h4CFqZwqFhGmDzb08Gw5yTR6itmiancXd0am9MfAQdYAH4sOgVEg4dOYiTp6vlLscIpthGc7i/TvUzhh4iDpAgIcWd/U2rx/yKXt5iFqlqr4BqWcuAOD6O9T+GHiIOshDjRuKbjp8FvUNRpmrIer6fsoug8EoEOXrgig/Tken9sXAQ9RBEnoGIMhDi4s1BqT8cl7ucoi6PG4WSh2JgYeogygVEqYNDgMAbDjINXmIrkcIgV2NNywncDo6dQAGHqIO9EDjbK29OeU4X1EnczVEXdepkioUNk5HH87p6NQBGHiIOlC4jwviI70hBPANNxQlalHTdPRh3B2dOggDD1EHu29ACABwB3Wi6+DqytTRGHiIOtjEfsFQKiQcPatDblm13OUQdTlV9Q04mMfp6NSxGHiIOpifmwYju/sBAL5OZy8P0dX2Nk5Hj/R1QTSno1MHYeAh6gRNw1pfHTkHIYTM1RB1LTtPcnVl6ngMPESdILFvEDQqBU6XVuOXwgq5yyHqMszT0bn+DnU8Bh6iTuCmUWFsbCAA4Kv0czJXQ9R1ZJdU4dylWqi5Ozp1MAYeok4y+YrZWkYTh7WIgMuzs4bF+MJZzeno1HEYeIg6yehe/nDXqnC+oh4Hci/IXQ5Rl7DzpHn9Hd6/Qx2NgYeok2hUSkyMCwbANXmIAKC6vgEHcy8C4Po71PEYeIg6UdNsrS0ZRdA3mGSuhkhee3PKoTeaEOHD6ejU8Rh4iDrR0BhfBLhroKs1YHfjVFwiR9W0ncToXv6QJEnmasjeMfAQdSKlQsK9tzWtycNhLXJcQghuJ0GdioGHqJM1DWttPVaM6voGmashkkdO6eXp6MNj/OQuhxwAAw9RJ7stzBNRvi6oM5iw9dh5ucshkkVT787QaB9OR6dOwcBD1MkkScLkAaEAOFuLHNdOrq5MnYyBh0gGk/ubh7V2nyzFhWq9zNUQda7q+gbLWlS8f4c6S4cGniVLlmDEiBFwcXGBl5dXs23y8/MxadIkuLq6ws/PD8888wz0eus3gIyMDCQkJMDZ2RmhoaFYvHjxNRsw7tq1C/Hx8dBqtYiJicHq1as76rSIbln3ADf0DfFAg0lgS0aR3OUQdap9jdPRw32cEcPp6NRJOjTw6PV6PPDAA/jtb3/b7OtGoxH33HMPqqur8eOPP2LDhg3YuHEj5s+fb2lTUVGBcePGISQkBAcPHsTKlSuxbNkyLF++3NImNzcXEydOxKhRo5CWloaXXnoJzzzzDDZu3NiRp0d0S5puXv46ncNa5Fgur64cwOno1GlUHXnw1157DQCwfv36Zl9PSUnBsWPHUFBQgJAQ8w//N998EzNnzsSSJUvg4eGBjz76CHV1dVi/fj00Gg3i4uJw8uRJLF++HMnJyZAkCatXr0ZERARWrFgBAIiNjUVqaiqWLVuGqVOnduQpEt20Sf1DsPS74ziQdwHnLtUi1MtZ7pKIOhyno5NcZL2HZ9++fYiLi7OEHQBISkpCfX09Dh06ZGmTkJAAjUZj1aawsBB5eXmWNomJiVbHTkpKQmpqKgwGQ7Pfu76+HhUVFVYPos4U7OmM26N8AADf8OZlchA5pdU4e7EWaqUCw7txd3TqPLIGnuLiYgQGBlo95+3tDbVajeLi4hbbNH1+ozYNDQ0oKytr9nsvXboUnp6elkd4eHi7nBNRW9zXOFvrKw5rkYNoWl15aIwPXNQdOshAZKXNgWfRokWQJOm6j9TU1FYfr7nxWyGE1fNXt2m6Ybmtba60cOFC6HQ6y6OgoKDVNRO1lwlxQXBSSsgqqsCp85Vyl0PU4XY1bqmSwN3RqZO1OV7PmzcP06dPv26bqKioVh0rKCgI+/fvt3ru4sWLMBgMlh6boKAgS09Ok5IS828IN2qjUqng69t8l6lGo7EaJiOSg7erGnf28Me24yX4+kgh5if2krskog5To2/A/tNN09G5/g51rjYHHj8/P/j5tc8y4MOHD8eSJUtQVFSE4OBgAOYbmTUaDeLj4y1tXnrpJej1eqjVakubkJAQS7AaPnw4vvnmG6tjp6SkYPDgwXBycmqXWok6yuQBIdh2vARfpRcieVxPzlohu9U0HT3M2xnd/DkdnTpXh97Dk5+fj/T0dOTn58NoNCI9PR3p6emoqqoCACQmJqJPnz549NFHkZaWhm3btmHBggV48skn4eHhAQB4+OGHodFoMHPmTGRmZuKLL77A66+/bpmhBQBz5szBmTNnkJycjKysLHzwwQdYu3YtFixY0JGnR9QuxvUJhLOTEvkXapBecEnucog6zJWzsxjsqbN1aOD54x//iIEDB+LVV19FVVUVBg4ciIEDB1ru8VEqldi8eTO0Wi3uuOMOTJs2Dffffz+WLVtmOYanpye2bt2Ks2fPYvDgwZg7dy6Sk5ORnJxsaRMdHY0tW7Zg586dGDBgAP70pz/h7bff5pR0sgkuahUS+5qHZ3nzMtkrIYTV+jtEnU0SVy9Z7KAqKirg6ekJnU5n6V0i6izbj5/HE+tT4eemwc8L74JKyV1fyL7klFbh7jd3Qa1UIP3VcZyhRe2mte/f/KlK1AWM6uEPbxcnlFXV4+fGmzqJ7EnTcNbt0ZyOTvJg4CHqApyUCkzsZ75x/6v0czJXQ9T+mtbf4erKJBcGHqIuomkH9f9mFqPOYJS5GqL2U6s3Yj93RyeZMfAQdRFDonwQ7KlFZX2D5bdhInuw+1Qp9A1N09Hd5C6HHBQDD1EXoVBIll4eztYie/L9L+aFYRP7BHE6OsmGgYeoC5k8wBx4th0vQUVd8xvfEtkSg9GEbVnmHsukvoE3aE3UcRh4iLqQPsEe6B7gBn2DCd9nFt/4C4i6uIO5F6CrNcDXVY3BUT5yl0MOjIGHqAuRJAn3NQ5rfX2Ew1pk+5qGs8bGBkKp4HAWyYeBh6iLmdQYeH7KLkNpZb3M1RDdPCEEUo6dBwAkxXE4i+TFwEPUxUT5uaJ/uBdMAth8lL08ZLuOntWhSFcHV7USI7q1z6bTRDeLgYeoC2oa1vqKw1pkw5qGs0b3CoDWSSlzNeToGHiIuqB7bwuGQgLS8i8hv7xG7nKIboplOjpnZ1EXwMBD1AUFeGgtQwBfH+FWE2R7skuqkFNaDSelhDG9uTs6yY+Bh6iLalqT56v0QgghZK6GqG2aendGdPODh9ZJ5mqIGHiIuqykvkFQKxU4VVKF48WVcpdD1CaW2Vl9g2SuhMiMgYeoi/J0dsKY3uaNFrnVBNmSYl0djhRcgiQBY/twOIu6BgYeoi7svgGhAIBvjhTCZOKwFtmGlGPm4axBEd4IcNfKXA2RGQMPURd2V+8AuGlUOHepFofyL8pdDlGrNN2/w72zqCth4CHqwrROSss9EF+lc7YWdX2XavT4+fQFALx/h7oWBh6iLu6+xtlam48WwWA0yVwN0fVtyyqB0STQO8gdkb6ucpdDZMHAQ9TFjejmCz83NS7WGPBjdpnc5RBd1+XFBtm7Q10LAw9RF6dSKnBPv2AAwNecrUVdWK3eiN2nSgHw/h3qehh4iGzA5MbZWt//UoxavVHmaoiat+tkKeoMJoR5O6NPsIfc5RBZYeAhsgGDIrwQ5u2MGr0RP2Sdl7scomY1TUdP6hsESZJkrobIGgMPkQ2QJMly8zIXIaSuyGA0YVtWCQAgsQ+Hs6jrYeAhshFNixDuOlmCSzV6mashsnYg9wJ0tQb4uqoxOMpH7nKIrsHAQ2Qjega6o3eQOwxGge8yi+Uuh8hK0+yssbGBUCo4nEVdDwMPkQ1p2kGds7WoKzGZBFJ+adwsNI7DWdQ1MfAQ2ZBJt5kDz8+55SjW1clcDZHZ0XM6FFfUwVWtxIhufnKXQ9QsBh4iGxLu44LBkd4QAvj2KHt5qGtoGs4a3TsAWielzNUQNY+Bh8jGcLYWdTWXNwvl6srUdTHwENmYif2CoVRIyDinQ05pldzlkIPLLqnE6dJqOCkljO7lL3c5RC1i4CGyMb5uGozqYb5P4vPUszJXQ47u+8ablUd084OH1knmaohaxsBDZIOmD4kAAHyWWoD6Bm41QfJJ4XAW2QgGHiIbNDY2AEEeWlyo1mNLRpHc5ZCDKtLV4shZHSQJGMfVlamLY+AhskEqpQIPDzX38vx73xmZqyFH1bT2TnyEN/zdNTJXQ3R9DDxENmr67eFQKSQczr+EzHM6ucshB8TZWWRLGHiIbFSAuxbj48xvNB/tZy8Pda6L1Xrsz70AgIGHbAMDD5ENe3RYJADgy7RC6GoNMldDjmTb8RIYTQK9g9wR4esidzlEN8TAQ2TDbo/2Qa9Ad9QajNh4iFPUqfM0DWclsneHbAQDD5ENkyQJjww39/J8+PMZCCFkrogcQa3eiD2nSgEASX05O4tsAwMPkY371cBQuKqVOF1WjZ+yy+UuhxzArpOlqDOYEObtjD7BHnKXQ9QqDDxENs5No8KUQWEAgH//nCdvMeQQrlxsUJIkmashah0GHiI78GjjsNbWY+dRpKuVuRqyZwajCT9kmdff4ewssiUMPER2oGegO4ZG+8AkgE/258tdDtmx/acvoKKuAb6uasRHestdDlGrMfAQ2YmmXp6PDxRA32CSuRqyV02zs8b1CYRSweEssh0MPER2IqlvEPzdNSirqre8KRG1J5NJIOVY03R0zs4i28LAQ2QnnJQKPHR74/5aP3PlZWp/R85ewvmKeriqlRjRzU/ucojapEMDz5IlSzBixAi4uLjAy8ur2TaSJF3zWL16tVWbjIwMJCQkwNnZGaGhoVi8ePE1643s2rUL8fHx0Gq1iImJueYYRI7g4dsjoFRIOJB7AceLK+Quh+zM942bhY7uHQCtk1LmaojapkMDj16vxwMPPIDf/va31223bt06FBUVWR4zZsywvFZRUYFx48YhJCQEBw8exMqVK7Fs2TIsX77c0iY3NxcTJ07EqFGjkJaWhpdeegnPPPMMNm7c2GHnRtQVBXlqkdjHPNTwIXt5qJ01DWdxdhbZIlVHHvy1114DAKxfv/667by8vBAU1Px/oI8++gh1dXVYv349NBoN4uLicPLkSSxfvhzJycmWHqGIiAisWLECABAbG4vU1FQsW7YMU6dObc9TIuryHh0Wie8yi/HF4XN4YXxvuGud5C6J7EB2SSVOl1ZDrVRgTC9/ucsharMucQ/PvHnz4OfnhyFDhmD16tUwmS7PMNm3bx8SEhKg0WgszyUlJaGwsBB5eXmWNomJiVbHTEpKQmpqKgyG5jdUrK+vR0VFhdWDyB4M7+aLbv6uqNYb8WXaObnLITvRNJw1orsvQzTZJNkDz5/+9Cd8/vnn+OGHHzB9+nTMnz8fr7/+uuX14uJiBAZazwZo+ry4uPi6bRoaGlBWVtbs9126dCk8PT0tj/Dw8PY8LSLZSJJk2UX9X/u4vxa1j+9/4XAW2bY2B55FixY1e6PxlY/U1NRWH++VV17B8OHDMWDAAMyfPx+LFy/GX//6V6s2Vy9d3vQD/MrnW9PmSgsXLoROp7M8CgoKWl0zUVc3JT4Mzk5KnCqpwv7cC3KXQzau8FItjp7VQZKAsbGcjk62qc338MybNw/Tp0+/bpuoqKibrQfDhg1DRUUFzp8/j8DAQAQFBVl6cpqUlJQAuNzT01IblUoFX1/fZr+PRqOxGiYjsiceWifcPzAUnxzIx79/PoNhMc3/PyBqjaa9s+IjvOHvzp+bZJvaHHj8/Pzg59dx6y+kpaVBq9VaprEPHz4cL730EvR6PdRqNQAgJSUFISEhlmA1fPhwfPPNN1bHSUlJweDBg+HkxLFmckyPDovEJwfy8X1mMUoq6hDgoZW7JLJRTffvcDiLbFmH3sOTn5+P9PR05Ofnw2g0Ij09Henp6aiqqgIAfPPNN1izZg0yMzORk5ODf/zjH3j55Zfxm9/8xtL78vDDD0Oj0WDmzJnIzMzEF198gddff90yQwsA5syZgzNnziA5ORlZWVn44IMPsHbtWixYsKAjT4+oS+sT4oHBkd5oMAl8coBDtnRzLlbrcSDPPCzKwEM2TXSgGTNmCADXPHbs2CGEEOK7774TAwYMEG5ubsLFxUXExcWJFStWCIPBYHWco0ePilGjRgmNRiOCgoLEokWLhMlksmqzc+dOMXDgQKFWq0VUVJR4991321SrTqcTAIROp7ulcybqSr5MOysiX/hW3L5kq9A3GOUuh2zQ56kFIvKFb0XSW7vkLoWoWa19/5aE4BQOwLzAoaenJ3Q6HTw8POQuh6hd1DcYMWLpdpRX67H6kUEYHxcsd0lkY578Vyq2HjuP39/dA8+N6yl3OUTXaO37t+zT0omo42hUSky/3bzkwr/2ceVlapsafQN2nywFwOEssn0MPER27qHbI6CQgL055cguqZS7HLIhu0+Wor7BhHAfZ8QGu8tdDtEtYeAhsnNh3i64q3fT/lr5MldDtqRpdlZin6AW1zQjshUMPEQO4LHh5pWXNx46i+r6BpmrIVtgMJqwLYvT0cl+MPAQOYCR3f0Q5euCyvoGfJVeKHc5ZAN+Pl2OiroG+LqqER/pLXc5RLeMgYfIASgUEh6x7K+Vx/216Iaa9s4a1ycQSgWHs8j2MfAQOYgH4sOhdVLgeHElDp25KHc51IWZTAIpXF2Z7AwDD5GD8HRxwuT+IQCAf//MKerUsiNnL6Gksh5uGhVGdOc+bGQfGHiIHMijw6IAAFsyilBWVS9vMdRlNc3OGt3LHxqVUuZqiNoHAw+RA+kX5on+4V4wGAU+Pcj9tehaQgjL7ugcziJ7wsBD5GAea7x5+eP9+TCaePMyWcsuqcLpsmqolQqM7uUvdzlE7YaBh8jB3HNbMLxcnHDuUi22Hy+RuxzqYppmZ43o7gt3rZPM1RC1HwYeIgejdVLiwcHm/bV48zJdqcFowmepZwEA4zmcRXaGgYfIAf3P0EhIknmvpNyyarnLoS7i6yOFyL9QAx9XNSYPCJG7HKJ2xcBD5IAifF0wuqf5/oyP2MtDAIwmgb/vyAYAzB4VDRe1SuaKiNoXAw+Rg3q0cX+tzw+dRa3eKHM1JLctGUU4XVoNT2cnPDY8Su5yiNodAw+Rg0roGYBwH2foag345ij313JkJpPA37ebe3eeuCMabhr27pD9YeAhclBKhYT/GWru5fn3vjPcX8uBpRw7jxPnK+GuUWHmHVFyl0PUIRh4iBzYtMHhUKsUyDinw5GzOrnLIRkIIbBy+ykAwIwRUfB05lR0sk8MPEQOzMdVjXtvCwZg7uUhx7PjRAl+KayAi1qJJ0ZGy10OUYdh4CFycI82rrz8zdFCXKjWy1wNdSYhBN7eZr5359FhkfBxVctcEVHHYeAhcnADwr0QF+oBfYMJn6dyfy1H8mN2GdILLkHrpMDsUTFyl0PUoRh4iBycJEl4rHEX9Q/3n4GJ+2s5jJWNvTsP3R4Bf3eNzNUQdSwGHiLCpP4h8NCqUHChFrtOlcpdDnWCn0+X40DeBaiVCjx1Zze5yyHqcAw8RARntRIPNO2vxZuXHULTzKwHBochyFMrczVEHY+Bh4gAAI803ry840QJskuqZK6GOtKhMxfxU3Y5VAoJvx3N3h1yDAw8RAQAiPZzxbg+gRACeH93jtzlUAdq6t2ZMigUYd4uMldD1DkYeIjIYk6C+bf9L9LOoVhXJ3M11BGOnr2EnSdKoZCAuaO7y10OUadh4CEii/hIb9we5QODUeCDn3LlLoc6wMrGPbPuGxCKKD9Xmash6jwMPERkZc5o83osH+/Ph67WIHM11J6yiiqw9dh5SBLw9Bj27pBjYeAhIitjegWgV6A7quob8OHPnLFlT/6+w9y7M7FfMLoHuMlcDVHnYuAhIiuSJOGpBHMvz7qf8lBnMMpcEbWH7JJKbMkoAgD87i727pDjYeAhomtM6h+CUC9nlFXVY+Phs3KXQ+3gnR05EAJI7BOI3kEecpdD1OkYeIjoGk5KBWY17py9ZvdpGLndhE3LK6vGV+nnAAC/u6uHzNUQyYOBh4iaNf32cHi5OCGvvAb/zSyWuxy6Bat2ZsMkgDG9/NEvzFPucohkwcBDRM1yUavw2PAoAMDqXTkQgr08tqjgQg02HW7s3bmbvTvkuBh4iKhFM0dEQeukQMY5HfbmlMtdDt2E1bty0GASGNndD4MivOUuh0g2DDxE1CIfVzUebNxUdPUubjdha4p1dfg81XzTOWdmkaNj4CGi65o9KgZKhYQ9p8qQeU4ndznUBqt35UBvNOH2aB8MjfGVuxwiWTHwENF1hfu44N7bggGwl8eWlFTW4ZMD+QDYu0MEMPAQUSs8dad5U9EtGUU4U14tczXUGv/Yk4v6BhMGhHthZHc/ucshkh0DDxHdUJ8QDyT09IdJAGv2nJa7HLqBC9V6y7Ygz9zdHZIkyVwRkfwYeIioVeYkmHt5Pk89i7KqepmroetZ++Np1OiNiAv1wJheAXKXQ9QlMPAQUasMi/FB/3Av1DeYsP6nPLnLoRboagz4515z7868MT3Yu0PUiIGHiFpFkiT8tnFT0X/ty0NVfYPMFVFz1u3NRVV9A3oFuiOxT6Dc5RB1GQw8RNRq4/oEIcbPFRV1DdjQOAOIuo7KOgPWNfa+zburOxQK9u4QNWHgIaJWUyok/OZOcy/PP/bkQt9gkrkiutK/fz4DXa0BMf6umNgvWO5yiLoUBh4iapNfDQpFgLsGxRV1+LJxB26SX42+Af/YkwsAmDemO5Ts3SGy0mGBJy8vD7NmzUJ0dDScnZ3RrVs3vPrqq9Dr9Vbt8vPzMWnSJLi6usLPzw/PPPPMNW0yMjKQkJAAZ2dnhIaGYvHixddsZLhr1y7Ex8dDq9UiJiYGq1ev7qhTI3JoGpUSs0ZGAwDe25UDk4mbinYFH+/Px4VqPSJ9XTC5f4jc5RB1OaqOOvDx48dhMpnw3nvvoXv37sjMzMSTTz6J6upqLFu2DABgNBpxzz33wN/fHz/++CPKy8sxY8YMCCGwcuVKAEBFRQXGjRuHMWPG4ODBgzh58iRmzpwJV1dXzJ8/HwCQm5uLiRMn4sknn8SHH36In376CXPnzoW/vz+mTp3aUadI5LAeHhqBv+/IRk5pNX7IOo/EvkFyl+TQ6gxGvLfbvD7S3NHdoFKy857oGqIT/d///Z+Ijo62fL5lyxahUCjEuXPnLM998sknQqPRCJ1OJ4QQYtWqVcLT01PU1dVZ2ixdulSEhIQIk8kkhBDi+eefF71797b6Xk899ZQYNmxYq2vT6XQCgOX7EtH1vfFdloh84Vvxq3d+tPxfJHms/ylXRL7wrRixdJuoNxjlLoeoU7X2/btTfw3Q6XTw8fGxfL5v3z7ExcUhJORy92tSUhLq6+tx6NAhS5uEhARoNBqrNoWFhcjLy7O0SUxMtPpeSUlJSE1NhcFgaLaW+vp6VFRUWD2IqPUevyMKapUCh/Mv4WDeRbnLcVj1DUbLHmdzRneDWsXeHaLmdNr/jJycHKxcuRJz5syxPFdcXIzAQOt1Iry9vaFWq1FcXNxim6bPb9SmoaEBZWVlzdazdOlSeHp6Wh7h4eG3doJEDibAXYupg8IAcFNROW08dA5FujoEemjwQHyY3OUQdVltDjyLFi2CJEnXfaSmplp9TWFhIcaPH48HHngAs2fPtnqtuVVAhRBWz1/dRjTesNzWNldauHAhdDqd5VFQUHCjUyeiq/zmzhhIErD9eAlOFFfKXY7DMRhNWLUzG4B5g1etk1Lmioi6rjbftDxv3jxMnz79um2ioqIsfy4sLMSYMWMwfPhwvP/++1btgoKCsH//fqvnLl68CIPBYOmxCQoKsvTkNCkpKQGAG7ZRqVTw9fVttkaNRmM1TEZEbRft54oJcUHYklGM93blYPmDA+QuyaF8mXYOZy/Wws9NjYduj5C7HKIurc2Bx8/PD35+fq1qe+7cOYwZMwbx8fFYt24dFArrDqXhw4djyZIlKCoqQnCweZGslJQUaDQaxMfHW9q89NJL0Ov1UKvVljYhISGWYDV8+HB88803VsdOSUnB4MGD4eTk1NZTJKI2mJPQDVsyivH1kULMT+qFUC9nuUtyCEaTwKqd5qHE2aNi4Kxm7w7R9XTYPTyFhYUYPXo0wsPDsWzZMpSWlqK4uNiqJyYxMRF9+vTBo48+irS0NGzbtg0LFizAk08+CQ8PDwDAww8/DI1Gg5kzZyIzMxNffPEFXn/9dSQnJ1uGq+bMmYMzZ84gOTkZWVlZ+OCDD7B27VosWLCgo06PiBrdFuaFEd180WAS+Mee03KX4zA2Hj6L3LJqeLk44ZFhkXKXQ9TldVjgSUlJQXZ2NrZv346wsDAEBwdbHk2USiU2b94MrVaLO+64A9OmTcP9999vWacHADw9PbF161acPXsWgwcPxty5c5GcnIzk5GRLm+joaGzZsgU7d+7EgAED8Kc//Qlvv/021+Ah6iRzEroBADYcKMDFav0NWtOtqtE3YNn3JwCY191x03TYkmpEdkMSQnCZVJgXOPT09IROp7P0LhFR6wghcO/KH/FLYQWeG9sTvx/bQ+6S7NpbW0/ib9tOIdzHGT8kJ0Cj4nAWOa7Wvn9zwQYiumWSJOGpxl6ef+7LQ63eKHNF9qtYV4f3dpvv3Vk4IZZhh6iVGHiIqF1MjAtCuI8zLlTr8Vkql3noKMtSTqDOYMLgSG9MiOOWHkStxcBDRO1CpVTgN6NiAABr9pxGg9Ekc0X2J/OcDhsPnwUAvHxPbIvrjBHRtRh4iKjdPDA4HL6uapy9WIvNGUVyl2NXhBD48+ZjEAK4b0AIBkZ4y10SkU1h4CGidqN1UmLmiCgAwOpdp8E5Ee3nh6wS/Hz6AtQqBf43qZfc5RDZHAYeImpXjw6PhItaiayiCuw6WSp3OXZB32DC61uyAACzR0YjzNtF5oqIbA8DDxG1Ky+Xy9sccFPR9vHR/jPILauGn5savx3dTe5yiGwSAw8RtbtZI6OhUkj4+fQFpOVflLscm6arMeBv204BAJLH9YK7ltvlEN0MBh4ianchXs64b0AoAPby3KqV20/hUo0BPQPdMG1wmNzlENksBh4i6hBzEsxT1FOOnUdOaZXM1dimvLJq/HNfHgDg5Xv6QKXkj2yim8X/PUTUIXoEumNsbACEAN7fxU1Fb8Zf/nscBqNAQk9/JPT0l7scIpvGwENEHabpBtsv0s7hfEWdzNXYlgO5F/BdZjEUknmRQSK6NQw8RNRh4iN9MCTKG3qjCUu3ZHFdnlYymcyLDALA9Nsj0DPQXeaKiGwfAw8RdagXxveGUiHhy/RCfLQ/X+5ybMLXRwpx9KwObhoVnhvbU+5yiOwCAw8RdajBUT54vnFl4MXfHEN6wSV5C+riavVG/OW/xwGYhwT93TUyV0RkHxh4iKjD/ebOGCT2CYTeaMLTHx3GhWq93CV1WWt/PI0iXR1CvZwxa2S03OUQ2Q0GHiLqcJIkYdm0/ojydcG5S7X4/YY0GE28n+dqJZV1eHened2i58f3gtZJKXNFRPaDgYeIOoWH1gnvPhIPrZMCe06V4e3G1YPpsre2nkS13ogB4V6Y3D9E7nKI7AoDDxF1mthgDyyd0g8A8Pb2U9hxokTmirqOrKIKfHqwAADwh3tjIUmSzBUR2RcGHiLqVL8aGIZHhkVACOC5T9NRcKFG7pJkJ4TA61uyYBLAPf2CER/pI3dJRHaHgYeIOt0f7u2D/mGeuFRjwNyPDqPOYJS7JFntPFmKPafKoFYq8ML43nKXQ2SXGHiIqNNpVEqseiQe3i5OyDinw+Jvj8ldkmwajCYs2ZwFAJh5RxQifF1krojIPjHwEJEsQr2csWL6QEgS8PH+fPzn0Fm5S5LFJwcLkF1SBW8XJzw9prvc5RDZLQYeIpJNQk9/PHu3eSXhl7/IwLHCCpkr6lwVdQas2HoSAPDcuJ7wdHaSuSIi+8XAQ0Sy+t1d3TG6lz/qG0z47UeHoKs1yF1Sp1m1Iwfl1XrE+Lviodsj5C6HyK4x8BCRrBQKCW9NG4BQL2ecKa/Bgs+PwOQAixIWXKjBBz/mAgBenhgLJyV/HBN1JP4PIyLZebuq8e4jg6BWKrD12Hm8t/u03CV1uP/7/gT0RhPu6O6Lu3oHyF0Okd1j4CGiLuG2MC8smtwXAPDX749jb06ZzBV1nENnLuKbI4WQJODliX24yCBRJ2DgIaIu46HbwzF1UBhMAnjmkzQU6+rkLqndCSHw583mafgPxIehT4iHzBUROQYGHiLqMiRJwp/vj0PvIHeUVenx9MeHYTCa5C6rXW3OKEJa/iW4qJWYn9hL7nKIHAYDDxF1Kc5qJVY/Eg93jQqHzlzE0i3H5S6p3dQZjHjjO/P5zEnohkAPrcwVETkOBh4i6nKi/Fzx5rT+AIAPfsrFt0cLZa6ofazfm4ezF2sR5KHFk6Ni5C6HyKEw8BBRl5TYNwhzEroBAF74z1Fkl1TKXNGtKa+qxzvbswEA/5vUC85qpcwVETkWBh4i6rIWJPbE8BhfVOuNmPPhYVTXN8hd0k1b8cMpVNY3IC7UA78aGCp3OUQOh4GHiLoslVKBtx8aiEAPDbJLqvDipgwIYXuLEmaXVOLjA/kAgFfu6QOFgtPQiTobAw8RdWn+7hq88/AgqBQSvjlSiH/uzZO7pDY5kHsB8z5Og9EkkNgnEMNifOUuicghMfAQUZc3OMoHL02MBQD8eXMWDp25KHNFN3byfCVmrT+Iae/tw/HiSnhoVVjYeA5E1PkYeIjIJjx+RxTuuS0YDSaBpz86jLKqerlLalaRrhbP/+cIxq/YjW3HS6BUSPifoRH4YX4Cov1c5S6PyGGp5C6AiKg1JEnCX6behuNFFcgprcYzn6Th37OGQtlF7ofR1RqwelcOPvgxF/UN5sUSJ8QFYUFSL3Tzd5O5OiJi4CEim+GmUWH1I/G4752fsDenHH/efAzzE3vBTSPfj7L6BiP+ve8M/r4jG5dqDACA26N88OLE3hgU4S1bXURkTRK2OOWhA1RUVMDT0xM6nQ4eHtzbhqgr+/pIIZ75JA0AoFEpkNDTHxP7BePu2AC4a506pQaTSeCrI+ew7PuTOHepFgDQI8ANL4zvjbtjA7ghKFEnae37N3t4iMjmTO4fgopaA/6x5zTyymuQcuw8Uo6dh1qpwJ09/RrDTyA8nds//AghsPtUGd747jiyiioAAIEeGiSP64mpg8KgUvLWSKKuiD08jdjDQ2R7hBDIKqrEd5lF2JxRhNOl1ZbXnJQSRnY3h5/EPkHwdLn18JNxVoc3/puFn7LLAQDuWhV+O7obHh8RzZWTiWTS2vdvBp5GDDxEtk0IgZPnq7AlowhbMopwqqTK8ppKIeGO7n6Y2C8IiX2C4O2qbtOx88trsCzlBL4+Yt7TS61U4LHhkXh6TPc2H4uI2hcDTxsx8BDZl1PnK/FdZjG2ZBThePHlfbiUCgkjuvk29vwEwtdN0+IxyqvqsXJ7Nj7afwYGo4AkAfcPCEXyuJ4I93HpjNMgohtg4GkjBh4i+5VTWoX/ZhZj89EiHGu87wYAFBIwLMYcfpL6BsHf3Rx+avQN+ODHXKzedRpVjft3jerhhxcn9EbfEE9ZzoGImsfA00YMPESOIa+sGlsyi/BdRjEyzukszysk4PZoHwyO9MFnqQUoqTQvbBgX6oEXx8diZA8/uUomoutg4GkjBh4ix1NwocZ8z09mMY4UXLJ6LdzHGQsSe2HSbSHc7JOoC2vt+3eHzZ/My8vDrFmzEB0dDWdnZ3Tr1g2vvvoq9Hq9VTtJkq55rF692qpNRkYGEhIS4OzsjNDQUCxevPiaHZN37dqF+Ph4aLVaxMTEXHMMIqKrhfu44KmEbvjq6Tvw4wtj8Mo9sRjfNwivTuqDH5ITcN+AUIYdIjvRYevwHD9+HCaTCe+99x66d++OzMxMPPnkk6iursayZcus2q5btw7jx4+3fO7peXmMvKKiAuPGjcOYMWNw8OBBnDx5EjNnzoSrqyvmz58PAMjNzcXEiRPx5JNP4sMPP8RPP/2EuXPnwt/fH1OnTu2oUyQiOxLm7YLZo2Iwe5TclRBRR+jUIa2//vWvePfdd3H69OnLBUgSvvjiC9x///3Nfs27776LhQsX4vz589BozDcUvvHGG1i5ciXOnj0LSZLwwgsv4Ouvv0ZWVpbl6+bMmYMjR45g3759raqNQ1pERES2R/YhrebodDr4+Phc8/y8efPg5+eHIUOGYPXq1TCZTJbX9u3bh4SEBEvYAYCkpCQUFhYiLy/P0iYxMdHqmElJSUhNTYXBYGi2lvr6elRUVFg9iIiIyD51WuDJycnBypUrMWfOHKvn//SnP+Hzzz/HDz/8gOnTp2P+/Pl4/fXXLa8XFxcjMDDQ6muaPi8uLr5um4aGBpSVlTVbz9KlS+Hp6Wl5hIeH3/I5EhERUdfU5sCzaNGiZm80vvKRmppq9TWFhYUYP348HnjgAcyePdvqtVdeeQXDhw/HgAEDMH/+fCxevBh//etfrdpcvQlf0yjclc+3ps2VFi5cCJ1OZ3kUFBS04SoQERGRLWnzTcvz5s3D9OnTr9smKirK8ufCwkKMGTMGw4cPx/vvv3/D4w8bNgwVFRU4f/48AgMDERQUZOnJaVJSUgLgck9PS21UKhV8fX2b/T4ajcZqmIyIiIjsV5sDj5+fH/z8WrcA17lz5zBmzBjEx8dj3bp1UChu3KGUlpYGrVYLLy8vAMDw4cPx0ksvQa/XQ60271mTkpKCkJAQS7AaPnw4vvnmG6vjpKSkYPDgwXByav/dkomIiMi2dNg9PIWFhRg9ejTCw8OxbNkylJaWori42Kon5ptvvsGaNWuQmZmJnJwc/OMf/8DLL7+M3/zmN5bel4cffhgajQYzZ85EZmYmvvjiC7z++utITk62DFfNmTMHZ86cQXJyMrKysvDBBx9g7dq1WLBgQUedHhEREdmQDluHJyUlBdnZ2cjOzkZYWJjVa0331zg5OWHVqlVITk6GyWRCTEwMFi9ejKefftrS1tPTE1u3bsXTTz+NwYMHw9vbG8nJyUhOTra0iY6OxpYtW/Dcc8/hnXfeQUhICN5++22uwUNEREQAuLWEBdfhISIisj1dch0eIiIiIjkw8BAREZHdY+AhIiIiu8fAQ0RERHaPgYeIiIjsXodNS7c1TZPVuIkoERGR7Wh6377RpHMGnkbl5eUAwE1EiYiIbFB5eTk8PT1bfJ2Bp5GPjw8AID8//7oXzN4NGTIEBw8elLsM2Tj6+QO8BgCvgaOfP8BrANjONdDpdIiIiLC8j7eEgadR0z5fnp6eDr3woFKp5Pk78PkDvAYAr4Gjnz/AawDY3jW40X6dvGmZrFy5rYcjcvTzB3gNAF4DRz9/gNcAsL9rwK0lGnFrCSIiItvDrSXaSKPR4NVXX7Xs0k5ERERdX2vfv9nDQ0RERHaPPTxERERk9xh47MSqVasQHR0NrVaL+Ph47Nmzx/LaokWL0Lt3b7i6usLb2xtjx47F/v37b3jMjIwMJCQkwNnZGaGhoVi8ePE1Czvt2rUL8fHx0Gq1iImJwerVq9v93FrretcAALKysjB58mR4enrC3d0dw4YNQ35+/nWPaU/X4Pz585g5cyZCQkLg4uKC8ePH49SpUzc8pq1cg927d2PSpEkICQmBJEn48ssvLa8ZDAa88MIL6NevH1xdXRESEoLHHnsMhYWFNzyurZw/cP1rAAAzZ86EJElWj2HDht3wuPZ0DaqqqjBv3jyEhYXB2dkZsbGxePfdd294XFu5BkuXLsWQIUPg7u6OgIAA3H///Thx4oRVm02bNiEpKQl+fn6QJAnp6emtOratXIMWCbJ5GzZsEE5OTmLNmjXi2LFj4ve//71wdXUVZ86cEUII8dFHH4mtW7eKnJwckZmZKWbNmiU8PDxESUlJi8fU6XQiMDBQTJ8+XWRkZIiNGzcKd3d3sWzZMkub06dPCxcXF/H73/9eHDt2TKxZs0Y4OTmJ//znPx1+zle70TXIzs4WPj4+4n//93/F4cOHRU5Ojvj222/F+fPnWzymPV0Dk8kkhg0bJkaNGiUOHDggjh8/Ln7zm9+IiIgIUVVV1eIxbekabNmyRbz88sti48aNAoD44osvLK9dunRJjB07Vnz66afi+PHjYt++fWLo0KEiPj7+use0pfMX4vrXQAghZsyYIcaPHy+Kioosj/Ly8use096uwezZs0W3bt3Ejh07RG5urnjvvfeEUqkUX375ZYvHtKVrkJSUJNatWycyMzNFenq6uOeee675f/6vf/1LvPbaa2LNmjUCgEhLS7vhcW3pGrTEbgLPO++8I6KiooRGoxGDBg0Su3fvtrxmMpnEq6++KoKDg4VWqxUJCQkiMzPzhsc8evSouPPOO4VWqxUhISHitddeEyaTyarNzp07xaBBg4RGoxHR0dHi3Xffbfdzu5Hbb79dzJkzx+q53r17ixdffLHZ9jqdTgAQP/zwQ4vHXLVqlfD09BR1dXWW55YuXSpCQkIs1+D5558XvXv3tvq6p556SgwbNuxmT+Wm3egaPPjgg+KRRx5p0zHt6RqcOHFCALD6d9/Q0CB8fHzEmjVrWjymrV2DJs290V3twIEDAoAlFDfHVs9fiOavwYwZM8R9993XpuPY2zXo27evWLx4sdVzgwYNEq+88kqLx7Hla1BSUiIAiF27dl3zWm5ubqsDjy1fgyZ2MaT16aef4tlnn8XLL7+MtLQ0jBo1ChMmTLAMV/zf//0fli9fjr///e84ePAggoKCMG7cOFRWVrZ4zIqKCowbNw4hISE4ePAgVq5ciWXLlmH58uWWNrm5uZg4cSJGjRqFtLQ0vPTSS3jmmWewcePGDj/nJnq9HocOHUJiYqLV84mJidi7d2+z7d9//314enqif//+ludnzpyJ0aNHWz7ft28fEhISrO56T0pKQmFhIfLy8ixtrv6+SUlJSE1NhcFgaIeza50bXQOTyYTNmzejZ8+eSEpKQkBAAIYOHdpsd7+9XoP6+noAgFartbymVCqhVqvx448/Wp6z5WvQVjqdDpIkwcvLy/KcI5z/zp07ERAQgJ49e+LJJ59ESUmJ1ev2fg1GjhyJr7/+GufOnYMQAjt27MDJkyeRlJRkaWNP10Cn0wHADVchvpo9XYMmdhF4li9fjlmzZmH27NmIjY3FihUrEB4ejnfffRdCCKxYsQIvv/wypkyZgri4OPzzn/9ETU0NPv744xaP+dFHH6Gurg7r169HXFwcpkyZgpdeegnLly+3jFmuXr0aERERWLFiBWJjYzF79mw88cQTWLZsWWedOsrKymA0GhEYGGj1fGBgIIqLiy2ff/vtt3Bzc4NWq8Vbb72FrVu3ws/Pz/J6cHAwIiIiLJ8XFxc3e8ym167XpqGhAWVlZe1zgq1wo2tQUlKCqqoqvPHGGxg/fjxSUlLwq1/9ClOmTMGuXbss7e35GvTu3RuRkZFYuHAhLl68CL1ejzfeeAPFxcUoKiqytLfla9AWdXV1ePHFF/Hwww9brdth7+c/YcIEfPTRR9i+fTvefPNNHDx4EHfddZclEAP2fw3efvtt9OnTB2FhYVCr1Rg/fjxWrVqFkSNHWtrYyzUQQiA5ORkjR45EXFxcm77WXq7BlWx+a4mm32xffPFFq+ebfrPNzc1FcXGxVerUaDRISEjA3r178dRTTwEwp9m8vDzs3LkTQMtpduHChcjLy0N0dHSLaXbt2rUwGAxwcnLqoLO+liRJVp8LIayeGzNmDNLT01FWVoY1a9Zg2rRp2L9/PwICAgCYb3RrzTGvfr41bTpLS9fAZDIBAO677z4899xzAIABAwZg7969WL16NRISEgDY9zVwcnLCxo0bMWvWLPj4+ECpVGLs2LGYMGGCVXt7uAY3YjAYMH36dJhMJqxatcrqNXs//wcffNDy57i4OAwePBiRkZHYvHkzpkyZAsD+r8Hbb7+Nn3/+GV9//TUiIyOxe/duzJ07F8HBwRg7diwA+7kG8+bNw9GjR616cVvLXq7BlWy+h+dGv9k2Jc8b9YDYapr18/ODUqm0OhcAKCkpsarN1dUV3bt3x7Bhw7B27VqoVCqsXbu2xeMGBQU1e0zg8nVoqY1KpYKvr+8tnVdb3Oga+Pn5QaVSoU+fPlavx8bGXneWlj1dAwCIj49Heno6Ll26hKKiIvz3v/9FeXk5oqOjWzyuLV2D1jAYDJg2bRpyc3OxdevWG66qbm/nf7Xg4GBERkZed7aePV2D2tpaS0/9pEmTcNttt2HevHl48MEHr9szb4vX4He/+x2+/vpr7NixA2FhYbd8PFu8Blez+cDT5EY9HDd6fenSpfjXv/51w2Ne/bzcaVatViM+Ph5bt261en7r1q0YMWJEi18nhLDqxr7a8OHDsXv3buj1estzKSkpCAkJQVRUlKXN1d83JSUFgwcP7tTerRtdA7VajSFDhlwzNfPkyZOIjIxs8bj2dA2u5OnpCX9/f5w6dQqpqam47777WjyuLV2DG2kKO6dOncIPP/zQqh/A9nT+zSkvL0dBQQGCg4NbbGNP18BgMMBgMFyzyaRSqbT0BDfHlq6BEALz5s3Dpk2bsH379uv+QtMWtnQNWtRJN0d3mPr6eqFUKsWmTZusnn/mmWfEnXfeKXJycgQAcfjwYavXJ0+eLB577LEWj/voo4+KyZMnWz13+PBhAUCcPn1aCCHEqFGjxDPPPGPVZtOmTUKlUgm9Xn8rp9UmTdOR165dK44dOyaeffZZ4erqKvLy8kRVVZVYuHCh2Ldvn8jLyxOHDh0Ss2bNEhqNxmrGzosvvigeffRRy+eXLl0SgYGB4qGHHhIZGRli06ZNwsPDo9kpiM8995w4duyYWLt2rexTspu7BkKY/16cnJzE+++/L06dOiVWrlwplEql2LNnj+UY9n4NPvvsM7Fjxw6Rk5MjvvzySxEZGSmmTJlidQxbvgaVlZUiLS1NpKWlCQBi+fLlIi0tTZw5c0YYDAYxefJkERYWJtLT062mZdfX19vF+Qtx/WtQWVkp5s+fL/bu3Styc3PFjh07xPDhw0VoaKioqKiwHMOer4EQQiQkJIi+ffuKHTt2iNOnT4t169YJrVYrVq1aZTmGLV+D3/72t8LT01Ps3LnT6t95TU2NpU15eblIS0sTmzdvFgDEhg0bRFpamigqKrK0seVr0BKbDzxCmKfj/va3v7V6LjY2Vrz44ovCZDKJoKAg8Ze//MXyWn19vfD09BSrV69u8ZirVq0SXl5eVj8M33jjjWum4MXGxlp93Zw5c2SZgvfOO++IyMhIoVarxaBBgyxTEGtra8WvfvUrERISItRqtQgODhaTJ08WBw4csPr6GTNmiISEBKvnjh49KkaNGiU0Go0ICgoSixYtanZa/sCBA4VarRZRUVGyTMtv0tI1aLJ27VrRvXt3odVqRf/+/a9Zd8Per8Hf/vY3ERYWJpycnERERIR45ZVXrP59C2Hb12DHjh0CwDWPGTNmWKbfNvfYsWOH5Ri2fP5CXP8a1NTUiMTEROHv72/5NzBjxgyRn59vdQx7vgZCCFFUVCRmzpwpQkJChFarFb169RJvvvmm1fnY8jVo6d/5unXrLG3WrVvXbJtXX33V0saWr0FL7CLw3Og32zfeeEN4enqKTZs2iYyMDPHQQw+J4OBgu/qthoiIiFpm87O0APPMg/LycixevBhFRUWIi4vDli1bLPdnPP/886itrcXcuXNx8eJFDB06FCkpKXB3d7cco6ioyOoGVk9PT2zduhVPP/00Bg8eDG9vbyQnJyM5OdnSJjo6Glu2bMFzzz2Hd955ByEhIXj77bcxderUzjt5IiIiuiHulk5ERER2z25maRERERG1hIGHiIiI7B4DDxEREdk9Bh4iIiKyeww8REREZPcYeIiIiMju2VTg2b17NyZNmoSQkBBIkoQvv/zS6vVFixahd+/ecHV1hbe3N8aOHYv9+/ff8LiSJEGSJPz8889Wz9fX18PX1xeSJFl2USciIiLbY1OBp7q6Gv3798ff//73Zl/v2bMn/v73vyMjIwM//vgjoqKikJiYiNLS0hseOzw8HOvWrbN67osvvoCbm1u71E5ERETysanAM2HCBPz5z3/GlClTmn394YcfxtixYxETE4O+ffti+fLlqKiowNGjR2947BkzZmDDhg2ora21PPfBBx9gxowZ17R94YUX0LNnT7i4uCAmJgZ/+MMfYDAYAAB5eXlQKBRITU21+pqVK1ciMjISXOeRiIio89lU4GkLvV6P999/H56enujfv/8N28fHxyM6OhobN24EABQUFGD37t149NFHr2nr7u6O9evX49ixY/jb3/6GNWvW4K233gIAREVFYezYsdf0Fq1btw4zZ86EJEntcHZERETUFnYXeL799lu4ublBq9XirbfewtatW+Hn59eqr3388cfxwQcfADAHlIkTJ8Lf3/+adq+88gpGjBiBqKgoTJo0CfPnz8dnn31meX327Nn45JNPUF9fDwA4cuQI0tPT8fjjj7fDGRIREVFb2V3gGTNmDNLT07F3716MHz8e06ZNQ0lJCQBgzpw5cHNzszyu9sgjj2Dfvn04ffo01q9fjyeeeKLZ7/Gf//wHI0eORFBQENzc3PCHP/zBauPR+++/HyqVCl988QUA89DYmDFjEBUV1f4nTERERDdkd4HH1dUV3bt3x7Bhw7B27VqoVCqsXbsWALB48WKkp6dbHlfz9fXFvffei1mzZqGurg4TJky4ps3PP/+M6dOnY8KECfj222+RlpaGl19+GXq93tJGrVbj0Ucfxbp166DX6/Hxxx+3GJ6IiIio46nkLqCjCSEsQ0sBAQEICAi4bvsnnngCEydOxAsvvAClUnnN6z/99BMiIyPx8ssvW547c+bMNe1mz56NuLg4rFq1CgaDocUbrYmIiKjj2VTgqaqqQnZ2tuXz3NxcpKenw8fHB76+vliyZAkmT56M4OBglJeXY9WqVTh79iweeOCBVn+P8ePHo7S0FB4eHs2+3r17d+Tn52PDhg0YMmQINm/ebBm6ulJsbCyGDRuGF154AU888QScnZ3bfsJERETULmxqSCs1NRUDBw7EwIEDAQDJyckYOHAg/vjHP0KpVOL48eOYOnUqevbsiXvvvRelpaXYs2cP+vbt2+rvIUkS/Pz8oFarm339vvvuw3PPPYd58+ZhwIAB2Lt3L/7whz8023bWrFnQ6/UcziIiIpKZJLgwTIdZsmQJNmzYgIyMDLlLISIicmg21cNjK6qqqnDw4EGsXLkSzzzzjNzlEBEROTwGng4wb948jBw5EgkJCRzOIiIi6gI4pEVERER2jz08REREZPcYeIiIiMjuMfAQERGR3WPgISIiIrvHwENEnWLnzp2QJAmXLl2SuxQickAMPETUIUaPHo1nn33W8vmIESNQVFQET09P2Wpi6CJyXDa1lxYR2S61Wo2goCC5yyAiB8UeHiJqdzNnzsSuXbvwt7/9DZIkQZIkrF+/3qp3Zf369fDy8sK3336LXr16wcXFBb/+9a9RXV2Nf/7zn4iKioK3tzd+97vfwWg0Wo6t1+vx/PPPIzQ0FK6urhg6dCh27txpef3MmTOYNGkSvL294erqir59+2LLli3Iy8vDmDFjAADe3t6QJAkzZ84EAPz3v//FyJEj4eXlBV9fX9x7773IycmxHDMvLw+SJOGzzz7DqFGj4OzsjCFDhuDkyZM4ePAgBg8eDDc3N8vmw1deh/vvvx+vvfYaAgIC4OHhgaeeegp6vb7jLj4RNYs9PETU7v72t7/h5MmTiIuLw+LFiwEAv/zyyzXtampq8Pbbb2PDhg2orKzElClTMGXKFHh5eWHLli04ffo0pk6dipEjR+LBBx8EADz++OPIy8vDhg0bEBISgi+++ALjx49HRkYGevTogaeffhp6vR67d++Gq6srjh07Bjc3N4SHh2Pjxo2YOnUqTpw4AQ8PDzg7OwMAqqurkZycjH79+qG6uhp//OMf8atf/Qrp6elQKC7/Xvjqq69ixYoViIiIwBNPPIGHHnoIHh4e+Nvf/gYXFxdMmzYNf/zjH/Huu+9avmbbtm3QarXYsWMH8vLy8Pjjj8PPzw9LlizpyL8CIrqaICLqAAkJCeL3v/+95fMdO3YIAOLixYtCCCHWrVsnAIjs7GxLm6eeekq4uLiIyspKy3NJSUniqaeeEkIIkZ2dLSRJEufOnbP6XnfffbdYuHChEEKIfv36iUWLFjVb09U1tKSkpEQAEBkZGUIIIXJzcwUA8Y9//MPS5pNPPhEAxLZt2yzPLV26VPTq1cvy+YwZM4SPj4+orq62PPfuu+8KNzc3YTQar1sDEbUvDmkRkWxcXFzQrVs3y+eBgYGIioqCm5ub1XMlJSUAgMOHD0MIgZ49e8LNzc3y2LVrl2UI6plnnsGf//xn3HHHHXj11Vdx9OjRG9aRk5ODhx9+GDExMfDw8EB0dDQAID8/36rdbbfdZlUXAPTr16/ZWpv0798fLi4uls+HDx+OqqoqFBQU3LAuImo/HNIiItk4OTlZfS5JUrPPmUwmAIDJZIJSqcShQ4egVCqt2jWFpNmzZyMpKQmbN29GSkoKli5dijfffBO/+93vWqxj0qRJCA8Px5o1axASEgKTyYS4uLhr7rW5sjZJkpp9rqnWG2n6eiLqHOzhIaIOoVarrW42bg8DBw6E0WhESUkJunfvbvW4cgZYeHg45syZg02bNmH+/PlYs2aNpSYAVnWVl5cjKysLr7zyCu6++27Exsbi4sWL7VbzkSNHUFtba/n8559/hpubG8LCwtrtexDRjTHwEFGHiIqKwv79+5GXl4eysrJW93xcT8+ePfE///M/eOyxx7Bp0ybk5ubi4MGD+Mtf/oItW7YAAJ599ll8//33yM3NxeHDh7F9+3bExsYCACIjIyFJEr799luUlpaiqqoK3t7e8PX1xfvvv4/s7Gxs374dycnJt1xrE71ej1mzZuHYsWP47rvv8Oqrr2LevHlWN0MTUcfj/zgi6hALFiyAUqlEnz594O/vf839MDdr3bp1eOyxxzB//nz06tULkydPxv79+xEeHg7A3Hvz9NNPIzY2FuPHj0evXr2watUqAEBoaChee+01vPjiiwgMDLQEjw0bNuDQoUOIi4vDc889h7/+9a/tUisA3H333ejRowfuvPNOTJs2DZMmTcKiRYva7fhE1DqSEELIXQQRkT2aOXMmLl26hC+//FLuUogcHnt4iIiIyO4x8BAREZHd45AWERER2T328BAREZHdY+AhIiIiu8fAQ0RERHaPgYeIiIjsHgMPERER2T0GHiIiIrJ7DDxERERk9xh4iIiIyO4x8BAREZHd+38dqf48WucjMgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "res_elec_resampled"
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-05-13'\n",
+    "(res_elec_resampled - rooftop_solar_energy).loc[time].plot(ax=ax)\n",
+    "res_elec_resampled.loc[time].plot(ax=ax)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2018-01-01 00:00:00    0.0\n",
-       "2018-01-01 01:00:00    0.0\n",
-       "2018-01-01 02:00:00    0.0\n",
-       "2018-01-01 03:00:00    0.0\n",
-       "2018-01-01 04:00:00    0.0\n",
-       "                      ... \n",
-       "2018-12-31 19:00:00    0.0\n",
-       "2018-12-31 20:00:00    0.0\n",
-       "2018-12-31 21:00:00    0.0\n",
-       "2018-12-31 22:00:00    0.0\n",
-       "2018-12-31 23:00:00    0.0\n",
-       "Name: ghi, Length: 8760, dtype: float64"
+       "net_load    1331.488437\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "rooftop_solar_energy"
+    "net_load[net_load>0].max()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='timestamp'>"
+       "net_load   -2506.148296\n",
+       "dtype: float64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "net_load[net_load<0].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>net_load</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>734.275500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>319.253001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>206.265092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>200.158683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>214.710827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 19:00:00</th>\n",
+       "      <td>756.661431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 20:00:00</th>\n",
+       "      <td>799.025894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 21:00:00</th>\n",
+       "      <td>814.918117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 22:00:00</th>\n",
+       "      <td>842.407166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:00:00</th>\n",
+       "      <td>790.741970</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8760 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       net_load\n",
+       "timestamp                      \n",
+       "2018-01-01 00:00:00  734.275500\n",
+       "2018-01-01 01:00:00  319.253001\n",
+       "2018-01-01 02:00:00  206.265092\n",
+       "2018-01-01 03:00:00  200.158683\n",
+       "2018-01-01 04:00:00  214.710827\n",
+       "...                         ...\n",
+       "2018-12-31 19:00:00  756.661431\n",
+       "2018-12-31 20:00:00  799.025894\n",
+       "2018-12-31 21:00:00  814.918117\n",
+       "2018-12-31 22:00:00  842.407166\n",
+       "2018-12-31 23:00:00  790.741970\n",
+       "\n",
+       "[8760 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load = (res_elec_resampled - rooftop_solar_energy).to_frame()\n",
+    "net_load.columns = ['net_load']\n",
+    "net_load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retail_price = 0.1129  # $/kWh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_metering_price = retail_price * 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "net_load    2.358429e+06\n",
+       "dtype: float64"
       ]
      },
+     "execution_count": 43,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load.where(net_load > 0).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "net_load   -2.358429e+06\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "(res_elec_resampled - rooftop_solar_energy/2).plot()"
+    "net_load.where(net_load <0).sum()"
    ]
   },
   {
@@ -543,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -552,13 +706,13 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -575,7 +729,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4135965.349731396"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4135965.349731396"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rooftop_solar_energy.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +778,7 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -628,26 +822,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "# rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()\n",
-    "rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * total_electrified_data.sum()"
+    "rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3574.700141052983"
+       "2807.397404775099"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/notebooks/10-pypsa-model.ipynb b/notebooks/10-pypsa-model.ipynb
index ab74e5a..1b287ea 100644
--- a/notebooks/10-pypsa-model.ipynb
+++ b/notebooks/10-pypsa-model.ipynb
@@ -2,14 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import pypsa\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "from unyt import MWh, kWh"
    ]
   },
   {
@@ -21,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,13 +66,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
     "n.add(class_name=\"Carrier\", name=\"grid\")\n",
     "n.add(class_name=\"Carrier\", name=\"solar\")\n",
-    "n.add(class_name=\"Carrier\", name=\"battery\")"
+    "n.add(class_name=\"Carrier\", name=\"battery\")\n",
+    "n.add(class_name=\"Carrier\", name='net metering')"
    ]
   },
   {
@@ -83,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +235,7 @@
        "2018-01-01 04:00:00              90    0    0    0  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +264,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "costs = pd.read_csv(\"../data/technology_costs.csv\", index_col='technology')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -323,13 +334,12 @@
        "Residential Battery Storage  78943.789878  3.157752e+06"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "costs = pd.read_csv(\"../data/technology_costs.csv\", index_col='technology')\n",
     "costs *= 1e3  # convert /kW to /MW\n",
     "costs"
    ]
@@ -343,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -353,28 +363,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.09439292574325567"
+       "0.05541531489055132"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "annuity_adj = annuity(0.07, 20)\n",
+    "annuity_adj = annuity(0.01, 20)\n",
     "annuity_adj"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -382,9 +392,9 @@
      "output_type": "stream",
      "text": [
       "ResPV\n",
-      "276446.11698458303\n",
+      "173900.3569535663\n",
       "Residential Battery Storage\n",
-      "377013.20171200635\n"
+      "253931.58886784827\n"
      ]
     }
    ],
@@ -408,115 +418,73 @@
     "                    p_nom_extendable=True,\n",
     "                    )\n",
     "        elif generator=='Residential Battery Storage':\n",
-    "            n.add(class_name=\"StorageUnit\",\n",
-    "                    name=generator,\n",
-    "                    bus=bus_name,\n",
-    "                    carrier=\"battery\",\n",
-    "                    capital_cost=annualized_cost,  # $/kW\n",
-    "                    p_nom_extendable=True,\n",
-    "                    max_hours=2.5,\n",
-    "                    cyclic_state_of_charge=False,\n",
-    "                    )\n",
+    "            pass\n",
+    "            # n.add(class_name=\"StorageUnit\",\n",
+    "            #         name=generator,\n",
+    "            #         bus=bus_name,\n",
+    "            #         carrier=\"battery\",\n",
+    "            #         capital_cost=annualized_cost,  # $/kW\n",
+    "            #         p_nom_extendable=True,\n",
+    "            #         max_hours=2.5,\n",
+    "            #         cyclic_state_of_charge=False,\n",
+    "            #         )\n",
     "        \n",
     "        "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 41,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from unyt import MWh, kWh"
+    "Add a \"net metering\" technology to capture the excess energy."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "unyt_quantity(112.9, '1/MWh')"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "(0.1129/kWh).to(1/MWh)"
+    "retail_price = 112.9  # $/MWh"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
-    "n.add(class_name='Generator',\n",
-    "      name='Evergy Import',\n",
+    "n.add(class_name=\"Generator\",\n",
+    "      name=f\"Net metering {bus_name}\",\n",
     "      bus=bus_name,\n",
-    "      carrier='grid',\n",
-    "      capital_cost=0,\n",
-    "      marginal_cost=112.9,\n",
-    "      p_nom_max=2,\n",
+    "      carrier='net metering',\n",
+    "      p_min_pu=-1,\n",
+    "      p_max_pu=0.0,\n",
+    "      marginal_cost=retail_price*0.0,\n",
+    "      capital_cost=0.0,\n",
     "      p_nom_extendable=True)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Step 6: Run the model"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 31.13it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 89.55it/s]\n",
-      "INFO:linopy.io: Writing time: 0.56s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 43803 primals, 105124 duals\n",
-      "Objective: 4.67e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "('ok', 'optimal')"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "n.optimize(solver_name='highs')"
+    "n.add(class_name='Generator',\n",
+    "      name='Evergy Import',\n",
+    "      bus=bus_name,\n",
+    "      carrier='grid',\n",
+    "      capital_cost=0,\n",
+    "      marginal_cost=retail_price,\n",
+    "      p_nom_extendable=True,\n",
+    "      # p_nom_max=1.3315\n",
+    "      )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -610,7 +578,31 @@
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>-0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Net metering Residential</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Evergy Import</th>\n",
@@ -621,7 +613,7 @@
        "      <td>0.0</td>\n",
        "      <td>True</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>inf</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
@@ -634,43 +626,54 @@
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2 rows × 34 columns</p>\n",
+       "<p>3 rows × 34 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "attribute              bus control type  p_nom  p_nom_mod  p_nom_extendable  \\\n",
-       "Generator                                                                     \n",
-       "ResPV          Residential      PQ         0.0        0.0              True   \n",
-       "Evergy Import  Residential      PQ         0.0        0.0              True   \n",
+       "attribute                         bus control type  p_nom  p_nom_mod  \\\n",
+       "Generator                                                              \n",
+       "ResPV                     Residential      PQ         0.0        0.0   \n",
+       "Net metering Residential  Residential      PQ         0.0        0.0   \n",
+       "Evergy Import             Residential      PQ         0.0        0.0   \n",
+       "\n",
+       "attribute                 p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
+       "Generator                                                                    \n",
+       "ResPV                                 True        0.0        inf       0.0   \n",
+       "Net metering Residential              True        0.0        inf      -1.0   \n",
+       "Evergy Import                         True        0.0        inf       0.0   \n",
        "\n",
-       "attribute      p_nom_min  p_nom_max  p_min_pu  p_max_pu  ...  min_up_time  \\\n",
-       "Generator                                                ...                \n",
-       "ResPV                0.0        inf       0.0       1.0  ...            0   \n",
-       "Evergy Import        0.0        2.0       0.0       1.0  ...            0   \n",
+       "attribute                 p_max_pu  ...  min_up_time  min_down_time  \\\n",
+       "Generator                           ...                               \n",
+       "ResPV                          1.0  ...            0              0   \n",
+       "Net metering Residential       0.0  ...            0              0   \n",
+       "Evergy Import                  1.0  ...            0              0   \n",
        "\n",
-       "attribute      min_down_time  up_time_before down_time_before  ramp_limit_up  \\\n",
-       "Generator                                                                      \n",
-       "ResPV                      0               1                0            NaN   \n",
-       "Evergy Import              0               1                0            NaN   \n",
+       "attribute                 up_time_before down_time_before  ramp_limit_up  \\\n",
+       "Generator                                                                  \n",
+       "ResPV                                  1                0            NaN   \n",
+       "Net metering Residential               1                0            NaN   \n",
+       "Evergy Import                          1                0            NaN   \n",
        "\n",
-       "attribute      ramp_limit_down  ramp_limit_start_up  ramp_limit_shut_down  \\\n",
-       "Generator                                                                   \n",
-       "ResPV                      NaN                  1.0                   1.0   \n",
-       "Evergy Import              NaN                  1.0                   1.0   \n",
+       "attribute                 ramp_limit_down  ramp_limit_start_up  \\\n",
+       "Generator                                                        \n",
+       "ResPV                                 NaN                  1.0   \n",
+       "Net metering Residential              NaN                  1.0   \n",
+       "Evergy Import                         NaN                  1.0   \n",
        "\n",
-       "attribute      weight  p_nom_opt  \n",
-       "Generator                         \n",
-       "ResPV             1.0       -0.0  \n",
-       "Evergy Import     1.0        2.0  \n",
+       "attribute                 ramp_limit_shut_down  weight  p_nom_opt  \n",
+       "Generator                                                          \n",
+       "ResPV                                      1.0     1.0        0.0  \n",
+       "Net metering Residential                   1.0     1.0        0.0  \n",
+       "Evergy Import                              1.0     1.0        0.0  \n",
        "\n",
-       "[2 rows x 34 columns]"
+       "[3 rows x 34 columns]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -679,11 +682,82 @@
     "n.generators"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Baseline\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* uses the sticker price for rooftop solar from NREL's ATB\n",
+    "* does NOT pay for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 6: Run the model"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 26.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 61.81it/s]\n",
+      "INFO:linopy.io: Writing time: 0.24s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 26283 primals, 61323 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -704,132 +778,947 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>attribute</th>\n",
-       "      <th>bus</th>\n",
-       "      <th>control</th>\n",
-       "      <th>type</th>\n",
-       "      <th>p_nom</th>\n",
-       "      <th>p_nom_mod</th>\n",
-       "      <th>p_nom_extendable</th>\n",
-       "      <th>p_nom_min</th>\n",
-       "      <th>p_nom_max</th>\n",
-       "      <th>p_min_pu</th>\n",
-       "      <th>p_max_pu</th>\n",
-       "      <th>...</th>\n",
-       "      <th>state_of_charge_initial_per_period</th>\n",
-       "      <th>state_of_charge_set</th>\n",
-       "      <th>cyclic_state_of_charge</th>\n",
-       "      <th>cyclic_state_of_charge_per_period</th>\n",
-       "      <th>max_hours</th>\n",
-       "      <th>efficiency_store</th>\n",
-       "      <th>efficiency_dispatch</th>\n",
-       "      <th>standing_loss</th>\n",
-       "      <th>inflow</th>\n",
-       "      <th>p_nom_opt</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>StorageUnit</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Residential Battery Storage</th>\n",
-       "      <td>Residential</td>\n",
-       "      <td>PQ</td>\n",
-       "      <td></td>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.430806</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
+       "      <td>0.329983</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>inf</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
+       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 7: Calculate the LCOE from the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Load\n",
+       "Load Residential    112.9\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.objective / n.loads_t.p_set.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the model has an LCOE of $112.9/MWh, which makes sense because it only uses electricity purchased from the grid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 8: Plot some data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TECH_ORDER = ['grid',\n",
+    "              'solar',\n",
+    "              'battery'\n",
+    "              ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def power_by_carrier(n):\n",
+    "    p_by_carrier = n.generators_t.p.T.groupby(\n",
+    "        n.generators.carrier).sum().T \n",
+    "    \n",
+    "    if not n.storage_units.empty:\n",
+    "        sto = n.storage_units_t.p.T.groupby(\n",
+    "            n.storage_units.carrier).sum().T\n",
+    "        p_by_carrier = pd.concat([p_by_carrier, sto], axis=1)\n",
+    "        \n",
+    "    last_cols = [col for col in p_by_carrier.columns if col not in TECH_ORDER]\n",
+    "\n",
+    "    p_by_carrier = p_by_carrier[TECH_ORDER+last_cols]\n",
+    "\n",
+    "    return p_by_carrier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mvX79+qCPi8FICFRUVODUqVPQG/QwpaibGdGZddDHuutWWEjpPxGMIG/w+0o6CdY8d92IOBgQEYXS7t270dbWBmusddAWEiIYWbM++P2RGIyEgJiiSRiaAEkfnCW73hDZkYoK1o34S6QvjcONg9zTzZrvDka2bNkStDEREfVHTNEkjUoaPJubbwUkoKqiCtXV1UEdF4OREBDFq5aMgWsKQkUUsTIz4p/y8nJUVFRAp9chZvjA6U5BZEbWfxP8tCdRuKmsrERLS4vaw4hqIhhBzuD31Vv1sAx1n7dEoX6wMBgJAZEZcaX2XywUSsqKGmZG/KJsjjciFTqzZx8lkRnZW7QXDocjaGMjCjcHDhzA8OHDcc0116g9lKgmghF9nmf9rKzD3ccsBiMRQGRGdBnh8esWjc/KK8pVHom2/ec//wEAmEd5XpRsTDFCb9Ojs6MTRUVFwRoaUdhZsmQJ2tvbsXr16pAsFaXeTpw4oVwcD1a8Kih1I+uCWzcSHmfHCCfefHOGuitpBNESno3PfFdVVYXPP/8cAGD6nudFyZIksW6EopIyPQDgn//8p4ojiV6bN28GACRmJcIQb/DoMSIY2bZtG9rb24M2NgYjQdbc3IyqqioAYRSMfFfAysyI79588004nU4MOXMILEO8qwUSdSPiwEAUDTZu3Kj8++133g7ZnifUZdOmTQCA2OGeN9c0pZugj9Wjw9GBHTt2BGlkDEaCTkzRxCbFQm8L3p4z3hA1I7VVtWx85gOXy4XXX38dAGCY5NnVRXciM7JhU3DnYInCxbFjx3DgwAEAgCHegKbGJnz88ccqjyr69LdT70AkSVIK9INZN8JgJMjEFE3c0DiVR9JFZEbaTrahsbFR5dFoT2FhIcrKyhATFwPbeO/3ERLByL7ifbw6pKggrsiTspOQeFkiAOCfb3CqJpS679RrGeZlNve7ItZgNj9jMBJkSvFqWvj8qnVmnZKl4Yoa77322msAgLSL0/rcZGowhkQDDPEGODud2LVrV6CHRxR2xEnQNtyGxIsSAQlYvmw5SktZtxYqZWVlOHr0KPQGvbJDvKdC0fwsfM6QESrclvUK7DXim5qaGnz22WcAAMOF3k/RACxipejTfXrAlGqCbaw7o/jmm2+qOayoImrUUob3vVPvQKzD3M3PqiurlRrIQGMwEmQiMzJY291QY68R3yxYsACdnZ3IOiNLaQbkC0ue+7Gbt7CIlSJb9+kB6zB3EJ44pWuqhptGhoZSs5Pp/UWU3hL85mcMRoLI6XSGzQZ5pxO9RpgZ8Vz3wlXjJM/av/dHZEbWb2QnVopsBw4cQENDAwwmA8zZ7uNg/Hnx0Nv0qDpShaVLl6o8wuhw+PBhAEBnQqdPjxdTNQxGNKi8vBwOhwMGkwHGFP9OXoEmMiNc3uu55cuXo6SkBNY4K2wTvC9c7U4s7z20/xDbY1NEE1mR1JGp0BncpxydSQf7hXYA7DkSKqI+x5Di2/SydYT7mLV63eqAjak7BiNBpGyQNyRh0A2JQk0EIyVlJSqPRDveeustAED65HSP27/3x5hghCHRAJfLFdS1+0RqE/1FTm8/LqZqPvn0EwbkISAyI77uHC+W9+7YviMozc8YjASRqBcJtykaoFvNSCVrRjwhyzKWLFni/s/ZgXlOkR1hEStFMpEZkXJ6XpBZc60wphjR0d6BNWuCv0V9NHM6nSgrKwMAGFN9y9Kb0k3Qx7mbnwWjnT+DkSAK15U0QFcwUnOkho3PPFBUVITa2lqYrCZl7tRfom6EnVgpUp06dQo7d+4E0NWrorvYse5OoMuWLQvpuKJNdXU1Ojo6oDPolD5T3pIkSdnPJhj9RhiMBJEIRvTp4dF5tTs2PvOOKLJLOyMNOmNgPjYiM8JOrBSptm3bhs7OTsQmxSp7YnUnlvh+Xfh1qIcWVUS9SHxavF8lAzEj3cHI2nVrAzKu7hiMBFG4LusFejY+44qawSkV/yMD95wiGCk9WIqmpqbAPTFRmBBTNAkjEyBJvU+CsQXuzEjRriLU19eHdGzRRNSLWFN7Z6e8IYKRNWvXBDyjzmAkSBobG1FTUwMgPGtGAPYa8VR7eztWrVoFALCM9b23yOkM8QblanH16uBUqBOpSRSvunL6nqo22A0wD3UfH1esWBGycUUbkRnRJft3yrfmWwE9UFdbp9SgBAqDkSARWZG4lDjoreE3TQOwC6unNm7ciNbWVsQmxcI8JLCBZfx58QCAv/3tbwF9XqJwIDIjpvz+s8MiO8K6keARmRFngn8N5nQmHay57uzKunXr/B1Wz+cO6LORQjQ7i83yfKvmUGPjM88UFhYCAJLOTAr4Eu3kq5MBvbuHidhMjCgSVFdXo7y8vMf2B31h3Ujw+dtjpDsxVRPoIlYGI0EiUlhScnj1F+lOZEbKygObbos0ol7ENSLwq6JMySYkfC8BAPDMM88E/PmJ1CKyIil5KQNmh21jbIAOOFxyGOXlbMIYDCIz4uuy3u7EasKVa1b6/VzdMRgJEvGhctrDd98FEYyUlnPnzP40NjYqGYuYsYFZ0nu6lGtSAAAff/wxiouLg/IaRKEmmvmZ8wae2tRb9UoxN6dqAq+zs1OpC/S14Vl3IjPy7Z5vA1p4z2AkSEQwYkjyPy0WLGx8NrgVK1bA5XIhOScZpuTgrIqyZFkQd14cAODZZ58NymsQhdru3bsBAM6MwS/I2G8keCorK+F0OmEwGWCw+38+MiYYYUw1wuVyKdmvQGAwEiRKt7sw25OmO9FrhI3P+iemaMS8drCkXpMKAHjnnXeYqqaIUFRUBAAeFX0rdSNLv+axKMCUHiPp/vUY6U5M1QSyiJXBSBDIsqycUET2IRyJsZ1qPcU+F/1Q+ouMCu7rxAyPga3Ahs7OTsyZMye4L0YUZKdOncLBgwcBQFm6O5CYETGQjBLqa+s5VRlgol7EnBq4lYBKv5F1gWvjH75zCBp2/PhxtLa2AgjvYEQ0PnO2OlFRUQG73a72kMJKRUUF9u3bB0knuYvsgiz1mlS0Frfitddfw+OPP46UlJSgv2ak27RpE7Zt24aysjKUl5ejvLwcer0eeXl5yM/PR15eHiZOnIgxY8aoPdSIUlxcDJfLhRh7jEdTAzqTDjEjY9C6txXLli3D2LFjQzDK6CAyI1JS4BZTiMzIxo0b4XQ6odf7376CwUgQiKxIbFIsdKbwTj4Zk4xwtjpRWVmJcePGqT2csCKyIhljMqCPCX6vGNsZNlhyLThVdgrvvfce7r///qC/ZiTbsWMHJk6c2Of3RBM7ADCZTCgpKcGQIUNCNbSIJ+pF7Ln2Pjuv9iV2bCxa97Zi6bKl/NsPIJEZcSUGbjWgZagFOosOJ1tOoqioCGef7f/uoeF9ptQoEYzEpAZn9UUgiV4j7MLam9il1zAmNDG7JEmIPcNdyCf61JDvxAaEcWlxyLoqCxk3ZSD73mwMvWco0n6ShqRLkmBKMKG9vb1rR2YKCFEvos/0PIgXdSMrVq6A0xm+qxC1RmRGAlm/KOkkZePDQNWNMBgJAlG8akgO/8STMeG7ItbvWteT265du/DBBx8AAIxjQzfVZkp1r9jZd3BfyF4zUomALn58PJJ+loSUaSmwX2BHwoUJSLs2DVl3ZME+xT01uXz5cjWHGnFEZsSV4fnVuDXPCp1Vh+bGZuXx5D+lx0iAF1PYRrqDx7VrA7NpHoORIBCZEdke/lXhhnh3wMRgpIvT6cTdd98Np9OJvMl5yvxoKIimRIdKD4XsNSOVCEZcKf2fEG0FXd0/uYojcERmxDLU872cJJ0ES7b7/nv37g3KuKKNw+HAkSNHAHRd6ASKKGJdvS4w+2oxGAkCJRhJDP+DmyguO1JzROWRhI9XXnkF33zzDaw2K8w3hHaTQ9GU6Ej5EZ4c/SSCEXN6/+9hzIgYSAYJdbV1yn5S5J+GhgblBOjtXk5iU1GuqAmMiooKyLIMk8UEfVxg696sw6yA5D5WVVVV+f18PgUj8+bNQ35+PiwWC8aPH481awZe3vPuu+/i7LPPRkxMDDIzM3HHHXfg2LFjPg1YC8Q0TbCaZAWSCEaqqv3/Y4oEVVVVmDVrFgAg68YspRdLqBiTjYAEOE45cPTo0ZC+diTp7OzEoUPu7JIpo//PoVjFAbDhVqCIrIg93e514bc5yx2MFO0tCvi4opGoF4nLiPO4kNhTeqteyWQFYp8ar4ORhQsXYsaMGXjsscewfft2TJkyBdOmTeu3UdPatWtx66234s4778SePXvwwQcfYPPmzbjrrrv8Hny4UnqMJIfvsl5Bmaap5TQNADzwwANobm5G1tgsWKZ4nmIOFJ1RpxQViwMJea+srAwdHR0wmAyDLq8XhZNLly0NxdAinqj3iM3xfpNQkRnZvYc1I4Gg9BhJCU6GV0xhi2Jxf3gdjMyZMwd33nkn7rrrLhQUFOCFF15AdnY25s+f3+f9N27ciLy8PDzwwAPIz8/HRRddhHvuuQdbtmzxe/DhqK2tTam/COfuq4IIRo4djdxMlac+//xzfPjhh9Ab9Ij5WUzAd+j1lJjbZTDiuwMHDgAAEoYkDPo+ii3sl69YzlUcASAyI3KG99OMIjNSVlKGzs7OgI4rGinHkKTgPL84x1Uc8X81plfBSHt7O7Zu3YqpU6f2uH3q1Kn9pmkmTZqEyspKLFq0CLIso7a2Fv/9739xzTXX+D7qMFZZWQkA7jk6W/B7U/hLTNOcbDmJtrY2lUejHlmWMWPGDABA3rV5SvpRDaJuhMGI75R6kYzBrwit+VboLDo0nWjCzp07gz20iCcyI7pM76sAjElGSCYJnR1d02zku2D0GOlOXMxW1YS4ZqS+vh5OpxPp6ek9bk9PT+93NcakSZPw7rvv4qabboLJZEJGRgYSEhLw8ssv9/s6DocDTU1NPb60Qml4lhYb8Dm6YNDF6CAZ3OOsra1VeTTqqaioQElJCfQGPcxXh7Zo9XQiM1JSUqLqOLTMk5U0gqSXYBvtnqph3Yh/ZFnu2pPGgzbwp5N0EotYAygYPUa6M8S5g5Hao/6fO3wqYD39JCvLcr8n3r179+KBBx7AE088ga1bt2Lx4sUoLS3F9OnT+33+2bNnw263K1/Z2dm+DFMVong1WHN0gSZJkhLdRnMwsn37dgBAUl4SdGZ1F5mJ5b37DnF1h69EMKJL8+y9ZN1IYBw5cgQnTpyATq9TggpvMRgJHJEZCfSyXkGcO+rr6v1+Lq+OuikpKdDr9b2yIEePHu2VLRFmz56NyZMn4ze/+Q3OOussXHXVVZg3bx7eeOMNVFdX9/mYWbNmobGxUfnSUndQkRmREsM/KyKIqZpo7jUighFLjnrTMwKnafwnghFTumcHYbGF/Zo1a9De3h60cUU6kRVJHJoIndG3oF7UjezZuydg44pGp06dCnr9oj7eXYpw4tgJv1sRePXXYjKZMH78eBQWFva4vbCwEJMmTerzMSdPnoRO1/NlxKY6/Q3ebDYjPj6+x5dWiMyIM0E7hXDMjADbtm0DADgz1X/fxFVMzZEaFlT64NSpU8pFgSc1I4C7H4Y+To9TJ09h06ZNwRxeRBP1ItZsq8/PITIju/bsCsiYopWSpbeZg1a/KKZpOjs6/S6n8Dp0nTlzJv7xj3/gjTfeQHFxMR588EGUl5cr0y6zZs3Crbfeqtz/2muvxUcffYT58+ejpKQE69atwwMPPIALLrgAWVlZfg0+HImDoCEp/FvBCyK6jeZgRGRGzDnqT68ZEgyQDBKcnU6lIJo8d+jQIciyDEusxeNGT5JOUrqxsjW870RmxJnmexAtMiMH9x9k4z8/KD1G0gPfY0TQmXTQWdxhhL99kbwORm666Sa88MILePrpp3HOOedg9erVWLRoEXJzcwEA1dXVPXqO3H777ZgzZw7mzp2LcePG4ac//SlGjx6Njz76yK+Bhyvxs2uh4ZlgtEf3/jT19fXKST8cpmkknaT0qOFUjffEst74IfFeHYTFVM2Spdw0z1ciM2Ic4vu0gCndBOiAU62nGIz7Idj1IoLIrPsbjPh0+X7vvffi3nvv7fN7CxYs6HXb/fffHxVbQrtcLk01PBOivSW8UryanQS9NTyWY5tSTWivbUdpaSkuvfRStYejKaJexJju3WdQZEa+2fgNTp48iZiY8N91O5w4nU5lTxlfVtIIOoMOpjQT2mva8e2332pqAUM4ERcywd6WRB+nB46qkBmh/tXV1cHhcLivbEPcRtwfIrI9UhWdwYioF4nJDZ+Tjyg4Y2bEeyIYcaZ4N1VgSjPBmGxEZ0cnli7lqhpvHTx4EA6HA0aL0e+rcTFVwxU1vlP6tCQH93UClRlhMBJAomAoLiVO6d2hBUrNSADWimuRyIyEQ/GqIA7mh0rY+MlbIhgxpHmX+JUkCfET3MXyL730UsDHFemUlTS5iX53L2Yw4r+DBw8CcAfZwSSKWBmMhBExRWNN8b2SXA2iZiRaW8KLYMSUEz51PiIY2X9ov8oj0R5vuq+eLnlqMqBzNz/bsWNHgEcW2US9iGmI/58jsaJmZxE74vpCluWujSI9XN7uK2ZGwpAWV9IAXTUjrc2tUdcSvqWlRSl4tOaGTxAppmkOlx5WdyAac+LECeWg6MtB2JRsgv0COwDg+eefD+jYIp2SYczwP8NoyXIXkn/77bd+P1c0qq2tRWtrq7tkIMh7pInMelWtfy3hGYwEkBZ7jADR3RJ+586dkGUZ8anxSoQfDkRmpL62PuoCRH+IwDI2OdbnYuSUq1MAAO+//76mGi6qTWx+asn3f0WaKdP9999Q34Djx4/7/XzRRkzR2NPt0BmCe5oXx83qmr6bmHqKwUgAabH7KuCeK4/WXiOieDU+P7wa6+lj9cr6fRHk0uBEMBKXFefzc1jzrLCNsaGzs5O1Ix6qqqpCVVUVJJ0Ea47/GUa9RQ9jkvuKnnUj3hNTNNb04Gd7A7U/DYORABInDS0t6xWitQurSC27soKzq6WvJEniihofeLsnTX9EduTV117V1EadahFZkZTclIDt7cQiVt+JzIguJfineHHuOFbvX80hg5EA0mLDM0EUsUZrMGLIDp8pGoG793rP12W9p4s9KxbmTDOam5rxj3/8IxBDi2giGDHnBa6DMTfM850IRpzJwS8ZEF2Om443+bV9BYORAGltbcWxY+7IUIuZkWicpmlvb8eePe7NuMKh8+rpuGGe97zdIK8/kk5C8tXuBg1/+9vf0NHR4ffYIpkIRlxDA5dhFJmR3Xt2B+w5o4WykibIy3qBrmkaWZb9qu9hMBIgIitiibVAHxMeXTy9EY079+7ZswcdHR2wxluDXnHuC2Oqe0wHDh1QeSTaIMtywIIRAEi4MAH6eD0qKysjdvuKQJBlOaDFqwJ37/VdqHqMAICkl6CPdZ/z/Fney2AkQEQwEpsWq/JIfCPm/Sqro2cvCFG8mjgsMWgbSflDTNMcKGEw4ona2lo0NzdDkqSAHIR1Jh0SJiYAAHfyHUBFRQXq6uqgN+hhGRrAYOS7aZrqymqcPHkyYM8b6Y4fP46GhgYAoQlGgMD0GmEwEiBKvUiS9upFgK6akapq/9aKa4moF9ENCc+PgZimqTjM5aWeEFkRe6YdOmNg3lNxMD9YejAgzxeJNm/eDABIzk+GzhS4z5Ih3gB9rB6yLGPfvn0Be95IJ6Zo4lLiAlZMPBhRN8JgJAwoOxUnqjsOX0VjzYgIRjBE3XH0R0zTNDc2o7GxUeXRhD+xrDcmM3B7DInpO7bl759SvJobuOJVQUzVbN26NeDPHanEFI0twxay1wxES3gGIwEiai2ccdpqeCaImpFjddHREt7pdGLnTneraUtu+BWvAu5eC+KKg0Wsg1N+R0mBe05RjF5ZET3Tl94SwYhzaOCPfbFnuKe9WbPjOZEZ0aeGrnaR0zRhRAQjerv2ileBrj+mlqaWqOj4efjwYbS2tsJoNipz0+GIK2o8J7KTroTAregQy/QbjzeitbU1YM8bKboXr1rzA99gK/58dzPCwqWFOHHiRMCfPxIpy3qTQndhHIg+VQxGAkQEI+HUUtwbepsekt5dxOnvhkdaUFnpvtKNTY31e4fRYBJTNQxGBqfsDZUcuM+g3qaHzspOuP0pKSnBiRMnYDAZYB4S+KDekmWBeYgZnR2d+OyzzwL+/JFIZEaMaaFbIRiI/WkYjASIEozYtRmMSJKkZHWioW6kutq9j4I5MXyzIgAzI94QwUig+/yI94DBSG9K8eqw5KDtgRI/wZ0d+eCDD4Ly/JEmlMt6BVEzUl3r+/40DEYCQJZl5QSu1WAEiK6W8FVV7gheZw/vj4A4sZaUsQvrQJxOp5LtEnuaBIqyg/LhwwF93kggpmiMucG7Cref795F+eslX7M1/yBaWlqUC+OQBiPfnTvq6up8fo7wPhJrRENDg9KhUavTNEB0BSMiM+KKC689aU5nTHQf5Csqubx3ILW1tejo6IBOr4MxIcDByHcBITMjvYlgRB4qB+01zEPMMGea0dHegc8//zxorxMJxNYRMfEx0NtCWMD6XWbkeB07sKpKRKLWeGvA+huoIZq6sIpgRI4L3kE0EEQwUl3l3/bckU5M0cSlxCm1T4EipmkOlXJ5b3cul0tZchuM4lVBkqSuqZr/cqpmIGKKxp9dq30hakZam1vhcDh8eg7tnjnDiBKMJAZ/u+ZgEsFINGVGAn0VHWiGhK4l1/5sQhXplO0YUgK/TFtkRtj4rKf9+/ejpaUFJosp6CvS7Be4p2q++uorNDc3B/W1tEwUrxrSQpuh18foge8SMb5O1TAYCQBljs6uze6rQjS1hBc1I+JkH64MdgOgA2SXHBVBoq9EMKJPCnxqWrTlryjjVFl3YoomeXhywLNRpzMPNcOUbkK7ox1ffvllUF9Ly0RmxJUU2ulnSScpUzUMRlQkThLhXgw5GBGMRENLeJEZCfdgRNJJSsbqyJEjKo8mfAWjx4ggMiPHjh6Lih48nhIrafQ5wa9NkCRJ6TnCqZr+iWBElxr6c5G/XVi1ffYMEyIz4ooN72LIwURLzcipU6eU9upiT55wJupGGIz0TwQjUmLgr9D1sXpIJvfzVlQwOyKIYATZoXk9sarmy0VfsgFdP8Q0TShX0giiboTBiIrEyVuODe9iyMGIzEikt4RX6kXMRuhiwv8jILI3YmqJegvmRpWSJLHXyGna29uVXa+tw0JTK2fJscCYaoTjlAOLFi0KyWtqicPhUD4H5rTQ909iZiQMaL3hmSDG39LU4nNFtBaIk7ot2QZJCt/uqwIzI4MLVsMzgb1Getq1axccDgdi4mNgSg/NVbgkSYg/xz1Vs3bt2pC8ppYcPnwYsizDHGNWshSh5O/+NAxGAiBSgpFoaQmvle6rAnuNDKy1tRXHjrmzecEKRpgZ6embb74BACSOTAxpQC9W7ew7uC9kr6kVol4kPjNelYssEYz42oWVwUgAREL3VeC7lvDxkd8SXgQjWtnUUEzTlFXyRNgXUcdhsVncSwyDQOmEW8pOuEBXMIKc0L6u2G/lwMEDoX1hDVDawIcoU3U6scN4dQ2DEVU4nU5lKZMWiiEHI6LbSC5iFdM04d7wTBCZkcojkb/k2hdiisaWZgvaa4hg5EAJT4JAVzBiyAvtBZgozKwsq4TLpe0FA4EmildDvaxXUM4dR307dzAY8VNdXR1cLhcknaREhloWDY3PtNIKXjAkfveeVEXue+IPpWgvOXjTbmKaRrxWNGtoaMD+/fsBhK54VTAlmQAJaHe0R/QFky/EeyKlqFMHJwpY6+vqfXo8gxE/iQ+ELcEW1lvReyqagpFw7zEiiC6xrc2tXNLYByVASAzea4gC1rrqOmUfqmi1adMmAEDikETlBBQqkkFSslQiE0DuzVq3b98OALBkB74LsSdEZuR4/XHIsvdZZwYjfhInbUuiOn8AgRYN0zRaaQUv6Kw66MzujyqX9/YmghGnPXjt8g3xBkgGCS6XK+pXNYkpmtjhsaq8vpiqEZvCkfuYdvToUej0OtWCEVFv2N7W7tNFE4MRP4mTdiTUiwBd6ehIrlbXSit4QZIkZaom2k+EfVGW9SYF7zMo6bquyKN9ea8IRpzZ6uyVJNrzMxjpInq+JOUkQWdS57SuM+uU5oC+rMZkMOInEYxIcdqfogG6KrH3H9iv8kiCw+Fw4Phx9zbXWglGAPYaGUiwe4wIYqommpf3yrKsBCOWYepcgYvMCFfUdBHBiCVXvQy9JPm3Pw2DET9FSit4oXu1eiTuEiveL71RD71NOwXH7MLaN5fLpSztDXYwYkpmr5GSkhIcO3YMBqMBlhyVgpHUyM/eekvUi7iy1D0P+dP4jMGIn5Tainh1xxEoxiQjJIOEzo7OiNyHQ9SLxCbHaqL7qsDMSN+OHj2K9vZ29zRKkGuARGYkmnuNiKxIyvAU6IzqnD7EBVNpSakqrx+ORGbEnKNuI0exopTBiAoipeGZIOkk5cpDNNGJJCKzoJXuq4IIRg5XHFZ3IGFGTNHEpcRBMgQ3uBT1VAdLI+9z4Sm1+ot0J45PDfUNaGlpUW0c4eLYsWPK50CtbJXAzIiKIqUVfHfiyiMSgxFlWa/G3i8xTcOW8D2Jg7A1Jfj9LljAql7n1e70tq4p1tJSZkfEFE3ikMSgdSD2FIMRFSnBSLy2Tm4DEUWskRyMyPHa6L4qiMwIa0Z6EsGIPin4B2ExTVNbVRuV3T8dDody4rMOD22zs9OJCyb2GukKRmx5wetA7ClRwFpV4/1xisGIHxwOBxoaGgBo70p7ICIYKd5frPJIAk/pvqqxgmOxtLe+tj4qT4T9EcGIKyH4vxNjghHQA50dncrfUTTZuXMn2tvbYbPblGBALcbU7+p3uLxXqRdRu3gV6Oo1Ulnl/dYVDEb8IOpF9AZtrcwYTCQv79VajxFBBLudHZ3KDrXUFYxICcEvRpb0UlftThRO1YgpmoRRCaoXf7PXSBeRGTHmqN/rSvT68WUfLQYjfhDBiC3JpvqHM5DMae7izvLS8oi7CtdaK3hBZ9ApVx1cUdMlVD1GBFHEGo3Le5V6kWx1xwF0TdNEYvbWG83NzV37BOWqO3UGdH0OayprvG4Jz2DED6JexJygrZUZgzEmu9PR7Y72iDvxaTUYAbi8ty+hDkaiuYhV7Emj5koaQWRGDpVEd83Izp07Icsy4lPjw6JuURyj2h3tXmdwGYz4IRJX0gDudLSyjDGCilg7OjqUzoBa2ZemOzFmBiNup06dUt5P0ZAs2EQRa7QFI+3t7UqxqFp7n3QngpEj5Ucisjmjp8QUTVx+nMojcdMZdUpQ5G2fKgYjflAanoXH30FAReKKmtraWsiy7J7yiNVejY8oYuWKGjdxsDPHmKGLCc2hTATpB0qjqxX54cOH4XK5YLKYwiKrKLK3nR2dUR2ci+JVeUj4rA4U2UNlN20PMRjxg6gZ0drKDE9EYq8RMUVjS7RB0mmvxkekQCsrvS8Oi0TiYBebFrpuutE6TSOyIvFZ8WFRHyfpurK30VzEKjIjhqHqB4iCKGJlZiSERGZEFx95v0ZzursO5tv936o8ksARwYg1Sf1CL1+IK9LSCjZ6ArqCkVBN0QBd0zRVlVVeF+hpmbgoEcXt4UCpG4nSXiMOhwN79uwBAFjy1J86ExiMqCBSa0aArszIvgORsxmVsqxXo+8XC1h7CuWyXsGYZAQkoL2t3aedSbVKnPBdyeGTBRbHqGjNjBQVFaGzsxMx9hglAAgHYiwlh717XxiM+CGig5HvakYOlxyOmCtArXZfFUQwUlNdo/JIwoM4CbkSQ3eC1Bl0SoYqmqZqRGZESlF/ikYQmZEDh6KrfkcQ9SIJw9Tv+9KdmMosLfMug8tgxA+RHIwYU4yADnCccnQV6mqc0n01Lnyu7rwhClgbjzfC4XCoPBr17dy5EwBgGhLabqDR2GtEBCNqd17tLhKzt94Q9SLS0PAJRIBujc8qvKttYzDio5aWFrS2tgKIzGBEZ9ApEe6BA5Fx5SGCEb1deytpAPcGYWJn2mhsR95de3s7iovdDa9CvVNptBWxOp1OZUO6cApGREv4w6WH1R2ISkRmRBoSZsHId5+Puto6r5ZdMxjxkVhJY7QYobdo8+Q2GFHEGikrarTaCl6QJEnJjkR73cjevXvR0dEBa7w15PPloog1WjIjlZWVaG9vh96gD1lzOU+IaZqmhiY0NjaqPJrQOnnypBKMWIeFV0G+wW4A9IDL6fLqosmnYGTevHnIz8+HxWLB+PHjsWbNmgHv73A48NhjjyE3NxdmsxnDhw/HG2+84ctLhw0xdWFLUn+nxGCJtOW94oOhxYZnAotY3cQUTUJ+6OfLlV4jJZGRMRyMKF61Z9rDakm83qqHPs59IRhtRazr1q1DR0cH7Gl2JSgLF5JOUo6x3vQa8ToYWbhwIWbMmIHHHnsM27dvx5QpUzBt2rQBX/TGG2/EsmXL8M9//hP79u3De++9hzFjxnj70mFFBCOmhPD6QwgkUcQaCXOyTqdTyWZpNTMCdAVS0d74bMeOHQAAXVbok7siO+DtagGtEhcjlvTwWT4qROuGeStWrAAA2Mfaw6p4VRCfEW+W93p9VJ4zZw7uvPNO3HXXXQCAF154AV9//TXmz5+P2bNn97r/4sWLsWrVKpSUlCApKQkAkJeX5+3Lhh0RjIjNyyKRyIxEQq+Ruro6uFwuSDopLPZw8BWnadxEMIKs0L+26GtypOIIZFkOy5NBICmZ0RR1x9EXU5oJp0pORV2vERGMOIeHZyt8X3qNeHVZ0d7ejq1bt2Lq1Kk9bp86dSrWr1/f52M+++wzTJgwAc8++yyGDBmCUaNG4eGHH8apU6e8eemwI66yEavuOIIpkpb3ikyCVruvCiIzUlYRHfUKfZFlWZmmCXXxKtBVM3Kq5RROnDgR8tcPNXGil5PD7xgQjY3PmpubsXnzZgCArSA8ywREMOLNNI1Xl4j19fVwOp1IT0/vcXt6enq/yz9LSkqwdu1aWCwWfPzxx6ivr8e9996L48eP91s34nA4eixdbGpq8maYISF+XmdceEamgWBKMQEScLLlJOrq6pCWlqb2kHwmghFrYngVe3lLZEaiORipqKhAQ0MD9AY9zFmh7wiqM+mgj9fD2eTE4cOHkZiYGPIxhFI4LusVlOW9B7U/leyptWvXwul0IjErUalfCje+ND7zacL19LTkQKlKl8sFSZLw7rvv4oILLsD3v/99zJkzBwsWLOg3OzJ79mzY7XblKzs725dhBpXSY0TDKf/B6Ew65Y9K60Ws4XxA9Ua0zpF3J7IiSblJ0BnVWRAYLb1GZFlWsg4iUxpORJaqpDR6Pg9iiiauIHx3aPWl8ZlXn+SUlBTo9fpeWZCjR4/2ypYImZmZGDJkCOx2u3JbQUEBZFnud8OvWbNmobGxUfnytsd9KCjBiIaLIT0RKStq9u1zXzm5UrTZ8EwwZ7gzAfU19WGZMQwFUS9izlZvn5Ro6TVSW1uL1tZW9wqJlPBbhSaOT9WV1V71tNAyEYy4hofvsUxcxFYd8bzQ3qtgxGQyYfz48SgsLOxxe2FhISZNmtTnYyZPnoyqqiq0tLQot+3fvx86nQ5Dhw7t8zFmsxnx8fE9vsJNJHdf7U5cDWm98ZkIRnTp2m6to7fplb858TNFGxGMuDLVOxiLItZIz4yIixB7uh06Q/h9doyJRkh6CZ0dnVFR1H3ixAmlv0i41osAXcH6iWMnPK4P9fqva+bMmfjHP/6BN954A8XFxXjwwQdRXl6O6dOnA3BnNW699Vbl/jfffDOSk5Nxxx13YO/evVi9ejV+85vf4Oc//zmsVm3O38uyHDXBiGh8pvXlveLELTILWmbO/G5H5W+1v8rJF0ob+Gz1pg1EluBgqbYzhoMRUzTW9PA8Vks6qWtKoDTyd7NevXo1XC4XkrKTlJ5D4Uhv00MyuUs3PG1D4HUwctNNN+GFF17A008/jXPOOQerV6/GokWLkJubC8DdWKp7BW1sbCwKCwtx4sQJTJgwAf/3f/+Ha6+9Fi+99JK3Lx02jh8/jo6ODgCRXTMCdGVGdu/ZrfJIfNfc3Kx8IBiMaFtTU5NygrRkq9f3QgQjh0oiexVHOG6QdzrRFj4aghExRRNbEN7LOCVJ6tqjpp9yjNP5dCa99957ce+99/b5vQULFvS6bcyYMb2mdrRMZEWs8VbVCuhCxTLUfcA/uP8gOjo6YDSGbzTeH5EViU2Khd6m/b4wpkx3gLhn7x6VRxJ6u3e7g+L4tHgY4tS7EBDTNN5uBqY1IvBzJYdvfYIp1YRWtEZVMCIPD79l1qczJhvRXtPu8fRZZJ9Jg0QJRjS+TNQTxhQjdBYdOts7sX//frWH4xMRjMQNCd/qc2+IzEhRcZHKIwk9US8Sl6vueykyI80nmtHc3KzqWIJJC6vQxMqmg4cie8rs2LFjyhSlbUz41osI3jY+YzDig2hoBS9IOknJjogPgtYoxatpkfHnLoKRspIydHZ2qjya0BJ/g3KWuleGeqteybJFchGrsqw3nIOR75a77z+kzYslT61atQoAkJKXoolaRW+naSLj6BxiYsM1nT06fn2iy+WuXbtUHolvRDDiTImMpX/GJCMkk3sFQTSkprsTmRH9EPWn2yJ9997jx4/j+PHjAMI7GBE1I4dLD6s7kCATUzTWMdrIyIupzJJyz3rARMfZNMCU7quxkXFyG4zIjGzdvlXlkfhGBCOi1kLrJJ2kFOJGUxFrZ2enUjOiRhv400V6rxGRFYlLiYPOHL6nCjFNU19bj7a2NpVHEzzLly93/2O4uuPwFDMjISCCESkufCvMA0msWti5S3vTNC6XS6l1iYSVNIJogx5NwciBAwfQ1tYGk9UUFlfqkd5rRAQjtozwrk/Qx+mhs7hPZZH6XlRVVWHv3r2QJEkT9SJAVzBSU9X3VjGnYzDig2jpMSKYh7pPfHU1daivr1d5NN6prKzEqVOnoDfolbnlSCDqRvbu3avySEJHTNEk5SeFxWaHyvLe0shc3iuKV/Wp6k+JDUSSurrDRuo2CV988QUAIGNMhqqryLwhModtJz3LVjEY8UG0BSN6q16Zl9Va3YjIHCQMSYCkV/8EFigiGNm9V7v9X7wlilfDoV4E6LaKoyQyV3GIzIgzKfyno8WFRqTWUIlgxDBOO+ccnVnnVSsFBiM+EAWskb4vTXfWHHfRlNaCEVEvYslUv8YgkEQwsn/ffshy+PccCIRvvvkGgPoraQRx5RepvUZEZsSYFv69hURgGInByKlTp7B06VIAgPksbU01i6kaTzAY8ZLD4VAqzKMlMwJ0TdVobXmvCEbk1PA4gQWKKd0ESEBzYzPq6urUHk7Q7dixAytXroROp4NtbHjMmYupgeN1xz3ef0NLtNBjRBBjPHBI23to9WXZsmU4deoU7Ol25TisFSJg9wSDES8dPXoUAKA36KGPCY90cSiIzMiW7VtUHol3RDAipUXOFA0A6Ew65WQYDUWsf/zjHwEAOVNylP2S1Ka3dRVOdt8CIxI0Nzd39VPSQDAiPguRGIyIKRr7uXZIkraOY8yMBJH4gNqSbGFRRBcqYrv2fcX7NNVoK5I2yDtdtOxRs2fPHnz44YcAAMPU8MlGSlLXJm2Rtopj8+bNANy79WphCwVRM1J+OLKCQlmWlWBEGqu98w0zI0EkghFzQuSd3AZiSjFBZ9Gho71DM23hW1tblVbEkdJjpLtoCUb+9Kc/AQDyLsqDZUh41f6IWoVI6zWybt06AED8qHiVR+IZkRlpaWrBiRMn1B1MAO3YsQNHjhyB2WrWzJLe7pgZCaJoLF4Fvmu09d18pVaKWA8ccKdsbQk2GGIj7/2KhhU1+/fvx8KFCwEA+ivD7wo9UjMjIhhx5YXvBnnd6S166OPdfx+RVMT6+eefAwDSzkmDzqS90zWDkSASmRFExp5rXhGdWLUSjETaBnmnE8FIcXGxyiMJntmzZ8PlciH3e7mw5oZfG2ylv0Vp5PS3cDqdWL9+PQDAMiK8MlEDicQVNSIYQYG64/CVMcWofEYGw2DESyIYccVq44ohkEQLbq20hRfTF4b0yMuKAF3BSFVFVUSs5pg7dy5mzZqFxYsXo6WlBaWlpXj77bcBAPqp4ZcVAbpOgAdKI6dwsqioCM3NzbDYLEr3ZS0QdSOR0visuroaW7a4FwzEnBWj8mh8Y0o2YcQfRnh038g8SgeRCEZ08dEXx2mtLXykbZB3On2ce+dYZ6sTBw4cwFlnnaX2kHy2Z88e3H///QCAZ555Bnq9HmlpaXA6ncgen42YYeF5MBYrTUoORsYJEOiaokkenaypIn3RmDFSMiNffvklACCzIBPGhPDv9eKv6Duj+knUjETDH8fpxDRNbVWt0mslnIlgxJgRme+VJEkRU8S6bNkyAEBMcgxi0mPgdDq76rOuCt9rJlOGOxhpqG/QxGfCEyIYQZ6qw/CayFLtO7hP5ZEEhtJ19Yzw/fsPJAYjXoq2VvDdhXNbeNGl0OFwAHAviYvEDfJOJ1YJab1uRGyPnnR5Eob9ZRhG/XUUhtw1BDkP5MA2KnxXEegteqVIT+vvgSCCEeMIbQXxSpYqAup32traUFhYCAAwnRl5KwH7wmDEC7IsR3UwAoRnEassy7jhhhtw5ZVXYuzYsfjwww9RVVWFlpYW6PQ6TbSz9pWyYV6xdjfMczqdWLlyJQDANMZ94DWlmpB4USLizwv/paWRtINyZWUlysrKoNPrYB0WfgXDAxGZkcqySrhc2q7pe+utt3Dy5EkkpCcotXqRjsGIF5qamtDW5t6B0BAfpcHIdx8MsYNqOPjkk0+waNEiAO7itRtuuAGXXnopACAhKwE6Q+T+mUfC8t4dO3bgxIkTsNgsYbliZjCRtKpJZEVSh6dCbwnPouH+GJOMgAR0tHd0rXrUoFOnTuGpp54CACRenai5rqu+ityjdBAoDc9sZujM0fmrE5mRrTtCu6KmtLQUe/f2vvpvbW3FjBkzAAB51+Yh9Yep0Jl0yr4a1kztndy8IU6Eh/Yf0lRn3O7EFE3KGSma3FlZCQj3aDcgFEQwYhquvakBySApU2ZaLmKdN28eqqqqkJiRCOtFkX386i46z6g+EgV1tqTwncMONnHlWrynOGTLSZ1OJyZNmoQzzzwTCxYs6PG9P//5zygvL0dSZhJifhCD9B+nY8TsEbBfaIekkyJ+vtWUZoLOqkO7o73PYE0Lli9fDgCQR2hzM0MxTbNn7x6VR+I/rRavCqJuRKvBSFNTE2bPng0ASLg2ATpj9Jyio+cnDQCRGYnGlTSCMdUIQ4IBHe0dypbuwXbw4EHU1NTA5XLhjjvuwIsvvgjA3Z3zr3/9KwAg8cZEJVtlSjYh+55sFLxWANuUyA4cJZ0Ea547QBT7iWhJR0cH1qxZAwCwjNbm3LgIRrTe76W5uVmZfo0ZGZ5LqQcjGmxpNRh5/vnncezYMaTmpSLme9p8D3ylqWBE7a3SRTAi2g5HI0mSlD0SRNFhsIkDpN7o/r3PmDEDTz31FO6//350dHQg54IcWM7pfSKL5FqR7rQcjGzZsgUtLS2IscdoqsFWd6LfS/cVXFr0zTffwOVyISEjwas23uFEND47eOigyiPxXl1dHebMmQMAiL02VpNTlv7Q1NF6925152SV7qtx2q7U9pcIRpavXB6S19u5091kLfOSTKT9KA0A8OSTT2LJkiUwmoww/8QcNUVefbHmu4OR9d+sV3kk3hNTNMnjtNVgq7vu/V60XMQqpmjiRmt3+wSxomb/Ie0FhbNnz0ZLSwsyR2ci5rzoyooADEa8IoIROVabc9uBYhvtDkY2btyorC4KJhGMuDJdSLsuDZn/l6l8L/uH2TCnR24fEU+IYGRv0d6QvB+BJIpXXcO0HeCLqZpICEZcudp9L0TNyMED2sqMVFZWYt68eQAA67XWqLy40lQwonYb8mjdsfd0pgwTDHYDOhwd2LRpU9BfT0zTiGXFyVcmI2dGDtJ/lA7LVG2m9gPJmGKEPk4PZ6dTCdy0wOFwKCdAa4G2Vw2IzEjR3iKVR+Ibp9OJjRs3AtDW5ninMw8xAxJQX1uPo0ePqj0cj3366adwOBzIGpcF6xna/iz4SlPByPYd21V9/WhveCaEsm6krq4OVVVVAADz0K4MSPw58Ui9LlWT22oHmiRJSnZES3UjIrMWlxynnMy1SmRGtLq8d9euXe7N8WItyvJ9LdJb9Up2REuBudIwLx9RmRUBNBaMlB4qVbVancFIFzFVs2zFsqC+jjigJA5JhN4avYXDgxHByKbNwc9UBYqoF0k4I0HzB2ARTJUeKoXTqb2NGcVFRUpBimZrdwRLbvg1ZhyMCEakNG3/7v2hqWBElmUUFamTBu3s7FRW8xjt2qw0DySRGdm4YaOyH0wwiGDElhvZS3T9pcUiVqVeZLh2axQEY4oRklFCZ3unJpeVivcCw9UdRyBYc9yfha3bQtuY0R8iGNF6htAfmgpGAPWi3bq6OsiyDEknQR/HK3RTpgmGeAPaHe1BrRvpXrxK/YvJd1ffl+wvQXNzs8qjGVxra6tSoxBToP2VA5JOUjZk1FoRq9PpxOrVqwEA5tHaPxmK2rIt27aoPBLPtLS0oLKyEkDXLtDRiMGIh7p3X9V6GjMQJElCzBj3SWTVqlVBex3xfhuGcmpsIAa7AcYkI2RZxrZt29QezqA2bNiAjo4O2NPtSm8IrdPqiprt27ejsbER1lirMsWhZSIYKT1YipMnT6o8msGJ3jS2RBsMsdF7nNNcMLJ5mzoFeqJexJoQnZXOfQl23YjD4VAO7NGyc6U/tFTEKrr3xo+O13y9iCCCEa215e+xN1AEXGgZ7Abo4/VwuVyqTet7Q0zRxA3Vbn+XQNBcMLJ7125Vtodm8Wpvom5kw/oNaG9vD/jzFxcXo7OzE9Y4q2Y7QoaSCEa+2RSaNv3+EFN7zqHaK/bsj5jv37V3l8oj8U4k1e4A360u+65uRAtFrPv27QMA6NOie/pfW8GIAWg72YZDhw6F/KWVLanjQ/7SYcucZYY+Tg9HmyMoV+PiQJI4LHq20faHdZj7ALxx00aVRzIwWZaVzIglP3IyXiIY2b9vP2RZG40RI2FvoL6ITOr27eq2g/CEyIy4UiMjGPSVpoIRyxD1lmwpreBjo/sPpjtJkpSpmmD0GxHFq1IWAxFPiB2VK8sqUV9fr/Jo+ldRUYHa2lroDfqIqFEQTBkmQAJam1q7Ll7C3NatWzW/N1BfRDCyaWv4L3UXwUg0F68CWgtGhqoXjIgCVl28pn5lQRfMfWqUpkVZAX/qiKS36ZUD2pYt4buSQEzRJA9LjqimdTqjTmm4pZUiVjFFk3yGdvcG6ouYptlbtDes+764XC6lgNWUyWBEM0QHTjWWbLFmpG8iGFm/bn1A+43IstyrDTwNTgtFrCIYMedpfxnp6bS2YV6k7A10OlOGCZJJQtupNhw8GL771JSXl6OtrQ0Go0HZ5C9aaSoYsWa7D7RqtIUXLckZjPRkzjLDkGhA26k2fPzxxwF73srKSjQ0NEBv0CurFGhwIhgJ57oRUS/iyo6sEyDQFYwo7b3DWHt7e9feQGMia5WgpJNUzaR7ShSvJgxNgKSPnMyULzQVjJiy3HOydTV1Id0EqaOjA2VlZe4xpEV39Ho6SSch8eJEAMD8V+YH7HnFFE1SThJ0Rk39mapKND/btGlTWBZRdnZ2KlNIouA2kojAeWdR+O+LsmnTJpw8eRKxibHuDeYijKihCudgROm8mhF5v39vaeoor9YmSGVlZXA6nTCajcyM9CHp0iRAB6xetTpgPRbEAcSczQ+pNyw5FkAH1B+tR3l5udrD6aW4uBgnT56ExWaJyNbXWsqMiCmapDOSInK1mpje3bw1fKcsuZKmi6aCEaDrDyyU0a6Yc7QPsUdUkVegGBONiDvH3bDnlVdeCchzKm3gM/gh9YbOrEPMMHd2ZMmSJSqPpjdRL5I0IikiP0tiC/u6mjpUVFSoPZwBRWq9iKAs71V5t/eBiGkaXbrmTsUBp7nfgPgD27Y9dC2vRTBiTou8K7lASfqfJADAgn8tQGtrq9/PJ4IRUw6nxbwVe2YsAGDRokUqj6Q3US8i5UZeIAK4s7eibmfZsuDuaO2PtrY2rF/v3lQx0upFBMtQCyABx+uOh+1Sa26Q10VzwYhYshXKFTUiGJGTw28OPlzEjo2FKc2E5qZmvP/++349V0tLi/I7j6TeB6ESd5Y7S1W4tBAdHR0qj6YnkRnR5Wju0OOx2LHuYDCcg5GNG927bcclx0XsklKdWafUYoRj3UhTU5PSMoI1IxoMRkRmpORAScg2QVKWhqWG5OU0SdJJSLzUXcg6b/48v55r5cqVkGUZCRkJMMSzRsdbllwL9HF6tLa0Kle/4aC1tVXZKyRmuPZ36u2P7Qz3cvfFSxaHZRExAHz66acAgMQzI7u7sWiqF47BiJiiiU2OhT4mulvBAxoMRgwJBhgSDXC5XErKN9hEMMKVNANLnJIIySBh29ZtfjXd+vDDDwEA9vPsgRpaVJF0EmLHua/OFy9erPJoumzbtg1OpxPxqfEwJkbuXkMxw2MgmSTUH60Py03zOjs78d5777n/c46qQwk6cfG6ddtWlUfSm7JB3pDo3iBP0Fww0r0F+erVq4P+ek6nEyUlJQAAUzqDkYEY4gyIP9+9ec/8+b4t8+3o6FCu2nRnae7PM2zEnek+wH3+5ecqj6SLmKKJHxHZGzzpTDrEjHRnfsJxqqawsBC1tbWITYxF3LjIPhGKYESNRpmDEcEIi1fdNPlbiBnl/qCvXLUy6K9VXl6Ojo4OGEyGiL6aCxRRyPruv9/F//3f/2HmzJl49tln8fnnn3uUsl65ciUaGhoQlxSnvM/kvdhxsYAE7Nm9R2nYpzYRjLiGRubqje5iz3BnppYUhn5FkyzLmDt3Lm688UbU1tb2+v5bb70FAEi+MBmSIXKnaICuGsOykjK0tLSoPJqexDSNKyXyPw+e0OSEvMiMbNjg3rreZApexkJZ1pvJZb2eiBkRA0uuBW1lbfj3v//d43uPPvoo/vSnPw34eDFFkzQhMpd+hooh3gBrnhWnSk/h66+/xh133BGQ562srMS7776Ld955B06nE2+++SYmTpzo0WNFMGLKj/wMY+zYWNSiFitXrkRnZycMhtAcah0OB+655x7861//AgBYrVbl34C7aPKTTz4BAOgnRH6dgiHeAIPdgM7GThQVFeF73/ue2kNSKBvkRWgBsbc0mRkxZ5qht7m3rt+2LbhLfJVVHelc1eEJSZKQ91Aeht49FBk3ZSDl6hQkXuAubH3mmWcG3DPF6XQqB0qcFYLBRrjYs9xX518t/srv51q8eDGuuOIK5OTk4JFHHkFRURGKi4tx0UUX4fnnn4fLNfDV3dGjR3H48GFIkgRrXmQuJe3OkmOB3uYuIg7VPkF1dXW44oor8K9//Qs6nfvQ/vbbb2P79q4+Gx9++CHa2tqQkpOiLEGOdGJFXigbZQ7G6XTiwIEDALisV9BkMCLpJCWFv2bNmqC+lrKsNyU8q+LDkSHegIRJCUiZloKM/83AkHuHwD7RDpfLhTvuuKPfDfXWr1+P2tpaxMTFKBvwke9E3cjirxejs7PT5+fZv38/fvCDH2DZsmWQZRlDzhqCnDtyEH9+PDo7O/Hwww/j2muvRX19fb/PoTQ7y0mKipUDkk6CrcD9N7x06dKgv97evXsxceJErF27FjGxMRj+8HDYv2eHLMt4+OGHlSnSt99+GwBg+54tolfRdCfqRsIpGDl8+DDa29thNBthTOb0P6DRYAQAbKPcH/Rg142wx0hgZP6/TOjj9dizZw+efvrpPu8jpmhSJ6RCZ9Dsn2bYsA6zQm/To7mxWQkGfPH+++/D6XQifWw6Rv11FBJnJiL+knhk35uNrNuyIBklLFq0COeee26/e0Zt2LABAJTusNFA9BtZsjS4dSMulws//OEPUVpaipShKch6NAvmsWak/yQdkkHC8uXL8dVXX6GiogIrV64EAJjPj56rcZEZ2bwtfNrCiymahKEJnI7+jk9H/Hnz5iE/Px8WiwXjx4/3ODuxbt06GAwGnHPOOb68bA8xo7/LjKxdM2iK2B/Ksl6upPGLIc6ArFuzAAB/+ctfei39lWUZH330kfvfZzHwCwRJJymFlF995dtUjSzLShM782QzTKldnwNJkpB0WRKGPzEcxhQjKisr8cEHH/T5POIY4cqLnmI90W9k44aNAelK3J+tW7fi0KFDsMRYkPLbFFiy3CdfU6oJyVOTAQAP/+ZhvPXWW0pmq/v7GOlEZmRP0Z6gnis81dbWhs8++wwAm51153UwsnDhQsyYMQOPPfYYtm/fjilTpmDatGmDbsrV2NiIW2+9FZdffrnPg+3OmmuFzqxDc2Oz0kgp0FwuFw4dOgSAreADwT7BDvsFdjidzl7TNZs3b0ZFRQUsMRblBEr+E3UjXy760qfHi9oQg9EA27l9T51Zsi3Kzs1iv5PuHA6HkpmJphVSpjQTjMlGdHZ0Yu3atUF7HVFnlXZeGgxxPQtlU69JhT5Wj+K9xUpGMhoKV7szZ5ghGSScaj2F0tJS1cbhcDgwf/58jBw5Eq+99pr7xmzVhhN2vA5G5syZgzvvvBN33XUXCgoK8MILLyA7O3vQvhL33HMPbr75Zlx44YU+D7Y7SS/BOsJdgBWsupEjR47A4XBAZ9BxXi9AMv9fJvRxehQVFeGKK67AunXrAHRN0aRPSIfOxCmaQBHNz7Zv297vFMpAFi5cCADIOj9rwFoPUR+xfOXyXku4N2/eDIfDgdikWJgyoueKXJIk2MYGv25EBCPyuN4ZRb1Nj7Tr0gDAXaNgMsI2PrrqsSS95N7AEOrVjSxduhSjRo3Cvffei8rKSiSkJyD/jnzYr2RjR8Gro357ezu2bt2KqVOn9rh96tSpA7adfvPNN3Ho0CH8/ve/9+h1HA4Hmpqaenz1RSzxXbVqlYc/gXfEFE1CRgIkPef1AsEQb8CQO4dAMkhYu3YtLrroIlxzzTXKVEBfB1TynTHBCEueO03917/+1avHdp+iwdkD39eab4VkktBwrKFX11FxsZA4JrJbj/dF1I0sXhKcTrgHDhzA3r17oTfolQ0ST5d4WaIyzZx1QRb0tujKjADq7PYuVFRU4IYbbkB5eTnsqXbk35aPrD9kwXaJjeeVbrwKRurr692FbOnpPW5PT0/vd1fEAwcO4JFHHsG7777r8Vr72bNnw263K1/Z2X3nspQi1tUrg7IHhLKsN4PLegMp/px4jPzLSCRenAhJ5y5+LC8vh9FshO2s6LpqC4X0H7k/r3PmzPFqKfy2bdtw6NAhmCwm2M4e+H3RGbq6jooiSUEEI848pxejjgwiGCnaVeRTZmowoltx5lmZ/QYZOoMOQ38xFPFnxcM0LXoyU92J5meh3O0dcE/133777WhsbERWQRaG/GkIbJfZmP3tg0+/kdOvbmRZ7vOKx+l04uabb8ZTTz2FUaNGefz8s2bNQmNjo/JVUVHR5/2sw6yQDBLqauuU2o5AUjbISwn4U0c9U7IJQ34+BCP+PAL279kBCRh62VDoLdF31RZscWfHwX6Be2n13Xff7fEyX5EVyTw/06P3RWQquwcjTqdTmYqzjoqOvhbdGewGZbM2pYdOAInnlMYNfIUdMyIGOTNzlOmKaCNW1GzbGdpg5OWXX8by5cthsVpgu41ByEC8+s2kpKRAr9f3yoIcPXq0V7YEAJqbm7Flyxb86le/gsFggMFgwNNPP42dO3fCYDBg+fLlfb6O2WxGfHx8j68+B2/SKY17glE3wmW9wWfOMCN7ejbGvjoWMTdGT3FjqGXcnAFdjA5bt27FSy+9NOj9XS4X/vOf/wAA5LM9+/sXdSPLVi5TMpW7d+9GU1MTzDFm5YQQbewXuOsC3nv/vYA+b21trTI9bj07+gI9b4i/veqKapw4cSIkr7l371789re/BQAMvXkoV84MwqtgxGQyYfz48SgsLOxxe2FhISZNmtTr/vHx8di9ezd27NihfE2fPh2jR4/Gjh07PG4jPRCxxDcYm+Zxt97Q0Zl0XG8fRMYEIzJuygAA/O53v8Phw4cHvP/GjRtRXl4Oi82irMgZjFI3Ut+A4uJiAFBWkaQUpETt/LgIRlavWt3nXjG++uKLLyDLMjLGZMCUzGPUQPQ2vbIIYdeuXUF/vfb2dvy///f/4HA4kHNBDswXMRAZjNc5o5kzZ+If//gH3njjDRQXF+PBBx9EeXk5pk+fDsA9xXLrrbe6n1ynw7hx43p8paWlwWKxYNy4cbDZ/K8PEHUjy1f1nWXxlSzL7DFCESVxSiJiRsfg5MmT+OUvfzlgnZVYRZN+vuerm/qqG1Eylvm+j1vrTKkmWPOtcLlcyqqxQBBTNMYzudLPE6FsC//0009j+/btiEuIg/Vma9QVbvvC62DkpptuwgsvvICnn34a55xzDlavXo1FixYhNzcXAFBdXT1oz5FAihkRA0hAeWk5KisrA/a8NTU1OHnyJCSdBGMKP+ykfZJOwpDb3CuZFi9ejBdffLHP+zmdTmWKBud49xqibmTFihWQZVkJRkwjojugD/RUTUtLi5KhNp/Nq25PiGAk2CtqnE4nXn75ZQBA2i1pMCbw/OEJn6pp7r33Xhw+fBgOhwNbt27FxRdfrHxvwYIFvarpu3vyyScD+segj9HDOsw9X9p9d0p/Kbv1ZtjZmpwihjnLjLQfuftOPPjgg5gzZ06P73d0dGDGjBmoqalBTHwMbOO8y152rxs5dOgQqquroTd0fUajVfwF7rq3dWvXoaqqyu/n+/rrr+FwOJA0NClqi1K9JZb3btm+ZZB7+qeoqAhNTU2w2CywnBuddVK+iIizbPLl7pbHL738Ur+bsHlLBCPWjOg+iFLkSfl+ClJ/kAoAeOihhzB79mwA7qzmZZddhrlz5wIAhv54qNeBePe6EdFlMm10WtSvIjAlm2AdYYUsy/jvf//r9/OJJb2x58ZyCsBDIjPy7d5v/do4cjCiTip5TDLr4LwQEUeI+AviYUg04Gjt0a4mTX4SwYguJSJ+RUQKSZKQfkO6kiF59NFHcffdd+O8887DunXrEBMbgxEPjoDpUu+nVrrXjcybNw8AoB/G5dpA4KZqOjo68MUXXwAA9OP4u/WUKc0EnVmHdkc7Dhw4ELTXUVr/R3GdlC8i4kyrM+iQfIU7O/L8nOcD0gBNBCPO5Ohr1ETRIe26NKT/1L0k//XXX0dNTQ3Sh6djyONDYDnb9/SyqBsRm8PphkfEYcZv9vPd/XQ2btjYb+8kT2zYsAENDQ2wJdiUwI8GJ+kkmIcGty189zop43DWingjYo4SSZcmQTJJ2L1rd7/9S7zBZb0UDVKvSXX3IDHqkP8/+Uj+/5L97odgG9NVZyJJEmwj2VUXAIyJRiV4UAqEfSB2YE4+m9MA3gr2ipqysjIcOXIEeoMeMcMYKHojYoIRvU2PxCnunUNPL8rzFpf1UjRJmZqCgvkFsN1qg87s/yFB1I0AQEp+SlTuhdIfMVXz/kLfp5NFMOIa7QrImKKJKGLdun1rUJ5fTNGkjkwNyGcpmkTUbyv5ymRAAhYtWqQ0XfLFgQMH0NTUBIPRAFMqgxGKfJIhcFfYOqPOveQegGUkVxN0Fz8hHpCALZu3oKSkxOvHV1VVYefOnZAkSdmRmTxnzXYvSNixc0dQnl8EI4bhnu3DRl0iKhgxZ5gRd04cAOCFF17w+XlWrFgBAEgrSIPOGFG/IqKQSL0mFbbhNpgv5rLT7owJRtjGuqetnn/+ea8fv3ixe/ff9NHpMMTzhOctUTNSV1OHurq6gD+/CEZ0+TxveCvifmMpV7t3tfvXW//y+Y9N9EnRjYi4Xw9RSMSeEYv83+XDMoSZkdOlXetexfTaa695nR0RUzTGsSyO9IXeqlcCEn/qdvpy/Phx7NmzBwBYWOyDiDvbxoyKgSXPAkebw6crD1mWlcyIcRQ/8EQUWLYxNsSOi0VnZyeeeuopjx/X2dmpdF1lMOK7pEuTALiz5y5X4OpuxKaFybnJzFr5IOKCEUmSkHad+8rjhRdfwJEjR7x6/Lfffova2loYzcao7xpJRMGRfoN7SfXbb7+tXE0PZsOGDWhsbIQtwcZjkx8SLkqAzqrDwYMHsWjRooA9r5iiYVbENxEXjABA3DlxiBkZA0ebA08++aRXjxVTNKkFqVHfNZKIgsOaZ0X8hHjIsozf/e53Hj1GWdJ7Fpf0+kNv0SPpkq7sSKCIYMSVx1VOvojIs60kSUi/0X3l8cYbb+Dbb7/1+LFiiob1IkQUTGk/SgMk4OOPP8bmzZsHvb+ypHcMT3b+SroiCZCAZcuWYffu3X4/X1tbm/IexoxiZsQXEXvGtY20Ie7cOLhcLjz66KMePUaWZSUzwnoRIgomyxALEiYlAAAee+yxAe9bXV2NHTt2cElvgJhSTO5l1ghMdmTLli1ob29HXHIc20H4KGKDEeC7ednvrjw2btw46P337t2Luro6mCwmWPM5J0tEwZV2fRokvYTCwkJ88MEH/d6PS3oDL2Wqe+XlO+++g6NHj/r1XGKKJmFMAjcu9FFEByOWIRYkXJQAAPjtb3876J41YoomtSCV/UWIKOhMqSYk/Y+7fuGmm27Cn/70pz6PU1zSG3jWEVZYh1nR7mjHK6+84tdzif1oWC/iu4g/46ZdnwbJKGH16tWDVk6LYAQjQjAwIiIA6TelI+l/kiDLMh5//HHccMMNaG5uVr5/9OhRLukNAkmSkDzVvcHq3L/PhcPh8Ol5XC6XsqyXHYd9F/HBiCnZ5G4TD+Chhx9CR0dHn/dzuVxYtWoVAMA8il0jiSg0dAYdsm7NQtYdWZAMEj766CNMnDgR119/PbKzs5Geno4TJ05wSW8Q2CfYYUg0oO5oHebOnevTc+zYsQMnTpyAxWZRNuIj70V8MAK4W1Pr4/TY9+2+ftNxRUVFOHbsGOtFiEgVSZckIf+RfBgSDCguLsann36KyspK9xV8TjKybszikt4Akwxdfakef/xx7Nu3z+vnWLp0KQAg9cxUSHq+P76KikoovU2P9B+no+pfVXji90/g5ptvRnJyco/7KPUiZ6QGdNMwIiJPxYyIwfAnh6NhVQP0Fj0seRZYcizQW7nzcbAkXpKIxs2NaN3Tittvvx1r166FXu/571sEI/KIgWsSaWBRkRkB3H9wlmwLTjSc6LMRmljSKw1nIEJE6jEmGJF2XRqSr0qGbbSNgUiQSZKEIT8fAp1Vh40bN2LOnDkeP7atrU0pXrWM5RSNP6ImGJF0EjJ+lgEAmD9/Pvbu3at8r729XakXMY3mGnEiomhiSjYh82eZAIDf/e53Pc4PA1m/fj3a2toQnxoPcyZrDf0RNcEIAMSOjUXceXFwOp2YOXMmmpubMWfOHIwYMQINDQ2w2Cyw5rJehIgo2iRMSUDsWbFwOBy4/fbb0dnZOehjxBRNwjj2F/FXVAUjAJDxvxmQ9BK+/vprZGVl4aGHHkJFRQXik+OR94s81osQEUUhSZIw5I4h0MXosHnzZsyfP3/Qx4hgxDWC/UX8FXXBiDnNrKwtb2lpQUpOCvLuzMPQ2UNhOCcq6nmJiKgPxkQj0n/s3tfs5bkvD9gos6GhAVu2bAEA2MbaQjK+SBaVZ9+0H6VBH6uHOcOMuHPjuFyOiIgAAAmTE1D7QS0O7D+ANWvW4OKLL+7zfitWrIAsy0jJS4Exkc3o/BV1mREA0Jl0SL0mFfHj4xmIEBGRQm/Vw/49OwDgtdde6/d+YorGWsA6w0CIymCEiIioP4mXJAIAPvjvBzh+/Hif9xHBiDSKF7SBwGCEiIioG2u+FZYcC9od7Xj77bd7fb+srAwHDhyATq+DbQzrRQKBwQgREVE3kiQh8VJ3duSVV1/pVci6bNkyAED6mHQ2pQsQBiNERESnSfheAiSThG+Lv1V25RXEFI1+NAORQGEwQkREdBp9jB72ie5C1tdff1253eVyKcGIcTRX0QQKgxEiIqI+JF2SBAB4f+H7OHDgAP72t7/h7LPPRl1dHcxWM6zDuZImUKKyzwgREdFgrMOtMA81w1HpwKhRo5TbjWYjcn6UA52B1/OBwmCEiIioD5IkIel/klD9VjUAIHNMJkzfMyHm/BjobawXCSQGI0RERP1IujQJhngDTGkmWHM4LRMsDEaIiIj6Iekk2CfY1R5GxOOEFxEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREanKp2Bk3rx5yM/Ph8Viwfjx47FmzZp+7/vRRx/hyiuvRGpqKuLj43HhhRfi66+/9nnAREREFFm8DkYWLlyIGTNm4LHHHsP27dsxZcoUTJs2DeXl5X3ef/Xq1bjyyiuxaNEibN26FZdddhmuvfZabN++3e/BExERkfZJsizL3jxg4sSJOO+88zB//nzltoKCAlx//fWYPXu2R89xxhln4KabbsITTzzh0f2bmppgt9tRML8Aeqvem+ESERGRSpynnCj+ZTEaGxsRHx/f7/28yoy0t7dj69atmDp1ao/bp06divXr13v0HC6XC83NzUhKSur3Pg6HA01NTT2+iIiIKDJ5FYzU19fD6XQiPT29x+3p6emoqanx6Dmef/55tLa24sYbb+z3PrNnz4bdble+srOzvRkmERERaYhPBaySJPX4vyzLvW7ry3vvvYcnn3wSCxcuRFpaWr/3mzVrFhobG5WviooKX4ZJREREGmDw5s4pKSnQ6/W9siBHjx7tlS053cKFC3HnnXfigw8+wBVXXDHgfc1mM8xmszdDIyIiIo3yKjNiMpkwfvx4FBYW9ri9sLAQkyZN6vdx7733Hm6//Xb8+9//xjXXXOPbSImIiCgieZUZAYCZM2filltuwYQJE3DhhRfitddeQ3l5OaZPnw7APcVy5MgRvPXWWwDcgcitt96KF198Ed/73veUrIrVaoXdbg/gj0JERERa5HUwctNNN+HYsWN4+umnUV1djXHjxmHRokXIzc0FAFRXV/foOfLqq6+is7MT9913H+677z7l9ttuuw0LFizw/ycgIiIiTfO6z4ga2GeEiIhIe4LSZ4SIiIgo0BiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqnwKRubNm4f8/HxYLBaMHz8ea9asGfD+q1atwvjx42GxWDBs2DC88sorPg2WiIiIIo/XwcjChQsxY8YMPPbYY9i+fTumTJmCadOmoby8vM/7l5aW4vvf/z6mTJmC7du349FHH8UDDzyADz/80O/BExERkfZJsizL3jxg4sSJOO+88zB//nzltoKCAlx//fWYPXt2r/v/9re/xWeffYbi4mLltunTp2Pnzp3YsGGDR6/Z1NQEu92OgvkF0Fv13gyXiIiIVOI85UTxL4vR2NiI+Pj4fu9n8OZJ29vbsXXrVjzyyCM9bp86dSrWr1/f52M2bNiAqVOn9rjtqquuwj//+U90dHTAaDT2eozD4YDD4VD+39jYCMD9QxEREZE2iPP2YHkPr4KR+vp6OJ1OpKen97g9PT0dNTU1fT6mpqamz/t3dnaivr4emZmZvR4ze/ZsPPXUU71u3z9zvzfDJSIiojBw7Ngx2O32fr/vVTAiSJLU4/+yLPe6bbD793W7MGvWLMycOVP5/4kTJ5Cbm4vy8vIBfxgKnfPPPx+bN29Wexj0Hb4f4YXvR3jh+6GexsZG5OTkICkpacD7eRWMpKSkQK/X98qCHD16tFf2Q8jIyOjz/gaDAcnJyX0+xmw2w2w297rdbrcPOOdEoaPX6/lehBG+H+GF70d44fuhPp1u4PUyXq2mMZlMGD9+PAoLC3vcXlhYiEmTJvX5mAsvvLDX/ZcsWYIJEyb0WS9C2nDfffepPQTqhu9HeOH7EV74foQ/r1fTLFy4ELfccgteeeUVXHjhhXjttdfw+uuvY8+ePcjNzcWsWbNw5MgRvPXWWwDcS3vHjRuHe+65B7/4xS+wYcMGTJ8+He+99x5+8pOfePSaYjXNYNW4REREFD48PX97XTNy00034dixY3j66adRXV2NcePGYdGiRcjNzQUAVFdX9+g5kp+fj0WLFuHBBx/E3//+d2RlZeGll17yOBAB3NM2v//97/ucuiEiIqLw5On52+vMCBEREVEgcW8aIiIiUhWDESIiIlIVgxEa0ECbItbW1uL2229HVlYWYmJicPXVV+PAgQMqjjayrV69Gtdeey2ysrIgSRI++eSTHt+XZRlPPvkksrKyYLVacemll2LPnj3qDDYKDPZ+fPTRR7jqqquQkpICSZKwY8cOVcYZLQZ6Pzo6OvDb3/4WZ555Jmw2G7KysnDrrbeiqqpKvQFTDwxGqF8DbYooyzKuv/56lJSU4NNPP8X27duRm5uLK664Aq2trWoPPSK1trbi7LPPxty5c/v8/rPPPos5c+Zg7ty52Lx5MzIyMnDllVeiubk5xCONDoO9H62trZg8eTKeeeaZEI8sOg30fpw8eRLbtm3D7373O2zbtg0fffQR9u/fjx/+8IcqjJT6JIeZv//973JeXp5sNpvl8847T169erXyvebmZvm+++6ThwwZIlssFnnMmDHyvHnzVBxtZLvgggvk6dOn97htzJgx8iOPPCLv27dPBiAXFRUp3+vs7JSTkpLk119/PdRDjToA5I8//lj5v8vlkjMyMuRnnnlGua2trU222+3yK6+8osIIo8vp70d3paWlMgB5+/btIR1TNBvo/RA2bdokA5DLyspCMygaUFhlRga6EgeABx98EIsXL8Y777yD4uJiPPjgg7j//vvx6aefqjzyyCM2RTx9k0OxKaLYyNBisSjf0+v1MJlMWLt2bUjHSu5+PjU1NT3eL7PZjEsuuaTfTSyJolljYyMkSUJCQoLaQyGE2TTNnDlzcOedd+Kuu+5CQUEBXnjhBWRnZ2P+/PkA3DsA33bbbbj00kuRl5eHu+++G2effTa2bNmi8sgjz2CbIo4ZM0ZpctfQ0ID29nY888wzqKmpQXV1tUqjjl5iywVvNrEkilZtbW145JFHcPPNN7ORZpgIm2BksCtxALjooovw2Wef4ciRI5BlGStWrMD+/ftx1VVXqTHkqNDfpohGoxEffvgh9u/fj6SkJMTExGDlypWYNm0a9Hq9SqMlbzexJIo2HR0d+N///V+4XC7MmzdP7eHQd3zatTcYBrsSB4CXXnoJv/jFLzB06FAYDAbodDr84x//wEUXXaTGkCOaJ5sijh8/Hjt27EBjYyPa29uRmpqKiRMnYsKECWoMOaplZGQAcGdIMjMzldsH2sSSKNp0dHTgxhtvRGlpKZYvX86sSBgJm8yIMNCV3UsvvYSNGzfis88+w9atW/H888/j3nvvxdKlS9UYakTzZlNEu92O1NRUHDhwAFu2bMF1110XyqES3NsuZGRk9Hi/2tvbsWrVqn43sSSKJiIQOXDgAJYuXdrvrvGkjrDJjAx2JX7q1Ck8+uij+Pjjj3HNNdcAAM466yzs2LEDzz33HK644go1hh3RZs6ciVtuuQUTJkxQNkUsLy/H9OnTAQAffPABUlNTkZOTg927d+PXv/41rr/++l5TbRQYLS0tOHjwoPL/0tJS7NixA0lJScjJycGMGTPw5z//GSNHjsTIkSPx5z//GTExMbj55ptVHHXkGuz9OH78OMrLy5VeFvv27QPgzmKJTBYFzkDvR1ZWFm644QZs27YNX3zxBZxOp3KuSUpKgslkUmvYJKi7mKenCy64QP7lL3/Z47aCggL5kUcekRsbG2UA8qJFi3p8/+6775avvPLKUA4zqvz973+Xc3NzZZPJJJ933nnyqlWrlO+9+OKL8tChQ2Wj0Sjn5OTIjz/+uOxwOFQcbWRbsWKFDKDX12233SbLsnt57+9//3s5IyNDNpvN8sUXXyzv3r1b3UFHsMHejzfffLPP7//+979XddyRaqD3Qyyv7utrxYoVag+dZFkOq43yFi5ciFtuuQWvvPKKciX++uuvY8+ePcjNzcWll16K+vp6zJ07F7m5uVi1ahV++ctfYs6cOfjlL3+p9vCJiIjIB2EVjADu9uPPPvssqqurMW7cOPztb3/DxRdfDMBdnDdr1iwsWbIEx48fR25uLu6++248+OCDXDFARESkUWEXjBAREVF0CbvVNERERBRdGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqjQdjEiShE8++UTtYRAREZEfwioYuf3223H99derPQwiIiIKobAKRoiIiCj6hG0wkpeXhxdeeKHHbeeccw6efPJJVcZDREREwRG2wQgRERFFBwYjREREpCoGI0RERKSqsA1GdDodTt/Dr6OjQ6XREBERUbCEbTCSmpqK6upq5f9NTU0oLS1VcUREREQUDGEbjPzP//wP3n77baxZswZFRUW47bbboNfr1R4WERERBZhB7QF053K5YDC4hzRr1iyUlJTgBz/4Aex2O/7whz8wM0JERBSBJPn0wgwVXX311RgxYgTmzp2r9lCIiIgoRMJimqahoQFffvklVq5ciSuuuELt4RAREVEIhcU0zc9//nNs3rwZDz30EK677jq1h0NEREQhFFbTNERERBR9wmKahoiIiKIXgxEiIiJSVUiCkdmzZ+P8889HXFwc0tLScP3112Pfvn097iPLMp588klkZWXBarXi0ksvxZ49e3rc57XXXsOll16K+Ph4SJKEEydO9Hqt/fv347rrrkNKSgri4+MxefJkrFixIpg/HhEREfkhJMHIqlWrcN9992Hjxo0oLCxEZ2cnpk6ditbWVuU+zz77LObMmYO5c+di8+bNyMjIwJVXXonm5mblPidPnsTVV1+NRx99tN/Xuuaaa9DZ2Ynly5dj69atOOecc/CDH/wANTU1Qf0ZiYiIyDeqFLDW1dUhLS0Nq1atwsUXXwxZlpGVlYUZM2bgt7/9LQDA4XAgPT0df/nLX3DPPff0ePzKlStx2WWXoaGhAQkJCcrt9fX1SE1NxerVqzFlyhQAQHNzM+Lj47F06VJcfvnlIfsZiYiIyDOq1Iw0NjYCAJKSkgAApaWlqKmpwdSpU5X7mM1mXHLJJVi/fr3Hz5ucnIyCggK89dZbaG1tRWdnJ1599VWkp6dj/Pjxgf0hiIiIKCBC3mdElmXMnDkTF110EcaNGwcAyhRKenp6j/ump6ejrKzM4+eWJAmFhYW47rrrEBcXB51Oh/T0dCxevLhHBoWIiIjCR8gzI7/61a+wa9cuvPfee72+J0lSj//LstzrtoHIsox7770XaWlpWLNmDTZt2oTrrrsOP/jBD3rsAExEREThI6TByP3334/PPvsMK1aswNChQ5XbMzIyAKBXkenRo0d7ZUsGsnz5cnzxxRd4//33MXnyZJx33nmYN28erFYr/vWvfwXmhyAiIqKACkkwIssyfvWrX+Gjjz7C8uXLkZ+f3+P7+fn5yMjIQGFhoXJbe3s7Vq1ahUmTJnn8OidPngQA6HQ9fyydTgeXy+XHT0BERETBEpKakfvuuw///ve/8emnnyIuLk7JgNjtdlitVkiShBkzZuDPf/4zRo4ciZEjR+LPf/4zYmJicPPNNyvPU1NTg5qaGhw8eBAAsHv3bsTFxSEnJwdJSUm48MILkZiYiNtuuw1PPPEErFYrXn/9dZSWluKaa64JxY9KREREXgrJ0t7+6j7efPNN3H777QDc2ZOnnnoKr776KhoaGjBx4kT8/e9/V4pcAeDJJ5/EU089NeDzbNmyBY899hi2bNmCjo4OnHHGGXjiiScwbdq0gP9cRERE5D9ulEdERESq4t40REREpCoGI0RERKQqBiNERESkKgYjREREpCoGI0RERKQqBiNERESkKgYjREREpCoGI0SkCStXroQkSThx4oTaQyGiAGMwQkRRRZIkfPLJJ2oPg4i6YTBCREREqmIwQkSK//73vzjzzDNhtVqRnJyMK664Aq2trbj99ttx/fXX47nnnkNmZiaSk5Nx3333oaOjQ3nsO++8gwkTJiAuLg4ZGRm4+eabcfToUeX7Yprlyy+/xNlnnw2LxYKJEydi9+7dyn3Kyspw7bXXIjExETabDWeccQYWLVrUY4xbt27FhAkTEBMTg0mTJmHfvn09vj9//nwMHz4cJpMJo0ePxttvv618Ly8vDwDwox/9CJIkKf8nInUxGCEiAEB1dTV+9rOf4ec//zmKi4uxcuVK/PjHP4bYvmrFihU4dOgQVqxYgX/9619YsGABFixYoDy+vb0df/jDH7Bz50588sknKC0tVTaw7O43v/kNnnvuOWzevBlpaWn44Q9/qAQ19913HxwOB1avXo3du3fjL3/5C2JjY3s8/rHHHsPzzz+PLVu2wGAw4Oc//7nyvY8//hi//vWv8dBDD6GoqAj33HMP7rjjDqxYsQIAsHnzZgDuzTWrq6uV/xORymQiIlmWt27dKgOQDx8+3Ot7t912m5ybmyt3dnYqt/30pz+Vb7rppn6fb9OmTTIAubm5WZZlWV6xYoUMQH7//feV+xw7dky2Wq3ywoULZVmW5TPPPFN+8skn+3w+8filS5cqt3355ZcyAPnUqVOyLMvypEmT5F/84hc9HvfTn/5U/v73v6/8H4D88ccf9ztuIgo9ZkaICABw9tln4/LLL8eZZ56Jn/70p3j99dfR0NCgfP+MM86AXq9X/p+ZmdljGmb79u247rrrkJubi7i4OFx66aUAgPLy8h6vc+GFFyr/TkpKwujRo1FcXAwAeOCBB/DHP/4RkydPxu9//3vs2rWr1zjPOuusHmMAoIyjuLgYkydP7nH/yZMnK89PROGJwQgRAQD0ej0KCwvx1VdfYezYsXj55ZcxevRolJaWAgCMRmOP+0uSBJfLBQBobW3F1KlTERsbi3feeQebN2/Gxx9/DMA9fTMYSZIAAHfddRdKSkpwyy23YPfu3ZgwYQJefvnlHvftPg7xODGO7rcJsiz3uo2IwguDESJSSJKEyZMn46mnnsL27dthMpmUoGIg3377Lerr6/HMM89gypQpGDNmTI+sSXcbN25U/t3Q0ID9+/djzJgxym3Z2dmYPn06PvroIzz00EN4/fXXPR5/QUEB1q5d2+O29evXo6CgQPm/0WiE0+n0+DmJKPgMag+AiMLDN998g2XLlmHq1KlIS0vDN998g7q6OhQUFPQ5XdJdTk4OTCYTXn75ZUyfPh1FRUX4wx/+0Od9n376aSQnJyM9PR2PPfYYUlJScP311wMAZsyYgWnTpmHUqFFoaGjA8uXLewQSg/nNb36DG2+8Eeeddx4uv/xyfP755/joo4+wdOlS5T55eXlYtmwZJk+eDLPZjMTERI+fn4iCg5kRIgIAxMfHY/Xq1fj+97+PUaNG4fHHH8fzzz+PadOmDfrY1NRULFiwAB988AHGjh2LZ555Bs8991yf933mmWfw61//GuPHj0d1dTU+++wzmEwmAIDT6cR9992HgoICXH311Rg9ejTmzZvn8c9w/fXX48UXX8Rf//pXnHHGGXj11Vfx5ptvKvUrAPD888+jsLAQ2dnZOPfccz1+biIKHkmWv1u3R0QURCtXrsRll12GhoYGJCQkqD0cIgojzIwQERGRqhiMEBERkao4TUNERESqYmaEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhU9f8DKliVHWPeARkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n",
+    "# n.storage_units_t.p_store.loc[time].plot(ax=ax, legend=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* does NOT pay for net metering\n",
+    "* does NOT include residential storage\n",
+    "\n",
+    "Apply the Residential Renewable Energy Tax Credit\n",
+    "\n",
+    "[DSIRE Data on the RRETC](https://programs.dsireusa.org/system/program/detail/1235/residential-renewable-energy-tax-credit) -- solar and storage each get a 30% tax credit. \n",
+    "\n",
+    "Apply the Investment Tax Credit (ITC)\n",
+    "\n",
+    "[EPA Data on ITC](https://www.epa.gov/green-power-markets/summary-inflation-reduction-act-provisions-related-renewable-energy) -- qualified residential units in a low-income area recieve +20%.\n",
+    "\n",
+    "[Homeowner's Guide to Federal Tax Credits](https://www.energy.gov/eere/solar/homeowners-guide-federal-tax-credit-solar-photovoltaics).\n",
+    "\n",
+    "This will be implemented as a direct 50% cost reduction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rretc_credit = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['ResPV', 'capital_cost'] *= (1-rretc_credit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 27.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 60.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.23s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 26283 primals, 61323 duals\n",
+      "Objective: 4.17e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.342852</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2964.049410</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>2964.049410</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.251973</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>334641.178424</td>\n",
+       "      <td>334641.178424</td>\n",
+       "      <td>112.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>0.669086</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>224.13732</td>\n",
+       "      <td>-224.137320</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.038241</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar</th>\n",
+       "      <td>0.947609</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1396.053259</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1396.053259</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>3.914040e-10</td>\n",
+       "      <td>82394.7361</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>82394.736100</td>\n",
+       "      <td>59.019766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.965350</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-417035.914525</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.342852                 0.0  2964.049410   \n",
+       "          net metering          0.669086                 0.0     0.000000   \n",
+       "          solar                 0.947609                 0.0  1396.053259   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
+       "\n",
+       "                        Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.00000  2964.049410           0.0   \n",
+       "          net metering   224.13732  -224.137320           0.0   \n",
+       "          solar            0.00000  1396.053259           0.0   \n",
+       "Load      -             4135.96535 -4135.965350           0.0   \n",
+       "\n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.251973  0.000000e+00               0.0000   \n",
+       "          net metering         0.038241  0.000000e+00               0.0000   \n",
+       "          solar                0.168178  3.914040e-10           82394.7361   \n",
+       "Load      -                         NaN  0.000000e+00               0.0000   \n",
+       "\n",
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    334641.178424  334641.178424    112.900000  \n",
+       "          net metering                 0.000000       0.000000      0.000000  \n",
+       "          solar                        0.000000   82394.736100     59.019766  \n",
+       "Load      -                            0.000000 -417035.914525           NaN  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the LCOE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Load\n",
+       "Load Residential    100.831578\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_2 = n.objective / n.loads_t.p_set.sum()\n",
+    "model_lcoe_2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the electricity price reduction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.689479185119586"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs((100.831578 - 112.9)/112.9)*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like almost a 10.7% reduction in electricity cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering + Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* Applies 50% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.0\n",
+       "Net metering Residential      0.0\n",
+       "Evergy Import               112.9\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators.marginal_cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.00\n",
+       "Net metering Residential     56.45\n",
+       "Evergy Import               112.90\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators.marginal_cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 27.96it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 62.62it/s]\n",
+      "INFO:linopy.io: Writing time: 0.23s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 26283 primals, 61323 duals\n",
+      "Objective: 3.75e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.322049</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2208.701032</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2208.701032</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.190715</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>249362.346567</td>\n",
+       "      <td>249362.346567</td>\n",
+       "      <td>112.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>4.032169</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4484.706259</td>\n",
+       "      <td>-4484.706259</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.126967</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-253161.668329</td>\n",
+       "      <td>-253161.668329</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar</th>\n",
+       "      <td>4.352297</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6411.970576</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>6411.970576</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>1.797677e-09</td>\n",
+       "      <td>378433.000344</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>378433.000344</td>\n",
+       "      <td>59.019766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.965350</td>\n",
+       "      <td>-4135.965350</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-374633.678582</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1 rows × 30 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "attribute                            bus control type  p_nom  p_nom_mod  \\\n",
-       "StorageUnit                                                               \n",
-       "Residential Battery Storage  Residential      PQ         0.0        0.0   \n",
-       "\n",
-       "attribute                    p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
-       "StorageUnit                                                                     \n",
-       "Residential Battery Storage              True        0.0        inf      -1.0   \n",
-       "\n",
-       "attribute                    p_max_pu  ...  \\\n",
-       "StorageUnit                            ...   \n",
-       "Residential Battery Storage       1.0  ...   \n",
-       "\n",
-       "attribute                    state_of_charge_initial_per_period  \\\n",
-       "StorageUnit                                                       \n",
-       "Residential Battery Storage                               False   \n",
-       "\n",
-       "attribute                    state_of_charge_set  cyclic_state_of_charge  \\\n",
-       "StorageUnit                                                                \n",
-       "Residential Battery Storage                  NaN                   False   \n",
-       "\n",
-       "attribute                   cyclic_state_of_charge_per_period  max_hours  \\\n",
-       "StorageUnit                                                                \n",
-       "Residential Battery Storage                              True        2.5   \n",
-       "\n",
-       "attribute                    efficiency_store  efficiency_dispatch  \\\n",
-       "StorageUnit                                                          \n",
-       "Residential Battery Storage               1.0                  1.0   \n",
-       "\n",
-       "attribute                    standing_loss  inflow  p_nom_opt  \n",
-       "StorageUnit                                                    \n",
-       "Residential Battery Storage            0.0     0.0       -0.0  \n",
-       "\n",
-       "[1 rows x 30 columns]"
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.322049                 0.0  2208.701032   \n",
+       "          net metering          4.032169                 0.0     0.000000   \n",
+       "          solar                 4.352297                 0.0  6411.970576   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
+       "\n",
+       "                         Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.000000  2208.701032           0.0   \n",
+       "          net metering  4484.706259 -4484.706259           0.0   \n",
+       "          solar            0.000000  6411.970576           0.0   \n",
+       "Load      -             4135.965350 -4135.965350           0.0   \n",
+       "\n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.190715  0.000000e+00             0.000000   \n",
+       "          net metering         0.126967  0.000000e+00             0.000000   \n",
+       "          solar                0.168178  1.797677e-09        378433.000344   \n",
+       "Load      -                         NaN  0.000000e+00             0.000000   \n",
+       "\n",
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    249362.346567  249362.346567    112.900000  \n",
+       "          net metering           -253161.668329 -253161.668329           NaN  \n",
+       "          solar                        0.000000  378433.000344     59.019766  \n",
+       "Load      -                            0.000000 -374633.678582           NaN  "
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90.57950125390316"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.objective/n.loads_t.p_set.sum().values[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering + Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* Applies 90% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.000\n",
+       "Net metering Residential    111.771\n",
+       "Evergy Import               112.900\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.storage_units"
+    "n.generators.marginal_cost"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['ResPV', 'p_nom_max'] = 2.807"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 27.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 57.13it/s]\n",
+      "INFO:linopy.io: Writing time: 0.25s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 26283 primals, 61324 duals\n",
+      "Objective: 2.47e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -880,35 +1769,65 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Generator</th>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
        "      <th>grid</th>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1.331491</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4135.96535</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>4135.96535</td>\n",
+       "      <td>2358.485524</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2358.485524</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.236071</td>\n",
+       "      <td>0.202205</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>112.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>2.505756</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2357.900052</td>\n",
+       "      <td>-2357.900052</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>466950.487985</td>\n",
-       "      <td>466950.487985</td>\n",
-       "      <td>112.9</td>\n",
+       "      <td>0.107419</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-263544.846758</td>\n",
+       "      <td>-263544.846758</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar</th>\n",
+       "      <td>2.807000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>1.159408e-09</td>\n",
+       "      <td>244069.150984</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>462664.672697</td>\n",
+       "      <td>111.879606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Load</th>\n",
        "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>4135.96535</td>\n",
-       "      <td>-4135.96535</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.965350</td>\n",
+       "      <td>-4135.965350</td>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-466950.487985</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-465392.841637</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -916,24 +1835,32 @@
        "</div>"
       ],
       "text/plain": [
-       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
-       "Generator grid               2.0                 0.0  4135.96535     0.00000   \n",
-       "Load      -                  0.0                 0.0     0.00000  4135.96535   \n",
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.331491                 0.0  2358.485524   \n",
+       "          net metering          2.505756                 0.0     0.000000   \n",
+       "          solar                 2.807000                 0.0  4135.379878   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
        "\n",
-       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
-       "Generator grid  4135.96535           0.0         0.236071          0.0   \n",
-       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "                         Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.000000  2358.485524           0.0   \n",
+       "          net metering  2357.900052 -2357.900052           0.0   \n",
+       "          solar            0.000000  4135.379878           0.0   \n",
+       "Load      -             4135.965350 -4135.965350           0.0   \n",
        "\n",
-       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
-       "Generator grid                  0.0            466950.487985  466950.487985   \n",
-       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.202205  0.000000e+00             0.000000   \n",
+       "          net metering         0.107419  0.000000e+00             0.000000   \n",
+       "          solar                0.168178  1.159408e-09        244069.150984   \n",
+       "Load      -                         NaN  0.000000e+00             0.000000   \n",
        "\n",
-       "                Market Value  \n",
-       "Generator grid         112.9  \n",
-       "Load      -              NaN  "
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    266273.015698  266273.015698    112.900000  \n",
+       "          net metering           -263544.846758 -263544.846758           NaN  \n",
+       "          solar                        0.000000  462664.672697    111.879606  \n",
+       "Load      -                            0.000000 -465392.841637           NaN  "
       ]
      },
-     "execution_count": 47,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -944,241 +1871,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Load\n",
-       "Load Residential    1.430806\n",
+       "Load Residential    59.671032\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.loads_t.p.max()"
+    "model_lcoe_3 = n.objective / n.loads_t.p_set.sum()\n",
+    "model_lcoe_3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>StorageUnit</th>\n",
-       "      <th>Residential Battery Storage</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>snapshot</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01 00:00:00</th>\n",
-       "      <td>-0.0</td>\n",
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-       "      <th>2018-12-31 22:00:00</th>\n",
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-       "<p>8760 rows × 1 columns</p>\n",
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       "text/plain": [
-       "StorageUnit          Residential Battery Storage\n",
-       "snapshot                                        \n",
-       "2018-01-01 00:00:00                         -0.0\n",
-       "2018-01-01 01:00:00                         -0.0\n",
-       "2018-01-01 02:00:00                         -0.0\n",
-       "2018-01-01 03:00:00                         -0.0\n",
-       "2018-01-01 04:00:00                         -0.0\n",
-       "...                                          ...\n",
-       "2018-12-31 19:00:00                         -0.0\n",
-       "2018-12-31 20:00:00                         -0.0\n",
-       "2018-12-31 21:00:00                         -0.0\n",
-       "2018-12-31 22:00:00                         -0.0\n",
-       "2018-12-31 23:00:00                         -0.0\n",
-       "\n",
-       "[8760 rows x 1 columns]"
+       "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
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hSXBGJMbVgzPghNcXOxABaHqGD7GvzigjIkyPQCSVjIivc0aEpmakxqXFe2fLd7ZINBLSHd9ARCtLeCkQ4UHohncAEAozCIa7vTkHHPD4DXHvQ4EId2J1VW3H902ZdGX3ds+ICJ2aoUBEask2veuO6kTkw2dqBtDOEl4KRHgQmg0BgFBID2/Y2+VnroArYUZkW4NT8Hl7C5qaUZfuNR2prJzpPJ1DUzPS47vZ45p9rVQnIhO+j41WuqtSIMKDGG+CwZAevlDXmg9XwIdQJP5DQRkR7mhqRl3axM6ICGiKRrjhU6wKAL4g1YnIhevOu+2oRiQNCd3wDogGIt5Q14yIJ04PkXbbG7SznbPSxJ6aoYyIMN2nZlLZb6Zz8BIIRXgVUxL+Ugm+qU5EHlSsStAiUUYkWSDS5PLTZl8ciT41I2B/FNJzk7qUpma63Yd6iUjLm0JdFNWJyIN3jQgFIulHaHt3APAHYmRE4jQz62x7I9WJcCH61AxdfQsiytRMt/uI8Tok8aVSoE11IvLgmxGhPiJpSIwaEX9ID1+YX0YEoDoRrsRuaOaivWZSxrJsz1UzAhuaAbRyRmqpTH35ghGs22eXYDSkM5qaISJlRJieUzNcMiIUiHBCLd7Vw+kPIdxtB8KUGpp1y4h079ZKxBMIRTjvZdIdvUdJj+/UTIs70OM1qEYUiPDQIsKVmD/YMyPii9PevTPahZcbWjWjHrHqmlLJiNipRkQ2QgqBGzXSs0LL+E4VR1hx2k5IjQIRHsTIiLAs02MHXsqIiIdWzahHrCkUoct3AaBNA2+sWiWkgZ9WpgG0jO/yXUAbjwsFIjyI1Uype5t3H4dA5ECbN6Vq9t5G7KkZvj0VyCGxplBSa2jWrViVMiKS4dtVtTPKiEgvlQsjCkTSjFjV+oFw90Ak+ZOLZYEdjZQVSUbsqZlwhKUAMEViZEQiEbbHmy/ViEhHSEZEK/uaaFkq7QQoEEkzYvQRAYBgt6t2X4jbk4sCkeTEzogAND2TqlhdUD2BMEIxdqCOx+kPge1Wa0erZqRDNSLqlkrNWpNT/YE7BSIc+YJh+ESqPwh0eyPmMjUDALua20Q5fzoTu0YEoILVVNnjBO58Vs7EKm6l/Wak4xGwXF0LV95axrJsSn2NtPC4UCDCkZiVx51rRALhAAIhhtP9Djioe2Ey3YM8MVBGJDXxMhd8Vs7EmsqhLsPSEdLAzxMI02tFQu5AGKmsxNXClBkFIhyJGYgEI4cCEUfAgVDIyOl+dc420caQrqTo7kgZkdTE26COT+V/rOJWyohIR2hxNk3PSCfV9yEtdFelQIQjMd/8OteIOANOBEMmTvdrdLsRDNPVYCKSTM1Qm/eUxM2I8ChYjZkR8QYR0UCTJi0SGnRTICKdVJbuAjQ1k1bEXDLYefrAFXAhEOCWEWl1mLDbsVu0caQjaYpVadVMKuxxVrfwmpqJcdsIm1o/EpIcZUTUK9VpLwpE0oiYG20FwoeeUM6AE/6AgdP9mu0m7GrbJdo40hFNzaiHOBmR2P/2tHJGGkKzf41OX/IbkZSkuhN4sysAtvvSM5WhQIQjUWtEOmVEHEEHfBwDkVBEh32ORtHGkY6kyIhQIJKaeDUifJqaxcueUJ2INISsmgG0URipValmREIRVvWBOwUiHIlaI9KpoZkr4ILHxy0QAYB6V5to40hHUtSI0EqA1HTfebed0BoRgDIiUqGpGfUS8j6k9ukZCkQ4kqpGxBlw8gpEGigQSYimZtTBEwghECc7xa9GJPa/PWVEpCGkoRkANGlghYZWuQTURak9U0WBCEdi1oiEOi3fdQe9CIS4PwxtfjdCEfpgjCfeh58QVKzKX6KMBZ+GZvGyKpQRkYaQFu8AZUSkJGxDQnUHiBSIcCRqQ7NOGRGuXVXbRYI27HXuFW0s6YZqRNQhYSAisKFZ9PjqfmPVKiGb3gEUiEgp1eW7ANCk8seFAhGOxEwFd95rg+s+Mx33DWRhZ9tO0caSbigQUYdEG9PxqhGJW6xKGREpCM2INLv9ql+hoVUuf+rPeaoRSQMsy6JZzKmZMIsIG/3A9PMMRLy+TOxo2yHaWNKNFDUiVKzKX6I27HxWzcS7CqQaEWkIrREJhlkKEiWS6vJdQP3dVSkQ4aDR5Re19iASMcAXiq635xuIOFxW7LBTIBJLJMIiGBb/aow6q/IXb+kuIE5GJF7tCBHGLUI9FE3PSENIrRplRNLAgVavqMeLhA3whqLH5BuItDisNDUThxTTMoA4b869TaIaEa5z3ZEIC1ecIJAyItLwihB0UyAiDZqa6eVq28TtFhgOG+ALR4/ZucsqF20uIw64ajumdsghUkzLADQ1k4pENSLuQAhhDnvFOH0hxCs3aHVTRkRsLMvCGxQhI+Ki7qpSENZHRN2BOwUiHNS2iZsRCYX1naZm+L3wWZaBzZCJ/c79oo4pHUiXEaFAhK9ENSIsCzg5TM8kmsKhVTPi8wZT22a+O8qISENIZpb6iKSBA2IHIqFojUg4Eu6ygoarbGMBFazGIEVXVSDabZJ2e+UnWQ0Hl4LVRMdwB8Jdtkogwok1BUmBiDSELN8NhCKq3iiSAhEORA9Ewnp4Q164gi6ANfO+v1WXRwWrMQTC0tVyUMEqP4mmZgBuBavJbkN1IqmLVXwvdMVMOwpEpCGkRgRQdy8RCkQ4EHtqJhCM1og4A06wERPv++vZXCpYjcEnUUYEoIJVvpJNvXBpapYsa0LdVVO3bn9bj5+JlhFR+TSAGiXrvRIKRwS/v6m5ToQCEQ7EzogEQzr4QqkHIghlY6edApHupKoRAahglS+nL/GHkSgZERF7+/Q2P+9q6fEzsTIiTU56XPhauLkh4e/FCBLVvHKGApEkPIGQ6FdeweChqZlwiP/UTMCfiV32XdTBsBupVs0AVLDKV/KMSPJ/z2RZk20NLl5jIof8sqe1x8oloV1V21FGhL//rqtFvSP+aqMWEaYhKRDRMLGnZQDA/3sg4gg4EA4Zed/f48mAJ+TBQfdB0cemZVJmRCgQ4S4UCcHlS/xYcMqIJAlEXli4HV6RPjx7mzqHD3tbPF1+JkYPESBau5NKEX5vVtvmw5e/xn8///LXWsHnoKkZDdsvcjMzAPAHmI6pmWCI/9SM3W1DyB7C4l8Wiz42LZNq1QxAUzN8OHxeBMJM4ttwqRFJskqgzuHDq9/TFGUqWt0BbKt3dvmZWDUiLKvuDz01qrV78UWcYINlWby3ap/gc+xtqxd8DKlQIJKE2M3MgGhGxBf2wRVwIRDknxGp3+/F9ru346LjLsKECRPw73//G36/etNucpF0aoZWzXBmT1IfAiQPMgBuwcorS3agwUkNtPhq8QSwvbHr1JZYNSIArZzhIxJhUe/wYc2+tpj1iD9sb8K+FuEXxHsd+xCKqPN9jAKRJKSYmglFdAiEgnAGnPAFDLzuy7Is9n3+CkKO6BNq2bJluOiii1BZWYm7774bDQ2Ji57SmbTFqjQFwBWXaZctdU74knTx5HIcdyCMp+dv5Tw2AngDYfiCEWyv7xqIiFUjAlB3VT6aXH4EwyxYFvgqxvTMuz8Lz4YAQItLj18bfxXlWGKjQCQJsVfMtAtGInAGnfDzDETcGxfBu205GD2Dqv+rQvGZxTDlm9DQ0ICHHnoIVVVVuOGGG7Bnzx5Jxq1mYm5M2B3ViHDHpWvqsp3NmPrMUvy4vSnubbju0vv+qv3YUudMfkMC4FD/lR4ZERGf45QR4e6g/VDQ1n16ptnlx/zfxJlSaWyxYNnBZaIcS2wUiCQhWSASDsMZcMLr4x6IhBxNaFnwCgBg6AUjkXV4FopnFGPgYwNRMbsCtn42+Hw+vPDCCxgwYACuuOIKuFy9Z2UBFauqg4PD1AwA7G724KLXVmDO+2tjLsXl2gkyHGHx16828Rpjb9by+7/1jgZXl5V3omZEKBDh7KD90GfMuv127G0+VET84er9CIhU+Ov2G7C2Yb0oxxIbBSJJSDE1A/yeEQk44fbpOd2eZVk0f/0sWL8bpj6DMOKsYzp+xxgY5ByRg5q7a1B9azUyh2UiFArhjTfewL333ivJ+NVIyhoRKlbljktGpLOPfzmAE59ago9Wd90/iUuNSLulWxuxdGsjr/P2Vu0ZEXcgjNpOV+NUI6KM7nWIX6w/lBV5b6U40zLt6lwNcAXUd3FKgUgCkQiLOrs0c52hcATuoIdzjYhr3Tfw7V4DxmBC4fSbYWDyetyGYRhkDs1E9V+qUX5NOQDgtX++1muyIlKumqGMCHeOJO3dY2lxB3DLB+vwx9dWYE+zO3ocnntrLNhCK2i4aOmUfeq8csYjao0IBSJcdc6IAOhYxrt8ZzN2NrlFPZdNn4ef634W9ZhikDQQWbp0KU4//XSUlZWBYRh8+umnUp5OdPVOH0ISbXYWDEcQCHO74gvU70Drwn8CAHKPvRTGggpEgtkJ75NzZA5MJSY47A68/fbbgserBdJOzVCxKldC9sT4YXsTTnlmKV5ctJ33SqW9DuG9FnqDztNg2zs1hRPzOU4ZEe4OdrvY3VjrwK4mN979ea/o5zIhH8sPLhf9uEJJGoi43W6MHDkSL7zwgpSnkYxU0zJAtEYkyGGTtmBbHeo/uBds0AdL1ShkjTsdAOD3Zya8H6NjUHBSAQDg2WefRSSS/g2GaGpGHZx+YT0kfMEIHv/fFvBtHNzodsLutws6d2/Q0qlTdOdARMypGeojwl33QAQA/rV8D77eUCf6uZhQLpbVqq9gVdJAZNq0aXjooYdw1llnSXkayUjRzKxdKBJBMJL4gzPssaPh/XsQcbfBWFSNojPngmGiD5nLbUt6jtxjcqGz6LBlyxYsWLBAlHGrGRWrqgPfGhGxtDqsql2eqCZtnjgZESpWVcTBGBe8r/+4S5L3s4A/G7sdu1HnFj/IEUJVNSJ+vx8Oh6PLl5KkaGYGACwbQUttM8IJqqEjQR8aPnwAodZa6LOLUHzu/dCZMzp+3+ZMHojorXrkTYrWkjz33HPCB65y1FlVHYRuV56qJrsF6xrXKXJuLelcI9J5Ca+Yy3dd/hC13+cgEmHRECNok6giAB5PNJOutqyIqgKRRx55BDk5OR1fFRUVio5HiqmZSMCLhvfuxrxLn8b619fG3LiODQfR9PljCBzcAp0lEyXnPgBDVkGX2zS3mcEgcRttAMg/MR9ggC+//BLbtm0T7e9QI7GWucVCnVW5cwmcmklVIKjD9tbdipxbS1o7ZUTaPMGO7IWYxaoAZUW4aHD6JatDjKXNGb2YVVs/EVUFInPnzoXdbu/42rdP3KVLfIndQyTsdaL+3bvg2xO9atv+xWa0LnilSzASdreh/t274N3+MxiDCUVn3wtjYc+ALBDWIduUk/Sc5lIzskZkAQBefPFFkf4SdfIn6dQpRLJCvv379+P111/H/fffj6uuugpTp07FWWedha1be1/XT1dAmYwIANQ66xFh078eSogWd9fHZ1tDdOWM2ME2dVdNrtYu3fR/LA2tFgDAioMrVLV7O7+2nhIzm80wm81KD6ODmBmRsKsV9e/fjWDjbugsWRh62lBs+GgFnL98AZZlkX/yNQg27ETDxw8h7GgEY7KiaMbtsJQPiXvMXFMh7IG2pOfOPzkfznVO/PP1f+LBBx9EVlaWaH+Xmkjb4j3+m/SaNWtw/PHHw27vWSi5ZMkSfP7555g4caJkY1Mbtz8IgP8eSmJgWDO2t23HoLxBipxfC7o3j9vR4MKE/oXwiLwyjDIiyR2UaPo/Hl9Aj0JTDpq9zfjm52/Qsr0Fq1atwqpVq9DU1ISrr74as2fPhsnEfzNWIVQViKiNWBmRkL0B9e/diVDrQegz81F83gMYffp+ZPXJxbLnv4VrzZcIOxrg2/Mr2JAfhrwyFJ91d8xMSGcZ+nxO588clglzHzNcB12YN28ebrjhBjH+LNWRctVMIBRBMByBUd81ifjbb79hypQpsNvtKKopgrGfEeHsMAx5BrQsbkHLzhaceOKJ+Ne//oWzzz5bsvGpRSAcgFvBzx8Tk4d1jesoEEmg89QMAGxrcCEUjog+tUmBSHLde4jIIUdXiHVPr8Opv57a43dz5szB3//+dzz11FM49dRTwTDJp//FIOnUjMvlwtq1a7F27VoAwK5du7B27Vrs3Sv++mixOXxBOHk2VIqFDQfR8OH90SAkpwQlF/4NpqJqgDVgwAlHoODUmwAw8O5YCTbkh6VmDEoveSppEAJE33S5YBgG+SdFg5annn4KYQ7LhrVIyowI0HPlzM6dO3HyySejqakJZYPLUHhrIfIvzkfRjCLkHZuHmttqkDU6C36/H+eeey6efvppScenBna/HW6vctc3TCgH6xqoYDUeTyDU43WyvcEl6oqZdjta99A0WRKxlu5KiWVZbHx5I1y/usAYGJQOK0X5tHL0vaov+lzSB8ZsI7Zu3YrTTjsNU6dOlW3PMkkDkVWrVmH06NEYPXo0gGi0NXr0aNxzzz1SnlYUYk3LOFZ8jGDTHuhsOSi98G8w5vWJ/oI1gY2YkTn8RBScNgf6zHxkH30Ois+5F3pL4h4h7Zhw4qZmneUdkwd9ph67d+3WXGM5rqRcNQN0nZ7Zv38/TjzxRNTW1qKkfwmyr8+G3tq1Xb/OrEPlDZXIPzEfLMtizpw5ePTRRyUdo9IcAQdcHuUCkWAgm1bOJNASY0+fbQ0uUXuItNvqWIMr/ncF6tx1YFlWVTUJaiF3RqRt6VvYs2Q7oAcqb6pE4V8KkXt+LvIm5qHghAIM+NsAFJ5aCJ1Bh2+//RYnnHAC6uqkX+oraSAyefLkjidg56958+ZJeVpRHBChh0iw5QDafnoXAJB/4lUwZBd2/I5lDUA4Og+XOex4lM9+C3nHXQZGx23vGQAIBbgHIjqzDvknRLMijz3+WFq+KUg5NQMcKlhdsmQJJk+ejN27d6Ooogi5N+TCkBn7w5fRMejzxz4oOacEQLQgO10DQQBw+nwIRZSrgfd4M7HHsYcam8XR6u5ZSNzo9EtyZe7z27C6fjVmfjQTE6ZMQGlpKV577bW0fO9JlVQtImJxrvkKjuUfAAD6XtYXWYf3rBXUW/UoPa8U/R/uD2ORETt37sS0adNi1r+JSVWrZtREaEaEZVk0/+9FIByEpXo0bEOO6/r7iAHhsLCCIJ+PW+akXcGJBWAMDH5e8TN+/PFHQedWI6mnZvbV1uGyyy7D5MmTsWPHDuSW5iLvz3kw5iYuzGQYBkWnFUWXUgP44x//iHXr0vOqXWhXVaEcThtYsJQViaPFE/vxWbevTfRzeTw2sBEWm1/ejOULlqOhoQFXXXUVjj/+eGzZskX082mRVHuZdefZvgIt8/8OABh21qkd/aXiMRebUf2Xauiz9Vi7di1mzJgBn0+6sVIgEscBgZGqe8N38O/9FYzBjPxTZvco+omEjQiFhAUiDlfypmadGXIMyJ2YCwB44oknBJ1bjaQKRFiWhXPd/3DG5NF48803AQbIPz4ffe7uA1Mh98ewz4V9kDEsA263G6effjrq6+slGa+SlOoh0q6pLbo8kQKR2NriBCK/7hf/itfuMqP+w3rYl9sBPVBwcgF0Zh2WLFmCESNG4MEHH0Qo1Hv784TCETQ4pQ9Ewh47mv77JMBGkDH8ZFRNOZfT/czFZlTfUg2dJfqYXXTRRZLVF1IgEoeQFTNhd1vHJnU5x1wIY25pj9tEIgaEQsKWOLbYrbzvUzg1Oj30+eefp91VSUCCQISNhNEy/2W0fPM8vE43LJUW9LurH8ouLYM+g/s0GgAwegaV11XCVGrCvn37cOaZZ0p6laEEpTvQOr1G2Aw2CkTiiFUjAkiTEdnx3QI0fdUEAOh7RV/0uagPBjw0AJnDMxEIBHDPPffgnHPOSbvXAFf1Tr9kHVQ7c6z4CGzAA2NxPxScMhutzozkd/qdtcqKyj9XgjEw+Pjjj3HttddKMrVGgUgcQqZmWhe+hojPCWNxP2QfMTPmbSJhAwJBYYFIq8sIo47fMcx9zMganQWWZdNuFYfYNSJsKIimzx+Ha81XABjkHncZjnv8RNj688tEdabP0KPqpirobDosW7YM48ePx1dffZU28+ZKNjNrl2cuxIamDbRiI4buPUTa7WoWd7t5z9af0Pi/VwEAxWcXI29idCrAVGRC1ZwqlF9dDsbI4LPPPsP06dPhdDpFPb8W1MlQqBpyNsP5yxcAgLxjLwajN6Cp1cKpK3e7zCGZKP9TOcAAr776Km677TbR368oEIkj1UAk0LAT7t8WA2BQMPX6uMWnobAegYCwQIRlGeSbC5PfsJv2rMi8N+ehoaFB0BjURMyMSMTvQcOH98Gz5QdAZ0DhjNuQc/Q5yI+cLPjY5lIzKq+vhM6iw9q1azF9+nQcc8wxWLx4sfCBK8yt8NQMAGQaCuAOurG9bTsAIBwOw+12w+FwwO/3p03Ql4p4NSJi/pMEm/dFpwLAovzkChSdVtTl9wzDIHdCLqrmVEFn0WHhwoU46aST0NLSIt4gNECOQlX7svfBhgIw9x0CS79xAKJduXPN3Fo/tMs5Igdll5UBAB5//HHRV/9RIBJDKBxBvSO1J4l9WbQq2Tb4GJj7xG+qFArr4RMYiABAtpF/IGIbZIO1nxV+nz9t2r4HQhFR0pwsy8J/YDPq370Dvj3rwJisKD73PmQMPgYAsHpjDe8XcSyZQzMx6LFBKJxaCJ1Jh59++gnHH388brzxRsHHVpIadik2Iw8HXj+AEeUjYDAYYDAYkJmZiZycHFgsFuh0OlgsFpSXl+Ptt99WeriyirVqRkxsOIim/z4R7YlUNQrjrj4mblOszCGZqL61GvoMPX7++Wcce+yxsiwVVQupl+6G7PVwrfsfACD32Iu7PA55pmLex8s/Lh+l50fLDO644w68/PLL4gwUFIjEVOfwpfShFmzaB8/mHwAAOePPS3jbUMgAr49fjUEsFh237qqdMQzTkRX5+yt/T4uCMaHTMmGPHY6Vn+Lg67NR987/IVC3HTprNkoueBjW6lGHzhPUo8Z0ksDRRhmyDSi9oBQD/zawY3PC559/Hr/88osox1eCWwVTMweW1aF1aSv8Hn/c4jq/348DBw7gkksuwfPPPy/zCJUTr0ZELG3fv4NA/Q7orNkomH4zrMbchLe39bOh5o4aGHIN2LhxIy6++OJek7GSOiPS9sN/gEgIlqpRsFSO6PI7m74gzr0SK5xWiKLToxmu2bNn4z//+Y/gcQLU4j2mVHuI2Je/D4CFdeDRMBXXJLxtMKSH1y/8n9/A5qZ0v6wxWdBn6dFQ34D58+dj2rRpgseiJCErZly/fovmb18CwtGAjDGYYRs8ETkT/nCoAV0nv205HMbyTxGMiPOha8wzouziMoTdYdiX23HHHXfgm2++EeXYcotmRJR7W4kEfFj51rcAgIKpBSg8pRCMkYHOqAMYgA2zYIMsIsEImv/XjOZvm3HjjTfCbrfjzjvvlK2ltVK6t3cXk3fPOjhWfAwAKJh2IwxZBTCwyTfmtPS1oOa2Gmy/ZzsWLFiA119/HbNmzZJsnGoh5dLdYPM+uDcuBBDNhnRnZPlfwLYrPqsYYU8YLd+14JprrsHMmTNhtfJfONEZZURiSGVHxGDrQbh/WwIAyBl/ftLbB4J6uP3CMyJskHtTs850Bh1yjoq+Sbz55puCx6G0VAMR3/7ffu/3EoKppD/yp1yH8uvfQuH0OTGDEABospsxNPtYIcONqfisYkAP/O9//8OSJUtEP74cPAFla0Tsy96Fp7kNxgIjSs4sgTHPCEOmATqzDjqTDnqrHoZsA0wFJpT+oRTFM6Mp6rvvvhu33npr2l+NSxWIhL0ONH/xFAAWmaOmwjbwaAAAG+bW68jcx4ySs6JN/+bMmYMDBw5IMk41kXJqpu2HfwNsBNYBR8FcdliP37Oh5AFiPAzDoM9FfWAsMMLhcODLL78UMlQAFIjElErKzLH8A4CNwFIzFuY+A5Pe3uk2g2WFX335/anvpJt3TLTW4ZNPP0FbW5vgsSjJH+Q/NRNytaDp00eASBi2wZNQeukzyBp9KnTm5Mvb6vYdmcowEzIXm5F/XPRKZe7cuZr8UJSiVThXweZ9cPz8KQCgz0V9oDMnfntjGAbFM4tR+ofovPcTTzyB6dOnY8OGDVIPVTGtHvGnzliWRcs3LyDsaoYhvxx5x1/Z8btwgPv7U8EpBbD2s8LhcOCaa67R5POfj1qJMiL+uu3wbP4eAJA76Y+xb+NL7QK2HaNjOi5kxZieoUAkBr49REKOBrg2RNNgORMu4HSfNpc46Wu3h/ua8O4sVRaY+5oR8Afw/vvvizIepfDdOZQNB9H06aMIu1thLKxEwbQbeaXlt+/PwaCcUTxHmVzRGUVgTAyWLVuGL774QvTjS80dVCYQYVk22jkyEkLOYWORNZr7B2DhKYXoO6svGD2Dr7/+GiNHjsQVV1yBffv2SThi+bn8IUl67Th+/gSerT9FV5ed/n/QmSwdv/P5uafsGR3T8Th88cUXotUfqFEwHEGzS/zdiVmWRet3/wAAZAydHLdEwOXm15U7lpyjo4HIF19+IbgFPAUiMfBduutY8REQCcFcOQKW8iGc7hNhxfmnb3WkPjfHMExHp9U35r0hyniUwnfDu9ZFr8N/4DcwJhuKzrwTOhP/f0drcETyG/FkzDWi4KRoIdkdd96BSEQ7vTA8QQ+8fmVqLDybv4+ucjKYkHX8n2DU81uRljcpDwMeGoDscdmIRCJ44403MHDgwLRZVQbE7yEihGf7CrQtjr535J0wC+bSAV1+73Lz67lj6WtB0YxoMeQNN96QVu0FOquzp7YgIhnPlh/h3/8bGIMZucddGvd2LW3CajoAwFJhgbkseiH7ySefCDoWBSIx8AlEwh47nOuixXG5HLMhYmqyW5LfKIHcCbkAAyxfthxbt24VZ1AK4FMj4v5tMZyr/wsAKDztFhjz+6Z0ztZW/kvguCiaXgSdVYcN6zdo6qrQEXDA5zfLft5I0I/Wha8BALKPPhf6nLKU+uuY+0T7u/S7qx8yDsuA3+/HzTffLNtW6FITuz4k0LgbTf99Au11IVljTutxG7uL//tT0alFsFRa0NLcgjPOOAN79+4VYbTqIsUmg2wogNbfg8Lso86CIbso7m0b7RboGWE1igzDdGRF/v3vfws6FgUiMfCpEXFvXASEgzCVDoC5criEo4otENQhx5Sb8v2NuUZkHh5N07311lsijUp+XJfvspEwWhfPAwBkjz8ftoFHpXzOXQfyBL+YY9Fn6FE0Pfomcvc9d0u2v4PY7H67KCvB+HL9+i3Crhbos4uRc9TZAIBsY2rLEwHANsCG6turkTEkA8FgEA8//LBYQ1WUmEt3w+42NHz4ANiAF5aqEcg/6ZqYU5utDhOvLp4AwBgY9L2yL3RWHVasWIFRo0bhs88+E2voqiBFoapj1WcI2+uhzyxA9pFnJ7xtOMIgz5L6a6Rde53Id999J2jvLApEurF7g5z3y2BZFq5f5wMAMkdMUWzpX545fuTLRfv0zJtvvampqYDOuE7NeHeuQtjZBJ01W3AGy+PXoyyjUtAx4ik4uQD6TD127dyF//73v5KcQ2yOgAMur/AmfXyw4WB0ahRAztHngDFENyG06oS9ybYXsgLA66+/nhZZEbEyImwoiMZPHkbY0QBDXh8UzpgLRh87AA2Edcgy8S+MtFZaMeD+AbDWWNHa2oqZM2fipptugt8vfl2FEsTOiIRdrbAvi9b55U6+rEudTjw5RmGfGwBgLjHDWmNFJBLBBx98kPJxKBDphs+0TKBuO4JNe8AYTMgYIv5yTq5s+tTXhANA9phs6Kw67N+3X7NtxrlOzbjWRvtzZA4/CYxB+IdmvqG/4GPEojPrkHdcdFXTc88/J8k5xOYMOOH2ypsRcW9chLCzCfrMfGQOP9RoLtX+Op1lHJaBjKEZCIVC+Otf/yr4eEprEaGrKstG0PTV09H6KnMGis++B3pr4sLgbGNqnYhNxSbU3FmDglOiQeWzzz6LM844Iy1W0xwUsJdZLG3fvw024IWpz0BkDD2O031sAoP1du3TM//6979SPgYFIt3wCURc6xcAAKwDx0NnEV6FnCoThLUc15l0yDlS2z1FuEzNhOwN8O5YBQDIHHmKKOdl/eWiHCeW/BOi3VYXLVyEjRs3SnYesbj8foQj8mUF2UgY9uUfAgCyj5jZkQ0BUu+v0117VuSNN97A7t27RTmmUsQoVm1b8iY8m5YCOj2KZs6FsaAi6X0yDKm/P+kMOvT5Qx9U3lQJnUmHb7/9Fp9++mnKx1OL1XtbRTuW/8Dmjsx83glXg2G4fawbWOFbVQCIfnb8XmeY6muEApFuuAYibCgAz2+LAQCZI4RvhCYEE069OU273GNyAQDvvf+eJpctcsmIRPddYGGpGplygWp3zc3SFKwCgKnAhOwx0Q9ULbQhl7u9u2fzDwi11kJnyULmqK6dgf1+cQKRjEEZyBgWzYpovVZE6NSM85cvO6bBCqbe2GXrg0RMjPDHIntUdkdm5NbbbkUwqPxWAqna2+zBhgMOUY4Vdrei8dNHALDIGDqZ86pNAIgEhX9uANHO0BmDo20k3n333ZSOQYFINwc4Fqp6ti5DxO+GPrsIlirxl3HyERLhTdc2wAbbIBv8Pj9uu+02EUYlr2QNzdhwCK5fo6ubun9oCbGrNgcGnXTTEfknRafd3nr7LbS2incVJQWPjIEIy0ZgXx6dk84ad0aP5dduD79lo4l0zors2rVLtOPKTUgg4tm2Ai0LXgEA5Ez6IzKHn8j5vvqIOEFh4amF0GfpsX3bdvzjH/8Q5ZhK+HL9QVGOw4ZDaPzsbx2N5PKnXMfr/n5f6s0wu2ufnnnn3++kdH8KRLrhmhFpn5bJPPxEzqkwqXi8qTc1a9fethdMtFPeDz/8IMLI5JOsoZl3+88Iu1uhy8gVtFKmO39Qj762xPsKCZExOAPmcjO8Hi/eeEPdvV48MjYz8+5YiWDjbjAmK7LGnt7j960O8QKRjIHpkRVJddWMv247mj5/DGAjyBwxhdMWFl2ExQlE9FZ9R1B43/33weEQJ6sgt69ECkRaF78B/74N0R3Cz7oTOjO/53xLmziPCwDkjMsBY2Cwcf1GLFu2jPf9KRDphksgEnI0wLd7LQAgY7g4O7EK4XSJ86ZrrbIib1J03vCmm27S1AqaZKtmnGu/BgBkDj8ZDM9mV8nkGfqJerzOGIbpaHD2/AvPq3opr1wZEZZlYf8pukIga/R06GPUZzXZLbyXjSZSMjO6D8q8efOwc+dO0Y4rp9YUilVZNoKW/70ANuSHpd9Y5J8ym/fqwGBA+IVSu/zj8mEqNaGpsQmPPfaYaMeVy74WD9YfENaFFPi9F9Kq6JLmwuk3c6rV6e5AY6ZorxF9hh4546NZkb/97W+870+BSDdcllVF27mzMFcOhzG3VPpBJdFoF94lr13J2SXQWXVYvXq1pgpXE9WIBFsPwrd7DQBGtCLVzsJe6QpWASB3fC70GXrs3rUbX331laTnEsIr0z4z7o0LETi4BYzBhOwjZsS8TSCoQ445V7Rz2gbakHl4pqazIi0pTM24NyxCoG47GJMVhafeDEbHv2+OzydedooxMCg9N/qe++RTT2puc7wvfhWeDfHXbUfz19Gasezx58E2aEJKx/EF9Ciyivf5VTStCGCAzz77DL/99huv+1Ig0kk4wqLekTgQYdkI3O3TMirIhgCAw22EWS+sw2o7Q44BxWdE05+3z71dM+nPRKtmXOuiS3YtNWMkCRwbm4Wvx09EZ9Z1ZKrUvJTXI0MBYaBxD1q+fQnA711UM+JX/uea+HdXTaR9WuDNN9/UZFakjWcgEgl40bY0ejGSM+EC6DNyUzqvyyPehRIAZI3Jgm2gDT6vD/fcc4+ox5aa0GkZ/4HNqH/3zmiGqmYMco+5SNDxCkziXUSZy8wdxfV8s1UUiHRS7/AhlGQDAP++jQi11YExWWEbNFGmkSWXSkvruMc6OR+mEhMa6hs00z8hXkYk7LHD9XsL/qxRUyU59+7abJh0puQ3FCD/xOhS3gXzF2D58uWSnitV3oC000aRgBeNnz4CNuiHpXo0csafl/D2Np2w/jo9jjfgUFZEK6+Ldk5fEMEwv/4bjhUfIexqgSG3FNljz0j53HanuG3/GYZB6fnRC4o33nhDM7slC52W8e1dj/r37wbrd8NcPhRFM25PKUPVmRniXpgVTo9+Dv3rX//itfqSApFOuNSHtK+8yBg8iVP3OrlkCWhp3Z3OoEPpBdEn6NNPP421a9eKdmypxKsRaV30OiI+J4xF1bAOOFKScwfDOpRnSNPYrJ2pyNTRAffKK69EICD+BmZCeSXMiLAsi+b/vYBQy37oMwtQePr/JX0TNjHi9EnoTKtZEb71ISFHAxw/fwwAyJt8haDmf61OE3QiF/TbBtiQPS4bLMvirrvuEvXYUhGyWsa7czUaPrj395b6o1B87gO8i1NjifjFzeba+tmQMSRa2M2n5QAFIp0cSBKIRPxueLb8BCDa0l1NLIy4V39Zo7KQNToLwWAQ559/PpxOp6jHF1usqRnfnl/h3vAdAAYFp8wWfPWQSLauWrJjtyu9oBT6LD02btyYUkGYlFiWhS/JEmohXGu/hue3JQCjQ+GMW6G3Je+BwITE6ZPQWXtWJBwOayorwrc+pHXxm2BDAZgrDod10HhB5w5FdMg2if9YlJxV0lGTkMpKDbmlOi3j3vwDGj5+EGwoAGv/I1B8zj2iXQQ7HLmiHKezotOiwc28N+dxvg8FIp0k2+zO/dsSsCE/jAWVMJUdJtOouNFHxH2hMwyDvrP6wpBvwNatW3HdddepurVy96kZNhRA87fRLdwzR0+DuS/3Rj+pCElcsAoAhkxDdIk1gIceegibNm2S/JxcuYIuhELi7zMTCfrhWPkZWr6L9o3IPe4yWMqHcbpvwC/+hx/QNSuyY8cOSc4hNj5dVf0HNsGzaQkABvknXiXKHlrZRnEvlIBoTUJ77dTcuXNV/f60r8WDX/fzm5Zh2QjafvgXmj57FAiHYBs0AUVn3tGlg7BQB5vEW8LbLmNoBizVFvh93PcFokCkk2RTM4c2uDtZsQ3u4omExH9CGTINqLimAtAB77zzjqpX0QS6BSL25R8g1HIA+ow85B17ieTnb2iStmC1Xc5ROcgckYlAIICrrrpKNUusHQEHAsHU3yDZULDLB0kk6Idj1WeofeVKtC58FQiHYB00HtlHnsn5mAfqSlIeTyK2ATZkDtdWVoRrD5GQswmN/30CQLQ1galEnClHmz5XlON0VzSjCIyBwZIlS/Dtt99Kcg4x8J2Waa+Hsv/4HwBA1tgzUDjjNtFbDzTZLcg0irs9CcMwHbuHc0WBSCeJApFAwy4E6rYBOgMyDj9BxlFxI2aXvM4yBmWg+MzoFeDs2bN5L8uSGsuyWL1uNXbu2AA2FH2zDTbv6+i6mXfi1bLsA7T7YBYsIq1cSoRhGJRdWgadWYcff/wRr7zyiuTn5MLutyMQ4F+UyIaDaPriSex98izsfXwG9j13IQ68ciUOvHw5Wr97FWF3K/TZxcg/5XoUnXEbrwuAfQ0Z6GOTJlNVPCP6mnjrrbc0sQ8Ql66qYY8d9e/ehbC9HobcPsibfJlo5zcz0mSnTAWmju7Dt8+9XTWBeXefr63lfNtgay3q3vkLvFuXAXoDCqb9GfknXS3Z1HKxhX8PkmSyx2bDWMw9aKJApJNENSLtRaq2AUdymp+Wm9MtXtOg7oqmFyFjWAY8Hg/OP/98uFwuyc7V3YEDB/D444/jiy++QEtLS8fPHQ4HXnrpJQwbPgzjRo3D4oeuwN4nz8b+ly5D/bt3Rq+g+42DbfAxsowzHGFQnjFAlnOZCkwoOSd6tX/bbbepYjM2R8ABr5/f1Vok6EfDxw/BvXERABZgI4h4HQi11SHidXQEIH2vfgVZo6bG3Wo+kRLjSN734cI2wIas0VkIh8OYNWuWqhvNAckDkYjPhYb374kWA2cVouSCv4r6PqeLSHOhBERrEnRWHdauWStoK3qpLPitHr8dTN4GIdCwC03/fQK1r16DYONu6DJyUfqHRyTfyyxLXyb6MRkdg5IzuWck5d2zO0Xvv/8+rrzySsnPEy8jwoaCcG9cDEB9Rart2uxWQNzl+h0YHYOKqyuw/e7t2LBhA6ZNm4avvvoKWVnSvbkAgNfrxbRp07B+/fqOnw0ePBiHHXYYFixYALfbDQDQGY1gGQPYgBdhZ1N0zEYz8qdcK+sUWiZTA0CepYT5J+ajbUUbnNudOP300/Hjjz8iO1v86TmuHH4HPF7ugUjE70HDxw/Cv3c9GIMZRTNvh7G4HyJ+NyI+NxAJwdx3sOBUtLttAERssNpF2cVl2LZ5G1asWIHnnnsON998szQnEkFLglUzkYAPDR/ej0D9DuhsuSi54K8w5Ii7mSMblu69wpBpQOG0QjR83IA777oTZ555JkwmaZfTc8WyLJ6avzXhbfwHt8H+w7/g3bmq42eW6tEomPZnGLLF7YUTCxOSZuPOrFHcH3NNZERuvPFGyacEXP4QHL7YnSE925Yh4nNCn1UIS81oSceRqia7WfQlcp0ZcgzRrbhtOvzwww845ZRTJG92dsMNN2D9+vXIzMlERt9oxmfz5s347LPP4Ha7YSmzYPDlQ1E++01U3PQ+ym/4F0r/+AQKTrsFpRc9DkOONDUC8QQ94l9ZxMPoGFRcWwFDrgEbNmzAH/7wB4RC8u310p0j4IDLy+26Juxzof79u6NBiMmK4vPuh7X/ETBkFcBUWAlL+RBYKoeLMh++aWexZD1ejPnGjn4Wd955p2oLV1mWxZb62IWSkYAPjR8/AP+BTdCZM1By/oOi7UzdmZht3mMpmFIAfbYeO7bvwO233y7pufj4ekNdwmxIsPUg6v99ezQIYXSwHXYMSi95GiXnPyhLEAIAXrf4hcR8aSIQ8Xq9OPvssyVdQpqoPqSjSPXwEyVdAipEKKJDrkn8vgmd2frZUHNrDfQZeixbtgxTpkxBW1ubJOd688038c9//hM6nQ6l15ai5q81GPz8YFT+uRIl55SgZm4N+v+1P0ad/Ccw5mwwDAO9LQfmvoOROex4mEqk2/8lnvpGed442pkKTKj6cxUYE4OvvvoKt9xyi6zn78zhd8DtS/7aiAS8aHjvbgRqt0BnyUTJBX+FpeJwycblDejRL2u4ZMfPOy4PGUMy4PV6ceWVV6qyRuG9Nevxy56egUjE70HDB/fAt+fX3wPCB2AqlmYDRzHbvMeit+jR97JoAPX000+rYoomEmHxdIJsCMuyaPnfi2BDfpjLBqPsqr+jaObtMPcZKOMogabWXFnPF4smAhF9rh6bN2/G1VdfLdkSrXj1ISF7pw3uJJ6rEypH5JbWsVirrai+tRr6DD1WrFiBk08+GQcPirObZLsNGzbg2muvBQD0O68fLIOjRaCGLAOyR2ej6LQiZByWgWJbKVZtkG7nW7721GXIUrDambXGivKrogWZzz33HF566SVZz9/OEwwgwiZ+O2EjYTR9/hgCddugs2aj5A+PwNxnkORjMweHSnZshmFQdnkZGBODxYsX49VXX5XsXKnY07YfLy7q2Xgt7HWi/r074d//2++ZkIdglrAlgdMt0bxxJ9ljslF4avQ98IorrsCWLVskP2cin6+rxbaG+PV07o0L4duzFozBhILT5sCYJ19GtbPahgwYGGWrNDQRiJRfWQ7ogXfffRcvvviiJOeIlxFxrPwUAAtL1QhVbHCXiE0vT4rNWmVF9W3V0GfpsWrVKhx22GF4+umnERShs6bL5cK5554Lr9eLinEVME+JvxKjDFMRDKvnKRxhdbIVrHaWc0ROR/HqjTfeiA8//FD2MfiCiaeFWJZF63f/gHfHSjAGE4rPvluyq+/u9tdWSXp8c7EZJWdH//3/8pe/YNu2bZKej6twJIwXf1qCfY1dp7iiq2PuQOBge0D4sKRBCAC0OsRt8x5PydklsB1mg8vlwtlnn91RRya3cITFs9/Ffx6EPXa0LvwnACBn4h8UC0IAIBDWodjWR7HzAxoJRGz9bSg9LxoEzJkzB0uXLhX9HLECkWDzPjjXfAkAyD7qHNHPKTYjpJ2a6cxaaUXN3BpY+1nhdDoxZ84cjB49GosWLUo5a9XS0oJzzjkHmzdvRl5xHjIuyQCji11pmGvOw+oN0l9N85XJSPuhF0/h9ELkHpOLcDiM8847D08++aSsDZ68SQIR56rP4PzlSwAMCk67RfIGc53tPpiJYqu0b7QFJxfAOiD6Wpg0aRLWrVsn6fkSaX/c//3bJ/jq565L1/21W1D3zl8QbNgFXUYuSv7wiGi9QhJpcxmhZ6Sf1mb0v9dO5RiwceNGXHPNNYo0Ovvol/3Y1RQ/CGpd+BoiXgeMRdXIPoJ7bxyp5BrErwviQxOrZoBoMZJnmweOVQ5Mnz4d//vf/zBhQmrbH8cSq6tqy8LXgEgY1gFHwlozRrRzSUWKltaJWMos6HdXP7R+34qGDxqwceNGnHBCtMeK0WiEyWSC2WzGuHHjcMkll+DMM8+EzRZ7rnjFihU4//zzsWfPHpjMJhRcVQBDdvynZ41xKvYF1FevE/Iq84JmGAZ9L+8LnVmHlu9a8H//93/YsWMHnnvuORgMqb/Mt23bhg8//BAHDx5EfX096uvr4XQ6MXDgQIwYMQIjRozAqFGj4Euw+7F7y48dV395x1+OjMPk3yyyzDwSDV5xpxA7Y3QMqm6owu4nd6N+bz2OO+44fPHFFzjmGGmXj7Msi3Xr1mHFihVYuXIlVq5cic2bN2P4mOGwjR8Dp+E0MLroyr+2H/8Fx4qPATYCfVYRSi54SJLC1FgirA45ply0+JslP5cx14iK6yqw62+78M4772DixIm45pprJD9vO38ojOcSZEO8u9b8vmSdQcHUG1Jali42M6tstp9hVdwX1+FwICcnB0NeHgK9VY9IIII9z+yB+zc3srKyMH/+fBx11FGinOu8V5bh512H+lR4d6xEw4f3AzoDyma9KNsLVoijR+zExuA/FDl3yBVCw8cNaF3cCjbODsaZmZk499xzMW3aNNTU1KCmpgb5+fl47rnn8Je//AXBYBCFFYXIvTIXlqr4tRYGxoDI3nthd4nfUlyo/mUONOQ8rNj5WZZF87fNqHu3DmCBadOm4V//+hfy8vhly1iWxd///nfMmTMHPl/irQ8AYNgpY+EccU+PYm7v7rVo/OgBsKEAMkdPR/7J1yjSlXjskAPYCu6bcKUq7A5jz7N74NnqgdVqxYcffohTTz1VknM5HA5cccUV+Oijj+LexpBTgsyRp8D922IEm/YCADKGTkbeSX+C3irt8vvuhh/5T+x2yjdt1fR1E+req4PZbMayZcswerQ8Kx6fXbANTy+IFqmy4RD8B35DsKUWIXt9tOZwzzpEPG3IGns68k/6kyxjSmb8yO3YEHhN1GOGvWFsunYT7HZ70tYCmgpEACDij2DP03vg3uxGdnY2FixYgCOOOELwuY7520Lsb41Oz7DhEGpfvx6hlv3IPvIs5B1/heDjy2HEwAbsMjyl6Bgi/ggi/gjYEAs2xCLsDcPxiwPOZU74Gnp+oFmtVni90X/36mOqYb3QCr0tcaZjYPZw/LLiIknGL5RexyJv6P3wh5N/eEvJsdqB/a/sRyQQgdVqxTnnnINZs2bh2GOPTRoINDY2YtasWfjvf/8LACgbUYZwZRhMFgNDjgE6sw7+g34E9gUQrg3Duc8JNsLCNuRYFJ52S0cw4tu3AQ3v3ws25Id14NEomjlXsVVnmZYQjP3vQygi/RLniD+CfS/tg3OdEwaDAc888wyuu+46UQOwTZs24ayzzsLmzZthMBpQPLwYTAUDfaUepmITsGwwdi5cgojv0EpDnS0XBadcB9sg8TLJfBx59EfYZF8p2/lYlsXeZ/fCudaJAQMGYPXq1ZL32tnT7MbJTy2GY89vcP+2BJ7N3yPi7bl815BTgj6XPy/KDrpiGFrTjH2Wx0U9ZloHIkD0hb77yd3wbPUgNzcX7733HqZMSb3RWCTC4rC7v0YwHP2ncKz8DK0LX4XOlou+V78CnVnaNfBi6VvkgaPwAaWHERPLsvBs88C+zI7IgQgCTQF4WjwAAL1Rj5qLamA+zszpzXp05h+wdKU0HTPFMOqot7DDoXwrfM9OD2pfr4Vv/6GgaMCAATjrrLMwadIkTJw4sSNTEggE8Ntvv+Hnn3/Gvffei7q6OhhNRlReUAnLZEvcWh3g96Dn5f2IhCKwDZqAwjNuRaBuG+rfvye6bXm/sSg+8y5BW8mLYezR72Krfa0s52JDLPb/cz/sy6LLZs844wz885//RGGh8JVtH330ES677DK4XC7kFeeh4E8FsPQ/lEGsyhyADSuvRCTog3vjIrjW/Q/GggrknXClol2hjxm/AOvaFsh6zpArhB337kCwOYjzzjsP7777rqQZuVNufRnf/eN+hO31HT/T2XJg7jMIhpxi6LNLYMgphrVmtKo+V3Iz/QhX3CvqMdM+EAGif+SeJ/fAsz36YTZ16lQ89thjGD6cf8+AOrsPRz/yXfS4HjsO/ONqsH438qfegKyRpwj/Q2Ri0EWQNfgehFjlGlvxEQlEEGwKQp+lhyGL+zxpqfN2bNufK93ABJp09BKstX+t9DAARANA7w4vWr9vhXOFE6FOTfsYhsHhhx8OgyFa2BcIHGoDXlxTjOzLs2Gp5LYcuXDLMCx+7CMgHIKlaiT8B7eBDXhgqRqForPvhs4oz6qJRCaN+xVr3f+W7Xwsy6J5fjPq368HG2JRVlaGt99+u6OOiq9IJIK7774bDz8cnforH1WOzMszYcjp+toZbrkcP61R1+7gAHDsUSuwxvGJ7Of1bPdg5yM7gTDw0ksvdbQGENsTb36KW6+6AGzQD8ZkhW3QeGQMOQ6W6lGq7T/VWfnIR2AP8NshOBE+gYgmVs3EorfqUXVLFQpOLgCjZ/DNN99g1KhRmDVrFvbt28frWLX236dkQkE0f/UMWL8bppL+yBx+khRDl0wookOhVd5uokLoTDqYy8y8gpA8cwG2H1DfXj+dhb3SbLSWCoZhYBtgQ9/L+2LQs4NQfnU58o7NQ0ZZBliWxfr167FmzRoEAgFYMi0oG1mGynMqUTi3kHMQAgDlY0ai+My7AL0Rvj3rwAY8MFccjqKz71JFEAIAdfWVsp6PYRgUTilEv7v7wdzHjNraWpx00kmYNWsW7x4XXq8XF1xwQUcQ0n9Gf+TckNMjCLEZbPh1k/SrYFIRCclbk9LONsCG0nOjxZg33XQTvvrqK9HPsfTHZbj9TxeBDfphqRmD8tlvo3D6HFj7jdVEEAIARRZ5Xx+daTYQAaLBSJ+L+mDAwwOQPS4bkUgEr7/+Ovr164eLL74Ya9euTXqMUCSET7Z90rEBV3ufg/yTr9XME6izXKO6e50IVWkdBZaVv9iRj4ZmeTuscqUz65A7IRd9r+iLmodrMPjZwai8oRIV11dg0OOD0P/5/si/OR/Zp2VDZ+L31hAJW2DtPw7F59wLnSUTlqoR0f83ytvgLZH99VlgpNp4JgFrlRX97+uPvMl5YFkWr7/+OoYMGYKzzz4bP//8c9L7NzQ04IQTTsAHH3wAg9GAgdcOhPVMKxh9z79lUOYkuHzKr8KIJeCXfhfseApOKUDW6CwEAgFMnz4d06ZNw4YN4uwLtW7dOkydOhVhvwfmyuEoOvMO6Ezqed5zlcEo10tEs1MzsXi2e1D/UT3cmw6t3z7ppJNwyy23YMqUKdDper653vfTfdi8K4xP7/gc/r2/gjGaUXT2PbBWqbcGIRE1TQtIYYj+Wvy8QZleHVwZ9RFkD74PgUjyrdfTxVD9tVjx++PChkOATq/I6phkakY/hSZfg2Ln92zzoPHLRjjXHioiHTx4MMaOHYsxY8ZgzJgxKCkpgdPphMvlQnNzM2699Vbs3r0bmTmZKJ1dCsug+B9ypa7bsW1frgx/CX8leT54Su9T7PwRfwT1H9WjZWEL2BALnU6HWbNm4Y477kB1dXVKx9y0aROOmTQJLc3NMJcNRvF5D6imAJWvSeM2YK37HdGO1ytqRBLx7vai6esmOFY6OpaSDhw4EDfccAMuvfTSjn+UNze8iUcXPoqmFwNo2rozut/CuffBUj5Mkr9HDmI/mdREx+jA7L0PbS517KyZyMgj52Gnc7PSw5DNgMifsWaLst0ZuRh39HvYYl+j9DDgO+BD01dNsC+3gw0nfwsuLC9E3uw8mPvEn+bqlzUY636+TMRRim/4ka9jtzPxbrRS89f7Uf9BPRyrDq1mOfbYY3HxxRfjnHPOQW5uLqfjrFq1ClOnTUVzUzNMJf1RcsFfobMol/URaszgWmxjnhPteHwCEXXm8ASyVltRcW0FAucE0Dy/GfYf7Ni2bRtuvPFG3HHHHTjyyCOxfe927N+3HxF/dJMqnTkDxec9IHmrY6l5PblKD0EyNVmDsVYDQQgAZOurAfSeQMQf0MbjYoU6pi4tfS0ov6ocpReUwrvLC+9uLwJ7AwjuDSLsDcNkM8FgNUBv0cNaZoXtTFvSWqqMgLTN08SQh9HYDWUDEXOJGZXXV8K9zY2GTxrg3uTG0qVLsXTpUlx//fW45JJL8PDDDydc4fTdd99h5syZcLlcyO/fB7ZTH9B0EAIAjS05QIEy507LQKSdqciEPhf2QfFZxWj7sQ2t37XCVevCwoULu96uoAwFp98mS6tjqbXaswFpl8orJpuVbhdVsYUV6rCqFJ9fG4EIGyxSeghdGLIMyBqRhawRwgo5M4yZWLtJPRtAxlNb2w9Qpma1h4yBGai5tQaB5gDsy+1wLHPAu9+LV199FR999BEee+wxXH755T2m9D/66CNceOGFCAQCyBiSgRP+7yas3KruAnouDjRaUVhihTcUfyd6qaR1INJOb9Gj4MQC5J+QD/dmN4ItQRjzjDDmG1FUWoIDm25XeoiiqWuyQZemgUh9fbXSQ+CsqbkI0PYFEmcZxkzUN0u/u6oY3K5cjZfoxzbQNgnf+9VfXL99fw76jSlBo7c++Y1lYiowoWh6EQpPLYRnmwcH3z6Iln0tuPLKK/HGG2/ghhtuQGtrK+rq6rBnzx689dZbiEQiyBmXg75/6gt3UB1ZNqEirA5ltn7Y4dgo+7l7RSDSjmEYZA7p+ulQkl2BAwqNRwpuvwGV5gK0yrCng5xyTLnYtlk7Vx2767KRMcggSydPpQ2ynYClfm28lTS1ZiuWfpZS7T552peLodw0Do3eL5UeRg8MwyBjUAb639cfzfOb0fBJA3788Uf8+OOPPW7b54Q+yP9jPhgdg9q69LniyGIqAVAgIrsMnfoL7PgqMPdJu0CkyjYK+1ntXMoGgjoMstVgj0sdW8JLRc/osX37KKWHwdnBJhuyCg2aafrHxWE5o7Fqk3bSoPaWQYBefYFIO0bPoHBqIbKPyEbDxw1gGhlY8ixAFhDODMNSYUHWmCwwDIMcUw72u9TRJ0cMYb8yn4e9PhBBUJ09H4TI0JUAEGeNvBKyjNE3VWfwUFV72K29IuI8Qw32IL0DkaE54/HTb9pZrhiK6FBs7YNaD7+mh2oWbD5e6SHwsmlHEfKHZcIddCk9lIRMBSaUX5W4OWGxpRL7ZRqPHFpaCgEFXs7aucSUiNfDb1dSLdCFtR1c9c+YgIYNt6MmNAejc6ajyFqCbbvLlB4WbxFf+hesttWNV3oIvOUatfdciqdf1hCs21as9DB4CYR16JcxRulhiMKmS4/6kHZ7DuZAx8gfFvT6jEhLW67SQxBdwJer9BAECXkqEIro8Ou2YmBbMYBJSg8pJU3NxYB69rUSXb+sIVi3SXsFF2ZoZxuEZIyuE5UeQkrCrqEAlio9DMGYYPo8lwDAG9Cj0laOWvdeWc/bqzMiDBgcaNROWpkrh1M7RZ2x1DVq6wovnh37c2A1aGM1SSosnuOUHkJKQn5tZwzblWdUY/UmbWbdNu3sCwOj/etgtytf6SGIrsBYLfs5e3UgUmQtgT+o/iVvfNU3a7eK22bIwJ6D6ZFGCEV0qMwYovQwJFFoKcYvm5TbJEsIpyNX6SGIIicwRfX7LsXjcBvRP1s7fYHiaWzRTpEwV/qQ/MFtrw5E8k3avJpIptlhgs2gzUxPRcYgRDS0OiYZW2Sg0kOQRLnhJATD2nyc6pu1/+FRbO2DVRuqlR6GIJagtgMRo86I2qb0uGjqzOWQPyOtzXcSkViQHlMAsRRZtFmQZ2OrlR6CqFqb0y/YtRky8Osm7WZ6Gu1mzU+Z9cEpCEW0/fa9c0+10kMQpMTaF+GINjNSieyvl38Bh7afyQKprd2zmLIM2iyi8jjT64N7274CGHVGpYchqoHGc2B3afdvYlkGxRbtPs9yzXlYtUH7mbbaJhv6ZWk3oM01avc5lEhjmwW5ZnmDkV4diLhd6bd0t52R1WaQtadWe6swEvEF9KjKHKT0METTP3soflqjvZ4u3WUbtNvIsMYyAb5AetS2ZYfHKj2ElBkj6bV0t7NSSz9Zz9erA5GmVpXsviSBsF971dyFlmI0tmk7ZR5LFrT/wQ0ABsYA576ZaVHDYwhrM2MIAC0Ng5Uegmi27+oPBtqc3gh402P1VSxWNnEjN7Fp/x0lRQbGgINN2izo5MLt1t4S3lKL9tPNsTjb5H1RS2V41hnYUav9Qk8ACPi0mXnLNxfgt13aHHssB1us6J89VOlhpKTVnqv0ECTj98ib7em1gUixtY/mi70SadRgtscQrFZ6CJLYvrdIkW6FYupjK8eKX7SbRu+uza69QB0AqixHpUVGqrOMkDafV7UN2m2TkExjs7zZnvR6RvOQTm2eY6lvtmiuYVBba3rOuTq9RlRkyDvnKjar47y06rlT26S9QB0AWhq1W9wZz9Yd/TQ3PZNvLoDTq92C7WT21mXApDPJdr5eG4ikU5vnWEIRHYqs2vlg1zE67NyfvsXDeXrt1omMyD1ec/uZJONwG5Fj0lZWJN9cgN92ps+0TLuGNgsGaKy5WaGlQukhSCoU0aFvRrVs5+u1gUjIn34v6O5yjdoJRMozquH2ayuDw4ffWa30EFJiM2Rg62/abOWeTKFFW7U76Tgt084a1NYmeDZod9UVV9n6KtnOlZ7Pag5czvS9+m5nhnaW8OYZ+is9BEnt3K/NjMIg85lotFuUHoYkMjW2c2prGk7LtNuyo0ZTdVTp3IOqg1++8gVZHvmXXnoJNTU1sFgsGDt2LL7//ns5TptQQ7M254h5CWkn6xPxanPfEq6a7GaUZWgrnds3owrL12hzRQMXTEg7HyZ55gJsTMNpmXZNdjMGZo9UehicuZzaa4/AV1ubfK8PyQOR9957DzfddBPuvPNOrFmzBpMmTcK0adOwd6+82wx3ZtZbUN+Wnld5nXk9uUoPgbP6Jm1mDPgoMmqr/4PFcZZm95PhwufRzodJleXItJ2WaWfya2d6Jh32K0pmz0H5aqgkf2Y/9dRTmDVrFq688koMGTIEzzzzDCoqKvDyyy9Lfeq4Sqx9NbtrJR+tdm28WCx6C3anSX+KRELuGqWHwNnhuROxdmt6F3S3tGmnWLWtMX0zU+02ba9Cvln9WR+z3oK6lvRrvNid02vEINyAikzp37ckDUQCgQBWr16NKVOmdPn5lClT8NNPP/W4vd/vh8Ph6PIlBS23d+Zjz8FMDNZdh1G5U9HHpt7CvIqMgWm5eVR3e2tLUWgphp5R9zJYs96C3VtPUnoYkjvQmIlhuePRx1au6sck3adl2rU6Tdj/6/+hf/gmjMidDLPerPSQYiq1lveKC1kAWL2pLzavugrDjH9CiVW6z01Jlyk0NTUhHA6jpKTrlVVJSQnq6up63P6RRx7B/fff3+Pnz0x+BhlZ4m23bHCF8HzDM9CFA6Idky9WZwQTCQo6RmPVdNT1P5fz7U2WNrD6RrBgBZ1XbHo2B/5h/K6+M9q2oGzrv2BxyT/Fx+qMaKw4BY1VpyJi4NuddzJ0TAQGoxuMwYEI41Hh45EJv4dfodqg5bfD7On5mlZSc98TUHvYJUluNR4AoNOFYDA3g9U51Pl4jOH3eFRs/Dty65dLNKLU1Pc7Cw3VZ3C89RkwGHxgzPvU93iELcjM24ri3Z/DEJDmYlkObaXjsW/onzje+mjomHOgt+0Ciwine7idbpyCUzjdVpb1kgzTNXpkWbbHzwBg7ty5mDNnTsf3DocDFRUVOLrsaGRni5y6jziA9y8BWG7/qKLL7gs4Dgg6RP6YmThsEJ+CoiIA6dJGvQgYNx7Y9i2w4hVg5yLpT5lXDYy9HBh1EQoyi6Ctig+JLd4B1K1RehRd5I+YioG8Xh9plCnNPA34xxNKj+IQczbyJ76FIRl8MzsqLfCuHAsEZwKbvwR+eQvYtUTpEfGWf/jJ6Mfr9QEA3Fea8ZnRkHRqprCwEHq9vkf2o6GhoUeWBADMZjOys7O7fElmyOnAGc9Ld/xkLLmA0NRjnnZqDiSh0wOHTQMu+RTof4L05zvjBeCYm4BM7ay2kI1FhfUWRb04VCwbDQzhmn2QwcQ/A7yDEJUzWoHh5wCXfg5c+l+gSGPLq8tGKz2CDpIGIiaTCWPHjsX8+fO7/Hz+/PmYMGGClKfmZvQfgVMeVubcRitgERho5cnXcEb1DDIUj5nTd28JwSy5So+gp8JBSo9AWcffCaihdXpWH+Do65QehbRqjgWu+R6Y+ihg1kjhfdkopUfQQfJVM3PmzMFrr72G119/HZs2bcLNN9+MvXv34pprrpH61NyMnx2N1uVmtAJmIb1MGCBHpWlLJRhkKGwzUSASl9oyIkYbvT6KBwMjL1B6FMDkuYApfXc676A3AkdfC9ywGigYoPRoEisYoKrXrOSByPnnn49nnnkGDzzwAEaNGoWlS5fiq6++QlWViq7mR5wv/zmNVmGRc3YZYEz/XiicyRKIiFcwnXZU9KYGIJoN0aV33w1OJt8O6BTcnK3wMGDURcqdXwmZxcBpzyg9isTK1NWzRZZX6nXXXYfdu3fD7/dj9erVOPbYY+U4LXe5CgRFQqdm8qpFG0paoIyIstQWiBRpd5NBUeVVA2MvVe78J90L6NN3D6m4aiapOwBTUX0I0Iv3munCnAlkyFyAaBCYEaFApCuDDNkhyojEZ81VegRdUSByyLF/kaeGqruKo4HDTpX/vGpx8oOAVaXde/v2woyIJsj9wS50akaJLI6aSZ0RMdqiq3RIbGorVu3NK2a6yyoFDpsq/3kHnQLEaNPQa2QUAKf8VelR9MTogNIRSo+iCwpE2skeiNhoakZMUmdEKBuSmNqmZgopI9JFpgLt+jMK5T+n2oz8A1A9SelRdFU0RHXFwxSItJM7wyA0I0KBSFdSZ0SoPiQxNQUiehO9PrqzKRAUyD3drUYMA5z2dPQ5qRZ91VUfAlAgcojsGRELZUTEJHlGhAKRhNQ0NVMwoHcWSCaiRDMxJYIfNSocCAw7S+lRHKKyQlWAApFDlJiaSbWPiMEaXSJGDpH6ioOmZhJTU0aEClV7UiQjkmadVIUYfo7SIzhEZUt3AQpEDtFSsWpede8uAotF6owIdVVNTFWBCBWq9qBEvQZlRA7pN1kdK2j0JqBkmNKj6IECkXbZZfI2/jEI6CNCrd17omJVZRktwvdOEktvb+0ei9xBgd4ksHN0mtEbgWEzlR5FNAiRo+cSTxSItNPpgVwZW0IbrYA5xatIqg/pSfJiVXpTTUotvUQoI9KT3BkRWyFlbbs7XAXTMyqclgEoEOlKzg94ITUiFIj0RBkR5alheobRAQX9lR6F+lhyo/82cqH6kJ4qxwNZZcqOQYWFqgAFIl3JGogIWDVDgUhPUmdEqEYkOTUEIvn9VJl6VpxOJ2+NAi3d7UmnAw5XePWMyjqqtqNApDPZMyIUiIhG8qkZyogkpYZAhKZl4pNzeoYKVWM7/Gzlzm20qbbRHwUinckaiFijH26ppEtzK8Ufj9ZRjYjy1NBLhApV45MzOKCuqrGVjY5m7ZRQOkK1/XUoEOlMzu6qRmu0mItvnUhGMV2dx0I1IsqjjIi6yVm3YaMakZgYRrmi1T7q2l+mMwpEOpMzI9K+GybflTM0LRMbTc0oTxWBiDpTz6pAGRF1UGp6JqtUmfNyQIFIZ9Zc+dLLxt8DEb4FqxSIxEYNzZSnhkCkcKDSI1AvqhFRh+LBQMnh8p9XDQ3V4qBApDu5PuiNv+9+yLdglQKR2GivGeUp3Uckt5IyV4lQRkQ9qo+R/5wqni6jQKQ7OT7oGV200x7Av0aEuqrGRrvvKk/pjIhKVwSohpw1IrR8NzElLihtlBHRDjk+6I22Q10HaWpGHFK3F6cr7eSUDkSoPiQxOTMiKr76VoW8GvnPqeLHhAKR7uT4oG+vDwFoakYsegPA6KU7Pu2bkZzSgUj5EcqeX+3k+iDSGZV/LqhdvgKBCNWIaIjcgQifjIjeBGT1EX886ULKOhHKiCSndB+R6knKnl/t5KrbsBXQPjPJ5FYBkPnfiKZmNESOQMTQOSPC40o7tzK6OR+JTao6EUYnfTFsOlAyECkZTvubJCNXRoQKVZMzWqI7vsvFnH2oLlGFKBDpLqdC+s2hUp2aoWmZxKQKFkxZdIXHRap7J4mh5ljlzq0V+gRTJmJOa6q4FkFV5KwTUXE2BKBApCe9Ecgul/Yc7Ut3AX5zqXJ2ftUiqTIiNC3Djd4IGBX6t+p3nDLn1Zp4BatiFvpSRoSb/Gr5zqXi+hCAApHYpF45Y+x05c4rI0KBSEJSBSLUzIw7JXqJMProFuskuXhBQulwEc9BS3c5kTUjou4sFQUisUg9BdI5I8KnRkSJJV9aQhkR5SmxWqLvGGWnhbQkXkZEzECEuqpyI+fKGZqa0SDJA5EUV80oseRLSySrEaGMCGdKBCJUH8JdvIJeUTMi6r76Vg3KiHSgQCQWqQMRQ6pTM9WiDyWtSJYRoUCEMwpE1C1WtkJvErcrLWVEuJHzwpJqRDRIzqkZrhmRjCJqqpWMZBkRmprhTO4lvHoTUHGUvOfUslg1InnV4k5tUbEqN9Y8+QJ3mprRIKlTZp2nZkwcgwuqD0mOilWVJ3dGpOKorq8nklisFH1+v98vjkRaok4ZEe7kel+nqRkNsuVLe2XXOSOiN3Bb8kj1IclRjYjy5A5EaFqGn1hBQn6/aJ8csZ7nlBHhTq73dcqIaBDDAAUDpDu+sdsHJpe0KGVEkpNq4zsKRLijQETdYhWS5veL/leMzB+jV77Vv5ZQRgQABSLxSRqI2Lp+z6X2gwpVk6Plu8qTs4+IMQMoGyPf+dJBzIzI7x+GYjzPbQWAjj5WOIuXESkeJu55qFhVowqlDES6zWlzWTlDUzPJSTU1QzUi3ImRETFlASfem/x2VeMBg0n4+XqTWNMm7RkRMTJ/NC3DT7yMyITrgZEXincemprRKCkzIt0/MGlqRhy0fFd5ggIRBhh7GXDjL8CkOckLuWuorTtvRmvXmjSdAcipjP6/GM9zlU8BqE7MC0wGGHAycNK94jwmpkzp3htFQoFIPLJOzSQJRIwZQGaxdONJF1SsqrxUA5GCAcA13wOnP3vouZ5smqd4aGrn6u0614nkVEQL5gFxMn+UEeEnq6xnbVvfMUBmEZBVChx3q/BzqDwbAlAgEl97ulIKPaZmklz55VXT7q9cUI2I8lItVJzxUs/unsmCGiX2tUkHnetEOr/PiVIjQoEILzpdzz3EBp5y6P+PuhbI7y/sHCqvDwEoEInPlAFk95Xm2N0zIsnecKk+hBvKiCgvlYzIuFlAZYymZMmCGmse/3ORrlmLLoEIZUQU0X3afdCUQ/9vMAFTHxF2fA1Ml1EgkohU0zPdl+8mm5qhFTPcSFW4SMWq3JmzwasxVmZpdC48lmRBDS0TTU3cjAjViCii84VmZglQOrLr7wedEq0ZSRVNzWicZIFI94xIkkCEMiLcUIt35el0/PZPmva3+AFHsqkXJfa1SQedP5g6ByKi1IgUCT9Gb9P5QnPgybGXP594d+rH10BwSIFIIpIFIinUiJDkaNWMOnANEAZNA4bOSHCc3Pi/M2UdKrIk/MSdmhEh4KapGf46T810rg/pLKci9eNTjYjGSRWIGHj2EaGlu9xQjYg6WDkEIsYM4NTHExdhJwpoqFA1dR1TM0zXQkkqVlVGe8ZbZwT6TY59GyEbntLUjMYVCKxWjqd7RiTR1AyjB3IrpRlHupEiI2Kw0JU3X1xqN4aeAeQmucpLFGxQfUjq2rMWORVdXzNcN+DkcmzCXW4VACbaoC/eZ4He2HNKnysKRDQutyoapYqt+5V7ooxITnn0SUiSkyIjQvUh/HGZmuGy4oUyItJoz1p0rz0T/FxnaCVTKowWILss/rRMu1SzIlQjonF6g/iFogZLz2KkRIEIFapyJ8WmdxSI8MclEOHyppoo60GFqqlrb2jW/b1FaLGqLR/Q6YUdo7fKq4mujkmETxF4ZxSIpAGx60S6T8sAiadmqD6EOymmZsRIV/c2XKZNuLypUkZEGh0ZkW5NG4XWQlF9SOoGnJj8sybVjAgVq6YBsetEYs3zUUZEHDQ1ow5cshVc9leiGhFpmLMAvUn8QISW7qZuzKXJu2dzec3EQjUiaUCOjIjREn1jiIUyItxJkRGhZmb8cZqa4ZIRyY3/O6pFSB3DRLMXPQIRgUF3hvqnAFSLy79dKhkRoy32Z47KUCCSTMFAcY/Xfeluu3hPMuohwh1lRNRBrIwITc1IJ6Ow53uL4BoRmpqRVCo1IhqoDwEoEElOjowIEP9JRlMz3FGNiDpwCRLMXHqNWONnCmlqRpjS4T2DbMFTMxSISCqVQEQjmUMKRJLJLBb3wyheIBLrCtFWKKyRTW9DGRF1ECsjwjCpt38niZWP6/kzvVHYyjPKiEgrlc8CyoikCYYRt2CVT0aEsiH8xLt6FoJqRPgTa/kuED/zYdHGlZ5qlR8Z++dCAu+sktTvS5JLpVhVA4WqAAUi3Ig5PcMnEKFCVX50OvGDEcqI8CdWsSoQP/NBGRFhiofE/rmQwLvk8NTvS5KjjEgvVyhiwWq8Nr2xol3KiPAn9vQM7TPDX7L6DZ2BeyV/vKCGakSEidd4LNXnuymLLpykllKNCGVE0gdlRLRD7IJVCkT4M2VE90iKx5ydvGdCu7hTM9RZVRKpPt9Lh8fevp6Ih1bN9HJi1ojEu2KPlREpGSbeeXsL0TMiNDXDW6IiU4DfXHes45iyaCNCqaT6fO8zUtxxkJ6oRqSXyxezWDXO1Ez3+b/SEdGrDMKP2BkRWrWUmkSBCJ8ru1i1IFQfIp1Ua0T6jBB3HKSnlGpEKBBJH5Zs8ZamcZ2aOWIW9/Q1OUTsje8oI5KahO3ZeUyrxJqaofoQ6aQ8NUOBiOSoRoQgq1Sc43DpI2LKAg4/R5zz9TZUI6IOCTMiPK7sYh2HMiLSSeX5rjcDRYeJPxbSFa2aIcgsFuc4cTMind5wR15A/StSRTUi6pCooyNNzahXKs/34iHRZmhEWqZMADyz5BSIpJlMsTIiHGpExl0hzrl6I8qIqEOilLDQYlWampFOKhdAVB8iD52OX1bEYAFMcT5vVIYCEa7E6hqYbNVM5XigZKg45+qNxM6IUGYqNYmK5PhkRGIFHZQRkU4qgTetmJEPn0BEI9kQgAIR7jJFCkTiZkR+f3OmbIgwomZEmPiPF0ks0Zsgn4xIrKCDMiLSSSUQKaVARDa8pjW1UagKUCDCnWiBSIJiVVsBMHSGOOfprcTMiJgyaeVSqhK9CfLKiFCxqqz41ogwOup3JCdeGREKRNKP1IGIMQMYc4k0W9n3JgYR95qhQtXUiZURMeegR4EeZUSkw3dlRsFAzdQhpAU+rx0KRKL++te/YsKECbDZbMjNzZXyVNKTevmuTgccdY045+jNxMyIUH1I6mwirZrR6XrenjIi0uEbfFOhqryoRoS/QCCAc889F9dee62Up5GH1BkRQLxgpzcTM6NEGZHUiTU1AwDWbtMzlgRBDhGG73OeGpnJK01rRCTdsOH+++8HAMybN0/K08jDnBmdPgm6hR2Hih+lJWqNCLV3T5lYUzPA71Mxew99TxkR6fAtVqUVM/KijAgRZQmv2MtLSVeUEVEHcxagi3Odwzcj0r1gNVGzNCIM30CE9sOSF5/tETJE2pZEBqrawtLv98Pv93d873A4FBxNDJmlQMtOYcdINDVDhBM1I0KBSMoYJpoadjf0/B3fjEj3DAifN2PCD5+6qJxKTRVEpgU+GZGcCunGITLeGZH77rsPDMMk/Fq1alVKg3nkkUeQk5PT8VVRobJ/SKFt3nVGaoUsNTE3vaNiVWFipoYZ/lfdnQMPczag0wsaFknAaAPnNuJUqCo/XoFIuXTjEBnvjMj111+PCy64IOFtqqurUxrM3LlzMWfOnI7vHQ6HuoIRocWklA2RnqhTMxSICBLratmczb83S+flurR0V1rM74FiwJn8tlSoKj+u05qMXlOLH3gHIoWFhSgslGbuyWw2w2xWcR8NoStnKBCRntgNzUjqYgUifKdlgK5TM91X0BDxmTK4BSKUEZEf14xIdl9NZQ4lrRHZu3cvWlpasHfvXoTDYaxduxYAMGDAAGRmavBNngIR9aNiVfWItXyQb6EqQBkRuZkzAReH25WNkXwopBuugXxOX2nHITJJA5F77rkHb775Zsf3o0ePBgAsWrQIkydPlvLU0hC6aoaW7kqPGpqph1gZkc7BBy3dlR6XADy/v3gbgRLuuAbyGqoPASRevjtv3jywLNvjS5NBCBBdNSMELd2VHtWIqEesYtWUMiKdpmMoIyI9Lv1zqsZLPw7SEwUiRPjUDGVEJEfLd9Uj1tSM4BqR3Hi3ImLh8ryvnCD9OEhPfGpENIQCET5sBfGbNHFBNSLSE3XTO8qICBJv1QxfVCMiLy5TkpQRUYbRyu0zSEM9RAAKRPjR6YAMAb1EjDQ1IzlaNaMeMadmUmib33lqhrqqSi9ZRiSzFMirkWcspCuG4fYaoqmZNCekqRlNzUiPilXVQ6ypmS6BSG7KwyEcJasRqRrPvxcMEQ+XrCIFImlOSJMYmpqRHhWrqodYUzNGy6EAk6ZmpJcsI0L1IcpK9hoyZWpuGwQKRPiijIi6iZURySjS3FWF6lhyAabbW0yqb5DtAQhlRKSXLBNI9SHKSjY1k1OuuYwVBSJ8CVnCS8t3pSdWRqTmWM29mFVHp+uZwUglIwIcCkAoIyK9RBkRcw5QPFS+sZCekk1vavACigIRvoQ08aGMiPTE2vSu32RxjtPbdS9YTaVGBDiUSaFiVeklqhGpPEpTrcPTEpeMiMZQIMKXkF4iVCMiPb0RnHcPTaTmOOHHID3rRFLNiLRnQjQ2961JiTIilTQto7hkr6FsCkTSn5CpGVq+Kz2GET4FllcN5FWJMpxer/vKmVSW7wLRAMScTVfjckhUI1JFhaqKo4wIoakZDRBaJ0LZEPGINTVjzaX6ELnEWy2mNwNlo+UdC+mJakSIsIZmNDUjC6EZkX4UiIjG1q2mQ8jUjJWmZWQRLxApHyfu8niSmmSvIY3tvAtQIMKf0ZL6lZmBAhFZUEZEPTpnREyZqU+tWHKoUFUu8WpEqD5EHZLWiFAg0juk2tSMMiLyEJIRKTkcyCgUbyy9XecakVSzIQBNzcgpXo0I9Q9Rh0Q1IpklmsxaUSCSilSbmlGNiDyEbHxH2RBxdV41k2p9CPB7RiRX8HAIB8YYGRFGB5QfKf9YSE+JXkcarA8BKBBJTaorZygjIg8hGRGqDxFX56kZIRkRSy5lRORiMAH6bsF86XBhgSQRT6KMiAanZQAKRFKT6soZCkTkkWogojPQ8kSxdZmaSXHpLhDNhlBGRD7dC1Zpfxn1SBTQ51TINw4RUSCSilSbmlEgIo9U50j7jhX2YUl6EnNqhjIi8ukeiFCArh4JAxGamuk9aGpG3VLNiFB9iPg6r3QROjVDGRH5dC9YpRUz6pHoYokCkV4k1akZWr4rj1QzIlQfIj69MbpRGiAsI2LKBGy0mkk2nZfwFgwEMouUGwvpymCKf7GlwR4iAAUiqUlpaobR5LIqTUolI2KwAuVHiD8Wcmh6xiygIZlOR2335dR5aoaW7apPvKwI1Yj0IqkEIkYbbSsvl+4V/1xUHEmBolTaAxGhqy40uJmXZnXOiFChqvrEmubUmzWbNaRAJBWWHP7bzVN9iHxSyYhUHyP+OEhU+8oZoYXAeoPwsRBuOj9WVKiqPrFeS9ll0cyhBmlz1EpjGP51IhSIyCeVzEbVRPHHQaLae4kIKVYl8mrPiGT3BXIrlR0L6SlWdlGjhaoABSKp49s4hgIR+fDNiOjN0aW7RBpiTc0Q+bTXiFSOpyllNYoV1Gu0PgSgQCR1ZWP43Z4CEfnwzYiUHxHdzJBIo2NqhgIRzWgPRKhQVZ1iBiKUEel9KniusKClu/LhmxGppmkZSVFGRHva+4hQoao6xaoR0ejSXYACkdTx3QCKdnSVD99N76g+RFpiLN8l8jJlRJvRFQ1WeiQkFqoRIQCi0SefObmSYdKNhXTFJyOiM1L/EKl1FKtS+3zNMGVG60M0ugoj7XV/LRkzNF3nRs8yIfh8gFEgIh8+gUjfsYDJJt1YSLRGxGDhn6kiymkPRIg6dQ9ERv+x63YKGkOBiBAVR3G/bcnh0o2DdMWnWJXqQ6Rny6dCVa0xZ1L/EDXr/HpidMDR1yo3FhFQhyAhKjjWiZgygbwaacdCDuGTEaH6EOlZ86lQVWsyiqmlvpp1DkSGnAHka/vzhTIiQpQO57YapngozbXKiWtGhNHzy2qR1Bgt0a6PRDsK+kc3LCTq1Dmwn3CDcuMQCX06CqE3An059BOh+hB5cc2IlI3uud05kUZetdIjIHxQEzN1a68RqRwPlI9TdiwioEBEKC7TM6VUHyIrrpveUX2IfCgQIUQ87VMzaZANASgQEY5Lar9kuPTjIIdwzYhQfYh8KBAhRDzmLCC/PzBomtIjEQUFIkJxWcJbMlT6cZBDuNSIMDqg8mjpx0KiKBAhRDzmLGDC9WlTe5gef4WSMgqjkWk8edXUyEluXDIifUYBFur0KRtaNUaIeHR6YNRFSo9CNBSIiCHR9Az1D5Efl4zIqAulHwc5RMPNlghRJb6be6oYBSJiSLQBHgUi8kuWETFYgeHnyjMWEkWrMAghcVAgIoZEGRFaMSO/ZFcKw2YC1lw5RkIIISQJCkTEUDQ4fgtryojIT6cHdAmaBo+5VL6xEEIISYgCETHo9LF3PjRlArnUJlkR8aZnCg+j1TKEEKIiFIiIZcwlPX9WMixtlldpTrzpmbGXUr0CIYSoCH1KimXYmUD1pK4/o2kZ5cTKiOhNwIgL5B8LIYSQuCgQEQvDAKc+Ht1IrR3tMaOcWBmRIacDGQXyj4UQQkhcFIiIqXgIcNQ1h74vpdbuiomVERl7mezDIIQQkhgFImKbfBuQURz9/2Jq7a6Y7hvf5ffrOXVGCCFEcRSIiM2SA5z8QLSlNW0xr5zuGZGJN1GRKiGEqFCCZgskZSPOBzzNSo+id+tcI1J+BDD6YuXGQgghJC7KiEhBpwPGz1Z6FL1be0aE0QHTn6Rl1IQQolL07iwVmgZQVntG5IirgD4jlR0LIYSQuCgQIenJYIkWDZ9wp9IjIYQQkgAFIiQ9GUzAlIeixcOEEEJUi4pVSXoacBIwdKbSoyCEEJIEZURIeho6k+p0CCFEAygQIemJghBCCNEECkQIIYQQohgKRAghhBCiGApECCGEEKIYCkQIIYQQohgKRAghhBCiGApECCGEEKIYCkQIIYQQohgKRAghhBCiGApECCGEEKIYyQKR3bt3Y9asWaipqYHVakX//v1x7733IhAISHVKQgghhGiMZJvebd68GZFIBK+88goGDBiADRs24KqrroLb7cYTTzwh1WkJIYQQoiEMy7KsXCd7/PHH8fLLL2Pnzp2cbu9wOJCTkwO73Y7s7GyJR0cIIYQQMfD5/Ja1RsRutyM/P1/OUxJCCCFExSSbmulux44deP755/Hkk0/GvY3f74ff7+/43m63A4hGVoQQQgjRhvbPbU6TLixP9957Lwsg4dfKlSu73OfAgQPsgAED2FmzZgk+Nn3RF33RF33RF31p42vHjh1J4wreNSJNTU1oampKeJvq6mpYLBYAQG1tLY4//ngcddRRmDdvHnS6+LNB3TMibW1tqKqqwt69e5GTk8NnmEQiRxxxBFauXKn0MMjv6PFQF3o81IUeD+XY7XZUVlaitbUVubm5CW/Le2qmsLAQhYWFnG574MABHH/88Rg7dizeeOONhEEIAJjNZpjN5h4/z8nJoWJVldDr9fRYqAg9HupCj4e60OOhvGSf+4CENSK1tbWYPHkyKisr8cQTT6CxsbHjd6WlpVKdlkhs9uzZSg+BdEKPh7rQ46Eu9Hhog2TLd+fNm4fLL7885u+4npKW7xJCCCHao4rlu5dddhlYlo35xZXZbMa9994bc7qGEEIIIerE5/Nb1oZmhBBCCCGd0aZ3hBBCCFEMBSKEEEIIUQwFIiSml156CTU1NbBYLBg7diy+//77jt/V19fjsssuQ1lZGWw2G6ZOnYpt27YpONr0tnTpUpx++ukoKysDwzD49NNPu/yeZVncd999KCsrg9VqxeTJk7Fx40ZlBtsLJHs8Pv74Y5xyyikoLCwEwzBYu3atIuPsLRI9HsFgELfddhuGDx+OjIwMlJWV4ZJLLkFtba1yAyY9UCBCenjvvfdw00034c4778SaNWswadIkTJs2DXv37gXLspg5cyZ27tyJzz77DGvWrEFVVRVOOukkuN1upYeeltxuN0aOHIkXXngh5u8fe+wxPPXUU3jhhRewcuVKlJaW4uSTT4bT6ZR5pL1DssfD7XZj4sSJePTRR2UeWe+U6PHweDz45ZdfcPfdd+OXX37Bxx9/jK1bt+KMM85QYKQkLn4N3qXz4osvstXV1azZbGbHjBnDLl26tON3TqeTnT17Ntu3b1/WYrGwgwcPZl966SUFR5vejjzySPaaa67p8rPBgwezt99+O7tlyxYWALthw4aO34VCITY/P5999dVX5R5qrwOA/eSTTzq+j0QibGlpKfvoo492/Mzn87E5OTns3//+dwVG2Lt0fzw627VrFwuAXbNmjaxj6s0SPR7tfv75ZxYAu2fPHnkGRZJSRUYk0RU4ANx888345ptv8M4772DTpk24+eabccMNN+Czzz5TeOTpJxAIYPXq1ZgyZUqXn0+ZMgU//fRTRwv+9hb+QLR7oclkwg8//CDrWAmwa9cu1NXVdXm8zGYzjjvuOPz0008KjowQdbLb7WAYJmnbcSIfVQQiTz31FGbNmoUrr7wSQ4YMwTPPPIOKigq8/PLLAIBly5bh0ksvxeTJk1FdXY2rr74aI0eOxKpVqxQeefppampCOBxGSUlJl5+XlJSgrq4OgwcPRlVVFebOnYvW1lYEAgE8+uijqKurw8GDBxUade9VV1cHAHEfL0LIIT6fD7fffjsuvPBCapKpIooHIsmuwAHgmGOOweeff44DBw6AZVksWrQIW7duxSmnnKLEkHsFhmG6fM+yLBiGgdFoxEcffYStW7ciPz8fNpsNixcvxrRp06DX6xUaLYn3eBFCooLBIC644AJEIhG89NJLSg+HdCLZXjNcJbsCB4DnnnsOV111FcrLy2EwGKDT6fDaa6/hmGOOUWLIaa2wsBB6vb7H1XRDQ0PHYzR27FisXbsWdrsdgUAARUVFOOqoozBu3Dglhtyrte/bVFdXhz59+nT8vPPjRUhvFwwGcd5552HXrl1YuHAhZUNURvGMSLtEV3TPPfccli9fjs8//xyrV6/Gk08+ieuuuw4LFixQYqhpzWQyYezYsZg/f36Xn8+fPx8TJkzo8rOcnBwUFRVh27ZtWLVqFWbMmCHnUAmAmpoalJaWdnm8AoEAlixZ0uPxIqQ3ag9Ctm3bhgULFqCgoEDpIZFuFM+IJLsC93q9uOOOO/DJJ59g+vTpAIARI0Zg7dq1eOKJJ3DSSScpMey0NmfOHFx88cUYN24cxo8fj3/84x/Yu3cvrrnmGgDABx98gKKiIlRWVmL9+vX485//jJkzZ/aYXiPicLlc2L59e8f3u3btwtq1a5Gfn4/KykrcdNNNePjhhzFw4EAMHDgQDz/8MGw2Gy688EIFR52+kj0eLS0t2Lt3b0evii1btgCIZq9o53HxJXo8ysrKcM455+CXX37BF198gXA43PFZk5+fD5PJpNSwSWfKLtqJOvLII9lrr722y8+GDBnC3n777azdbmcBsF999VWX31999dXsySefLOcwe5UXX3yRraqqYk0mEztmzBh2yZIlHb979tln2fLyctZoNLKVlZXsXXfdxfr9fgVHm94WLVrEAujxdemll7IsG13Ce++997KlpaWs2Wxmjz32WHb9+vXKDjqNJXs83njjjZi/v/feexUdd7pK9Hi0L6GO9bVo0SKlh05+p4pN79577z1cfPHF+Pvf/95xBf7qq69i48aNqKqqwuTJk9HU1IQXXngBVVVVWLJkCa699lo89dRTuPbaa5UePiGEEEJSpIpABIi2FH/sscdw8OBBHH744Xj66adx7LHHAogW4s2dOxfffvstWlpaUFVVhauvvho333wzrQwghBBCNEw1gQghhBBCeh/VrJohhBBCSO9DgQghhBBCFEOBCCGEEEIUQ4EIIYQQQhRDgQghhBBCFKPJQIRhGHz66adKD4MQQgghAqkiELnsssswc+ZMpYdBCCGEEJmpIhAhhBBCSO+kukCkuroazzzzTJefjRo1Cvfdd58i4yGEEEKIdFQXiBBCCCGk96BAhBBCCCGKoUCEEEIIIYpRXSCi0+nQfR++YDCo0GgIIYQQIiXVBSJFRUU4ePBgx/cOhwO7du1ScESEEEIIkYrqApETTjgBb7/9Nr7//nts2LABl156KfR6vdLDIoQQQogEDEoPAAAikQgMhuhQ5s6di507d+K0005DTk4OHnzwQcqIEEIIIWmKYbsXZChg6tSpGDBgAF544QWlh0IIIYQQGSk6NdPa2oovv/wSixcvxkknnaTkUAghhBCiAEWnZq644gqsXLkSt9xyC2bMmKHkUAghhBCiAFVMzRBCCCGkd1LdqhlCCCGE9B4UiBBCCCFEMZIGIo888giOOOIIZGVlobi4GDNnzsSWLVu63IZlWdx3330oKyuD1WrF5MmTsXHjxi63+cc//oHJkycjOzsbDMOgra2tx7m2bt2KGTNmoLCwENnZ2Zg4cSIWLVok5Z9HCCGEEIEkDUSWLFmC2bNnY/ny5Zg/fz5CoRCmTJkCt9vdcZvHHnsMTz31FF544QWsXLkSpaWlOPnkk+F0Ojtu4/F4MHXqVNxxxx1xzzV9+nSEQiEsXLgQq1evxqhRo3Daaaehrq5Oyj+REEIIIQLIWqza2NiI4uJiLFmyBMceeyxYlkVZWRluuukm3HbbbQAAv9+PkpIS/O1vf8Of/vSnLvdfvHgxjj/+eLS2tiI3N7fj501NTSgqKsLSpUsxadIkAIDT6UR2djYWLFiAE088Ua4/kRBCCCE8yFojYrfbAQD5+fkAgF27dqGurg5TpkzpuI3ZbMZxxx2Hn376ifNxCwoKMGTIELz11ltwu90IhUJ45ZVXUFJSgrFjx4r7RxBCCCFENLL1EWFZFnPmzMExxxyDww8/HAA6pk1KSkq63LakpAR79uzhfGyGYTB//nzMmDEDWVlZ0Ol0KCkpwTfffNMlc0IIIYQQdZEtI3L99dfj119/xX/+858ev2MYpsv3LMv2+FkiLMviuuuuQ3FxMb7//nv8/PPPmDFjBk477bQuO/kSQgghRF1kCURuuOEGfP7551i0aBHKy8s7fl5aWgoAPQpKGxoaemRJElm4cCG++OILvPvuu5g4cSLGjBmDl156CVarFW+++aY4fwQhhBBCRCdpIMKyLK6//np8/PHHWLhwIWpqarr8vqamBqWlpZg/f37HzwKBAJYsWYIJEyZwPo/H4wEA6HRd/xydTodIJCLgLyCEEEKIlCStEZk9ezb+/e9/47PPPkNWVlZH5iMnJwdWqxUMw+Cmm27Cww8/jIEDB2LgwIF4+OGHYbPZcOGFF3Ycp66uDnV1ddi+fTsAYP369cjKykJlZSXy8/Mxfvx45OXl4dJLL8U999wDq9WKV199Fbt27cL06dOl/BMJIYQQIoCky3fj1Xm88cYbuOyyywBEsyb3338/XnnlFbS2tuKoo47Ciy++2FHQCgD33Xcf7r///oTHWbVqFe68806sWrUKwWAQw4YNwz333INp06aJ/ncRQgghRBy06R0hhBBCFEN7zRBCCCFEMRSIEEIIIUQxFIgQQgghRDEUiBBCCCFEMRSIEEIIIUQxFIgQQgghRDEUiBBCCCFEMRSIEEJUbfHixWAYBm1tbUoPhRAiAQpECCG9AsMw+PTTT5UeBiGkGwpECCGEEKIYCkQIIfjwww8xfPhwWK1WFBQU4KSTToLb7cZll12GmTNn4oknnkCfPn1QUFCA2bNnIxgMdtz3nXfewbhx45CVlYXS0lJceOGFaGho6Ph9+9TKl19+iZEjR8JiseCoo47C+vXrO26zZ88enH766cjLy0NGRgaGDRuGr776qssYV69ejXHjxsFms2HChAnYsmVLl9+//PLL6N+/P0wmEw477DC8/fbbHb+rrq4GAJx55plgGKbje0KI8igQIaSXO3jwIP7whz/giiuuwKZNm7B48WKcddZZaN+GatGiRdixYwcWLVqEN998E/PmzcO8efM67h8IBPDggw9i3bp1+PTTT7Fr166OzSg7+8tf/oInnngCK1euRHFxMc4444yOgGb27Nnw+/1YunQp1q9fj7/97W/IzMzscv8777wTTz75JFatWgWDwYArrrii43effPIJ/vznP+OWW27Bhg0b8Kc//QmXX345Fi1aBABYuXIlgOhGmQcPHuz4nhCiAiwhpFdbvXo1C4DdvXt3j99deumlbFVVFRsKhTp+du6557Lnn39+3OP9/PPPLADW6XSyLMuyixYtYgGw7777bsdtmpubWavVyr733nssy7Ls8OHD2fvuuy/m8drvv2DBgo6fffnllywA1uv1sizLshMmTGCvuuqqLvc799xz2VNPPbXjewDsJ598EnfchBBlUEaEkF5u5MiROPHEEzF8+HCce+65ePXVV9Ha2trx+2HDhkGv13d836dPny5TL2vWrMGMGTNQVVWFrKwsTJ48GQCwd+/eLucZP358x//n5+fjsMMOw6ZNmwAAN954Ix566CFMnDgR9957L3799dce4xwxYkSXMQDoGMemTZswceLELrefOHFix/EJIepFgQghvZxer8f8+fPx9ddfY+jQoXj++edx2GGHYdeuXQAAo9HY5fYMwyASiQAA3G43pkyZgszMTLzzzjtYuXIlPvnkEwDRKZtkGIYBAFx55ZXYuXMnLr74Yqxfvx7jxo3D888/3+W2ncfRfr/2cXT+WTuWZXv8jBCiPhSIEELAMAwmTpyI+++/H2vWrIHJZOoIKBLZvHkzmpqa8Oijj2LSpEkYPHhwl2xJZ8uXL+/4/9bWVmzduhWDBw/u+FlFRQWuueYafPzxx7jlllvw6quvch7/kCFD8MMPP3T52U8//YQhQ4Z0fG80GhEOhzkfkxAiD4PSAyCEKGvFihX47rvvMGXKFBQXF2PFihVobGzEkCFDYk6RdFZZWQmTyYTnn38e11xzDTZs2IAHH3ww5m0feOABFBQUoKSkBHfeeScKCwsxc+ZMAMBNN92EadOmYdCgQWhtbcXChQu7BBHJ/OUvf8F5552HMWPG4MQTT8R///tffPzxx1iwYEHHbaqrq/Hdd99h4sSJMJvNyMvL43x8Qoh0KCNCSC+XnZ2NpUuX4tRTT8WgQYNw11134cknn8S0adOS3reoqAjz5s3DBx98gKFDh+LRRx/FE088EfO2jz76KP785z9j7NixOHjwID7//HOYTCYAQDgcxuzZszFkyBBMnToVhx12GF566SXOf8PMmTPx7LPP4vHHH8ewYcPwyiuv4I033uioVwGAJ598EvPnz0dFRQVGjx7N+diEEGkxLPv7Gj1CCJHA4sWLcfzxx6O1tRW5ublKD4cQojKUESGEEEKIYigQIYQQQohiaGqGEEIIIYqhjAghhBBCFEOBCCGEEEIUQ4EIIYQQQhRDgQghhBBCFEOBCCGEEEIUQ4EIIYQQQhRDgQghhBBCFEOBCCGEEEIUQ4EIIYQQQhTz/2qe2bOsgC3YAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "n.storage_units_t.p_store"
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "TECH_ORDER = ['grid',\n",
-    "              'solar',\n",
-    "              'battery'\n",
-    "              ]"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "def power_by_carrier(n):\n",
-    "    p_by_carrier = n.generators_t.p.T.groupby(\n",
-    "        n.generators.carrier).sum().T \n",
-    "    \n",
-    "    if not n.storage_units.empty:\n",
-    "        sto = n.storage_units_t.p.T.groupby(\n",
-    "            n.storage_units.carrier).sum().T\n",
-    "        p_by_carrier = pd.concat([p_by_carrier, sto], axis=1)\n",
-    "        \n",
-    "    last_cols = [col for col in p_by_carrier.columns if col not in TECH_ORDER]\n",
-    "\n",
-    "    p_by_carrier = p_by_carrier[TECH_ORDER+last_cols]\n",
-    "\n",
-    "    return p_by_carrier"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_dispatch(n, year=2025, month=7):\n",
-    "\n",
-    "    time = (year, f'{year}-0{month}')\n",
-    "    p_by_carrier = power_by_carrier(n).div(1e3)\n",
-    "\n",
-    "    # y-limits\n",
-    "    y_min = -n.storage_units_t.p_store.max().max() / 1e3\n",
-    "    y_max = n.loads_t.p_set.sum(axis=1).max() / 1e3\n",
-    "    margin = 0.1\n",
-    "    y_low = (1 + margin) * y_min\n",
-    "    y_high = (1 + margin) * y_max\n",
-    "\n",
-    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
-    "\n",
-    "    color = p_by_carrier.columns.map(n.carriers.color)\n",
-    "\n",
-    "    display(p_by_carrier)\n",
-    "\n",
-    "    p_by_carrier.where(p_by_carrier > 0).loc[time].plot.area(\n",
-    "        ax=ax,\n",
-    "        linewidth=0,\n",
-    "        color=color,\n",
-    "        ylim=(y_low - margin, y_high + margin)\n",
-    "    )\n",
-    "\n",
-    "    charge = p_by_carrier.where(\n",
-    "        p_by_carrier < 0).dropna(\n",
-    "        how=\"all\",\n",
-    "        axis=1).loc[time]\n",
-    "\n",
-    "    if not charge.empty:\n",
-    "        charge.plot.area(\n",
-    "            ax=ax,\n",
-    "            linewidth=0,\n",
-    "            color=charge.columns.map(n.carriers.color),\n",
-    "            ylim=(y_low - margin, y_high + margin)\n",
-    "        )\n",
-    "\n",
-    "    n.loads_t.p_set.sum(axis=1).loc[time].div(1e3).plot(ax=ax, c=\"k\")\n",
-    "\n",
-    "    ax.legend(loc=(1.05, 0))\n",
-    "    ax.set_ylabel(\"GW\", fontsize=16)\n",
-    "    plt.tight_layout()\n",
-    "\n",
-    "    return fig, ax"
+    "rates = np.linspace(1e-4, 0.1, 10000)\n",
+    "y1 = annuity(rates, 20)\n",
+    "y2 = y1 / annuity(0.1,20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='snapshot'>"
+       "[<matplotlib.lines.Line2D at 0x28aff24e440>]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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AgOn/6IRXQrvBxoYdVa0RwwgREZmV7KJyPLchESeyiqGQ2yD60Z54TN1O6rLIhBhGiIjIbBzNLMRzGxKRV6KBm6MCH0eoEdyhldRlkYkxjBARkVn45tgVvLztKDQ6A7p5OOOTycHwaeUgdVnUBBhGiIhIUkIIfPDjOfz3hzMAgPv82+D98X3grLKTuDJqKgwjREQkmYpKPV7dcQxfH70CAHh2iB/mjgyAnB1VmxWGESIikkRecQWe+zwJRzMLYWsjw1vhgRjfv73UZZEEGEaIiKjJncgqwnMbEpFdVIEWDnaInajGwE5uUpdFEmEYISKiJvX9yRxEbklBeaUenVo7Yu3kfujg7ih1WSQhhhEiImoSQgjE7D+P5fvSIAQwtIs7PpwQBFd7dlRt7hhGiIjI5Coq9XhtxzF89WdH1UkDffHGw91hK7eRuDIyBwwjRERkUjlFFXj+80Qcu1wEWxsZFo7pgYkhvlKXRWaEYYSIiEwmJbMQz/85o2pLBzvEsKMq1YFhhIiITOJ/KVl4dccxaHUGdPVwwieT+qG9G2dUpdoYRoiIqFEZDALL9qUhdv95AMDwgDb47zjOqEq3xjBCRESNplSjQ+SWZPxwKg8AMP0fnfBKaDfYcEZVug2GESIiahQZBWV4dsNhnMkthcLWBksf64Xwvt5Sl0UWgGGEiIju2m/nCzB9UxKul1WijbMSqycFo49PC6nLIgvBMEJERHdl4++XsOCrk9AZBHq1c8XqiGC0dVVJXRZZEIYRIiJqkEq9AYu+TsXnv18CAIzq7YVlj/eCyk4ucWVkaRhGiIjIaIVlWkzfdAQJ5wsAAK+O6Ibp/+gEmYwdVcl4DCNERGSUs7kleHZDIi4VlMFBIceKcX0Q2qOt1GWRBWMYISKievv5dB5e2pyMUo0O7Vra45PJwfBv6yJ1WWThGEaIiOiOhBBY/Us6luw9DSGA/n6tEDsxCG5OSqlLIyvAMEJERLdVUanHv748ji+TswAAT/b3wcLRgVDYcsVdahwMI0REdEt5JRV44fMkJGcUQm4jw/yHAjB5UAd2VKVGxTBCRER1OpFVhOc2JCK7qAIuKlvETFRjSBd3qcsiK8QwQkREtXx19Ape23EUFZUGdGztiLWT+8HP3VHqsshKMYwQEVE1vUFg+V9W3L23a2t88GRfuNpzxV0yHYYRIiICABSVV2LWlmTsT7sKAHjh3o54bYQ/5Fxxl0yMYYSIiHAurxTPb0hEev4NKG1tsPTxXhjThyvuUtNgGCEiauZ+Op2LWZtTUKLRwctVhY8jgtGznavUZVEzwjBCRNRMCSEQs/88lu9LgxBAvw4tEfuUGu6cyIyaGMMIEVEzVKbV4bUdx/DNsWwAwMSQ9nhzVA9OZEaSYBghImpmLl8vw/MbkpCaXQxbGxkWjO6Bpwb4Sl0WNWMMI0REzcjv6QWYvukIrt3Qws1Rgdin1Ojv10rqsqiZa9D9uJiYGPj5+UGlUkGtViM+Pv62+2/atAm9e/eGg4MDPD09MXXqVBQUFDSoYCIiMp4QAp//dhFPffIHrt3QItDbBV+9NIRBhMyC0WFk69atiIyMxLx585CcnIyhQ4ciLCwMGRkZde5/8OBBTJo0Cc888wxOnjyJ7du34/Dhw3j22WfvungiIrozjU6PuV8ex/z/nYTOIDC6txe2vzAI3i3spS6NCAAgE0IIYw4ICQlBUFAQYmNjq7cFBAQgPDwc0dHRtfZfvnw5YmNjcf78+eptK1euxNKlS5GZmVmv7ywuLoarqyuKiorg4uJiTLlERM1aXkkF/rnxCJIuXYdMBrz+oD9euKcjF7qjJlHf67dRd0a0Wi2SkpIQGhpaY3toaCgSEhLqPGbQoEG4fPky9uzZAyEEcnNzsWPHDjz00EPGfDURERnp2OVCjF75K5IuXYezyhbrpvTDtHs7MYiQ2TGqA2t+fj70ej08PDxqbPfw8EBOTk6dxwwaNAibNm3CuHHjUFFRAZ1Oh9GjR2PlypW3/B6NRgONRlP9vri42JgyiYiavV3Jl/H6zuPQ6gzo1NoRayYFo2NrJ6nLIqpTgzqw/j1VCyFumbRTU1Mxc+ZMvPHGG0hKSsLevXtx4cIFTJs27ZafHx0dDVdX1+qXj49PQ8okImp2dHoD3v42FbO3HoVWZ8B9/m2wa8ZgBhEya0b1GdFqtXBwcMD27dvxyCOPVG+fNWsWUlJScODAgVrHREREoKKiAtu3b6/edvDgQQwdOhRXrlyBp6dnrWPqujPi4+PDPiNERLdRVFaJFzcfQfzZfADAjGGdEPVANy50R5IxSZ8RhUIBtVqNuLi4Gtvj4uIwaNCgOo8pKyuDjU3Nr5HL5QCq7qjURalUwsXFpcaLiIhu7WxuCcasOoj4s/mwt5Pjwwl98SpX3CULYfSkZ1FRUYiIiEBwcDAGDhyI1atXIyMjo/qxy9y5c5GVlYUNGzYAAEaNGoXnnnsOsbGxGDFiBLKzsxEZGYn+/fvDy8urcc+GiKgZ2ncyB7O3puCGVg/vFvZYPUmNHl5c6I4sh9FhZNy4cSgoKMCiRYuQnZ2NwMBA7NmzB76+VVMJZ2dn15hzZMqUKSgpKcGHH36Il19+GS1atMB9992Hd955p/HOgoioGTIYBFb8eBYf/HgWABDi1woxE4PgxoXuyMIYPc+IFDjPCBFRTUXllYjamoIfT+cBAKYM6oB5DwXATs6F7sh81Pf6zbVpiIgszNncEjz/eRIu5N+AwtYGix/picfV7aQui6jBGEaIiCzI3hM5eHlbVf8QL1cVPopQo1e7FlKXRXRXGEaIiCyAwSDw3x/OYOVP5wBU9Q9ZNTEI7uwfQlaAYYSIyMwVlVcicksyfk67CgB4erAf5o70Z/8QshoMI0REZuxMbgme35CIiwVlUNraIPrRnng0iP1DyLowjBARmanvjmfj5e1HUfbn/CEfR6gR6M35Q8j6MIwQEZkZvUHgvbg0rPr5PABgUCc3rHyyL+cPIavFMEJEZEaKyioxc0syDpyp6h/yzBA/zA3zhy37h5AVYxghIjITaTkleP7zRFwqKIPKzgZLHu2F8L7eUpdFZHIMI0REZuDbY9l4dQf7h1DzxDBCRCQhvUFg+b40xO6v6h8yuLMbVj4ZhFaOCokrI2o6DCNERBIpLNNi5pYU/PJn/5Dn7+mI10Z0Y/8QanYYRoiIJHAquxgvfJ6EjGtV/UOWPt4bo3t7SV0WkSQYRoiImtg3x67g1e3HUF6ph08re3z8VDC6e3FFcmq+GEaIiJqITm/Asu/T8PEv6QCAoV3c8cH4vmjJ/iHUzDGMEBE1gfxSDWZuTkbC+QIAwAv3dsSroewfQgQwjBARmVxKZiH+uTEJ2UUVcFDIsezx3niol6fUZRGZDYYRIiITEUJg86FMLPjqJLR6Azq6O+LjCDW6eDhLXRqRWWEYISIygYpKPd743wlsS7wMABjRwwPLn+gNZ5WdxJURmR+GESKiRnb5ehn+ufEIjmcVwUYGvDrCH9Pu7QiZTCZ1aURmiWGEiKgRxZ+9ipmbk3G9rBItHeyw8skgDOniLnVZRGaNYYSIqBEYDAKxB85j+b40CAH0aueK2KfU8G5hL3VpRGaPYYSI6C4VV1Ti5W1HEZeaCwAY388HC0b3gMpOLnFlRJaBYYSI6C6cyS3BtM+TkJ5/Awq5DRaO6YEn+7eXuiwii8IwQkTUQN8cu4LXdhxDmVYPL1cVYp5So49PC6nLIrI4DCNEREbS6Q1Y8t1pfHLwAgBgUCc3rHyyL9yclBJXRmSZGEaIiIxwtUSDF784gj8uXAPAad2JGgPDCBFRPR3JuI5/bkxCbrEGjgo5lj/RG2E9Oa070d1iGCEiugMhBDb+kYFFX59EpV6gU2tHfBwRjM5tnKQujcgqMIwQEd1GRaUe83adwM4jVdO6hwW2xbInesNJyT+fRI2F/5qIiG4h81oZpm1MwskrxbCRAa8/6I/n7+G07kSNjWGEiKgOP53OReSWFBRX6NDKUYEPn+yLQZ05rTuRKTCMEBH9hd4g8F5cGlb9fB4A0MenBWImBsGL07oTmQzDCBHRn/JLNZi1JRm/nisAAEwe6It5D3WHwpbDdolMiWGEiAhA0qVrmLEpGTnFFbC3k2PJYz0xpo+31GURNQsMI0TUrAkhsD7hIt7+9hR0hqphux89pUYXD2epSyNqNhhGiKjZKtXo8PrOY/j2WDYA4KFennjnsV4ctkvUxPgvjoiapbO5JZi2MQnnr96ArY0M8x4KwJRBHThsl0gCDCNE1Oz8LyULc788jjKtHh4uSsRMDILat5XUZRE1WwwjRNRsaHUGvP1tKj777RKAqtV2P3iyL9y52i6RpBhGiKhZuFJYjumbjiAlsxAA8OKwzpj9QFfIbfhYhkhqDCNEZPV+OXMVs7Yk43pZJVxUtvjvuD64P8BD6rKI6E8MI0RktQwGgZU/ncOKH89ACCDQ2wWxE9XwaeUgdWlE9BcMI0Rkla7f0GL2thTsT7sKAHiyvw/eHNUDKju5xJUR0d8xjBCR1TmaWYjpm44gq7AcSlsbvBUeiCeCfaQui4hugWGEiKyGEAJfHMrAwq9SodUb4OvmgNiJanT3cpG6NCK6DYYRIrIKZVod/r3rBL5MzgIAhHb3wLInesPV3k7iyojoThhGiMjincsrwT83HsHZvFLYyIDXH/TH8/d05GyqRBaiQetix8TEwM/PDyqVCmq1GvHx8bfcd8qUKZDJZLVePXr0aHDRREQ37Uq+jFErf8XZvFK0dlbii+cG4IV7OzGIEFkQo8PI1q1bERkZiXnz5iE5ORlDhw5FWFgYMjIy6tz//fffR3Z2dvUrMzMTrVq1whNPPHHXxRNR81VRqcfcL49h9tajKK/UY3BnN+yZORQDOrpJXRoRGUkmhBDGHBASEoKgoCDExsZWbwsICEB4eDiio6PvePzu3bvx6KOP4sKFC/D19a3XdxYXF8PV1RVFRUVwcWFHNKLm7kL+DUzfdASnsoshkwEz7+uCmfd34WyqRGamvtdvo/qMaLVaJCUlYc6cOTW2h4aGIiEhoV6fsXbtWgwfPvy2QUSj0UCj0VS/Ly4uNqZMIrJie45n47Udx1Cq0cHNUYEV4/tgaJfWUpdFRHfBqDCSn58PvV4PD4+a0yh7eHggJyfnjsdnZ2fju+++wxdffHHb/aKjo7Fw4UJjSiMiK6fVGbB4zymsT7gIAOjXoSVWPhmEtq4qaQsjorvWoA6sf+8YJoSoV2ex9evXo0WLFggPD7/tfnPnzkVRUVH1KzMzsyFlEpGVyLxWhic+/q06iEy7txM2PzeAQYTIShh1Z8Td3R1yubzWXZC8vLxad0v+TgiBdevWISIiAgqF4rb7KpVKKJVc0puIgB9Sc/Hy9qMoKq+Eq70d3hvbm4vcEVkZo+6MKBQKqNVqxMXF1dgeFxeHQYMG3fbYAwcO4Ny5c3jmmWeMr5KImp1KvQHRe07h2Q2JKCqvRG+fFvh25hAGESIrZPSkZ1FRUYiIiEBwcDAGDhyI1atXIyMjA9OmTQNQ9YglKysLGzZsqHHc2rVrERISgsDAwMapnIisVnZROV76IhmJl64DAKYO7oC5YQFQ2DboyTIRmTmjw8i4ceNQUFCARYsWITs7G4GBgdizZ0/16Jjs7Oxac44UFRVh586deP/99xunaiKyWr+cuYrIrSm4dkMLZ6Utlj7eC2E9PaUui4hMyOh5RqTAeUaIrJ/eIPD+D2ew8udzEALo7umCmIlB6ODuKHVpRNRAJplnhIjIFPJKKjBrcwp+Sy8AAEwIaY83Hu4OlZ1c4sqIqCkwjBCRpH47X4CZW5JxtUQDB4Ucix/pifC+3lKXRURNiGGEiCRhMAjEHjiPd/elwSCArh5OiJmoRuc2TlKXRkRNjGGEiJpcfqkGs7emIP5sPgDgsaB2+E94Dzgo+CeJqDniv3wialIJ5/Mxa0sKrpZooLKzwaLRgRjbz0fqsohIQgwjRNQk9AaBlT+dxQc/noVBAF3aOGHVxCB09XCWujQikhjDCBGZXF5xBWZt+f/RMk+o22HhGD6WIaIq/EtARCb1y5mrmL01BQU3tHBQyPFWeCAeDWondVlEZEYYRojIJHR6A/77wxnE7D8PIQD/ts74cEIQR8sQUS0MI0TU6LKLyjFzczIOX6xaW2ZiSHvM5yRmRHQLDCNE1Kh+Op2Ll7cdxfWySjgpbbHksZ54uJeX1GURkRljGCGiRlGpN2DZ92lY/Us6AKCntys+nNAXvm5cW4aIbo9hhIjuWua1Mry0ORkpmYUAgCmDOmDuSH8obflYhojujGGEiO7K3hM5eG3HURRX6OCissWyJ3pjRI+2UpdFRBaEYYSIGkSj0yN6z2msT7gIAOjj0wIrn+wLn1YO0hZGRBaHYYSIjHYx/wZe3HwEJ7KKAQDP39MRr47oBju5jcSVEZElYhghIqN8c+wK5uw8jlKNDi0d7PDu2N64z99D6rKIyIIxjBBRvVRU6rHom1R88UcGAKBfh5b44Mm+8HS1l7gyIrJ0DCNEdEdnckvw4hdHcCa3FDIZMP0fnTB7eFfY8rEMETUChhEiuiUhBL44lIFFX6dCozPA3UmJ98b2xj1dW0tdGhFZEYYRIqpTUVkl5u46hj3HcwAA93RtjXef6I3WzkqJKyMia8MwQkS1JF26hpmbU5BVWA5bGxlee7Abnh3SETY2MqlLIyIrxDBCRNX0BoHY/efw3x/OQm8QaN/KASuf7IvePi2kLo2IrBjDCBEBAHKLKxC5JQW/pRcAAMb08cJb4YFwVtlJXBkRWTuGESLCj6dy8cr2qpV2HRRyLBoTiMeCvCGT8bEMEZkewwhRM6bR6bHku9P49NeLAIDuni5YOaEvOrV2krYwImpWGEaImqn0q6V4aXMyTl6pmtJ96uAOmBPGlXaJqOkxjBA1M0II7DyShTf+dwJlWj1aOthh+RO9cX8Ap3QnImkwjBA1IyUVlZi/+wR2p1wBAAzo2AorxvVFW1eVxJURUXPGMELUTBzNLMTMLcm4VFAGuY0Mkfd3wfRhnSHn3CFEJDGGESIrZzAIfHIwHUv3pkFnEPBuYY/3x/dBcIdWUpdGRASAYYTIql0t0eCV7Udx4MxVAEBYYFssebQXXB04dwgRmQ+GESIrtT8tD69sP4r8Ui2UtjZ4Y1R3TOjfnnOHEJHZYRghsjIVlXq8s/f/5w7p6uGElU8GoVtbZ2kLIyK6BYYRIityJrcEMzcn43ROCQBg8kBfzB0ZAJUd5w4hIvPFMEJkBYQQ2Pj7Jbz17SlodAa4OSqw7IleuM+fc4cQkfljGCGycAWlGry+8xh+OJUHALina2ssf6IX2jhz7hAisgwMI0QWLP7sVURtO4qrJRoo5DZ4PcwfUwd1gA3nDiEiC8IwQmSBNDo9lu1NwycHLwAAOrdxwgfj+6K7l4vElRERGY9hhMjCnMsrxczNyUjNrlrg7qkB7TFvZHfYK9hJlYgsE8MIkYUQQuCLQxn4zzepqKg0oKWDHZY+3hsPdGcnVSKybAwjRBbg+g0tXt95DPtScwEAQzq7492xveHhwk6qRGT5GEaIzNyv5/IRtS0FucUa2MlleG2EP54Z4sdOqkRkNRhGiMyUVmfAu/vSsDo+HUIAnVo74v3xfRHo7Sp1aUREjYphhMgMpV8txawtKTieVQQAmBDSHvMfYidVIrJODCNEZkQIgW2JmVjwVSrKK/Vo4WCHJY/2woOBbaUujYjIZBhGiMzEtRtazPlLJ9VBndzw3tg+aOvKTqpEZN1sGnJQTEwM/Pz8oFKpoFarER8ff9v9NRoN5s2bB19fXyiVSnTq1Anr1q1rUMFE1ujntDyMWPEL9qXmwk4uw5wwf2x8JoRBhIiaBaPvjGzduhWRkZGIiYnB4MGD8fHHHyMsLAypqalo3759nceMHTsWubm5WLt2LTp37oy8vDzodLq7Lp7I0pVr9Yj+7hQ2/HYJANCljRNWjO+DHl7spEpEzYdMCCGMOSAkJARBQUGIjY2t3hYQEIDw8HBER0fX2n/v3r0YP3480tPT0apVqwYVWVxcDFdXVxQVFcHFhdNdk3U4kVWEWVuScf7qDQDA1MEd8PqD/lDZsZMqEVmH+l6/jXpMo9VqkZSUhNDQ0BrbQ0NDkZCQUOcxX331FYKDg7F06VJ4e3uja9eueOWVV1BeXm7MVxNZDb1BYNXP5xC+6lecv3oDbZyV2PB0f7w5qgeDCBE1S0Y9psnPz4der4eHR83ppz08PJCTk1PnMenp6Th48CBUKhV27dqF/Px8TJ8+HdeuXbtlvxGNRgONRlP9vri42JgyicxW5rUyRG1LweGL1wEAYYFtsfiRnmjpqJC4MiIi6TRoNI1MVnPmRyFErW03GQwGyGQybNq0Ca6uVc/B33vvPTz++ONYtWoV7O3tax0THR2NhQsXNqQ0IrMkhMCXR7Lw5lcnUarRwUlpiwWje+CxIO9b/tshImoujHpM4+7uDrlcXusuSF5eXq27JTd5enrC29u7OogAVX1MhBC4fPlyncfMnTsXRUVF1a/MzExjyiQyK4VlWrz4RTJe3n4UpRodgn1b4rtZQ/G4uh2DCBERjAwjCoUCarUacXFxNbbHxcVh0KBBdR4zePBgXLlyBaWlpdXbzpw5AxsbG7Rr167OY5RKJVxcXGq8iCxR/NmrGLHiF3x7PBu2NjK8OqIbtr4wED6tHKQujYjIbBg9z0hUVBQ++eQTrFu3DqdOncLs2bORkZGBadOmAai6qzFp0qTq/SdMmAA3NzdMnToVqamp+OWXX/Dqq6/i6aefrvMRDZE1qKjUY9HXqYhYewi5xRp0bO2IL6cPwoxhnSHnAndERDUY3Wdk3LhxKCgowKJFi5CdnY3AwEDs2bMHvr6+AIDs7GxkZGRU7+/k5IS4uDi89NJLCA4OhpubG8aOHYu33nqr8c6CyIykXilG5NZknMmtuhsYMcAX/xoZwHVliIhuweh5RqTAeUbIEhgMAp8cTMfy789AqzfA3UmJZY/3wjD/NlKXRkQkifpev7k2DVEjuFJYjqhtKfg9/RoA4IHuHljyaE+4OSklroyIyPwxjBDdBSEE/pdyBfP/dwIlFTo4KOR44+HuGNfPhyNliIjqiWGEqIGu3dBi3q7j+O5E1VD3Pj4tsGJcH3Rwd5S4MiIiy8IwQtQAP57Kxes7jyO/VANbGxkih3fBtHs7wVbeoIWwiYiaNYYRIiOUanR465tUbDlcNRFflzZO+O+4Pgj05iq7REQNxTBCVE9/pBfg5e1Hcfl6OWQy4Nkhfng5tBsXtyMiuksMI0R3UFGpx3txZ7AmPh1CAN4t7PHu2N4Y0NFN6tKIiKwCwwjRbZzIKkLUtpTqCczGBfvg3w8HwFllJ3FlRETWg2GEqA46vQEf/5KOFT+cQaVewN1JgSWP9sLw7nUvCElERA3HMEL0NxfybyBqWwqSMwoBAA/2aIu3HwnkBGZERCbCMEL0JyEEPv/9EhbvOYWKSgOcVbZYNKYHwvt4cwIzIiITYhghApBdVI7XdhxD/Nl8AMDgzm5Y9nhveLXgytJERKbGMELNmhACXx29gvm7T6C4QgelrQ3mhvlj0sAOsLHh3RAioqbAMELN1vUbWvx79wl8ezwbANC7nSveHdsHnds4SVwZEVHzwjBCzdIPqbmYu+s4rpZUTef+0n1dMGMYp3MnIpICwwg1K0XllVj0dSp2HrkMAOjcxgnvje2NXu1aSFsYEVEzxjBCzcb+tDzM2XkcOcUVkMmA54d2xOwHunI6dyIiiTGMkNUrqajE29+eql7czs/dEcuf6AW1byuJKyMiIoBhhKzcr+fy8dqOY8gqrFrcbuogP7w6ohvsFbwbQkRkLhhGyCrd0OgQ/d0pbPw9AwDQvpUDlj3eCyFc3I6IyOwwjJDV+T29AK/uOIrMa+UAgIgBvpgT5g9HJX/diYjMEf86k9Uo1+qx9PvT+PTXiwAA7xb2WPp4Lwzu7C5tYUREdFsMI2QVki5dwyvbj+FC/g0AwJP9ffCvkQFwVtlJXBkREd0JwwhZtIpKPd6LO4M18ekQAmjrosI7j/fCvV1bS10aERHVE8MIWayUzEK8vC0F569W3Q15XN0O8x/uDld73g0hIrIkDCNkcTQ6PT748Sxi95+HQQCtnZVY8mhP3B/gIXVpRETUAAwjZFFOZBXh5W1HkZZbAgAI7+OFBaN7oIWDQuLKiIiooRhGyCLcvBvy0YF06A0Cbo4KvP1ITzwY2Fbq0oiI6C4xjJDZS8ksxKvbj+JsXikA4KFenlg0ugfcnJQSV0ZERI2BYYTMVkWlHv/9c6SMQQDuTgq8FR6IBwM9pS6NiIgaEcMImaXEi9fw2o5jSP9z3pBH+nrjjYe7o6Uj+4YQEVkbhhEyK2VaHZZ9n4b1CRchBODhosTiRzhShojImjGMkNn47XwBXt95DBnXygAAT6jb4d+cN4SIyOoxjJDkSjU6LPnLCrteripEP8ZZVImImguGEZJU/NmrmLPzOLIKq1bYnRDSHnPD/LmmDBFRM8IwQpIorqjE4m9PYcvhTABAu5b2WPpYLwziCrtERM0Owwg1uZ9P52Hul8eRU1wBAJgyqANeHdENjkr+OhIRNUf8609NprBMi0XfpOLLI1kAgA5uDlj6eG/092slcWVERCQlhhFqEt+fzMG/d5/A1RINZDLg2SF+iHqgG+wVcqlLIyIiiTGMkEldLdFgwdcn8e2xbABAp9aOWPZEbwS1bylxZUREZC4YRsgkhBDYeSQL//kmFUXllZDbyPD8PR0x6/4uUNnxbggREf0/hhFqdJnXyvCvXccRfzYfANDDywXvPNYLgd6uEldGRETmiGGEGo3eILA+4SKWf5+G8ko9lLY2iBzeFc8O9YOd3Ebq8oiIyEwxjFCjSMspwes7jyElsxAAEOLXCkse6wU/d0dpCyMiIrPHMEJ3RaPTY9XP5xG7/xwq9QLOSlvMHRmA8f18YGMjk7o8IiKyAAwj1GBJl65jzs5jOJtXCgAYHuCBt8ID0dZVJXFlRERkSRhGyGg3NDos+z4Nn/12EUIA7k4KLBjdAw/19IRMxrshRERkHIYRMsqBM1fxry//f2G7R4O8Mf+h7mjpqJC4MiIislQNGuIQExMDPz8/qFQqqNVqxMfH33Lf/fv3QyaT1XqdPn26wUVT07t+Q4uorSmYvO4QsgrL4d3CHp893R/vje3DIEJERHfF6DsjW7duRWRkJGJiYjB48GB8/PHHCAsLQ2pqKtq3b3/L49LS0uDi4lL9vnXr1g2rmJqUEAJfH8vGwq9OouCGFjJZ1cJ2r4RyYTsiImocMiGEMOaAkJAQBAUFITY2tnpbQEAAwsPDER0dXWv//fv3Y9iwYbh+/TpatGjRoCKLi4vh6uqKoqKiGoGGTCu7qBz/3nUCP57OAwB09XDCksd6cSp3IiKql/pev416TKPVapGUlITQ0NAa20NDQ5GQkHDbY/v27QtPT0/cf//9+Pnnn2+7r0ajQXFxcY0XNR29QWD9rxcw/N0D+PF0HuzkMkQO74JvXhrKIEJERI3OqPvs+fn50Ov18PDwqLHdw8MDOTk5dR7j6emJ1atXQ61WQ6PR4PPPP8f999+P/fv345577qnzmOjoaCxcuNCY0qiRnMouxpwvj+Pon5OX9W3fAu881gtdPZylLYyIiKxWgx76/334phDilkM6u3Xrhm7dulW/HzhwIDIzM7F8+fJbhpG5c+ciKiqq+n1xcTF8fHwaUirVU7lWj/d/PIs18enQG6omL3stzB8T+7fn5GVERGRSRoURd3d3yOXyWndB8vLyat0tuZ0BAwZg48aNt/y5UqmEUqk0pjS6C7+cuYp5u48j81rVcN2wwLZYMLoHPFw4eRkREZmeUWFEoVBArVYjLi4OjzzySPX2uLg4jBkzpt6fk5ycDE9PT2O+mkwgv1SDt75Jxe6UKwAAT1cVFo0JxAPd6x8siYiI7pbRj2mioqIQERGB4OBgDBw4EKtXr0ZGRgamTZsGoOoRS1ZWFjZs2AAAWLFiBTp06IAePXpAq9Vi48aN2LlzJ3bu3Nm4Z0L1JoTA9qTLWLznFArLKquH674c2g1OHK5LRERNzOgrz7hx41BQUIBFixYhOzsbgYGB2LNnD3x9fQEA2dnZyMjIqN5fq9XilVdeQVZWFuzt7dGjRw98++23GDlyZOOdBdVb+tVS/GvXcfyefg0AEODpgiWP9kRvnxbSFkZERM2W0fOMSIHzjNw9rc6Ajw6cx4c/n4NWZ4DKzgZRD3TF04P9YCtv0ES8REREt1Xf6zfvyTcDiRevYe6Xx6tX1723a2u8FR4In1YOEldGRETEMGLVisor8c7e0/jij6rHZu5OCsx/uDtG9/bi6rpERGQ2GEaskBACe47nYMHXJ3G1RAMAGBfsg7kj/dHCgYvaERGReWEYsTKZ18rw5lcn8dOf68l0bO2IxY/0xICObhJXRkREVDeGESuh1RmwJj4dK386i4pKA+zkMkz/R2dMH9YJSlu51OURERHdEsOIFfgjvQDzdp/AuT87qA7o2ApvhfdE5zZOEldGRER0ZwwjFqygVIPo705jR9JlAICbowL/fjgA4X282UGViIgsBsOIBTIYBLYlZiL6u9MoKq+aQfXJ/u3x+gh/uDrYSV0eERGRURhGLMzpnGLM23UCSZeuA6iaQfXtRwIR1L6lxJURERE1DMOIhbih0eH9H89i7cEL0BsEHBVyzH6gK6YM6sAZVImIyKIxjFiAfSdzsOCrk7hSVAEACAtsizdGdYenq73ElREREd09hhEzdvl6GRZ8lYofTuUCANq1tMeiMT1wn7+HxJURERE1HoYRM1SpN2DtwQt4/4ezKK/Uw04uw/P3dMSLw7rAXsE5Q4iIyLowjJiZwxev4d+7TiAttwQA0N+vFd4OD0QXD2eJKyMiIjINhhEzUVCqwTt7T2NbYtWcIa0cFfjXyAA8FsQ5Q4iIyLoxjEhMbxD44lAGln+fhqLySgDAk/198NoIf7R05KJ2RERk/RhGJJSSWYj5u0/geFYRAKC7pwv+Ex4ItS/nDCEiouaDYUQC125osez709hyOBNCAM4qW7w6ohsmhvhCbsNHMkRE1LwwjDQhvUFg6+FMLP3+NArLqh7JPK5uh9cf9EdrZ6XE1REREUmDYaSJHM0sxBv/O4Gjl6seyfi3dcZb4YEI7tBK4sqIiIikxTBiYtdvaLFsXxo2H8qoeiSjtEVUaFdEDPDlNO5ERERgGDGZmyvrvrP3NK7/+Ujm0b7emDPSH22cVRJXR0REZD4YRkzg+OUizP/fCaRkFgIAunk4Y9GYHgjp6CZtYURERGaIYaQRFZZpsXxfGjb9UfVIxklpi9kPdMWkgb6w4yMZIiKiOjGMNAKDQWBH0mUs2Xsa125oAQBj+nhh3sgAtHHhIxkiIqLbYRi5S8cuF+LNr04iOaMQANCljRMWjQnEwE58JENERFQfDCMNlF+qwbK9adiWVDVxmaNCjsjhXTFlcAc+kiEiIjICw4iRKvUGbPz9Et6LO4OSCh2AqlEyr4f5w4OPZIiIiIzGMGKEhHP5WPD1SZzJLQUA9PBywcLRPThxGRER0V1gGKmHy9fL8Pa3p/DdiRwAQEsHO7w6wh/j+vlwLRkiIqK7xDByGxWVenx04Dxi95+HRmeAjQyYNLADZg/vClcHO6nLIyIisgoMI3UQQuD7kzn4zzenkFVYDgAY0LEVFozuAf+2LhJXR0REZF0YRv7mTG4JFn59Er+eKwAAeLmqMO+h7hjZsy1kMj6SISIiamwMI38qKq/E+z+cxWe/XYTeIKCwtcG0ezpi2j86wUHBZiIiIjKVZn+VNRgEtidlYuneNBT8OXtqaHcP/Puh7mjv5iBxdURERNavWYeRIxnXseCrkzh2uQgA0LG1IxaM6oF7uraWuDIiIqLmo9mGESEEFv4ZRJyUtogc3gWTBnaAwpazpxIRETWlZhtGZDIZ3hzdA5t+z8DrYd3QxpmzpxIREUmh2YYRAAhq3xJB7VtKXQYREVGzxmcSREREJCmGESIiIpIUwwgRERFJimGEiIiIJMUwQkRERJJiGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCSpBoWRmJgY+Pn5QaVSQa1WIz4+vl7H/frrr7C1tUWfPn0a8rVERERkhYwOI1u3bkVkZCTmzZuH5ORkDB06FGFhYcjIyLjtcUVFRZg0aRLuv//+BhdLRERE1kcmhBDGHBASEoKgoCDExsZWbwsICEB4eDiio6Nvedz48ePRpUsXyOVy7N69GykpKfX+zuLiYri6uqKoqAguLi7GlEtEREQSqe/126g7I1qtFklJSQgNDa2xPTQ0FAkJCbc87tNPP8X58+fx5ptv1ut7NBoNiouLa7yIiIjIOhm1am9+fj70ej08PDxqbPfw8EBOTk6dx5w9exZz5sxBfHw8bG3r93XR0dFYuHBhre0MJURERJbj5nX7Tg9hjAojN8lkshrvhRC1tgGAXq/HhAkTsHDhQnTt2rXenz937lxERUVVv8/KykL37t3h4+PTkHKJiIhIQiUlJXB1db3lz40KI+7u7pDL5bXuguTl5dW6W3LzyxMTE5GcnIwXX3wRAGAwGCCEgK2tLfbt24f77ruv1nFKpRJKpbL6vZOTEzIzM+Hs7Fxn6Gmo4uJi+Pj4IDMzk31RTIjt3DTYzk2Hbd002M5Nw5TtLIRASUkJvLy8brufUWFEoVBArVYjLi4OjzzySPX2uLg4jBkzptb+Li4uOH78eI1tMTEx+Omnn7Bjxw74+fnV63ttbGzQrl07Y0o1iouLC3/RmwDbuWmwnZsO27ppsJ2bhqna+XZ3RG4y+jFNVFQUIiIiEBwcjIEDB2L16tXIyMjAtGnTAFQ9YsnKysKGDRtgY2ODwMDAGse3adMGKpWq1nYiIiJqnowOI+PGjUNBQQEWLVqE7OxsBAYGYs+ePfD19QUAZGdn33HOESIiIqKbGtSBdfr06Zg+fXqdP1u/fv1tj12wYAEWLFjQkK9tdEqlEm+++WaN/inU+NjOTYPt3HTY1k2D7dw0zKGdjZ70jIiIiKgxcaE8IiIikhTDCBEREUmKYYSIiIgkxTBCREREkrKqMBITEwM/Pz+oVCqo1WrEx8ffdv8DBw5ArVZDpVKhY8eO+Oijj2rts3PnTnTv3h1KpRLdu3fHrl27TFW+RWnstl6zZg2GDh2Kli1bomXLlhg+fDgOHTpkylOwCKb4nb5py5YtkMlkCA8Pb+SqLY8p2rmwsBAzZsyAp6cnVCoVAgICsGfPHlOdgkUwRTuvWLEC3bp1g729PXx8fDB79mxUVFSY6hQshjFtnZ2djQkTJqBbt26wsbFBZGRknfuZ9HoorMSWLVuEnZ2dWLNmjUhNTRWzZs0Sjo6O4tKlS3Xun56eLhwcHMSsWbNEamqqWLNmjbCzsxM7duyo3ichIUHI5XKxePFicerUKbF48WJha2srfv/996Y6LbNkiraeMGGCWLVqlUhOThanTp0SU6dOFa6uruLy5ctNdVpmxxTtfNPFixeFt7e3GDp0qBgzZoyJz8S8maKdNRqNCA4OFiNHjhQHDx4UFy9eFPHx8SIlJaWpTsvsmKKdN27cKJRKpdi0aZO4cOGC+P7774Wnp6eIjIxsqtMyS8a29YULF8TMmTPFZ599Jvr06SNmzZpVax9TXw+tJoz0799fTJs2rcY2f39/MWfOnDr3f+2114S/v3+NbS+88IIYMGBA9fuxY8eKBx98sMY+I0aMEOPHj2+kqi2TKdr673Q6nXB2dhafffbZ3RdsoUzVzjqdTgwePFh88sknYvLkyc0+jJiinWNjY0XHjh2FVqtt/IItlCnaecaMGeK+++6rsU9UVJQYMmRII1VtmYxt67+699576wwjpr4eWsVjGq1Wi6SkJISGhtbYHhoaioSEhDqP+e2332rtP2LECCQmJqKysvK2+9zqM5sDU7X135WVlaGyshKtWrVqnMItjCnbedGiRWjdujWeeeaZxi/cwpiqnb/66isMHDgQM2bMgIeHBwIDA7F48WLo9XrTnIiZM1U7DxkyBElJSdWPdNPT07Fnzx489NBDJjgLy9CQtq4PU18PGzQDq7nJz8+HXq+vtXKwh4dHrRWGb8rJyalzf51Oh/z8fHh6et5yn1t9ZnNgqrb+uzlz5sDb2xvDhw9vvOItiKna+ddff8XatWuRkpJiqtItiqnaOT09HT/99BMmTpyIPXv24OzZs5gxYwZ0Oh3eeOMNk52PuTJVO48fPx5Xr17FkCFDIISATqfDP//5T8yZM8dk52LuGtLW9WHq66FVhJGbZDJZjfdCiFrb7rT/37cb+5nNhSna+qalS5di8+bN2L9/P1QqVSNUa7kas51LSkrw1FNPYc2aNXB3d2/8Yi1YY/8+GwwGtGnTBqtXr4ZcLodarcaVK1ewbNmyZhlGbmrsdt6/fz/efvttxMTEICQkBOfOncOsWbPg6emJ+fPnN3L1lsUU1y5TXg+tIoy4u7tDLpfXSmh5eXm1ktxNbdu2rXN/W1tbuLm53XafW31mc2Cqtr5p+fLlWLx4MX744Qf06tWrcYu3IKZo55MnT+LixYsYNWpU9c8NBgMAwNbWFmlpaejUqVMjn4l5M9Xvs6enJ+zs7CCXy6v3CQgIQE5ODrRaLRQKRSOfiXkzVTvPnz8fERERePbZZwEAPXv2xI0bN/D8889j3rx5sLGxip4IRmlIW9eHqa+HVvFfSqFQQK1WIy4ursb2uLg4DBo0qM5jBg4cWGv/ffv2ITg4GHZ2drfd51af2RyYqq0BYNmyZfjPf/6DvXv3Ijg4uPGLtyCmaGd/f38cP34cKSkp1a/Ro0dj2LBhSElJgY+Pj8nOx1yZ6vd58ODBOHfuXHXYA4AzZ87A09Oz2QURwHTtXFZWVitwyOVyiKrBGY14BpajIW1dHya/HjZKN1gzcHMo09q1a0VqaqqIjIwUjo6O4uLFi0IIIebMmSMiIiKq9785bGz27NkiNTVVrF27ttawsV9//VXI5XKxZMkScerUKbFkyRIO7RWmaet33nlHKBQKsWPHDpGdnV39KikpafLzMxemaOe/42ga07RzRkaGcHJyEi+++KJIS0sT33zzjWjTpo146623mvz8zIUp2vnNN98Uzs7OYvPmzSI9PV3s27dPdOrUSYwdO7bJz8+cGNvWQgiRnJwskpOThVqtFhMmTBDJycni5MmT1T839fXQasKIEEKsWrVK+Pr6CoVCIYKCgsSBAweqfzZ58mRx77331th///79om/fvkKhUIgOHTqI2NjYWp+5fft20a1bN2FnZyf8/f3Fzp07TX0aFqGx29rX11cAqPV68803m+BszJcpfqf/imGkiinaOSEhQYSEhAilUik6duwo3n77baHT6Ux9Kmatsdu5srJSLFiwQHTq1EmoVCrh4+Mjpk+fLq5fv94EZ2PejG3ruv7++vr61tjHlNdD2Z9FEBEREUnCKvqMEBERkeViGCEiIiJJMYwQERGRpBhGiIiISFIMI0RERCQphhEiIiKSFMMIERERSYphhIiIiCTFMEJERESSYhghIiIiSTGMEBERkaQYRoiIiEhS/wfEBu+C2kSxnwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1188,10 +1977,7 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "time = '2018-07'\n",
-    "n.generators_t.p.loc[time].plot(ax=ax, legend=False)\n",
-    "n.storage_units_t.p_store.loc[time].plot(ax=ax, legend=False)\n"
+    "plt.plot(rates, y2)"
    ]
   },
   {

From 4ad1397064f7aff8dbb1b122bdbb11ab807d3f8c Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Fri, 27 Sep 2024 16:45:00 -0400
Subject: [PATCH 44/52] adds a net metering only scenario

---
 notebooks/10-pypsa-model.ipynb | 614 ++++++++++++++++++++-------------
 1 file changed, 383 insertions(+), 231 deletions(-)

diff --git a/notebooks/10-pypsa-model.ipynb b/notebooks/10-pypsa-model.ipynb
index 1b287ea..ef8bf60 100644
--- a/notebooks/10-pypsa-model.ipynb
+++ b/notebooks/10-pypsa-model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,12 +17,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PyPSA Network"
+    "## PyPSA Network"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +235,7 @@
        "2018-01-01 04:00:00              90    0    0    0  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -334,7 +334,7 @@
        "Residential Battery Storage  78943.789878  3.157752e+06"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -372,7 +372,7 @@
        "0.05541531489055132"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -384,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -416,6 +416,7 @@
     "                    p_min_pu=ghi,\n",
     "                    p_max_pu=ghi,\n",
     "                    p_nom_extendable=True,\n",
+    "                    p_nom_max = 2.807,\n",
     "                    )\n",
     "        elif generator=='Residential Battery Storage':\n",
     "            pass\n",
@@ -441,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +566,7 @@
        "      <td>0.0</td>\n",
        "      <td>True</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>inf</td>\n",
+       "      <td>2.807</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>...</td>\n",
@@ -642,7 +643,7 @@
        "\n",
        "attribute                 p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
        "Generator                                                                    \n",
-       "ResPV                                 True        0.0        inf       0.0   \n",
+       "ResPV                                 True        0.0      2.807       0.0   \n",
        "Net metering Residential              True        0.0        inf      -1.0   \n",
        "Evergy Import                         True        0.0        inf       0.0   \n",
        "\n",
@@ -673,7 +674,7 @@
        "[3 rows x 34 columns]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,7 +705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -713,14 +714,14 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 26.09it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 61.81it/s]\n",
-      "INFO:linopy.io: Writing time: 0.24s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 20.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 41.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.31s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61323 duals\n",
+      "Solution: 26283 primals, 61324 duals\n",
       "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
@@ -734,7 +735,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -745,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -849,13 +850,14 @@
        "Load      -              NaN  "
       ]
      },
-     "execution_count": 53,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.statistics()"
+    "results_1 = n.statistics().copy()\n",
+    "results_1"
    ]
   },
   {
@@ -867,24 +869,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Load\n",
-       "Load Residential    112.9\n",
-       "dtype: float64"
+       "112.9000000000018"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.objective / n.loads_t.p_set.sum()"
+    "model_lcoe_1 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_1"
    ]
   },
   {
@@ -903,41 +904,241 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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mvX79+qCPi8FICFRUVODUqVPQG/QwpaibGdGZddDHuutWWEjpPxGMIG/w+0o6CdY8d92IOBgQEYXS7t270dbWBmusddAWEiIYWbM++P2RGIyEgJiiSRiaAEkfnCW73hDZkYoK1o34S6QvjcONg9zTzZrvDka2bNkStDEREfVHTNEkjUoaPJubbwUkoKqiCtXV1UEdF4OREBDFq5aMgWsKQkUUsTIz4p/y8nJUVFRAp9chZvjA6U5BZEbWfxP8tCdRuKmsrERLS4vaw4hqIhhBzuD31Vv1sAx1n7dEoX6wMBgJAZEZcaX2XywUSsqKGmZG/KJsjjciFTqzZx8lkRnZW7QXDocjaGMjCjcHDhzA8OHDcc0116g9lKgmghF9nmf9rKzD3ccsBiMRQGRGdBnh8esWjc/KK8pVHom2/ec//wEAmEd5XpRsTDFCb9Ojs6MTRUVFwRoaUdhZsmQJ2tvbsXr16pAsFaXeTpw4oVwcD1a8Kih1I+uCWzcSHmfHCCfefHOGuitpBNESno3PfFdVVYXPP/8cAGD6nudFyZIksW6EopIyPQDgn//8p4ojiV6bN28GACRmJcIQb/DoMSIY2bZtG9rb24M2NgYjQdbc3IyqqioAYRSMfFfAysyI79588004nU4MOXMILEO8qwUSdSPiwEAUDTZu3Kj8++133g7ZnifUZdOmTQCA2OGeN9c0pZugj9Wjw9GBHTt2BGlkDEaCTkzRxCbFQm8L3p4z3hA1I7VVtWx85gOXy4XXX38dAGCY5NnVRXciM7JhU3DnYInCxbFjx3DgwAEAgCHegKbGJnz88ccqjyr69LdT70AkSVIK9INZN8JgJMjEFE3c0DiVR9JFZEbaTrahsbFR5dFoT2FhIcrKyhATFwPbeO/3ERLByL7ifbw6pKggrsiTspOQeFkiAOCfb3CqJpS679RrGeZlNve7ItZgNj9jMBJkSvFqWvj8qnVmnZKl4Yoa77322msAgLSL0/rcZGowhkQDDPEGODud2LVrV6CHRxR2xEnQNtyGxIsSAQlYvmw5SktZtxYqZWVlOHr0KPQGvbJDvKdC0fwsfM6QESrclvUK7DXim5qaGnz22WcAAMOF3k/RACxipejTfXrAlGqCbaw7o/jmm2+qOayoImrUUob3vVPvQKzD3M3PqiurlRrIQGMwEmQiMzJY291QY68R3yxYsACdnZ3IOiNLaQbkC0ue+7Gbt7CIlSJb9+kB6zB3EJ44pWuqhptGhoZSs5Pp/UWU3hL85mcMRoLI6XSGzQZ5pxO9RpgZ8Vz3wlXjJM/av/dHZEbWb2QnVopsBw4cQENDAwwmA8zZ7uNg/Hnx0Nv0qDpShaVLl6o8wuhw+PBhAEBnQqdPjxdTNQxGNKi8vBwOhwMGkwHGFP9OXoEmMiNc3uu55cuXo6SkBNY4K2wTvC9c7U4s7z20/xDbY1NEE1mR1JGp0BncpxydSQf7hXYA7DkSKqI+x5Di2/SydYT7mLV63eqAjak7BiNBpGyQNyRh0A2JQk0EIyVlJSqPRDveeustAED65HSP27/3x5hghCHRAJfLFdS1+0RqE/1FTm8/LqZqPvn0EwbkISAyI77uHC+W9+7YviMozc8YjASRqBcJtykaoFvNSCVrRjwhyzKWLFni/s/ZgXlOkR1hEStFMpEZkXJ6XpBZc60wphjR0d6BNWuCv0V9NHM6nSgrKwMAGFN9y9Kb0k3Qx7mbnwWjnT+DkSAK15U0QFcwUnOkho3PPFBUVITa2lqYrCZl7tRfom6EnVgpUp06dQo7d+4E0NWrorvYse5OoMuWLQvpuKJNdXU1Ojo6oDPolD5T3pIkSdnPJhj9RhiMBJEIRvTp4dF5tTs2PvOOKLJLOyMNOmNgPjYiM8JOrBSptm3bhs7OTsQmxSp7YnUnlvh+Xfh1qIcWVUS9SHxavF8lAzEj3cHI2nVrAzKu7hiMBFG4LusFejY+44qawSkV/yMD95wiGCk9WIqmpqbAPTFRmBBTNAkjEyBJvU+CsQXuzEjRriLU19eHdGzRRNSLWFN7Z6e8IYKRNWvXBDyjzmAkSBobG1FTUwMgPGtGAPYa8VR7eztWrVoFALCM9b23yOkM8QblanH16uBUqBOpSRSvunL6nqo22A0wD3UfH1esWBGycUUbkRnRJft3yrfmWwE9UFdbp9SgBAqDkSARWZG4lDjoreE3TQOwC6unNm7ciNbWVsQmxcI8JLCBZfx58QCAv/3tbwF9XqJwIDIjpvz+s8MiO8K6keARmRFngn8N5nQmHay57uzKunXr/B1Wz+cO6LORQjQ7i83yfKvmUGPjM88UFhYCAJLOTAr4Eu3kq5MBvbuHidhMjCgSVFdXo7y8vMf2B31h3Ujw+dtjpDsxVRPoIlYGI0EiUlhScnj1F+lOZEbKygObbos0ol7ENSLwq6JMySYkfC8BAPDMM88E/PmJ1CKyIil5KQNmh21jbIAOOFxyGOXlbMIYDCIz4uuy3u7EasKVa1b6/VzdMRgJEvGhctrDd98FEYyUlnPnzP40NjYqGYuYsYFZ0nu6lGtSAAAff/wxiouLg/IaRKEmmvmZ8wae2tRb9UoxN6dqAq+zs1OpC/S14Vl3IjPy7Z5vA1p4z2AkSEQwYkjyPy0WLGx8NrgVK1bA5XIhOScZpuTgrIqyZFkQd14cAODZZ58NymsQhdru3bsBAM6MwS/I2G8keCorK+F0OmEwGWCw+38+MiYYYUw1wuVyKdmvQGAwEiRKt7sw25OmO9FrhI3P+iemaMS8drCkXpMKAHjnnXeYqqaIUFRUBAAeFX0rdSNLv+axKMCUHiPp/vUY6U5M1QSyiJXBSBDIsqycUET2IRyJsZ1qPcU+F/1Q+ouMCu7rxAyPga3Ahs7OTsyZMye4L0YUZKdOncLBgwcBQFm6O5CYETGQjBLqa+s5VRlgol7EnBq4lYBKv5F1gWvjH75zCBp2/PhxtLa2AgjvYEQ0PnO2OlFRUQG73a72kMJKRUUF9u3bB0knuYvsgiz1mlS0Frfitddfw+OPP46UlJSgv2ak27RpE7Zt24aysjKUl5ejvLwcer0eeXl5yM/PR15eHiZOnIgxY8aoPdSIUlxcDJfLhRh7jEdTAzqTDjEjY9C6txXLli3D2LFjQzDK6CAyI1JS4BZTiMzIxo0b4XQ6odf7376CwUgQiKxIbFIsdKbwTj4Zk4xwtjpRWVmJcePGqT2csCKyIhljMqCPCX6vGNsZNlhyLThVdgrvvfce7r///qC/ZiTbsWMHJk6c2Of3RBM7ADCZTCgpKcGQIUNCNbSIJ+pF7Ln2Pjuv9iV2bCxa97Zi6bKl/NsPIJEZcSUGbjWgZagFOosOJ1tOoqioCGef7f/uoeF9ptQoEYzEpAZn9UUgiV4j7MLam9il1zAmNDG7JEmIPcNdyCf61JDvxAaEcWlxyLoqCxk3ZSD73mwMvWco0n6ShqRLkmBKMKG9vb1rR2YKCFEvos/0PIgXdSMrVq6A0xm+qxC1RmRGAlm/KOkkZePDQNWNMBgJAlG8akgO/8STMeG7ItbvWteT265du/DBBx8AAIxjQzfVZkp1r9jZd3BfyF4zUomALn58PJJ+loSUaSmwX2BHwoUJSLs2DVl3ZME+xT01uXz5cjWHGnFEZsSV4fnVuDXPCp1Vh+bGZuXx5D+lx0iAF1PYRrqDx7VrA7NpHoORIBCZEdke/lXhhnh3wMRgpIvT6cTdd98Np9OJvMl5yvxoKIimRIdKD4XsNSOVCEZcKf2fEG0FXd0/uYojcERmxDLU872cJJ0ES7b7/nv37g3KuKKNw+HAkSNHAHRd6ASKKGJdvS4w+2oxGAkCJRhJDP+DmyguO1JzROWRhI9XXnkF33zzDaw2K8w3hHaTQ9GU6Ej5EZ4c/SSCEXN6/+9hzIgYSAYJdbV1yn5S5J+GhgblBOjtXk5iU1GuqAmMiooKyLIMk8UEfVxg696sw6yA5D5WVVVV+f18PgUj8+bNQ35+PiwWC8aPH481awZe3vPuu+/i7LPPRkxMDDIzM3HHHXfg2LFjPg1YC8Q0TbCaZAWSCEaqqv3/Y4oEVVVVmDVrFgAg68YspRdLqBiTjYAEOE45cPTo0ZC+diTp7OzEoUPu7JIpo//PoVjFAbDhVqCIrIg93e514bc5yx2MFO0tCvi4opGoF4nLiPO4kNhTeqteyWQFYp8ar4ORhQsXYsaMGXjsscewfft2TJkyBdOmTeu3UdPatWtx66234s4778SePXvwwQcfYPPmzbjrrrv8Hny4UnqMJIfvsl5Bmaap5TQNADzwwANobm5G1tgsWKZ4nmIOFJ1RpxQViwMJea+srAwdHR0wmAyDLq8XhZNLly0NxdAinqj3iM3xfpNQkRnZvYc1I4Gg9BhJCU6GV0xhi2Jxf3gdjMyZMwd33nkn7rrrLhQUFOCFF15AdnY25s+f3+f9N27ciLy8PDzwwAPIz8/HRRddhHvuuQdbtmzxe/DhqK2tTam/COfuq4IIRo4djdxMlac+//xzfPjhh9Ab9Ij5WUzAd+j1lJjbZTDiuwMHDgAAEoYkDPo+ii3sl69YzlUcASAyI3KG99OMIjNSVlKGzs7OgI4rGinHkKTgPL84x1Uc8X81plfBSHt7O7Zu3YqpU6f2uH3q1Kn9pmkmTZqEyspKLFq0CLIso7a2Fv/9739xzTXX+D7qMFZZWQkA7jk6W/B7U/hLTNOcbDmJtrY2lUejHlmWMWPGDABA3rV5SvpRDaJuhMGI75R6kYzBrwit+VboLDo0nWjCzp07gz20iCcyI7pM76sAjElGSCYJnR1d02zku2D0GOlOXMxW1YS4ZqS+vh5OpxPp6ek9bk9PT+93NcakSZPw7rvv4qabboLJZEJGRgYSEhLw8ssv9/s6DocDTU1NPb60Qml4lhYb8Dm6YNDF6CAZ3OOsra1VeTTqqaioQElJCfQGPcxXh7Zo9XQiM1JSUqLqOLTMk5U0gqSXYBvtnqph3Yh/ZFnu2pPGgzbwp5N0EotYAygYPUa6M8S5g5Hao/6fO3wqYD39JCvLcr8n3r179+KBBx7AE088ga1bt2Lx4sUoLS3F9OnT+33+2bNnw263K1/Z2dm+DFMVong1WHN0gSZJkhLdRnMwsn37dgBAUl4SdGZ1F5mJ5b37DnF1h69EMKJL8+y9ZN1IYBw5cgQnTpyATq9TggpvMRgJHJEZCfSyXkGcO+rr6v1+Lq+OuikpKdDr9b2yIEePHu2VLRFmz56NyZMn4ze/+Q3OOussXHXVVZg3bx7eeOMNVFdX9/mYWbNmobGxUfnSUndQkRmREsM/KyKIqZpo7jUighFLjnrTMwKnafwnghFTumcHYbGF/Zo1a9De3h60cUU6kRVJHJoIndG3oF7UjezZuydg44pGp06dCnr9oj7eXYpw4tgJv1sRePXXYjKZMH78eBQWFva4vbCwEJMmTerzMSdPnoRO1/NlxKY6/Q3ebDYjPj6+x5dWiMyIM0E7hXDMjADbtm0DADgz1X/fxFVMzZEaFlT64NSpU8pFgSc1I4C7H4Y+To9TJ09h06ZNwRxeRBP1ItZsq8/PITIju/bsCsiYopWSpbeZg1a/KKZpOjs6/S6n8Dp0nTlzJv7xj3/gjTfeQHFxMR588EGUl5cr0y6zZs3Crbfeqtz/2muvxUcffYT58+ejpKQE69atwwMPPIALLrgAWVlZfg0+HImDoCEp/FvBCyK6jeZgRGRGzDnqT68ZEgyQDBKcnU6lIJo8d+jQIciyDEusxeNGT5JOUrqxsjW870RmxJnmexAtMiMH9x9k4z8/KD1G0gPfY0TQmXTQWdxhhL99kbwORm666Sa88MILePrpp3HOOedg9erVWLRoEXJzcwEA1dXVPXqO3H777ZgzZw7mzp2LcePG4ac//SlGjx6Njz76yK+Bhyvxs2uh4ZlgtEf3/jT19fXKST8cpmkknaT0qOFUjffEst74IfFeHYTFVM2Spdw0z1ciM2Ic4vu0gCndBOiAU62nGIz7Idj1IoLIrPsbjPh0+X7vvffi3nvv7fN7CxYs6HXb/fffHxVbQrtcLk01PBOivSW8UryanQS9NTyWY5tSTWivbUdpaSkuvfRStYejKaJexJju3WdQZEa+2fgNTp48iZiY8N91O5w4nU5lTxlfVtIIOoMOpjQT2mva8e2332pqAUM4ERcywd6WRB+nB46qkBmh/tXV1cHhcLivbEPcRtwfIrI9UhWdwYioF4nJDZ+Tjyg4Y2bEeyIYcaZ4N1VgSjPBmGxEZ0cnli7lqhpvHTx4EA6HA0aL0e+rcTFVwxU1vlP6tCQH93UClRlhMBJAomAoLiVO6d2hBUrNSADWimuRyIyEQ/GqIA7mh0rY+MlbIhgxpHmX+JUkCfET3MXyL730UsDHFemUlTS5iX53L2Yw4r+DBw8CcAfZwSSKWBmMhBExRWNN8b2SXA2iZiRaW8KLYMSUEz51PiIY2X9ov8oj0R5vuq+eLnlqMqBzNz/bsWNHgEcW2US9iGmI/58jsaJmZxE74vpCluWujSI9XN7uK2ZGwpAWV9IAXTUjrc2tUdcSvqWlRSl4tOaGTxAppmkOlx5WdyAac+LECeWg6MtB2JRsgv0COwDg+eefD+jYIp2SYczwP8NoyXIXkn/77bd+P1c0qq2tRWtrq7tkIMh7pInMelWtfy3hGYwEkBZ7jADR3RJ+586dkGUZ8anxSoQfDkRmpL62PuoCRH+IwDI2OdbnYuSUq1MAAO+//76mGi6qTWx+asn3f0WaKdP9999Q34Djx4/7/XzRRkzR2NPt0BmCe5oXx83qmr6bmHqKwUgAabH7KuCeK4/WXiOieDU+P7wa6+lj9cr6fRHk0uBEMBKXFefzc1jzrLCNsaGzs5O1Ix6qqqpCVVUVJJ0Ea47/GUa9RQ9jkvuKnnUj3hNTNNb04Gd7A7U/DYORABInDS0t6xWitQurSC27soKzq6WvJEniihofeLsnTX9EduTV117V1EadahFZkZTclIDt7cQiVt+JzIguJfineHHuOFbvX80hg5EA0mLDM0EUsUZrMGLIDp8pGoG793rP12W9p4s9KxbmTDOam5rxj3/8IxBDi2giGDHnBa6DMTfM850IRpzJwS8ZEF2Om443+bV9BYORAGltbcWxY+7IUIuZkWicpmlvb8eePe7NuMKh8+rpuGGe97zdIK8/kk5C8tXuBg1/+9vf0NHR4ffYIpkIRlxDA5dhFJmR3Xt2B+w5o4WykibIy3qBrmkaWZb9qu9hMBIgIitiibVAHxMeXTy9EY079+7ZswcdHR2wxluDXnHuC2Oqe0wHDh1QeSTaIMtywIIRAEi4MAH6eD0qKysjdvuKQJBlOaDFqwJ37/VdqHqMAICkl6CPdZ/z/Fney2AkQEQwEpsWq/JIfCPm/Sqro2cvCFG8mjgsMWgbSflDTNMcKGEw4ona2lo0NzdDkqSAHIR1Jh0SJiYAAHfyHUBFRQXq6uqgN+hhGRrAYOS7aZrqymqcPHkyYM8b6Y4fP46GhgYAoQlGgMD0GmEwEiBKvUiS9upFgK6akapq/9aKa4moF9ENCc+PgZimqTjM5aWeEFkRe6YdOmNg3lNxMD9YejAgzxeJNm/eDABIzk+GzhS4z5Ih3gB9rB6yLGPfvn0Be95IJ6Zo4lLiAlZMPBhRN8JgJAwoOxUnqjsOX0VjzYgIRjBE3XH0R0zTNDc2o7GxUeXRhD+xrDcmM3B7DInpO7bl759SvJobuOJVQUzVbN26NeDPHanEFI0twxay1wxES3gGIwEiai2ccdpqeCaImpFjddHREt7pdGLnTneraUtu+BWvAu5eC+KKg0Wsg1N+R0mBe05RjF5ZET3Tl94SwYhzaOCPfbFnuKe9WbPjOZEZ0aeGrnaR0zRhRAQjerv2ileBrj+mlqaWqOj4efjwYbS2tsJoNipz0+GIK2o8J7KTroTAregQy/QbjzeitbU1YM8bKboXr1rzA99gK/58dzPCwqWFOHHiRMCfPxIpy3qTQndhHIg+VQxGAkQEI+HUUtwbepsekt5dxOnvhkdaUFnpvtKNTY31e4fRYBJTNQxGBqfsDZUcuM+g3qaHzspOuP0pKSnBiRMnYDAZYB4S+KDekmWBeYgZnR2d+OyzzwL+/JFIZEaMaaFbIRiI/WkYjASIEozYtRmMSJKkZHWioW6kutq9j4I5MXyzIgAzI94QwUig+/yI94DBSG9K8eqw5KDtgRI/wZ0d+eCDD4Ly/JEmlMt6BVEzUl3r+/40DEYCQJZl5QSu1WAEiK6W8FVV7gheZw/vj4A4sZaUsQvrQJxOp5LtEnuaBIqyg/LhwwF93kggpmiMucG7Cref795F+eslX7M1/yBaWlqUC+OQBiPfnTvq6up8fo7wPhJrRENDg9KhUavTNEB0BSMiM+KKC689aU5nTHQf5Csqubx3ILW1tejo6IBOr4MxIcDByHcBITMjvYlgRB4qB+01zEPMMGea0dHegc8//zxorxMJxNYRMfEx0NtCWMD6XWbkeB07sKpKRKLWeGvA+huoIZq6sIpgRI4L3kE0EEQwUl3l3/bckU5M0cSlxCm1T4EipmkOlXJ5b3cul0tZchuM4lVBkqSuqZr/cqpmIGKKxp9dq30hakZam1vhcDh8eg7tnjnDiBKMJAZ/u+ZgEsFINGVGAn0VHWiGhK4l1/5sQhXplO0YUgK/TFtkRtj4rKf9+/ejpaUFJosp6CvS7Be4p2q++uorNDc3B/W1tEwUrxrSQpuh18foge8SMb5O1TAYCQBljs6uze6rQjS1hBc1I+JkH64MdgOgA2SXHBVBoq9EMKJPCnxqWrTlryjjVFl3YoomeXhywLNRpzMPNcOUbkK7ox1ffvllUF9Ly0RmxJUU2ulnSScpUzUMRlQkThLhXgw5GBGMRENLeJEZCfdgRNJJSsbqyJEjKo8mfAWjx4ggMiPHjh6Lih48nhIrafQ5wa9NkCRJ6TnCqZr+iWBElxr6c5G/XVi1ffYMEyIz4ooN72LIwURLzcipU6eU9upiT55wJupGGIz0TwQjUmLgr9D1sXpIJvfzVlQwOyKIYATZoXk9sarmy0VfsgFdP8Q0TShX0giiboTBiIrEyVuODe9iyMGIzEikt4RX6kXMRuhiwv8jILI3YmqJegvmRpWSJLHXyGna29uVXa+tw0JTK2fJscCYaoTjlAOLFi0KyWtqicPhUD4H5rTQ909iZiQMaL3hmSDG39LU4nNFtBaIk7ot2QZJCt/uqwIzI4MLVsMzgb1Getq1axccDgdi4mNgSg/NVbgkSYg/xz1Vs3bt2pC8ppYcPnwYsizDHGNWshSh5O/+NAxGAiBSgpFoaQmvle6rAnuNDKy1tRXHjrmzecEKRpgZ6embb74BACSOTAxpQC9W7ew7uC9kr6kVol4kPjNelYssEYz42oWVwUgAREL3VeC7lvDxkd8SXgQjWtnUUEzTlFXyRNgXUcdhsVncSwyDQOmEW8pOuEBXMIKc0L6u2G/lwMEDoX1hDVDawIcoU3U6scN4dQ2DEVU4nU5lKZMWiiEHI6LbSC5iFdM04d7wTBCZkcojkb/k2hdiisaWZgvaa4hg5EAJT4JAVzBiyAvtBZgozKwsq4TLpe0FA4EmildDvaxXUM4dR307dzAY8VNdXR1cLhcknaREhloWDY3PtNIKXjAkfveeVEXue+IPpWgvOXjTbmKaRrxWNGtoaMD+/fsBhK54VTAlmQAJaHe0R/QFky/EeyKlqFMHJwpY6+vqfXo8gxE/iQ+ELcEW1lvReyqagpFw7zEiiC6xrc2tXNLYByVASAzea4gC1rrqOmUfqmi1adMmAEDikETlBBQqkkFSslQiE0DuzVq3b98OALBkB74LsSdEZuR4/XHIsvdZZwYjfhInbUuiOn8AgRYN0zRaaQUv6Kw66MzujyqX9/YmghGnPXjt8g3xBkgGCS6XK+pXNYkpmtjhsaq8vpiqEZvCkfuYdvToUej0OtWCEVFv2N7W7tNFE4MRP4mTdiTUiwBd6ehIrlbXSit4QZIkZaom2k+EfVGW9SYF7zMo6bquyKN9ea8IRpzZ6uyVJNrzMxjpInq+JOUkQWdS57SuM+uU5oC+rMZkMOInEYxIcdqfogG6KrH3H9iv8kiCw+Fw4Phx9zbXWglGAPYaGUiwe4wIYqommpf3yrKsBCOWYepcgYvMCFfUdBHBiCVXvQy9JPm3Pw2DET9FSit4oXu1eiTuEiveL71RD71NOwXH7MLaN5fLpSztDXYwYkpmr5GSkhIcO3YMBqMBlhyVgpHUyM/eekvUi7iy1D0P+dP4jMGIn5Tainh1xxEoxiQjJIOEzo7OiNyHQ9SLxCbHaqL7qsDMSN+OHj2K9vZ29zRKkGuARGYkmnuNiKxIyvAU6IzqnD7EBVNpSakqrx+ORGbEnKNuI0exopTBiAoipeGZIOkk5cpDNNGJJCKzoJXuq4IIRg5XHFZ3IGFGTNHEpcRBMgQ3uBT1VAdLI+9z4Sm1+ot0J45PDfUNaGlpUW0c4eLYsWPK50CtbJXAzIiKIqUVfHfiyiMSgxFlWa/G3i8xTcOW8D2Jg7A1Jfj9LljAql7n1e70tq4p1tJSZkfEFE3ikMSgdSD2FIMRFSnBSLy2Tm4DEUWskRyMyPHa6L4qiMwIa0Z6EsGIPin4B2ExTVNbVRuV3T8dDody4rMOD22zs9OJCyb2GukKRmx5wetA7ClRwFpV4/1xisGIHxwOBxoaGgBo70p7ICIYKd5frPJIAk/pvqqxgmOxtLe+tj4qT4T9EcGIKyH4vxNjghHQA50dncrfUTTZuXMn2tvbYbPblGBALcbU7+p3uLxXqRdRu3gV6Oo1Ulnl/dYVDEb8IOpF9AZtrcwYTCQv79VajxFBBLudHZ3KDrXUFYxICcEvRpb0UlftThRO1YgpmoRRCaoXf7PXSBeRGTHmqN/rSvT68WUfLQYjfhDBiC3JpvqHM5DMae7izvLS8oi7CtdaK3hBZ9ApVx1cUdMlVD1GBFHEGo3Le5V6kWx1xwF0TdNEYvbWG83NzV37BOWqO3UGdH0OayprvG4Jz2DED6JexJygrZUZgzEmu9PR7Y72iDvxaTUYAbi8ty+hDkaiuYhV7Emj5koaQWRGDpVEd83Izp07Icsy4lPjw6JuURyj2h3tXmdwGYz4IRJX0gDudLSyjDGCilg7OjqUzoBa2ZemOzFmBiNup06dUt5P0ZAs2EQRa7QFI+3t7UqxqFp7n3QngpEj5Ucisjmjp8QUTVx+nMojcdMZdUpQ5G2fKgYjflAanoXH30FAReKKmtraWsiy7J7yiNVejY8oYuWKGjdxsDPHmKGLCc2hTATpB0qjqxX54cOH4XK5YLKYwiKrKLK3nR2dUR2ci+JVeUj4rA4U2UNlN20PMRjxg6gZ0drKDE9EYq8RMUVjS7RB0mmvxkekQCsrvS8Oi0TiYBebFrpuutE6TSOyIvFZ8WFRHyfpurK30VzEKjIjhqHqB4iCKGJlZiSERGZEFx95v0ZzursO5tv936o8ksARwYg1Sf1CL1+IK9LSCjZ6ArqCkVBN0QBd0zRVlVVeF+hpmbgoEcXt4UCpG4nSXiMOhwN79uwBAFjy1J86ExiMqCBSa0aArszIvgORsxmVsqxXo+8XC1h7CuWyXsGYZAQkoL2t3aedSbVKnPBdyeGTBRbHqGjNjBQVFaGzsxMx9hglAAgHYiwlh717XxiM+CGig5HvakYOlxyOmCtArXZfFUQwUlNdo/JIwoM4CbkSQ3eC1Bl0SoYqmqZqRGZESlF/ikYQmZEDh6KrfkcQ9SIJw9Tv+9KdmMosLfMug8tgxA+RHIwYU4yADnCccnQV6mqc0n01Lnyu7rwhClgbjzfC4XCoPBr17dy5EwBgGhLabqDR2GtEBCNqd17tLhKzt94Q9SLS0PAJRIBujc8qvKttYzDio5aWFrS2tgKIzGBEZ9ApEe6BA5Fx5SGCEb1deytpAPcGYWJn2mhsR95de3s7iovdDa9CvVNptBWxOp1OZUO6cApGREv4w6WH1R2ISkRmRBoSZsHId5+Puto6r5ZdMxjxkVhJY7QYobdo8+Q2GFHEGikrarTaCl6QJEnJjkR73cjevXvR0dEBa7w15PPloog1WjIjlZWVaG9vh96gD1lzOU+IaZqmhiY0NjaqPJrQOnnypBKMWIeFV0G+wW4A9IDL6fLqosmnYGTevHnIz8+HxWLB+PHjsWbNmgHv73A48NhjjyE3NxdmsxnDhw/HG2+84ctLhw0xdWFLUn+nxGCJtOW94oOhxYZnAotY3cQUTUJ+6OfLlV4jJZGRMRyMKF61Z9rDakm83qqHPs59IRhtRazr1q1DR0cH7Gl2JSgLF5JOUo6x3vQa8ToYWbhwIWbMmIHHHnsM27dvx5QpUzBt2rQBX/TGG2/EsmXL8M9//hP79u3De++9hzFjxnj70mFFBCOmhPD6QwgkUcQaCXOyTqdTyWZpNTMCdAVS0d74bMeOHQAAXVbok7siO+DtagGtEhcjlvTwWT4qROuGeStWrAAA2Mfaw6p4VRCfEW+W93p9VJ4zZw7uvPNO3HXXXQCAF154AV9//TXmz5+P2bNn97r/4sWLsWrVKpSUlCApKQkAkJeX5+3Lhh0RjIjNyyKRyIxEQq+Ruro6uFwuSDopLPZw8BWnadxEMIKs0L+26GtypOIIZFkOy5NBICmZ0RR1x9EXU5oJp0pORV2vERGMOIeHZyt8X3qNeHVZ0d7ejq1bt2Lq1Kk9bp86dSrWr1/f52M+++wzTJgwAc8++yyGDBmCUaNG4eGHH8apU6e8eemwI66yEavuOIIpkpb3ikyCVruvCiIzUlYRHfUKfZFlWZmmCXXxKtBVM3Kq5RROnDgR8tcPNXGil5PD7xgQjY3PmpubsXnzZgCArSA8ywREMOLNNI1Xl4j19fVwOp1IT0/vcXt6enq/yz9LSkqwdu1aWCwWfPzxx6ivr8e9996L48eP91s34nA4eixdbGpq8maYISF+XmdceEamgWBKMQEScLLlJOrq6pCWlqb2kHwmghFrYngVe3lLZEaiORipqKhAQ0MD9AY9zFmh7wiqM+mgj9fD2eTE4cOHkZiYGPIxhFI4LusVlOW9B7U/leyptWvXwul0IjErUalfCje+ND7zacL19LTkQKlKl8sFSZLw7rvv4oILLsD3v/99zJkzBwsWLOg3OzJ79mzY7XblKzs725dhBpXSY0TDKf/B6Ew65Y9K60Ws4XxA9Ua0zpF3J7IiSblJ0BnVWRAYLb1GZFlWsg4iUxpORJaqpDR6Pg9iiiauIHx3aPWl8ZlXn+SUlBTo9fpeWZCjR4/2ypYImZmZGDJkCOx2u3JbQUEBZFnud8OvWbNmobGxUfnytsd9KCjBiIaLIT0RKStq9u1zXzm5UrTZ8EwwZ7gzAfU19WGZMQwFUS9izlZvn5Ro6TVSW1uL1tZW9wqJlPBbhSaOT9WV1V71tNAyEYy4hofvsUxcxFYd8bzQ3qtgxGQyYfz48SgsLOxxe2FhISZNmtTnYyZPnoyqqiq0tLQot+3fvx86nQ5Dhw7t8zFmsxnx8fE9vsJNJHdf7U5cDWm98ZkIRnTp2m6to7fplb858TNFGxGMuDLVOxiLItZIz4yIixB7uh06Q/h9doyJRkh6CZ0dnVFR1H3ixAmlv0i41osAXcH6iWMnPK4P9fqva+bMmfjHP/6BN954A8XFxXjwwQdRXl6O6dOnA3BnNW699Vbl/jfffDOSk5Nxxx13YO/evVi9ejV+85vf4Oc//zmsVm3O38uyHDXBiGh8pvXlveLELTILWmbO/G5H5W+1v8rJF0ob+Gz1pg1EluBgqbYzhoMRUzTW9PA8Vks6qWtKoDTyd7NevXo1XC4XkrKTlJ5D4Uhv00MyuUs3PG1D4HUwctNNN+GFF17A008/jXPOOQerV6/GokWLkJubC8DdWKp7BW1sbCwKCwtx4sQJTJgwAf/3f/+Ha6+9Fi+99JK3Lx02jh8/jo6ODgCRXTMCdGVGdu/ZrfJIfNfc3Kx8IBiMaFtTU5NygrRkq9f3QgQjh0oiexVHOG6QdzrRFj4aghExRRNbEN7LOCVJ6tqjpp9yjNP5dCa99957ce+99/b5vQULFvS6bcyYMb2mdrRMZEWs8VbVCuhCxTLUfcA/uP8gOjo6YDSGbzTeH5EViU2Khd6m/b4wpkx3gLhn7x6VRxJ6u3e7g+L4tHgY4tS7EBDTNN5uBqY1IvBzJYdvfYIp1YRWtEZVMCIPD79l1qczJhvRXtPu8fRZZJ9Jg0QJRjS+TNQTxhQjdBYdOts7sX//frWH4xMRjMQNCd/qc2+IzEhRcZHKIwk9US8Sl6vueykyI80nmtHc3KzqWIJJC6vQxMqmg4cie8rs2LFjyhSlbUz41osI3jY+YzDig2hoBS9IOknJjogPgtYoxatpkfHnLoKRspIydHZ2qjya0BJ/g3KWuleGeqteybJFchGrsqw3nIOR75a77z+kzYslT61atQoAkJKXoolaRW+naSLj6BxiYsM1nT06fn2iy+WuXbtUHolvRDDiTImMpX/GJCMkk3sFQTSkprsTmRH9EPWn2yJ9997jx4/j+PHjAMI7GBE1I4dLD6s7kCATUzTWMdrIyIupzJJyz3rARMfZNMCU7quxkXFyG4zIjGzdvlXlkfhGBCOi1kLrJJ2kFOJGUxFrZ2enUjOiRhv400V6rxGRFYlLiYPOHL6nCjFNU19bj7a2NpVHEzzLly93/2O4uuPwFDMjISCCESkufCvMA0msWti5S3vTNC6XS6l1iYSVNIJogx5NwciBAwfQ1tYGk9UUFlfqkd5rRAQjtozwrk/Qx+mhs7hPZZH6XlRVVWHv3r2QJEkT9SJAVzBSU9X3VjGnYzDig2jpMSKYh7pPfHU1daivr1d5NN6prKzEqVOnoDfolbnlSCDqRvbu3avySEJHTNEk5SeFxWaHyvLe0shc3iuKV/Wp6k+JDUSSurrDRuo2CV988QUAIGNMhqqryLwhModtJz3LVjEY8UG0BSN6q16Zl9Va3YjIHCQMSYCkV/8EFigiGNm9V7v9X7wlilfDoV4E6LaKoyQyV3GIzIgzKfyno8WFRqTWUIlgxDBOO+ccnVnnVSsFBiM+EAWskb4vTXfWHHfRlNaCEVEvYslUv8YgkEQwsn/ffshy+PccCIRvvvkGgPoraQRx5RepvUZEZsSYFv69hURgGInByKlTp7B06VIAgPksbU01i6kaTzAY8ZLD4VAqzKMlMwJ0TdVobXmvCEbk1PA4gQWKKd0ESEBzYzPq6urUHk7Q7dixAytXroROp4NtbHjMmYupgeN1xz3ef0NLtNBjRBBjPHBI23to9WXZsmU4deoU7Ol25TisFSJg9wSDES8dPXoUAKA36KGPCY90cSiIzMiW7VtUHol3RDAipUXOFA0A6Ew65WQYDUWsf/zjHwEAOVNylP2S1Ka3dRVOdt8CIxI0Nzd39VPSQDAiPguRGIyIKRr7uXZIkraOY8yMBJH4gNqSbGFRRBcqYrv2fcX7NNVoK5I2yDtdtOxRs2fPHnz44YcAAMPU8MlGSlLXJm2Rtopj8+bNANy79WphCwVRM1J+OLKCQlmWlWBEGqu98w0zI0EkghFzQuSd3AZiSjFBZ9Gho71DM23hW1tblVbEkdJjpLtoCUb+9Kc/AQDyLsqDZUh41f6IWoVI6zWybt06AED8qHiVR+IZkRlpaWrBiRMn1B1MAO3YsQNHjhyB2WrWzJLe7pgZCaJoLF4Fvmu09d18pVaKWA8ccKdsbQk2GGIj7/2KhhU1+/fvx8KFCwEA+ivD7wo9UjMjIhhx5YXvBnnd6S166OPdfx+RVMT6+eefAwDSzkmDzqS90zWDkSASmRFExp5rXhGdWLUSjETaBnmnE8FIcXGxyiMJntmzZ8PlciH3e7mw5oZfG2ylv0Vp5PS3cDqdWL9+PQDAMiK8MlEDicQVNSIYQYG64/CVMcWofEYGw2DESyIYccVq44ohkEQLbq20hRfTF4b0yMuKAF3BSFVFVUSs5pg7dy5mzZqFxYsXo6WlBaWlpXj77bcBAPqp4ZcVAbpOgAdKI6dwsqioCM3NzbDYLEr3ZS0QdSOR0visuroaW7a4FwzEnBWj8mh8Y0o2YcQfRnh038g8SgeRCEZ08dEXx2mtLXykbZB3On2ce+dYZ6sTBw4cwFlnnaX2kHy2Z88e3H///QCAZ555Bnq9HmlpaXA6ncgen42YYeF5MBYrTUoORsYJEOiaokkenaypIn3RmDFSMiNffvklACCzIBPGhPDv9eKv6Duj+knUjETDH8fpxDRNbVWt0mslnIlgxJgRme+VJEkRU8S6bNkyAEBMcgxi0mPgdDq76rOuCt9rJlOGOxhpqG/QxGfCEyIYQZ6qw/CayFLtO7hP5ZEEhtJ19Yzw/fsPJAYjXoq2VvDdhXNbeNGl0OFwAHAviYvEDfJOJ1YJab1uRGyPnnR5Eob9ZRhG/XUUhtw1BDkP5MA2KnxXEegteqVIT+vvgSCCEeMIbQXxSpYqAup32traUFhYCAAwnRl5KwH7wmDEC7IsR3UwAoRnEassy7jhhhtw5ZVXYuzYsfjwww9RVVWFlpYW6PQ6TbSz9pWyYV6xdjfMczqdWLlyJQDANMZ94DWlmpB4USLizwv/paWRtINyZWUlysrKoNPrYB0WfgXDAxGZkcqySrhc2q7pe+utt3Dy5EkkpCcotXqRjsGIF5qamtDW5t6B0BAfpcHIdx8MsYNqOPjkk0+waNEiAO7itRtuuAGXXnopACAhKwE6Q+T+mUfC8t4dO3bgxIkTsNgsYbliZjCRtKpJZEVSh6dCbwnPouH+GJOMgAR0tHd0rXrUoFOnTuGpp54CACRenai5rqu+ityjdBAoDc9sZujM0fmrE5mRrTtCu6KmtLQUe/f2vvpvbW3FjBkzAAB51+Yh9Yep0Jl0yr4a1kztndy8IU6Eh/Yf0lRn3O7EFE3KGSma3FlZCQj3aDcgFEQwYhquvakBySApU2ZaLmKdN28eqqqqkJiRCOtFkX386i46z6g+EgV1tqTwncMONnHlWrynOGTLSZ1OJyZNmoQzzzwTCxYs6PG9P//5zygvL0dSZhJifhCD9B+nY8TsEbBfaIekkyJ+vtWUZoLOqkO7o73PYE0Lli9fDgCQR2hzM0MxTbNn7x6VR+I/rRavCqJuRKvBSFNTE2bPng0ASLg2ATpj9Jyio+cnDQCRGYnGlTSCMdUIQ4IBHe0dypbuwXbw4EHU1NTA5XLhjjvuwIsvvgjA3Z3zr3/9KwAg8cZEJVtlSjYh+55sFLxWANuUyA4cJZ0Ea547QBT7iWhJR0cH1qxZAwCwjNbm3LgIRrTe76W5uVmZfo0ZGZ5LqQcjGmxpNRh5/vnncezYMaTmpSLme9p8D3ylqWBE7a3SRTAi2g5HI0mSlD0SRNFhsIkDpN7o/r3PmDEDTz31FO6//350dHQg54IcWM7pfSKL5FqR7rQcjGzZsgUtLS2IscdoqsFWd6LfS/cVXFr0zTffwOVyISEjwas23uFEND47eOigyiPxXl1dHebMmQMAiL02VpNTlv7Q1NF6925152SV7qtx2q7U9pcIRpavXB6S19u5091kLfOSTKT9KA0A8OSTT2LJkiUwmoww/8QcNUVefbHmu4OR9d+sV3kk3hNTNMnjtNVgq7vu/V60XMQqpmjiRmt3+wSxomb/Ie0FhbNnz0ZLSwsyR2ci5rzoyooADEa8IoIROVabc9uBYhvtDkY2btyorC4KJhGMuDJdSLsuDZn/l6l8L/uH2TCnR24fEU+IYGRv0d6QvB+BJIpXXcO0HeCLqZpICEZcudp9L0TNyMED2sqMVFZWYt68eQAA67XWqLy40lQwonYb8mjdsfd0pgwTDHYDOhwd2LRpU9BfT0zTiGXFyVcmI2dGDtJ/lA7LVG2m9gPJmGKEPk4PZ6dTCdy0wOFwKCdAa4G2Vw2IzEjR3iKVR+Ibp9OJjRs3AtDW5ninMw8xAxJQX1uPo0ePqj0cj3366adwOBzIGpcF6xna/iz4SlPByPYd21V9/WhveCaEsm6krq4OVVVVAADz0K4MSPw58Ui9LlWT22oHmiRJSnZES3UjIrMWlxynnMy1SmRGtLq8d9euXe7N8WItyvJ9LdJb9Up2REuBudIwLx9RmRUBNBaMlB4qVbVancFIFzFVs2zFsqC+jjigJA5JhN4avYXDgxHByKbNwc9UBYqoF0k4I0HzB2ARTJUeKoXTqb2NGcVFRUpBimZrdwRLbvg1ZhyMCEakNG3/7v2hqWBElmUUFamTBu3s7FRW8xjt2qw0DySRGdm4YaOyH0wwiGDElhvZS3T9pcUiVqVeZLh2axQEY4oRklFCZ3unJpeVivcCw9UdRyBYc9yfha3bQtuY0R8iGNF6htAfmgpGAPWi3bq6OsiyDEknQR/HK3RTpgmGeAPaHe1BrRvpXrxK/YvJd1ffl+wvQXNzs8qjGVxra6tSoxBToP2VA5JOUjZk1FoRq9PpxOrVqwEA5tHaPxmK2rIt27aoPBLPtLS0oLKyEkDXLtDRiMGIh7p3X9V6GjMQJElCzBj3SWTVqlVBex3xfhuGcmpsIAa7AcYkI2RZxrZt29QezqA2bNiAjo4O2NPtSm8IrdPqiprt27ejsbER1lirMsWhZSIYKT1YipMnT6o8msGJ3jS2RBsMsdF7nNNcMLJ5mzoFeqJexJoQnZXOfQl23YjD4VAO7NGyc6U/tFTEKrr3xo+O13y9iCCCEa215e+xN1AEXGgZ7Abo4/VwuVyqTet7Q0zRxA3Vbn+XQNBcMLJ7125Vtodm8Wpvom5kw/oNaG9vD/jzFxcXo7OzE9Y4q2Y7QoaSCEa+2RSaNv3+EFN7zqHaK/bsj5jv37V3l8oj8U4k1e4A360u+65uRAtFrPv27QMA6NOie/pfW8GIAWg72YZDhw6F/KWVLanjQ/7SYcucZYY+Tg9HmyMoV+PiQJI4LHq20faHdZj7ALxx00aVRzIwWZaVzIglP3IyXiIY2b9vP2RZG40RI2FvoL6ITOr27eq2g/CEyIy4UiMjGPSVpoIRyxD1lmwpreBjo/sPpjtJkpSpmmD0GxHFq1IWAxFPiB2VK8sqUV9fr/Jo+ldRUYHa2lroDfqIqFEQTBkmQAJam1q7Ll7C3NatWzW/N1BfRDCyaWv4L3UXwUg0F68CWgtGhqoXjIgCVl28pn5lQRfMfWqUpkVZAX/qiKS36ZUD2pYt4buSQEzRJA9LjqimdTqjTmm4pZUiVjFFk3yGdvcG6ouYptlbtDes+764XC6lgNWUyWBEM0QHTjWWbLFmpG8iGFm/bn1A+43IstyrDTwNTgtFrCIYMedpfxnp6bS2YV6k7A10OlOGCZJJQtupNhw8GL771JSXl6OtrQ0Go0HZ5C9aaSoYsWa7D7RqtIUXLckZjPRkzjLDkGhA26k2fPzxxwF73srKSjQ0NEBv0CurFGhwIhgJ57oRUS/iyo6sEyDQFYwo7b3DWHt7e9feQGMia5WgpJNUzaR7ShSvJgxNgKSPnMyULzQVjJiy3HOydTV1Id0EqaOjA2VlZe4xpEV39Ho6SSch8eJEAMD8V+YH7HnFFE1SThJ0Rk39mapKND/btGlTWBZRdnZ2KlNIouA2kojAeWdR+O+LsmnTJpw8eRKxibHuDeYijKihCudgROm8mhF5v39vaeoor9YmSGVlZXA6nTCajcyM9CHp0iRAB6xetTpgPRbEAcSczQ+pNyw5FkAH1B+tR3l5udrD6aW4uBgnT56ExWaJyNbXWsqMiCmapDOSInK1mpje3bw1fKcsuZKmi6aCEaDrDyyU0a6Yc7QPsUdUkVegGBONiDvH3bDnlVdeCchzKm3gM/gh9YbOrEPMMHd2ZMmSJSqPpjdRL5I0IikiP0tiC/u6mjpUVFSoPZwBRWq9iKAs71V5t/eBiGkaXbrmTsUBp7nfgPgD27Y9dC2vRTBiTou8K7lASfqfJADAgn8tQGtrq9/PJ4IRUw6nxbwVe2YsAGDRokUqj6Q3US8i5UZeIAK4s7eibmfZsuDuaO2PtrY2rF/v3lQx0upFBMtQCyABx+uOh+1Sa26Q10VzwYhYshXKFTUiGJGTw28OPlzEjo2FKc2E5qZmvP/++349V0tLi/I7j6TeB6ESd5Y7S1W4tBAdHR0qj6YnkRnR5Wju0OOx2LHuYDCcg5GNG927bcclx0XsklKdWafUYoRj3UhTU5PSMoI1IxoMRkRmpORAScg2QVKWhqWG5OU0SdJJSLzUXcg6b/48v55r5cqVkGUZCRkJMMSzRsdbllwL9HF6tLa0Kle/4aC1tVXZKyRmuPZ36u2P7Qz3cvfFSxaHZRExAHz66acAgMQzI7u7sWiqF47BiJiiiU2OhT4mulvBAxoMRgwJBhgSDXC5XErKN9hEMMKVNANLnJIIySBh29ZtfjXd+vDDDwEA9vPsgRpaVJF0EmLHua/OFy9erPJoumzbtg1OpxPxqfEwJkbuXkMxw2MgmSTUH60Py03zOjs78d5777n/c46qQwk6cfG6ddtWlUfSm7JB3pDo3iBP0Fww0r0F+erVq4P+ek6nEyUlJQAAUzqDkYEY4gyIP9+9ec/8+b4t8+3o6FCu2nRnae7PM2zEnek+wH3+5ecqj6SLmKKJHxHZGzzpTDrEjHRnfsJxqqawsBC1tbWITYxF3LjIPhGKYESNRpmDEcEIi1fdNPlbiBnl/qCvXLUy6K9VXl6Ojo4OGEyGiL6aCxRRyPruv9/F//3f/2HmzJl49tln8fnnn3uUsl65ciUaGhoQlxSnvM/kvdhxsYAE7Nm9R2nYpzYRjLiGRubqje5iz3BnppYUhn5FkyzLmDt3Lm688UbU1tb2+v5bb70FAEi+MBmSIXKnaICuGsOykjK0tLSoPJqexDSNKyXyPw+e0OSEvMiMbNjg3rreZApexkJZ1pvJZb2eiBkRA0uuBW1lbfj3v//d43uPPvoo/vSnPw34eDFFkzQhMpd+hooh3gBrnhWnSk/h66+/xh133BGQ562srMS7776Ld955B06nE2+++SYmTpzo0WNFMGLKj/wMY+zYWNSiFitXrkRnZycMhtAcah0OB+655x7861//AgBYrVbl34C7aPKTTz4BAOgnRH6dgiHeAIPdgM7GThQVFeF73/ue2kNSKBvkRWgBsbc0mRkxZ5qht7m3rt+2LbhLfJVVHelc1eEJSZKQ91Aeht49FBk3ZSDl6hQkXuAubH3mmWcG3DPF6XQqB0qcFYLBRrjYs9xX518t/srv51q8eDGuuOIK5OTk4JFHHkFRURGKi4tx0UUX4fnnn4fLNfDV3dGjR3H48GFIkgRrXmQuJe3OkmOB3uYuIg7VPkF1dXW44oor8K9//Qs6nfvQ/vbbb2P79q4+Gx9++CHa2tqQkpOiLEGOdGJFXigbZQ7G6XTiwIEDALisV9BkMCLpJCWFv2bNmqC+lrKsNyU8q+LDkSHegIRJCUiZloKM/83AkHuHwD7RDpfLhTvuuKPfDfXWr1+P2tpaxMTFKBvwke9E3cjirxejs7PT5+fZv38/fvCDH2DZsmWQZRlDzhqCnDtyEH9+PDo7O/Hwww/j2muvRX19fb/PoTQ7y0mKipUDkk6CrcD9N7x06dKgv97evXsxceJErF27FjGxMRj+8HDYv2eHLMt4+OGHlSnSt99+GwBg+54tolfRdCfqRsIpGDl8+DDa29thNBthTOb0P6DRYAQAbKPcH/Rg142wx0hgZP6/TOjj9dizZw+efvrpPu8jpmhSJ6RCZ9Dsn2bYsA6zQm/To7mxWQkGfPH+++/D6XQifWw6Rv11FBJnJiL+knhk35uNrNuyIBklLFq0COeee26/e0Zt2LABAJTusNFA9BtZsjS4dSMulws//OEPUVpaipShKch6NAvmsWak/yQdkkHC8uXL8dVXX6GiogIrV64EAJjPj56rcZEZ2bwtfNrCiymahKEJnI7+jk9H/Hnz5iE/Px8WiwXjx4/3ODuxbt06GAwGnHPOOb68bA8xo7/LjKxdM2iK2B/Ksl6upPGLIc6ArFuzAAB/+ctfei39lWUZH330kfvfZzHwCwRJJymFlF995dtUjSzLShM782QzTKldnwNJkpB0WRKGPzEcxhQjKisr8cEHH/T5POIY4cqLnmI90W9k44aNAelK3J+tW7fi0KFDsMRYkPLbFFiy3CdfU6oJyVOTAQAP/+ZhvPXWW0pmq/v7GOlEZmRP0Z6gnis81dbWhs8++wwAm51153UwsnDhQsyYMQOPPfYYtm/fjilTpmDatGmDbsrV2NiIW2+9FZdffrnPg+3OmmuFzqxDc2Oz0kgp0FwuFw4dOgSAreADwT7BDvsFdjidzl7TNZs3b0ZFRQUsMRblBEr+E3UjXy760qfHi9oQg9EA27l9T51Zsi3Kzs1iv5PuHA6HkpmJphVSpjQTjMlGdHZ0Yu3atUF7HVFnlXZeGgxxPQtlU69JhT5Wj+K9xUpGMhoKV7szZ5ghGSScaj2F0tJS1cbhcDgwf/58jBw5Eq+99pr7xmzVhhN2vA5G5syZgzvvvBN33XUXCgoK8MILLyA7O3vQvhL33HMPbr75Zlx44YU+D7Y7SS/BOsJdgBWsupEjR47A4XBAZ9BxXi9AMv9fJvRxehQVFeGKK67AunXrAHRN0aRPSIfOxCmaQBHNz7Zv297vFMpAFi5cCADIOj9rwFoPUR+xfOXyXku4N2/eDIfDgdikWJgyoueKXJIk2MYGv25EBCPyuN4ZRb1Nj7Tr0gDAXaNgMsI2PrrqsSS95N7AEOrVjSxduhSjRo3Cvffei8rKSiSkJyD/jnzYr2RjR8Gro357ezu2bt2KqVOn9rh96tSpA7adfvPNN3Ho0CH8/ve/9+h1HA4Hmpqaenz1RSzxXbVqlYc/gXfEFE1CRgIkPef1AsEQb8CQO4dAMkhYu3YtLrroIlxzzTXKVEBfB1TynTHBCEueO03917/+1avHdp+iwdkD39eab4VkktBwrKFX11FxsZA4JrJbj/dF1I0sXhKcTrgHDhzA3r17oTfolQ0ST5d4WaIyzZx1QRb0tujKjADq7PYuVFRU4IYbbkB5eTnsqXbk35aPrD9kwXaJjeeVbrwKRurr692FbOnpPW5PT0/vd1fEAwcO4JFHHsG7777r8Vr72bNnw263K1/Z2X3nspQi1tUrg7IHhLKsN4PLegMp/px4jPzLSCRenAhJ5y5+LC8vh9FshO2s6LpqC4X0H7k/r3PmzPFqKfy2bdtw6NAhmCwm2M4e+H3RGbq6jooiSUEEI848pxejjgwiGCnaVeRTZmowoltx5lmZ/QYZOoMOQ38xFPFnxcM0LXoyU92J5meh3O0dcE/133777WhsbERWQRaG/GkIbJfZmP3tg0+/kdOvbmRZ7vOKx+l04uabb8ZTTz2FUaNGefz8s2bNQmNjo/JVUVHR5/2sw6yQDBLqauuU2o5AUjbISwn4U0c9U7IJQ34+BCP+PAL279kBCRh62VDoLdF31RZscWfHwX6Be2n13Xff7fEyX5EVyTw/06P3RWQquwcjTqdTmYqzjoqOvhbdGewGZbM2pYdOAInnlMYNfIUdMyIGOTNzlOmKaCNW1GzbGdpg5OWXX8by5cthsVpgu41ByEC8+s2kpKRAr9f3yoIcPXq0V7YEAJqbm7Flyxb86le/gsFggMFgwNNPP42dO3fCYDBg+fLlfb6O2WxGfHx8j68+B2/SKY17glE3wmW9wWfOMCN7ejbGvjoWMTdGT3FjqGXcnAFdjA5bt27FSy+9NOj9XS4X/vOf/wAA5LM9+/sXdSPLVi5TMpW7d+9GU1MTzDFm5YQQbewXuOsC3nv/vYA+b21trTI9bj07+gI9b4i/veqKapw4cSIkr7l371789re/BQAMvXkoV84MwqtgxGQyYfz48SgsLOxxe2FhISZNmtTr/vHx8di9ezd27NihfE2fPh2jR4/Gjh07PG4jPRCxxDcYm+Zxt97Q0Zl0XG8fRMYEIzJuygAA/O53v8Phw4cHvP/GjRtRXl4Oi82irMgZjFI3Ut+A4uJiAFBWkaQUpETt/LgIRlavWt3nXjG++uKLLyDLMjLGZMCUzGPUQPQ2vbIIYdeuXUF/vfb2dvy///f/4HA4kHNBDswXMRAZjNc5o5kzZ+If//gH3njjDRQXF+PBBx9EeXk5pk+fDsA9xXLrrbe6n1ynw7hx43p8paWlwWKxYNy4cbDZ/K8PEHUjy1f1nWXxlSzL7DFCESVxSiJiRsfg5MmT+OUvfzlgnZVYRZN+vuerm/qqG1Eylvm+j1vrTKkmWPOtcLlcyqqxQBBTNMYzudLPE6FsC//0009j+/btiEuIg/Vma9QVbvvC62DkpptuwgsvvICnn34a55xzDlavXo1FixYhNzcXAFBdXT1oz5FAihkRA0hAeWk5KisrA/a8NTU1OHnyJCSdBGMKP+ykfZJOwpDb3CuZFi9ejBdffLHP+zmdTmWKBud49xqibmTFihWQZVkJRkwjojugD/RUTUtLi5KhNp/Nq25PiGAk2CtqnE4nXn75ZQBA2i1pMCbw/OEJn6pp7r33Xhw+fBgOhwNbt27FxRdfrHxvwYIFvarpu3vyyScD+segj9HDOsw9X9p9d0p/Kbv1ZtjZmpwihjnLjLQfuftOPPjgg5gzZ06P73d0dGDGjBmoqalBTHwMbOO8y152rxs5dOgQqquroTd0fUajVfwF7rq3dWvXoaqqyu/n+/rrr+FwOJA0NClqi1K9JZb3btm+ZZB7+qeoqAhNTU2w2CywnBuddVK+iIizbPLl7pbHL738Ur+bsHlLBCPWjOg+iFLkSfl+ClJ/kAoAeOihhzB79mwA7qzmZZddhrlz5wIAhv54qNeBePe6EdFlMm10WtSvIjAlm2AdYYUsy/jvf//r9/OJJb2x58ZyCsBDIjPy7d5v/do4cjCiTip5TDLr4LwQEUeI+AviYUg04Gjt0a4mTX4SwYguJSJ+RUQKSZKQfkO6kiF59NFHcffdd+O8887DunXrEBMbgxEPjoDpUu+nVrrXjcybNw8AoB/G5dpA4KZqOjo68MUXXwAA9OP4u/WUKc0EnVmHdkc7Dhw4ELTXUVr/R3GdlC8i4kyrM+iQfIU7O/L8nOcD0gBNBCPO5Ohr1ETRIe26NKT/1L0k//XXX0dNTQ3Sh6djyONDYDnb9/SyqBsRm8PphkfEYcZv9vPd/XQ2btjYb+8kT2zYsAENDQ2wJdiUwI8GJ+kkmIcGty189zop43DWingjYo4SSZcmQTJJ2L1rd7/9S7zBZb0UDVKvSXX3IDHqkP8/+Uj+/5L97odgG9NVZyJJEmwj2VUXAIyJRiV4UAqEfSB2YE4+m9MA3gr2ipqysjIcOXIEeoMeMcMYKHojYoIRvU2PxCnunUNPL8rzFpf1UjRJmZqCgvkFsN1qg87s/yFB1I0AQEp+SlTuhdIfMVXz/kLfp5NFMOIa7QrImKKJKGLdun1rUJ5fTNGkjkwNyGcpmkTUbyv5ymRAAhYtWqQ0XfLFgQMH0NTUBIPRAFMqgxGKfJIhcFfYOqPOveQegGUkVxN0Fz8hHpCALZu3oKSkxOvHV1VVYefOnZAkSdmRmTxnzXYvSNixc0dQnl8EI4bhnu3DRl0iKhgxZ5gRd04cAOCFF17w+XlWrFgBAEgrSIPOGFG/IqKQSL0mFbbhNpgv5rLT7owJRtjGuqetnn/+ea8fv3ixe/ff9NHpMMTzhOctUTNSV1OHurq6gD+/CEZ0+TxveCvifmMpV7t3tfvXW//y+Y9N9EnRjYi4Xw9RSMSeEYv83+XDMoSZkdOlXetexfTaa695nR0RUzTGsSyO9IXeqlcCEn/qdvpy/Phx7NmzBwBYWOyDiDvbxoyKgSXPAkebw6crD1mWlcyIcRQ/8EQUWLYxNsSOi0VnZyeeeuopjx/X2dmpdF1lMOK7pEuTALiz5y5X4OpuxKaFybnJzFr5IOKCEUmSkHad+8rjhRdfwJEjR7x6/Lfffova2loYzcao7xpJRMGRfoN7SfXbb7+tXE0PZsOGDWhsbIQtwcZjkx8SLkqAzqrDwYMHsWjRooA9r5iiYVbENxEXjABA3DlxiBkZA0ebA08++aRXjxVTNKkFqVHfNZKIgsOaZ0X8hHjIsozf/e53Hj1GWdJ7Fpf0+kNv0SPpkq7sSKCIYMSVx1VOvojIs60kSUi/0X3l8cYbb+Dbb7/1+LFiiob1IkQUTGk/SgMk4OOPP8bmzZsHvb+ypHcMT3b+SroiCZCAZcuWYffu3X4/X1tbm/IexoxiZsQXEXvGtY20Ie7cOLhcLjz66KMePUaWZSUzwnoRIgomyxALEiYlAAAee+yxAe9bXV2NHTt2cElvgJhSTO5l1ghMdmTLli1ob29HXHIc20H4KGKDEeC7ednvrjw2btw46P337t2Luro6mCwmWPM5J0tEwZV2fRokvYTCwkJ88MEH/d6PS3oDL2Wqe+XlO+++g6NHj/r1XGKKJmFMAjcu9FFEByOWIRYkXJQAAPjtb3876J41YoomtSCV/UWIKOhMqSYk/Y+7fuGmm27Cn/70pz6PU1zSG3jWEVZYh1nR7mjHK6+84tdzif1oWC/iu4g/46ZdnwbJKGH16tWDVk6LYAQjQjAwIiIA6TelI+l/kiDLMh5//HHccMMNaG5uVr5/9OhRLukNAkmSkDzVvcHq3L/PhcPh8Ol5XC6XsqyXHYd9F/HBiCnZ5G4TD+Chhx9CR0dHn/dzuVxYtWoVAMA8il0jiSg0dAYdsm7NQtYdWZAMEj766CNMnDgR119/PbKzs5Geno4TJ05wSW8Q2CfYYUg0oO5oHebOnevTc+zYsQMnTpyAxWZRNuIj70V8MAK4W1Pr4/TY9+2+ftNxRUVFOHbsGOtFiEgVSZckIf+RfBgSDCguLsann36KyspK9xV8TjKybszikt4Akwxdfakef/xx7Nu3z+vnWLp0KQAg9cxUSHq+P76KikoovU2P9B+no+pfVXji90/g5ptvRnJyco/7KPUiZ6QGdNMwIiJPxYyIwfAnh6NhVQP0Fj0seRZYcizQW7nzcbAkXpKIxs2NaN3Tittvvx1r166FXu/571sEI/KIgWsSaWBRkRkB3H9wlmwLTjSc6LMRmljSKw1nIEJE6jEmGJF2XRqSr0qGbbSNgUiQSZKEIT8fAp1Vh40bN2LOnDkeP7atrU0pXrWM5RSNP6ImGJF0EjJ+lgEAmD9/Pvbu3at8r729XakXMY3mGnEiomhiSjYh82eZAIDf/e53Pc4PA1m/fj3a2toQnxoPcyZrDf0RNcEIAMSOjUXceXFwOp2YOXMmmpubMWfOHIwYMQINDQ2w2Cyw5rJehIgo2iRMSUDsWbFwOBy4/fbb0dnZOehjxBRNwjj2F/FXVAUjAJDxvxmQ9BK+/vprZGVl4aGHHkJFRQXik+OR94s81osQEUUhSZIw5I4h0MXosHnzZsyfP3/Qx4hgxDWC/UX8FXXBiDnNrKwtb2lpQUpOCvLuzMPQ2UNhOCcq6nmJiKgPxkQj0n/s3tfs5bkvD9gos6GhAVu2bAEA2MbaQjK+SBaVZ9+0H6VBH6uHOcOMuHPjuFyOiIgAAAmTE1D7QS0O7D+ANWvW4OKLL+7zfitWrIAsy0jJS4Exkc3o/BV1mREA0Jl0SL0mFfHj4xmIEBGRQm/Vw/49OwDgtdde6/d+YorGWsA6w0CIymCEiIioP4mXJAIAPvjvBzh+/Hif9xHBiDSKF7SBwGCEiIioG2u+FZYcC9od7Xj77bd7fb+srAwHDhyATq+DbQzrRQKBwQgREVE3kiQh8VJ3duSVV1/pVci6bNkyAED6mHQ2pQsQBiNERESnSfheAiSThG+Lv1V25RXEFI1+NAORQGEwQkREdBp9jB72ie5C1tdff1253eVyKcGIcTRX0QQKgxEiIqI+JF2SBAB4f+H7OHDgAP72t7/h7LPPRl1dHcxWM6zDuZImUKKyzwgREdFgrMOtMA81w1HpwKhRo5TbjWYjcn6UA52B1/OBwmCEiIioD5IkIel/klD9VjUAIHNMJkzfMyHm/BjobawXCSQGI0RERP1IujQJhngDTGkmWHM4LRMsDEaIiIj6Iekk2CfY1R5GxOOEFxEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREamKwQgRERGpisEIERERqYrBCBEREanKp2Bk3rx5yM/Ph8Viwfjx47FmzZp+7/vRRx/hyiuvRGpqKuLj43HhhRfi66+/9nnAREREFFm8DkYWLlyIGTNm4LHHHsP27dsxZcoUTJs2DeXl5X3ef/Xq1bjyyiuxaNEibN26FZdddhmuvfZabN++3e/BExERkfZJsizL3jxg4sSJOO+88zB//nzltoKCAlx//fWYPXu2R89xxhln4KabbsITTzzh0f2bmppgt9tRML8Aeqvem+ESERGRSpynnCj+ZTEaGxsRHx/f7/28yoy0t7dj69atmDp1ao/bp06divXr13v0HC6XC83NzUhKSur3Pg6HA01NTT2+iIiIKDJ5FYzU19fD6XQiPT29x+3p6emoqanx6Dmef/55tLa24sYbb+z3PrNnz4bdble+srOzvRkmERERaYhPBaySJPX4vyzLvW7ry3vvvYcnn3wSCxcuRFpaWr/3mzVrFhobG5WviooKX4ZJREREGmDw5s4pKSnQ6/W9siBHjx7tlS053cKFC3HnnXfigw8+wBVXXDHgfc1mM8xmszdDIyIiIo3yKjNiMpkwfvx4FBYW9ri9sLAQkyZN6vdx7733Hm6//Xb8+9//xjXXXOPbSImIiCgieZUZAYCZM2filltuwYQJE3DhhRfitddeQ3l5OaZPnw7APcVy5MgRvPXWWwDcgcitt96KF198Ed/73veUrIrVaoXdbg/gj0JERERa5HUwctNNN+HYsWN4+umnUV1djXHjxmHRokXIzc0FAFRXV/foOfLqq6+is7MT9913H+677z7l9ttuuw0LFizw/ycgIiIiTfO6z4ga2GeEiIhIe4LSZ4SIiIgo0BiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqnwKRubNm4f8/HxYLBaMHz8ea9asGfD+q1atwvjx42GxWDBs2DC88sorPg2WiIiIIo/XwcjChQsxY8YMPPbYY9i+fTumTJmCadOmoby8vM/7l5aW4vvf/z6mTJmC7du349FHH8UDDzyADz/80O/BExERkfZJsizL3jxg4sSJOO+88zB//nzltoKCAlx//fWYPXt2r/v/9re/xWeffYbi4mLltunTp2Pnzp3YsGGDR6/Z1NQEu92OgvkF0Fv13gyXiIiIVOI85UTxL4vR2NiI+Pj4fu9n8OZJ29vbsXXrVjzyyCM9bp86dSrWr1/f52M2bNiAqVOn9rjtqquuwj//+U90dHTAaDT2eozD4YDD4VD+39jYCMD9QxEREZE2iPP2YHkPr4KR+vp6OJ1OpKen97g9PT0dNTU1fT6mpqamz/t3dnaivr4emZmZvR4ze/ZsPPXUU71u3z9zvzfDJSIiojBw7Ngx2O32fr/vVTAiSJLU4/+yLPe6bbD793W7MGvWLMycOVP5/4kTJ5Cbm4vy8vIBfxgKnfPPPx+bN29Wexj0Hb4f4YXvR3jh+6GexsZG5OTkICkpacD7eRWMpKSkQK/X98qCHD16tFf2Q8jIyOjz/gaDAcnJyX0+xmw2w2w297rdbrcPOOdEoaPX6/lehBG+H+GF70d44fuhPp1u4PUyXq2mMZlMGD9+PAoLC3vcXlhYiEmTJvX5mAsvvLDX/ZcsWYIJEyb0WS9C2nDfffepPQTqhu9HeOH7EV74foQ/r1fTLFy4ELfccgteeeUVXHjhhXjttdfw+uuvY8+ePcjNzcWsWbNw5MgRvPXWWwDcS3vHjRuHe+65B7/4xS+wYcMGTJ8+He+99x5+8pOfePSaYjXNYNW4REREFD48PX97XTNy00034dixY3j66adRXV2NcePGYdGiRcjNzQUAVFdX9+g5kp+fj0WLFuHBBx/E3//+d2RlZeGll17yOBAB3NM2v//97/ucuiEiIqLw5On52+vMCBEREVEgcW8aIiIiUhWDESIiIlIVgxEa0ECbItbW1uL2229HVlYWYmJicPXVV+PAgQMqjjayrV69Gtdeey2ysrIgSRI++eSTHt+XZRlPPvkksrKyYLVacemll2LPnj3qDDYKDPZ+fPTRR7jqqquQkpICSZKwY8cOVcYZLQZ6Pzo6OvDb3/4WZ555Jmw2G7KysnDrrbeiqqpKvQFTDwxGqF8DbYooyzKuv/56lJSU4NNPP8X27duRm5uLK664Aq2trWoPPSK1trbi7LPPxty5c/v8/rPPPos5c+Zg7ty52Lx5MzIyMnDllVeiubk5xCONDoO9H62trZg8eTKeeeaZEI8sOg30fpw8eRLbtm3D7373O2zbtg0fffQR9u/fjx/+8IcqjJT6JIeZv//973JeXp5sNpvl8847T169erXyvebmZvm+++6ThwwZIlssFnnMmDHyvHnzVBxtZLvgggvk6dOn97htzJgx8iOPPCLv27dPBiAXFRUp3+vs7JSTkpLk119/PdRDjToA5I8//lj5v8vlkjMyMuRnnnlGua2trU222+3yK6+8osIIo8vp70d3paWlMgB5+/btIR1TNBvo/RA2bdokA5DLyspCMygaUFhlRga6EgeABx98EIsXL8Y777yD4uJiPPjgg7j//vvx6aefqjzyyCM2RTx9k0OxKaLYyNBisSjf0+v1MJlMWLt2bUjHSu5+PjU1NT3eL7PZjEsuuaTfTSyJolljYyMkSUJCQoLaQyGE2TTNnDlzcOedd+Kuu+5CQUEBXnjhBWRnZ2P+/PkA3DsA33bbbbj00kuRl5eHu+++G2effTa2bNmi8sgjz2CbIo4ZM0ZpctfQ0ID29nY888wzqKmpQXV1tUqjjl5iywVvNrEkilZtbW145JFHcPPNN7ORZpgIm2BksCtxALjooovw2Wef4ciRI5BlGStWrMD+/ftx1VVXqTHkqNDfpohGoxEffvgh9u/fj6SkJMTExGDlypWYNm0a9Hq9SqMlbzexJIo2HR0d+N///V+4XC7MmzdP7eHQd3zatTcYBrsSB4CXXnoJv/jFLzB06FAYDAbodDr84x//wEUXXaTGkCOaJ5sijh8/Hjt27EBjYyPa29uRmpqKiRMnYsKECWoMOaplZGQAcGdIMjMzldsH2sSSKNp0dHTgxhtvRGlpKZYvX86sSBgJm8yIMNCV3UsvvYSNGzfis88+w9atW/H888/j3nvvxdKlS9UYakTzZlNEu92O1NRUHDhwAFu2bMF1110XyqES3NsuZGRk9Hi/2tvbsWrVqn43sSSKJiIQOXDgAJYuXdrvrvGkjrDJjAx2JX7q1Ck8+uij+Pjjj3HNNdcAAM466yzs2LEDzz33HK644go1hh3RZs6ciVtuuQUTJkxQNkUsLy/H9OnTAQAffPABUlNTkZOTg927d+PXv/41rr/++l5TbRQYLS0tOHjwoPL/0tJS7NixA0lJScjJycGMGTPw5z//GSNHjsTIkSPx5z//GTExMbj55ptVHHXkGuz9OH78OMrLy5VeFvv27QPgzmKJTBYFzkDvR1ZWFm644QZs27YNX3zxBZxOp3KuSUpKgslkUmvYJKi7mKenCy64QP7lL3/Z47aCggL5kUcekRsbG2UA8qJFi3p8/+6775avvPLKUA4zqvz973+Xc3NzZZPJJJ933nnyqlWrlO+9+OKL8tChQ2Wj0Sjn5OTIjz/+uOxwOFQcbWRbsWKFDKDX12233SbLsnt57+9//3s5IyNDNpvN8sUXXyzv3r1b3UFHsMHejzfffLPP7//+979XddyRaqD3Qyyv7utrxYoVag+dZFkOq43yFi5ciFtuuQWvvPKKciX++uuvY8+ePcjNzcWll16K+vp6zJ07F7m5uVi1ahV++ctfYs6cOfjlL3+p9vCJiIjIB2EVjADu9uPPPvssqqurMW7cOPztb3/DxRdfDMBdnDdr1iwsWbIEx48fR25uLu6++248+OCDXDFARESkUWEXjBAREVF0CbvVNERERBRdGIwQERGRqhiMEBERkaoYjBAREZGqGIwQERGRqjQdjEiShE8++UTtYRAREZEfwioYuf3223H99derPQwiIiIKobAKRoiIiCj6hG0wkpeXhxdeeKHHbeeccw6efPJJVcZDREREwRG2wQgRERFFBwYjREREpCoGI0RERKSqsA1GdDodTt/Dr6OjQ6XREBERUbCEbTCSmpqK6upq5f9NTU0oLS1VcUREREQUDGEbjPzP//wP3n77baxZswZFRUW47bbboNfr1R4WERERBZhB7QF053K5YDC4hzRr1iyUlJTgBz/4Aex2O/7whz8wM0JERBSBJPn0wgwVXX311RgxYgTmzp2r9lCIiIgoRMJimqahoQFffvklVq5ciSuuuELt4RAREVEIhcU0zc9//nNs3rwZDz30EK677jq1h0NEREQhFFbTNERERBR9wmKahoiIiKIXgxEiIiJSVUiCkdmzZ+P8889HXFwc0tLScP3112Pfvn097iPLMp588klkZWXBarXi0ksvxZ49e3rc57XXXsOll16K+Ph4SJKEEydO9Hqt/fv347rrrkNKSgri4+MxefJkrFixIpg/HhEREfkhJMHIqlWrcN9992Hjxo0oLCxEZ2cnpk6ditbWVuU+zz77LObMmYO5c+di8+bNyMjIwJVXXonm5mblPidPnsTVV1+NRx99tN/Xuuaaa9DZ2Ynly5dj69atOOecc/CDH/wANTU1Qf0ZiYiIyDeqFLDW1dUhLS0Nq1atwsUXXwxZlpGVlYUZM2bgt7/9LQDA4XAgPT0df/nLX3DPPff0ePzKlStx2WWXoaGhAQkJCcrt9fX1SE1NxerVqzFlyhQAQHNzM+Lj47F06VJcfvnlIfsZiYiIyDOq1Iw0NjYCAJKSkgAApaWlqKmpwdSpU5X7mM1mXHLJJVi/fr3Hz5ucnIyCggK89dZbaG1tRWdnJ1599VWkp6dj/Pjxgf0hiIiIKCBC3mdElmXMnDkTF110EcaNGwcAyhRKenp6j/ump6ejrKzM4+eWJAmFhYW47rrrEBcXB51Oh/T0dCxevLhHBoWIiIjCR8gzI7/61a+wa9cuvPfee72+J0lSj//LstzrtoHIsox7770XaWlpWLNmDTZt2oTrrrsOP/jBD3rsAExEREThI6TByP3334/PPvsMK1aswNChQ5XbMzIyAKBXkenRo0d7ZUsGsnz5cnzxxRd4//33MXnyZJx33nmYN28erFYr/vWvfwXmhyAiIqKACkkwIssyfvWrX+Gjjz7C8uXLkZ+f3+P7+fn5yMjIQGFhoXJbe3s7Vq1ahUmTJnn8OidPngQA6HQ9fyydTgeXy+XHT0BERETBEpKakfvuuw///ve/8emnnyIuLk7JgNjtdlitVkiShBkzZuDPf/4zRo4ciZEjR+LPf/4zYmJicPPNNyvPU1NTg5qaGhw8eBAAsHv3bsTFxSEnJwdJSUm48MILkZiYiNtuuw1PPPEErFYrXn/9dZSWluKaa64JxY9KREREXgrJ0t7+6j7efPNN3H777QDc2ZOnnnoKr776KhoaGjBx4kT8/e9/V4pcAeDJJ5/EU089NeDzbNmyBY899hi2bNmCjo4OnHHGGXjiiScwbdq0gP9cRERE5D9ulEdERESq4t40REREpCoGI0RERKQqBiNERESkKgYjREREpCoGI0RERKQqBiNERESkKgYjREREpCoGI0SkCStXroQkSThx4oTaQyGiAGMwQkRRRZIkfPLJJ2oPg4i6YTBCREREqmIwQkSK//73vzjzzDNhtVqRnJyMK664Aq2trbj99ttx/fXX47nnnkNmZiaSk5Nx3333oaOjQ3nsO++8gwkTJiAuLg4ZGRm4+eabcfToUeX7Yprlyy+/xNlnnw2LxYKJEydi9+7dyn3Kyspw7bXXIjExETabDWeccQYWLVrUY4xbt27FhAkTEBMTg0mTJmHfvn09vj9//nwMHz4cJpMJo0ePxttvv618Ly8vDwDwox/9CJIkKf8nInUxGCEiAEB1dTV+9rOf4ec//zmKi4uxcuVK/PjHP4bYvmrFihU4dOgQVqxYgX/9619YsGABFixYoDy+vb0df/jDH7Bz50588sknKC0tVTaw7O43v/kNnnvuOWzevBlpaWn44Q9/qAQ19913HxwOB1avXo3du3fjL3/5C2JjY3s8/rHHHsPzzz+PLVu2wGAw4Oc//7nyvY8//hi//vWv8dBDD6GoqAj33HMP7rjjDqxYsQIAsHnzZgDuzTWrq6uV/xORymQiIlmWt27dKgOQDx8+3Ot7t912m5ybmyt3dnYqt/30pz+Vb7rppn6fb9OmTTIAubm5WZZlWV6xYoUMQH7//feV+xw7dky2Wq3ywoULZVmW5TPPPFN+8skn+3w+8filS5cqt3355ZcyAPnUqVOyLMvypEmT5F/84hc9HvfTn/5U/v73v6/8H4D88ccf9ztuIgo9ZkaICABw9tln4/LLL8eZZ56Jn/70p3j99dfR0NCgfP+MM86AXq9X/p+ZmdljGmb79u247rrrkJubi7i4OFx66aUAgPLy8h6vc+GFFyr/TkpKwujRo1FcXAwAeOCBB/DHP/4RkydPxu9//3vs2rWr1zjPOuusHmMAoIyjuLgYkydP7nH/yZMnK89PROGJwQgRAQD0ej0KCwvx1VdfYezYsXj55ZcxevRolJaWAgCMRmOP+0uSBJfLBQBobW3F1KlTERsbi3feeQebN2/Gxx9/DMA9fTMYSZIAAHfddRdKSkpwyy23YPfu3ZgwYQJefvnlHvftPg7xODGO7rcJsiz3uo2IwguDESJSSJKEyZMn46mnnsL27dthMpmUoGIg3377Lerr6/HMM89gypQpGDNmTI+sSXcbN25U/t3Q0ID9+/djzJgxym3Z2dmYPn06PvroIzz00EN4/fXXPR5/QUEB1q5d2+O29evXo6CgQPm/0WiE0+n0+DmJKPgMag+AiMLDN998g2XLlmHq1KlIS0vDN998g7q6OhQUFPQ5XdJdTk4OTCYTXn75ZUyfPh1FRUX4wx/+0Od9n376aSQnJyM9PR2PPfYYUlJScP311wMAZsyYgWnTpmHUqFFoaGjA8uXLewQSg/nNb36DG2+8Eeeddx4uv/xyfP755/joo4+wdOlS5T55eXlYtmwZJk+eDLPZjMTERI+fn4iCg5kRIgIAxMfHY/Xq1fj+97+PUaNG4fHHH8fzzz+PadOmDfrY1NRULFiwAB988AHGjh2LZ555Bs8991yf933mmWfw61//GuPHj0d1dTU+++wzmEwmAIDT6cR9992HgoICXH311Rg9ejTmzZvn8c9w/fXX48UXX8Rf//pXnHHGGXj11Vfx5ptvKvUrAPD888+jsLAQ2dnZOPfccz1+biIKHkmWv1u3R0QURCtXrsRll12GhoYGJCQkqD0cIgojzIwQERGRqhiMEBERkao4TUNERESqYmaEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhUxWCEiIiIVMVghIiIiFTFYISIiIhU9f8DKliVHWPeARkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "TECH_ORDER = ['grid',\n",
-    "              'solar',\n",
-    "              'battery'\n",
-    "              ]"
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 56,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def power_by_carrier(n):\n",
-    "    p_by_carrier = n.generators_t.p.T.groupby(\n",
-    "        n.generators.carrier).sum().T \n",
-    "    \n",
-    "    if not n.storage_units.empty:\n",
-    "        sto = n.storage_units_t.p.T.groupby(\n",
-    "            n.storage_units.carrier).sum().T\n",
-    "        p_by_carrier = pd.concat([p_by_carrier, sto], axis=1)\n",
-    "        \n",
-    "    last_cols = [col for col in p_by_carrier.columns if col not in TECH_ORDER]\n",
+    "## Model Version: Net Metering\n",
     "\n",
-    "    p_by_carrier = p_by_carrier[TECH_ORDER+last_cols]\n",
+    "At this moment, the model\n",
     "\n",
-    "    return p_by_carrier"
+    "* uses the sticker price for rooftop solar from NREL's ATB\n",
+    "* applies 50% retail price for net metering\n",
+    "* does NOT include residential storage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 21.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 48.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.3s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 26283 primals, 61324 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.430806</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.329983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
+       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Load\n",
+       "Load Residential    112.9\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_2 = n.objective / n.loads_t.p_set.sum()\n",
+    "model_lcoe_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -946,7 +1147,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -965,8 +1166,7 @@
     "fig, ax = plt.subplots()\n",
     "time = '2018-07-08'\n",
     "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
-    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n",
-    "# n.storage_units_t.p_store.loc[time].plot(ax=ax, legend=False)\n"
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
    ]
   },
   {
@@ -996,7 +1196,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reset the price of net metering\n",
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1005,7 +1215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1014,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1023,14 +1233,14 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 27.82it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 60.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.23s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 20.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 42.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.3s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61323 duals\n",
+      "Solution: 26283 primals, 61324 duals\n",
       "Objective: 4.17e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
@@ -1044,7 +1254,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1055,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1197,7 +1407,7 @@
        "Load      -                            0.000000 -417035.914525           NaN  "
       ]
      },
-     "execution_count": 61,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1215,25 +1425,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Load\n",
-       "Load Residential    100.831578\n",
-       "dtype: float64"
+       "100.83157842499818"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_lcoe_2 = n.objective / n.loads_t.p_set.sum()\n",
-    "model_lcoe_2"
+    "model_lcoe_3 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_3"
    ]
   },
   {
@@ -1245,7 +1453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1254,7 +1462,7 @@
        "10.689479185119586"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1272,7 +1480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1281,7 +1489,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1318,7 +1526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1331,7 +1539,7 @@
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1342,7 +1550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1351,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1364,7 +1572,7 @@
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1375,7 +1583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1384,15 +1592,15 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 27.96it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 62.62it/s]\n",
-      "INFO:linopy.io: Writing time: 0.23s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 22.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 56.54it/s]\n",
+      "INFO:linopy.io: Writing time: 0.3s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61323 duals\n",
-      "Objective: 3.75e+05\n",
+      "Solution: 26283 primals, 61324 duals\n",
+      "Objective: 3.77e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
@@ -1405,7 +1613,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1416,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1469,48 +1677,48 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
        "      <th>grid</th>\n",
-       "      <td>1.322049</td>\n",
+       "      <td>1.331491</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2208.701032</td>\n",
+       "      <td>2358.485524</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>2208.701032</td>\n",
+       "      <td>2358.485524</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.190715</td>\n",
+       "      <td>0.202205</td>\n",
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>249362.346567</td>\n",
-       "      <td>249362.346567</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>266273.015698</td>\n",
        "      <td>112.900000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>net metering</th>\n",
-       "      <td>4.032169</td>\n",
+       "      <td>2.505756</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>4484.706259</td>\n",
-       "      <td>-4484.706259</td>\n",
+       "      <td>2357.900052</td>\n",
+       "      <td>-2357.900052</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.126967</td>\n",
+       "      <td>0.107419</td>\n",
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-253161.668329</td>\n",
-       "      <td>-253161.668329</td>\n",
+       "      <td>-133103.457958</td>\n",
+       "      <td>-133103.457958</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>solar</th>\n",
-       "      <td>4.352297</td>\n",
+       "      <td>2.807000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6411.970576</td>\n",
+       "      <td>4135.379878</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>6411.970576</td>\n",
+       "      <td>4135.379878</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.168178</td>\n",
-       "      <td>1.797677e-09</td>\n",
-       "      <td>378433.000344</td>\n",
+       "      <td>1.159408e-09</td>\n",
+       "      <td>244069.150984</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>378433.000344</td>\n",
-       "      <td>59.019766</td>\n",
+       "      <td>255898.612865</td>\n",
+       "      <td>61.880316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Load</th>\n",
@@ -1525,7 +1733,7 @@
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-374633.678582</td>\n",
+       "      <td>-389068.170605</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1534,31 +1742,31 @@
       ],
       "text/plain": [
        "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
-       "Generator grid                  1.322049                 0.0  2208.701032   \n",
-       "          net metering          4.032169                 0.0     0.000000   \n",
-       "          solar                 4.352297                 0.0  6411.970576   \n",
+       "Generator grid                  1.331491                 0.0  2358.485524   \n",
+       "          net metering          2.505756                 0.0     0.000000   \n",
+       "          solar                 2.807000                 0.0  4135.379878   \n",
        "Load      -                     0.000000                 0.0     0.000000   \n",
        "\n",
        "                         Withdrawal     Dispatch  Transmission  \\\n",
-       "Generator grid             0.000000  2208.701032           0.0   \n",
-       "          net metering  4484.706259 -4484.706259           0.0   \n",
-       "          solar            0.000000  6411.970576           0.0   \n",
+       "Generator grid             0.000000  2358.485524           0.0   \n",
+       "          net metering  2357.900052 -2357.900052           0.0   \n",
+       "          solar            0.000000  4135.379878           0.0   \n",
        "Load      -             4135.965350 -4135.965350           0.0   \n",
        "\n",
        "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
-       "Generator grid                 0.190715  0.000000e+00             0.000000   \n",
-       "          net metering         0.126967  0.000000e+00             0.000000   \n",
-       "          solar                0.168178  1.797677e-09        378433.000344   \n",
+       "Generator grid                 0.202205  0.000000e+00             0.000000   \n",
+       "          net metering         0.107419  0.000000e+00             0.000000   \n",
+       "          solar                0.168178  1.159408e-09        244069.150984   \n",
        "Load      -                         NaN  0.000000e+00             0.000000   \n",
        "\n",
        "                        Operational Expenditure        Revenue  Market Value  \n",
-       "Generator grid                    249362.346567  249362.346567    112.900000  \n",
-       "          net metering           -253161.668329 -253161.668329           NaN  \n",
-       "          solar                        0.000000  378433.000344     59.019766  \n",
-       "Load      -                            0.000000 -374633.678582           NaN  "
+       "Generator grid                    266273.015698  266273.015698    112.900000  \n",
+       "          net metering           -133103.457958 -133103.457958           NaN  \n",
+       "          solar                        0.000000  255898.612865     61.880316  \n",
+       "Load      -                            0.000000 -389068.170605           NaN  "
       ]
      },
-     "execution_count": 70,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1569,27 +1777,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "90.57950125390316"
+       "91.20934940819072"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.objective/n.loads_t.p_set.sum().values[0]"
+    "model_lcoe_4 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1598,13 +1807,13 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1629,35 +1838,35 @@
     "At this moment, the model\n",
     "\n",
     "* reduces the price for rooftop solar by applying federal tax credits.\n",
-    "* Applies 90% retail price for net metering\n",
+    "* Applies 99% retail price for net metering\n",
     "* does NOT include residential storage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
-    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.99"
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.00"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "Generator\n",
-       "ResPV                         0.000\n",
-       "Net metering Residential    111.771\n",
-       "Evergy Import               112.900\n",
+       "ResPV                         0.0\n",
+       "Net metering Residential    112.9\n",
+       "Evergy Import               112.9\n",
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1668,7 +1877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1677,7 +1886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1686,15 +1895,15 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 27.20it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 57.13it/s]\n",
-      "INFO:linopy.io: Writing time: 0.25s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 21.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 55.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.3s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 26283 primals, 61324 duals\n",
-      "Objective: 2.47e+05\n",
+      "Objective: 2.44e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
@@ -1707,7 +1916,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1718,7 +1927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1782,7 +1991,7 @@
        "      <td>0.000000</td>\n",
        "      <td>266273.015698</td>\n",
        "      <td>266273.015698</td>\n",
-       "      <td>112.900000</td>\n",
+       "      <td>112.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>net metering</th>\n",
@@ -1795,8 +2004,8 @@
        "      <td>0.107419</td>\n",
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-263544.846758</td>\n",
-       "      <td>-263544.846758</td>\n",
+       "      <td>-266206.915917</td>\n",
+       "      <td>-266206.915917</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1811,8 +2020,8 @@
        "      <td>1.159408e-09</td>\n",
        "      <td>244069.150984</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>462664.672697</td>\n",
-       "      <td>111.879606</td>\n",
+       "      <td>466884.388204</td>\n",
+       "      <td>112.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Load</th>\n",
@@ -1827,7 +2036,7 @@
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-465392.841637</td>\n",
+       "      <td>-466950.487985</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1854,13 +2063,13 @@
        "Load      -                         NaN  0.000000e+00             0.000000   \n",
        "\n",
        "                        Operational Expenditure        Revenue  Market Value  \n",
-       "Generator grid                    266273.015698  266273.015698    112.900000  \n",
-       "          net metering           -263544.846758 -263544.846758           NaN  \n",
-       "          solar                        0.000000  462664.672697    111.879606  \n",
-       "Load      -                            0.000000 -465392.841637           NaN  "
+       "Generator grid                    266273.015698  266273.015698         112.9  \n",
+       "          net metering           -266206.915917 -266206.915917           NaN  \n",
+       "          solar                        0.000000  466884.388204         112.9  \n",
+       "Load      -                            0.000000 -466950.487985           NaN  "
       ]
      },
-     "execution_count": 94,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1871,30 +2080,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Load\n",
-       "Load Residential    59.671032\n",
-       "dtype: float64"
+       "59.02739266934207"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_lcoe_3 = n.objective / n.loads_t.p_set.sum()\n",
-    "model_lcoe_3"
+    "model_lcoe_5 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1903,7 +2110,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1925,61 +2132,6 @@
     "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rates = np.linspace(1e-4, 0.1, 10000)\n",
-    "y1 = annuity(rates, 20)\n",
-    "y2 = y1 / annuity(0.1,20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x28aff24e440>]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(rates, y2)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 507b2b49969ba7678eedbaeaf453ccf59ea1a8b5 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 30 Sep 2024 14:29:01 -0400
Subject: [PATCH 45/52] adds simulations to pypsa model

---
 notebooks/10-pypsa-model.ipynb | 7661 +++++++++++++++++++++++++++++++-
 1 file changed, 7487 insertions(+), 174 deletions(-)

diff --git a/notebooks/10-pypsa-model.ipynb b/notebooks/10-pypsa-model.ipynb
index ef8bf60..1aec720 100644
--- a/notebooks/10-pypsa-model.ipynb
+++ b/notebooks/10-pypsa-model.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -235,7 +235,7 @@
        "2018-01-01 04:00:00              90    0    0    0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -334,7 +334,7 @@
        "Residential Battery Storage  78943.789878  3.157752e+06"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,28 +363,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.05541531489055132"
+       "0.09439292574325567"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "annuity_adj = annuity(0.01, 20)\n",
+    "annuity_adj = annuity(0.07, 20)\n",
     "annuity_adj"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "      <th>annualized_cost</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DistributedWind</th>\n",
+       "      <td>35912.100000</td>\n",
+       "      <td>5.678577e+06</td>\n",
+       "      <td>571929.570926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ResPV</th>\n",
+       "      <td>28108.825392</td>\n",
+       "      <td>2.630889e+06</td>\n",
+       "      <td>276446.116985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>78943.789878</td>\n",
+       "      <td>3.157752e+06</td>\n",
+       "      <td>377013.201712</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Fixed O&M           OCC  annualized_cost\n",
+       "technology                                                              \n",
+       "DistributedWind              35912.100000  5.678577e+06    571929.570926\n",
+       "ResPV                        28108.825392  2.630889e+06    276446.116985\n",
+       "Residential Battery Storage  78943.789878  3.157752e+06    377013.201712"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "costs = costs.assign(annualized_cost = costs['OCC']*annuity_adj + costs['Fixed O&M'])\n",
+    "costs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -392,9 +470,9 @@
      "output_type": "stream",
      "text": [
       "ResPV\n",
-      "173900.3569535663\n",
+      "276446.11698458303\n",
       "Residential Battery Storage\n",
-      "253931.58886784827\n"
+      "377013.20171200635\n"
      ]
     }
    ],
@@ -404,7 +482,7 @@
     "        pass\n",
     "    else:\n",
     "        print(generator)\n",
-    "        annualized_cost = costs.at[generator, 'OCC']*annuity_adj + costs.at[generator,'Fixed O&M']\n",
+    "        annualized_cost = costs.at[generator, 'annualized_cost']\n",
     "        print(annualized_cost)\n",
     "        \n",
     "        if generator=='ResPV':\n",
@@ -420,15 +498,15 @@
     "                    )\n",
     "        elif generator=='Residential Battery Storage':\n",
     "            pass\n",
-    "            # n.add(class_name=\"StorageUnit\",\n",
-    "            #         name=generator,\n",
-    "            #         bus=bus_name,\n",
-    "            #         carrier=\"battery\",\n",
-    "            #         capital_cost=annualized_cost,  # $/kW\n",
-    "            #         p_nom_extendable=True,\n",
-    "            #         max_hours=2.5,\n",
-    "            #         cyclic_state_of_charge=False,\n",
-    "            #         )\n",
+    "            n.add(class_name=\"StorageUnit\",\n",
+    "                    name=generator,\n",
+    "                    bus=bus_name,\n",
+    "                    carrier=\"battery\",\n",
+    "                    capital_cost=annualized_cost,  # $/kW\n",
+    "                    p_nom_extendable=True,\n",
+    "                    max_hours=2.5,\n",
+    "                    cyclic_state_of_charge=False,\n",
+    "                    )\n",
     "        \n",
     "        "
    ]
@@ -442,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -451,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -674,7 +752,7 @@
        "[3 rows x 34 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -705,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -714,19 +792,19 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 20.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 41.48it/s]\n",
-      "INFO:linopy.io: Writing time: 0.31s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 29.39it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.58s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61324 duals\n",
+      "Solution: 52564 primals, 122645 duals\n",
       "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
      ]
     },
     {
@@ -735,7 +813,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -746,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -797,131 +875,7213 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Generator</th>\n",
-       "      <th>grid</th>\n",
-       "      <td>1.430806</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4135.96535</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>4135.96535</td>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.430806</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.329983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
+       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_1 = n.statistics().copy()\n",
+    "results_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StorageUnit\n",
+       "Residential Battery Storage   -0.0\n",
+       "Name: p_nom_opt, dtype: float64"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.storage_units.p_nom_opt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 7: Calculate the LCOE from the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "112.9000000000018"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_1 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the model has an LCOE of $112.9/MWh, which makes sense because it only uses electricity purchased from the grid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 8: Plot some data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Tax Credit Simulation\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* does NOT pay for net metering\n",
+    "\n",
+    "The purpose of this simulation is to test the effect of price reductions on solar penetration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.10it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.45it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.55it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.92it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.25it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.82it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.68it/s]\n",
+      "INFO:linopy.io: Writing time: 0.6s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 80.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.6s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 27.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 74.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.63s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 81.62it/s]\n",
+      "INFO:linopy.io: Writing time: 0.61s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.59e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.81it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.23it/s]\n",
+      "INFO:linopy.io: Writing time: 0.64s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.51e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.51it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.92s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.30e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.17e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.03e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.70it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.86e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.30it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.07e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.38it/s]\n",
+      "INFO:linopy.io: Writing time: 0.64s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {'discount':[],\n",
+    "        'solar_capacity':[]}\n",
+    "\n",
+    "discounts = np.linspace(0, 1, 20)\n",
+    "\n",
+    "for discount in discounts:\n",
+    "        n.generators.loc['ResPV', 'capital_cost'] = costs.at['ResPV','annualized_cost'] * (1-discount)\n",
+    "        \n",
+    "        n.optimize(solver_name='highs')\n",
+    "        \n",
+    "        data['discount'].append(discount)\n",
+    "        data['solar_capacity'].append(np.abs(n.generators.p_nom_opt['ResPV']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>discount</th>\n",
+       "      <th>solar_capacity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.052632</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.105263</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.210526</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.263158</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.315789</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.421053</td>\n",
+       "      <td>0.366176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.479037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.526316</td>\n",
+       "      <td>0.593604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.578947</td>\n",
+       "      <td>0.711923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.631579</td>\n",
+       "      <td>0.826754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>0.944494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.736842</td>\n",
+       "      <td>1.074690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.789474</td>\n",
+       "      <td>1.245800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.842105</td>\n",
+       "      <td>1.484248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>1.944705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.947368</td>\n",
+       "      <td>2.807000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.807000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    discount  solar_capacity\n",
+       "0   0.000000        0.000000\n",
+       "1   0.052632        0.000000\n",
+       "2   0.105263        0.000000\n",
+       "3   0.157895        0.000000\n",
+       "4   0.210526        0.000000\n",
+       "5   0.263158        0.000000\n",
+       "6   0.315789        0.000000\n",
+       "7   0.368421        0.000000\n",
+       "8   0.421053        0.366176\n",
+       "9   0.473684        0.479037\n",
+       "10  0.526316        0.593604\n",
+       "11  0.578947        0.711923\n",
+       "12  0.631579        0.826754\n",
+       "13  0.684211        0.944494\n",
+       "14  0.736842        1.074690\n",
+       "15  0.789474        1.245800\n",
+       "16  0.842105        1.484248\n",
+       "17  0.894737        1.944705\n",
+       "18  0.947368        2.807000\n",
+       "19  1.000000        2.807000"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df = pd.DataFrame(data)\n",
+    "results_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df = results_df.assign(solar_penetration=results_df['solar_capacity'] / n.generators.p_nom_max.ResPV)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "results_df.plot(ax=ax, x='discount', y='solar_penetration')\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.65it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 27.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.28it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.53it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.41it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 52.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.84it/s]\n",
+      "INFO:linopy.io: Writing time: 0.9s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.60it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.61it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.93s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 52.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.50it/s]\n",
+      "INFO:linopy.io: Writing time: 0.96s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.93s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.08it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.92s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.13it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.92it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.03it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.38it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.50it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.76it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.78it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.78it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 77.24it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 80.16it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.31it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.23it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.82it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.16it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.00it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.88it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.97it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.10it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.31it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.43it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.32it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.94it/s]\n",
+      "INFO:linopy.io: Writing time: 0.85s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.88it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.13it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.23it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.46it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.76it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.76it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.93it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.47it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.92it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.55it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.15it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.97it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.35it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 74.32it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.52it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.37it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.62it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 77.00it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.81it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 52.30it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.62it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.37it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.50it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.40it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.87it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.38it/s]\n",
+      "INFO:linopy.io: Writing time: 1.22s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.03it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.50it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.39it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.17it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.38it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.11it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.17it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.03it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.41it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.56it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.96it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.92it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.90it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.18it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.17it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.49it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.36it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.00it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 79.47it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.39it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.41it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.18it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.22it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.18it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.89it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.20it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.25it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.70it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.29it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.95it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.53it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.47it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.81it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.20it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.28it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.77it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.29it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.23it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.96it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.20it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.30it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.51it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.85it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.08it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.68it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.41it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.49it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.56it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.13it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.55it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.76it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.63e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.62e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.59e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.59e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.25it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.74it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.44it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.94it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.50it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.15it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.29it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.00it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.05it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.56e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.28it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.56e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.84it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.55e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.54e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.52e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.77it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.10it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.22e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.55it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.51e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.51e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.24it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.13it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.43it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.13it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.78it/s]\n",
+      "INFO:linopy.io: Writing time: 0.85s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.48e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.47e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.26it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.46e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.30it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.45e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.70it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.44e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.62it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.47it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.42e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.37it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.40e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.35e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.55it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.96it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.10e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.88it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.96e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.82e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.68e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.89it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.40e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.40e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.43it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.39e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.00it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.38e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.97it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.36e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.10it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.93s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.34e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.05it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.32e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.68it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.30e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.58it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.27e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.87it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.22e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.66it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.11e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.32it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.97e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.08it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.83e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.82it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.69e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.55e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.39it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.27e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.16it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.30e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.50it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.29e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.27it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.28e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.27e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.26e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.75it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.45it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.22e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.95it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.20e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.82it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.18e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 30.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.14e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.57it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.98e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.96it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.84e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.77it/s]\n",
+      "INFO:linopy.io: Writing time: 0.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.70e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.71it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.56e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.25it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.42e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.03it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.28e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.44it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.14e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.00e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.84it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.86e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.66it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.17e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.05it/s]\n",
+      "INFO:linopy.io: Writing time: 0.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.16e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.14e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.23it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.13e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.11e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.89it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.78it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.24it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.05e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.01e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.38it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.27it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.95e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.85e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.60it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.71e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.44it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.17it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.43e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.96it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.29e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.22it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.15e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.96it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.94it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.01e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.65it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.87e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.73e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.51it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.59e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.43it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.45e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.39it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.03e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.01e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.98e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.81it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.95e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.92e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.88e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.31it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.82e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.72e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.68it/s]\n",
+      "INFO:linopy.io: Writing time: 1.81s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.31it/s]\n",
+      "INFO:linopy.io: Writing time: 2.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.44e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 18.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.30e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 28.45it/s]\n",
+      "INFO:linopy.io: Writing time: 1.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.16e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.85it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.02e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.88e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.60it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.74e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.21it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.90it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.60e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.46e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.32e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.08it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.18e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.04e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.86e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.83e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.79e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.31it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.75e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.10it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.77it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.68e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.95it/s]\n",
+      "INFO:linopy.io: Writing time: 0.96s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.59e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.46e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.08it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.32e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.18e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.31it/s]\n",
+      "INFO:linopy.io: Writing time: 0.67s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.04e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.90e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.15it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.76e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.62e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.03it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.93it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.47e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.51it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.19e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.18it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.05e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.88it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.91e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.03it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.77e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.94it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.63e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.32it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.61e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.08it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.55e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.46e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.74it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.16it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.19e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.05e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.89it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.69it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.91e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.15it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.70it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.77e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.36it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.63e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.21it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 74.21it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.35e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.21e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.31it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.07e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.44it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 35.05it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.93e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  7.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.73it/s]\n",
+      "INFO:linopy.io: Writing time: 2.4s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.79e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 18.11it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.89it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 12.30it/s]\n",
+      "INFO:linopy.io: Writing time: 2.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.51e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.13it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.68it/s]\n",
+      "INFO:linopy.io: Writing time: 1.63s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.23e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.53it/s]\n",
+      "INFO:linopy.io: Writing time: 1.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.03it/s]\n",
+      "INFO:linopy.io: Writing time: 1.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.20e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.68it/s]\n",
+      "INFO:linopy.io: Writing time: 1.59s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.06e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.10it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.65it/s]\n",
+      "INFO:linopy.io: Writing time: 1.58s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.92e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.99it/s]\n",
+      "INFO:linopy.io: Writing time: 1.58s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.78e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.77it/s]\n",
+      "INFO:linopy.io: Writing time: 1.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.66s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.36e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.64it/s]\n",
+      "INFO:linopy.io: Writing time: 1.63s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.22e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.53it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.92s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.19it/s]\n",
+      "INFO:linopy.io: Writing time: 2.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.94e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.81it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.49it/s]\n",
+      "INFO:linopy.io: Writing time: 1.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.80e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.86it/s]\n",
+      "INFO:linopy.io: Writing time: 1.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.05it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.05it/s]\n",
+      "INFO:linopy.io: Writing time: 1.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.52e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.30it/s]\n",
+      "INFO:linopy.io: Writing time: 1.61s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.38e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 20.70it/s]\n",
+      "INFO:linopy.io: Writing time: 1.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.19it/s]\n",
+      "INFO:linopy.io: Writing time: 1.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.10e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.41it/s]\n",
+      "INFO:linopy.io: Writing time: 1.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 9.58e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.51it/s]\n",
+      "INFO:linopy.io: Writing time: 1.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 8.17e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 20.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.58s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.07e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.52it/s]\n",
+      "INFO:linopy.io: Writing time: 1.9s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.93e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.79e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.95it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.51e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.56it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.21it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.24it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.23e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.8s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.09e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.95e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.96it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.30it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.81e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.53e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.61it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.39e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.15it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.25e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.11e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 9.70e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.55it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 8.29e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 6.89e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.10it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.70it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 5.49e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.53it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.65it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.09e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.58it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.52e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.03it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.03it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.38e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.10e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.35it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.96e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.06it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.82e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.68e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.54e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.76it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.40e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.26e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.12e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.28it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 9.81e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.93it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 8.41e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.23it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 7.01e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.85it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 5.61e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.87it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.58it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.21e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.22it/s]\n",
+      "INFO:linopy.io: Writing time: 0.84s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.81e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.10it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.64it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.41e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 6.61e+01\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {'discount':[],\n",
+    "        'percent_retail_price':[],\n",
+    "        'solar_capacity':[],\n",
+    "        'battery_capacity':[],\n",
+    "        'objective_value':[],\n",
+    "        }\n",
+    "\n",
+    "discounts = np.linspace(0, 1, 20)\n",
+    "retail_prices = np.linspace(0, 1, 20)\n",
+    "for discount in discounts:\n",
+    "    for pct_retail in retail_prices:\n",
+    "        n.generators.loc['ResPV', 'capital_cost'] = costs.at['ResPV','annualized_cost'] * (1-discount)\n",
+    "        n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*pct_retail\n",
+    "        n.optimize(solver_name='highs')\n",
+    "        \n",
+    "        data['discount'].append(discount)\n",
+    "        data['solar_capacity'].append(np.abs(n.generators.p_nom_opt['ResPV']))\n",
+    "        data['battery_capacity'].append(np.abs(n.storage_units.p_nom_opt['Residential Battery Storage']))\n",
+    "        data['percent_retail_price'].append(pct_retail)\n",
+    "        data['objective_value'].append(n.objective)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>discount</th>\n",
+       "      <th>percent_retail_price</th>\n",
+       "      <th>solar_capacity</th>\n",
+       "      <th>battery_capacity</th>\n",
+       "      <th>objective_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.157895</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>...</th>\n",
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+       "      <th>395</th>\n",
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+       "      <td>0.0</td>\n",
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+       "      <td>2.807</td>\n",
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+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.894737</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>28087.880403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.947368</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14076.990092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66.099781</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>400 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     discount  percent_retail_price  solar_capacity  battery_capacity  \\\n",
+       "0         0.0              0.000000           0.000               0.0   \n",
+       "1         0.0              0.052632           0.000               0.0   \n",
+       "2         0.0              0.105263           0.000               0.0   \n",
+       "3         0.0              0.157895           0.000               0.0   \n",
+       "4         0.0              0.210526           0.000               0.0   \n",
+       "..        ...                   ...             ...               ...   \n",
+       "395       1.0              0.789474           2.807               0.0   \n",
+       "396       1.0              0.842105           2.807               0.0   \n",
+       "397       1.0              0.894737           2.807               0.0   \n",
+       "398       1.0              0.947368           2.807               0.0   \n",
+       "399       1.0              1.000000           2.807               0.0   \n",
+       "\n",
+       "     objective_value  \n",
+       "0      466950.487985  \n",
+       "1      466950.487985  \n",
+       "2      466950.487985  \n",
+       "3      466950.487985  \n",
+       "4      466950.487985  \n",
+       "..               ...  \n",
+       "395     56109.661027  \n",
+       "396     42098.770715  \n",
+       "397     28087.880403  \n",
+       "398     14076.990092  \n",
+       "399        66.099781  \n",
+       "\n",
+       "[400 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df_large = pd.DataFrame(data)\n",
+    "results_df_large"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df_large = results_df_large.assign(solar_penetration=results_df_large['solar_capacity'] / n.generators.p_nom_max.ResPV)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 100.0)"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large[['discount','solar_penetration','percent_retail_price']]*100,\n",
+    "            x='discount',\n",
+    "            y='solar_penetration',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis')\n",
+    "\n",
+    "ax.set_ylabel(\"Affordable Solar Capacity \\n (% of Total Electricity)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "ax.set_ylim(0,1.05*100)\n",
+    "ax.set_xlim(0,1.0*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>discount</th>\n",
+       "      <th>percent_retail_price</th>\n",
+       "      <th>solar_capacity</th>\n",
+       "      <th>battery_capacity</th>\n",
+       "      <th>objective_value</th>\n",
+       "      <th>solar_penetration</th>\n",
+       "      <th>lcoe</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.0</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "      <td>400.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.218476</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.329983</td>\n",
+       "      <td>381557.144405</td>\n",
+       "      <td>0.434085</td>\n",
+       "      <td>0.092253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.303869</td>\n",
+       "      <td>0.303869</td>\n",
+       "      <td>1.240130</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>117928.563407</td>\n",
+       "      <td>0.441799</td>\n",
+       "      <td>0.028513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>466950.487985</td>\n",
-       "      <td>466950.487985</td>\n",
-       "      <td>112.9</td>\n",
+       "      <td>66.099781</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Load</th>\n",
-       "      <th>-</th>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.250000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>4135.96535</td>\n",
-       "      <td>-4135.96535</td>\n",
+       "      <td>317253.149899</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.076706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.758327</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>449455.279045</td>\n",
+       "      <td>0.270156</td>\n",
+       "      <td>0.108670</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>2.807000</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.112900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.807000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-466950.487985</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.112900</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
-       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
-       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
-       "\n",
-       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
-       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
-       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "         discount  percent_retail_price  solar_capacity  battery_capacity  \\\n",
+       "count  400.000000            400.000000      400.000000             400.0   \n",
+       "mean     0.500000              0.500000        1.218476               0.0   \n",
+       "std      0.303869              0.303869        1.240130               0.0   \n",
+       "min      0.000000              0.000000        0.000000               0.0   \n",
+       "25%      0.250000              0.250000        0.000000               0.0   \n",
+       "50%      0.500000              0.500000        0.758327               0.0   \n",
+       "75%      0.750000              0.750000        2.807000               0.0   \n",
+       "max      1.000000              1.000000        2.807000               0.0   \n",
        "\n",
-       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
-       "Generator grid                  0.0            466950.487985  466950.487985   \n",
-       "Load      -                     0.0                 0.000000 -466950.487985   \n",
-       "\n",
-       "                Market Value  \n",
-       "Generator grid         112.9  \n",
-       "Load      -              NaN  "
+       "       objective_value  solar_penetration        lcoe  \n",
+       "count       400.000000         400.000000  400.000000  \n",
+       "mean     381557.144405           0.434085    0.092253  \n",
+       "std      117928.563407           0.441799    0.028513  \n",
+       "min          66.099781           0.000000    0.000016  \n",
+       "25%      317253.149899           0.000000    0.076706  \n",
+       "50%      449455.279045           0.270156    0.108670  \n",
+       "75%      466950.487985           1.000000    0.112900  \n",
+       "max      466950.487985           1.000000    0.112900  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "results_1 = n.statistics().copy()\n",
-    "results_1"
+    "results_df_large.describe()"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 114,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Step 7: Calculate the LCOE from the model"
+    "results_df_large = results_df_large.assign(lcoe=results_df_large['objective_value'] / n.loads_t.p_set.sum().values[0] / 1000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "112.9000000000018"
+       "(0.0, 1.0)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "model_lcoe_1 = n.objective / n.loads_t.p_set.sum().values[0]\n",
-    "model_lcoe_1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We observe that the model has an LCOE of $112.9/MWh, which makes sense because it only uses electricity purchased from the grid."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Step 8: Plot some data"
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large,\n",
+    "            x='discount',\n",
+    "            y='lcoe',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis'\n",
+    "            )\n",
+    "\n",
+    "ax.set_ylabel(\"Retail Price of Electricity \\n ($ / kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "# ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='snapshot'>"
+       "<seaborn.axisgrid.PairGrid at 0x2aa92e7ab60>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1750x1750 with 35 Axes>"
       ]
      },
      "metadata": {},
@@ -929,12 +8089,23 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "time = '2018-07-08'\n",
-    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
-    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
+    "sb.pairplot(results_df_large, corner=True)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -944,7 +8115,7 @@
     "At this moment, the model\n",
     "\n",
     "* uses the sticker price for rooftop solar from NREL's ATB\n",
-    "* applies 50% retail price for net metering\n",
+    "* applies 100% retail price for net metering\n",
     "* does NOT include residential storage"
    ]
   },
@@ -968,19 +8139,19 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 21.58it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 48.14it/s]\n",
-      "INFO:linopy.io: Writing time: 0.3s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.10it/s]\n",
+      "INFO:linopy.io: Writing time: 0.62s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61324 duals\n",
+      "Solution: 52564 primals, 122645 duals\n",
       "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
      ]
     },
     {
@@ -1217,15 +8388,164 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>attribute</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>control</th>\n",
+       "      <th>type</th>\n",
+       "      <th>p_nom</th>\n",
+       "      <th>p_nom_mod</th>\n",
+       "      <th>p_nom_extendable</th>\n",
+       "      <th>p_nom_min</th>\n",
+       "      <th>p_nom_max</th>\n",
+       "      <th>p_min_pu</th>\n",
+       "      <th>p_max_pu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>state_of_charge_initial_per_period</th>\n",
+       "      <th>state_of_charge_set</th>\n",
+       "      <th>cyclic_state_of_charge</th>\n",
+       "      <th>cyclic_state_of_charge_per_period</th>\n",
+       "      <th>max_hours</th>\n",
+       "      <th>efficiency_store</th>\n",
+       "      <th>efficiency_dispatch</th>\n",
+       "      <th>standing_loss</th>\n",
+       "      <th>inflow</th>\n",
+       "      <th>p_nom_opt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>StorageUnit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "attribute                            bus control type  p_nom  p_nom_mod  \\\n",
+       "StorageUnit                                                               \n",
+       "Residential Battery Storage  Residential      PQ         0.0        0.0   \n",
+       "\n",
+       "attribute                    p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
+       "StorageUnit                                                                     \n",
+       "Residential Battery Storage              True        0.0        inf      -1.0   \n",
+       "\n",
+       "attribute                    p_max_pu  ...  \\\n",
+       "StorageUnit                            ...   \n",
+       "Residential Battery Storage       1.0  ...   \n",
+       "\n",
+       "attribute                    state_of_charge_initial_per_period  \\\n",
+       "StorageUnit                                                       \n",
+       "Residential Battery Storage                               False   \n",
+       "\n",
+       "attribute                    state_of_charge_set  cyclic_state_of_charge  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                  NaN                   False   \n",
+       "\n",
+       "attribute                   cyclic_state_of_charge_per_period  max_hours  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                              True        2.5   \n",
+       "\n",
+       "attribute                    efficiency_store  efficiency_dispatch  \\\n",
+       "StorageUnit                                                          \n",
+       "Residential Battery Storage               1.0                  1.0   \n",
+       "\n",
+       "attribute                    standing_loss  inflow  p_nom_opt  \n",
+       "StorageUnit                                                    \n",
+       "Residential Battery Storage            0.0     0.0       -0.0  \n",
+       "\n",
+       "[1 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "n.generators.loc['ResPV', 'capital_cost'] *= (1-rretc_credit)"
+    "n.storage_units"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 31,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['ResPV', 'capital_cost'] *= (1-rretc_credit)\n",
+    "n.storage_units.loc['Residential Battery Storage', 'capital_cost'] *= (1-rretc_credit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -1233,19 +8553,19 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 20.73it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 42.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.3s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61324 duals\n",
+      "Solution: 52564 primals, 122645 duals\n",
       "Objective: 4.17e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
      ]
     },
     {
@@ -1254,7 +8574,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1265,7 +8585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1407,7 +8727,7 @@
        "Load      -                            0.000000 -417035.914525           NaN  "
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1425,7 +8745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1434,7 +8754,7 @@
        "100.83157842499818"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1453,7 +8773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1462,7 +8782,7 @@
        "10.689479185119586"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1480,7 +8800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1489,7 +8809,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1526,7 +8846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1539,7 +8859,7 @@
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1550,7 +8870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1559,7 +8879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1572,7 +8892,7 @@
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1583,7 +8903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1592,19 +8912,19 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 22.14it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 56.54it/s]\n",
-      "INFO:linopy.io: Writing time: 0.3s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61324 duals\n",
+      "Solution: 52564 primals, 122645 duals\n",
       "Objective: 3.77e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
      ]
     },
     {
@@ -1613,7 +8933,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1624,7 +8944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1766,7 +9086,7 @@
        "Load      -                            0.000000 -389068.170605           NaN  "
       ]
      },
-     "execution_count": 56,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1777,7 +9097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1786,7 +9106,7 @@
        "91.20934940819072"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1798,7 +9118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1807,7 +9127,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1844,7 +9164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1853,7 +9173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1866,7 +9186,7 @@
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1877,7 +9197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1886,7 +9206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1895,19 +9215,19 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 5/5 [00:00<00:00, 21.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 2/2 [00:00<00:00, 55.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.3s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.62it/s]\n",
+      "INFO:linopy.io: Writing time: 0.63s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
-      "Solution: 26283 primals, 61324 duals\n",
+      "Solution: 52564 primals, 122645 duals\n",
       "Objective: 2.44e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper were not assigned to the network.\n"
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
      ]
     },
     {
@@ -1916,7 +9236,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1927,7 +9247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2069,7 +9389,7 @@
        "Load      -                            0.000000 -466950.487985           NaN  "
       ]
      },
-     "execution_count": 63,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2080,7 +9400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -2089,7 +9409,7 @@
        "59.02739266934207"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2101,7 +9421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -2110,7 +9430,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -2131,13 +9451,6 @@
     "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
     "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From a0b31bcb6e7c39dbc1c8106f098d5ec5214997b3 Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 28 Oct 2024 13:25:55 -0400
Subject: [PATCH 46/52] updates pypsa model

---
 notebooks/09-electricity-use.ipynb         |  355 +-
 notebooks/10-pypsa-model.ipynb             | 6407 +++++++-------------
 notebooks/gis_notebooks/power_plants.ipynb |  115 +-
 3 files changed, 2271 insertions(+), 4606 deletions(-)

diff --git a/notebooks/09-electricity-use.ipynb b/notebooks/09-electricity-use.ipynb
index 0a4a156..49950ff 100644
--- a/notebooks/09-electricity-use.ipynb
+++ b/notebooks/09-electricity-use.ipynb
@@ -23,192 +23,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>multi-family_with_2_-_4_units</th>\n",
-       "      <th>multi-family_with_5plus_units</th>\n",
-       "      <th>single-family_attached</th>\n",
-       "      <th>single-family_detached</th>\n",
-       "      <th>mobile_home</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>timestamp</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01 00:15:00</th>\n",
-       "      <td>113.972825</td>\n",
-       "      <td>2.678051</td>\n",
-       "      <td>11.182543</td>\n",
-       "      <td>591.611432</td>\n",
-       "      <td>1.316949</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01 00:30:00</th>\n",
-       "      <td>122.073324</td>\n",
-       "      <td>2.689016</td>\n",
-       "      <td>11.389590</td>\n",
-       "      <td>603.432355</td>\n",
-       "      <td>1.402989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01 00:45:00</th>\n",
-       "      <td>124.810963</td>\n",
-       "      <td>2.734115</td>\n",
-       "      <td>11.064188</td>\n",
-       "      <td>600.966481</td>\n",
-       "      <td>1.501680</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01 01:00:00</th>\n",
-       "      <td>130.157708</td>\n",
-       "      <td>2.679941</td>\n",
-       "      <td>11.317656</td>\n",
-       "      <td>605.812913</td>\n",
-       "      <td>1.601132</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-01-01 01:15:00</th>\n",
-       "      <td>16.824577</td>\n",
-       "      <td>0.403772</td>\n",
-       "      <td>2.361815</td>\n",
-       "      <td>133.074632</td>\n",
-       "      <td>0.205631</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-12-31 23:00:00</th>\n",
-       "      <td>155.102361</td>\n",
-       "      <td>2.646652</td>\n",
-       "      <td>14.567161</td>\n",
-       "      <td>695.344421</td>\n",
-       "      <td>1.082926</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-12-31 23:15:00</th>\n",
-       "      <td>128.287742</td>\n",
-       "      <td>2.732418</td>\n",
-       "      <td>14.002470</td>\n",
-       "      <td>622.587237</td>\n",
-       "      <td>1.316956</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-12-31 23:30:00</th>\n",
-       "      <td>138.694172</td>\n",
-       "      <td>2.711169</td>\n",
-       "      <td>13.979686</td>\n",
-       "      <td>610.486911</td>\n",
-       "      <td>1.224628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018-12-31 23:45:00</th>\n",
-       "      <td>130.288110</td>\n",
-       "      <td>2.756182</td>\n",
-       "      <td>13.871853</td>\n",
-       "      <td>610.047892</td>\n",
-       "      <td>1.236934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019-01-01 00:00:00</th>\n",
-       "      <td>134.371567</td>\n",
-       "      <td>2.746753</td>\n",
-       "      <td>13.342888</td>\n",
-       "      <td>615.014955</td>\n",
-       "      <td>1.295301</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>35040 rows × 5 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                     multi-family_with_2_-_4_units  \\\n",
-       "timestamp                                            \n",
-       "2018-01-01 00:15:00                     113.972825   \n",
-       "2018-01-01 00:30:00                     122.073324   \n",
-       "2018-01-01 00:45:00                     124.810963   \n",
-       "2018-01-01 01:00:00                     130.157708   \n",
-       "2018-01-01 01:15:00                      16.824577   \n",
-       "...                                            ...   \n",
-       "2018-12-31 23:00:00                     155.102361   \n",
-       "2018-12-31 23:15:00                     128.287742   \n",
-       "2018-12-31 23:30:00                     138.694172   \n",
-       "2018-12-31 23:45:00                     130.288110   \n",
-       "2019-01-01 00:00:00                     134.371567   \n",
-       "\n",
-       "                     multi-family_with_5plus_units  single-family_attached  \\\n",
-       "timestamp                                                                    \n",
-       "2018-01-01 00:15:00                       2.678051               11.182543   \n",
-       "2018-01-01 00:30:00                       2.689016               11.389590   \n",
-       "2018-01-01 00:45:00                       2.734115               11.064188   \n",
-       "2018-01-01 01:00:00                       2.679941               11.317656   \n",
-       "2018-01-01 01:15:00                       0.403772                2.361815   \n",
-       "...                                            ...                     ...   \n",
-       "2018-12-31 23:00:00                       2.646652               14.567161   \n",
-       "2018-12-31 23:15:00                       2.732418               14.002470   \n",
-       "2018-12-31 23:30:00                       2.711169               13.979686   \n",
-       "2018-12-31 23:45:00                       2.756182               13.871853   \n",
-       "2019-01-01 00:00:00                       2.746753               13.342888   \n",
-       "\n",
-       "                     single-family_detached  mobile_home  \n",
-       "timestamp                                                 \n",
-       "2018-01-01 00:15:00              591.611432     1.316949  \n",
-       "2018-01-01 00:30:00              603.432355     1.402989  \n",
-       "2018-01-01 00:45:00              600.966481     1.501680  \n",
-       "2018-01-01 01:00:00              605.812913     1.601132  \n",
-       "2018-01-01 01:15:00              133.074632     0.205631  \n",
-       "...                                     ...          ...  \n",
-       "2018-12-31 23:00:00              695.344421     1.082926  \n",
-       "2018-12-31 23:15:00              622.587237     1.316956  \n",
-       "2018-12-31 23:30:00              610.486911     1.224628  \n",
-       "2018-12-31 23:45:00              610.047892     1.236934  \n",
-       "2019-01-01 00:00:00              615.014955     1.295301  \n",
-       "\n",
-       "[35040 rows x 5 columns]"
+       "332.6447407810212"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "res_elec"
+    "(res_elec.resample('h').mean().resample('M').sum().loc['2018','single-family_detached']/886).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "multi-family_with_2_-_4_units    0.203935\n",
+       "multi-family_with_5plus_units    0.003292\n",
+       "single-family_attached           0.016574\n",
+       "single-family_detached           1.044017\n",
+       "mobile_home                      0.004240\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec.resample('h').mean().max() / 886 *2 / (1 / 0.37)"
    ]
   },
   {
@@ -476,44 +331,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constant_energy = np.ones(len(rooftop_solar_energy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constant_energy /= constant_energy.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "net_load    1331.488437\n",
-       "dtype: float64"
+       "4135965.349731396"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "net_load[net_load>0].max()"
+    "res_elec_resampled.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "constant_energy *= res_elec_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unmet_demand = res_elec_resampled-constant_energy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "net_load   -2506.148296\n",
-       "dtype: float64"
+       "794018.7630375257"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "net_load[net_load<0].min()"
+    "unmet_demand.where(unmet_demand>0).fillna(0).sum()"
    ]
   },
   {
@@ -697,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -706,7 +595,7 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -727,6 +616,38 @@
     "res_elec_resampled.plot(ax=ax, label='electricity', lw=0.3)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "timestamp\n",
+       "2018-01-01 00:15:00    591.611432\n",
+       "2018-01-01 00:30:00    603.432355\n",
+       "2018-01-01 00:45:00    600.966481\n",
+       "2018-01-01 01:00:00    605.812913\n",
+       "2018-01-01 01:15:00    133.074632\n",
+       "                          ...    \n",
+       "2018-12-31 23:00:00    695.344421\n",
+       "2018-12-31 23:15:00    622.587237\n",
+       "2018-12-31 23:30:00    610.486911\n",
+       "2018-12-31 23:45:00    610.047892\n",
+       "2019-01-01 00:00:00    615.014955\n",
+       "Name: single-family_detached, Length: 35040, dtype: float64"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec['single-family_detached']"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 29,
@@ -769,7 +690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +699,7 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -879,6 +800,64 @@
    "source": [
     "calculate_battery_needs(net_load)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,5))\n",
+    "start = 24*3*30\n",
+    "end = 24*3*32\n",
+    "rooftop_solar_energy[start:end].plot(ax=ax, color='gold', lw=1, label='solar production')\n",
+    "res_elec_resampled[start:end].plot(ax=ax, label='electricity', lw=1)\n",
+    "ax.fill_between(x=rooftop_solar_energy.index[start:end],\n",
+    "                y1=res_elec_resampled[start:end],\n",
+    "                y2=rooftop_solar_energy[start:end],\n",
+    "                where=rooftop_solar_energy[start:end] >= res_elec_resampled[start:end],\n",
+    "                color='lightgray',\n",
+    "                label='excess energy')\n",
+    "# ax.fill_between(x=rooftop_solar_energy.index[start:end],\n",
+    "#                 y1=0,\n",
+    "#                 y2=res_elec_resampled[start:end],\n",
+    "#                 where=rooftop_solar_energy[start:end]>0)\n",
+    "# ax.fill_between(x=rooftop_solar_energy.index[start:end],\n",
+    "#                 y1=res_elec_resampled[start:end],\n",
+    "#                 y2=rooftop_solar_energy[start:end],\n",
+    "#                 where=rooftop_solar_energy[start:end] < res_elec_resampled[start:end],\n",
+    "#                 color='lightgray')\n",
+    "ax.set_ylabel(\"Electricity (kW)\")\n",
+    "ax.set_xlabel('')\n",
+    "ax.set_ylim(0, 2550)\n",
+    "ax.legend(facecolor='None', framealpha=1, loc=(0.2,-0.15), edgecolor='None', ncols=3)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/10-pypsa-model.ipynb b/notebooks/10-pypsa-model.ipynb
index 1aec720..bfcf88e 100644
--- a/notebooks/10-pypsa-model.ipynb
+++ b/notebooks/10-pypsa-model.ipynb
@@ -1039,18 +1039,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Version: Tax Credit Simulation\n",
+    "## Model Version: Policy Simulation\n",
     "\n",
-    "At this moment, the model\n",
-    "\n",
-    "* does NOT pay for net metering\n",
-    "\n",
-    "The purpose of this simulation is to test the effect of price reductions on solar penetration."
+    "The purpose of this simulation is to test the effect of price reductions and net metering on solar penetration."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 274,
    "metadata": {},
    "outputs": [
     {
@@ -1059,9 +1055,9 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.10it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.83s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.11it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.69it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1074,9 +1070,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.50it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1089,9 +1085,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.45it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.49it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1104,9 +1100,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.55it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.92it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.94s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1119,9 +1115,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.25it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.3s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1134,9 +1130,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.14it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.82it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.95s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1149,9 +1145,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.68it/s]\n",
-      "INFO:linopy.io: Writing time: 0.6s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.95it/s]\n",
+      "INFO:linopy.io: Writing time: 1.86s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1164,9 +1160,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 80.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.6s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.44it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1179,430 +1175,189 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 27.36it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 74.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.63s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.95s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.94it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 81.62it/s]\n",
-      "INFO:linopy.io: Writing time: 0.61s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.96it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.59e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.81it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.23it/s]\n",
-      "INFO:linopy.io: Writing time: 0.64s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.00it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.84s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.51e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.51it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.9s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.41e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.83it/s]\n",
-      "INFO:linopy.io: Writing time: 0.92s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.30e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.17e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.42it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.75it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.03e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.70it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.86e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.33it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.30it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.9s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.66e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.77it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.44it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.85s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.41e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.45it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.59it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.91s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.07e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.38it/s]\n",
-      "INFO:linopy.io: Writing time: 0.64s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.66e+05\n",
+      "Objective: 4.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
-     ]
-    }
-   ],
-   "source": [
-    "data = {'discount':[],\n",
-    "        'solar_capacity':[]}\n",
-    "\n",
-    "discounts = np.linspace(0, 1, 20)\n",
-    "\n",
-    "for discount in discounts:\n",
-    "        n.generators.loc['ResPV', 'capital_cost'] = costs.at['ResPV','annualized_cost'] * (1-discount)\n",
-    "        \n",
-    "        n.optimize(solver_name='highs')\n",
-    "        \n",
-    "        data['discount'].append(discount)\n",
-    "        data['solar_capacity'].append(np.abs(n.generators.p_nom_opt['ResPV']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>discount</th>\n",
-       "      <th>solar_capacity</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
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-       "      <td>0.000000</td>\n",
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-       "    <tr>\n",
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-       "      <td>0.000000</td>\n",
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-       "      <th>7</th>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.421053</td>\n",
-       "      <td>0.366176</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.473684</td>\n",
-       "      <td>0.479037</td>\n",
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-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.526316</td>\n",
-       "      <td>0.593604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0.578947</td>\n",
-       "      <td>0.711923</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0.631579</td>\n",
-       "      <td>0.826754</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>0.684211</td>\n",
-       "      <td>0.944494</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>0.736842</td>\n",
-       "      <td>1.074690</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>0.789474</td>\n",
-       "      <td>1.245800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>0.842105</td>\n",
-       "      <td>1.484248</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.894737</td>\n",
-       "      <td>1.944705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0.947368</td>\n",
-       "      <td>2.807000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>2.807000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    discount  solar_capacity\n",
-       "0   0.000000        0.000000\n",
-       "1   0.052632        0.000000\n",
-       "2   0.105263        0.000000\n",
-       "3   0.157895        0.000000\n",
-       "4   0.210526        0.000000\n",
-       "5   0.263158        0.000000\n",
-       "6   0.315789        0.000000\n",
-       "7   0.368421        0.000000\n",
-       "8   0.421053        0.366176\n",
-       "9   0.473684        0.479037\n",
-       "10  0.526316        0.593604\n",
-       "11  0.578947        0.711923\n",
-       "12  0.631579        0.826754\n",
-       "13  0.684211        0.944494\n",
-       "14  0.736842        1.074690\n",
-       "15  0.789474        1.245800\n",
-       "16  0.842105        1.484248\n",
-       "17  0.894737        1.944705\n",
-       "18  0.947368        2.807000\n",
-       "19  1.000000        2.807000"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results_df = pd.DataFrame(data)\n",
-    "results_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_df = results_df.assign(solar_penetration=results_df['solar_capacity'] / n.generators.p_nom_max.ResPV)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 1.0)"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "results_df.plot(ax=ax, x='discount', y='solar_penetration')\n",
-    "ax.minorticks_on()\n",
-    "ax.grid(which='major')\n",
-    "ax.grid(which='minor', alpha=0.2)\n",
-    "ax.set_ylim(0,1.005)\n",
-    "ax.set_xlim(0,1.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.26it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.65it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1615,9 +1370,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 27.09it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.28it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.75it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1630,9 +1385,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.53it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.41it/s]\n",
-      "INFO:linopy.io: Writing time: 0.86s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.43it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1645,8 +1400,8 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.97it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 52.01it/s]\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.59it/s]\n",
       "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
@@ -1660,9 +1415,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.94it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.84it/s]\n",
-      "INFO:linopy.io: Writing time: 0.9s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.90it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1675,9 +1430,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.60it/s]\n",
-      "INFO:linopy.io: Writing time: 0.86s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.90it/s]\n",
+      "INFO:linopy.io: Writing time: 0.92s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1690,9 +1445,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.77it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.61it/s]\n",
-      "INFO:linopy.io: Writing time: 0.89s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.91s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1705,9 +1460,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.59it/s]\n",
-      "INFO:linopy.io: Writing time: 0.93s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.34it/s]\n",
+      "INFO:linopy.io: Writing time: 2.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1720,9 +1475,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.54it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.85s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1735,9 +1490,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.54it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 52.11it/s]\n",
-      "INFO:linopy.io: Writing time: 0.88s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.02it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.04it/s]\n",
+      "INFO:linopy.io: Writing time: 2.11s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1750,9 +1505,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.50it/s]\n",
-      "INFO:linopy.io: Writing time: 0.96s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 18.77it/s]\n",
+      "INFO:linopy.io: Writing time: 2.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1765,9 +1520,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.19it/s]\n",
-      "INFO:linopy.io: Writing time: 0.93s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.51it/s]\n",
+      "INFO:linopy.io: Writing time: 0.96s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1780,9 +1535,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.31it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.80it/s]\n",
-      "INFO:linopy.io: Writing time: 0.89s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1795,9 +1550,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.74it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.08it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1810,9 +1565,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.72it/s]\n",
-      "INFO:linopy.io: Writing time: 0.92s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.95it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1826,8 +1581,8 @@
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
       "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.47it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.72it/s]\n",
-      "INFO:linopy.io: Writing time: 0.89s\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.27it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1840,9 +1595,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.36it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.13it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1855,9 +1610,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.17it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.92it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1870,9 +1625,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.03it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1885,9 +1640,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.38it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.50it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1900,8 +1655,8 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.16it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.34it/s]\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.21it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.98it/s]\n",
       "INFO:linopy.io: Writing time: 0.65s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
@@ -1915,9 +1670,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.82it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.76it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1930,9 +1685,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.55it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.93it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1945,9 +1700,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.78it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1960,9 +1715,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.64it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.07it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.13it/s]\n",
+      "INFO:linopy.io: Writing time: 0.82s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1975,9 +1730,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.78it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.18it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -1990,9 +1745,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.44it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.25it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2005,9 +1760,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.59it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 77.24it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2020,9 +1775,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 80.16it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.66it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2035,9 +1790,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.50it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.31it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2050,9 +1805,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.23it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2065,9 +1820,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.82it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2080,9 +1835,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2095,9 +1850,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.56it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.40it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2110,9 +1865,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.88it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2125,9 +1880,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.16it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.24it/s]\n",
+      "INFO:linopy.io: Writing time: 0.81s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2140,9 +1895,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.97it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.00it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2155,9 +1910,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.67it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2170,9 +1925,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2185,9 +1940,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.88it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.97it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2200,9 +1955,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.97it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.69it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2215,9 +1970,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.10it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.05it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2230,9 +1985,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.04it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2245,9 +2000,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.67it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.31it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.78it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2260,9 +2015,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.43it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.84s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2275,9 +2030,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.32it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.81s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2290,9 +2045,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.94it/s]\n",
-      "INFO:linopy.io: Writing time: 0.85s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2305,9 +2060,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.75it/s]\n",
+      "INFO:linopy.io: Writing time: 2.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2320,9 +2075,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.88it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 30.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2335,9 +2090,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.94it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.25it/s]\n",
+      "INFO:linopy.io: Writing time: 1.45s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2350,9 +2105,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 28.66it/s]\n",
+      "INFO:linopy.io: Writing time: 1.91s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2365,9 +2120,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.13it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.17it/s]\n",
+      "INFO:linopy.io: Writing time: 1.77s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2380,9 +2135,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.23it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.73s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2395,9 +2150,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.74s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2410,9 +2165,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.8s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.81it/s]\n",
+      "INFO:linopy.io: Writing time: 2.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2425,9 +2180,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.46it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.00it/s]\n",
+      "INFO:linopy.io: Writing time: 1.7s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2440,9 +2195,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.19it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.24it/s]\n",
+      "INFO:linopy.io: Writing time: 1.75s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2455,9 +2210,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.01it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.76s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2470,9 +2225,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.65it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.76it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2485,9 +2240,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.67it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 35.82it/s]\n",
+      "INFO:linopy.io: Writing time: 1.63s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2500,9 +2255,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.76it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.14it/s]\n",
+      "INFO:linopy.io: Writing time: 1.69s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2515,9 +2270,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.93it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.47it/s]\n",
+      "INFO:linopy.io: Writing time: 1.48s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2530,9 +2285,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.47it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.53it/s]\n",
+      "INFO:linopy.io: Writing time: 1.19s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2545,9 +2300,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.44it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  7.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.77it/s]\n",
+      "INFO:linopy.io: Writing time: 2.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2560,9 +2315,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.44s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2575,9 +2330,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.17it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.44s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2590,9 +2345,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.92it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.28it/s]\n",
+      "INFO:linopy.io: Writing time: 1.4s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2605,9 +2360,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.94it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.53it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.48it/s]\n",
+      "INFO:linopy.io: Writing time: 1.48s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2620,9 +2375,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.55it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.15it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.94it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2635,9 +2390,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.97it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.40it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2650,9 +2405,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.72it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.79it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.4s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2665,9 +2420,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.65it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.35it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 30.98it/s]\n",
+      "INFO:linopy.io: Writing time: 1.48s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2680,9 +2435,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.58it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 74.32it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.59s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2695,9 +2450,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.52it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.51it/s]\n",
+      "INFO:linopy.io: Writing time: 1.38s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2710,9 +2465,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.37it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2725,9 +2480,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.72it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.62it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.83it/s]\n",
+      "INFO:linopy.io: Writing time: 1.34s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2740,9 +2495,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.56it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 77.00it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.20it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2755,9 +2510,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.81it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.48it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2770,9 +2525,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 52.30it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.58s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2785,9 +2540,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.35it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.71it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2800,9 +2555,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.62it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.44it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2815,9 +2570,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.53it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2830,9 +2585,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.06it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.82it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2845,9 +2600,9 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.37it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.50it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
@@ -2860,4740 +2615,2265 @@
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.97it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 31.52it/s]\n",
+      "INFO:linopy.io: Writing time: 1.19s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.66e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.40it/s]\n",
-      "INFO:linopy.io: Writing time: 1.08s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.85it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.65e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.87it/s]\n",
-      "INFO:linopy.io: Writing time: 0.83s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.10it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.65e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.22it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.38it/s]\n",
-      "INFO:linopy.io: Writing time: 1.22s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.09it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.65e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.36s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.65e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.8s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.73it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.50e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.63e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.03it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.03it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.44it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.63e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.54it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.50it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.62e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.51it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.39it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.69it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.61e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.42it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.47it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.35e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.64it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.78it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.60e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.60e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.33it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.02it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.59e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.17it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.57e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.34it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.19e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.38it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.09it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.58e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.11it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.17it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.34it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.57e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.73it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.03it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.56e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.41it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.95it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.52e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.42it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.31it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.04e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.35it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.56it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.70it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.55e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.96it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.92it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.15it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.41it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.54e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.80it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.90it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.40it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.52e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.18it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.85it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.45e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.25it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.88e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.47it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.33it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.32it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.52e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.28it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.50e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.17it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.47e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.45it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.49it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.92it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.37e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.51it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.36it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.59it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.73e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.49e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.73it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.00it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.39it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.47e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 79.47it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.29it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.17it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.42e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.39it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.41it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.48it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.24e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.63it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.18it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.37it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.57e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.22it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.45e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.18it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.74it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.42e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.80it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.66it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.37e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.50it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.43it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.08e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.20it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.33it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.41e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.89it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.20it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.41e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.41it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.44it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.62it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.38e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.54it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.11it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.31e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.2s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.93e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.61it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.25it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.26e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.79it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.80it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.91it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.37e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.99it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.70it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.15it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.33e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.53it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.92it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.24e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.29it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.28it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.77e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.94it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.21it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.10e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.59it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.27it/s]\n",
-      "INFO:linopy.io: Writing time: 1.02s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.41it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.33e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.18it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.28e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.56it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.14it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.2s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.17e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.05it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.61e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.95it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.33it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 2.95e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.64it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.28e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.72it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 35.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.22e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.53it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.47it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.21s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.08e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.81it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.91it/s]\n",
+      "INFO:linopy.io: Writing time: 1.2s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.46e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.31it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.20it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.85it/s]\n",
+      "INFO:linopy.io: Writing time: 1.29s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 2.79e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.28it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.07it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.56it/s]\n",
+      "INFO:linopy.io: Writing time: 1.21s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.23e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.79it/s]\n",
+      "INFO:linopy.io: Writing time: 1.33s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.16e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.66it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.77it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.82it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.96e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.86it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.30e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.29it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.83it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 2.64e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.23it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.36it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.18e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.96it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.11it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.76it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.61it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.20it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.10e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.97it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.30it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.22it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.81e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.10it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 3.15e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.50it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.62it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.76it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 2.48e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.58it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.42it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.13e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.51it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.50it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 4.03e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.85it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 3.66e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.08it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 2.99e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.68it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.49it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 2.33e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.06it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.84it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 4.08e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.29it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 3.96e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.41it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.73it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 3.50e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.72it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.11it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.35it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 2.84e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.79it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.49it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.69it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 2.17e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.56it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.42it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 4.02e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.12it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.84it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 3.88e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.54it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.07it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.78it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 3.35e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.90it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.72it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.65e+05\n",
+      "Objective: 2.68e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.13it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.44it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.99it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.64e+05\n",
+      "Objective: 2.02e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.55it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.07it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.64e+05\n",
+      "Objective: 3.96e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.07it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.80it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.64e+05\n",
+      "Objective: 3.78e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.75it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.94s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.64e+05\n",
+      "Objective: 3.19e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.80it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.76it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.38it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.63e+05\n",
+      "Objective: 2.53e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.62e+05\n",
+      "Objective: 1.86e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.76it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.91it/s]\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.74it/s]\n",
       "INFO:linopy.io: Writing time: 0.68s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.49e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.27it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.14it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.59e+05\n",
+      "Objective: 3.89e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.83it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.65it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.59e+05\n",
+      "Objective: 3.68e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.27it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.04it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.58e+05\n",
+      "Objective: 3.04e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.76it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.25it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.49it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.58e+05\n",
+      "Objective: 2.37e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.74it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.58e+05\n",
+      "Objective: 1.71e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.48it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.97s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.58e+05\n",
+      "Objective: 3.82e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.44it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.94it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.58e+05\n",
+      "Objective: 3.55e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.64it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.50it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.58e+05\n",
+      "Objective: 2.88e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.15it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.51it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.57e+05\n",
+      "Objective: 2.22e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.29it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.71it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.57e+05\n",
+      "Objective: 1.55e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.67it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.00it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.84it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.57e+05\n",
+      "Objective: 3.75e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.30it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.05it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.30it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.56e+05\n",
+      "Objective: 3.39e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.28it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.8s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.56e+05\n",
+      "Objective: 2.73e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.86it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.84it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.55e+05\n",
+      "Objective: 2.06e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.19it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.61it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.54e+05\n",
+      "Objective: 1.40e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.56it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.12it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.04it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.52e+05\n",
+      "Objective: 3.67e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 70.77it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.39it/s]\n",
+      "INFO:linopy.io: Writing time: 0.97s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.49e+05\n",
+      "Objective: 3.24e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.01it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.33it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.37e+05\n",
+      "Objective: 2.57e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.10it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.22e+05\n",
+      "Objective: 1.91e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.22it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.08e+05\n",
+      "Objective: 1.24e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.84it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.55it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.64it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.51e+05\n",
+      "Objective: 3.58e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.04it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.52it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.51e+05\n",
+      "Objective: 3.08e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.40it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.24it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.98s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.50e+05\n",
+      "Objective: 2.42e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.61it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.39it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.50e+05\n",
+      "Objective: 1.75e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.13it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.98s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.50e+05\n",
+      "Objective: 1.09e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.27it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.43it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.49e+05\n",
+      "Objective: 3.49e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.86s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.49e+05\n",
+      "Objective: 2.93e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.13it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.78it/s]\n",
-      "INFO:linopy.io: Writing time: 0.85s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.19it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.48e+05\n",
+      "Objective: 2.26e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.05it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.47e+05\n",
+      "Objective: 1.60e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.26it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.26it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.46e+05\n",
+      "Objective: 9.32e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.61it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.30it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.45e+05\n",
+      "Objective: 3.39e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.12it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.70it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.97it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.44e+05\n",
+      "Objective: 2.77e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.62it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.47it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.42e+05\n",
+      "Objective: 2.11e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.37it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.11it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.40e+05\n",
+      "Objective: 1.44e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.45it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.57it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.35e+05\n",
+      "Objective: 7.77e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.75it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.55it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.32it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.24e+05\n",
+      "Objective: 3.27e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.96it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.10e+05\n",
+      "Objective: 2.62e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.16it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.88it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.92it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.96e+05\n",
+      "Objective: 1.95e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.48it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.50it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.82e+05\n",
+      "Objective: 1.29e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.00it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.68e+05\n",
+      "Objective: 6.21e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.88it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.07it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.36it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.41e+05\n",
+      "Objective: 3.13e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.33it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.41e+05\n",
+      "Objective: 2.46e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.89it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.67it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.33s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.40e+05\n",
+      "Objective: 1.80e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.40e+05\n",
+      "Objective: 1.13e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.43it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.23s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.39e+05\n",
+      "Objective: 4.66e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.00it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.56it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.38e+05\n",
+      "Objective: 2.97e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.66it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.97it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.00it/s]\n",
+      "INFO:linopy.io: Writing time: 1.31s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.37e+05\n",
+      "Objective: 2.31e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.04it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.61it/s]\n",
-      "INFO:linopy.io: Writing time: 1.13s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.29s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.36e+05\n",
+      "Objective: 1.64e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.10it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.48it/s]\n",
-      "INFO:linopy.io: Writing time: 0.93s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.95it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.34e+05\n",
+      "Objective: 9.77e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.49it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.05it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.33it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.32e+05\n",
+      "Objective: 3.11e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.68it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.45it/s]\n",
+      "INFO:linopy.io: Writing time: 1.34s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.30e+05\n",
+      "Objective: 2.82e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.65it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.58it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.35it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.27e+05\n",
+      "Objective: 2.15e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.87it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.59it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.22e+05\n",
+      "Objective: 1.49e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.20it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.66it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.15it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.72it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.11e+05\n",
+      "Objective: 8.21e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.32it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.38it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.97e+05\n",
+      "Objective: 1.56e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.08it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.83e+05\n",
+      "Objective: 2.66e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.82it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.98it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.69e+05\n",
+      "Objective: 2.00e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.07it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.55e+05\n",
+      "Objective: 1.33e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.39it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.41e+05\n",
+      "Objective: 6.66e+04\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
       "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.33it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.98it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.27e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.69it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.16it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.30e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.50it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.29e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.27it/s]\n",
-      "INFO:linopy.io: Writing time: 0.83s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.28e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.17it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.48it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.27e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.80it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.26e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.14it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.75it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.24e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.56it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.45it/s]\n",
-      "INFO:linopy.io: Writing time: 0.79s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.22e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.74it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.95it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.20e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.37it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.82it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.18e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.19it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 30.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.8s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.14e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.72it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.57it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.08e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.98e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.74it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.96it/s]\n",
-      "INFO:linopy.io: Writing time: 0.79s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.84e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.01it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.77it/s]\n",
-      "INFO:linopy.io: Writing time: 0.8s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.70e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.20it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.71it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.56e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.74it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.25it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.42e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.03it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.28e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.44it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.91it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.14e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.65s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.00e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.84it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.86e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.12it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.66it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.17e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.05it/s]\n",
-      "INFO:linopy.io: Writing time: 0.66s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.16e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.14e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.23it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.13e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.50it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.59it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.11e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.89it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.78it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.08e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.54it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.24it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.05e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.14it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.01e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.38it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.27it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.95e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.59it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.85e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.23it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.60it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.71e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.57e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.44it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.17it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.43e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.96it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.29e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.63it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.22it/s]\n",
-      "INFO:linopy.io: Writing time: 0.83s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.15e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.96it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.94it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.01e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.65it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.87e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.64it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.73e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.51it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.59e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.98it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.43it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.45e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.39it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.12it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.03e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.83it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.01e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.95it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.09it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.98e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.01it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.81it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.95e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.34it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.92e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.88e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.31it/s]\n",
-      "INFO:linopy.io: Writing time: 1.11s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.82e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.60it/s]\n",
-      "INFO:linopy.io: Writing time: 1.83s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.72e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.68it/s]\n",
-      "INFO:linopy.io: Writing time: 1.81s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.58e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.93it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.31it/s]\n",
-      "INFO:linopy.io: Writing time: 2.01s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.44e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 18.75it/s]\n",
-      "INFO:linopy.io: Writing time: 1.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.30e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.65it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 28.45it/s]\n",
-      "INFO:linopy.io: Writing time: 1.8s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.16e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.85it/s]\n",
-      "INFO:linopy.io: Writing time: 1.89s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.02e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.62it/s]\n",
-      "INFO:linopy.io: Writing time: 1.79s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.88e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.67it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.60it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.74e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.21it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.90it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.60e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.44it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.46e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.22it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.32e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.70it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.08it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.18e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.30it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.04e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.99it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.04it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.86e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.41it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.12it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.83e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.19it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.79e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.26it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.31it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.75e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.10it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.77it/s]\n",
-      "INFO:linopy.io: Writing time: 0.86s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.68e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.51it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.95it/s]\n",
-      "INFO:linopy.io: Writing time: 0.96s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.59e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.66it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.67it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.46e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 76.08it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.32e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.34it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.18e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.31it/s]\n",
-      "INFO:linopy.io: Writing time: 0.67s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.04e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 61.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.90e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 72.15it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.76e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.84it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.89it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.62e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.03it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.93it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.47e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.17it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.51it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.33e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.76it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.83it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.19e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.97it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.18it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.05e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.45it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.88it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.91e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.99it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.03it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.77e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.71it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.94it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.63e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.14it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.66e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.82it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.32it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.61e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.31it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.08it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.55e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.09it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.98it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.46e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.68it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.74it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.33e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.90it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.16it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.19e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.80it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.05e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.89it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.69it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.91e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.15it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.70it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.77e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.02it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.36it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.63e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.14it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.49e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.21it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 74.21it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.35e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.65it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.73it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.21e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.97it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.31it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.07e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.44it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 35.05it/s]\n",
-      "INFO:linopy.io: Writing time: 1.01s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.93e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  7.24it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.73it/s]\n",
-      "INFO:linopy.io: Writing time: 2.4s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.79e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.60it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 18.11it/s]\n",
-      "INFO:linopy.io: Writing time: 1.89s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.65e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.89it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 12.30it/s]\n",
-      "INFO:linopy.io: Writing time: 2.15s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.51e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.13it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.01it/s]\n",
-      "INFO:linopy.io: Writing time: 1.99s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.37e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.80it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.68it/s]\n",
-      "INFO:linopy.io: Writing time: 1.63s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.23e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.01it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.61it/s]\n",
-      "INFO:linopy.io: Writing time: 1.89s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.41e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.12it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.53it/s]\n",
-      "INFO:linopy.io: Writing time: 1.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.33e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.03it/s]\n",
-      "INFO:linopy.io: Writing time: 1.62s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.20e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.68it/s]\n",
-      "INFO:linopy.io: Writing time: 1.59s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.06e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.10it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.65it/s]\n",
-      "INFO:linopy.io: Writing time: 1.58s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.92e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.77it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.99it/s]\n",
-      "INFO:linopy.io: Writing time: 1.58s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.78e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.77it/s]\n",
-      "INFO:linopy.io: Writing time: 1.62s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.64e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.63it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.54it/s]\n",
-      "INFO:linopy.io: Writing time: 1.66s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.50e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.59it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.46it/s]\n",
-      "INFO:linopy.io: Writing time: 1.62s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.36e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.07it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.64it/s]\n",
-      "INFO:linopy.io: Writing time: 1.63s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.22e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.53it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 16.62it/s]\n",
-      "INFO:linopy.io: Writing time: 1.92s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.08e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.42it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.19it/s]\n",
-      "INFO:linopy.io: Writing time: 2.0s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.94e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.81it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.49it/s]\n",
-      "INFO:linopy.io: Writing time: 1.79s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.80e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.86it/s]\n",
-      "INFO:linopy.io: Writing time: 1.62s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.66e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.05it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.05it/s]\n",
-      "INFO:linopy.io: Writing time: 1.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.52e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.30it/s]\n",
-      "INFO:linopy.io: Writing time: 1.61s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.38e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.82it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 20.70it/s]\n",
-      "INFO:linopy.io: Writing time: 1.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.24e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.65it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.19it/s]\n",
-      "INFO:linopy.io: Writing time: 1.83s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.10e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.16it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.41it/s]\n",
-      "INFO:linopy.io: Writing time: 1.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 9.58e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.51it/s]\n",
-      "INFO:linopy.io: Writing time: 1.62s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 8.17e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.32it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 20.08it/s]\n",
-      "INFO:linopy.io: Writing time: 1.58s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.07e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.43it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 17.52it/s]\n",
-      "INFO:linopy.io: Writing time: 1.9s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.93e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.45it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.72it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.79e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.91it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.19it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.65e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.41it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.95it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.51e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.57it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.56it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.37e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.21it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.24it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.23e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.8s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.09e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.07it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.95e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.96it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 75.30it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.81e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.75it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.53it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.67e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.88it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.63it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.53e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.45it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.61it/s]\n",
-      "INFO:linopy.io: Writing time: 0.76s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.39e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.67it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.15it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.25e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.91it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.11e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.35it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.99it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 9.70e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.18it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.55it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 8.29e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.41it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.86it/s]\n",
-      "INFO:linopy.io: Writing time: 0.68s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 6.89e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.10it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.70it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 5.49e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.53it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.65it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.09e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.99it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.58it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.66e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.67it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.02it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.52e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.03it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.03it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.38e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.52it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 57.12it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.24e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.46it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.83it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.10e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.09it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.35it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.96e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.09it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.06it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.82e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.91it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.68e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.92it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.34it/s]\n",
-      "INFO:linopy.io: Writing time: 0.75s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.54e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.76it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.40e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.48it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.26e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.80it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.53it/s]\n",
-      "INFO:linopy.io: Writing time: 0.73s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.12e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.49it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.28it/s]\n",
-      "INFO:linopy.io: Writing time: 0.74s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 9.81e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.92it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.93it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 8.41e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.08it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.23it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 7.01e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.83it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.85it/s]\n",
-      "INFO:linopy.io: Writing time: 0.77s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 5.61e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.87it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.58it/s]\n",
-      "INFO:linopy.io: Writing time: 0.78s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.21e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.25it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.22it/s]\n",
-      "INFO:linopy.io: Writing time: 0.84s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.81e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.10it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.64it/s]\n",
-      "INFO:linopy.io: Writing time: 0.72s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 1.41e+04\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 25.14it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.69s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 6.61e+01\n",
+      "Objective: 6.61e+01\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
@@ -7609,8 +4889,10 @@
     "        'objective_value':[],\n",
     "        }\n",
     "\n",
-    "discounts = np.linspace(0, 1, 20)\n",
-    "retail_prices = np.linspace(0, 1, 20)\n",
+    "\n",
+    "delta = 0.02\n",
+    "discounts = np.arange(0, 1+delta, delta)\n",
+    "retail_prices = np.linspace(0, 1, 5)\n",
     "for discount in discounts:\n",
     "    for pct_retail in retail_prices:\n",
     "        n.generators.loc['ResPV', 'capital_cost'] = costs.at['ResPV','annualized_cost'] * (1-discount)\n",
@@ -7626,7 +4908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 275,
    "metadata": {},
    "outputs": [
     {
@@ -7661,7 +4943,7 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
@@ -7669,7 +4951,7 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.052632</td>\n",
+       "      <td>0.25</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
@@ -7677,7 +4959,7 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.105263</td>\n",
+       "      <td>0.50</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
@@ -7685,7 +4967,7 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.157895</td>\n",
+       "      <td>0.75</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
@@ -7693,7 +4975,7 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.210526</td>\n",
+       "      <td>1.00</td>\n",
        "      <td>0.000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
@@ -7707,63 +4989,63 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>395</th>\n",
+       "      <th>250</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.789474</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>2.807</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>56109.661027</td>\n",
+       "      <td>266273.015698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>396</th>\n",
+       "      <th>251</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.842105</td>\n",
+       "      <td>0.25</td>\n",
        "      <td>2.807</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>42098.770715</td>\n",
+       "      <td>199721.286719</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>397</th>\n",
+       "      <th>252</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.894737</td>\n",
+       "      <td>0.50</td>\n",
        "      <td>2.807</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>28087.880403</td>\n",
+       "      <td>133169.557739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>398</th>\n",
+       "      <th>253</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.947368</td>\n",
+       "      <td>0.75</td>\n",
        "      <td>2.807</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>14076.990092</td>\n",
+       "      <td>66617.828760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>399</th>\n",
+       "      <th>254</th>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>1.00</td>\n",
        "      <td>2.807</td>\n",
        "      <td>0.0</td>\n",
        "      <td>66.099781</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>400 rows × 5 columns</p>\n",
+       "<p>255 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "     discount  percent_retail_price  solar_capacity  battery_capacity  \\\n",
-       "0         0.0              0.000000           0.000               0.0   \n",
-       "1         0.0              0.052632           0.000               0.0   \n",
-       "2         0.0              0.105263           0.000               0.0   \n",
-       "3         0.0              0.157895           0.000               0.0   \n",
-       "4         0.0              0.210526           0.000               0.0   \n",
+       "0         0.0                  0.00           0.000               0.0   \n",
+       "1         0.0                  0.25           0.000               0.0   \n",
+       "2         0.0                  0.50           0.000               0.0   \n",
+       "3         0.0                  0.75           0.000               0.0   \n",
+       "4         0.0                  1.00           0.000               0.0   \n",
        "..        ...                   ...             ...               ...   \n",
-       "395       1.0              0.789474           2.807               0.0   \n",
-       "396       1.0              0.842105           2.807               0.0   \n",
-       "397       1.0              0.894737           2.807               0.0   \n",
-       "398       1.0              0.947368           2.807               0.0   \n",
-       "399       1.0              1.000000           2.807               0.0   \n",
+       "250       1.0                  0.00           2.807               0.0   \n",
+       "251       1.0                  0.25           2.807               0.0   \n",
+       "252       1.0                  0.50           2.807               0.0   \n",
+       "253       1.0                  0.75           2.807               0.0   \n",
+       "254       1.0                  1.00           2.807               0.0   \n",
        "\n",
        "     objective_value  \n",
        "0      466950.487985  \n",
@@ -7772,16 +5054,16 @@
        "3      466950.487985  \n",
        "4      466950.487985  \n",
        "..               ...  \n",
-       "395     56109.661027  \n",
-       "396     42098.770715  \n",
-       "397     28087.880403  \n",
-       "398     14076.990092  \n",
-       "399        66.099781  \n",
+       "250    266273.015698  \n",
+       "251    199721.286719  \n",
+       "252    133169.557739  \n",
+       "253     66617.828760  \n",
+       "254        66.099781  \n",
        "\n",
-       "[400 rows x 5 columns]"
+       "[255 rows x 5 columns]"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 275,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7793,40 +5075,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import seaborn as sb"
+    "results_df_large = results_df_large.assign(solar_penetration=results_df_large['solar_capacity'] / n.generators.p_nom_max.ResPV)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 354,
    "metadata": {},
    "outputs": [],
    "source": [
-    "results_df_large = results_df_large.assign(solar_penetration=results_df_large['solar_capacity'] / n.generators.p_nom_max.ResPV)\n"
+    "results_df_large = pd.read_csv('simulation_data.csv', index_col=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 355,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.0, 100.0)"
+       "<matplotlib.lines.Line2D at 0x2aa82cfd1e0>"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 355,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -7844,20 +5135,74 @@
     "            hue='percent_retail_price',\n",
     "            palette='viridis')\n",
     "\n",
-    "ax.set_ylabel(\"Affordable Solar Capacity \\n (% of Total Electricity)\")\n",
-    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.set_ylabel(\"% of total electricity covered by solar\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost\\n (%)\")\n",
     "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
     "\n",
     "ax.minorticks_on()\n",
     "ax.grid(which='major')\n",
     "ax.grid(which='minor', alpha=0.2)\n",
     "ax.set_ylim(0,1.05*100)\n",
-    "ax.set_xlim(0,1.0*100)"
+    "ax.set_xlim(0,1.0*100)\n",
+    "\n",
+    "ax.axvline(x=50, color='tab:red', linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df_large['solar_penetration'] = results_df_large['solar_penetration'].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = results_df_large.loc[results_df_large['discount']==0.5, ['percent_retail_price','solar_penetration']].plot.bar(x='percent_retail_price',\n",
+    "                                                                                                               y='solar_penetration',\n",
+    "                                                                                                               legend=False,\n",
+    "                                                                                                               zorder=3,\n",
+    "                                                                                                               color='gold')\n",
+    "ax.set_xlabel('Net Metering Price \\n (fraction of retail price)')\n",
+    "ax.set_ylabel(\"Solar penetration \\n (fraction)\")\n",
+    "ax.set_title(\"Solar potential with 50% Tax Credits\")\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major', zorder=0)\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "\n",
+    "ax.patches[0].set_edgecolor('red')\n",
+    "ax.patches[0].set_linewidth(3)\n",
+    "ax.bar_label(ax.containers[0])\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 279,
    "metadata": {},
    "outputs": [
     {
@@ -7887,39 +5232,35 @@
        "      <th>battery_capacity</th>\n",
        "      <th>objective_value</th>\n",
        "      <th>solar_penetration</th>\n",
-       "      <th>lcoe</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
-       "      <td>400.000000</td>\n",
-       "      <td>400.000000</td>\n",
-       "      <td>400.000000</td>\n",
-       "      <td>400.0</td>\n",
-       "      <td>400.000000</td>\n",
-       "      <td>400.000000</td>\n",
-       "      <td>400.000000</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>0.500000</td>\n",
        "      <td>0.500000</td>\n",
-       "      <td>1.218476</td>\n",
+       "      <td>1.217456</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>381557.144405</td>\n",
-       "      <td>0.434085</td>\n",
-       "      <td>0.092253</td>\n",
+       "      <td>382392.224079</td>\n",
+       "      <td>0.433721</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.303869</td>\n",
-       "      <td>0.303869</td>\n",
-       "      <td>1.240130</td>\n",
+       "      <td>0.294971</td>\n",
+       "      <td>0.354249</td>\n",
+       "      <td>1.236635</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>117928.563407</td>\n",
-       "      <td>0.441799</td>\n",
-       "      <td>0.028513</td>\n",
+       "      <td>117536.626041</td>\n",
+       "      <td>0.440554</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
@@ -7929,37 +5270,33 @@
        "      <td>0.0</td>\n",
        "      <td>66.099781</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.000016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>0.250000</td>\n",
+       "      <td>0.240000</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>317253.149899</td>\n",
+       "      <td>324929.125859</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.076706</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>0.500000</td>\n",
        "      <td>0.500000</td>\n",
-       "      <td>0.758327</td>\n",
+       "      <td>0.757254</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>449455.279045</td>\n",
-       "      <td>0.270156</td>\n",
-       "      <td>0.108670</td>\n",
+       "      <td>448625.587086</td>\n",
+       "      <td>0.269774</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>0.750000</td>\n",
+       "      <td>0.760000</td>\n",
        "      <td>0.750000</td>\n",
        "      <td>2.807000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.112900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
@@ -7969,7 +5306,6 @@
        "      <td>0.0</td>\n",
        "      <td>466950.487985</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.112900</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -7977,27 +5313,27 @@
       ],
       "text/plain": [
        "         discount  percent_retail_price  solar_capacity  battery_capacity  \\\n",
-       "count  400.000000            400.000000      400.000000             400.0   \n",
-       "mean     0.500000              0.500000        1.218476               0.0   \n",
-       "std      0.303869              0.303869        1.240130               0.0   \n",
+       "count  255.000000            255.000000      255.000000             255.0   \n",
+       "mean     0.500000              0.500000        1.217456               0.0   \n",
+       "std      0.294971              0.354249        1.236635               0.0   \n",
        "min      0.000000              0.000000        0.000000               0.0   \n",
-       "25%      0.250000              0.250000        0.000000               0.0   \n",
-       "50%      0.500000              0.500000        0.758327               0.0   \n",
-       "75%      0.750000              0.750000        2.807000               0.0   \n",
+       "25%      0.240000              0.250000        0.000000               0.0   \n",
+       "50%      0.500000              0.500000        0.757254               0.0   \n",
+       "75%      0.760000              0.750000        2.807000               0.0   \n",
        "max      1.000000              1.000000        2.807000               0.0   \n",
        "\n",
-       "       objective_value  solar_penetration        lcoe  \n",
-       "count       400.000000         400.000000  400.000000  \n",
-       "mean     381557.144405           0.434085    0.092253  \n",
-       "std      117928.563407           0.441799    0.028513  \n",
-       "min          66.099781           0.000000    0.000016  \n",
-       "25%      317253.149899           0.000000    0.076706  \n",
-       "50%      449455.279045           0.270156    0.108670  \n",
-       "75%      466950.487985           1.000000    0.112900  \n",
-       "max      466950.487985           1.000000    0.112900  "
+       "       objective_value  solar_penetration  \n",
+       "count       255.000000         255.000000  \n",
+       "mean     382392.224079           0.433721  \n",
+       "std      117536.626041           0.440554  \n",
+       "min          66.099781           0.000000  \n",
+       "25%      324929.125859           0.000000  \n",
+       "50%      448625.587086           0.269774  \n",
+       "75%      466950.487985           1.000000  \n",
+       "max      466950.487985           1.000000  "
       ]
      },
-     "execution_count": 134,
+     "execution_count": 279,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8008,7 +5344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 280,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -8017,7 +5353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 281,
    "metadata": {},
    "outputs": [
     {
@@ -8026,13 +5362,13 @@
        "(0.0, 1.0)"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 281,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -8062,26 +5398,254 @@
     "ax.set_xlim(0,1.0)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "results_df_large.to_csv(\"simulation_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.18761865362970692"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(costs.at['ResPV','annualized_cost'] * 2.807) / load_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* uses the sticker price for rooftop solar from NREL's ATB\n",
+    "* applies 100% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.10it/s]\n",
+      "INFO:linopy.io: Writing time: 0.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.430806</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.329983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
+       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Load\n",
+       "Load Residential    112.9\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_2 = n.objective / n.loads_t.p_set.sum()\n",
+    "model_lcoe_2"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x2aa92e7ab60>"
+       "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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z9eJ/TdSeepf652ap2e3R7rpm2dNbz5GGFnfU62HVNLhCAi5/+0jBWEeiOa6iFcswLhWxf2CEPc2iFl/re9v273V7ZKTLHvryBQAAAAAJh5ALKaOmwaWbOrhAfHObC8Q+n8JO8eT/+Y5presBtbR45JNPNY0tyrGny+tr/cRrXXOLcjKsamlhMe7uyOXxRpxqz+XxRtUux54eMg2Zv03bKe8SvZ3UWrURaVq+tlVDRtoZDdhiHa4ZnWLNaLuowjADoUQgiCgzFkQM6JWlhy8Yq6o6V6CiqaBHaEWHkXaxDkFSgSM7fHVMTYNLWytrtcvZpLa1EKVtpjMszM3UyAEOPb12W9A6XWOL8gLb/AGX/1xYvmabHrpgrG7628eB7b97Y2vQWl03nTlCFln02C+PDoRef/pPuX5+TFHg2He5PappCA66jB6D0TB6/EWD4zAy9g+M8koR3+t62wRfAAAAAJCoCLmQMoxenGtyeyKGD03u1vDKJ6nF59PXu+sDi3E3trQuxt2np11WS/B//JkWqHuIZpq/SFJxSj7J2LR8RtsZDdhiHa4V9LDplBF9NSzM2lifVzijnuIvXmFYWx0FMe111s6MECRVObJtGtw7W3162lVV2xTYfuWUEi1b/bUmlRSo2e3V2IG9tPStssA6Xf41u5a+VaY5k4ZIkhb8+HDVNLbo0klD5PP59NyG7WHX6sq2WQNh2A0vfRzo86ThffTcZcfqjtc+CTr221f2eHw+PXXR+JDXNf80ZrWdHKOR9kUsjwuOw8jYPzDKJ8ntCz8lodvnC3mvCwAAAACJiJALKcPoBeK2aw6E47/dI6mqtjnkdt/+7f0dP0z3s3Nfo/Y2tsgnyeX2yuX26vuaJjW5PCpkWqCUEuvqomim5Evkqfsk49PyGW0X6zWvjLZzZNt0s8G1se4xsDZWvMIwM8Sz72TUb/90hvY0i1q8vkBF141njJTL41V9szuwZlfbtbua3V5l26zq3ytTn75bI0kaX5QnR3aGMtPTdO2pw7XwlGFqcHk0rihPY4vytHzNNs2eVBw2GB45wKE7XvskZHvbyp56l0e/ffUTrW43je9DF4zVVS9sVoPLEzgG24rHBzw4DiNj/8AorySLWt/b+GRRmsUja5pFjqwMWfbfDgAAAACJjpALKcPoBeJeWZHb+W/3eH0R1+7y7F+Mu6bBJZe7dcIXBU3r4pPL7Q2ZDgrJLdbVRbFeG8uMqfuMtJMSf82reK2NZXRdoliHYWaIZ9/JKtJ0hh/s2Kei3q3rhtnTf1j8xp6eptmTinXfG58F1uY6amAvPfzWlx2u0TX9ykPU4vHqxyP66vrT01Rd75LH69PG8mqNL8oLet1o699fVmlfQ4tu+tvHQQGX9MM0vrMnFeujHfsCx6BfvNZ94jiMjP0Do9IkpXdQrZVuSRNLcgEAAABIBoRcSBlGLxD37WnXj0f01fAwU5F9VuFU3552SZLXJy1b/bXGFuVpdmlxULtlq7/WjWeMlCTVN7bIK582l+8LTGtY72qd1vC4w/JV39hCyJVCYl1dFOu1scyYus9IOynx17yK59pYA3pl6b6fHanqepecTW7lZqUrL9umwtzMwP1iHYa1FatqmwPpG+E5sm068fA+2tfYokn71+ka4MgMrNk1cUi+JAWtzRVpja67Xv80EIC1X5+r0JEZtD5X2ykIJane5e7wmF9btke/OrFEvzimKGHWZ4t0HJ4yoq96ZKbrq1113Xb6YKNTrgL+aq2O3sNaWZMLAAAAQBIg5ELKMHqB2JFt0y0GpiJzeTyBC4btL+5fUlosl6f1AqFX0q7aJg3pk6Mce7rqmjzqmZmubFuadtU2qX8u0xWmklhXF5mxNlYsp+6Lpp0Za14ZaRev0CyadW+MVrwM6JWlhy8Yq6o6l2qbWtQzM0MFPYIv0Bt9rfOLZbVNtH0jsv69spRts+quc8bot3//VOOK8n5Ys+uwgqD1utr+23YtLv/3/gAs0vpc/tDrz1ccq2+rG2VPt2rLd/tkTbNEXIsrM8Oq/u2OlXiu+9TRcXjKiL66+ayRuub/fdjl1WWJxOiUqwDvYQEAAACkAkIupBQjF4iNTkVmkaXDyhlJuvms1kout9en3j3s2vD13sCnYBtbWj8Fe8yQ3nJ7Qxf0jscaJoiNaNbaiiRZpu4z2s4v1mteGWkXTWgWj7Wxoq146Wh6u7aMvNYdSN9GGO0bxvh/37//2ZHaU++S1+fTzWeOVJPbq70NLkkKWbfLH35l26yaOrJQYwf2Uo4tXUdHWJ+ro9DrqYvG687XPo24Flev7IyQv1sen0/ZNmuH61yave5TuOOwR2Z6SMAldU11WSKJZspVdG9ur0/5PezaFWYN2vwe9rDvYQEAAAAg0RByIeV0doE4mk+fd1Q5s7ZsT+tiPJLSLBZV1DSFXbtrcEGODnEEfwq2cl+jvtnboBy7VU0tXmVYPdpaW6vBvbPVr5t8yjyZGa1WMmNtrEjiHa5JxgM2oxdgjbaLJjSLx9pYZlW8GAnD4tk3otN+n9Y0uFRd3xpy+dfr8v/b7PYGQqv73vhMG/evzzW+KE9TRxbqzDH9Vdvk1rgIoZfUutbW0rfDB9P+2z+vcMpmTdOVL2wOOpaOH1oQFIS11xXrPrXfZ1/tqotbdVkiiWeVHZKL1WKR19dxkGXtYL0uAAAAAEgkhFzodoxetG9wuSO281/Uc/t8EdfuuunMkYH71DS41NDiUSAhC/CpocWjmgYuPCUDI9VKZqyNZXTqvnisiyXFfpq/aNbGMlq1EI+1saIJCmMtnn3j4DiybRqUnx1Yr6u0zb/29LRAaOVfn+v5Ddt11MBeuueNz7R5f+g1dmAvjR3YS3+ZO1H2dKuOLsrTLyYMCrwmjC/KC3rdacu/FtcFPxqo6/+6JeRcXP1llXw+X2tQ1u4x/Md/V1ctc7y3Yj/AMItC35K2vx0AAAAAEhwhF7odoxftHVm2iGGBI6u1XXNL5LW7mt0/fMK9rrFFPvn09e76kKkN+/S0q66xhZArwUUzfV8s17yKZuq+WK+LZSTIkeJXbZboa2NFExRKsZ3ONNq+kVgOycvW7849Qre9+okuKS3W8xu265LSYu10NmnikPyg9bn8a3L5Qy//36S20xSuLdsT+Ls2cUi+bNY0PX3xj7Sx3RpcfpkZrRXHHZ1fa8r2aN6JJUGvNScMLdC9M45QvcsTs7XgjOJ4b2X0ww6A1JpxdfS+FAAAAACSASEXup1oLu4bCQssFoue37A9bCXX8xu269pThwfu65FUFWbdA9/+7f0dTFeY6OK1NlY01UqxXhfrnhlH6J0vdqtvT3vg+N7pbNKUw/sEhS+xnuYv3mtj+fmkDj/NbiQMiyYoNBrCGRVN3xLrBSaiQ3tnB9brWnT6CHl9Pg3qna2axtbj2r8+1+zS4qDQq+2Ug20DrrYBmNS6LtdNZ47Qn684Vt9WN8qeHvxhjs7Or8wMq/61cHLQ8S8pZHpDyfy1sdof722DHkny+nzdomK6oIdNT4d5/3J8Sb6ebvP+BfC//+xoO+9LAQAAACQDQi50O0Yv7kvGwgKLRRErudouZ+DpZAHvzm5H/CV6tZIZoZlP0usfVQQ97glDCzT58D5B9zUaiBkNXuK5NlY0YVNn61MZrfg60BAuEqN9R/uc0bXCHWNf7aqT1Lo+V9t//aGXX9uf26/L1Tb0uuGljwP3afthjvQ0i566aHxINZC/6suRlaHD+vYIGVs81oRqe7y/v706JNCTus8xvfStL7Wm3ev76rI9UrtKYnRvvC8FAAAAkAoIudDtGL24bzQssEhBFw39/D/fetaowLbOLhW0v52qisQTzdpYkZhVrRTr0CwQvJQZC16MBGJGw7B4rY1lRthkpOIrmhAu1n2b8ZxhLv9xb09Pk6TAv/6wS2oNsXrn2AIhVVHvbEnS5vJ9anB5QkIvv7Vle5SVYdVtZ4/STS9/HHQ+l5bk66ELxuqqFzar9LB89chM11e76oL+TtU1x29NKP/xvq+hRTe9vCXkuXWHY3pXbXNroBXG6i+rtKu2OWWfO6Ljk5RmkYb0yVGOPV11TR71zExXti1NaZbO37cCAAAAQCIg5EK3Y/RCstEL4l6fQi6i+a0t2yOvL/gSgdcn/X1LRdB9SkvydeWUkqB2lfsa9c3eBuXYW9dEybB6tLW2VoN7Z6tfin8CPZFFszaW0WkxEzk0iyZ4iSYQM1odFo+1scwKmzqr+DL6OzkQnfVt1nOGefzH/aovdqu0JF+bd+xTaUl+IOzyV2k9sOJzbSzfp9mTimVPT9P4ojxNv/IQ7Wt0qcXj09FFeRpblBeyJtfw/rla9NePQsIS/9+u26eN0oTi3rrm/30YUv13x09GK9tmDVnjy8/sNaF++KBK+L/NqX5M72uM/FpR08nt6D4skvJ72LUrzJSF+T3sHc3UCwAAAAAJhZAL3Y7RC8lGL4g3uNwR27W9yOfzSctWfx12/a5lq7/WjWeMlNQaFtS3ePT17rqwC4F3hzVFEpXRSsBo1ryKdWgWyyn+oglezKoO8+uqtbHMDJsiMfo7MUO8njMOzoBeWTpjdD9NHJKvO//+qS4pLdZOZ5NKS/I1tihPT6/dps3l+wLT9i1fs00PXTBWt7zycdCUheHW5BpflBf0mtTW2rI9uuuc0brx5Y/DVv/d8rePW1/X/rol6LZsm1U3nzVSXp9Pm8urTa1Q7s7HdI7NGvH27E5uR/eRbrHI5fPp6931Yd9vZliIuQAAAAAkPkIudDtGLyQbDRUcWbagxe3br1viyPqhP5fHE3H9LpenNRCrbWxRVW1TyNhaFwJvkj3NQsgVJ9EEOUbCMDNCs1iuixVN8GJGdVg81saKNmyK1bSiRn8nse5Xim/AhoPjP+5//7MjtafepcH52Zo4JF8VNU1a+laZrjypJDAlYdvvpY7X5DppeB/NGHuIll/8IzW1eMKuxdXg8kQ8j288c0TQ8Zxts2r5xT/SI2+VBYVfZq2R1Z2P6Rxbuk4fXaifjhuovrn2wBR0O51N+svGHcqx8fYfrXwWqcoZWsXV+n6zWf0dzBwAAAAAIPHxv1x0O4bDqygqbJZf/CM9/NaXIcHV8ot/FHRh2mKx6PkN28NWcj2/YbuuPW24JKmlk4W+O7sd5ol1kBPr0EyK7bpY0QQvsa4Oi3adKCOhTywrvqToQrjOGA3hYt2vFN+ADbHRPuStbnBJksYO7BX429T2e0lh1+TKtlk1c8Ig3fS3j0Om1X1k5tH68Nt9GnOIQ3XNbi2/+Ech4Zdfo8sTdK7lZdtC1veSzFsjy39Mv7+9OuRDKDudTSHHdCrplZ2h604boRvbrUk2qSRfd54zRr2yUzfgQ3Q8nbyf7Ox2AAAAAEgEhFzodoyGV9GECo+8VRbSbm3ZHqVZLFp6wdjANosUsZLLPymMT5HX7uKSQ/zEOsiJdWgW63WxogleYl0dlugVX9GGcEYYCeHM6DeeARvM4chq/Z01u72BbW2/l0JDLyl88CVJm8v3KTMjTe9/s1dL/vllYHtpSb4eumCsrnphc1DQ1TMzI+hc+2pXXcjrkp8Za2T5g/ztextCPoRy/P7XOUd2zLpLKE1ub0jAJfnfv2zR7887So44jQ2JhfebAAAAAFIBIRe6HaPhVVSVOB1cuFvd7sKdRQp78dD/861njZJkfO0udL1YBzmxDs3MCMMG9MrSfT87UtX1Ljmb3MrNSldetk2FuZlB9zUalMR6bax4hU3RhHDR6CyEM6vfeAVsMIf/PLOnpwW2tf1eCg29pPDBl9Qafi19O/wHOvy3L1+zTbMnFeu4IfmqaXTpq911gSq/eKyRlW2zhv0QyuoUP16r610hz9lvTdkeVde7Ql6/0T3xfhMAAABAKkiYkGvx4sW64YYb9Jvf/EZLliyRJPl8Pt1+++168sknVV1drQkTJuiRRx7RqFGjAvdrbm7WNddcoxdeeEGNjY06+eST9eijj+rQQw8NtKmurtZVV12lV155RZI0bdo0Pfzww+rVq1egTXl5uX71q1/prbfeUlZWlmbOnKn7779fNtsPFz+2bNmiK6+8Uv/5z3/Uu3dvXXHFFbr55ptlYVHmpGL0AnGsK3Gk1k/LdnThaW3ZHnl9rZ+ZdXk8mnXsYFXUNAa1GeDI1LiivMDaXX47nU2dBhCIjWim+TMyLWasQ7N4r4tlJBCL9dpY8Qqb4nHR3ux+4xWwIfb859mqL3artCRfa8v2aPOOfYHvpdbQq/2akv1y7XrqovGyWCxB63GNL8oLG35JrX+/5kwaoqMu6BVSqex/rWi7PmVb/v4zM6zaXF4d0+kvI30IJZWP185eIzq7Hd2Hy+PR7OOKld/TJp8sqm1snYL2zDH9dOzg3iHvNwEAAAAgESVEyPXee+/pySef1BFHHBG0/d5779UDDzygZ555RocffrjuvPNOnXLKKfr888/Vs2dPSdL8+fP16quv6k9/+pPy8/N19dVX66yzztLGjRtltVolSTNnztS3336rN954Q5J0+eWXa9asWXr11VclSR6PR2eeeab69OmjNWvWaM+ePbrooovk8/n08MMPS5KcTqdOOeUUTZkyRe+9956++OILXXzxxcrJydHVV1/dVbsKMWD0AnGsK3EkqcHljtjWP9VTWppFmRlpHU4fk5b2Q7Bavqdei14KXXfj7uljVJSfE7E/HBij0/wZmRYz1tVP8V4Xy2ggFsu1seIVNkVz7qdCv1L89jUOzIBeWTpjdD9NHJKvm//2sZav2aaH9k+hu7Zsjz7+vkZPXTReS98u09K3ypRts+qhC8aGVByXluTr7CP6K9tmDVl7y6+fI1N3/f3TkA9y+F8r7vvZkSHnc9v+wgVjBzv9ZXc9Xjt7DTDzNQLJJd1q0aG9s0JmOGhdv220Wryh1Z4AAAAAkGjiHnLV1dXpF7/4hZYtW6Y777wzsN3n82nJkiW68cYbde6550qS/vu//1uFhYV6/vnndcUVV6impkZPPfWU/ud//kc//vGPJUnPPvusBg4cqH/+85869dRTtXXrVr3xxht69913NWHCBEnSsmXLNHHiRH3++ecaNmyYVqxYoU8//VQ7duzQgAEDJEm///3vdfHFF+uuu+5Sbm6unnvuOTU1NemZZ56R3W7X6NGj9cUXX+iBBx7QwoULqeZKIkYvEBu9wG60YkdqXSOl/afm/W2Xr9kW+KS7LS0t4rRQd/5ktKTWCq7bXv0k7DQzt7/6ie4+9wgqumLM6DR/0azpFsvqp3iuixVtIBartbGiDX1qGlyqqnPJ2dRyUFUjRvd1vPuNpXgGbDgw/vNs6f5Qub65RXefM0YuT+vF6zte/SRoysGO1uPatH2f/jj7GO2qbQ76u9U29FrdQaXyv7+sUl2TO+R87qi/WE1/2V2P1x42q84YXagZ4waqb65ddU0e9cxM105nk17cuEM9bNZ4DxEJItNq1a0dvI/87Wuf6razR3X+IAAAAAAQZ3EPuX71q1/pzDPP1I9//OOgkGvbtm2qrKzU1KlTA9vsdrsmT56sdevW6YorrtDGjRvV0tIS1GbAgAEaPXq01q1bp1NPPVXr16+Xw+EIBFySdOyxx8rhcGjdunUaNmyY1q9fr9GjRwcCLkk69dRT1dzcrI0bN2rKlClav369Jk+eLLvdHtRm0aJF+uabb1RcXGzWLkKMGb1AbHRaOqMVO/6+l1/8Iz381pdBbUtL8rX84h8F+m5s8Whz+T5deVJJ2DCssaX1ouK+BlfEaQ33NbDuRqxFtVZbjKcDjEcYFk0lRLRT2RkJfWJZ8SVFN/ViZ4zu63j265cKARsOTrhQ+atddUHBVLj1uIKqu176oe1Jw/voT5cfq6o6l3w+n7w+n648qSQk+PKrbWrRYX17BJ3PmRnWDqdAjMV0gv7j9f3t1SEfLtnpbErZ4zU3K0PXnjZCN74cWuV95zljlNvB1JHofupbIk+PXd/CdIUAAAAAEl9cQ64//elP2rRpk957772Q2yorKyVJhYWFQdsLCwu1ffv2QBubzaa8vLyQNv77V1ZWqm/fviGP37dv36A27fvJy8uTzWYLajN48OCQfvy3dRRyNTc3q7m5OfCz0+kM2w5dJ5oLxEampYumYkeSHnkrfIVWmsWipfunkWps8YSdwqm0JF8PXTBWTW0uOmTb0sI+z462I1Q052ms12qLpvopHmFYNJUQ0QRi0YQ+sar4irbSzAgjIVy8+vWLd8AWS/xNja3252yzO3RqsnDVVtk2q2ZOGKR73vgsZFrDhy4Yq6te2By4byBYsllV0+AKOp83l1dHHN/BTifo/7DK9r0NIR8uOX7/33JH9kF1kZCa3N6QgEvyvy/Zot+fd5QcJvbPeZo8LIr8PpJ5KgAAAAAkg7iFXDt27NBvfvMbrVixQpmZHVeatJ8G0OfzdTo1YPs24drHoo3P5+vwvn6LFy/W7bffHnG86HpRXZjuZFq6aKpXqupcIY/nt7pN2/wcmx5Y+UWH0xXedU7rdIU2a5q8PnW4dpfNStBlRDTnaazXajN6/JgVhsWySsroc45X6BNtpZlRnYVw8epXin/AFmv8TY2t9uesPT30b0a46q6Ophn0/3zF5CEaNcDR6VpbXTGdYLbNGvbDJatjNCViIqqud4U8X781ZXtUXW9ulTfnafLgfSQAAACAVBC3/7ls3LhRu3bt0rhx45Senq709HStWrVKDz30kNLT04OqpNratWtX4LZ+/frJ5XKpuro6YpudO3eG9L979+6gNu37qa6uVktLS8Q2u3btkhRabdbWokWLVFNTE/jasWNH5B2DLuPItumwvj10VFGeDuvbI+Qil5EL01J01StG27Z4fB1eoFpbtkctntaA1euTlq3+WmOL8vTUReP16C+O1vKLf6SxRXlatvpreX0Ru8N+0Zyn/tAnnPZrtRlpZ/SYMHo8dhZq1DS4wj6GT1K4j2z7K3faP5dwlTtGn7PR5xKtzs7paM7VWIpXv1L89rVZ+JsaW+3P2c079qm0JD+oTbjqrrEDe0X8GzVlWN+Ia235X4fCvWZk26y68qQSPX/ZBNU0uvTV7roOX7eMiPThkoM5BxJZZ685nd1+sDhPkwfvIwEAAACkgrhVcp188snasmVL0LZLLrlEw4cP13XXXachQ4aoX79+WrlypcaObZ3CzeVyadWqVbrnnnskSePGjVNGRoZWrlyp8847T5JUUVGhjz/+WPfee68kaeLEiaqpqdF//vMfHXPMMZKkDRs2qKamRscdd1ygzV133aWKigr1799fkrRixQrZ7XaNGzcu0OaGG26Qy+WSzWYLtBkwYEDINIZt2e32oHW8kDyMXpiO5pPoRtvWNbuVbbOGrCHiX5OrvtktSXJ5PJo5YVDYaQ0vKS2WyxO8lkKs1uVJNdGcp9Gs1RbL6QBjGYaZUfEVzXOONvSJ1XHbFVUjidSvFN+AzQz8TY2t9ufs8jXb9NAFY2VRa8WPFFrdlW2zqneOTU9dND7kb5N/LS5rmqXDEKzt61D7/tuu/xWpAiwaqXYOGJGbldHhmp4NLo+przkS52kycXkir8nV/n0kAAAAACSiuIVcPXv21OjRo4O25eTkKD8/P7B9/vz5uvvuuzV06FANHTpUd999t7KzszVz5kxJksPh0KWXXqqrr75a+fn56t27t6655hqNGTNGP/7xjyVJI0aM0GmnnaY5c+boiSeekCRdfvnlOuusszRs2DBJ0tSpUzVy5EjNmjVL9913n/bu3atrrrlGc+bMUW5uriRp5syZuv3223XxxRfrhhtu0Jdffqm7775bt9xyS6fTJyI5Gb0wXdDDplNG9NWw/rkhF5Q+r3AGTedmtK0jKyPimlz+ReMtFkvEKaNuPmtkYFvFvsagYKa6oUX/2bZXJx7eR/0P4MJhd2ZkrTbJ2NpYRqcDjHUYFu00dkamxpOMBWLRhD6xXE8qmqkXpdiFa/HqV4pvwIbk0P6czc3K0O/PO0p1TW7VNrUoL/uH49cfQj2w4nOtbvN356ThffSny49VVZ1LTS0eWSRdeVJJUPDVVttgqW3/Xp9Pd7z6SYcVYAcytWB3PAeyMqz6oLw6eA2ykgI9P+dYPbmqTD1s1jiODokkzWKJuCZXWhr/xwEAAACQ+OIWchlx7bXXqrGxUfPmzVN1dbUmTJigFStWqGfPnoE2Dz74oNLT03XeeeepsbFRJ598sp555hlZrT/8B/65557TVVddpalTp0qSpk2bpqVLlwZut1qt+vvf/6558+aptLRUWVlZmjlzpu6///5AG4fDoZUrV+pXv/qVxo8fr7y8PC1cuFALFy7sgj2BeDAcSGXbdPNZI7XopS1BF5QmleTr7uljQoICI21z7OkdhlcWSb8/76jWDT5pc/m+Dj+xrf3TzNQ0uLR9b4Ne++j7kDUXigtylG2zUtFlkNG12iRjAY3R6qdYh2HRrhMVTfDSWSBm9LnEej0po/taim24Fq9+pfgGbEge4c7Zwtwfvvcfv0cM7BXytynbZtXMCYN0zxufhfx9eeiCsbrqhc0hQVf7YMnf/1e76oLCs7YOdP06/znw/vbqkOronc6mkHMg2dU0uHTTSx8HKvH8VpdVySef7p4+JvBBGaDTNbnSWJMLAAAAQOKz+Hw+ZlvvQk6nUw6HQzU1NYEqMSSu8j31WvTSlqD/+PsDqaL8HEmtF5SufGFz2MDghKEFQRfijbb9aledTn5gVYfj+tfCyTqsbw99UL5XVfUtIRcd/dMVFuTYdFRRnrZX1ev21z7RyAGOkDDs0+9rdOtZozSoIOeA91OqiXSeGv3dRHNcSNJOZ1PEii+pNQDpKCjxV+PVNLj06xc2dxhq+PvdXF6t6Y+u6/B5vDzvOB1VlBfoN5bBi9HnYnRfR8sf5HRUaRbt7y7R+zWyr/3tYv17Nht/U7tOTYNLFTVNOu3/rg7afuVJJdpcXh12esLSknyNLcrT0rfKAlPwHjckX5kZaXJk20JC1Ghel6JRsa9R2/c26OG3vgwa5/H7z4NEPb4PRGevm89dNkElfXuE/H0xE+dp4vp6V53ufP3TDt8f3nTGSA05gL+zAAAAANCVErqSC4inmgaXbnz545ALd2vK9uimlz8OXHCOpiLGaFujU87lZdt134ovOpyu8O5zxkiSGlrcEdfuamhxR+wPP0j0tbFivRZYrKuponkuZq2l01mlWbRVboner5F9bdbvGanDkW3T11X1IdvHDuwV9HelrbVlezS7tNjwWltmTS2YbbPqkbfKQv5Wrk7B47uz182axhZV17u6NORC4mruZG3XZtbkAgAAAJAECLmADsQ6kJKMX7Q3eqHP5fGG/fS81Hpx0eXxSmpdu+v5Dds1tihPs0uLgz6p+/yG7brutOER+8MP4r02lp9PkjpYKsNIqGF0Gjuzghep89AnXmvpmBWuxbPfeAVsSC3hzslmtzfifRxZGfrbr0oNrbXV9nXJX/nlry7Jy85Qj8wDe9taVecKmWK27RhS6fju7HXTnp7W6WsNug+LIr8/vPZU3h8CAAAASHyEXEAHYh1IRdPW6Hpgdc3ukAuBbdfkqm9urdCySBE/qQvj4rk2VjTTyXUWahit+DqQ4CVW6zrFaz2peIVr8epXil+wh+QS7py0p0desydv/zloZK0t/+vSrX/7WOcfU9Rp5ZdRyX58R/PaVtDDpuOHFmhjmDXIKmsa9fH3NZo6sl8XPwMkLEsn7w87+DANAAAAACQSQi6gA9EEUkYvxBsNrxzZNt181kgtemlL0EUH/3pg/otbjqyMsFNAlZbk66ELxgYWl7dIIet2ST9Ma3jrWaOM7pZuz2g4ZPS4MLviKxIjFV/RBi+xXNfJ6L6Odb/xCtei7TeW4hmwIXmEOyc379inSSX5WhMmxPIft+GmOWyrbcg0oFeW7pw+Rtf87wedVn4ZlczHd7SvbY5sm+46Z7S+29eopW+XBb03OH5ogX59Uol62qxdMnYkvs7eH95y1sg4jAoAAAAAokPIBXQgmkDqnhlH6J0vdqtvT3ug3U5nk6Yc3idkvSQj4ZXR9cBy7OkdXpywSPr9eUdJkrw+aXP5Pl15UknYii+vz2fCHkxd8VgbK9rp5IwGL51VfEUTvMQriIt1v/EK16LpV4pduCbFL9hD8ml/TuZmZejn4wfqhpe2dHjc5ma6Ij5m+5CprsltqPLLKP/x/X6Y6qadziZTA+SDcaCvbfYMqx59O/waZJJ0/8+ONG/QSCqdvz+M9wgBAAAAoHOEXEAHjAZSUuv6SK9/VBG05scJQws0+fA+QY9pNLwyGmjUNbk7XJNrTdke1TW5VZgrNbW4I1Z8NbUELyzOBWzjumptrGim24pX8GLWuk7xWE8qHuGa0X6l2P6OpfgFe0hO4c7JSMdttGttxXp6Qf+HUbbvbdDDb30ZUt00+fA+cmRH9ZBd4kBf25yNLR2GhKu/rJKzsUWFuZkxHSuSU6PLrUdmHq2Kmsag7QMcmXpk5tFqdLnjNDIAAAAAMI6QC+iA0UAqcKG7rPML3UYvWBm9wGe0XV62Xfet+KLD6WjuPmdMYBsXsDsXj7WxjFZ8xTN4ide6N2b1G49wzUi/ZvyOpfgFe0gNkY7baNfaMmN6wWybVY+8Fb66KVGP3QN9bdvXEPl++xoTew0ydJ1e2TY1u736+5aKoHOjtCRfV04pUa8EOycAAAAAIBxCLqADRi9gR3Oh2+gFK6MX+Iy2c3m8HVZ8rS3bI5fHK6n1AvYtf/tYRw7spYuPGxw0bc2tf/tY9//syIS7CNjVor3Ib6QqLpYVX/EKXqT4rXsTr37jFeqZ9TuW4hfsIfVFs9ZWtJVfRlTVuUI+jNK2/0Q8dg/0tS3bHnnNrWzW5MJ+FknLVn+tsUV5ml1aHPS+b9nqr3XzmazJBQAAACDxEXIBHYh1NZVk/IKV0UDD6Lphdc3ukAuFbddcqG9unY5mT71LPw/zKfvSknxdUlqsPfWJdxGwq0VzkT8eFV/RBi+psK5TtP3GSncL1+LdN5Kf0bW2oq38MiKRj92OXgsP9LUtM92qk4b30cgBjpC/+Z9+X6PMdEIutGpyezRzwqAO3/c1uT0R7g0AAAAAiYGQC+hArKupJOOhlH/9kHf+P3t3Hh9Vdf9//D0JmeyZQIYgUUBqIoKAIliLYCiutUXF0tpCS0Wt1q8LpaiVpS5YNq1bi9X6tW61StuvFKqt9Sd1Y3OpgAKKSCwaLZuJyQxZJ8v8/sAZM5nJzJ3kTu4sr+fj4eMh957cczI5526fOefzwWcqzs/0lzvgbtLkY/v7AwFG84Y5sjPC5uQqyD7cxtZ2rx7duKfLZQ1vPe/4sL9rKjD6otSqZQOj6Y/Jktcpmnol64JrZtVrVXDN6rqR+KIJNEUz88uIeO27kc6F0ZzbfPrYpAXfHKFFz+wIzD9WWqRbzh+pPl3kkUTqsdlsYe/7bprCTC4AAAAA8Y8gF9AFs2dTScaDUpLklfTctn0ByyuVlzk16dj+/n8bzRuWm9mny5cYNkl3XXSiJKm93autlbW65vTSkDO+2tq9UX+Oycboi9Jol3UzGgCJNOPLaL9NtrxORvOGWRVcM7Pe7szuSPRZc0gO0QaajM78MiKaa3VvMXIuNHpu68ieka5fPP1O0Ge3vqJai559V3dMGx2T3weJx+tV2Ps+L7d9AAAAABIAQS6gC0ZfYEcTuDIalPK/+KoIHwQwGkipa2rtMifXhopq1TW1akCB1NTSGnbGV1MLy9YYfckfzYwFKwIvyZjXKVK9VgXXzK432plrVs6aAzqKNkhq5hKD0Vyre4vRc6GRnIgdHQoTHFy/u0qHmlo1sFstRrJp9IS/72v0tFrYOgAAAAAwhiAXEIbRF9hGAleS8RdaRsuZnTesb06mfvXCB10uW7N06qiwx0kFRl/yG52xYFXgJRXzOlkVXItFvUZnd1g5aw7oLNogqZlLDEZzre4tsToXuhrD/1yk/Ugdjmy77lrb9X3fbeePtKJZAAAAABAVglxABGa+wDY7KGV23jBPW3uXM742VlTL09Ye9jipwshLfqMzFqwKvKRiXiergmuxqtfI7A6r+hfQlWiCpGYujxnL2avdFatzYU5meo/2I3W0tIe/72tp574PAAAAQPwjyAX0UDQvsM0OShnNMWK0XF1zq3Ls6bp04tCQuRnqm1m2xifSS36jMxaiDYBYlVvJrHqtrNuq4JqVAUUrZ+wBXTEaJDVzecx4Ggu+c5qr0aOVl5+ijR9W65ENe9Tg+XJJ4J7kuMvOSNe5IwfoO2MHqbggU3VNbcrP6qMD7iY9vfkTZWcQ5MJhdU3h7/vqmrjvAwAAABD/CHIBPRTNC2yzg1JGc4wYLpedETY3Q0F27F7GJyMjMxai6T9W5VYys14r67YquGZlQNHKABtgBrOWx4yXsRDqnDbxi2vs7JVb1eBp63GOu6w0m278xnAtXLM9YJbOxNIiLZ46SnZbj38NJImC7D4R7vt4VAQAAAAQ/2xer9fbnR984okn9Lvf/U579uzRa6+9piFDhujee+/V0KFDdcEFF5jdzqThdrvlcDjkcrlUUFBgdXNgAleDR9f/3ztdBqTu/O4JAS+qKqvrNX918IunpReO0uCi3KjKuRo8umbl1pBLMJWXOf05RoyWO+Bu0ty/vB1y6ZqJpUW666ITNaAgK+B3N+tlfDwxMk7N+t1dDR5du3JrlwGQaP+G3ak/Us65WNRrVd17axu7DK4N7BA0MzuwZ1W9RvtXx/KJNKa5psKoaK/VsWpDV+e008qcunnKCKXZbD3Ocbe3tlE3PP1Ol9fyO75zQrfOJ93FOI1fn9Y06Oa/7dCIEkfQuNi516VFF4zUUX1zrG4mAAAAAITVra/nPfDAA7r55ps1Z84cLVmyRG1th5dXKSws1L333kuQCynF6CwpyXjie6PljOYYMVqurqm1y9wMGyqqVdfUqgFfvJ8y+2V8IrFiRpVVuZVimcfGirqNzApxNXiC/r6+Ouet2tat4JpV9Vo5Yw+IJ9Fcq2Ml3Dlt/e4qpdlsOqY4r8f1uBtbwl7L3Y0tjGlIOrxM9YxThoScyXXJhKGqY5lqAAAAAAmgW0GuFStW6KGHHtLUqVO1fPly//Zx48bp+uuvN61xQCIwGpCSjL+0N1rOaI4Rs8vF4mV8orAqAGJVPhkr89jEqm6rAntW1WtVgA2IJ9Fcq2Olt86n7sbwx4nUDqQQr/Toxj1B48L375u+NcKKVgEAAABAVLoV5NqzZ4/GjBkTtD0zM1P19fU9bhSQSKJ5MW12sMlojpFoyoVLQO4rV1Xn0eaPa3TN6aUhy/Vkdk+8izYQYXT5t0gBkGjzyZi17JyVeWysqjsZA4pWztgD4oEVfbzzebhfjl059nQ1eNpCljfrnJafbexaDnglba2s7fJ+rltr2gMAAABAL+tWkGvo0KF6++23NWTIkIDt//znPzViBN/4Q2qJ5sW02UEpZ55dZw0v7jLHiDPPHnW5R2adrBUv7Q5atuaRWSf7y9U1t4RNVF7fnLzfEo/m723m8m/OPLvKy5xd5lby/W2srNfHrABbtHUnemDPyoCilTP2gN7Q2328q/PwI7NO1qWP/Tso0NXV+bQ7cu3pejTEtfy00iI9Outk5drTTakHia/R0xr2fq7Rw3KFAAAAAOJft4JcN9xwg66++mo1NTXJ6/XqzTff1MqVK7Vs2TL9/ve/N7uNQFyL5sW00Zf2RoNSRnOMRJOL5LcvVYRctibNZtN90w/P4CzMtuuO/7ery+Vtlk4dFfYzSWRG/95mL/9mNLeSVfX6WJGvzOx6rQqudSegaBYrA2xAb+jNPh7uPOyVDl+P/7rdv72r82l3pUu676Xd2tDpGr2+olqy2bR86khT6kHi65tj111rP+jyfm7xBfQVAAAAAPGvW0GuSy65RK2trfr5z3+uhoYGzZgxQ0ceeaR+/etf6/vf/77ZbQTimtGAlHT4pf3t00brlQ8+U3F+pr/sAXeTJh/bP+qglNEcI0bLVdV5tL4i9HJO6zss5+Rpa+8yqf3Gimp52tqj/yAThNFARCyWxjKSW8mqeiXr8pVZGdizKqgnmRdck6wL7AG9pTeDyOHOw+t3V+nmKSP04txJYc+nPVHX0nY4oNVF/XUtoZdLROppjnA/15zE93MAAAAAkke3glySdPnll+vyyy9XVVWV2tvbVVxcbGa7gIQRzSwp6XD+g+e27QsIJpWXOTXp2P7+f0cVlDIQ0DBazuhyTnXN4ZevqY+wP5EZDUTEammsSLmVrKpXil3OGyvySVkRXDNar2RucE2yLrAH9JZog8g9Eek8XN/cqhMH9zWtvqD6G8NfgyPtR+pwN4bvq5H2AwAAAEA86HaQy8fpdJrRDiBhGQ1I+creuGpb0Gypzi/FzQ5KGS1nds6wZGUkEEFep2CxyutkVWDPqqBeLIJrknWBPaC3GA0i95TV18icCDm3cjLJyYXDIvXFZL+fAwAAAJAcuhXkGjp0qGw2W5f7//Of/3S7QUCiieZFt9nBK7ODUmbnDEtmkQIR5HUKlmyBPauCerEKrknWBfaA3mJkVmpPWXkelqRse7pOP66/RpQ4gq7R7+11KTuDIBcOy7Gn64zj+mt4iL6yc68rYsAUAAAAAOJBt4Jcc+bMCfh3S0uLtm7dqueff1433HCDGe0CEkY0L7rNDl6ZHZQyO2dYKiOvU6BkDOylWnDN6rqBRNGbSyOGkmGzacE3R2jRMzsCrtGnlRbplvNHKiPMF9WQWtK90k1TjtfCNcH3c4unjlK618LGAQAAAIBB3Qpy/fSnPw25/be//a3eeuutHjUISDTRvOiOJnhlVVDKzJxhqY68Tl8eMxkDe1YF9axcCs3qZdiARNFbSyOGZJMWPbND6ztdo9dXVGvRs+9q6dSRsW8DEoLXJt20envI+7mb/raDvgIAAAAgIfQ4J1dH5557rubPn69HH33UzMMCcS2aF93RBK+sCEqZnTMM5HVK5sCeVUG97sxcS/RZc0Ai6o2lEUNpaGkLCnD5rN9dpYaWtl5uEeIVfQUAAABAMjA1yPX000+rX79+Zh4SiHvRvOg2GryyKihlds4wREZeJ/PqtiKwZ0VQL9qZa1bOmgPQ+9yNrT3aj9RBXwEAAACQDLoV5BozZoxsHdbz93q92r9/vz777DPdf//9pjUOSBRGZ5EYDV5ZFZQyO2cYIiOvU+/VbVVwLRb1RnPOsWrWHABr5NjTw+/PDL8fqYO+AgAAACAZdCvINXXq1IB/p6WlqX///vr617+u4447zox2AQnHyLJE8R6UMjtnmI9ZS6XFE/I6dV+qBfZiVa+Z55xY1A3AGtn2dJ07coC+M3aQigsyVdfUpvysPjrgbtLTmz9RdgaBCxxGXwEAAACQDLoV5LrlllvMbgeQEuI9KBWLnGFmLpUWL1Ixr5OZgcpUC+xZGVBkaVEg9djTbLrxG8O1cM32gJnjE0uLtHjqKPWxhflhpBT6CgAAAIBkkGa0oNvtNvyfUQ888IBGjx6tgoICFRQUaPz48frnP//p3+/1enXrrbeqpKRE2dnZ0aW3IQABAABJREFU+vrXv65333034BjNzc269tpr5XQ6lZubq/PPP1+ffvppQJmamhrNnDlTDodDDodDM2fOVG1tbUCZyspKnXfeecrNzZXT6dTs2bPl8XgCymzfvl2TJk1Sdna2jjzySN12223yer2Gf18g2qDUNaeX6uGLx+n+H5ykR2adrGtOL9VZw4uDglKRyvmCUlsra3TZ42/pqie36NLH/q23K2t005QR/oCB0XKRll10NXj85cItleYrl0hi8Tv5ln97ce4krbnqVL04d5JWTB+jgR0CV2bX6wuulZc5A7Z3FVy7ZuVWnXH3q7rw/k06465Xde3Krdpb2xj172pl3b7gWihdBdc+PFinrZU1+vCzum73V6vqlVhaFEhVnYMWku8avd2iFiFe0VcAAAAAJDrDM7kKCwsD8nCF4vV6ZbPZ1NbWZuiYRx11lJYvX67S0lJJ0uOPP64LLrhAW7du1fHHH6877rhDd999tx577DEde+yxWrx4sc466yzt2rVL+fn5kqQ5c+bo2Wef1Z/+9CcVFRXpuuuu05QpU7R582alpx9eYmPGjBn69NNP9fzzz0uSrrjiCs2cOVPPPvusJKmtrU3f+ta31L9/f23YsEHV1dW6+OKL5fV6tWLFCkmHg3xnnXWWJk+erH//+9/64IMPNGvWLOXm5uq6664z+jEixZk9U8poOaO5wKzKGZZIUi2vUyxyOllVt1Wz5qyqV7J2xh4AaxzytAVdx302VFTrkMfYfTqSH30FAAAAQDIwHOR6+eWXTa/8vPPOC/j3kiVL9MADD+j111/XiBEjdO+992rhwoX69re/LelwEGzAgAF66qmn9JOf/EQul0sPP/ywnnjiCZ155pmSpD/+8Y8aNGiQ/vWvf+mcc87Rzp079fzzz+v111/XKaecIkl66KGHNH78eO3atUvDhg3TCy+8oPfee0+ffPKJSkpKJEl33XWXZs2apSVLlqigoEBPPvmkmpqa9NhjjykzM1MjR47UBx98oLvvvltz586NGAAEpOQJSpmdMyyRpFpep1gGKlMpsGdVvVYG2ABY41Bj8l17ERv0FQAAAADJwHCQa9KkSbFsh9ra2vR///d/qq+v1/jx47Vnzx7t379fZ599tr9MZmamJk2apE2bNuknP/mJNm/erJaWloAyJSUlGjlypDZt2qRzzjlHr732mhwOhz/AJUlf+9rX5HA4tGnTJg0bNkyvvfaaRo4c6Q9wSdI555yj5uZmbd68WZMnT9Zrr72mSZMmKTMzM6DM/Pnz9dFHH2no0KEx/XyQHJIlKGV2zrBEkmp5nawMVCZbYM+qeq2csQeg9+VnJ9+1F7FBXwEAAACQDAwHuUJpaGhQZWVlUO6q0aNHGz7G9u3bNX78eDU1NSkvL0+rV6/WiBEjtGnTJknSgAEDAsoPGDBAH3/8sSRp//79stvt6tu3b1CZ/fv3+8sUFxcH1VtcXBxQpnM9ffv2ld1uDyhz9NFHB9Xj29dVkKu5uVnNzc3+f0eTswzJJ1mCUkaXXTRazmrRjFOrln+zql4rA5WpFtiLZb1WztgzE9dUILIce7rOOK6/hpc4gq69O/e6lGNPj2n9jNPEYXVfAQAAAAAzdCvI9dlnn+mSSy7RP//5z5D7jebkkqRhw4bp7bffVm1trVatWqWLL75Yr776qn9/52UAfXm/wulcJlR5M8p4vd4uf9Zn2bJlWrRoUdj2InVYGZQyEiCxKmeY1aIZp6mW1yna4JpkXWDPLKk2W09KnKVFuaYCkfWRdNOU47VwTfC1d/HUUT37hpsBjNPEYXVfAQAAAAAzdOvZZc6cOaqpqdHrr7+uyZMna/Xq1Tpw4IAWL16su+66K6pj2e12lZaWSpLGjRunf//73/r1r3+tG2+8UdLhWVIDBw70lz948KB/BtURRxwhj8ejmpqagNlcBw8e1Kmnnuovc+DAgaB6P/vss4DjvPHGGwH7a2pq1NLSElDGN6urYz1S8GyzjubPn6+5c+f6/+12uzVo0KBwHwmSmJVBqdunjdYrH3ym4vxMf7kD7iZNPrZ/1EEps3OGWS3acZpKeZ2iCa5J1gX2pMSfNWdVUE9KnKVFuaYCkXkl/WLN9i6uvdu1/MJRMa2fcZo4rO4rAAAAAGCGbgW5XnrpJf3tb3/TySefrLS0NA0ZMkRnnXWWCgoKtGzZMn3rW9/qdoO8Xq+am5s1dOhQHXHEEVq7dq3GjBkjSfJ4PHr11Vd1++23S5LGjh2rjIwMrV27VhdddJEkad++fdqxY4fuuOMOSdL48ePlcrn05ptv6qtf/aok6Y033pDL5fIHwsaPH68lS5Zo3759/oDaCy+8oMzMTI0dO9ZfZsGCBfJ4PLLb7f4yJSUlQcsYdpSZmRmQxwupzaqglHT4RcZz2/ZpfUVgsGDSsf39/7YqZ5jVujNOUymvk5HgmmRdYE9KjllzVgX1JOsCe9HimgpEVudp04ZO13GfDRXVqvMYX3GhOxinicPqvgIAAAAAZuhWkKu+vt6f56pfv3767LPPdOyxx2rUqFHasmWL4eMsWLBA5557rgYNGqRDhw7pT3/6k1555RU9//zzstlsmjNnjpYuXaqysjKVlZVp6dKlysnJ0YwZMyRJDodDl112ma677joVFRWpX79+uv766zVq1CideeaZkqThw4frG9/4hi6//HI9+OCDkqQrrrhCU6ZM0bBhwyRJZ599tkaMGKGZM2fqV7/6lT7//HNdf/31uvzyy1VQUCBJmjFjhhYtWqRZs2ZpwYIF2r17t5YuXaqbb7454vKJgI9VQSl/8KEifPDBqpxhySjZ8jpFCq5J1gX2kmXWnNF6JXODa5J1gT0A5nM3hj/PR7pOIHXQVwAAAAAkg24FuYYNG6Zdu3bp6KOP1oknnqgHH3xQRx99tH73u98FLC0YyYEDBzRz5kzt27dPDodDo0eP1vPPP6+zzjpLkvTzn/9cjY2Nuuqqq1RTU6NTTjlFL7zwgvLz8/3HuOeee9SnTx9ddNFFamxs1BlnnKHHHntM6elfJkp+8sknNXv2bJ199tmSpPPPP1/33Xeff396err+8Y9/6KqrrtKECROUnZ2tGTNm6M477/SXcTgcWrt2ra6++mqNGzdOffv21dy5cwOWYwEisSooZXbwyuycYcmIvE7BYhXYS6ZZc0bqjUVwTbIusAfAXPnZqXvtRXToKwAAAACSQbdzcu3bt0+SdMstt+icc87Rk08+Kbvdrscee8zwcR5++OGw+202m2699VbdeuutXZbJysrSihUrtGLFii7L9OvXT3/84x/D1jV48GD9/e9/D1tm1KhRWrduXdgyQDhWBaXMDl6ZnTMsGZHXKVisXpYl26y5SGK5DKhVgT0A5snJSNcZx/XX8BJH0LV3516XcjLSIx8EKYG+AgAAACAZdCvI9YMf/MD//2PGjNFHH32k999/X4MHD5bT6TStcUCysSooFU3wyqqcYcmGvE6BYhnYS7VZc1YuA5rKS5ACCcPr1U1TjtfCNcHX3sVTR0ler4WNQ1yhrwAAAABIAt0Kcr366quaNGmS/985OTk66aSTTGsUkKysCkpFE7yyImdYsiKv05fHjGVgL1FmzSV6UM/qugEYk55m0/zV20Nee2/62w4tmzrSopYh3tBXAAAAACSDbgW5zjrrLB1xxBGaMWOGfvjDH2rkSB6AACPMXubPaFDKaDmrcoYlM/I6xT6wlwiz5qwM6knJsRwmAGPqW9q0vtN13Gf97irVt7T1cosQr+grAAAAAJJBt4Jce/fu1Z/+9CetXLlSd9xxh0aOHKkf/vCHmjFjho466iiz2wgkDaMvxWMRlDJSzqqcYamMvE7m1B3Ps+asDOpJ1i6HCaD3uRtbe7QfqYO+AgAAACAZdCvI5XQ6dc011+iaa67Rnj179NRTT+kPf/iDFixYoPLycr300ktmtxNIGkZfisdzUMrsnGGpjLxO5tUdr7PmrArqSdYuhwnAGjn29PD7M8PvR+qgrwAAAABIBt0KcnU0dOhQzZs3TyeccIJuuukmvfrqq2a0C0hqZr0UtyooZXbOMB+zllTrDeR16r5UC+xZFdSTrAvsAbBOtj1d544coO+MHaTigkzVNbUpP6uPDrib9PTmT5SdQeACh9FXAAAAACSDHgW5Nm7cqCeffFJPP/20mpqadP7552vp0qVmtQ1IWfEelDI7Z5hk7pJqsZaKeZ3MDECmWmDPyoAiS4YCqceeZtON3xiuhWu2B8wIn1hapMVTR6mPzcLGIa7QVwAAAAAkg24FuRYsWKCVK1dq7969OvPMM3Xvvfdq6tSpysnJMbt9QEqK96BULHKGmb2kWqykYl4nswOQqRbYszKgyJKhQGrqHLSQfNfe7Vp+4SiLWoV4RF8BAAAAkOi6FeR65ZVXdP311+t73/uenE6n2W0CUl4iBKWsyBkWD1Itr1OsApCpFNizMqBoZYANgDUOedqCrs8+GyqqdcjT1sstQryirwAAAABIBt0Kcm3atMlQuW9961v6/e9/r4EDB3anGiBlJUtQyuycYfEg1fI6xTIAmUqBPavqtTLABsAahxoT55oKa9FXAAAAACSDHuXkimTdunVqbGyMZRVAUkqWoJTZOcPiQarldbIyAJlsgT2r6rVyxh6A3pefnTjXVFiLvgIAAAAgGcQ0yAWge5IlKGV2zrB4kGp5nawMQKZaYC+W9Vo5Yw9A78qxp+uM4/preIkj6Jq6c69LOfZ0q5uIOEFfAQAAAJAMCHIBccjKoJSRQIpVOcPiQarldYo2uCZZF9gzS6rN1pMSa8lQAOH1kXTTlOO1cE3wNXXx1FHc/MOPvgIAAAAgGfDsAsQhK4NSt08brVc++EzF+Zn+cgfcTZp8bP+og1Jm5wyLF6mU1yma4JpkXWBPSvxZc1YF9aTEWjIUQHheSb9Ys72La+p2Lb9wlDUNQ9yhrwBINZWVlaqqCr16gRFOp1ODBw82sUUAAMAMBLmAOGRVUEo6/MLjuW37tL4iMKgw6dj+/n9blTMsnqRSXicjwTXJusCelByz5qwK6knWBfYAmK/O06YNna7PPhsqqlXnaevlFiFe0VcApJLKykodd9xwNTY2dPsY2dk5ev/9nQS6AACIMwS5gDhkVVDKH6SoCB+ksCpnWCJJtrxOkYJrknWBvWSZNWe0Xsnc4JpkXWAPgPncjeHP85GuE0gd9BUAqaSqqkqNjQ065dJbVDDw6Kh/3r3vI73xyCJVVVUR5AIAIM7ENMi1YMEC9evXL5ZVAEnJqqCU2cErs3OGJRLyOgWLVWAvmWbNGak3FsE1ybrAHgBz5Wcn3zUVsUFfAZCKCgYerX6Dh1ndDAAAYKJuB7meeOIJ/e53v9OePXv02muvaciQIbr33ns1dOhQXXDBBZKk+fPnm9ZQIJVYFZQyO3hlds6wREJep2CxelmWbLPmIonl8p5WBfYAmCcnI11nHNdfw0scQdfUnXtdyslIt7qJiBP0FQAAAADJoFtBrgceeEA333yz5syZoyVLlqit7fB67YWFhbr33nv9QS4A3WNVUCqa4JVVOcMSBXmdAsUysJdqs+asXN4zGZcWBZKO16ubphyvhWuCr6mLp46SvF4LG4e4Ql8BAAAAkAS6FeRasWKFHnroIU2dOlXLly/3bx83bpyuv/560xoHpCqrglLRBK+syBmWaMjr9OUxYxnYS5RZc4ke1LO6bgDGpKfZNH/19pDX1Jv+tkPLpo60qGWIN/QVAAAAAMmgW0GuPXv2aMyYMUHbMzMzVV9f3+NGAanO7GX+jAaljJazKmdYIiKvU+wDe4kwa87KoJ6UHMthAjCmvqVN6ztdn33W765SfUtbL7cI8Yq+AgAAACAZdCvINXToUL399tsaMmRIwPZ//vOfGjFihCkNA1KZ0ZfnsQhKGSlnVc6wZEReJ3PqjudZc1YG9SRrl8ME0Pvcja092o/UQV8BAAAAkAy6FeS64YYbdPXVV6upqUler1dvvvmmVq5cqWXLlun3v/+92W0EUpLRl+fxHJQyO2dYMiKvk3l1x+usOauCepK1y2ECsEaOPT38/szw+5E66CsAAAAAkkG3glyXXHKJWltb9fOf/1wNDQ2aMWOGjjzySP3617/W97//fbPbCKQss16eWxWUMjtnWDIir1Pv1W1VYM+qoJ5kXWAPgHWy7ek6/bj+GlHiCLqmvrfXpewMAhc4jL4CAAAAIBl0K8glSZdffrkuv/xyVVVVqb29XcXFxWa2C4AB8R6UMjtnWDJKlLxOZgXXrKzbquCalQHFVF4KFEhVGWk2LfjmCN3yzI6ga+qt549URprNwtYhntBXAAAAACSDbgW5Fi1apB/+8Ic65phj5HQ6zW4TAIPiPShlds6wZBXveZ3MDK5ZWbdVwTUrA4qpvBQokKrSJC16JvQ1ddGz72r51JHWNAxxh74CAAAAIBl0K8i1atUq3XbbbTr55JP1wx/+UN/73vfUv39/s9sGIIJ4D0qZnTMsmcVrXqdY5HSyqm6rgmtWBhStDLABsEZdS5vWd7ru+qzfXaW6lrZebhHiFX0FAAAAQDLoVpBr27Ztevfdd/Xkk0/q7rvv1ty5c3XmmWfqhz/8oaZOnaqcnByz2wkghHgPSpmdMyyVWZXXKZYByFQK7FlVr5UBNgDWcDe29mg/Ugd9BQAAAEAy6HZOruOPP15Lly7V0qVLtXHjRj311FOaM2eOrrzySrndbjPbCKAL8R6UMjtnWCqz6jOyMgCZbIE9q+q1csYegN6XY08Pvz8z/H6kDvoKAAAAgGSQZsZBcnNzlZ2dLbvdrpYWZlwAvcXIS3HJeLDA7KBUNDnDystC5/cLtaRaKrLqM7IyAJlqgb1Y1uvIseuY4jydOLivjinOCwpYGT2XAIh/2fZ0TSgtCrlvQmmRsjMIXOAw+goAAACAZNDtmVx79uzRU089pSeffFIffPCBysvLdeutt+q73/2ume0DEIbVQalIeX6iyRl2+7TReuWDz1Scn+kvd8DdpMnH9mcGiaJbdk4yL7dStDmdrK7bDFYF16wMKLJkKJA8+qTZNOeMMk0ZNVDFBVlfXlNdjSotzlOfNJvVTUScoK8AAAAASAbdCnKNHz9eb775pkaNGqVLLrlEM2bM0JFHHml22wBEEO9BKaM5wyTJK+m5bfu0viIwgDPp2P7d+3CSkJFl5yRzcytFG1yzsm6rgmuJHtSTWDIUSCYZkorzs/SbF3drfYfljE8rLdLiqaPEaIYPfQUAAABAMuhWkGvy5Mn6/e9/r+OPP97s9gCIQrwHpYzmDPPnA6ogH1AkkfI6xSK3ktHgmpV1WxVcS4agnmRdYA+A+bzpaVrw9DtB1971FdVauGa77vjOCRa1DPGGvgIAAAAgGXQryLV06VKz2wGgG+I9KGUkz0805RBZrD7LSME1K+u2KriWLEE9ybrAHgDzuRtbgq7jPhsqquVubGGsQhJ9BQAAAEBySDNacO7cuaqvr/f/f7j/jFq2bJlOPvlk5efnq7i4WFOnTtWuXbsCyni9Xt16660qKSlRdna2vv71r+vdd98NKNPc3Kxrr71WTqdTubm5Ov/88/Xpp58GlKmpqdHMmTPlcDjkcDg0c+ZM1dbWBpSprKzUeeedp9zcXDmdTs2ePVsejyegzPbt2zVp0iRlZ2fryCOP1G233Sav12v4dwbMFCko5Wrw+MuFC0r5yhkJUkRTzmieH/IBmcfKz9Kquo32x2g5cuw6pjhPJw7uq2OK84ICTVbVGym45hvP0fIF2F6cO0lrrjpVL86dpBXTx2hghxecsaobgHncjeHPtZHO1Ugd9BUAAAAAycDwTK6tW7eqpaXF//9mePXVV3X11Vfr5JNPVmtrqxYuXKizzz5b7733nnJzcyVJd9xxh+6++2499thjOvbYY7V48WKdddZZ2rVrl/Lz8yVJc+bM0bPPPqs//elPKioq0nXXXacpU6Zo8+bNSk9PlyTNmDFDn376qZ5//nlJ0hVXXKGZM2fq2WeflSS1tbXpW9/6lvr3768NGzaourpaF198sbxer1asWCFJcrvdOuusszR58mT9+9//1gcffKBZs2YpNzdX1113nSmfCRANs2dKmR2UMprnh3xA5rHys7SqbquCa/Ec1OvuzMdIs+aYdQnEv/xsrqkwhr4CAAAAIBkYDnK9/PLLIf+/J3wBJ59HH31UxcXF2rx5s8rLy+X1enXvvfdq4cKF+va3vy1JevzxxzVgwAA99dRT+slPfiKXy6WHH35YTzzxhM4880xJ0h//+EcNGjRI//rXv3TOOedo586dev755/X666/rlFNOkSQ99NBDGj9+vHbt2qVhw4bphRde0HvvvadPPvlEJSUlkqS77rpLs2bN0pIlS1RQUKAnn3xSTU1Neuyxx5SZmamRI0fqgw8+0N133625c+fKZrOZ8rkARsV7UMpozjCj5RCZlbmVrKrbquBaqgX1rK4bgDE59nSdcVx/DS9xBF1Td+51KceebnUTESfoKwAAAACSQbdycl166aX69a9/7Z9J5VNfX69rr71WjzzySLca43K5JEn9+vWTJO3Zs0f79+/X2Wef7S+TmZmpSZMmadOmTfrJT36izZs3q6WlJaBMSUmJRo4cqU2bNumcc87Ra6+9JofD4Q9wSdLXvvY1ORwObdq0ScOGDdNrr72mkSNH+gNcknTOOeeoublZmzdv1uTJk/Xaa69p0qRJyszMDCgzf/58ffTRRxo6dGjQ79Tc3Kzm5mb/v91ud7c+GyCUeA9KGc0ZFk1usVhIpnFqZW4lq+q2KriWakE9q+uWkmusArGSIemmKcdr4Zrga+riqaMU67k5jNPEYXVfAQAAAAAzdCvI9fjjj2v58uVBQa7Gxkb94Q9/6FaQy+v1au7cuZo4caJGjhwpSdq/f78kacCAAQFlBwwYoI8//thfxm63q2/fvkFlfD+/f/9+FRcXB9VZXFwcUKZzPX379pXdbg8oc/TRRwfV49sXKsi1bNkyLVq0KPIHAHSD0ZfsVgWlIuUMWzF9jBw5dsPlYiXZxqkvt1JVnUeHmlqUn5UhZ15gQCVSbqXufuZW1G1VcC1RgnqSdYE9syXbWAViwZtm0y9Wbevimrpdt08bHdP6GaeJw+q+AgAAAABmiCrI5Xa75fV65fV6dejQIWVlZfn3tbW16bnnngsZTDLimmuu0bZt27Rhw4agfZ2XAfR6vRGXBuxcJlR5M8p4vd4uf1aS5s+fr7lz5/r/7Xa7NWjQoLBtB4wy+pLdqqCU2TnDYiUZx6mVuZWsqNuqwF68B/Uk6wJ7sZCMYxUw26GmVm3odH322VBRrUNNrTGtn3GaOKzuKwAAAABghqiCXIWFhbLZbLLZbDr22GOD9ttstm59c/Paa6/VM888o3Xr1umoo47ybz/iiCMkHZ4lNXDgQP/2gwcP+mdQHXHEEfJ4PKqpqQmYzXXw4EGdeuqp/jIHDhwIqvezzz4LOM4bb7wRsL+mpkYtLS0BZXyzujrWIwXPNvPJzMwMWN4QMJvRl+xWBKXMzhkWK6k4TpMxr5NVgb14DepJ1gX2YiUVxyoQLXdj+HNopHNwTzFOE4fVfQUAAAAAzBBVkOvll1+W1+vV6aefrlWrVvlzZ0mS3W7XkCFDAnJaReL1enXttddq9erVeuWVV4KW+xs6dKiOOOIIrV27VmPGjJEkeTwevfrqq7r99tslSWPHjlVGRobWrl2riy66SJK0b98+7dixQ3fccYckafz48XK5XHrzzTf11a9+VZL0xhtvyOVy+QNh48eP15IlS7Rv3z5/QO2FF15QZmamxo4d6y+zYMECeTwe2e12f5mSkpKgZQyB3mTWS3azg1Jm5wyDeVIxr5NVgT2rgnqSdYE9ANbJz+aaCmPoKwAAAACSQVRBrkmTJkmS9uzZo8GDB0dcMjCSq6++Wk899ZT+9re/KT8/3z9LyuFwKDs7WzabTXPmzNHSpUtVVlamsrIyLV26VDk5OZoxY4a/7GWXXabrrrtORUVF6tevn66//nqNGjVKZ555piRp+PDh+sY3vqHLL79cDz74oCTpiiuu0JQpUzRs2DBJ0tlnn60RI0Zo5syZ+tWvfqXPP/9c119/vS6//HIVFBRIkmbMmKFFixZp1qxZWrBggXbv3q2lS5fq5ptv7vFnAcSSVUEpo7nAjJaDeaLNrWRWTicr67YquGZlQNHqWZIAel+OPV1nHNdfw0scQdfUnXtdyrGnW91ExAn6CgAAAIBkEFWQy+ell15SXl6evvvd7wZs/7//+z81NDTo4osvNnScBx54QJL09a9/PWD7o48+qlmzZkmSfv7zn6uxsVFXXXWVampqdMopp+iFF15Qfn6+v/w999yjPn366KKLLlJjY6POOOMMPfbYY0pP//LB7Mknn9Ts2bN19tlnS5LOP/983Xffff796enp+sc//qGrrrpKEyZMUHZ2tmbMmKE777zTX8bhcGjt2rW6+uqrNW7cOPXt21dz584NyDsAxCOrglJGc4EZLQfzRJNbycycTlbWbVVwzcqAIrMkgdRjl3TTlOO1cE3wNXXx1FHiigof+goAAACAZGDzer3eaH9o2LBh+t3vfqfJkycHbH/11Vd1xRVXaNeuXaY1MNm43W45HA65XC7/DDEg1lwNHl3/f+90GZS687sn+F+iV1bXa/7q7QH5u3zBpsFFuf5tRsq5Gjy6ZuXWkMullZc5/fmAjJbrLak0Tn0BlXD53GL1t7Gi7r21jV0G1wZ2CJqZHdizql5Xg0fXrtzaZYCt82doZoCtN6TSWAWM2lvbqJ8//Y42dMrDKR2+Tt/xnRO6dT7pLsZp/Iq3vgIAsbRlyxaNHTtWZy18VP0GD4v65z+v3KW1Sy7R5s2bddJJJ8WghQAAoLu6NZPr448/DsqfJUlDhgxRZWVljxsFwFxGZ0q5GjxauGZHQOBKkjZUVOsXa3YEBKWMlDOaDyhWeYMQmVn53BKl7pLCbK2YPiZicK1zoMlX57xV27oVXLOqXitn7AGwhruxJWTQQjp8nXY3tjCmIYm+AgAAACA5dCvIVVxcrG3btunoo48O2P7OO++oqKjIjHYBMJFVQSmj+YDIGxS/rPzbxKpuqwJ7VtVrVYANgDXcjeHPjZHOrUgd9BUAAAAAyaBbQa7vf//7mj17tvLz81VeXi7p8FKFP/3pT/X973/f1AYC6DmrglJG8wGRNyh+Wfm3sapuqwJ7sazXyhl7AHpXfjbXVBhDXwEAAACQDLoV5Fq8eLE+/vhjnXHGGerT5/Ah2tvb9aMf/UhLly41tYEAes6qoJQzz67yMmeX+YCceXZ/ubOGF3eZM8xXDr3P6N+wI7PyOnWnbjNYFVyzMqDIbEogeeTY03XGcf01vMQRdE3dudelHHu61U1EnKCvAAAAAEgG3Qpy2e12/fnPf9Yvf/lLvfPOO8rOztaoUaM0ZMgQs9sHwARWBaUcOXbdPm20XvngMxXnZ/rLHXA3afKx/f2BD6M5w9D7osnpJJmb1ynauq0KriV6UE9iNiWQTDIk3TTleC1cE3xNXTx1lBjN8KGvAAAAAEgG3Qpy+Rx99NHyer065phj/DO6AMQfK4NSXknPbdun9RWBQYpJx/b3/9tozjBYw0hOJyk2eZ2M1m1VcC0ZgnqSdYE9AOZLz0jXTX95O+Q19aa/7dCd3z3BopYh3tBXAAAAACSDbkWmGhoadO211+rxxx+XJH3wwQf6yle+otmzZ6ukpETz5s0ztZEAesaqoJQ/6FERPuhBPqD4FymnkxS7vE6R6rYquJYsQT3JusAeAPO5Glq0vtP12Wf97iq5Glo0oCCrl1uFeERfAQAAAJAM0rrzQ/Pnz9c777yjV155RVlZXz74nHnmmfrzn/9sWuMAmCNSUMrV4PGXCxeU8pUzEsyIphz5gJKDVX9Ho/0sWo4cu44pztOJg/vqmOK8oECTVfVGCq75xmm0fAG2F+dO0pqrTtWLcydpxfQxGtghcBWrugGYx9UY/lwbaT9SB30FAAAAQDLo1kyuNWvW6M9//rO+9rWvyWaz+bePGDFCH374oWmNA2AOozNsjJYzGswwWo58QMnBqr+jVcG1eA7qdXfmY6RZc8y6BOJfjj09/P7M8PuROugrAAAAAJJBt4Jcn332mYqLi4O219fXBwS9AMQHq4JSRssZzRmG+GZVXiergmupFtSzum4AxmTb03X6cf01osQRdE19b69L2RkELnAYfQUAAABAMuhWkOvkk0/WP/7xD1177bWS5A9sPfTQQxo/frx5rQNgCquCUkbLRZMzDPHLqrxOVgXXUi2oZ3XdAIyxp9m04JsjdMszO4KuqbeeP1L2NL6QhsPoKwAAAACSQbeCXMuWLdM3vvENvffee2ptbdWvf/1rvfvuu3rttdf06quvmt1GAD1k9GW82UEpo+Ui5QxbMX0Mga4E4cvrVFXn0aGmFuVnZciZFxjMiZTXKdq/t1XBtUQJ6knWBfYA9L709DQtWv1OyGvqomff1R3TRlvUMsQb+goAAACAZNCtINepp56qjRs36s4779QxxxyjF154QSeddJJee+01jRo1yuw2Aughoy/jzQ5KGS1Hnp/kYkVeJyuCa1bVG01wTbIusAfAGoeaWrW+03XXZ/3uKh1qatXAXm4T4hN9BQAAAEAy6FaQS5JGjRqlxx9/3My2AIghoy/jzQxKGS1Hnp/UEqu/txXBNavqNTKeJesCewCs42oMfw6NtB+pg74CAAAAIBkYDnK53W4VFBT4/z+cnJwc9enT7fgZgBgx62W80SCF0XLk+UktVv29rQqmWhXUk6wL7AGwTk5meo/2I3XQVwAAAAAkA8ORqL59+2rfvn0qLi5WYWGhbLauExHbbDaVlZXp/vvv1+TJk01pKIDYMzsoZbSc0VxgSA7R5nUyK5+UVcE1K4O4zJIEUk92RrpOP66/RpQ4gq6p7+11KTuDwAUOo68AAAAASAaGg1wvvfSS+vXrJ0l6+eWXw5Ztbm7WmjVr9D//8z96//33e9ZCAL3G7KCU0XJGc4EhOUST18nMfFJWBdesqldiliSQijLTbFrwzRG65ZkdQdfUW88fqcy0rr+ohtRCXwEAAACQDAwHuSZNmhTy/7ty4okn6s033+xeqwBYwuyglNFyRnOBIXkYzRFnZj4pq4JrVtUrWRtgA2CNPhnpWrT67ZDX1EXPvqs7v3uCRS1DvKGvAAAAAEgG3U6c1dbWptWrV2vnzp2y2WwaPny4LrjgAn8uruLiYr311lumNRRA7JkdlDJaLlZ5gxDfzMoRFw0rgmtW1mtlgA2ANVwNLVrf6brrs353lVwNLRpQkNXLrUI8oq8AAAAASAbdCnLt2LFDF1xwgfbv369hw4ZJkj744AP1799fzzzzjEaNGmVqIwH0DrODUkbLkTcIocSqX1gRXLOyXqsCbACs4WoMf26MtB+pg74CAAAAIBl0K8j14x//WMcff7zeeust9e3bV5JUU1OjWbNm6YorrtBrr71maiMB9A6zg1JGy5E3CKFY1S+sCrrGsl6rAmwAel9OZnqP9iN10FcAAAAAJIO07vzQO++8o2XLlvkDXJLUt29fLVmyRG+//bZZbQPQy8wOShkt58sbFEqovEFIDVb1C6uCa1YGe5lNCSSP7Ix0TSgtCrlvQmmRsjMIXOAw+goAAACAZNCtmVzDhg3TgQMHdPzxxwdsP3jwoEpLS01pGIDeF21Qal2ImR8dgw/OPLvOGl6sYQMLNGZQoZpb25WVka4tlTXatc/tL+fIsev2aaP1ygefqTg/01/ugLtJk4/tzwySFBVNPinp8JJ7VXUeuZtaVJCdIWdu+NlLXTHav62u10zMpgSShz3NpjlnlGnKqIEqLsj68prqalRpcZ7saTarm4g4QV8BAAAAkAwMB7ncbrf//5cuXarZs2fr1ltv1de+9jVJ0uuvv67bbrtNt99+u/mtBNArzA5KOXLsumnKCM1fvV33vVThr2diaZGWXjgqIBDglfTctn1aXxEYzJh0bP/e+eURl4zkk5KkvbWNQTmlysucWj5ttEoKs6OqM5rgmlX1SuYF1yTrAnsAzJefnaE2r/SbF3drfYccm6eVFmnx1FHKzyZojcPoKwAAAACSgc3r9XqNFExLS5PN9uW3+Xw/5tvW8d9tbW1mtzNpuN1uORwOuVwuFRQUWN0cIEhldb3mr96ujR1edviCUoOLcv3b9tY26santwUFpTq+3Hc1eHTNyq0hc/2Ulzm1YvoYOXLshsv1FsZpYolV//EFcroKrllVr2RucK3jMbsKsA3scMxY1N1djFUg2AF3k+b+5e2A67jPxNIi3XXRiRpQkNVr7WGcxq946ysAEEtbtmzR2LFjddbCR9Vv8LCof/7zyl1au+QSbd68WSeddFIMWggAALrL8Eyul19+OZbtABAHXA0eLVyzI+hlx4aKav1izY6AoNSNqwIDXJK0bneV5q3a5i9XVecJGQDwla2q80RVDgglVv3HkRN+dpJV9frH3+7w4y9aRmbNxapuAOapqfeEDFpIh6/nNfUeAheQRF8BAAAAkBwMB7kmTZoU8O/a2lo9/PDD2rlzp2w2m4YPH67LLrtMDofD9EYC6B1mB6XcTS1h6zv0xX6j5YBQrOo/VtUby6CwVYE9AOZxN4Y/90Q6dyF10FcAAAAAJIO07vzQW2+9pdLSUt1zzz36/PPPVVVVpXvuuUfHHHOMtmzZYnYbAfQSs4NSBVnhcznkf7HfaDkgFKv6j1X1WhkUJiANxL9IeZS4psKHvgIAAAAgGRieydXRz372M5133nl66KGH1KfP4UO0trbqxz/+sebMmaN169aZ2kgAvcPsoJQzz66zhhdr2MACjRlUqObWdmVlpGtLZY127XPLmWePqhwQijPPrvIyZ0AuKZ/yMmdQ//HlvHI3taggO0PO3PCzl+KtXiuDwgSkgfiXa0/XGcf11/ASR9A1dedel3Lt6VY3EXGCvgIAAAAgGXQryPXWW28FBLgkqU+fPvr5z3+ucePGmdY4AL3L6Et7o0EpR45dN00Zofmrt+u+lyr8x5pYWqSlF47yv+A3Wg4IxZFj1/JpozVv1baAvlte5tTt00YH9J+9tY1BOaXKy5xaPm20SgqzE6LeaINrknWBPQC9LzsjXTdNOV4L1wRfUxdPHaXsDAIXOIy+AgAAACAZdCvIVVBQoMrKSh133HEB2z/55BPl5+eb0jAAvc/oS3ujQSlXg0cL1+wISmq+oaJav1izQyumj5Ejx264HNCVksJsrZg+RlV1Hh1qalF+VoaceYGBHFeDJyjQJB3OJTVv1bZu9TMr6o0muCZZF9gDYI12Sb9Ys72La+p23XXRiZa0C/GHvgIAAAAgGXQryPW9731Pl112me68806deuqpstls2rBhg2644QZNnz7d7DYC6EVGX9obCUpV1XmCXuz7rNtdpao6T1TlgHAcOeFnJ8Wqn1lRr5FxKlkX2ANgnZp6jzZ0uj77bKioVk29RwMKsnq5VYhH9BUAAAAAyaBbQa4777xTNptNP/rRj9Ta2ipJysjI0P/8z/9o+fLlpjYQQO8z66W9u6klbD2HvthvtBzQE1b1s1jVG2mcStYF9gBYJ9I5J9J+pA76CgAAAIBkkNadH7Lb7fr1r3+tmpoavf3229q6das+//xz3XPPPcrMzDR8nHXr1um8885TSUmJbDab1qxZE7Df6/Xq1ltvVUlJibKzs/X1r39d7777bkCZ5uZmXXvttXI6ncrNzdX555+vTz/9NKBMTU2NZs6cKYfDIYfDoZkzZ6q2tjagTGVlpc477zzl5ubK6XRq9uzZ8ng8AWW2b9+uSZMmKTs7W0ceeaRuu+02eb1ew78vkCyMvrQvyMoIWy7/i/1GywE9YVU/s7J/E0AGUk+kcwrXVPjQVwAAAAAkg27N5PLJycnRqFGjuv3z9fX1OuGEE3TJJZdo2rRpQfvvuOMO3X333Xrsscd07LHHavHixTrrrLO0a9cuf+6vOXPm6Nlnn9Wf/vQnFRUV6brrrtOUKVO0efNmpacfTpY8Y8YMffrpp3r++eclSVdccYVmzpypZ599VpLU1tamb33rW+rfv782bNig6upqXXzxxfJ6vVqxYoUkye1266yzztLkyZP173//Wx988IFmzZql3NxcXXfddd3+DIBEZPSlvTPPrvIyZ0DuHp/yMqeceXZ/ubOGF2vYwAKNGVSo5tZ2ZWWka0tljXbtc/vLAT1htD/6uBo8qqrzyN3UooLsDDlzuzd7Kdp6zUQAGUg9efZ0nXFcfw0vcQRdU3fudSnPnm51ExEn6CsAAAAAkkGPglw9de655+rcc88Nuc/r9eree+/VwoUL9e1vf1uS9Pjjj2vAgAF66qmn9JOf/EQul0sPP/ywnnjiCZ155pmSpD/+8Y8aNGiQ/vWvf+mcc87Rzp079fzzz+v111/XKaecIkl66KGHNH78eO3atUvDhg3TCy+8oPfee0+ffPKJSkpKJEl33XWXZs2apSVLlqigoEBPPvmkmpqa9NhjjykzM1MjR47UBx98oLvvvltz586VzWbrhU8MiA9Gg1KOHLtunzZar3zwmYrzM/3lDribNPnY/v6AgSPHrpumjND81dt130sV/nomlhZp6YWjWBYNpnDk2LV82mjNW7UtIOBUXubU7dNGB/SzvbWNQbmsysucWj5ttEoKs2NWr2RecE2yLrAHwDoF2Rm6acrxWrgm+Jq6eOooFWQT3MZh9BUAAAAAycDSIFc4e/bs0f79+3X22Wf7t2VmZmrSpEnatGmTfvKTn2jz5s1qaWkJKFNSUqKRI0dq06ZNOuecc/Taa6/J4XD4A1yS9LWvfU0Oh0ObNm3SsGHD9Nprr2nkyJH+AJcknXPOOWpubtbmzZs1efJkvfbaa5o0aVLAcoznnHOO5s+fr48++khDhw4N+Xs0NzerubnZ/2+3223K5wNYKZqglFfSc9v2aX1F4Mv9Scf29//b1eDRwjU7tLFT8vMNFdX6xZodWjF9TExftDNOU0dJYbZWTB+jqjqPDjW1KD8rQ868wECOq8ETFOCSDuewmrdqW7f6o5F6JXODa5J1gb1YYawCxty0ZnvIa+pNf9uh+6aPiWndjNPEYmVfAQAAAAAzdCsnV2/Yv3+/JGnAgAEB2wcMGODft3//ftntdvXt2zdsmeLi4qDjFxcXB5TpXE/fvn1lt9vDlvH921cmlGXLlvlzgTkcDg0aNCj8Lw4kgEhBKVeDx1/uxlXbAgJc0pfBAl+5qjpPUEChY9mqOk/IfWZhnKYWR45dxxTn6cTBfXVMcV5QoClW/TFSvZGCa77xEi1fgO3FuZO05qpT9eLcSVoxfYwGdghcxapuszFWgcgOuJu1vtP12Wf97iodcDeH3GcWxmnisLqvAAAAAIAZ4jbI5dN5GUCv1xtxacDOZUKVN6OM1+vt8md95s+fL5fL5f/vk08+Cdt2IBEYDQIYLeduaglb36EI+3uKcYqOrOqPsQz2WhXYMxtjFYjM1Rj+HBVpf08xThOH1X0FAAAAAMwQt8sVHnHEEZIOz5IaOHCgf/vBgwf9M6iOOOIIeTwe1dTUBMzmOnjwoE499VR/mQMHDgQd/7PPPgs4zhtvvBGwv6amRi0tLQFlOs/YOnjwoKTg2WYdZWZmBixxCCQDo0EAo+UKssLnfMiPsL+nGKfoyKr+aGWw1+pAs1GMVSCynMz0Hu3vKcZp4rC6rwAAAACAGeI2yDV06FAdccQRWrt2rcaMObwevMfj0auvvqrbb79dkjR27FhlZGRo7dq1uuiiiyRJ+/bt044dO3THHXdIksaPHy+Xy6U333xTX/3qVyVJb7zxhlwulz8QNn78eC1ZskT79u3zB9ReeOEFZWZmauzYsf4yCxYskMfjkd1u95cpKSnR0Ucf3TsfChAnjAYBjJZz5tl11vBiDRtYoDGDCtXc2q6sjHRtqazRrn1uOfNil48L6MyZZ1d5mTMgh5VPeZkzqD+6GjyqqvPI3dSiguwMOXODc20ZYWWw1+pAMwDz5GSk6/Tj+mtEiSPomvreXpdyMghc4DD6CgAAAIBkYGmQq66uThUVFf5/79mzR2+//bb69eunwYMHa86cOVq6dKnKyspUVlampUuXKicnRzNmzJAkORwOXXbZZbruuutUVFSkfv366frrr9eoUaN05plnSpKGDx+ub3zjG7r88sv14IMPSpKuuOIKTZkyRcOGDZMknX322RoxYoRmzpypX/3qV/r88891/fXX6/LLL1dBQYEkacaMGVq0aJFmzZqlBQsWaPfu3Vq6dKluvvnmiMsnAsnGaFDKaDlHjl03TRmh+au3676XvjwnTCwt0tILR3UrYAB0lyPHruXTRmveqm0Bga7yMqdunzY6oD/urW0MymVVXubU8mmjVdIh55URVgXXrK4bgLnyMvtowTdH6JZndgRdU289f6TyMuP2O27oZfQVAAAAAMnA5vUllrLAK6+8osmTJwdtv/jii/XYY4/J6/Vq0aJFevDBB1VTU6NTTjlFv/3tbzVy5Eh/2aamJt1www166qmn1NjYqDPOOEP3339/QJLrzz//XLNnz9YzzzwjSTr//PN13333qbCw0F+msrJSV111lV566SVlZ2drxowZuvPOOwOWW9m+fbuuvvpqvfnmm+rbt6+uvPLKqINcbrdbDodDLpfLH0ADElFldb3mr96ujR0SlvuCUoOLcqMq52rw6JqVW0PmBCovc2rF9DG9+gKdcQrpy0DOoaYW5WdlyJkXGMiJRb/dW9vYZXBtYIegmZnBtXiou7sYq0AwV4NH1zy1Res7XHd9Titz6j6uqfhCvPUVAIilLVu2aOzYsTpr4aPqN3hY1D//eeUurV1yiTZv3qyTTjopBi0EAADdZWmQKxXxoI9kYPTlvtFyHx6s0xl3v9plfS/OnaRjivNM/R3CYZzCiFj1WyuCa/FQd3cwVoFgH+w/pLPvXdfl/hfmlOvYI/J7rT2M0/gVb30FAGKJIBcAAMmLNSgARK2qzhPyJbckrdtdpao6jxw5dsPl3E0tYes7FGE/YIVY9VtHTvil/4yOq0SrG4A5XI3hzz2R9iN10FcAAAAAJIM0qxsAIPEYfblvtFxBVkbYcvkR9gNWsKrfWhkUJiANxL+czPQe7UfqoK8AAAAASAbM5AIQNaMv942Wc+bZddbwYg0bWKAxgwrV3NqurIx0bams0a59bjnzmBmC+OPMs6u8zBmQw8qnvMwZ1G99SwG6m1pUkJ0hZ274WVNdsTIoTEAaiH85Gek6/bj+GlHiCLqmvrfXpZwMAhc4jL4CAAAAIBkQ5AIQNaMv940Grxw5dt00ZYTmr96u+16q8B9rYmmRll44iuXPEJccOXYtnzZa81ZtCxgL5WVO3T5tdEC/3VvbqBtXbQtY6q+8zKnl00arpDA7qnqjDa5J5gXYulM3gN6Vl9lHC745Qrc8syPomnrr+SOVl8ntPw6jrwAAAABIBjy5AIia0Zf7RoNXrgaPFq7ZoY0V1QH1bKio1i/W7NCK6WMIdCEulRRma8X0Maqq8+hQU4vyszLkzAsMILkaPEEBLulwDqt5q7ZF3b+jCa5J5gbYoq0bQO+z90nTomdCX1MXPfuu7ps+xqKWId7QVwAAAAAkA4JcALrF6Mt9I8GrqjpPUADAZ93uKlXVeXh5jrjlyAk/KyoW/dvI+JPMD7BFUzcAaxw81Kz1na67Put3V+ngoWbGKyTRVwAAAAAkB4JcALrNrJf77qaWsPUcirAfiGex6t+Rxp8UmwCb0boBWKO2Mfw5xRVhP1IHfQUAAABAMkizugEAkpfRl/sFWRlhy+VH2A/EMyv7NwFkIPXk2tPD7s+JsB+pg74CAAAAIBkQ5AIQM0Zf7jvz7Covc4YsU17mlDOPGSNIXFb2bwLIQOrJtffRhNKikPsmlBYp185CDjiMvgIAAAAgGfDkAiBmnHl2nTW8WMMGFmjMoEI1t7YrKyNdWyprtGuf2/9y35Fj1+3TRuuVDz5TcX6mv9wBd5MmH9ufZdGQ0Bw5di2fNlrzVm3Tug5LB5aXOXX7tNEh82hV1XnkbmpRQXaGnLndXxrQF2BbF2LJwlABNjPrBmCNwpwMzTmjTFNGDVRxQdaX11RXo0qL81SYQ3Abh9FXAAAAACQDglwAYsaRY9dNU0Zo/urtuu+lCv/2iaVFWnrhqICX515Jz23bp/UVgUGAScf2780mAzFRUpitFdPHqKrOo0NNLcrPypAzLziAtLe2UTeu2haQR6u8zKnl00arpDA76nqjCbCZXTcAazhy7CouyNJvXtyt9RXV/u2nlRZpcadrL1IbfQUAAABAMmC5QgAx42rwaOGaHdrY4cWJJG2oqNYv1uyQq8HjL3fjqm0BAS5JWre7SvNWbfOXAxKZI8euY4rzdOLgvjqmOC/kDK7OQSap5+PAF2B7ce4krbnqVL04d5JWTB+jgR0CV7GqG0DvO+Bu0oLV2wOCFpK0vqJaC1dv1wF3k0UtQ7yhrwAAAABIBszkAhAzVXWeoJfmPut2V6mqziNHjt1wOSCZxXIcOHLCLzvIGASSR029J+jLJT4bKqpVU+/RgIKsXm4V4hF9BQAAAEAyYCYXgJhxN7WE3X/oi/1GywHJzMpxwBgEkoe7qbVH+5E66CsAAAAAkgFBLgAxU5AVPmF5/hf7jZYDkpmV44AxCCSPgqzwCzVE2o/UQV8BAAAAkAx4cgEQM848u84aXqxhAws0ZlChmlvblZWRri2VNdq1zy1nnj2qckAyc+bZVV7m1LoQywaWlzmDxoGrwaOqOo/cTS0qyM6QMzf8koTxWjcAc/XNteuM4/preIkj6Jq6c69LfXMZqziMvgIAAAAgGRDkAhAzjhy7bpoyQvNXb9d9L1X4t08sLdLSC0f5X4obLQckM0eOXcunjda8VdsCgk3lZU7dPm10wDjYW9uoG1dtC8ijVV7m1PJpo1VSmJ1QdQMw14CCLN183vFaEOKauuTCUeRYgh99BQAAAEAyIMgFIGZcDR4tXLMjKKn5hopq/WLNDq2YPkaOHLvhckCyKynM1orpY1RV59GhphblZ2XImRc4S8rV4AkKMknSut1VmrdqW7fHi5V1AzDPAXeTFq7eHvKaunD1dt110YkELyCJvgIAAAAgORDkAhAzVXWeoJfhPut2V6mqziNHjt1wOSAVOHLCL/0Xy/FiZd0AzFFT79GGTkELnw0V1aqp9xC4gCT6CgAAAIDkkGZ1AwAkL3dTS9j9h77Yb7QcAGvHC2MViH/uptYe7UfqoK8AAAAASAYEuQDETEFWRtj9+V/sN1oOgLXjhbEKxL+CrPALNUTaj9RBXwEAAACQDHhyARAzzjy7ysucWhdiebPyMqeceXZ/ubOGF2vYwAKNGVSo5tZ2ZWWka0tljXbtc/vLATA+rjpyNXhUVeeRu6lFBdkZcuaGX5bQzLoB9K6+uXadcVx/DS9xBF1Td+51qW8u4xSH0VcAAAAAJAOCXABixpFj1/JpozVv1baAl+LlZU7dPm20/yW7I8eum6aM0PzV23XfSxX+chNLi7T0wlHk+AE6MDqufPbWNurGVdsCcmmVlzm1fNpolRRmx7RuAL1vQEGWbj7veC0IcU1dcuEocizBj74CAAAAIBkQ5AIQUyWF2VoxfYyq6jw61NSi/KwMOfMCZ5G4GjxauGaHNnZKfr6holq/WLNDK6aP4eU50IGRcSUdHludA1yStG53leat2tatsWW0bgDWcDV49Isurqk3cU1FB/QVAAAAAMmAIBeAmHPkhH8BXlXnCXoJ77Nud5Wq6jy8ZAE6iTSupNiNLSN1A7AG11QYRV8BAAAAkAzSrG4AALibWsLuPxRhP4DQGFtA6mHcwyj6CgAAAIBkQJALgOUKsjLC7s+PsB9AaIwtIPUw7mEUfQUAAABAMmC5QgCWc+bZddbwYg0bWKAxgwrV3NqurIx0bams0a59bjnzWCoH6A5nnl3lZU6tC7EcVXmZM2hsuRo8qqrzyN3UooLsDDlzWZYQSDRcU2EUfQUAAABAMiDIBcByjhy7bpoyQvNXb9d9L1X4t08sLdLSC0fxkh3oJkeOXcunjda8VdsCAl3lZU7dPm10wNjaW9uoG1dtC8jPUl7m1PJpo1VSmN2r7QbQfVxTYRR9BQAAAEAyIMgFwHKuBo8WrtmhjRXVAds3VFTrF2t2aMX0MbxoAbqppDBbK6aPUVWdR4eaWpSflSFnXuAMLVeDJyjAJUnrdldp3qptjEEggXBNhVH0FQCJprKyUlVVwSsUGLFz506TWwMAAOIFQS4Alquq8wS9XPdZt7tKVXUeXrIAPeDICb/sIGMQSB6MZxhFXwGQSCorK3XcccPV2NjQo+O0NHtMahEAAIgXBLkAWM7d1BJ2/6EI+wH0DGMQSB6MZxhFXwGQSKqqqtTY2KB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",
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",
       "text/plain": [
-       "<Figure size 1750x1750 with 35 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -8089,48 +5653,217 @@
     }
    ],
    "source": [
-    "sb.pairplot(results_df_large, corner=True)"
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* does NOT pay for net metering\n",
+    "* does NOT include residential storage\n",
+    "\n",
+    "Apply the Residential Renewable Energy Tax Credit\n",
+    "\n",
+    "[DSIRE Data on the RRETC](https://programs.dsireusa.org/system/program/detail/1235/residential-renewable-energy-tax-credit) -- solar and storage each get a 30% tax credit. \n",
+    "\n",
+    "Apply the Investment Tax Credit (ITC)\n",
+    "\n",
+    "[EPA Data on ITC](https://www.epa.gov/green-power-markets/summary-inflation-reduction-act-provisions-related-renewable-energy) -- qualified residential units in a low-income area recieve +20%.\n",
+    "\n",
+    "[Homeowner's Guide to Federal Tax Credits](https://www.energy.gov/eere/solar/homeowners-guide-federal-tax-credit-solar-photovoltaics).\n",
+    "\n",
+    "This will be implemented as a direct 50% cost reduction."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# reset the price of net metering\n",
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.0"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "rretc_credit = 0.5"
+   ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 30,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>attribute</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>control</th>\n",
+       "      <th>type</th>\n",
+       "      <th>p_nom</th>\n",
+       "      <th>p_nom_mod</th>\n",
+       "      <th>p_nom_extendable</th>\n",
+       "      <th>p_nom_min</th>\n",
+       "      <th>p_nom_max</th>\n",
+       "      <th>p_min_pu</th>\n",
+       "      <th>p_max_pu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>state_of_charge_initial_per_period</th>\n",
+       "      <th>state_of_charge_set</th>\n",
+       "      <th>cyclic_state_of_charge</th>\n",
+       "      <th>cyclic_state_of_charge_per_period</th>\n",
+       "      <th>max_hours</th>\n",
+       "      <th>efficiency_store</th>\n",
+       "      <th>efficiency_dispatch</th>\n",
+       "      <th>standing_loss</th>\n",
+       "      <th>inflow</th>\n",
+       "      <th>p_nom_opt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>StorageUnit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "attribute                            bus control type  p_nom  p_nom_mod  \\\n",
+       "StorageUnit                                                               \n",
+       "Residential Battery Storage  Residential      PQ         0.0        0.0   \n",
+       "\n",
+       "attribute                    p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
+       "StorageUnit                                                                     \n",
+       "Residential Battery Storage              True        0.0        inf      -1.0   \n",
+       "\n",
+       "attribute                    p_max_pu  ...  \\\n",
+       "StorageUnit                            ...   \n",
+       "Residential Battery Storage       1.0  ...   \n",
+       "\n",
+       "attribute                    state_of_charge_initial_per_period  \\\n",
+       "StorageUnit                                                       \n",
+       "Residential Battery Storage                               False   \n",
+       "\n",
+       "attribute                    state_of_charge_set  cyclic_state_of_charge  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                  NaN                   False   \n",
+       "\n",
+       "attribute                   cyclic_state_of_charge_per_period  max_hours  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                              True        2.5   \n",
+       "\n",
+       "attribute                    efficiency_store  efficiency_dispatch  \\\n",
+       "StorageUnit                                                          \n",
+       "Residential Battery Storage               1.0                  1.0   \n",
+       "\n",
+       "attribute                    standing_loss  inflow  p_nom_opt  \n",
+       "StorageUnit                                                    \n",
+       "Residential Battery Storage            0.0     0.0       -0.0  \n",
+       "\n",
+       "[1 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## Model Version: Net Metering\n",
-    "\n",
-    "At this moment, the model\n",
-    "\n",
-    "* uses the sticker price for rooftop solar from NREL's ATB\n",
-    "* applies 100% retail price for net metering\n",
-    "* does NOT include residential storage"
+    "n.storage_units"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
-    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.0"
+    "n.generators.loc['ResPV', 'capital_cost'] *= (1-rretc_credit)\n",
+    "n.storage_units.loc['Residential Battery Storage', 'capital_cost'] *= (1-rretc_credit)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -8139,15 +5872,15 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.82it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.10it/s]\n",
-      "INFO:linopy.io: Writing time: 0.62s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.67e+05\n",
+      "Objective: 4.17e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
@@ -8160,7 +5893,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8171,7 +5904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -8219,38 +5952,68 @@
        "      <th>Revenue</th>\n",
        "      <th>Market Value</th>\n",
        "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.342852</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2964.049410</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>2964.049410</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.251973</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>334641.178424</td>\n",
+       "      <td>334641.178424</td>\n",
+       "      <td>112.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>0.669086</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>224.13732</td>\n",
+       "      <td>-224.137320</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.038241</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
        "    <tr>\n",
-       "      <th>Generator</th>\n",
-       "      <th>grid</th>\n",
-       "      <td>1.430806</td>\n",
+       "      <th>solar</th>\n",
+       "      <td>0.947609</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4135.96535</td>\n",
+       "      <td>1396.053259</td>\n",
        "      <td>0.00000</td>\n",
-       "      <td>4135.96535</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.329983</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1396.053259</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>466950.487985</td>\n",
-       "      <td>466950.487985</td>\n",
-       "      <td>112.9</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>3.914040e-10</td>\n",
+       "      <td>82394.7361</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>82394.736100</td>\n",
+       "      <td>59.019766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Load</th>\n",
        "      <th>-</th>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>4135.96535</td>\n",
-       "      <td>-4135.96535</td>\n",
+       "      <td>-4135.965350</td>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-466950.487985</td>\n",
+       "      <td>-417035.914525</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -8258,24 +6021,32 @@
        "</div>"
       ],
       "text/plain": [
-       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
-       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
-       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.342852                 0.0  2964.049410   \n",
+       "          net metering          0.669086                 0.0     0.000000   \n",
+       "          solar                 0.947609                 0.0  1396.053259   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
        "\n",
-       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
-       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
-       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "                        Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.00000  2964.049410           0.0   \n",
+       "          net metering   224.13732  -224.137320           0.0   \n",
+       "          solar            0.00000  1396.053259           0.0   \n",
+       "Load      -             4135.96535 -4135.965350           0.0   \n",
        "\n",
-       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
-       "Generator grid                  0.0            466950.487985  466950.487985   \n",
-       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.251973  0.000000e+00               0.0000   \n",
+       "          net metering         0.038241  0.000000e+00               0.0000   \n",
+       "          solar                0.168178  3.914040e-10           82394.7361   \n",
+       "Load      -                         NaN  0.000000e+00               0.0000   \n",
        "\n",
-       "                Market Value  \n",
-       "Generator grid         112.9  \n",
-       "Load      -              NaN  "
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    334641.178424  334641.178424    112.900000  \n",
+       "          net metering                 0.000000       0.000000      0.000000  \n",
+       "          solar                        0.000000   82394.736100     59.019766  \n",
+       "Load      -                            0.000000 -417035.914525           NaN  "
       ]
      },
-     "execution_count": 25,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8285,266 +6056,173 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 26,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Load\n",
-       "Load Residential    112.9\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "model_lcoe_2 = n.objective / n.loads_t.p_set.sum()\n",
-    "model_lcoe_2"
+    "Calculate the LCOE"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='snapshot'>"
+       "100.83157842499818"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "time = '2018-07-08'\n",
-    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
-    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
+    "model_lcoe_3 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_3"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Version: Tax Credits\n",
-    "\n",
-    "At this moment, the model\n",
-    "\n",
-    "* reduces the price for rooftop solar by applying federal tax credits.\n",
-    "* does NOT pay for net metering\n",
-    "* does NOT include residential storage\n",
-    "\n",
-    "Apply the Residential Renewable Energy Tax Credit\n",
-    "\n",
-    "[DSIRE Data on the RRETC](https://programs.dsireusa.org/system/program/detail/1235/residential-renewable-energy-tax-credit) -- solar and storage each get a 30% tax credit. \n",
-    "\n",
-    "Apply the Investment Tax Credit (ITC)\n",
-    "\n",
-    "[EPA Data on ITC](https://www.epa.gov/green-power-markets/summary-inflation-reduction-act-provisions-related-renewable-energy) -- qualified residential units in a low-income area recieve +20%.\n",
-    "\n",
-    "[Homeowner's Guide to Federal Tax Credits](https://www.energy.gov/eere/solar/homeowners-guide-federal-tax-credit-solar-photovoltaics).\n",
-    "\n",
-    "This will be implemented as a direct 50% cost reduction."
+    "Calculate the electricity price reduction"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.689479185119586"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# reset the price of net metering\n",
-    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.0"
+    "np.abs((100.831578 - 112.9)/112.9)*100"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 29,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "rretc_credit = 0.5"
+    "Looks like almost a 10.7% reduction in electricity cost."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>attribute</th>\n",
-       "      <th>bus</th>\n",
-       "      <th>control</th>\n",
-       "      <th>type</th>\n",
-       "      <th>p_nom</th>\n",
-       "      <th>p_nom_mod</th>\n",
-       "      <th>p_nom_extendable</th>\n",
-       "      <th>p_nom_min</th>\n",
-       "      <th>p_nom_max</th>\n",
-       "      <th>p_min_pu</th>\n",
-       "      <th>p_max_pu</th>\n",
-       "      <th>...</th>\n",
-       "      <th>state_of_charge_initial_per_period</th>\n",
-       "      <th>state_of_charge_set</th>\n",
-       "      <th>cyclic_state_of_charge</th>\n",
-       "      <th>cyclic_state_of_charge_per_period</th>\n",
-       "      <th>max_hours</th>\n",
-       "      <th>efficiency_store</th>\n",
-       "      <th>efficiency_dispatch</th>\n",
-       "      <th>standing_loss</th>\n",
-       "      <th>inflow</th>\n",
-       "      <th>p_nom_opt</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>StorageUnit</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Residential Battery Storage</th>\n",
-       "      <td>Residential</td>\n",
-       "      <td>PQ</td>\n",
-       "      <td></td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>inf</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 30 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "attribute                            bus control type  p_nom  p_nom_mod  \\\n",
-       "StorageUnit                                                               \n",
-       "Residential Battery Storage  Residential      PQ         0.0        0.0   \n",
-       "\n",
-       "attribute                    p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
-       "StorageUnit                                                                     \n",
-       "Residential Battery Storage              True        0.0        inf      -1.0   \n",
-       "\n",
-       "attribute                    p_max_pu  ...  \\\n",
-       "StorageUnit                            ...   \n",
-       "Residential Battery Storage       1.0  ...   \n",
-       "\n",
-       "attribute                    state_of_charge_initial_per_period  \\\n",
-       "StorageUnit                                                       \n",
-       "Residential Battery Storage                               False   \n",
-       "\n",
-       "attribute                    state_of_charge_set  cyclic_state_of_charge  \\\n",
-       "StorageUnit                                                                \n",
-       "Residential Battery Storage                  NaN                   False   \n",
-       "\n",
-       "attribute                   cyclic_state_of_charge_per_period  max_hours  \\\n",
-       "StorageUnit                                                                \n",
-       "Residential Battery Storage                              True        2.5   \n",
-       "\n",
-       "attribute                    efficiency_store  efficiency_dispatch  \\\n",
-       "StorageUnit                                                          \n",
-       "Residential Battery Storage               1.0                  1.0   \n",
-       "\n",
-       "attribute                    standing_loss  inflow  p_nom_opt  \n",
-       "StorageUnit                                                    \n",
-       "Residential Battery Storage            0.0     0.0       -0.0  \n",
-       "\n",
-       "[1 rows x 30 columns]"
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 30,
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering + Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* Applies 50% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.0\n",
+       "Net metering Residential      0.0\n",
+       "Evergy Import               112.9\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.storage_units"
+    "n.generators.marginal_cost"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "n.generators.loc['ResPV', 'capital_cost'] *= (1-rretc_credit)\n",
-    "n.storage_units.loc['Residential Battery Storage', 'capital_cost'] *= (1-rretc_credit)"
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.00\n",
+       "Net metering Residential     56.45\n",
+       "Evergy Import               112.90\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators.marginal_cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -8553,15 +6231,15 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.35it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.29it/s]\n",
-      "INFO:linopy.io: Writing time: 0.7s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 4.17e+05\n",
+      "Objective: 3.77e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
@@ -8574,7 +6252,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8585,7 +6263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -8638,48 +6316,48 @@
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
        "      <th>grid</th>\n",
-       "      <td>1.342852</td>\n",
+       "      <td>1.331491</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2964.049410</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>2964.049410</td>\n",
+       "      <td>2358.485524</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2358.485524</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.251973</td>\n",
+       "      <td>0.202205</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>334641.178424</td>\n",
-       "      <td>334641.178424</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>266273.015698</td>\n",
        "      <td>112.900000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>net metering</th>\n",
-       "      <td>0.669086</td>\n",
+       "      <td>2.505756</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>224.13732</td>\n",
-       "      <td>-224.137320</td>\n",
+       "      <td>2357.900052</td>\n",
+       "      <td>-2357.900052</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.038241</td>\n",
+       "      <td>0.107419</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>-133103.457958</td>\n",
+       "      <td>-133103.457958</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>solar</th>\n",
-       "      <td>0.947609</td>\n",
+       "      <td>2.807000</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1396.053259</td>\n",
-       "      <td>0.00000</td>\n",
-       "      <td>1396.053259</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.379878</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.168178</td>\n",
-       "      <td>3.914040e-10</td>\n",
-       "      <td>82394.7361</td>\n",
+       "      <td>1.159408e-09</td>\n",
+       "      <td>244069.150984</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>82394.736100</td>\n",
-       "      <td>59.019766</td>\n",
+       "      <td>255898.612865</td>\n",
+       "      <td>61.880316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Load</th>\n",
@@ -8687,14 +6365,14 @@
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>4135.96535</td>\n",
+       "      <td>4135.965350</td>\n",
        "      <td>-4135.965350</td>\n",
        "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.000000e+00</td>\n",
-       "      <td>0.0000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-417035.914525</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-389068.170605</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -8703,31 +6381,31 @@
       ],
       "text/plain": [
        "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
-       "Generator grid                  1.342852                 0.0  2964.049410   \n",
-       "          net metering          0.669086                 0.0     0.000000   \n",
-       "          solar                 0.947609                 0.0  1396.053259   \n",
+       "Generator grid                  1.331491                 0.0  2358.485524   \n",
+       "          net metering          2.505756                 0.0     0.000000   \n",
+       "          solar                 2.807000                 0.0  4135.379878   \n",
        "Load      -                     0.000000                 0.0     0.000000   \n",
        "\n",
-       "                        Withdrawal     Dispatch  Transmission  \\\n",
-       "Generator grid             0.00000  2964.049410           0.0   \n",
-       "          net metering   224.13732  -224.137320           0.0   \n",
-       "          solar            0.00000  1396.053259           0.0   \n",
-       "Load      -             4135.96535 -4135.965350           0.0   \n",
+       "                         Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.000000  2358.485524           0.0   \n",
+       "          net metering  2357.900052 -2357.900052           0.0   \n",
+       "          solar            0.000000  4135.379878           0.0   \n",
+       "Load      -             4135.965350 -4135.965350           0.0   \n",
        "\n",
        "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
-       "Generator grid                 0.251973  0.000000e+00               0.0000   \n",
-       "          net metering         0.038241  0.000000e+00               0.0000   \n",
-       "          solar                0.168178  3.914040e-10           82394.7361   \n",
-       "Load      -                         NaN  0.000000e+00               0.0000   \n",
+       "Generator grid                 0.202205  0.000000e+00             0.000000   \n",
+       "          net metering         0.107419  0.000000e+00             0.000000   \n",
+       "          solar                0.168178  1.159408e-09        244069.150984   \n",
+       "Load      -                         NaN  0.000000e+00             0.000000   \n",
        "\n",
        "                        Operational Expenditure        Revenue  Market Value  \n",
-       "Generator grid                    334641.178424  334641.178424    112.900000  \n",
-       "          net metering                 0.000000       0.000000      0.000000  \n",
-       "          solar                        0.000000   82394.736100     59.019766  \n",
-       "Load      -                            0.000000 -417035.914525           NaN  "
+       "Generator grid                    266273.015698  266273.015698    112.900000  \n",
+       "          net metering           -133103.457958 -133103.457958           NaN  \n",
+       "          solar                        0.000000  255898.612865     61.880316  \n",
+       "Load      -                            0.000000 -389068.170605           NaN  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8736,71 +6414,30 @@
     "n.statistics()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate the LCOE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "100.83157842499818"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model_lcoe_3 = n.objective / n.loads_t.p_set.sum().values[0]\n",
-    "model_lcoe_3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate the electricity price reduction"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "10.689479185119586"
+       "91.20934940819072"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.abs((100.831578 - 112.9)/112.9)*100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks like almost a 10.7% reduction in electricity cost."
+    "model_lcoe_4 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_4"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -8809,13 +6446,13 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -8840,13 +6477,22 @@
     "At this moment, the model\n",
     "\n",
     "* reduces the price for rooftop solar by applying federal tax credits.\n",
-    "* Applies 50% retail price for net metering\n",
+    "* Applies 99% retail price for net metering\n",
     "* does NOT include residential storage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.00"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -8854,12 +6500,12 @@
       "text/plain": [
        "Generator\n",
        "ResPV                         0.0\n",
-       "Net metering Residential      0.0\n",
+       "Net metering Residential    112.9\n",
        "Evergy Import               112.9\n",
        "Name: marginal_cost, dtype: float64"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8870,40 +6516,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Generator\n",
-       "ResPV                         0.00\n",
-       "Net metering Residential     56.45\n",
-       "Evergy Import               112.90\n",
-       "Name: marginal_cost, dtype: float64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n.generators.marginal_cost"
+    "n.generators.loc['ResPV', 'p_nom_max'] = 2.807"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -8912,15 +6534,15 @@
      "text": [
       "INFO:linopy.model: Solve problem using Highs solver\n",
       "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.79it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.33it/s]\n",
-      "INFO:linopy.io: Writing time: 0.71s\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.62it/s]\n",
+      "INFO:linopy.io: Writing time: 0.63s\n",
       "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
       "INFO:linopy.constants: Optimization successful: \n",
       "Status: ok\n",
       "Termination condition: optimal\n",
       "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 3.77e+05\n",
+      "Objective: 2.44e+05\n",
       "Solver model: available\n",
       "Solver message: optimal\n",
       "\n",
@@ -8933,7 +6555,7 @@
        "('ok', 'optimal')"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8944,7 +6566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -9008,7 +6630,7 @@
        "      <td>0.000000</td>\n",
        "      <td>266273.015698</td>\n",
        "      <td>266273.015698</td>\n",
-       "      <td>112.900000</td>\n",
+       "      <td>112.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>net metering</th>\n",
@@ -9021,8 +6643,8 @@
        "      <td>0.107419</td>\n",
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-133103.457958</td>\n",
-       "      <td>-133103.457958</td>\n",
+       "      <td>-266206.915917</td>\n",
+       "      <td>-266206.915917</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -9037,8 +6659,8 @@
        "      <td>1.159408e-09</td>\n",
        "      <td>244069.150984</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>255898.612865</td>\n",
-       "      <td>61.880316</td>\n",
+       "      <td>466884.388204</td>\n",
+       "      <td>112.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Load</th>\n",
@@ -9053,7 +6675,7 @@
        "      <td>0.000000e+00</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>-389068.170605</td>\n",
+       "      <td>-466950.487985</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -9080,13 +6702,13 @@
        "Load      -                         NaN  0.000000e+00             0.000000   \n",
        "\n",
        "                        Operational Expenditure        Revenue  Market Value  \n",
-       "Generator grid                    266273.015698  266273.015698    112.900000  \n",
-       "          net metering           -133103.457958 -133103.457958           NaN  \n",
-       "          solar                        0.000000  255898.612865     61.880316  \n",
-       "Load      -                            0.000000 -389068.170605           NaN  "
+       "Generator grid                    266273.015698  266273.015698         112.9  \n",
+       "          net metering           -266206.915917 -266206.915917           NaN  \n",
+       "          solar                        0.000000  466884.388204         112.9  \n",
+       "Load      -                            0.000000 -466950.487985           NaN  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9097,28 +6719,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "91.20934940819072"
+       "59.02739266934207"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_lcoe_4 = n.objective / n.loads_t.p_set.sum().values[0]\n",
-    "model_lcoe_4"
+    "model_lcoe_5 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_5"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -9127,7 +6749,7 @@
        "<Axes: xlabel='snapshot'>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -9153,304 +6775,359 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Version: Net Metering + Tax Credits\n",
+    "## Basic Analysis: How much solar capacity is required to meet 100% demand?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solar_100 = ghi / ghi.sum() * load_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_load = load_resampled - solar_100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8073974047750987"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solar_cap = solar_100.max()\n",
+    "solar_cap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_unmet_load = net_load.where(net_load>0).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "266266.65710289485"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_unmet_load*retail_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounts = np.linspace(0,1,20)\n",
+    "full_solar_cost_data = np.array([(costs.at['ResPV','annualized_cost'] * (1-discount) * solar_cap \n",
+    "                                  + total_unmet_load*retail_price) \n",
+    "                                 / load_resampled.sum() \n",
+    "                                 for discount in discounts])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1129"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retail_price/1e3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2aa83eda5c0>"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "y1 = ax.plot(discounts, full_solar_cost_data/1e3, label='100% Solar Power')\n",
+    "y2 = ax.axhline(y=retail_price/1e3, xmax=0.74, color='tab:red', linestyle='--', label='Retail Price')\n",
+    "y2 = ax.axvline(x=0.74, ymax=0.1129*2.5, linestyle=':', color='tab:purple', label='Subsidy needed to breakeven')\n",
+    "ax.set_xlim(0,1)\n",
+    "ax.set_ylabel(\"Levelized Cost of Electricity \\n ($/kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost\\n (%)\")\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor',alpha=0.2)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large,\n",
+    "            x='discount',\n",
+    "            y='lcoe',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis'\n",
+    "            )\n",
     "\n",
-    "At this moment, the model\n",
+    "y1 = ax.plot(discounts, full_solar_cost_data/1000)\n",
+    "y2 = ax.axhline(y=retail_price/1000, xmax=0.74, color='tab:red', linestyle='--')\n",
+    "\n",
+    "ax.set_ylabel(\"Retail Price of Electricity \\n ($ / kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
     "\n",
-    "* reduces the price for rooftop solar by applying federal tax credits.\n",
-    "* Applies 99% retail price for net metering\n",
-    "* does NOT include residential storage"
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "# ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 229,
    "metadata": {},
    "outputs": [],
    "source": [
-    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.00"
+    "lcoe_costs = full_solar_cost_data.copy()\n",
+    "lcoe_costs[lcoe_costs>=retail_price] = retail_price"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 230,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Generator\n",
-       "ResPV                         0.0\n",
-       "Net metering Residential    112.9\n",
-       "Evergy Import               112.9\n",
-       "Name: marginal_cost, dtype: float64"
+       "(0.0, 1.0)"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "n.generators.marginal_cost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n.generators.loc['ResPV', 'p_nom_max'] = 2.807"
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large,\n",
+    "            x='discount',\n",
+    "            y='lcoe',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis'\n",
+    "            )\n",
+    "\n",
+    "y1 = ax.plot(discounts, lcoe_costs/1000)\n",
+    "y2 = ax.axhline(y=retail_price/1000, xmax=0.74, color='tab:red', linestyle='--')\n",
+    "\n",
+    "ax.set_ylabel(\"Retail Price of Electricity \\n ($ / kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "# ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 243,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:linopy.model: Solve problem using Highs solver\n",
-      "INFO:linopy.io:Writing objective.\n",
-      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.47it/s]\n",
-      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.62it/s]\n",
-      "INFO:linopy.io: Writing time: 0.63s\n",
-      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
-      "INFO:linopy.constants: Optimization successful: \n",
-      "Status: ok\n",
-      "Termination condition: optimal\n",
-      "Solution: 52564 primals, 122645 duals\n",
-      "Objective: 2.44e+05\n",
-      "Solver model: available\n",
-      "Solver message: optimal\n",
-      "\n",
-      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "('ok', 'optimal')"
+       "6.660796450820109"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 243,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "n.optimize(solver_name='highs')"
+    "((lcoe_costs/1000) - results_df_large.loc[results_df_large['percent_retail_price']==0].lcoe.values).sum()*100"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 214,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
-      "\n",
-      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
-      "\n"
-     ]
+     "data": {
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+     "execution_count": 214,
+     "metadata": {},
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",
       "text/plain": [
-       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
-       "Generator grid                  1.331491                 0.0  2358.485524   \n",
-       "          net metering          2.505756                 0.0     0.000000   \n",
-       "          solar                 2.807000                 0.0  4135.379878   \n",
-       "Load      -                     0.000000                 0.0     0.000000   \n",
-       "\n",
-       "                         Withdrawal     Dispatch  Transmission  \\\n",
-       "Generator grid             0.000000  2358.485524           0.0   \n",
-       "          net metering  2357.900052 -2357.900052           0.0   \n",
-       "          solar            0.000000  4135.379878           0.0   \n",
-       "Load      -             4135.965350 -4135.965350           0.0   \n",
-       "\n",
-       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
-       "Generator grid                 0.202205  0.000000e+00             0.000000   \n",
-       "          net metering         0.107419  0.000000e+00             0.000000   \n",
-       "          solar                0.168178  1.159408e-09        244069.150984   \n",
-       "Load      -                         NaN  0.000000e+00             0.000000   \n",
-       "\n",
-       "                        Operational Expenditure        Revenue  Market Value  \n",
-       "Generator grid                    266273.015698  266273.015698         112.9  \n",
-       "          net metering           -266206.915917 -266206.915917           NaN  \n",
-       "          solar                        0.000000  466884.388204         112.9  \n",
-       "Load      -                            0.000000 -466950.487985           NaN  "
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 48,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "n.statistics()"
+    "fig, ax = plt.subplots()\n",
+    "y1 = ax.plot(discounts, full_solar_cost_data)\n",
+    "y2 = ax.axhline(y=retail_price, xmax=0.74, color='tab:red', linestyle='--')\n",
+    "ax.set_xlim(0,1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 247,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "59.02739266934207"
+       "15.51968500751449"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 247,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_lcoe_5 = n.objective / n.loads_t.p_set.sum().values[0]\n",
-    "model_lcoe_5"
+    "costs.at['ResPV', 'annualized_cost'] * 2.807 * 20 / 1e6"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 248,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='snapshot'>"
+       "7.384904875130196"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 248,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
-    "time = '2018-07-08'\n",
-    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
-    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+    "costs.at['ResPV', 'OCC'] * 2.807 /  1e6"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/gis_notebooks/power_plants.ipynb b/notebooks/gis_notebooks/power_plants.ipynb
index e4062e6..f4ac9e9 100644
--- a/notebooks/gis_notebooks/power_plants.ipynb
+++ b/notebooks/gis_notebooks/power_plants.ipynb
@@ -422,7 +422,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>POLYGON ((-93.84042 39.08375, -93.84920 39.013...</td>\n",
+       "      <td>POLYGON ((-93.84042 39.08375, -93.8492 39.0133...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -430,7 +430,7 @@
       ],
       "text/plain": [
        "                                                    0\n",
-       "20  POLYGON ((-93.84042 39.08375, -93.84920 39.013..."
+       "20  POLYGON ((-93.84042 39.08375, -93.8492 39.0133..."
       ]
      },
      "execution_count": 23,
@@ -449,7 +449,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -545,7 +545,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -700,7 +700,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -731,7 +731,16 @@
    "cell_type": "code",
    "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sdotson\\AppData\\Local\\Temp\\ipykernel_10668\\1296099471.py:1: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead.\n",
+      "  kc_outline = gpd.GeoDataFrame(geometry=[kc_ksmo.unary_union], crs='epsg:3857')\n"
+     ]
+    }
+   ],
    "source": [
     "kc_outline = gpd.GeoDataFrame(geometry=[kc_ksmo.unary_union], crs='epsg:3857')"
    ]
@@ -743,7 +752,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 2 Axes>"
       ]
@@ -889,42 +898,42 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Kansas</th>\n",
-       "      <td>4.0</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>9771.2</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>9019.2</td>\n",
-       "      <td>2535.4</td>\n",
-       "      <td>1323.2</td>\n",
-       "      <td>80.4</td>\n",
-       "      <td>18210.6</td>\n",
+       "      <td>19542.4</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>18053.8</td>\n",
+       "      <td>5070.8</td>\n",
+       "      <td>2637.0</td>\n",
+       "      <td>160.8</td>\n",
+       "      <td>36421.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missouri</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5576.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>7929.3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11152.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>15791.9</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1440.0</td>\n",
-       "      <td>148.8</td>\n",
-       "      <td>2197.4</td>\n",
+       "      <td>2960.0</td>\n",
+       "      <td>302.6</td>\n",
+       "      <td>4394.8</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "technology  All Other  Batteries    Coal  Hydro  Natural Gas  Nuclear  \\\n",
-       "stateName                                                               \n",
-       "Kansas            4.0        NaN  9771.2   14.0       9019.2   2535.4   \n",
-       "Missouri          NaN        2.0  5576.0   32.0       7929.3      NaN   \n",
+       "technology  All Other  Batteries     Coal  Hydro  Natural Gas  Nuclear  \\\n",
+       "stateName                                                                \n",
+       "Kansas            8.0        NaN  19542.4   28.0      18053.8   5070.8   \n",
+       "Missouri          NaN        4.0  11152.0   64.0      15791.9      NaN   \n",
        "\n",
        "technology  Petroleum  Solar PV  Wind Turbine  \n",
        "stateName                                      \n",
-       "Kansas         1323.2      80.4       18210.6  \n",
-       "Missouri       1440.0     148.8        2197.4  "
+       "Kansas         2637.0     160.8       36421.2  \n",
+       "Missouri       2960.0     302.6        4394.8  "
       ]
      },
      "execution_count": 39,
@@ -985,22 +994,22 @@
        "    <tr>\n",
        "      <th>Kansas</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>1556.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>2167.2</td>\n",
-       "      <td>332.8</td>\n",
-       "      <td>4.6</td>\n",
+       "      <td>3112.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4334.4</td>\n",
+       "      <td>665.6</td>\n",
+       "      <td>9.2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missouri</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4588.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9176.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>4224.6</td>\n",
-       "      <td>1214.2</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>401.8</td>\n",
+       "      <td>8449.2</td>\n",
+       "      <td>2428.4</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>803.6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1009,13 +1018,13 @@
       "text/plain": [
        "technology  Batteries    Coal  Hydro  Natural Gas  Petroleum  Solar PV  \\\n",
        "stateName                                                                \n",
-       "Kansas            NaN  1556.0   14.0       2167.2      332.8       4.6   \n",
-       "Missouri          2.0  4588.0    NaN       4224.6     1214.2      49.0   \n",
+       "Kansas            NaN  3112.0   28.0       4334.4      665.6       9.2   \n",
+       "Missouri          4.0  9176.0    NaN       8449.2     2428.4      98.0   \n",
        "\n",
        "technology  Wind Turbine  \n",
        "stateName                 \n",
        "Kansas               NaN  \n",
-       "Missouri           401.8  "
+       "Missouri           803.6  "
       ]
      },
      "execution_count": 40,
@@ -1072,18 +1081,18 @@
        "    <tr>\n",
        "      <th>Kansas</th>\n",
        "      <td>NaN</td>\n",
-       "      <td>522.0</td>\n",
-       "      <td>1017.6</td>\n",
-       "      <td>287.2</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1044.0</td>\n",
+       "      <td>2035.2</td>\n",
+       "      <td>574.4</td>\n",
+       "      <td>4.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Missouri</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1138.0</td>\n",
-       "      <td>964.4</td>\n",
-       "      <td>980.4</td>\n",
-       "      <td>20.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2276.0</td>\n",
+       "      <td>1928.8</td>\n",
+       "      <td>1960.8</td>\n",
+       "      <td>40.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1092,8 +1101,8 @@
       "text/plain": [
        "technology  Batteries    Coal  Natural Gas  Petroleum  Solar PV\n",
        "stateName                                                      \n",
-       "Kansas            NaN   522.0       1017.6      287.2       2.0\n",
-       "Missouri          2.0  1138.0        964.4      980.4      20.0"
+       "Kansas            NaN  1044.0       2035.2      574.4       4.0\n",
+       "Missouri          4.0  2276.0       1928.8     1960.8      40.0"
       ]
      },
      "execution_count": 41,

From f2b56106bfa8a96e905201afb3b42c71a87bbe8c Mon Sep 17 00:00:00 2001
From: Sam Dotson <sdotson@ucsusa.org>
Date: Mon, 28 Oct 2024 13:27:18 -0400
Subject: [PATCH 47/52] adds notebooks and data

---
 09-electricity-use.ipynb                  |  905 +++
 functions/nrel_data_api.py                |  159 +
 notebooks/03-retrieve-resweather.ipynb    |  221 +
 notebooks/04-project-sunroof.ipynb        |  794 +++
 notebooks/05-heatpump-model.ipynb         |   39 +
 notebooks/06-resstock-metadata.ipynb      | 1042 +++
 notebooks/07-nrel-atb.ipynb               | 2824 ++++++++
 notebooks/08-data_download_test.ipynb     | 1840 +++++
 notebooks/11-pypsa-model-more-buses.ipynb | 7371 +++++++++++++++++++++
 notebooks/11-results-analysis.ipynb       |  136 +
 notebooks/12-cashflow-analysis.ipynb      |   44 +
 notebooks/simulation_data.csv             |  256 +
 notebooks/simulation_data_detailed.csv    |  401 ++
 notebooks/simulation_data_sparse.csv      |  101 +
 scripts/retrieve_renewables.py            |  102 +
 15 files changed, 16235 insertions(+)
 create mode 100644 09-electricity-use.ipynb
 create mode 100644 functions/nrel_data_api.py
 create mode 100644 notebooks/03-retrieve-resweather.ipynb
 create mode 100644 notebooks/04-project-sunroof.ipynb
 create mode 100644 notebooks/05-heatpump-model.ipynb
 create mode 100644 notebooks/06-resstock-metadata.ipynb
 create mode 100644 notebooks/07-nrel-atb.ipynb
 create mode 100644 notebooks/08-data_download_test.ipynb
 create mode 100644 notebooks/11-pypsa-model-more-buses.ipynb
 create mode 100644 notebooks/11-results-analysis.ipynb
 create mode 100644 notebooks/12-cashflow-analysis.ipynb
 create mode 100644 notebooks/simulation_data.csv
 create mode 100644 notebooks/simulation_data_detailed.csv
 create mode 100644 notebooks/simulation_data_sparse.csv
 create mode 100644 scripts/retrieve_renewables.py

diff --git a/09-electricity-use.ipynb b/09-electricity-use.ipynb
new file mode 100644
index 0000000..0a4a156
--- /dev/null
+++ b/09-electricity-use.ipynb
@@ -0,0 +1,905 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_elec = pd.read_csv('../data/timeseries/residential_elec_load_rescaled.csv', parse_dates=True, index_col='timestamp')\n",
+    "res_heat = pd.read_csv('../data/timeseries/residential_heat_load_rescaled.csv', parse_dates=True, index_col='timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <th>mobile_home</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:15:00</th>\n",
+       "      <td>113.972825</td>\n",
+       "      <td>2.678051</td>\n",
+       "      <td>11.182543</td>\n",
+       "      <td>591.611432</td>\n",
+       "      <td>1.316949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:30:00</th>\n",
+       "      <td>122.073324</td>\n",
+       "      <td>2.689016</td>\n",
+       "      <td>11.389590</td>\n",
+       "      <td>603.432355</td>\n",
+       "      <td>1.402989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:45:00</th>\n",
+       "      <td>124.810963</td>\n",
+       "      <td>2.734115</td>\n",
+       "      <td>11.064188</td>\n",
+       "      <td>600.966481</td>\n",
+       "      <td>1.501680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>130.157708</td>\n",
+       "      <td>2.679941</td>\n",
+       "      <td>11.317656</td>\n",
+       "      <td>605.812913</td>\n",
+       "      <td>1.601132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:15:00</th>\n",
+       "      <td>16.824577</td>\n",
+       "      <td>0.403772</td>\n",
+       "      <td>2.361815</td>\n",
+       "      <td>133.074632</td>\n",
+       "      <td>0.205631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:00:00</th>\n",
+       "      <td>155.102361</td>\n",
+       "      <td>2.646652</td>\n",
+       "      <td>14.567161</td>\n",
+       "      <td>695.344421</td>\n",
+       "      <td>1.082926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:15:00</th>\n",
+       "      <td>128.287742</td>\n",
+       "      <td>2.732418</td>\n",
+       "      <td>14.002470</td>\n",
+       "      <td>622.587237</td>\n",
+       "      <td>1.316956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:30:00</th>\n",
+       "      <td>138.694172</td>\n",
+       "      <td>2.711169</td>\n",
+       "      <td>13.979686</td>\n",
+       "      <td>610.486911</td>\n",
+       "      <td>1.224628</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:45:00</th>\n",
+       "      <td>130.288110</td>\n",
+       "      <td>2.756182</td>\n",
+       "      <td>13.871853</td>\n",
+       "      <td>610.047892</td>\n",
+       "      <td>1.236934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-01-01 00:00:00</th>\n",
+       "      <td>134.371567</td>\n",
+       "      <td>2.746753</td>\n",
+       "      <td>13.342888</td>\n",
+       "      <td>615.014955</td>\n",
+       "      <td>1.295301</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35040 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     multi-family_with_2_-_4_units  \\\n",
+       "timestamp                                            \n",
+       "2018-01-01 00:15:00                     113.972825   \n",
+       "2018-01-01 00:30:00                     122.073324   \n",
+       "2018-01-01 00:45:00                     124.810963   \n",
+       "2018-01-01 01:00:00                     130.157708   \n",
+       "2018-01-01 01:15:00                      16.824577   \n",
+       "...                                            ...   \n",
+       "2018-12-31 23:00:00                     155.102361   \n",
+       "2018-12-31 23:15:00                     128.287742   \n",
+       "2018-12-31 23:30:00                     138.694172   \n",
+       "2018-12-31 23:45:00                     130.288110   \n",
+       "2019-01-01 00:00:00                     134.371567   \n",
+       "\n",
+       "                     multi-family_with_5plus_units  single-family_attached  \\\n",
+       "timestamp                                                                    \n",
+       "2018-01-01 00:15:00                       2.678051               11.182543   \n",
+       "2018-01-01 00:30:00                       2.689016               11.389590   \n",
+       "2018-01-01 00:45:00                       2.734115               11.064188   \n",
+       "2018-01-01 01:00:00                       2.679941               11.317656   \n",
+       "2018-01-01 01:15:00                       0.403772                2.361815   \n",
+       "...                                            ...                     ...   \n",
+       "2018-12-31 23:00:00                       2.646652               14.567161   \n",
+       "2018-12-31 23:15:00                       2.732418               14.002470   \n",
+       "2018-12-31 23:30:00                       2.711169               13.979686   \n",
+       "2018-12-31 23:45:00                       2.756182               13.871853   \n",
+       "2019-01-01 00:00:00                       2.746753               13.342888   \n",
+       "\n",
+       "                     single-family_detached  mobile_home  \n",
+       "timestamp                                                 \n",
+       "2018-01-01 00:15:00              591.611432     1.316949  \n",
+       "2018-01-01 00:30:00              603.432355     1.402989  \n",
+       "2018-01-01 00:45:00              600.966481     1.501680  \n",
+       "2018-01-01 01:00:00              605.812913     1.601132  \n",
+       "2018-01-01 01:15:00              133.074632     0.205631  \n",
+       "...                                     ...          ...  \n",
+       "2018-12-31 23:00:00              695.344421     1.082926  \n",
+       "2018-12-31 23:15:00              622.587237     1.316956  \n",
+       "2018-12-31 23:30:00              610.486911     1.224628  \n",
+       "2018-12-31 23:45:00              610.047892     1.236934  \n",
+       "2019-01-01 00:00:00              615.014955     1.295301  \n",
+       "\n",
+       "[35040 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'kWh')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "res_elec.resample('h').mean().sum(axis=1).plot(ax=ax, label='electricity')\n",
+    "res_heat.resample('h').mean().sum(axis=1).plot(ax=ax, legend=True, label='heat')\n",
+    "ax.set_xlabel('')\n",
+    "ax.set_ylabel('kWh')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_electrified_data = res_elec.resample('h').mean().sum(axis=1)+res_heat.resample('h').mean().sum(axis=1)/4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5266385.120228281"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_electrified_data.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1430.8057224551974"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec.resample('h').mean().sum(axis=1).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1450.0)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "res_elec.resample('h').mean().sum(axis=1).plot(ax=ax, label='electricity')\n",
+    "# res_heat.resample('h').mean().sum(axis=1).plot(ax=ax, legend=True, label='heat')\n",
+    "res_elec.resample('h').mean().sum(axis=1).plot.area(ax=ax, lw=0, color='lightgray')\n",
+    "ax.set_xlabel('')\n",
+    "ax.set_ylabel('kWh', fontsize=16)\n",
+    "ax.set_ylim(0, 1450)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Average Day')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "res_elec.sum(axis=1).groupby(res_elec.index.hour).mean().plot(ax=ax)\n",
+    "ax.grid(zorder=0)\n",
+    "ax.set_xlim(0,23)\n",
+    "ax.set_ylim(300,650)\n",
+    "ax.set_ylabel(\"kWh\", fontsize=16)\n",
+    "ax.set_xlabel('Hour of day')\n",
+    "ax.set_title('Average Day', size=18)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4315674125864762"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec.resample('h').mean().sum(axis=1).sum() / (6079*8760*0.18)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather = pd.read_csv(\"../data/timeseries/weather_year.csv\", parse_dates=True, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.16817788689562815"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather.ghi.mean() / weather.ghi.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_elec_resampled = res_elec.loc['2018'].resample('h').mean().sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rooftop_solar_energy = (weather.ghi / weather.ghi.sum() * res_elec_resampled.sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Kc+bhpopC858riwDdOcBQ3branVzN+0PVXXWzvMIJCI2/3IMTfru5Hgf3r315+ONXv0DrpMDmZ0ahm7+b3CXZlAaDHsd++gb6wx+jr2631SKHv6hvQ3XsA4i96xGr7TOuxsDTRgw81Gomk3mxvzM/mXtw8vc1v9Cff2zjb74jzL05HsGdXyt1DVWlQMXZxiDjZQ4KnblasBDmYTRLELoiEFmWAThnXiagiZOLOdRE3mEOOaHxNrX4X2c4XVqFiW/vQZ3BhEWT+mDmHdFyl2TTKnUXkLX9Q7hm/Qd99Ucsz9cKNX7xvBPqgQ+hz8jJUDlZ36/GwNNGDDzUIqPBPCTV1HuTv8/6jQEwb80Q3N/ccxM5wvzRxT7X3yA7pq8xhx9Djfl+HBvYwkEuDUYTHnhvH9LyL2FEN198OGsoFAoOS7eXojMncGbHPxGS/yUiTJd/oSyDF7IDJyBg1OOIiRsKgIGnzRh4yEJfbZ6Fc2af+R6Gs6nXrsmicgbChzTOQBlunlWllmdZdSLqfO/syMZfvz8Bd40K/33uToR6sferIwiTCafS9+Di3n+iZ1kKvHH5HrjTiiiUxPwKgUN+jZje/Rh4WouBx4FVFJmnHBccMM+8KUq/dsqx1utyz03kCHNvDn/7JXJIvxTqcP87P8FgFFj2QH/8Ot7B12DqJPr6OhzbvQmm9E8QV7UXasn8c/piHeDzl4obvn/bxwYfRK1lNADnM83hpumhy7+2nUdoY7hpvAfHvzf3VSIi1DcYkfzpERiMAol9AjF1kGNMve8K1BotBox7GBj3MHTl55G27V/wPLUJIfW/tOrr2cPTiD08dqq6/HLvTcEBoPDwtcNTksI8gyp8CBA+1Bx0vCI4TZyIrvHGd8exelcOfF3V+P65O+HnppG7JIeXlfYz+gwazh4eciAmk3ntkIL95nBz9gBQnn1tO62n+Z6b8KHmWSih8eb1TIiIriM17wLe250DAFjyq34MO11EaLfWbVPCwEO2q05nvqH47EFzyDmbap56ezW/nuZgEz7UvCuzX08OTxFRm1TXN2D+50cgBDBlUCjGxwXJXRK1EQPPVVJX/wYuIb2hDewJ7/DeCIrqDY32Jpdjp3ZhMhpRdOYUSnMOo+5cJlTlxxFQk41wYz6kq1ezdXIFwuIvh5uwwZweTkS37PUtWThTXoMQTy1endRX7nLoJjDwXGXwxS3wqPkOaBwJMQoJhQp/lGnCUesWCeHTDc5BPeETEYugiJ5wUrNLs70IkwnlxQUoyj6M6oIMKEqz4FWVjVDDGYRK9Wju1kDhHQUpfOjlIaqAPuZVYomI2smuk6X4aL95csNfH+gPT2fO0LRFfGe4yoHg/4G3vhCetQUIbjgHV6kOIaIEIXUlQN0hoAxA464BBqHEWUUAyjVhqPWIhuTbDS5BPeEb0QeB4d2hVPHytkR3oRSFpw5Dd+YoUHIM7hWnEKLPgx8qcc1uPJJ5Z90CVQQuunaDwa83Psxxwf76KLx3/3jER7IHh4g6hq7GgOf/Y171d+aIKNzR3fH2C7MXfEe+yu0z/2K5y1uYTCgrOYuSvGOoLjyBhtJsaCpy4V1bgCBjIZwlPcJEEcLqioC6g0AJgCzzcfRChbPKIFzShEKv9oZR4wmh9YLk7AWVqzec3LyhcfOFs4cvXL384O7lZxdDZ8Jkgl5fB319HRr09Wgw1ONSSQEu5h6BqTgTLrpTCKo7jQBcgGczX28UEs4pQ1Dm3A31Pr2gCekLv24DERLdB92uWE7880/TUZZ2Dl+nFzLwEFGH+ePXmThfUY8YP1e8ML633OXQLWDguQ5JoYBfUAT8giIAjLd6zWQ04nxRHkrPHEN14UmI8hxoKnLhU1eAYGMR1FIDIk1nEVl7Fqht3ferFWpUSm6oUbihVumOeicPGJw8rMKS0sUbTm4+UDo5QxgNMJkaIIwNEE0fG/8MkxEmYwNgaoAwGc0L6Zkuv4am54QRUuOfJVMDYDJAYdRDMhmgMBmgMOmhEAYoTXooTQ1QCT2UogEqYYBKGOAEA1RogFo0fpSM0AC4cqDPv4XzLYY/zjtHo8arJ1RBfeET3R+hPQYgwtkVETe4VpMGhGBT2jlszijCH+7tw92JiajdbT5ahK/SC6GQgDen9YezWil3SXQLGHhukkKpRGBYNwSGdbvmNWNDAwrP5qA8/xhqS3JhrLkI1F6Cov4SnPQ6OBkqoG2ohIupEm6iCu6iBgpJwFnSwxkXANMFwATA0PnnddNaWLKmQShQIbmhSB2NKs8eQEAfeEb2R0jPgQjy8sXNznMY2d0P3i5OKKvSY9/pcozq0VKsIiJqu5KKOrzyZQYA4Okx3TEwwlvmiuhWMfB0AKVKhZCoXgiJ6tWq9iajEbqKi6i+VIaaijLUVZRDX3UBDdUXYaq5CFF3CYp6HVT1OqgNFdAaK6E0GWCSlOYHzB+FdO1HISkhFE1/VkFICgiFyrzZZdOfG9tAoQKUakhKNaBSQ1JpICnVUDhpIKnUUKg0UDhpoFSpoVRroXTSQOmkhcpJDZXaGSq1GionDZw0zlCrNXBSa6FSqeADoL0HnZyUCkzsF4yP9ufjq/RCBh4iajdCCLy4KQMXawzoG+KB393VQ+6SqB0w8HQBCqUSnt5+8PTmzXBtMbl/CD7an4/vM4vx5/vjoHVidzMR3brPUguw/XgJ1EoFlk8bALWKQ+b2gH+LZLOGRPkg2FOLyvoG7DxRKnc5RGQHavVG/Plb8+yT+Yk90SuIq7DbCwYeslkKhYRJ/UMAAN8cKZS5GiKyBztPlKCyvgFh3s6YPSpG7nKoHTHwkE2b3Bh4fsg6j6r6BpmrISJbtyWzGAAwsV8wlApuIGxPGHjIpvUN8UCMnyvqG0zYeqxY7nKIyIbVGYzYnnUeADCBe2XZHQYesmmSdHlY66t0DmsR0c3bc6oM1XojQjy1GBDuJXc51M4YeMjmTR5gDjw/nirDhWq9zNUQka36LqMIADA+LhiSxOEse8PAQzavm78b4kI90GAS2NL4A4uIqC3qG4zY2jicNbEfh7PsEQMP2YWmm5e/5mwtIroJe7PLUVnXgAB3DQZxVWW7xMBDduHe28yB52DeBRReauXmZUREjZp6hyfEBUHB2Vl2iYGH7EKIlzNuj/KBEMC3R9nLQ0StZzCakHLMPJw1Pi5Y5mqoo9hV4Fm1ahWio6Oh1WoRHx+PPXv2yF0SdaJJAzisRURtty+nHLpaA3xd1bg9ur13/qOuwm4Cz6effopnn30WL7/8MtLS0jBq1ChMmDAB+fn5cpdGneSefsFQKSRknqvA6dIqucshIhvxXaZ5OCspLoiLDdoxuwk8y5cvx6xZszB79mzExsZixYoVCA8Px7vvvit3adRJfFzVGNnDvAEre3mIqDUajCZ8/0vj7CwOZ9k1uwg8er0ehw4dQmJiotXziYmJ2Lt3b7NfU19fj4qKCqsH2b4rZ2sJIWSuhoi6ugO5F3ChWg9vFycMjeFwlj2zi8BTVlYGo9GIwMBAq+cDAwNRXNz8dgNLly6Fp6en5REeHt4ZpVIHS+wbBI1KgdOl1filkCGWiK5vS+NwVmKfIDgp7eItkVpgV3+7V6+MKYRocbXMhQsXQqfTWR4FBQWdUSJ1MDeNCnfHBgDgsBYRXZ/RJPDfzMa9s7jYoN2zi8Dj5+cHpVJ5TW9OSUnJNb0+TTQaDTw8PKweZB+ahrW+OVIIk4nDWkTUvNS8CyirqoeHVoUR3fzkLoc6mF0EHrVajfj4eGzdutXq+a1bt2LEiBEyVUVyGd0rAO4aFYp0dUg9c1Hucoioi/ou0/xL8rg+QVCr7OLtkK7Dbv6Gk5OT8Y9//AMffPABsrKy8NxzzyE/Px9z5syRuzTqZFonJZLizN3TXx85J3M1RNQVmUwC/20MPNw7yzHYTeB58MEHsWLFCixevBgDBgzA7t27sWXLFkRGRspdGsmgaVhrS0YxDEaTzNUQUVeTVnAJxRV1cNOoLMtZkH1TyV1Ae5o7dy7mzp0rdxnUBYzo5gs/NzXKqvT4MbsMY3oFyF0SEXUh3zXunTU2NgAalVLmaqgz2E0PD9GVVEoFJvYzLyL2TTpnaxHRZUIIy/07E/pxsUFHwcBDdqtpWOv7X4pRZzDKXA0RdRVHz+pw7lItXNRKJPT0l7sc6iQMPGS3BkV4I9TLGdV6I7YfL5G7HCLqIpoWGxzTOwBaJw5nOQoGHrJbCoWESU1bTXBYi4jQOJyV0Tg7i3tnORQGHrJrTcNa20+UoKLOIHM1RCS3XworkH+hBlonBUb34nCWI2HgIbsWG+yO7gFu0DeY8H1m8/uqEZHj+K5xOGt0zwC4auxqojLdAAMP2TVJkqx2UCcixyWEwJaMptlZXGzQ0TDwkN1rCjx7c8pRVlUvczVEJJcT5yuRW1YNtUqBu3pzbS5Hw8BDdi/KzxX9wzxhNAlsaVxsjIgcT1Pvzp09/OGudZK5GupsDDzkEDhbi4iaVlfm3lmOiYGHHMKk/iGQJCD1zEWcvVgjdzlE1MmySypxqqQKTkoJd8cGyl0OyYCBhxxCoIcWQ6N9AADfHOGwFpGjaVp7Z2R3P3g6czjLETHwkMOY3D8UAGdrETmiLdw7y+Ex8JDDmBAXBCelhKyiCmSXVMpdDhF1ktyyamQVVUClkJDYh8NZjoqBhxyGt6sad/Ywr6zKm5eJHEfTYoPDu/nCy0UtczUkFwYeciiTB1xehFAIIXM1RNQZmu7fmcC9sxwaAw85lLGxgdA6KZBXXoOjZ3Vyl0NEHazgQg0yzumgkIDEvhzOcmQMPORQXDUqjG2cksqbl4nsX9Nw1tBoX/i5aWSuhuTEwEMOp2mriW+PFsJo4rAWkT1rWl2Ziw0SAw85nIRe/vDQqnC+oh4Hci/IXQ4RdZBzl2qRXnAJkgQk9WXgcXQMPORwNCql5eZFDmsR2a//Nq69MyTSBwEeWpmrIbkx8JBDapqt9V1mEfQNJpmrIaKO0LR31gQOZxEYeMhBDYvxhb+7BpdqDNhzqlTucoionZ2vqEPqmYsAgPFxDDzEwEMOSqmQcE8/DmsR2avvfzEPZw2K8EKwp7PM1VBXwMBDDqtpWGvrsfOo1RtlroaI2tOWxuGsidw7ixox8JDDGhjuhXAfZ9Tojfgh67zc5RBROymtvDwDk8NZ1ISBhxyWJEmWNXk4rEVkP1KOFcMkgP5hngjzdpG7HOoiGHjIoU3uHwoA2HmiBLoag8zVEFF7sOydxeEsugIDDzm0XkHu6BXoDoNR4L+/FMldDhHdogvVeuw7XQ4AmMDhLLoCAw85vCt3UCci27b1WDGMJoE+wR6I9HWVuxzqQhh4yOFNus0cePbllKOksk7maojoVnDvLGoJAw85vAhfFwyM8IJJAJuPcliLyFbpagz4KbsMAO/foWsx8BABnK1FZAe2Zp1Hg0mgV6A7uvm7yV0OdTEMPEQA7rktGAoJSMu/hPzyGrnLIaKbwL2z6HoYeIgABLhrcUd3PwDAfw4VyFwNEbVVRZ0Be06Zh7O4ujI1h4GHqNG0weEAgM9Sz6LByB3UiWzJ9qwS6I0mdPN3RY8ADmfRtRh4iBol9g2Et4sTiivqsOskd1AnsiXfZV7eO0uSJJmroa6IgYeokUalxJRBYQCADQc5rEVkK6rrG7DzhPmXlAlxHM6i5jHwEF3hodvNw1rbj5egpIJr8hDZgh0nSlDfYEKUrwtig93lLoe6KAYeoit0D3DH4EhvGE0Cnx86K3c5RNQKV+6dxeEsagkDD9FVHhxi7uX59GABTCYhczVEdD21eiO2Hy8BAEzkcBZdBwMP0VXuuS0Y7hoV8i/UWDYhJKKuadfJEtQajAjzdkZcqIfc5VAXxsBDdBUXtQr3DTSvvMybl4m6tqa9sybEBXE4i66LgYeoGdOHRAAAvs8sxoVqvczVEFFz6gxGbMs6D4B7Z9GNMfAQNSMu1BNxoR7QG03YdJg3LxN1RXtOlaFab0SwpxYDwrzkLoe6OAYeohY82NjL8+nBAgjBm5eJuprvfzEPZyX1DYJCweEsuj4GHqIW3DcgBM5OSpwqqcLh/Ityl0NEVzCZhGVF9LGxgTJXQ7aAgYeoBR5aJ9xzm/m+gA0HePMyUVdyrKgCpZX1cFErMSTaW+5yyAbIGniioqIgSZLV48UXX7Rqk5+fj0mTJsHV1RV+fn545plnoNdb30SakZGBhIQEODs7IzQ0FIsXL+YQBLWL6Y1r8nx7tAiVdQaZqyGiJk29OyO6+UKjUspcDdkCldwFLF68GE8++aTlcze3y7vcGo1G3HPPPfD398ePP/6I8vJyzJgxA0IIrFy5EgBQUVGBcePGYcyYMTh48CBOnjyJmTNnwtXVFfPnz+/08yH7Eh/pje4BbsguqcJX6YV4ZFik3CUREYCdJ8yLDSb0CpC5ErIVsgced3d3BAUFNftaSkoKjh07hoKCAoSEmNdFefPNNzFz5kwsWbIEHh4e+Oijj1BXV4f169dDo9EgLi4OJ0+exPLly5GcnMx1GeiWSJKE6UPC8efNWfj0YAEDD1EXoKs14HD+JQDA6J7+8hZDNkP2e3j+8pe/wNfXFwMGDMCSJUushqv27duHuLg4S9gBgKSkJNTX1+PQoUOWNgkJCdBoNFZtCgsLkZeX1+L3ra+vR0VFhdWDqDlTBoVBrVQg45wOmed0cpdD5PB+PFUGo0mge4Abwn1c5C6HbISsgef3v/89NmzYgB07dmDevHlYsWIF5s6da3m9uLgYgYHWd997e3tDrVajuLi4xTZNnze1ac7SpUvh6elpeYSHh7fXaZGd8XFVI7Gv+d/UhoP5MldDRE3DWezdobZo98CzaNGia25EvvqRmpoKAHjuueeQkJCA2267DbNnz8bq1auxdu1alJdf3r+ouSEpIYTV81e3abph+XrDWQsXLoROp7M8Cgo4C4da1rTy8ldphajVG2WuhshxCXF5Ovpo3r9DbdDu9/DMmzcP06dPv26bqKioZp8fNmwYACA7Oxu+vr4ICgrC/v37rdpcvHgRBoPB0osTFBR0TU9OSYk5/V/d83MljUZjNQxGdD0juvki3McZBRdqsTmjCL+OD5O7JCKHdKyoAiWcjk43od0Dj5+fH/z8/G7qa9PS0gAAwcHmtU+GDx+OJUuWoKioyPJcSkoKNBoN4uPjLW1eeukl6PV6qNVqS5uQkJAWgxVRWykUEqYPicBfvz+BTw/mM/AQyWTnCU5Hp5sj2z08+/btw1tvvYX09HTk5ubis88+w1NPPYXJkycjIsI8fJCYmIg+ffrg0UcfRVpaGrZt24YFCxbgySefhIeHBwDg4YcfhkajwcyZM5GZmYkvvvgCr7/+OmdoUbv7dXwYlAoJB/MuIrukUu5yiBzSrsbAw+no1FayBR6NRoNPP/0Uo0ePRp8+ffDHP/4RTz75JD755BNLG6VSic2bN0Or1eKOO+7AtGnTcP/992PZsmWWNp6enti6dSvOnj2LwYMHY+7cuUhOTkZycrIcp0V2LNBDizGNP2S58jJR59PVGnCocZsX3rBMbSUJLkkMwLyAoaenJ3Q6naX3iOhq27LOY9Y/U+Hjqsa+hXexS52oE23JKMLcjw6jm78rts0fLXc51EW09v1b9nV4iGxJQk9/BHpocKFaj63HzstdDpFDsUxH53AW3QQGHqI2UCkVmDbYvGYTh7WIOo/1dHQOZ1HbMfAQtVFT4PkxuwwFF2pkrobIMWQVVeJ8RT2cnZS4PdpH7nLIBjHwELVRuI8LRvUwL73w6UH28hB1hp0nzcNZnI5ON4uBh+gmNK28/PmhAjQYTTJXQ2T/mtbf4XAW3SwGHqKbMLZPAHxc1ThfUW/5QUxEHaOizoBDZxqno/OGZbpJDDxEN0GjUmLqoFAA3FCUqKP91Lg7eoy/K3dHp5vGwEN0kx5sHNbafrwExbo6mashsl+W4aye7N2hm8fAQ3STuge4YUiUN0wC+M8h3rxM1BE4HZ3aCwMP0S1ounn509QCmExctJyovR0vrkRxRR2no9MtY+AhugUT+wXDXatCwYVa7M0pl7scIrvTNJw1vJsvtE6cjk43j4GH6BY4q5W4f4D55uVPePMyUbu7vJ0Eh7Po1jDwEN2i6bebV15O+aUYF6r1MldDZD8qr5yOzhuW6RYx8BDdor4hnugX6gmDUWDT4bNyl0NkN37KLkODSSDGzxURvpyOTreGgYeoHTT18nxyIB9C8OZlovbQdP9OAoezqB0w8BC1g8n9Q+DspEROabWlC56Ibp4Q4ortJDicRbeOgYeoHbhrnXDvbcEAgE8OcE0eolt14rx5OrrWSYGhnI5O7YCBh6idTL/dvCbP5oxC6GoNMldDZNss09FjOB2d2gcDD1E7GRThhR4BbqgzmPD1kUK5yyGyaZeno3M4i9oHAw9RO5EkydLLs+EA1+QhulmVdQak5jXtjs4blql9MPAQtaMpA0OhVirwS2EFMs/p5C6HyCb9lF2OBpNAtJ8rIn1d5S6H7AQDD1E78nZVIykuCIB5ijoRtd2uk+bhrISe7N2h9sPAQ9TOHhpiXpPnq/RC1OgbZK6GyLZYT0dn4KH2w8BD1M6Gxfgi0tcFVfUN2Hy0SO5yiGzKyfNVKNLVQaNSYFiMr9zlkB1h4CFqZwqFhGmDzb08Gw5yTR6itmiancXd0am9MfAQdYAH4sOgVEg4dOYiTp6vlLscIpthGc7i/TvUzhh4iDpAgIcWd/U2rx/yKXt5iFqlqr4BqWcuAOD6O9T+GHiIOshDjRuKbjp8FvUNRpmrIer6fsoug8EoEOXrgig/Tken9sXAQ9RBEnoGIMhDi4s1BqT8cl7ucoi6PG4WSh2JgYeogygVEqYNDgMAbDjINXmIrkcIgV2NNywncDo6dQAGHqIO9EDjbK29OeU4X1EnczVEXdepkioUNk5HH87p6NQBGHiIOlC4jwviI70hBPANNxQlalHTdPRh3B2dOggDD1EHu29ACABwB3Wi6+DqytTRGHiIOtjEfsFQKiQcPatDblm13OUQdTlV9Q04mMfp6NSxGHiIOpifmwYju/sBAL5OZy8P0dX2Nk5Hj/R1QTSno1MHYeAh6gRNw1pfHTkHIYTM1RB1LTtPcnVl6ngMPESdILFvEDQqBU6XVuOXwgq5yyHqMszT0bn+DnU8Bh6iTuCmUWFsbCAA4Kv0czJXQ9R1ZJdU4dylWqi5Ozp1MAYeok4y+YrZWkYTh7WIgMuzs4bF+MJZzeno1HEYeIg6yehe/nDXqnC+oh4Hci/IXQ5Rl7DzpHn9Hd6/Qx2NgYeok2hUSkyMCwbANXmIAKC6vgEHcy8C4Po71PEYeIg6UdNsrS0ZRdA3mGSuhkhee3PKoTeaEOHD6ejU8Rh4iDrR0BhfBLhroKs1YHfjVFwiR9W0ncToXv6QJEnmasjeMfAQdSKlQsK9tzWtycNhLXJcQghuJ0GdioGHqJM1DWttPVaM6voGmashkkdO6eXp6MNj/OQuhxwAAw9RJ7stzBNRvi6oM5iw9dh5ucshkkVT787QaB9OR6dOwcBD1MkkScLkAaEAOFuLHNdOrq5MnYyBh0gGk/ubh7V2nyzFhWq9zNUQda7q+gbLWlS8f4c6S4cGniVLlmDEiBFwcXGBl5dXs23y8/MxadIkuLq6ws/PD8888wz0eus3gIyMDCQkJMDZ2RmhoaFYvHjxNRsw7tq1C/Hx8dBqtYiJicHq1as76rSIbln3ADf0DfFAg0lgS0aR3OUQdap9jdPRw32cEcPp6NRJOjTw6PV6PPDAA/jtb3/b7OtGoxH33HMPqqur8eOPP2LDhg3YuHEj5s+fb2lTUVGBcePGISQkBAcPHsTKlSuxbNkyLF++3NImNzcXEydOxKhRo5CWloaXXnoJzzzzDDZu3NiRp0d0S5puXv46ncNa5Fgur64cwOno1GlUHXnw1157DQCwfv36Zl9PSUnBsWPHUFBQgJAQ8w//N998EzNnzsSSJUvg4eGBjz76CHV1dVi/fj00Gg3i4uJw8uRJLF++HMnJyZAkCatXr0ZERARWrFgBAIiNjUVqaiqWLVuGqVOnduQpEt20Sf1DsPS74ziQdwHnLtUi1MtZ7pKIOhyno5NcZL2HZ9++fYiLi7OEHQBISkpCfX09Dh06ZGmTkJAAjUZj1aawsBB5eXmWNomJiVbHTkpKQmpqKgwGQ7Pfu76+HhUVFVYPos4U7OmM26N8AADf8OZlchA5pdU4e7EWaqUCw7txd3TqPLIGnuLiYgQGBlo95+3tDbVajeLi4hbbNH1+ozYNDQ0oKytr9nsvXboUnp6elkd4eHi7nBNRW9zXOFvrKw5rkYNoWl15aIwPXNQdOshAZKXNgWfRokWQJOm6j9TU1FYfr7nxWyGE1fNXt2m6Ybmtba60cOFC6HQ6y6OgoKDVNRO1lwlxQXBSSsgqqsCp85Vyl0PU4XY1bqmSwN3RqZO1OV7PmzcP06dPv26bqKioVh0rKCgI+/fvt3ru4sWLMBgMlh6boKAgS09Ok5IS828IN2qjUqng69t8l6lGo7EaJiOSg7erGnf28Me24yX4+kgh5if2krskog5To2/A/tNN09G5/g51rjYHHj8/P/j5tc8y4MOHD8eSJUtQVFSE4OBgAOYbmTUaDeLj4y1tXnrpJej1eqjVakubkJAQS7AaPnw4vvnmG6tjp6SkYPDgwXBycmqXWok6yuQBIdh2vARfpRcieVxPzlohu9U0HT3M2xnd/DkdnTpXh97Dk5+fj/T0dOTn58NoNCI9PR3p6emoqqoCACQmJqJPnz549NFHkZaWhm3btmHBggV48skn4eHhAQB4+OGHodFoMHPmTGRmZuKLL77A66+/bpmhBQBz5szBmTNnkJycjKysLHzwwQdYu3YtFixY0JGnR9QuxvUJhLOTEvkXapBecEnucog6zJWzsxjsqbN1aOD54x//iIEDB+LVV19FVVUVBg4ciIEDB1ru8VEqldi8eTO0Wi3uuOMOTJs2Dffffz+WLVtmOYanpye2bt2Ks2fPYvDgwZg7dy6Sk5ORnJxsaRMdHY0tW7Zg586dGDBgAP70pz/h7bff5pR0sgkuahUS+5qHZ3nzMtkrIYTV+jtEnU0SVy9Z7KAqKirg6ekJnU5n6V0i6izbj5/HE+tT4eemwc8L74JKyV1fyL7klFbh7jd3Qa1UIP3VcZyhRe2mte/f/KlK1AWM6uEPbxcnlFXV4+fGmzqJ7EnTcNbt0ZyOTvJg4CHqApyUCkzsZ75x/6v0czJXQ9T+mtbf4erKJBcGHqIuomkH9f9mFqPOYJS5GqL2U6s3Yj93RyeZMfAQdRFDonwQ7KlFZX2D5bdhInuw+1Qp9A1N09Hd5C6HHBQDD1EXoVBIll4eztYie/L9L+aFYRP7BHE6OsmGgYeoC5k8wBx4th0vQUVd8xvfEtkSg9GEbVnmHsukvoE3aE3UcRh4iLqQPsEe6B7gBn2DCd9nFt/4C4i6uIO5F6CrNcDXVY3BUT5yl0MOjIGHqAuRJAn3NQ5rfX2Ew1pk+5qGs8bGBkKp4HAWyYeBh6iLmdQYeH7KLkNpZb3M1RDdPCEEUo6dBwAkxXE4i+TFwEPUxUT5uaJ/uBdMAth8lL08ZLuOntWhSFcHV7USI7q1z6bTRDeLgYeoC2oa1vqKw1pkw5qGs0b3CoDWSSlzNeToGHiIuqB7bwuGQgLS8i8hv7xG7nKIboplOjpnZ1EXwMBD1AUFeGgtQwBfH+FWE2R7skuqkFNaDSelhDG9uTs6yY+Bh6iLalqT56v0QgghZK6GqG2aendGdPODh9ZJ5mqIGHiIuqykvkFQKxU4VVKF48WVcpdD1CaW2Vl9g2SuhMiMgYeoi/J0dsKY3uaNFrnVBNmSYl0djhRcgiQBY/twOIu6BgYeoi7svgGhAIBvjhTCZOKwFtmGlGPm4axBEd4IcNfKXA2RGQMPURd2V+8AuGlUOHepFofyL8pdDlGrNN2/w72zqCth4CHqwrROSss9EF+lc7YWdX2XavT4+fQFALx/h7oWBh6iLu6+xtlam48WwWA0yVwN0fVtyyqB0STQO8gdkb6ucpdDZMHAQ9TFjejmCz83NS7WGPBjdpnc5RBd1+XFBtm7Q10LAw9RF6dSKnBPv2AAwNecrUVdWK3eiN2nSgHw/h3qehh4iGzA5MbZWt//UoxavVHmaoiat+tkKeoMJoR5O6NPsIfc5RBZYeAhsgGDIrwQ5u2MGr0RP2Sdl7scomY1TUdP6hsESZJkrobIGgMPkQ2QJMly8zIXIaSuyGA0YVtWCQAgsQ+Hs6jrYeAhshFNixDuOlmCSzV6mashsnYg9wJ0tQb4uqoxOMpH7nKIrsHAQ2Qjega6o3eQOwxGge8yi+Uuh8hK0+yssbGBUCo4nEVdDwMPkQ1p2kGds7WoKzGZBFJ+adwsNI7DWdQ1MfAQ2ZBJt5kDz8+55SjW1clcDZHZ0XM6FFfUwVWtxIhufnKXQ9QsBh4iGxLu44LBkd4QAvj2KHt5qGtoGs4a3TsAWielzNUQNY+Bh8jGcLYWdTWXNwvl6srUdTHwENmYif2CoVRIyDinQ05pldzlkIPLLqnE6dJqOCkljO7lL3c5RC1i4CGyMb5uGozqYb5P4vPUszJXQ47u+8ablUd084OH1knmaohaxsBDZIOmD4kAAHyWWoD6Bm41QfJJ4XAW2QgGHiIbNDY2AEEeWlyo1mNLRpHc5ZCDKtLV4shZHSQJGMfVlamLY+AhskEqpQIPDzX38vx73xmZqyFH1bT2TnyEN/zdNTJXQ3R9DDxENmr67eFQKSQczr+EzHM6ucshB8TZWWRLGHiIbFSAuxbj48xvNB/tZy8Pda6L1Xrsz70AgIGHbAMDD5ENe3RYJADgy7RC6GoNMldDjmTb8RIYTQK9g9wR4esidzlEN8TAQ2TDbo/2Qa9Ad9QajNh4iFPUqfM0DWclsneHbAQDD5ENkyQJjww39/J8+PMZCCFkrogcQa3eiD2nSgEASX05O4tsAwMPkY371cBQuKqVOF1WjZ+yy+UuhxzArpOlqDOYEObtjD7BHnKXQ9QqDDxENs5No8KUQWEAgH//nCdvMeQQrlxsUJIkmashah0GHiI78GjjsNbWY+dRpKuVuRqyZwajCT9kmdff4ewssiUMPER2oGegO4ZG+8AkgE/258tdDtmx/acvoKKuAb6uasRHestdDlGrMfAQ2YmmXp6PDxRA32CSuRqyV02zs8b1CYRSweEssh0MPER2IqlvEPzdNSirqre8KRG1J5NJIOVY03R0zs4i28LAQ2QnnJQKPHR74/5aP3PlZWp/R85ewvmKeriqlRjRzU/ucojapEMDz5IlSzBixAi4uLjAy8ur2TaSJF3zWL16tVWbjIwMJCQkwNnZGaGhoVi8ePE1643s2rUL8fHx0Gq1iImJueYYRI7g4dsjoFRIOJB7AceLK+Quh+zM942bhY7uHQCtk1LmaojapkMDj16vxwMPPIDf/va31223bt06FBUVWR4zZsywvFZRUYFx48YhJCQEBw8exMqVK7Fs2TIsX77c0iY3NxcTJ07EqFGjkJaWhpdeegnPPPMMNm7c2GHnRtQVBXlqkdjHPNTwIXt5qJ01DWdxdhbZIlVHHvy1114DAKxfv/667by8vBAU1Px/oI8++gh1dXVYv349NBoN4uLicPLkSSxfvhzJycmWHqGIiAisWLECABAbG4vU1FQsW7YMU6dObc9TIuryHh0Wie8yi/HF4XN4YXxvuGud5C6J7EB2SSVOl1ZDrVRgTC9/ucsharMucQ/PvHnz4OfnhyFDhmD16tUwmS7PMNm3bx8SEhKg0WgszyUlJaGwsBB5eXmWNomJiVbHTEpKQmpqKgyG5jdUrK+vR0VFhdWDyB4M7+aLbv6uqNYb8WXaObnLITvRNJw1orsvQzTZJNkDz5/+9Cd8/vnn+OGHHzB9+nTMnz8fr7/+uuX14uJiBAZazwZo+ry4uPi6bRoaGlBWVtbs9126dCk8PT0tj/Dw8PY8LSLZSJJk2UX9X/u4vxa1j+9/4XAW2bY2B55FixY1e6PxlY/U1NRWH++VV17B8OHDMWDAAMyfPx+LFy/GX//6V6s2Vy9d3vQD/MrnW9PmSgsXLoROp7M8CgoKWl0zUVc3JT4Mzk5KnCqpwv7cC3KXQzau8FItjp7VQZKAsbGcjk62qc338MybNw/Tp0+/bpuoqKibrQfDhg1DRUUFzp8/j8DAQAQFBVl6cpqUlJQAuNzT01IblUoFX1/fZr+PRqOxGiYjsiceWifcPzAUnxzIx79/PoNhMc3/PyBqjaa9s+IjvOHvzp+bZJvaHHj8/Pzg59dx6y+kpaVBq9VaprEPHz4cL730EvR6PdRqNQAgJSUFISEhlmA1fPhwfPPNN1bHSUlJweDBg+HkxLFmckyPDovEJwfy8X1mMUoq6hDgoZW7JLJRTffvcDiLbFmH3sOTn5+P9PR05Ofnw2g0Ij09Henp6aiqqgIAfPPNN1izZg0yMzORk5ODf/zjH3j55Zfxm9/8xtL78vDDD0Oj0WDmzJnIzMzEF198gddff90yQwsA5syZgzNnziA5ORlZWVn44IMPsHbtWixYsKAjT4+oS+sT4oHBkd5oMAl8coBDtnRzLlbrcSDPPCzKwEM2TXSgGTNmCADXPHbs2CGEEOK7774TAwYMEG5ubsLFxUXExcWJFStWCIPBYHWco0ePilGjRgmNRiOCgoLEokWLhMlksmqzc+dOMXDgQKFWq0VUVJR4991321SrTqcTAIROp7ulcybqSr5MOysiX/hW3L5kq9A3GOUuh2zQ56kFIvKFb0XSW7vkLoWoWa19/5aE4BQOwLzAoaenJ3Q6HTw8POQuh6hd1DcYMWLpdpRX67H6kUEYHxcsd0lkY578Vyq2HjuP39/dA8+N6yl3OUTXaO37t+zT0omo42hUSky/3bzkwr/2ceVlapsafQN2nywFwOEssn0MPER27qHbI6CQgL055cguqZS7HLIhu0+Wor7BhHAfZ8QGu8tdDtEtYeAhsnNh3i64q3fT/lr5MldDtqRpdlZin6AW1zQjshUMPEQO4LHh5pWXNx46i+r6BpmrIVtgMJqwLYvT0cl+MPAQOYCR3f0Q5euCyvoGfJVeKHc5ZAN+Pl2OiroG+LqqER/pLXc5RLeMgYfIASgUEh6x7K+Vx/216Iaa9s4a1ycQSgWHs8j2MfAQOYgH4sOhdVLgeHElDp25KHc51IWZTAIpXF2Z7AwDD5GD8HRxwuT+IQCAf//MKerUsiNnL6Gksh5uGhVGdOc+bGQfGHiIHMijw6IAAFsyilBWVS9vMdRlNc3OGt3LHxqVUuZqiNoHAw+RA+kX5on+4V4wGAU+Pcj9tehaQgjL7ugcziJ7wsBD5GAea7x5+eP9+TCaePMyWcsuqcLpsmqolQqM7uUvdzlE7YaBh8jB3HNbMLxcnHDuUi22Hy+RuxzqYppmZ43o7gt3rZPM1RC1HwYeIgejdVLiwcHm/bV48zJdqcFowmepZwEA4zmcRXaGgYfIAf3P0EhIknmvpNyyarnLoS7i6yOFyL9QAx9XNSYPCJG7HKJ2xcBD5IAifF0wuqf5/oyP2MtDAIwmgb/vyAYAzB4VDRe1SuaKiNoXAw+Rg3q0cX+tzw+dRa3eKHM1JLctGUU4XVoNT2cnPDY8Su5yiNodAw+Rg0roGYBwH2foag345ij313JkJpPA37ebe3eeuCMabhr27pD9YeAhclBKhYT/GWru5fn3vjPcX8uBpRw7jxPnK+GuUWHmHVFyl0PUIRh4iBzYtMHhUKsUyDinw5GzOrnLIRkIIbBy+ykAwIwRUfB05lR0sk8MPEQOzMdVjXtvCwZg7uUhx7PjRAl+KayAi1qJJ0ZGy10OUYdh4CFycI82rrz8zdFCXKjWy1wNdSYhBN7eZr5359FhkfBxVctcEVHHYeAhcnADwr0QF+oBfYMJn6dyfy1H8mN2GdILLkHrpMDsUTFyl0PUoRh4iBycJEl4rHEX9Q/3n4GJ+2s5jJWNvTsP3R4Bf3eNzNUQdSwGHiLCpP4h8NCqUHChFrtOlcpdDnWCn0+X40DeBaiVCjx1Zze5yyHqcAw8RARntRIPNO2vxZuXHULTzKwHBochyFMrczVEHY+Bh4gAAI803ry840QJskuqZK6GOtKhMxfxU3Y5VAoJvx3N3h1yDAw8RAQAiPZzxbg+gRACeH93jtzlUAdq6t2ZMigUYd4uMldD1DkYeIjIYk6C+bf9L9LOoVhXJ3M11BGOnr2EnSdKoZCAuaO7y10OUadh4CEii/hIb9we5QODUeCDn3LlLoc6wMrGPbPuGxCKKD9Xmash6jwMPERkZc5o83osH+/Ph67WIHM11J6yiiqw9dh5SBLw9Bj27pBjYeAhIitjegWgV6A7quob8OHPnLFlT/6+w9y7M7FfMLoHuMlcDVHnYuAhIiuSJOGpBHMvz7qf8lBnMMpcEbWH7JJKbMkoAgD87i727pDjYeAhomtM6h+CUC9nlFXVY+Phs3KXQ+3gnR05EAJI7BOI3kEecpdD1OkYeIjoGk5KBWY17py9ZvdpGLndhE3LK6vGV+nnAAC/u6uHzNUQyYOBh4iaNf32cHi5OCGvvAb/zSyWuxy6Bat2ZsMkgDG9/NEvzFPucohkwcBDRM1yUavw2PAoAMDqXTkQgr08tqjgQg02HW7s3bmbvTvkuBh4iKhFM0dEQeukQMY5HfbmlMtdDt2E1bty0GASGNndD4MivOUuh0g2DDxE1CIfVzUebNxUdPUubjdha4p1dfg81XzTOWdmkaNj4CGi65o9KgZKhYQ9p8qQeU4ndznUBqt35UBvNOH2aB8MjfGVuxwiWTHwENF1hfu44N7bggGwl8eWlFTW4ZMD+QDYu0MEMPAQUSs8dad5U9EtGUU4U14tczXUGv/Yk4v6BhMGhHthZHc/ucshkh0DDxHdUJ8QDyT09IdJAGv2nJa7HLqBC9V6y7Ygz9zdHZIkyVwRkfwYeIioVeYkmHt5Pk89i7KqepmroetZ++Np1OiNiAv1wJheAXKXQ9QlMPAQUasMi/FB/3Av1DeYsP6nPLnLoRboagz4515z7868MT3Yu0PUiIGHiFpFkiT8tnFT0X/ty0NVfYPMFVFz1u3NRVV9A3oFuiOxT6Dc5RB1GQw8RNRq4/oEIcbPFRV1DdjQOAOIuo7KOgPWNfa+zburOxQK9u4QNWHgIaJWUyok/OZOcy/PP/bkQt9gkrkiutK/fz4DXa0BMf6umNgvWO5yiLoUBh4iapNfDQpFgLsGxRV1+LJxB26SX42+Af/YkwsAmDemO5Ts3SGy0mGBJy8vD7NmzUJ0dDScnZ3RrVs3vPrqq9Dr9Vbt8vPzMWnSJLi6usLPzw/PPPPMNW0yMjKQkJAAZ2dnhIaGYvHixddsZLhr1y7Ex8dDq9UiJiYGq1ev7qhTI3JoGpUSs0ZGAwDe25UDk4mbinYFH+/Px4VqPSJ9XTC5f4jc5RB1OaqOOvDx48dhMpnw3nvvoXv37sjMzMSTTz6J6upqLFu2DABgNBpxzz33wN/fHz/++CPKy8sxY8YMCCGwcuVKAEBFRQXGjRuHMWPG4ODBgzh58iRmzpwJV1dXzJ8/HwCQm5uLiRMn4sknn8SHH36In376CXPnzoW/vz+mTp3aUadI5LAeHhqBv+/IRk5pNX7IOo/EvkFyl+TQ6gxGvLfbvD7S3NHdoFKy857oGqIT/d///Z+Ijo62fL5lyxahUCjEuXPnLM998sknQqPRCJ1OJ4QQYtWqVcLT01PU1dVZ2ixdulSEhIQIk8kkhBDi+eefF71797b6Xk899ZQYNmxYq2vT6XQCgOX7EtH1vfFdloh84Vvxq3d+tPxfJHms/ylXRL7wrRixdJuoNxjlLoeoU7X2/btTfw3Q6XTw8fGxfL5v3z7ExcUhJORy92tSUhLq6+tx6NAhS5uEhARoNBqrNoWFhcjLy7O0SUxMtPpeSUlJSE1NhcFgaLaW+vp6VFRUWD2IqPUevyMKapUCh/Mv4WDeRbnLcVj1DUbLHmdzRneDWsXeHaLmdNr/jJycHKxcuRJz5syxPFdcXIzAQOt1Iry9vaFWq1FcXNxim6bPb9SmoaEBZWVlzdazdOlSeHp6Wh7h4eG3doJEDibAXYupg8IAcFNROW08dA5FujoEemjwQHyY3OUQdVltDjyLFi2CJEnXfaSmplp9TWFhIcaPH48HHngAs2fPtnqtuVVAhRBWz1/dRjTesNzWNldauHAhdDqd5VFQUHCjUyeiq/zmzhhIErD9eAlOFFfKXY7DMRhNWLUzG4B5g1etk1Lmioi6rjbftDxv3jxMnz79um2ioqIsfy4sLMSYMWMwfPhwvP/++1btgoKCsH//fqvnLl68CIPBYOmxCQoKsvTkNCkpKQGAG7ZRqVTw9fVttkaNRmM1TEZEbRft54oJcUHYklGM93blYPmDA+QuyaF8mXYOZy/Wws9NjYduj5C7HKIurc2Bx8/PD35+fq1qe+7cOYwZMwbx8fFYt24dFArrDqXhw4djyZIlKCoqQnCweZGslJQUaDQaxMfHW9q89NJL0Ov1UKvVljYhISGWYDV8+HB88803VsdOSUnB4MGD4eTk1NZTJKI2mJPQDVsyivH1kULMT+qFUC9nuUtyCEaTwKqd5qHE2aNi4Kxm7w7R9XTYPTyFhYUYPXo0wsPDsWzZMpSWlqK4uNiqJyYxMRF9+vTBo48+irS0NGzbtg0LFizAk08+CQ8PDwDAww8/DI1Gg5kzZyIzMxNffPEFXn/9dSQnJ1uGq+bMmYMzZ84gOTkZWVlZ+OCDD7B27VosWLCgo06PiBrdFuaFEd180WAS+Mee03KX4zA2Hj6L3LJqeLk44ZFhkXKXQ9TldVjgSUlJQXZ2NrZv346wsDAEBwdbHk2USiU2b94MrVaLO+64A9OmTcP9999vWacHADw9PbF161acPXsWgwcPxty5c5GcnIzk5GRLm+joaGzZsgU7d+7EgAED8Kc//Qlvv/021+Ah6iRzEroBADYcKMDFav0NWtOtqtE3YNn3JwCY191x03TYkmpEdkMSQnCZVJgXOPT09IROp7P0LhFR6wghcO/KH/FLYQWeG9sTvx/bQ+6S7NpbW0/ib9tOIdzHGT8kJ0Cj4nAWOa7Wvn9zwQYiumWSJOGpxl6ef+7LQ63eKHNF9qtYV4f3dpvv3Vk4IZZhh6iVGHiIqF1MjAtCuI8zLlTr8Vkql3noKMtSTqDOYMLgSG9MiOOWHkStxcBDRO1CpVTgN6NiAABr9pxGg9Ekc0X2J/OcDhsPnwUAvHxPbIvrjBHRtRh4iKjdPDA4HL6uapy9WIvNGUVyl2NXhBD48+ZjEAK4b0AIBkZ4y10SkU1h4CGidqN1UmLmiCgAwOpdp8E5Ee3nh6wS/Hz6AtQqBf43qZfc5RDZHAYeImpXjw6PhItaiayiCuw6WSp3OXZB32DC61uyAACzR0YjzNtF5oqIbA8DDxG1Ky+Xy9sccFPR9vHR/jPILauGn5savx3dTe5yiGwSAw8RtbtZI6OhUkj4+fQFpOVflLscm6arMeBv204BAJLH9YK7ltvlEN0MBh4ianchXs64b0AoAPby3KqV20/hUo0BPQPdMG1wmNzlENksBh4i6hBzEsxT1FOOnUdOaZXM1dimvLJq/HNfHgDg5Xv6QKXkj2yim8X/PUTUIXoEumNsbACEAN7fxU1Fb8Zf/nscBqNAQk9/JPT0l7scIpvGwENEHabpBtsv0s7hfEWdzNXYlgO5F/BdZjEUknmRQSK6NQw8RNRh4iN9MCTKG3qjCUu3ZHFdnlYymcyLDALA9Nsj0DPQXeaKiGwfAw8RdagXxveGUiHhy/RCfLQ/X+5ybMLXRwpx9KwObhoVnhvbU+5yiOwCAw8RdajBUT54vnFl4MXfHEN6wSV5C+riavVG/OW/xwGYhwT93TUyV0RkHxh4iKjD/ebOGCT2CYTeaMLTHx3GhWq93CV1WWt/PI0iXR1CvZwxa2S03OUQ2Q0GHiLqcJIkYdm0/ojydcG5S7X4/YY0GE28n+dqJZV1eHened2i58f3gtZJKXNFRPaDgYeIOoWH1gnvPhIPrZMCe06V4e3G1YPpsre2nkS13ogB4V6Y3D9E7nKI7AoDDxF1mthgDyyd0g8A8Pb2U9hxokTmirqOrKIKfHqwAADwh3tjIUmSzBUR2RcGHiLqVL8aGIZHhkVACOC5T9NRcKFG7pJkJ4TA61uyYBLAPf2CER/pI3dJRHaHgYeIOt0f7u2D/mGeuFRjwNyPDqPOYJS7JFntPFmKPafKoFYq8ML43nKXQ2SXGHiIqNNpVEqseiQe3i5OyDinw+Jvj8ldkmwajCYs2ZwFAJh5RxQifF1krojIPjHwEJEsQr2csWL6QEgS8PH+fPzn0Fm5S5LFJwcLkF1SBW8XJzw9prvc5RDZLQYeIpJNQk9/PHu3eSXhl7/IwLHCCpkr6lwVdQas2HoSAPDcuJ7wdHaSuSIi+8XAQ0Sy+t1d3TG6lz/qG0z47UeHoKs1yF1Sp1m1Iwfl1XrE+Lviodsj5C6HyK4x8BCRrBQKCW9NG4BQL2ecKa/Bgs+PwOQAixIWXKjBBz/mAgBenhgLJyV/HBN1JP4PIyLZebuq8e4jg6BWKrD12Hm8t/u03CV1uP/7/gT0RhPu6O6Lu3oHyF0Okd1j4CGiLuG2MC8smtwXAPDX749jb06ZzBV1nENnLuKbI4WQJODliX24yCBRJ2DgIaIu46HbwzF1UBhMAnjmkzQU6+rkLqndCSHw583mafgPxIehT4iHzBUROQYGHiLqMiRJwp/vj0PvIHeUVenx9MeHYTCa5C6rXW3OKEJa/iW4qJWYn9hL7nKIHAYDDxF1Kc5qJVY/Eg93jQqHzlzE0i3H5S6p3dQZjHjjO/P5zEnohkAPrcwVETkOBh4i6nKi/Fzx5rT+AIAPfsrFt0cLZa6ofazfm4ezF2sR5KHFk6Ni5C6HyKEw8BBRl5TYNwhzEroBAF74z1Fkl1TKXNGtKa+qxzvbswEA/5vUC85qpcwVETkWBh4i6rIWJPbE8BhfVOuNmPPhYVTXN8hd0k1b8cMpVNY3IC7UA78aGCp3OUQOh4GHiLoslVKBtx8aiEAPDbJLqvDipgwIYXuLEmaXVOLjA/kAgFfu6QOFgtPQiTobAw8RdWn+7hq88/AgqBQSvjlSiH/uzZO7pDY5kHsB8z5Og9EkkNgnEMNifOUuicghMfAQUZc3OMoHL02MBQD8eXMWDp25KHNFN3byfCVmrT+Iae/tw/HiSnhoVVjYeA5E1PkYeIjIJjx+RxTuuS0YDSaBpz86jLKqerlLalaRrhbP/+cIxq/YjW3HS6BUSPifoRH4YX4Cov1c5S6PyGGp5C6AiKg1JEnCX6behuNFFcgprcYzn6Th37OGQtlF7ofR1RqwelcOPvgxF/UN5sUSJ8QFYUFSL3Tzd5O5OiJi4CEim+GmUWH1I/G4752fsDenHH/efAzzE3vBTSPfj7L6BiP+ve8M/r4jG5dqDACA26N88OLE3hgU4S1bXURkTRK2OOWhA1RUVMDT0xM6nQ4eHtzbhqgr+/pIIZ75JA0AoFEpkNDTHxP7BePu2AC4a506pQaTSeCrI+ew7PuTOHepFgDQI8ANL4zvjbtjA7ghKFEnae37N3t4iMjmTO4fgopaA/6x5zTyymuQcuw8Uo6dh1qpwJ09/RrDTyA8nds//AghsPtUGd747jiyiioAAIEeGiSP64mpg8KgUvLWSKKuiD08jdjDQ2R7hBDIKqrEd5lF2JxRhNOl1ZbXnJQSRnY3h5/EPkHwdLn18JNxVoc3/puFn7LLAQDuWhV+O7obHh8RzZWTiWTS2vdvBp5GDDxEtk0IgZPnq7AlowhbMopwqqTK8ppKIeGO7n6Y2C8IiX2C4O2qbtOx88trsCzlBL4+Yt7TS61U4LHhkXh6TPc2H4uI2hcDTxsx8BDZl1PnK/FdZjG2ZBThePHlfbiUCgkjuvk29vwEwtdN0+IxyqvqsXJ7Nj7afwYGo4AkAfcPCEXyuJ4I93HpjNMgohtg4GkjBh4i+5VTWoX/ZhZj89EiHGu87wYAFBIwLMYcfpL6BsHf3Rx+avQN+ODHXKzedRpVjft3jerhhxcn9EbfEE9ZzoGImsfA00YMPESOIa+sGlsyi/BdRjEyzukszysk4PZoHwyO9MFnqQUoqTQvbBgX6oEXx8diZA8/uUomoutg4GkjBh4ix1NwocZ8z09mMY4UXLJ6LdzHGQsSe2HSbSHc7JOoC2vt+3eHzZ/My8vDrFmzEB0dDWdnZ3Tr1g2vvvoq9Hq9VTtJkq55rF692qpNRkYGEhIS4OzsjNDQUCxevPiaHZN37dqF+Ph4aLVaxMTEXHMMIqKrhfu44KmEbvjq6Tvw4wtj8Mo9sRjfNwivTuqDH5ITcN+AUIYdIjvRYevwHD9+HCaTCe+99x66d++OzMxMPPnkk6iursayZcus2q5btw7jx4+3fO7peXmMvKKiAuPGjcOYMWNw8OBBnDx5EjNnzoSrqyvmz58PAMjNzcXEiRPx5JNP4sMPP8RPP/2EuXPnwt/fH1OnTu2oUyQiOxLm7YLZo2Iwe5TclRBRR+jUIa2//vWvePfdd3H69OnLBUgSvvjiC9x///3Nfs27776LhQsX4vz589BozDcUvvHGG1i5ciXOnj0LSZLwwgsv4Ouvv0ZWVpbl6+bMmYMjR45g3759raqNQ1pERES2R/YhrebodDr4+Phc8/y8efPg5+eHIUOGYPXq1TCZTJbX9u3bh4SEBEvYAYCkpCQUFhYiLy/P0iYxMdHqmElJSUhNTYXBYGi2lvr6elRUVFg9iIiIyD51WuDJycnBypUrMWfOHKvn//SnP+Hzzz/HDz/8gOnTp2P+/Pl4/fXXLa8XFxcjMDDQ6muaPi8uLr5um4aGBpSVlTVbz9KlS+Hp6Wl5hIeH3/I5EhERUdfU5sCzaNGiZm80vvKRmppq9TWFhYUYP348HnjgAcyePdvqtVdeeQXDhw/HgAEDMH/+fCxevBh//etfrdpcvQlf0yjclc+3ps2VFi5cCJ1OZ3kUFBS04SoQERGRLWnzTcvz5s3D9OnTr9smKirK8ufCwkKMGTMGw4cPx/vvv3/D4w8bNgwVFRU4f/48AgMDERQUZOnJaVJSUgLgck9PS21UKhV8fX2b/T4ajcZqmIyIiIjsV5sDj5+fH/z8WrcA17lz5zBmzBjEx8dj3bp1UChu3KGUlpYGrVYLLy8vAMDw4cPx0ksvQa/XQ60271mTkpKCkJAQS7AaPnw4vvnmG6vjpKSkYPDgwXByav/dkomIiMi2dNg9PIWFhRg9ejTCw8OxbNkylJaWori42Kon5ptvvsGaNWuQmZmJnJwc/OMf/8DLL7+M3/zmN5bel4cffhgajQYzZ85EZmYmvvjiC7z++utITk62DFfNmTMHZ86cQXJyMrKysvDBBx9g7dq1WLBgQUedHhEREdmQDluHJyUlBdnZ2cjOzkZYWJjVa0331zg5OWHVqlVITk6GyWRCTEwMFi9ejKefftrS1tPTE1u3bsXTTz+NwYMHw9vbG8nJyUhOTra0iY6OxpYtW/Dcc8/hnXfeQUhICN5++22uwUNEREQAuLWEBdfhISIisj1dch0eIiIiIjkw8BAREZHdY+AhIiIiu8fAQ0RERHaPgYeIiIjsXodNS7c1TZPVuIkoERGR7Wh6377RpHMGnkbl5eUAwE1EiYiIbFB5eTk8PT1bfJ2Bp5GPjw8AID8//7oXzN4NGTIEBw8elLsM2Tj6+QO8BgCvgaOfP8BrANjONdDpdIiIiLC8j7eEgadR0z5fnp6eDr3woFKp5Pk78PkDvAYAr4Gjnz/AawDY3jW40X6dvGmZrFy5rYcjcvTzB3gNAF4DRz9/gNcAsL9rwK0lGnFrCSIiItvDrSXaSKPR4NVXX7Xs0k5ERERdX2vfv9nDQ0RERHaPPTxERERk9xh47MSqVasQHR0NrVaL+Ph47Nmzx/LaokWL0Lt3b7i6usLb2xtjx47F/v37b3jMjIwMJCQkwNnZGaGhoVi8ePE1Czvt2rUL8fHx0Gq1iImJwerVq9v93FrretcAALKysjB58mR4enrC3d0dw4YNQ35+/nWPaU/X4Pz585g5cyZCQkLg4uKC8ePH49SpUzc8pq1cg927d2PSpEkICQmBJEn48ssvLa8ZDAa88MIL6NevH1xdXRESEoLHHnsMhYWFNzyurZw/cP1rAAAzZ86EJElWj2HDht3wuPZ0DaqqqjBv3jyEhYXB2dkZsbGxePfdd294XFu5BkuXLsWQIUPg7u6OgIAA3H///Thx4oRVm02bNiEpKQl+fn6QJAnp6emtOratXIMWCbJ5GzZsEE5OTmLNmjXi2LFj4ve//71wdXUVZ86cEUII8dFHH4mtW7eKnJwckZmZKWbNmiU8PDxESUlJi8fU6XQiMDBQTJ8+XWRkZIiNGzcKd3d3sWzZMkub06dPCxcXF/H73/9eHDt2TKxZs0Y4OTmJ//znPx1+zle70TXIzs4WPj4+4n//93/F4cOHRU5Ojvj222/F+fPnWzymPV0Dk8kkhg0bJkaNGiUOHDggjh8/Ln7zm9+IiIgIUVVV1eIxbekabNmyRbz88sti48aNAoD44osvLK9dunRJjB07Vnz66afi+PHjYt++fWLo0KEiPj7+use0pfMX4vrXQAghZsyYIcaPHy+Kioosj/Ly8use096uwezZs0W3bt3Ejh07RG5urnjvvfeEUqkUX375ZYvHtKVrkJSUJNatWycyMzNFenq6uOeee675f/6vf/1LvPbaa2LNmjUCgEhLS7vhcW3pGrTEbgLPO++8I6KiooRGoxGDBg0Su3fvtrxmMpnEq6++KoKDg4VWqxUJCQkiMzPzhsc8evSouPPOO4VWqxUhISHitddeEyaTyarNzp07xaBBg4RGoxHR0dHi3Xffbfdzu5Hbb79dzJkzx+q53r17ixdffLHZ9jqdTgAQP/zwQ4vHXLVqlfD09BR1dXWW55YuXSpCQkIs1+D5558XvXv3tvq6p556SgwbNuxmT+Wm3egaPPjgg+KRRx5p0zHt6RqcOHFCALD6d9/Q0CB8fHzEmjVrWjymrV2DJs290V3twIEDAoAlFDfHVs9fiOavwYwZM8R9993XpuPY2zXo27evWLx4sdVzgwYNEq+88kqLx7Hla1BSUiIAiF27dl3zWm5ubqsDjy1fgyZ2MaT16aef4tlnn8XLL7+MtLQ0jBo1ChMmTLAMV/zf//0fli9fjr///e84ePAggoKCMG7cOFRWVrZ4zIqKCowbNw4hISE4ePAgVq5ciWXLlmH58uWWNrm5uZg4cSJGjRqFtLQ0vPTSS3jmmWewcePGDj/nJnq9HocOHUJiYqLV84mJidi7d2+z7d9//314enqif//+ludnzpyJ0aNHWz7ft28fEhISrO56T0pKQmFhIfLy8ixtrv6+SUlJSE1NhcFgaIeza50bXQOTyYTNmzejZ8+eSEpKQkBAAIYOHdpsd7+9XoP6+noAgFartbymVCqhVqvx448/Wp6z5WvQVjqdDpIkwcvLy/KcI5z/zp07ERAQgJ49e+LJJ59ESUmJ1ev2fg1GjhyJr7/+GufOnYMQAjt27MDJkyeRlJRkaWNP10Cn0wHADVchvpo9XYMmdhF4li9fjlmzZmH27NmIjY3FihUrEB4ejnfffRdCCKxYsQIvv/wypkyZgri4OPzzn/9ETU0NPv744xaP+dFHH6Gurg7r169HXFwcpkyZgpdeegnLly+3jFmuXr0aERERWLFiBWJjYzF79mw88cQTWLZsWWedOsrKymA0GhEYGGj1fGBgIIqLiy2ff/vtt3Bzc4NWq8Vbb72FrVu3ws/Pz/J6cHAwIiIiLJ8XFxc3e8ym167XpqGhAWVlZe1zgq1wo2tQUlKCqqoqvPHGGxg/fjxSUlLwq1/9ClOmTMGuXbss7e35GvTu3RuRkZFYuHAhLl68CL1ejzfeeAPFxcUoKiqytLfla9AWdXV1ePHFF/Hwww9brdth7+c/YcIEfPTRR9i+fTvefPNNHDx4EHfddZclEAP2fw3efvtt9OnTB2FhYVCr1Rg/fjxWrVqFkSNHWtrYyzUQQiA5ORkjR45EXFxcm77WXq7BlWx+a4mm32xffPFFq+ebfrPNzc1FcXGxVerUaDRISEjA3r178dRTTwEwp9m8vDzs3LkTQMtpduHChcjLy0N0dHSLaXbt2rUwGAxwcnLqoLO+liRJVp8LIayeGzNmDNLT01FWVoY1a9Zg2rRp2L9/PwICAgCYb3RrzTGvfr41bTpLS9fAZDIBAO677z4899xzAIABAwZg7969WL16NRISEgDY9zVwcnLCxo0bMWvWLPj4+ECpVGLs2LGYMGGCVXt7uAY3YjAYMH36dJhMJqxatcrqNXs//wcffNDy57i4OAwePBiRkZHYvHkzpkyZAsD+r8Hbb7+Nn3/+GV9//TUiIyOxe/duzJ07F8HBwRg7diwA+7kG8+bNw9GjR616cVvLXq7BlWy+h+dGv9k2Jc8b9YDYapr18/ODUqm0OhcAKCkpsarN1dUV3bt3x7Bhw7B27VqoVCqsXbu2xeMGBQU1e0zg8nVoqY1KpYKvr+8tnVdb3Oga+Pn5QaVSoU+fPlavx8bGXneWlj1dAwCIj49Heno6Ll26hKKiIvz3v/9FeXk5oqOjWzyuLV2D1jAYDJg2bRpyc3OxdevWG66qbm/nf7Xg4GBERkZed7aePV2D2tpaS0/9pEmTcNttt2HevHl48MEHr9szb4vX4He/+x2+/vpr7NixA2FhYbd8PFu8Blez+cDT5EY9HDd6fenSpfjXv/51w2Ne/bzcaVatViM+Ph5bt261en7r1q0YMWJEi18nhLDqxr7a8OHDsXv3buj1estzKSkpCAkJQVRUlKXN1d83JSUFgwcP7tTerRtdA7VajSFDhlwzNfPkyZOIjIxs8bj2dA2u5OnpCX9/f5w6dQqpqam47777WjyuLV2DG2kKO6dOncIPP/zQqh/A9nT+zSkvL0dBQQGCg4NbbGNP18BgMMBgMFyzyaRSqbT0BDfHlq6BEALz5s3Dpk2bsH379uv+QtMWtnQNWtRJN0d3mPr6eqFUKsWmTZusnn/mmWfEnXfeKXJycgQAcfjwYavXJ0+eLB577LEWj/voo4+KyZMnWz13+PBhAUCcPn1aCCHEqFGjxDPPPGPVZtOmTUKlUgm9Xn8rp9UmTdOR165dK44dOyaeffZZ4erqKvLy8kRVVZVYuHCh2Ldvn8jLyxOHDh0Ss2bNEhqNxmrGzosvvigeffRRy+eXLl0SgYGB4qGHHhIZGRli06ZNwsPDo9kpiM8995w4duyYWLt2rexTspu7BkKY/16cnJzE+++/L06dOiVWrlwplEql2LNnj+UY9n4NPvvsM7Fjxw6Rk5MjvvzySxEZGSmmTJlidQxbvgaVlZUiLS1NpKWlCQBi+fLlIi0tTZw5c0YYDAYxefJkERYWJtLT062mZdfX19vF+Qtx/WtQWVkp5s+fL/bu3Styc3PFjh07xPDhw0VoaKioqKiwHMOer4EQQiQkJIi+ffuKHTt2iNOnT4t169YJrVYrVq1aZTmGLV+D3/72t8LT01Ps3LnT6t95TU2NpU15eblIS0sTmzdvFgDEhg0bRFpamigqKrK0seVr0BKbDzxCmKfj/va3v7V6LjY2Vrz44ovCZDKJoKAg8Ze//MXyWn19vfD09BSrV69u8ZirVq0SXl5eVj8M33jjjWum4MXGxlp93Zw5c2SZgvfOO++IyMhIoVarxaBBgyxTEGtra8WvfvUrERISItRqtQgODhaTJ08WBw4csPr6GTNmiISEBKvnjh49KkaNGiU0Go0ICgoSixYtanZa/sCBA4VarRZRUVGyTMtv0tI1aLJ27VrRvXt3odVqRf/+/a9Zd8Per8Hf/vY3ERYWJpycnERERIR45ZVXrP59C2Hb12DHjh0CwDWPGTNmWKbfNvfYsWOH5Ri2fP5CXP8a1NTUiMTEROHv72/5NzBjxgyRn59vdQx7vgZCCFFUVCRmzpwpQkJChFarFb169RJvvvmm1fnY8jVo6d/5unXrLG3WrVvXbJtXX33V0saWr0FL7CLw3Og32zfeeEN4enqKTZs2iYyMDPHQQw+J4OBgu/qthoiIiFpm87O0APPMg/LycixevBhFRUWIi4vDli1bLPdnPP/886itrcXcuXNx8eJFDB06FCkpKXB3d7cco6ioyOoGVk9PT2zduhVPP/00Bg8eDG9vbyQnJyM5OdnSJjo6Glu2bMFzzz2Hd955ByEhIXj77bcxderUzjt5IiIiuiHulk5ERER2z25maRERERG1hIGHiIiI7B4DDxEREdk9Bh4iIiKyeww8REREZPcYeIiIiMju2VTg2b17NyZNmoSQkBBIkoQvv/zS6vVFixahd+/ecHV1hbe3N8aOHYv9+/ff8LiSJEGSJPz8889Wz9fX18PX1xeSJFl2USciIiLbY1OBp7q6Gv3798ff//73Zl/v2bMn/v73vyMjIwM//vgjoqKikJiYiNLS0hseOzw8HOvWrbN67osvvoCbm1u71E5ERETysanAM2HCBPz5z3/GlClTmn394YcfxtixYxETE4O+ffti+fLlqKiowNGjR2947BkzZmDDhg2ora21PPfBBx9gxowZ17R94YUX0LNnT7i4uCAmJgZ/+MMfYDAYAAB5eXlQKBRITU21+pqVK1ciMjISXOeRiIio89lU4GkLvV6P999/H56enujfv/8N28fHxyM6OhobN24EABQUFGD37t149NFHr2nr7u6O9evX49ixY/jb3/6GNWvW4K233gIAREVFYezYsdf0Fq1btw4zZ86EJEntcHZERETUFnYXeL799lu4ublBq9XirbfewtatW+Hn59eqr3388cfxwQcfADAHlIkTJ8Lf3/+adq+88gpGjBiBqKgoTJo0CfPnz8dnn31meX327Nn45JNPUF9fDwA4cuQI0tPT8fjjj7fDGRIREVFb2V3gGTNmDNLT07F3716MHz8e06ZNQ0lJCQBgzpw5cHNzszyu9sgjj2Dfvn04ffo01q9fjyeeeKLZ7/Gf//wHI0eORFBQENzc3PCHP/zBauPR+++/HyqVCl988QUA89DYmDFjEBUV1f4nTERERDdkd4HH1dUV3bt3x7Bhw7B27VqoVCqsXbsWALB48WKkp6dbHlfz9fXFvffei1mzZqGurg4TJky4ps3PP/+M6dOnY8KECfj222+RlpaGl19+GXq93tJGrVbj0Ucfxbp166DX6/Hxxx+3GJ6IiIio46nkLqCjCSEsQ0sBAQEICAi4bvsnnngCEydOxAsvvAClUnnN6z/99BMiIyPx8ssvW547c+bMNe1mz56NuLg4rFq1CgaDocUbrYmIiKjj2VTgqaqqQnZ2tuXz3NxcpKenw8fHB76+vliyZAkmT56M4OBglJeXY9WqVTh79iweeOCBVn+P8ePHo7S0FB4eHs2+3r17d+Tn52PDhg0YMmQINm/ebBm6ulJsbCyGDRuGF154AU888QScnZ3bfsJERETULmxqSCs1NRUDBw7EwIEDAQDJyckYOHAg/vjHP0KpVOL48eOYOnUqevbsiXvvvRelpaXYs2cP+vbt2+rvIUkS/Pz8oFarm339vvvuw3PPPYd58+ZhwIAB2Lt3L/7whz8023bWrFnQ6/UcziIiIpKZJLgwTIdZsmQJNmzYgIyMDLlLISIicmg21cNjK6qqqnDw4EGsXLkSzzzzjNzlEBEROTwGng4wb948jBw5EgkJCRzOIiIi6gI4pEVERER2jz08REREZPcYeIiIiMjuMfAQERGR3WPgISIiIrvHwENEnWLnzp2QJAmXLl2SuxQickAMPETUIUaPHo1nn33W8vmIESNQVFQET09P2Wpi6CJyXDa1lxYR2S61Wo2goCC5yyAiB8UeHiJqdzNnzsSuXbvwt7/9DZIkQZIkrF+/3qp3Zf369fDy8sK3336LXr16wcXFBb/+9a9RXV2Nf/7zn4iKioK3tzd+97vfwWg0Wo6t1+vx/PPPIzQ0FK6urhg6dCh27txpef3MmTOYNGkSvL294erqir59+2LLli3Iy8vDmDFjAADe3t6QJAkzZ84EAPz3v//FyJEj4eXlBV9fX9x7773IycmxHDMvLw+SJOGzzz7DqFGj4OzsjCFDhuDkyZM4ePAgBg8eDDc3N8vmw1deh/vvvx+vvfYaAgIC4OHhgaeeegp6vb7jLj4RNYs9PETU7v72t7/h5MmTiIuLw+LFiwEAv/zyyzXtampq8Pbbb2PDhg2orKzElClTMGXKFHh5eWHLli04ffo0pk6dipEjR+LBBx8EADz++OPIy8vDhg0bEBISgi+++ALjx49HRkYGevTogaeffhp6vR67d++Gq6srjh07Bjc3N4SHh2Pjxo2YOnUqTpw4AQ8PDzg7OwMAqqurkZycjH79+qG6uhp//OMf8atf/Qrp6elQKC7/Xvjqq69ixYoViIiIwBNPPIGHHnoIHh4e+Nvf/gYXFxdMmzYNf/zjH/Huu+9avmbbtm3QarXYsWMH8vLy8Pjjj8PPzw9LlizpyL8CIrqaICLqAAkJCeL3v/+95fMdO3YIAOLixYtCCCHWrVsnAIjs7GxLm6eeekq4uLiIyspKy3NJSUniqaeeEkIIkZ2dLSRJEufOnbP6XnfffbdYuHChEEKIfv36iUWLFjVb09U1tKSkpEQAEBkZGUIIIXJzcwUA8Y9//MPS5pNPPhEAxLZt2yzPLV26VPTq1cvy+YwZM4SPj4+orq62PPfuu+8KNzc3YTQar1sDEbUvDmkRkWxcXFzQrVs3y+eBgYGIioqCm5ub1XMlJSUAgMOHD0MIgZ49e8LNzc3y2LVrl2UI6plnnsGf//xn3HHHHXj11Vdx9OjRG9aRk5ODhx9+GDExMfDw8EB0dDQAID8/36rdbbfdZlUXAPTr16/ZWpv0798fLi4uls+HDx+OqqoqFBQU3LAuImo/HNIiItk4OTlZfS5JUrPPmUwmAIDJZIJSqcShQ4egVCqt2jWFpNmzZyMpKQmbN29GSkoKli5dijfffBO/+93vWqxj0qRJCA8Px5o1axASEgKTyYS4uLhr7rW5sjZJkpp9rqnWG2n6eiLqHOzhIaIOoVarrW42bg8DBw6E0WhESUkJunfvbvW4cgZYeHg45syZg02bNmH+/PlYs2aNpSYAVnWVl5cjKysLr7zyCu6++27Exsbi4sWL7VbzkSNHUFtba/n8559/hpubG8LCwtrtexDRjTHwEFGHiIqKwv79+5GXl4eysrJW93xcT8+ePfE///M/eOyxx7Bp0ybk5ubi4MGD+Mtf/oItW7YAAJ599ll8//33yM3NxeHDh7F9+3bExsYCACIjIyFJEr799luUlpaiqqoK3t7e8PX1xfvvv4/s7Gxs374dycnJt1xrE71ej1mzZuHYsWP47rvv8Oqrr2LevHlWN0MTUcfj/zgi6hALFiyAUqlEnz594O/vf839MDdr3bp1eOyxxzB//nz06tULkydPxv79+xEeHg7A3Hvz9NNPIzY2FuPHj0evXr2watUqAEBoaChee+01vPjiiwgMDLQEjw0bNuDQoUOIi4vDc889h7/+9a/tUisA3H333ejRowfuvPNOTJs2DZMmTcKiRYva7fhE1DqSEELIXQQRkT2aOXMmLl26hC+//FLuUogcHnt4iIiIyO4x8BAREZHd45AWERER2T328BAREZHdY+AhIiIiu8fAQ0RERHaPgYeIiIjsHgMPERER2T0GHiIiIrJ7DDxERERk9xh4iIiIyO4x8BAREZHd+38dqf48WucjMgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-05-13'\n",
+    "(res_elec_resampled - rooftop_solar_energy).loc[time].plot(ax=ax)\n",
+    "res_elec_resampled.loc[time].plot(ax=ax)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "net_load    1331.488437\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load[net_load>0].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "net_load   -2506.148296\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load[net_load<0].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>net_load</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>734.275500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>319.253001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>206.265092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>200.158683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>214.710827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 19:00:00</th>\n",
+       "      <td>756.661431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 20:00:00</th>\n",
+       "      <td>799.025894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 21:00:00</th>\n",
+       "      <td>814.918117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 22:00:00</th>\n",
+       "      <td>842.407166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31 23:00:00</th>\n",
+       "      <td>790.741970</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8760 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       net_load\n",
+       "timestamp                      \n",
+       "2018-01-01 00:00:00  734.275500\n",
+       "2018-01-01 01:00:00  319.253001\n",
+       "2018-01-01 02:00:00  206.265092\n",
+       "2018-01-01 03:00:00  200.158683\n",
+       "2018-01-01 04:00:00  214.710827\n",
+       "...                         ...\n",
+       "2018-12-31 19:00:00  756.661431\n",
+       "2018-12-31 20:00:00  799.025894\n",
+       "2018-12-31 21:00:00  814.918117\n",
+       "2018-12-31 22:00:00  842.407166\n",
+       "2018-12-31 23:00:00  790.741970\n",
+       "\n",
+       "[8760 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load = (res_elec_resampled - rooftop_solar_energy).to_frame()\n",
+    "net_load.columns = ['net_load']\n",
+    "net_load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retail_price = 0.1129  # $/kWh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_metering_price = retail_price * 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "net_load    2.358429e+06\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load.where(net_load > 0).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "net_load   -2.358429e+06\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net_load.where(net_load <0).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()).plot(ax=ax, color='gold', lw=0.3)\n",
+    "res_elec_resampled.plot(ax=ax, label='electricity', lw=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4135965.349731396"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4135965.349731396"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rooftop_solar_energy.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum())[24*30*3:24*32*3].plot(ax=ax, color='gold', lw=1)\n",
+    "res_elec_resampled[24*30*3:24*32*3].plot(ax=ax, label='electricity', lw=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_battery_needs(dataframe):\n",
+    "    df = dataframe.copy()\n",
+    "    # calculate max discharge power\n",
+    "    max_storage_power = df['net_load'].max()\n",
+    "    \n",
+    "    # calculate max storage duration\n",
+    "    df['grp'] = df['net_load'].gt(0).astype(int).diff().abs().cumsum().fillna(0)\n",
+    "    df_grouped = df.groupby(by='grp').sum()\n",
+    "    df_grouped['battery_duration'] = df_grouped['net_load']/max_storage_power\n",
+    "    \n",
+    "    max_storage_duration = df_grouped['battery_duration'].max()\n",
+    "    \n",
+    "    return max_storage_power, max_storage_duration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()\n",
+    "rooftop_solar_energy = rooftop_solar_energy/rooftop_solar_energy.sum() * res_elec_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2807.397404775099"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rooftop_solar_energy.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_load = rooftop_solar_energy.to_frame()\n",
+    "net_load['net_load'] = res_elec_resampled - net_load['ghi']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1331.4884367473771, 34.817758877371986)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calculate_battery_needs(net_load)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/functions/nrel_data_api.py b/functions/nrel_data_api.py
new file mode 100644
index 0000000..9f6467e
--- /dev/null
+++ b/functions/nrel_data_api.py
@@ -0,0 +1,159 @@
+import numpy as np
+import sys
+import os
+from pathlib import Path
+import pandas as pd
+from shapely import Point
+from dotenv import load_dotenv
+
+load_dotenv("../.env")
+
+personal_data = {'api_key':os.environ.get('NREL_API_KEY'),
+                 'name':os.environ.get('NAME').replace(' ', '+'),
+                 'reason':os.environ.get('REASON').replace(' ', '+'),
+                 'affiliation':os.environ.get('AFFIL').replace(' ', '+'),
+                 'email':os.environ.get('EMAIL'),
+                 'mailing_list':os.environ.get('MAILING_LIST')
+                 }
+
+parameters = {'lon':40.09,
+              'lat':-88.26,
+              'year':2019,
+              'leap_day':'false',
+              'selector':'POINT',
+              'utc':'true',
+              'interval':'60',
+              'attr_list':['ghi']}
+
+
+def make_wkt(selector, lat, lon):
+    """
+    This function generates a well known text (wkt)
+    string for use with the NREL API.
+
+    Parameters
+    ----------
+    selector : String
+        Indicates how you want to access the data.
+        Accepts: 'POINT', 'MULTIPOINT', 'POLYGON'
+    lat : Float or List
+        The latitude, or set of latitudes, of interest.
+    lon : Float or List
+        The longitude, or set of longitudes, of interest.
+
+    Returns
+    -------
+    wkt : String
+        The well known text string.
+    """
+
+    method = selector.upper()
+
+    if method == 'POINT':
+        wkt = '{method}({lat}%20{lon})'.format(method=method,
+                                               lon = lon,
+                                               lat = lat)
+    else:
+        try:
+            combinations = [f'{i}%20{j}' for i, j in zip(lon, lat)]
+        except ValueError:
+            "Longitude and Latitudes are different sizes."
+        coord_list = ('%2C').join(combinations)
+        wkt = "{method}({coordinates})".format(method=method,
+                                                             coordinates=coord_list)
+
+    return wkt
+
+
+def make_csv_url(parameters, personal_data=personal_data, kind='solar'):
+    """
+    This function generates a url to access renewable energy
+    data through the NREL API. This function requires your
+    personal API key. If you want to sign up with NREL and
+    get your own API key, visit this website:
+    https://developer.nrel.gov/signup/
+
+    Parameters
+    ----------
+    parameters : dictionary
+        This dictionary contains all of the information about
+        the dataset you wish to download. Required values are:
+        * 'lon' : The longitude of the location of interest, float or list
+        * 'lat' : The latitude of the location of interest, float or list
+        * 'year' : The year of interest, integer
+        * 'leap_day' : Boolean. If 'true', includes leap day.
+        * 'utc' : Boolean. If 'true', uses the time at the location.
+                  Else, uses your local time.
+        * 'selector' : String. Indicates how you want to access the
+                       data. Accepts: 'POINT', 'MULTIPOINT', 'POLYGON'
+        * 'interval' : String or integer. The desired resolution in minutes.
+                       For solar, resolutions are: 30 or 60
+                       For wind, resolutions are: 5, 10, 15, 30, or 60
+        * 'attr_list' : List. The list of desired columns in your dataset.
+
+    personal_data : dictionary
+        This dictionary contains all of the information about
+        the user seeking data. Required values are:
+        * 'api_key' : The API key generated for you by NREL
+        * 'name' : The name of the user
+        * 'reason' : How you will use the data
+        * 'affiliation' : The institution you are affiliated with
+        * 'mailing_list' : 'true' or 'false.' If true, will add you
+                            to the mailing list.
+        * 'email' : The email of the user
+
+    kind : string
+        Indicates what kind of data should be downloaded. Currently
+        accepts 'solar' or 'wind.' Default is 'solar'.
+
+    Returns
+    -------
+    url : string
+        A well formed URL to access NREL data.
+    """
+    databases = {'wind': 'api/wind-toolkit/v2/wind/wtk-download',
+                 'solar': 'api/solar/nsrdb_psm3_download'}
+
+    # Which NREL database should be accessed?
+    db_to_access = databases[kind.lower()]
+
+    # Generate the WKT string
+    wkt = make_wkt(parameters['selector'],
+                   parameters['lon'],
+                   parameters['lat'])
+
+    # Make sure the dictionary values are properly formatte
+    name = personal_data['name'].replace(' ', '+')
+    affiliation = personal_data['affiliation'].replace(' ', '+')
+    reason=personal_data['reason'].replace(' ', '+')
+    attributes = (',').join(parameters['attr_list'])
+    leap_day = str(parameters['leap_day']).lower()
+    year = int(parameters['year'])
+    interval = int(parameters['interval'])
+    utc = str(parameters['utc']).lower()
+    mailing_list = str(personal_data['mailing_list']).lower()
+    email = personal_data['email']
+    key = personal_data['api_key']
+
+    # print(wkt)
+    # print(db_to_access)
+
+    # Generate string
+    url = ("https://developer.nrel.gov/{db}.csv?wkt={wkt}&names={year}"
+           "&leap_day={leap}&interval={interval}&utc={utc}&full_name={name}"
+           "&email={email}&affiliation={affiliation}&mailing_list={mailing_list}"
+           "&reason={reason}&api_key={api}&attributes={attr}").format(db=db_to_access,
+                                                                      wkt=wkt,
+                                                                      year=year,
+                                                                      leap=leap_day,
+                                                                      interval=interval,
+                                                                      utc=utc,
+                                                                      name=name,
+                                                                      email=email,
+                                                                      affiliation=affiliation,
+                                                                      mailing_list=mailing_list,
+                                                                      reason=reason,
+                                                                      api=key,
+                                                                      attr=attributes)
+
+    return url
\ No newline at end of file
diff --git a/notebooks/03-retrieve-resweather.ipynb b/notebooks/03-retrieve-resweather.ipynb
new file mode 100644
index 0000000..204e4c4
--- /dev/null
+++ b/notebooks/03-retrieve-resweather.ipynb
@@ -0,0 +1,221 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "puma_county_id = \"G5600330\"\n",
+    "puma_county_id = \"G2002090\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_URL = (\n",
+    "    \"https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock\"\n",
+    "    \"/end-use-load-profiles-for-us-building-stock\")\n",
+    "def create_weather_url(puma_id,\n",
+    "                        year=2021,\n",
+    "                        product='resstock',\n",
+    "                        weather_version='tmy3',\n",
+    "                        release=1,\n",
+    "                        ):\n",
+    "\n",
+    "    data_route = (f\"/{year}\"\n",
+    "                  f\"/{product}_{weather_version}_release_{release}\"\n",
+    "                  \"/weather/tmy3/\")\n",
+    "\n",
+    "    file = f\"{puma_id.upper()}.csv\"\n",
+    "\n",
+    "    return BASE_URL + data_route + file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather_url = create_weather_url(puma_id=puma_county_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather_url == url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/resstock_tmy3_release_1/weather/tmy3/G2002090_tmy3.csv'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/resstock_tmy3_release_1/weather/tmy3/G2002090.csv'\n",
+    "'https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/resstock_tmy3_release_1/weather/tmy3/G2002090_tmy3.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = f'https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/resstock_tmy3_release_1/weather/tmy3/{puma_county_id}_tmy3.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(url, parse_dates=True)\n",
+    "timestamps = pd.date_range(start='2018-01-01', freq='h', periods=8760)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.index=timestamps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['date_time', 'Dry Bulb Temperature [°C]', 'Relative Humidity [%]',\n",
+       "       'Wind Speed [m/s]', 'Wind Direction [Deg]',\n",
+       "       'Global Horizontal Radiation [W/m2]', 'Direct Normal Radiation [W/m2]',\n",
+       "       'Diffuse Horizontal Radiation [W/m2]'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.loc[:, 'Dry Bulb Temperature [°C]'].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/04-project-sunroof.ipynb b/notebooks/04-project-sunroof.ipynb
new file mode 100644
index 0000000..514f4bc
--- /dev/null
+++ b/notebooks/04-project-sunroof.ipynb
@@ -0,0 +1,794 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = gpd.read_file(\"../data/spatial_data/kansas_rooftop_potential.gpkg\")\n",
+    "armourdale = gpd.read_file(\"../data/spatial_data/armourdale_shape.gpkg\")\n",
+    "lead = pd.read_csv(\"../data/armourdale_energy_expenses.csv\", index_col='BLD')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined = df.sjoin(armourdale, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "312    28.61974\n",
+       "Name: yearly_sunlight_kwh_total, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined.loc[:,'yearly_sunlight_kwh_total']/1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "312    22833.75\n",
+       "Name: kw_total, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined.loc[:,'kw_total'] # kW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "combined.plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None', ec='k')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "res_structures = pd.read_csv(\"../data/residential_buildings.csv\", index_col=0)\n",
+    "res_elec_load = pd.read_csv(\"../data/timeseries/residential_elec_load.csv\", parse_dates=True, index_col='timestamp')\n",
+    "res_heat_load = pd.read_csv(\"../data/timeseries/residential_heat_load.csv\", parse_dates=True, index_col='timestamp')\n",
+    "\n",
+    "total_elec_load = res_elec_load*res_structures.T.loc['n_units']\n",
+    "total_heat_load = res_heat_load*res_structures.T.loc['n_units']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "electricity_price = 0.1129"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([ 10973.01188612464,  6058.058224311011, 14042.677596446287,\n",
+       "       15966.966005418804,  5035.694118660718],\n",
+       "      dtype='float64')"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec_load.columns.map(lead['ELEP*UNITS'])/electricity_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>n_units</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>191.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>886.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               n_units\n",
+       "multi-family_with_2_-_4_units    191.0\n",
+       "multi-family_with_5plus_units      6.0\n",
+       "single-family_attached            16.0\n",
+       "single-family_detached           886.0\n",
+       "mobile_home                        8.0"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_structures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rescaled_elec_load = (res_elec_load.div(res_elec_load.sum(),axis=1)*\n",
+    "                      (res_elec_load.columns.map(lead['ELEP*UNITS'])/electricity_price)*\n",
+    "                      res_structures.T.loc['n_units'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.918441588455463"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined.at[312,'yearly_sunlight_kwh_total']/rescaled_elec_load.resample('h').mean().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
+       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
+       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
+       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
+       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'geometry',\n",
+       "       'index_right', 'CITY', 'WARD'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "312    62.810559\n",
+       "Name: percent_qualified, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined.percent_qualified"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>temp_db</th>\n",
+       "      <th>rel_humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "      <th>wind_direction</th>\n",
+       "      <th>ghi</th>\n",
+       "      <th>dni</th>\n",
+       "      <th>dhi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>2005-01-01 01:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>2005-01-01 02:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>2005-01-01 03:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>2005-01-01 04:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>2005-01-01 05:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               date_time  temp_db  rel_humidity  wind_speed  \\\n",
+       "2018-01-01 00:00:00  2005-01-01 01:00:00      8.0            61         5.7   \n",
+       "2018-01-01 01:00:00  2005-01-01 02:00:00      8.0            57         5.1   \n",
+       "2018-01-01 02:00:00  2005-01-01 03:00:00      8.0            57         5.1   \n",
+       "2018-01-01 03:00:00  2005-01-01 04:00:00      7.0            56         6.2   \n",
+       "2018-01-01 04:00:00  2005-01-01 05:00:00      7.0            56         5.1   \n",
+       "\n",
+       "                     wind_direction  ghi  dni  dhi  \n",
+       "2018-01-01 00:00:00              80    0    0    0  \n",
+       "2018-01-01 01:00:00              90    0    0    0  \n",
+       "2018-01-01 02:00:00              90    0    0    0  \n",
+       "2018-01-01 03:00:00              80    0    0    0  \n",
+       "2018-01-01 04:00:00              90    0    0    0  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather = pd.read_csv(\"../data/timeseries/weather_year.csv\", parse_dates=True, index_col=0)\n",
+    "weather.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "982"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather['ghi'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33.63955478615071"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(weather['ghi'] / weather['ghi'].max() * 22833.75).sum()/1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3399638.8051750576"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_elec_load.resample('h').mean().resample('ME').sum().loc['2018'].sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "single-family_detached    2.872939e+06\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(res_elec_load.loc[:, 'single-family_detached'].to_frame()/47699.807).resample('h').mean().resample('ME').sum().sum()*886"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "multi-family_with_2_-_4_units    254.733880\n",
+       "multi-family_with_5plus_units    227.087262\n",
+       "single-family_attached           241.053293\n",
+       "single-family_detached           347.351145\n",
+       "mobile_home                      326.329977\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec_load.resample('h').mean().resample('ME').sum().loc['2018'].sum() * 0.10714"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Monthly Building Energy \\n ResStock'}, ylabel='kWh'>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "res_elec_load.resample('h').mean().resample('ME').sum().loc['2018'].plot(marker='o', linestyle='--', xlabel='', ylabel='kWh', title='Monthly Building Energy \\n ResStock')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <th>mobile_home</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-31</th>\n",
+       "      <td>40.044137</td>\n",
+       "      <td>36.222490</td>\n",
+       "      <td>28.861290</td>\n",
+       "      <td>38.560354</td>\n",
+       "      <td>45.482042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-02-28</th>\n",
+       "      <td>23.800901</td>\n",
+       "      <td>22.107862</td>\n",
+       "      <td>18.935128</td>\n",
+       "      <td>25.684372</td>\n",
+       "      <td>26.586742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-03-31</th>\n",
+       "      <td>20.441317</td>\n",
+       "      <td>19.408657</td>\n",
+       "      <td>17.751216</td>\n",
+       "      <td>23.574588</td>\n",
+       "      <td>20.531207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-04-30</th>\n",
+       "      <td>16.173697</td>\n",
+       "      <td>15.372410</td>\n",
+       "      <td>15.683319</td>\n",
+       "      <td>20.697393</td>\n",
+       "      <td>16.495946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-05-31</th>\n",
+       "      <td>17.182721</td>\n",
+       "      <td>15.646410</td>\n",
+       "      <td>19.744576</td>\n",
+       "      <td>27.220587</td>\n",
+       "      <td>22.465727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-06-30</th>\n",
+       "      <td>20.103903</td>\n",
+       "      <td>17.323828</td>\n",
+       "      <td>22.867785</td>\n",
+       "      <td>34.144244</td>\n",
+       "      <td>29.819897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-07-31</th>\n",
+       "      <td>23.905924</td>\n",
+       "      <td>20.240604</td>\n",
+       "      <td>27.984748</td>\n",
+       "      <td>42.135665</td>\n",
+       "      <td>37.782434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-08-31</th>\n",
+       "      <td>26.406142</td>\n",
+       "      <td>21.691362</td>\n",
+       "      <td>29.986353</td>\n",
+       "      <td>47.135824</td>\n",
+       "      <td>42.987269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-09-30</th>\n",
+       "      <td>19.434027</td>\n",
+       "      <td>16.739112</td>\n",
+       "      <td>23.157401</td>\n",
+       "      <td>33.345445</td>\n",
+       "      <td>28.425835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-10-31</th>\n",
+       "      <td>15.210282</td>\n",
+       "      <td>14.248996</td>\n",
+       "      <td>15.561638</td>\n",
+       "      <td>20.528057</td>\n",
+       "      <td>16.367420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-11-30</th>\n",
+       "      <td>23.679779</td>\n",
+       "      <td>22.065150</td>\n",
+       "      <td>19.011665</td>\n",
+       "      <td>25.052458</td>\n",
+       "      <td>26.502078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-12-31</th>\n",
+       "      <td>30.351051</td>\n",
+       "      <td>28.020382</td>\n",
+       "      <td>23.508174</td>\n",
+       "      <td>31.272159</td>\n",
+       "      <td>34.883380</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            multi-family_with_2_-_4_units  multi-family_with_5plus_units  \\\n",
+       "timestamp                                                                  \n",
+       "2018-01-31                      40.044137                      36.222490   \n",
+       "2018-02-28                      23.800901                      22.107862   \n",
+       "2018-03-31                      20.441317                      19.408657   \n",
+       "2018-04-30                      16.173697                      15.372410   \n",
+       "2018-05-31                      17.182721                      15.646410   \n",
+       "2018-06-30                      20.103903                      17.323828   \n",
+       "2018-07-31                      23.905924                      20.240604   \n",
+       "2018-08-31                      26.406142                      21.691362   \n",
+       "2018-09-30                      19.434027                      16.739112   \n",
+       "2018-10-31                      15.210282                      14.248996   \n",
+       "2018-11-30                      23.679779                      22.065150   \n",
+       "2018-12-31                      30.351051                      28.020382   \n",
+       "\n",
+       "            single-family_attached  single-family_detached  mobile_home  \n",
+       "timestamp                                                                \n",
+       "2018-01-31               28.861290               38.560354    45.482042  \n",
+       "2018-02-28               18.935128               25.684372    26.586742  \n",
+       "2018-03-31               17.751216               23.574588    20.531207  \n",
+       "2018-04-30               15.683319               20.697393    16.495946  \n",
+       "2018-05-31               19.744576               27.220587    22.465727  \n",
+       "2018-06-30               22.867785               34.144244    29.819897  \n",
+       "2018-07-31               27.984748               42.135665    37.782434  \n",
+       "2018-08-31               29.986353               47.135824    42.987269  \n",
+       "2018-09-30               23.157401               33.345445    28.425835  \n",
+       "2018-10-31               15.561638               20.528057    16.367420  \n",
+       "2018-11-30               19.011665               25.052458    26.502078  \n",
+       "2018-12-31               23.508174               31.272159    34.883380  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_elec_load.resample('h').mean().resample('ME').sum().loc['2018'] * 0.10714 + (22/12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.10714"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(6466+4248)/1e5"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/05-heatpump-model.ipynb b/notebooks/05-heatpump-model.ipynb
new file mode 100644
index 0000000..861a27b
--- /dev/null
+++ b/notebooks/05-heatpump-model.ipynb
@@ -0,0 +1,39 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hplib as hpl\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "import numpy\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/06-resstock-metadata.ipynb b/notebooks/06-resstock-metadata.ipynb
new file mode 100644
index 0000000..2cd1c1b
--- /dev/null
+++ b/notebooks/06-resstock-metadata.ipynb
@@ -0,0 +1,1042 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import polars as pl\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('C:/Users/sdotson/OneDrive - Union of Concerned Scientists/Documents/Analysis/metadata-resstock.tsv', \n",
+    "                 sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Unnamed: 0',\n",
+       " 'bldg_id',\n",
+       " 'in.county',\n",
+       " 'in.puma',\n",
+       " 'in.ashrae_iecc_climate_zone_2004',\n",
+       " 'in.building_america_climate_zone',\n",
+       " 'in.iso_rto_region',\n",
+       " 'applicability',\n",
+       " 'weight',\n",
+       " 'in.sqft',\n",
+       " 'in.ahs_region',\n",
+       " 'in.applicable',\n",
+       " 'in.bathroom_spot_vent_hour',\n",
+       " 'in.bedrooms',\n",
+       " 'in.cec_climate_zone',\n",
+       " 'in.ceiling_fan',\n",
+       " 'in.census_division',\n",
+       " 'in.census_division_recs',\n",
+       " 'in.census_region',\n",
+       " 'in.clothes_dryer',\n",
+       " 'in.clothes_washer',\n",
+       " 'in.clothes_washer_presence',\n",
+       " 'in.cooking_range',\n",
+       " 'in.cooling_setpoint',\n",
+       " 'in.cooling_setpoint_has_offset',\n",
+       " 'in.cooling_setpoint_offset_magnitude',\n",
+       " 'in.cooling_setpoint_offset_period',\n",
+       " 'in.corridor',\n",
+       " 'in.dehumidifier',\n",
+       " 'in.dishwasher',\n",
+       " 'in.door_area',\n",
+       " 'in.doors',\n",
+       " 'in.ducts',\n",
+       " 'in.eaves',\n",
+       " 'in.electric_vehicle',\n",
+       " 'in.geometry_attic_type',\n",
+       " 'in.geometry_building_horizontal_location_mf',\n",
+       " 'in.geometry_building_horizontal_location_sfa',\n",
+       " 'in.geometry_building_level_mf',\n",
+       " 'in.geometry_building_number_units_mf',\n",
+       " 'in.geometry_building_number_units_sfa',\n",
+       " 'in.geometry_building_type_acs',\n",
+       " 'in.geometry_building_type_height',\n",
+       " 'in.geometry_building_type_recs',\n",
+       " 'in.geometry_floor_area',\n",
+       " 'in.geometry_floor_area_bin',\n",
+       " 'in.geometry_foundation_type',\n",
+       " 'in.geometry_garage',\n",
+       " 'in.geometry_stories',\n",
+       " 'in.geometry_stories_low_rise',\n",
+       " 'in.geometry_wall_exterior_finish',\n",
+       " 'in.geometry_wall_type',\n",
+       " 'in.geometry_wall_type_and_exterior_finish',\n",
+       " 'in.has_pv',\n",
+       " 'in.heating_fuel',\n",
+       " 'in.heating_setpoint',\n",
+       " 'in.heating_setpoint_has_offset',\n",
+       " 'in.heating_setpoint_offset_magnitude',\n",
+       " 'in.heating_setpoint_offset_period',\n",
+       " 'in.holiday_lighting',\n",
+       " 'in.hot_water_distribution',\n",
+       " 'in.hot_water_fixtures',\n",
+       " 'in.hvac_cooling_efficiency',\n",
+       " 'in.hvac_cooling_type',\n",
+       " 'in.hvac_has_ducts',\n",
+       " 'in.hvac_has_shared_system',\n",
+       " 'in.hvac_has_zonal_electric_heating',\n",
+       " 'in.hvac_heating_efficiency',\n",
+       " 'in.hvac_heating_type',\n",
+       " 'in.hvac_heating_type_and_fuel',\n",
+       " 'in.hvac_secondary_heating_efficiency',\n",
+       " 'in.hvac_secondary_heating_type_and_fuel',\n",
+       " 'in.hvac_shared_efficiencies',\n",
+       " 'in.hvac_system_is_faulted',\n",
+       " 'in.hvac_system_single_speed_ac_airflow',\n",
+       " 'in.hvac_system_single_speed_ac_charge',\n",
+       " 'in.hvac_system_single_speed_ashp_airflow',\n",
+       " 'in.hvac_system_single_speed_ashp_charge',\n",
+       " 'in.infiltration',\n",
+       " 'in.insulation_ceiling',\n",
+       " 'in.insulation_floor',\n",
+       " 'in.insulation_foundation_wall',\n",
+       " 'in.insulation_roof',\n",
+       " 'in.insulation_slab',\n",
+       " 'in.insulation_wall',\n",
+       " 'in.interior_shading',\n",
+       " 'in.lighting',\n",
+       " 'in.lighting_interior_use',\n",
+       " 'in.lighting_other_use',\n",
+       " 'in.location_region',\n",
+       " 'in.mechanical_ventilation',\n",
+       " 'in.misc_extra_refrigerator',\n",
+       " 'in.misc_freezer',\n",
+       " 'in.misc_gas_fireplace',\n",
+       " 'in.misc_gas_grill',\n",
+       " 'in.misc_gas_lighting',\n",
+       " 'in.misc_hot_tub_spa',\n",
+       " 'in.misc_pool',\n",
+       " 'in.misc_pool_heater',\n",
+       " 'in.misc_pool_pump',\n",
+       " 'in.misc_well_pump',\n",
+       " 'in.natural_ventilation',\n",
+       " 'in.neighbors',\n",
+       " 'in.occupants',\n",
+       " 'in.orientation',\n",
+       " 'in.overhangs',\n",
+       " 'in.plug_load_diversity',\n",
+       " 'in.plug_loads',\n",
+       " 'in.pv_orientation',\n",
+       " 'in.pv_system_size',\n",
+       " 'in.radiant_barrier',\n",
+       " 'in.range_spot_vent_hour',\n",
+       " 'in.reeds_balancing_area',\n",
+       " 'in.refrigerator',\n",
+       " 'in.roof_material',\n",
+       " 'in.schedules',\n",
+       " 'in.setpoint_demand_response',\n",
+       " 'in.solar_hot_water',\n",
+       " 'in.state',\n",
+       " 'in.units_represented',\n",
+       " 'in.usage_level',\n",
+       " 'in.vacancy_status',\n",
+       " 'in.vintage',\n",
+       " 'in.vintage_acs',\n",
+       " 'in.water_heater_efficiency',\n",
+       " 'in.water_heater_fuel',\n",
+       " 'in.water_heater_in_unit',\n",
+       " 'in.weather_file_city',\n",
+       " 'in.weather_file_latitude',\n",
+       " 'in.weather_file_longitude',\n",
+       " 'in.window_areas',\n",
+       " 'in.windows',\n",
+       " 'in.nhgis_county_gisjoin',\n",
+       " 'in.nhgis_puma_gisjoin',\n",
+       " 'in.state_name',\n",
+       " 'in.american_housing_survey_region',\n",
+       " 'in.weather_file_2018',\n",
+       " 'in.weather_file_TMY3',\n",
+       " 'in.resstock_county_id',\n",
+       " 'in.resstock_puma_id',\n",
+       " 'out.electricity.bath_fan.energy_consumption',\n",
+       " 'out.electricity.bath_fan.energy_consumption_intensity',\n",
+       " 'out.electricity.ceiling_fan.energy_consumption',\n",
+       " 'out.electricity.ceiling_fan.energy_consumption_intensity',\n",
+       " 'out.electricity.clothes_dryer.energy_consumption',\n",
+       " 'out.electricity.clothes_dryer.energy_consumption_intensity',\n",
+       " 'out.electricity.clothes_washer.energy_consumption',\n",
+       " 'out.electricity.clothes_washer.energy_consumption_intensity',\n",
+       " 'out.electricity.cooking_range.energy_consumption',\n",
+       " 'out.electricity.cooking_range.energy_consumption_intensity',\n",
+       " 'out.electricity.cooling.energy_consumption',\n",
+       " 'out.electricity.cooling.energy_consumption_intensity',\n",
+       " 'out.electricity.dishwasher.energy_consumption',\n",
+       " 'out.electricity.dishwasher.energy_consumption_intensity',\n",
+       " 'out.electricity.ext_holiday_light.energy_consumption',\n",
+       " 'out.electricity.ext_holiday_light.energy_consumption_intensity',\n",
+       " 'out.electricity.exterior_lighting.energy_consumption',\n",
+       " 'out.electricity.exterior_lighting.energy_consumption_intensity',\n",
+       " 'out.electricity.extra_refrigerator.energy_consumption',\n",
+       " 'out.electricity.extra_refrigerator.energy_consumption_intensity',\n",
+       " 'out.electricity.fans_cooling.energy_consumption',\n",
+       " 'out.electricity.fans_cooling.energy_consumption_intensity',\n",
+       " 'out.electricity.fans_heating.energy_consumption',\n",
+       " 'out.electricity.fans_heating.energy_consumption_intensity',\n",
+       " 'out.electricity.freezer.energy_consumption',\n",
+       " 'out.electricity.freezer.energy_consumption_intensity',\n",
+       " 'out.electricity.garage_lighting.energy_consumption',\n",
+       " 'out.electricity.garage_lighting.energy_consumption_intensity',\n",
+       " 'out.electricity.heating.energy_consumption',\n",
+       " 'out.electricity.heating.energy_consumption_intensity',\n",
+       " 'out.electricity.heating_supplement.energy_consumption',\n",
+       " 'out.electricity.heating_supplement.energy_consumption_intensity',\n",
+       " 'out.electricity.hot_tub_heater.energy_consumption',\n",
+       " 'out.electricity.hot_tub_heater.energy_consumption_intensity',\n",
+       " 'out.electricity.hot_tub_pump.energy_consumption',\n",
+       " 'out.electricity.hot_tub_pump.energy_consumption_intensity',\n",
+       " 'out.electricity.house_fan.energy_consumption',\n",
+       " 'out.electricity.house_fan.energy_consumption_intensity',\n",
+       " 'out.electricity.interior_lighting.energy_consumption',\n",
+       " 'out.electricity.interior_lighting.energy_consumption_intensity',\n",
+       " 'out.electricity.plug_loads.energy_consumption',\n",
+       " 'out.electricity.plug_loads.energy_consumption_intensity',\n",
+       " 'out.electricity.pool_heater.energy_consumption',\n",
+       " 'out.electricity.pool_heater.energy_consumption_intensity',\n",
+       " 'out.electricity.pool_pump.energy_consumption',\n",
+       " 'out.electricity.pool_pump.energy_consumption_intensity',\n",
+       " 'out.electricity.pumps_cooling.energy_consumption',\n",
+       " 'out.electricity.pumps_cooling.energy_consumption_intensity',\n",
+       " 'out.electricity.pumps_heating.energy_consumption',\n",
+       " 'out.electricity.pumps_heating.energy_consumption_intensity',\n",
+       " 'out.electricity.pv.energy_consumption',\n",
+       " 'out.electricity.pv.energy_consumption_intensity',\n",
+       " 'out.electricity.range_fan.energy_consumption',\n",
+       " 'out.electricity.range_fan.energy_consumption_intensity',\n",
+       " 'out.electricity.recirc_pump.energy_consumption',\n",
+       " 'out.electricity.recirc_pump.energy_consumption_intensity',\n",
+       " 'out.electricity.refrigerator.energy_consumption',\n",
+       " 'out.electricity.refrigerator.energy_consumption_intensity',\n",
+       " 'out.electricity.vehicle.energy_consumption',\n",
+       " 'out.electricity.vehicle.energy_consumption_intensity',\n",
+       " 'out.electricity.water_systems.energy_consumption',\n",
+       " 'out.electricity.water_systems.energy_consumption_intensity',\n",
+       " 'out.electricity.well_pump.energy_consumption',\n",
+       " 'out.electricity.well_pump.energy_consumption_intensity',\n",
+       " 'out.fuel_oil.heating.energy_consumption',\n",
+       " 'out.fuel_oil.heating.energy_consumption_intensity',\n",
+       " 'out.fuel_oil.water_systems.energy_consumption',\n",
+       " 'out.fuel_oil.water_systems.energy_consumption_intensity',\n",
+       " 'out.natural_gas.clothes_dryer.energy_consumption',\n",
+       " 'out.natural_gas.clothes_dryer.energy_consumption_intensity',\n",
+       " 'out.natural_gas.cooking_range.energy_consumption',\n",
+       " 'out.natural_gas.cooking_range.energy_consumption_intensity',\n",
+       " 'out.natural_gas.fireplace.energy_consumption',\n",
+       " 'out.natural_gas.fireplace.energy_consumption_intensity',\n",
+       " 'out.natural_gas.grill.energy_consumption',\n",
+       " 'out.natural_gas.grill.energy_consumption_intensity',\n",
+       " 'out.natural_gas.heating.energy_consumption',\n",
+       " 'out.natural_gas.heating.energy_consumption_intensity',\n",
+       " 'out.natural_gas.hot_tub_heater.energy_consumption',\n",
+       " 'out.natural_gas.hot_tub_heater.energy_consumption_intensity',\n",
+       " 'out.natural_gas.lighting.energy_consumption',\n",
+       " 'out.natural_gas.lighting.energy_consumption_intensity',\n",
+       " 'out.natural_gas.pool_heater.energy_consumption',\n",
+       " 'out.natural_gas.pool_heater.energy_consumption_intensity',\n",
+       " 'out.natural_gas.water_systems.energy_consumption',\n",
+       " 'out.natural_gas.water_systems.energy_consumption_intensity',\n",
+       " 'out.propane.clothes_dryer.energy_consumption',\n",
+       " 'out.propane.clothes_dryer.energy_consumption_intensity',\n",
+       " 'out.propane.cooking_range.energy_consumption',\n",
+       " 'out.propane.cooking_range.energy_consumption_intensity',\n",
+       " 'out.propane.heating.energy_consumption',\n",
+       " 'out.propane.heating.energy_consumption_intensity',\n",
+       " 'out.propane.water_systems.energy_consumption',\n",
+       " 'out.propane.water_systems.energy_consumption_intensity',\n",
+       " 'out.electricity.total.energy_consumption',\n",
+       " 'out.electricity.total.energy_consumption_intensity',\n",
+       " 'out.fuel_oil.total.energy_consumption',\n",
+       " 'out.fuel_oil.total.energy_consumption_intensity',\n",
+       " 'out.natural_gas.total.energy_consumption',\n",
+       " 'out.natural_gas.total.energy_consumption_intensity',\n",
+       " 'out.propane.total.energy_consumption',\n",
+       " 'out.propane.total.energy_consumption_intensity',\n",
+       " 'out.wood.total.energy_consumption',\n",
+       " 'out.wood.total.energy_consumption_intensity',\n",
+       " 'out.wood.heating.energy_consumption',\n",
+       " 'out.wood.heating.energy_consumption_intensity',\n",
+       " 'out.site_energy.total.energy_consumption',\n",
+       " 'out.site_energy.total.energy_consumption_intensity',\n",
+       " 'upgrade',\n",
+       " 'metadata_index',\n",
+       " 'qoi_report.average_maximum_daily_timing_cooling_hour',\n",
+       " 'qoi_report.average_maximum_daily_timing_heating_hour',\n",
+       " 'qoi_report.average_maximum_daily_timing_overlap_hour',\n",
+       " 'qoi_report.average_maximum_daily_use_cooling_kw',\n",
+       " 'qoi_report.average_maximum_daily_use_heating_kw',\n",
+       " 'qoi_report.average_maximum_daily_use_overlap_kw',\n",
+       " 'qoi_report.average_minimum_daily_use_cooling_kw',\n",
+       " 'qoi_report.average_minimum_daily_use_heating_kw',\n",
+       " 'qoi_report.average_minimum_daily_use_overlap_kw',\n",
+       " 'qoi_report.average_of_top_ten_highest_peaks_timing_cooling_hour',\n",
+       " 'qoi_report.average_of_top_ten_highest_peaks_timing_heating_hour',\n",
+       " 'qoi_report.average_of_top_ten_highest_peaks_use_cooling_kw',\n",
+       " 'qoi_report.average_of_top_ten_highest_peaks_use_heating_kw',\n",
+       " 'qoi_report.peak_magnitude_timing_hour',\n",
+       " 'qoi_report.peak_magnitude_use_kw',\n",
+       " 'in.door_area_ft_2',\n",
+       " 'in.duct_unconditioned_surface_area_ft_2',\n",
+       " 'in.floor_area_attic_ft_2',\n",
+       " 'in.floor_area_conditioned_ft_2',\n",
+       " 'in.floor_area_lighting_ft_2',\n",
+       " 'in.roof_area_ft_2',\n",
+       " 'in.wall_area_above_grade_conditioned_ft_2',\n",
+       " 'in.wall_area_above_grade_exterior_ft_2',\n",
+       " 'in.wall_area_below_grade_ft_2',\n",
+       " 'in.window_area_ft_2']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns.to_list()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= df.loc[df.loc[:,'in.state_name']=='Kansas']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wyandotte = df.loc[df.loc[:,'in.county']=='G2002090']\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Filter out the buildings with more than 20 units. I don't believe there are any in Armourdale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_types = {'Single-Family Detached':'single-family_detached',\n",
+    " 'Single-Family Attached':'single-family_attached',\n",
+    " '5 to 9 Unit':'multi-family_with_5plus_units',\n",
+    " '50 or more Unit':'multi-family_with_5plus_units',\n",
+    " '3 or 4 Unit':'multi-family_with_2_-_4_units',\n",
+    " '20 to 49 Unit':'multi-family_with_5plus_units',\n",
+    " '10 to 19 Unit':'multi-family_with_5plus_units',\n",
+    " 'Mobile Home':'mobile_home',\n",
+    " '2 Unit':'multi-family_with_2_-_4_units',\n",
+    " '<1940':'1939 >',\n",
+    " }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "LEAD DATA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_abbr = \"KS\"\n",
+    "dataset = f\"../data/spatial_data/armourdale_lead.csv\"\n",
+    "lead_df = pd.read_csv(dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_types_lead = {'1 ATTACHED':\"single-family_attached\", \n",
+    "'1 DETACHED':\"single-family_detached\",\n",
+    "'2 UNIT':\"multi-family_with_2_-_4_units\",\n",
+    "'3-4 UNIT':\"multi-family_with_2_-_4_units\",\n",
+    "'50+ UNIT':\"multi-family_with_5plus_units\",\n",
+    "'MOBILE_TRAILER':\"mobile_home\",\n",
+    "'BEFORE 1940':'1939 >'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lead_df = lead_df.replace(bldg_types_lead)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ybl_bld_grouped = lead_df.groupby(['YBL6', 'BLD']).sum(numeric_only=True)[['UNITS', 'ELEP*UNITS', 'GASP*UNITS','HINCP*UNITS']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>UNITS</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YBL6</th>\n",
+       "      <th>BLD</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">1939 &gt;</th>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>12.197597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>22.404520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>5.897407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>240.519377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1940-59</th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>14.140583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>9.528065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>329.987636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1960-79</th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>15.135973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>4.570091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>67.676301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1980-99</th>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>5.397922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>2.487823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>26.728819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">2000-09</th>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>4.650487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>3.823413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>14.853985</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            UNITS\n",
+       "YBL6    BLD                                      \n",
+       "1939 >  mobile_home                     12.197597\n",
+       "        multi-family_with_2_-_4_units   22.404520\n",
+       "        single-family_attached           5.897407\n",
+       "        single-family_detached         240.519377\n",
+       "1940-59 multi-family_with_2_-_4_units   14.140583\n",
+       "        single-family_attached           9.528065\n",
+       "        single-family_detached         329.987636\n",
+       "1960-79 multi-family_with_2_-_4_units   15.135973\n",
+       "        single-family_attached           4.570091\n",
+       "        single-family_detached          67.676301\n",
+       "1980-99 mobile_home                      5.397922\n",
+       "        single-family_attached           2.487823\n",
+       "        single-family_detached          26.728819\n",
+       "2000-09 multi-family_with_5plus_units    4.650487\n",
+       "        single-family_attached           3.823413\n",
+       "        single-family_detached          14.853985"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ybl_bld_grouped[['UNITS']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1960-79    90\n",
+       "1940-59    77\n",
+       "<1940      55\n",
+       "1980-99    32\n",
+       "2000-09    21\n",
+       "2010s       3\n",
+       "Name: in.vintage_acs, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wyandotte['in.vintage_acs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ybl_bld_grouped_resstock = wyandotte.replace(bldg_types).pivot_table(index=['in.vintage_acs','in.geometry_building_type_acs'],\n",
+    "                      columns=['in.county'],\n",
+    "                      values='bldg_id',\n",
+    "                      aggfunc='count')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ybl_bld_grouped_resstock.index.names = ['YBL6','BLD']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weighted_building_models = pd.concat([ybl_bld_grouped_resstock, ybl_bld_grouped[['UNITS']]], axis=1).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>G2002090</th>\n",
+       "      <th>UNITS</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YBL6</th>\n",
+       "      <th>BLD</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">1939 &gt;</th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>22.404520</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.897407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>240.519377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">1940-59</th>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>14.140583</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>9.528065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>65.0</td>\n",
+       "      <td>329.987636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1960-79</th>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15.135973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>4.570091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>52.0</td>\n",
+       "      <td>67.676301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1980-99</th>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.397922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.487823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>26.728819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">2000-09</th>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_2_-_4_units</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.650487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_attached</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.823413</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>14.853985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">2010s</th>\n",
+       "      <th>multi-family_with_5plus_units</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>single-family_detached</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1939 &gt;</th>\n",
+       "      <th>mobile_home</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.197597</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                       G2002090       UNITS\n",
+       "YBL6    BLD                                                \n",
+       "1939 >  multi-family_with_2_-_4_units       2.0   22.404520\n",
+       "        multi-family_with_5plus_units       3.0    0.000000\n",
+       "        single-family_attached              3.0    5.897407\n",
+       "        single-family_detached             47.0  240.519377\n",
+       "1940-59 multi-family_with_2_-_4_units       1.0   14.140583\n",
+       "        multi-family_with_5plus_units       6.0    0.000000\n",
+       "        single-family_attached              5.0    9.528065\n",
+       "        single-family_detached             65.0  329.987636\n",
+       "1960-79 mobile_home                         3.0    0.000000\n",
+       "        multi-family_with_2_-_4_units       7.0   15.135973\n",
+       "        multi-family_with_5plus_units      19.0    0.000000\n",
+       "        single-family_attached              9.0    4.570091\n",
+       "        single-family_detached             52.0   67.676301\n",
+       "1980-99 mobile_home                         3.0    5.397922\n",
+       "        multi-family_with_2_-_4_units       1.0    0.000000\n",
+       "        multi-family_with_5plus_units       8.0    0.000000\n",
+       "        single-family_attached              3.0    2.487823\n",
+       "        single-family_detached             17.0   26.728819\n",
+       "2000-09 mobile_home                         1.0    0.000000\n",
+       "        multi-family_with_2_-_4_units       1.0    0.000000\n",
+       "        multi-family_with_5plus_units       2.0    4.650487\n",
+       "        single-family_attached              3.0    3.823413\n",
+       "        single-family_detached             14.0   14.853985\n",
+       "2010s   multi-family_with_5plus_units       1.0    0.000000\n",
+       "        single-family_detached              2.0    0.000000\n",
+       "1939 >  mobile_home                         0.0   12.197597"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weighted_building_models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = wyandotte.loc[(wyandotte['in.geometry_building_type_acs']=='Single-Family Detached') & (wyandotte['in.sqft'] < 1500)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = armourdale.replace(bldg_types)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "building_ids = armourdale.set_index(['in.vintage_acs','in.geometry_building_type_acs',])[['bldg_id']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sdotson\\AppData\\Local\\Temp\\ipykernel_6600\\1869341699.py:1: PerformanceWarning: indexing past lexsort depth may impact performance.\n",
+      "  test_buildings = building_ids.xs(('1939 >','single-family_detached')).values.flatten()\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_buildings = building_ids.xs(('1939 >','single-family_detached')).values.flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 64/64 [01:44<00:00,  1.64s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "frames = []\n",
+    "for bldg_id in tqdm(building_ids.values.flatten()):\n",
+    "    url = f\"https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/resstock_tmy3_release_1/timeseries_individual_buildings/by_state/upgrade=0/state=KS/{bldg_id}-0.parquet\"\n",
+    "    bld = pd.read_parquet(url)\n",
+    "    bld.index = pd.to_datetime(bld['timestamp'])\n",
+    "    bld = bld.drop(columns='timestamp')\n",
+    "    bld = bld[['out.electricity.total.energy_consumption']]\n",
+    "    bld.columns = [bldg_id]\n",
+    "    frames.append(bld)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined = pd.concat(frames, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='kWh'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "combined.resample('h').mean().resample('M').sum().plot(legend=False, ylabel='kWh', xlabel='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bldg_id = 471403\n",
+    "url = f\"https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/resstock_tmy3_release_1/timeseries_individual_buildings/by_state/upgrade=0/state=KS/{bldg_id}-0.parquet\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bld = pd.read_parquet(url)rraofrooaeresrrooofasdfsdfa   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bld.index = pd.to_datetime(bld['timestamp'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bld = bld.drop(columns='timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bld.resample('h').mean().resample('M').sum()['out.electricity.total.energy_consumption'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/07-nrel-atb.ipynb b/notebooks/07-nrel-atb.ipynb
new file mode 100644
index 0000000..c94ef65
--- /dev/null
+++ b/notebooks/07-nrel-atb.ipynb
@@ -0,0 +1,2824 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from nrelpy.atb import ATBe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "atbe = ATBe(year=2023)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Utility', 'Commercial', 'Residential']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atbe.get_index_values('scale')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import yaml\n",
+    "\n",
+    "with open(\"../config.yml\", 'r') as file:\n",
+    "    config = yaml.safe_load(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'atb_year': 2023,\n",
+       " 'case': 'Market',\n",
+       " 'scenario': 'Moderate',\n",
+       " 'scale': 'Residential',\n",
+       " 'maturity': 'Y',\n",
+       " 'crp': 30,\n",
+       " 'cost_year': 2025}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "config['atb_params']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>temp_db</th>\n",
+       "      <th>rel_humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "      <th>wind_direction</th>\n",
+       "      <th>ghi</th>\n",
+       "      <th>dni</th>\n",
+       "      <th>dhi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>2005-01-01 01:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>2005-01-01 02:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>2005-01-01 03:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>2005-01-01 04:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>2005-01-01 05:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               date_time  temp_db  rel_humidity  wind_speed  \\\n",
+       "2018-01-01 00:00:00  2005-01-01 01:00:00      8.0            61         5.7   \n",
+       "2018-01-01 01:00:00  2005-01-01 02:00:00      8.0            57         5.1   \n",
+       "2018-01-01 02:00:00  2005-01-01 03:00:00      8.0            57         5.1   \n",
+       "2018-01-01 03:00:00  2005-01-01 04:00:00      7.0            56         6.2   \n",
+       "2018-01-01 04:00:00  2005-01-01 05:00:00      7.0            56         5.1   \n",
+       "\n",
+       "                     wind_direction  ghi  dni  dhi  \n",
+       "2018-01-01 00:00:00              80    0    0    0  \n",
+       "2018-01-01 01:00:00              90    0    0    0  \n",
+       "2018-01-01 02:00:00              90    0    0    0  \n",
+       "2018-01-01 03:00:00              80    0    0    0  \n",
+       "2018-01-01 04:00:00              90    0    0    0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather = pd.read_csv('../data/timeseries/weather_year.csv', index_col=0)\n",
+    "weather.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Resource Class</th>\n",
+       "      <th>GHI Bin (kilowatt-hours/square meters/day [kWh/m2/day])</th>\n",
+       "      <th>Mean Direct Current (DC) Capacity Factor</th>\n",
+       "      <th>Population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>&gt;5.75</td>\n",
+       "      <td>19.6%</td>\n",
+       "      <td>12554678.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.5–5.75</td>\n",
+       "      <td>19.3%</td>\n",
+       "      <td>21403290.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.25–5.5</td>\n",
+       "      <td>18.0%</td>\n",
+       "      <td>13476871.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5–5.25</td>\n",
+       "      <td>17.0%</td>\n",
+       "      <td>30603630.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.75–5</td>\n",
+       "      <td>16.1%</td>\n",
+       "      <td>45176116.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.5–4.75</td>\n",
+       "      <td>15.9%</td>\n",
+       "      <td>39880837.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>4.25–4.5</td>\n",
+       "      <td>15.2%</td>\n",
+       "      <td>31742606.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>4–4.25</td>\n",
+       "      <td>14.5%</td>\n",
+       "      <td>80155804.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.75–4</td>\n",
+       "      <td>13.9%</td>\n",
+       "      <td>40755023.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>&lt;3.75</td>\n",
+       "      <td>12.7%</td>\n",
+       "      <td>10255830.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Resource Class GHI Bin (kilowatt-hours/square meters/day [kWh/m2/day])  \\\n",
+       "0             1.0                                              >5.75        \n",
+       "1             2.0                                           5.5–5.75        \n",
+       "2             3.0                                           5.25–5.5        \n",
+       "3             4.0                                             5–5.25        \n",
+       "4             5.0                                             4.75–5        \n",
+       "5             6.0                                           4.5–4.75        \n",
+       "6             7.0                                           4.25–4.5        \n",
+       "7             8.0                                             4–4.25        \n",
+       "8             9.0                                             3.75–4        \n",
+       "9            10.0                                              <3.75        \n",
+       "\n",
+       "  Mean Direct Current (DC) Capacity Factor  Population  \n",
+       "0                                    19.6%  12554678.0  \n",
+       "1                                    19.3%  21403290.0  \n",
+       "2                                    18.0%  13476871.0  \n",
+       "3                                    17.0%  30603630.0  \n",
+       "4                                    16.1%  45176116.0  \n",
+       "5                                    15.9%  39880837.0  \n",
+       "6                                    15.2%  31742606.0  \n",
+       "7                                    14.5%  80155804.0  \n",
+       "8                                    13.9%  40755023.0  \n",
+       "9                                    12.7%  10255830.0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resource_class = pd.read_html(\"https://atb.nrel.gov/electricity/2024/residential_pv\", header=0)[0][:-1]\n",
+    "resource_class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resource_class = resource_class.set_index(resource_class['Resource Class'].astype(int)).drop(columns=['Resource Class'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resource_class.columns = ['ghi_bin', 'avg_cf', 'population']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\sdotson\\AppData\\Local\\Temp\\ipykernel_15808\\1129787922.py:1: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
+      "  resource_class.loc[:, 'avg_cf'] = resource_class['avg_cf'].apply(lambda x: float(x.strip('%'))/100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "resource_class.loc[:, 'avg_cf'] = resource_class['avg_cf'].apply(lambda x: float(x.strip('%'))/100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ghi_bin</th>\n",
+       "      <th>avg_cf</th>\n",
+       "      <th>population</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Resource Class</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>&gt;5.75</td>\n",
+       "      <td>0.196</td>\n",
+       "      <td>12554678.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.5–5.75</td>\n",
+       "      <td>0.193</td>\n",
+       "      <td>21403290.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.25–5.5</td>\n",
+       "      <td>0.180</td>\n",
+       "      <td>13476871.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5–5.25</td>\n",
+       "      <td>0.170</td>\n",
+       "      <td>30603630.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4.75–5</td>\n",
+       "      <td>0.161</td>\n",
+       "      <td>45176116.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.5–4.75</td>\n",
+       "      <td>0.159</td>\n",
+       "      <td>39880837.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4.25–4.5</td>\n",
+       "      <td>0.152</td>\n",
+       "      <td>31742606.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4–4.25</td>\n",
+       "      <td>0.145</td>\n",
+       "      <td>80155804.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3.75–4</td>\n",
+       "      <td>0.139</td>\n",
+       "      <td>40755023.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>&lt;3.75</td>\n",
+       "      <td>0.127</td>\n",
+       "      <td>10255830.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 ghi_bin  avg_cf  population\n",
+       "Resource Class                              \n",
+       "1                  >5.75   0.196  12554678.0\n",
+       "2               5.5–5.75   0.193  21403290.0\n",
+       "3               5.25–5.5   0.180  13476871.0\n",
+       "4                 5–5.25   0.170  30603630.0\n",
+       "5                 4.75–5   0.161  45176116.0\n",
+       "6               4.5–4.75   0.159  39880837.0\n",
+       "7               4.25–4.5   0.152  31742606.0\n",
+       "8                 4–4.25   0.145  80155804.0\n",
+       "9                 3.75–4   0.139  40755023.0\n",
+       "10                 <3.75   0.127  10255830.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resource_class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "avg_cf = weather.ghi.mean()/weather.ghi.max() # W/m^2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(resource_class['avg_cf'] - avg_cf).abs().sort_values().index[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection = atbe(\n",
+    "    core_metric_case='Market',\n",
+    "     crpyears=30,\n",
+    "     maturity='Y',\n",
+    "     scale='Commercial',\n",
+    "     scenario='Moderate',\n",
+    "     core_metric_variable=2025,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>Commercial Battery Storage 1Hr</th>\n",
+       "      <th>Commercial Battery Storage 2Hr</th>\n",
+       "      <th>Commercial Battery Storage 4Hr</th>\n",
+       "      <th>Commercial Battery Storage 6Hr</th>\n",
+       "      <th>Commercial Battery Storage 8Hr</th>\n",
+       "      <th>Commercial DW - Class 1</th>\n",
+       "      <th>Commercial DW - Class 10</th>\n",
+       "      <th>Commercial DW - Class 2</th>\n",
+       "      <th>Commercial DW - Class 3</th>\n",
+       "      <th>Commercial DW - Class 4</th>\n",
+       "      <th>Commercial DW - Class 5</th>\n",
+       "      <th>Commercial DW - Class 6</th>\n",
+       "      <th>Commercial DW - Class 7</th>\n",
+       "      <th>Commercial DW - Class 8</th>\n",
+       "      <th>Commercial DW - Class 9</th>\n",
+       "      <th>Commercial PV - Class 1</th>\n",
+       "      <th>Commercial PV - Class 10</th>\n",
+       "      <th>Commercial PV - Class 2</th>\n",
+       "      <th>Commercial PV - Class 3</th>\n",
+       "      <th>Commercial PV - Class 4</th>\n",
+       "      <th>Commercial PV - Class 5</th>\n",
+       "      <th>Commercial PV - Class 6</th>\n",
+       "      <th>Commercial PV - Class 7</th>\n",
+       "      <th>Commercial PV - Class 8</th>\n",
+       "      <th>Commercial PV - Class 9</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th>core_metric_parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">CommPV</th>\n",
+       "      <th>CAPEX</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "      <td>1731.273000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CFC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "      <td>61.870938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "      <td>17.591546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "      <td>1669.402062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CF</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.191976</td>\n",
+       "      <td>0.123419</td>\n",
+       "      <td>0.185015</td>\n",
+       "      <td>0.175138</td>\n",
+       "      <td>0.165884</td>\n",
+       "      <td>0.158300</td>\n",
+       "      <td>0.156201</td>\n",
+       "      <td>0.148828</td>\n",
+       "      <td>0.141347</td>\n",
+       "      <td>0.135677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FCR</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "      <td>0.044491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCOE</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>56.262649</td>\n",
+       "      <td>87.515822</td>\n",
+       "      <td>58.379664</td>\n",
+       "      <td>61.671863</td>\n",
+       "      <td>65.112419</td>\n",
+       "      <td>68.231939</td>\n",
+       "      <td>69.148898</td>\n",
+       "      <td>72.574405</td>\n",
+       "      <td>76.415676</td>\n",
+       "      <td>79.608753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Commercial Battery Storage</th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>32.989528</td>\n",
+       "      <td>38.207464</td>\n",
+       "      <td>48.643337</td>\n",
+       "      <td>59.079209</td>\n",
+       "      <td>69.515082</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCC</th>\n",
+       "      <td>1319.581113</td>\n",
+       "      <td>1528.298565</td>\n",
+       "      <td>1945.733470</td>\n",
+       "      <td>2363.168375</td>\n",
+       "      <td>2780.603279</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">DistributedWind</th>\n",
+       "      <th>CAPEX</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4474.789672</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>4422.927131</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CFC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>160.703184</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>158.840644</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4314.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>4264.086488</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CF</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.505564</td>\n",
+       "      <td>0.169145</td>\n",
+       "      <td>0.467740</td>\n",
+       "      <td>0.454523</td>\n",
+       "      <td>0.440773</td>\n",
+       "      <td>0.423729</td>\n",
+       "      <td>0.398052</td>\n",
+       "      <td>0.361518</td>\n",
+       "      <td>0.316618</td>\n",
+       "      <td>0.265176</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FCR</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCOE</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>53.576918</td>\n",
+       "      <td>160.138743</td>\n",
+       "      <td>57.909558</td>\n",
+       "      <td>59.593436</td>\n",
+       "      <td>62.064058</td>\n",
+       "      <td>63.924318</td>\n",
+       "      <td>68.047776</td>\n",
+       "      <td>74.924589</td>\n",
+       "      <td>85.549875</td>\n",
+       "      <td>102.145890</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "display_name                                      Commercial Battery Storage 1Hr  \\\n",
+       "technology                 core_metric_parameter                                   \n",
+       "CommPV                     CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                   32.989528   \n",
+       "                           OCC                                       1319.581113   \n",
+       "DistributedWind            CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "\n",
+       "display_name                                      Commercial Battery Storage 2Hr  \\\n",
+       "technology                 core_metric_parameter                                   \n",
+       "CommPV                     CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                   38.207464   \n",
+       "                           OCC                                       1528.298565   \n",
+       "DistributedWind            CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "\n",
+       "display_name                                      Commercial Battery Storage 4Hr  \\\n",
+       "technology                 core_metric_parameter                                   \n",
+       "CommPV                     CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                   48.643337   \n",
+       "                           OCC                                       1945.733470   \n",
+       "DistributedWind            CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "\n",
+       "display_name                                      Commercial Battery Storage 6Hr  \\\n",
+       "technology                 core_metric_parameter                                   \n",
+       "CommPV                     CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                   59.079209   \n",
+       "                           OCC                                       2363.168375   \n",
+       "DistributedWind            CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "\n",
+       "display_name                                      Commercial Battery Storage 8Hr  \\\n",
+       "technology                 core_metric_parameter                                   \n",
+       "CommPV                     CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                   69.515082   \n",
+       "                           OCC                                       2780.603279   \n",
+       "DistributedWind            CAPEX                                             NaN   \n",
+       "                           CFC                                               NaN   \n",
+       "                           Fixed O&M                                         NaN   \n",
+       "                           OCC                                               NaN   \n",
+       "                           CF                                                NaN   \n",
+       "                           FCR                                               NaN   \n",
+       "                           LCOE                                              NaN   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 1  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.505564   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 53.576918   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 10  \\\n",
+       "technology                 core_metric_parameter                             \n",
+       "CommPV                     CAPEX                                       NaN   \n",
+       "                           CFC                                         NaN   \n",
+       "                           Fixed O&M                                   NaN   \n",
+       "                           OCC                                         NaN   \n",
+       "                           CF                                          NaN   \n",
+       "                           FCR                                         NaN   \n",
+       "                           LCOE                                        NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                   NaN   \n",
+       "                           OCC                                         NaN   \n",
+       "DistributedWind            CAPEX                               4422.927131   \n",
+       "                           CFC                                  158.840644   \n",
+       "                           Fixed O&M                             35.912100   \n",
+       "                           OCC                                 4264.086488   \n",
+       "                           CF                                     0.169145   \n",
+       "                           FCR                                    0.045528   \n",
+       "                           LCOE                                 160.138743   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 2  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.467740   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 57.909558   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 3  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.454523   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 59.593436   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 4  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4474.789672   \n",
+       "                           CFC                                 160.703184   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4314.086488   \n",
+       "                           CF                                    0.440773   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 62.064058   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 5  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.423729   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 63.924318   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 6  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.398052   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 68.047776   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 7  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.361518   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 74.924589   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 8  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.316618   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                 85.549875   \n",
+       "\n",
+       "display_name                                      Commercial DW - Class 9  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                              4422.927131   \n",
+       "                           CFC                                 158.840644   \n",
+       "                           Fixed O&M                            35.912100   \n",
+       "                           OCC                                4264.086488   \n",
+       "                           CF                                    0.265176   \n",
+       "                           FCR                                   0.045528   \n",
+       "                           LCOE                                102.145890   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 1  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.191976   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 56.262649   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 10  \\\n",
+       "technology                 core_metric_parameter                             \n",
+       "CommPV                     CAPEX                               1731.273000   \n",
+       "                           CFC                                   61.870938   \n",
+       "                           Fixed O&M                             17.591546   \n",
+       "                           OCC                                 1669.402062   \n",
+       "                           CF                                     0.123419   \n",
+       "                           FCR                                    0.044491   \n",
+       "                           LCOE                                  87.515822   \n",
+       "Commercial Battery Storage Fixed O&M                                   NaN   \n",
+       "                           OCC                                         NaN   \n",
+       "DistributedWind            CAPEX                                       NaN   \n",
+       "                           CFC                                         NaN   \n",
+       "                           Fixed O&M                                   NaN   \n",
+       "                           OCC                                         NaN   \n",
+       "                           CF                                          NaN   \n",
+       "                           FCR                                         NaN   \n",
+       "                           LCOE                                        NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 2  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.185015   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 58.379664   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 3  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.175138   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 61.671863   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 4  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.165884   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 65.112419   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 5  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.158300   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 68.231939   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 6  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.156201   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 69.148898   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 7  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.148828   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 72.574405   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 8  \\\n",
+       "technology                 core_metric_parameter                            \n",
+       "CommPV                     CAPEX                              1731.273000   \n",
+       "                           CFC                                  61.870938   \n",
+       "                           Fixed O&M                            17.591546   \n",
+       "                           OCC                                1669.402062   \n",
+       "                           CF                                    0.141347   \n",
+       "                           FCR                                   0.044491   \n",
+       "                           LCOE                                 76.415676   \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "DistributedWind            CAPEX                                      NaN   \n",
+       "                           CFC                                        NaN   \n",
+       "                           Fixed O&M                                  NaN   \n",
+       "                           OCC                                        NaN   \n",
+       "                           CF                                         NaN   \n",
+       "                           FCR                                        NaN   \n",
+       "                           LCOE                                       NaN   \n",
+       "\n",
+       "display_name                                      Commercial PV - Class 9  \n",
+       "technology                 core_metric_parameter                           \n",
+       "CommPV                     CAPEX                              1731.273000  \n",
+       "                           CFC                                  61.870938  \n",
+       "                           Fixed O&M                            17.591546  \n",
+       "                           OCC                                1669.402062  \n",
+       "                           CF                                    0.135677  \n",
+       "                           FCR                                   0.044491  \n",
+       "                           LCOE                                 79.608753  \n",
+       "Commercial Battery Storage Fixed O&M                                  NaN  \n",
+       "                           OCC                                        NaN  \n",
+       "DistributedWind            CAPEX                                      NaN  \n",
+       "                           CFC                                        NaN  \n",
+       "                           Fixed O&M                                  NaN  \n",
+       "                           OCC                                        NaN  \n",
+       "                           CF                                         NaN  \n",
+       "                           FCR                                        NaN  \n",
+       "                           LCOE                                       NaN  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4422.927131418264"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selection.loc[('DistributedWind', 'CFC'), 'Commercial DW - Class 1'] + selection.loc[('DistributedWind', 'OCC'), 'Commercial DW - Class 1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>Residential Battery Storage - 5 kW - 12.5 kWh</th>\n",
+       "      <th>Residential Battery Storage - 5 kW - 20 kWh</th>\n",
+       "      <th>Residential DW - Class 1</th>\n",
+       "      <th>Residential DW - Class 10</th>\n",
+       "      <th>Residential DW - Class 2</th>\n",
+       "      <th>Residential DW - Class 3</th>\n",
+       "      <th>Residential DW - Class 4</th>\n",
+       "      <th>Residential DW - Class 5</th>\n",
+       "      <th>Residential DW - Class 6</th>\n",
+       "      <th>Residential DW - Class 7</th>\n",
+       "      <th>Residential DW - Class 8</th>\n",
+       "      <th>Residential DW - Class 9</th>\n",
+       "      <th>Residential PV - Class 1</th>\n",
+       "      <th>Residential PV - Class 10</th>\n",
+       "      <th>Residential PV - Class 2</th>\n",
+       "      <th>Residential PV - Class 3</th>\n",
+       "      <th>Residential PV - Class 4</th>\n",
+       "      <th>Residential PV - Class 5</th>\n",
+       "      <th>Residential PV - Class 6</th>\n",
+       "      <th>Residential PV - Class 7</th>\n",
+       "      <th>Residential PV - Class 8</th>\n",
+       "      <th>Residential PV - Class 9</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th>core_metric_parameter</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">DistributedWind</th>\n",
+       "      <th>CAPEX</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>5890.108263</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CFC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>211.531540</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>5678.576723</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CF</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.440416</td>\n",
+       "      <td>0.190760</td>\n",
+       "      <td>0.409000</td>\n",
+       "      <td>0.398158</td>\n",
+       "      <td>0.386772</td>\n",
+       "      <td>0.372443</td>\n",
+       "      <td>0.350504</td>\n",
+       "      <td>0.319187</td>\n",
+       "      <td>0.280306</td>\n",
+       "      <td>0.237586</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FCR</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>0.045528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCOE</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>78.816044</td>\n",
+       "      <td>181.965916</td>\n",
+       "      <td>84.870241</td>\n",
+       "      <td>87.181144</td>\n",
+       "      <td>89.747729</td>\n",
+       "      <td>93.200596</td>\n",
+       "      <td>99.034138</td>\n",
+       "      <td>108.750812</td>\n",
+       "      <td>123.835909</td>\n",
+       "      <td>146.102714</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"6\" valign=\"top\">ResPV</th>\n",
+       "      <th>CAPEX</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "      <td>28.108825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "      <td>2630.888805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CF</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.185816</td>\n",
+       "      <td>0.120424</td>\n",
+       "      <td>0.182942</td>\n",
+       "      <td>0.170668</td>\n",
+       "      <td>0.161277</td>\n",
+       "      <td>0.152756</td>\n",
+       "      <td>0.150860</td>\n",
+       "      <td>0.144209</td>\n",
+       "      <td>0.137374</td>\n",
+       "      <td>0.131906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FCR</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "      <td>0.044490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCOE</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>89.176026</td>\n",
+       "      <td>137.599803</td>\n",
+       "      <td>90.577303</td>\n",
+       "      <td>97.090948</td>\n",
+       "      <td>102.744516</td>\n",
+       "      <td>108.476271</td>\n",
+       "      <td>109.839283</td>\n",
+       "      <td>114.905065</td>\n",
+       "      <td>120.622382</td>\n",
+       "      <td>125.622858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Residential Battery Storage</th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>78.943790</td>\n",
+       "      <td>95.931801</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OCC</th>\n",
+       "      <td>3157.751595</td>\n",
+       "      <td>3837.272054</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "display_name                                       Residential Battery Storage - 5 kW - 12.5 kWh  \\\n",
+       "technology                  core_metric_parameter                                                  \n",
+       "DistributedWind             CAPEX                                                            NaN   \n",
+       "                            CFC                                                              NaN   \n",
+       "                            Fixed O&M                                                        NaN   \n",
+       "                            OCC                                                              NaN   \n",
+       "                            CF                                                               NaN   \n",
+       "                            FCR                                                              NaN   \n",
+       "                            LCOE                                                             NaN   \n",
+       "ResPV                       CAPEX                                                            NaN   \n",
+       "                            Fixed O&M                                                        NaN   \n",
+       "                            OCC                                                              NaN   \n",
+       "                            CF                                                               NaN   \n",
+       "                            FCR                                                              NaN   \n",
+       "                            LCOE                                                             NaN   \n",
+       "Residential Battery Storage Fixed O&M                                                  78.943790   \n",
+       "                            OCC                                                      3157.751595   \n",
+       "\n",
+       "display_name                                       Residential Battery Storage - 5 kW - 20 kWh  \\\n",
+       "technology                  core_metric_parameter                                                \n",
+       "DistributedWind             CAPEX                                                          NaN   \n",
+       "                            CFC                                                            NaN   \n",
+       "                            Fixed O&M                                                      NaN   \n",
+       "                            OCC                                                            NaN   \n",
+       "                            CF                                                             NaN   \n",
+       "                            FCR                                                            NaN   \n",
+       "                            LCOE                                                           NaN   \n",
+       "ResPV                       CAPEX                                                          NaN   \n",
+       "                            Fixed O&M                                                      NaN   \n",
+       "                            OCC                                                            NaN   \n",
+       "                            CF                                                             NaN   \n",
+       "                            FCR                                                            NaN   \n",
+       "                            LCOE                                                           NaN   \n",
+       "Residential Battery Storage Fixed O&M                                                95.931801   \n",
+       "                            OCC                                                    3837.272054   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 1  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.440416   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                  78.816044   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 10  \\\n",
+       "technology                  core_metric_parameter                              \n",
+       "DistributedWind             CAPEX                                5890.108263   \n",
+       "                            CFC                                   211.531540   \n",
+       "                            Fixed O&M                              35.912100   \n",
+       "                            OCC                                  5678.576723   \n",
+       "                            CF                                      0.190760   \n",
+       "                            FCR                                     0.045528   \n",
+       "                            LCOE                                  181.965916   \n",
+       "ResPV                       CAPEX                                        NaN   \n",
+       "                            Fixed O&M                                    NaN   \n",
+       "                            OCC                                          NaN   \n",
+       "                            CF                                           NaN   \n",
+       "                            FCR                                          NaN   \n",
+       "                            LCOE                                         NaN   \n",
+       "Residential Battery Storage Fixed O&M                                    NaN   \n",
+       "                            OCC                                          NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 2  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.409000   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                  84.870241   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 3  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.398158   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                  87.181144   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 4  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.386772   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                  89.747729   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 5  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.372443   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                  93.200596   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 6  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.350504   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                  99.034138   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 7  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.319187   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                 108.750812   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 8  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.280306   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                 123.835909   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential DW - Class 9  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                               5890.108263   \n",
+       "                            CFC                                  211.531540   \n",
+       "                            Fixed O&M                             35.912100   \n",
+       "                            OCC                                 5678.576723   \n",
+       "                            CF                                     0.237586   \n",
+       "                            FCR                                    0.045528   \n",
+       "                            LCOE                                 146.102714   \n",
+       "ResPV                       CAPEX                                       NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 1  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.185816   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                  89.176026   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 10  \\\n",
+       "technology                  core_metric_parameter                              \n",
+       "DistributedWind             CAPEX                                        NaN   \n",
+       "                            CFC                                          NaN   \n",
+       "                            Fixed O&M                                    NaN   \n",
+       "                            OCC                                          NaN   \n",
+       "                            CF                                           NaN   \n",
+       "                            FCR                                          NaN   \n",
+       "                            LCOE                                         NaN   \n",
+       "ResPV                       CAPEX                                2630.888805   \n",
+       "                            Fixed O&M                              28.108825   \n",
+       "                            OCC                                  2630.888805   \n",
+       "                            CF                                      0.120424   \n",
+       "                            FCR                                     0.044490   \n",
+       "                            LCOE                                  137.599803   \n",
+       "Residential Battery Storage Fixed O&M                                    NaN   \n",
+       "                            OCC                                          NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 2  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.182942   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                  90.577303   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 3  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.170668   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                  97.090948   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 4  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.161277   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                 102.744516   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 5  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.152756   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                 108.476271   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 6  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.150860   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                 109.839283   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 7  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.144209   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                 114.905065   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 8  \\\n",
+       "technology                  core_metric_parameter                             \n",
+       "DistributedWind             CAPEX                                       NaN   \n",
+       "                            CFC                                         NaN   \n",
+       "                            Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "                            CF                                          NaN   \n",
+       "                            FCR                                         NaN   \n",
+       "                            LCOE                                        NaN   \n",
+       "ResPV                       CAPEX                               2630.888805   \n",
+       "                            Fixed O&M                             28.108825   \n",
+       "                            OCC                                 2630.888805   \n",
+       "                            CF                                     0.137374   \n",
+       "                            FCR                                    0.044490   \n",
+       "                            LCOE                                 120.622382   \n",
+       "Residential Battery Storage Fixed O&M                                   NaN   \n",
+       "                            OCC                                         NaN   \n",
+       "\n",
+       "display_name                                       Residential PV - Class 9  \n",
+       "technology                  core_metric_parameter                            \n",
+       "DistributedWind             CAPEX                                       NaN  \n",
+       "                            CFC                                         NaN  \n",
+       "                            Fixed O&M                                   NaN  \n",
+       "                            OCC                                         NaN  \n",
+       "                            CF                                          NaN  \n",
+       "                            FCR                                         NaN  \n",
+       "                            LCOE                                        NaN  \n",
+       "ResPV                       CAPEX                               2630.888805  \n",
+       "                            Fixed O&M                             28.108825  \n",
+       "                            OCC                                 2630.888805  \n",
+       "                            CF                                     0.131906  \n",
+       "                            FCR                                    0.044490  \n",
+       "                            LCOE                                 125.622858  \n",
+       "Residential Battery Storage Fixed O&M                                   NaN  \n",
+       "                            OCC                                         NaN  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5890.108262857458"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selection.loc[('DistributedWind', 'CAPEX'), 'Residential DW - Class 1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = atbe.raw_dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_selection = df[(df['core_metric_case']=='Market')\n",
+    "   &(df['scale']=='Utility')\n",
+    "   &(df['maturity']=='Y')\n",
+    "   &(df['scenario']=='Moderate')\n",
+    "   &(df['core_metric_variable']==2025)\n",
+    "   &(df['default']==0)\n",
+    "   &(df['crpyears']==30)\n",
+    "   &(df['core_metric_parameter'].isin(['Fixed O&M', 'OCC']))\n",
+    "   ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_selection_pivot = new_selection.pivot_table(index=['technology'],\n",
+    "                          columns=['core_metric_parameter'],\n",
+    "                          values='value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>core_metric_parameter</th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CSP</th>\n",
+       "      <td>61.424000</td>\n",
+       "      <td>5912.402755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Coal_FE</th>\n",
+       "      <td>113.200000</td>\n",
+       "      <td>3891.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DistributedWind</th>\n",
+       "      <td>35.912100</td>\n",
+       "      <td>2460.146024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Geothermal</th>\n",
+       "      <td>143.354017</td>\n",
+       "      <td>5743.220762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Hydropower</th>\n",
+       "      <td>94.727273</td>\n",
+       "      <td>8688.909091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LandbasedWind</th>\n",
+       "      <td>29.503704</td>\n",
+       "      <td>1265.613783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaturalGas_FE</th>\n",
+       "      <td>27.900000</td>\n",
+       "      <td>1060.533333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OffShoreWind</th>\n",
+       "      <td>98.936957</td>\n",
+       "      <td>2149.057325</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pumped Storage Hydropower</th>\n",
+       "      <td>18.660000</td>\n",
+       "      <td>3215.893928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utility-Scale Battery Storage</th>\n",
+       "      <td>53.840076</td>\n",
+       "      <td>2153.603041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utility-Scale PV-Plus-Battery</th>\n",
+       "      <td>57.635124</td>\n",
+       "      <td>1906.394926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtilityPV</th>\n",
+       "      <td>20.483037</td>\n",
+       "      <td>1204.174657</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "core_metric_parameter           Fixed O&M          OCC\n",
+       "technology                                            \n",
+       "CSP                             61.424000  5912.402755\n",
+       "Coal_FE                        113.200000  3891.400000\n",
+       "DistributedWind                 35.912100  2460.146024\n",
+       "Geothermal                     143.354017  5743.220762\n",
+       "Hydropower                      94.727273  8688.909091\n",
+       "LandbasedWind                   29.503704  1265.613783\n",
+       "NaturalGas_FE                   27.900000  1060.533333\n",
+       "OffShoreWind                    98.936957  2149.057325\n",
+       "Pumped Storage Hydropower       18.660000  3215.893928\n",
+       "Utility-Scale Battery Storage   53.840076  2153.603041\n",
+       "Utility-Scale PV-Plus-Battery   57.635124  1906.394926\n",
+       "UtilityPV                       20.483037  1204.174657"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_selection_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_selection_pivot.to_csv('../data/utility_technology_costs.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def annuity(r, n):\n",
+    "    return r / (1 - 1 / (1 + r)**n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.11964493366309899"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2630*2802)*annuity(0.03, 20)/ 4.14e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "battery_capacity = 1331.488  # power, kW\n",
+    "battery_duration = 34.818  # hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.7045"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "battery_duration / 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11589.937296"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_capacity = battery_capacity * battery_duration / 4\n",
+    "total_capacity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_batt_cost = total_capacity*new_selection_pivot.at['Residential Battery Storage','OCC']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_solar_cost = (2630*2802)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1820320494485113"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(total_batt_cost+total_solar_cost)*annuity(0.07, 20) / 4.14e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.16802077583641167"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_solar_cost*annuity(0.07, 20)/4.14e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/08-data_download_test.ipynb b/notebooks/08-data_download_test.ipynb
new file mode 100644
index 0000000..dccaa88
--- /dev/null
+++ b/notebooks/08-data_download_test.ipynb
@@ -0,0 +1,1840 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "from unyt import foot, meter, kW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieve all buildings within the Armourdale core."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sunroof = gpd.read_file(\"../data/spatial_data/armourdale_rooftop_potential.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = gpd.read_file(\"../data/spatial_data/armourdale_shape.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sunroof.plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None', ec='k')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buildings = gpd.read_file('../data/spatial_data/armourdale/building_footprints.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = buildings.sjoin(sunroof, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
+       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
+       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
+       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry',\n",
+       "       'index_right', 'region_name', 'state_name', 'lat_max', 'lat_min',\n",
+       "       'lng_max', 'lng_min', 'lat_avg', 'lng_avg',\n",
+       "       'yearly_sunlight_kwh_kw_threshold_avg', 'count_qualified',\n",
+       "       'percent_covered', 'percent_qualified', 'number_of_panels_n',\n",
+       "       'number_of_panels_s', 'number_of_panels_e', 'number_of_panels_w',\n",
+       "       'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY_right',\n",
+       "       'WARD'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "core_buildings.plot(ax=ax, column='FEATURECOD', legend=True, legend_kwds=dict(loc=(0.05,-0.15), ncol=3))\n",
+    "# armourdale.plot(ax=ax, fc='lightgray', ec='k', zorder=0, alpha=0.5)\n",
+    "sunroof.plot(ax=ax, fc='lightgray', ec='b', lw=1, zorder=0)\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate the area of each building"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.assign(building_area=core_buildings.to_crs(epsg=5070).area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.assign(area_fraction = core_buildings['building_area'] / core_buildings['building_area'].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kw_total = core_buildings['kw_total'].unique()[0] * kW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area_fraction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Building General</th>\n",
+       "      <td>9963.985320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Commercial and Retail</th>\n",
+       "      <td>5193.889979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Industry</th>\n",
+       "      <td>6815.730465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       area_fraction\n",
+       "FEATURECOD                          \n",
+       "Building General         9963.985320\n",
+       "Commercial and Retail    5193.889979\n",
+       "Industry                 6815.730465"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.groupby(['FEATURECOD']).sum(numeric_only=True)['area_fraction'].to_frame().loc[['Building General',\n",
+    "                                                                                               'Commercial and Retail',\n",
+    "                                                                                               'Industry']]*kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_area = core_buildings['building_area'].sum()*meter**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01895735, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unit_area = (4*kW) / (211*foot**2)\n",
+    "unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(22833.75, 'kW')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "core_buildings.plot(ax=ax, column='building_area', legend=True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Building General'])].plot(ax=ax, column='building_area', legend=True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Building General                             1526\n",
+       "Commercial and Retail                         202\n",
+       "Industry                                       57\n",
+       "Public Attractions and Landmark Buildings      16\n",
+       "Government and Military                         3\n",
+       "Information and Communication                   2\n",
+       "Education                                       2\n",
+       "Name: FEATURECOD, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings['FEATURECOD'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kyFJnAb0kVp75l1UOMUpENaOoAhS1jr/KLHMsAEhISHBY/9JLL2HmzJm12ve//vUv3HPPPQgIcLxm6qGHHrL/3qFDB7Rp0wbdu3fH3r170bVrVwDOPydUVXXr84MBPRHRVaVvnvfc0RMJsY0A4eooBgA+W70Lv5+5iDLxPELCg7D08FsQRcGhdj1lUEdEx0dAVVVIdRRgl7r7sVtw92O3YP5Lq/DbofMwBhnq5DhSFR8szNATkT85c+YMwsLC7Mu1zc7/+OOPOHLkCFasWFFt265du0Kv1+PYsWPo2rUrYmJikJVVcXSyCxcuIDo62uU+MKAnIrqqNGTt3a0lOiXFO9z37ZZD+P3MRYcaelEU0TgmvMJ+ruuUgOs6JVRYX5eefHlUne5fJzBDT0SeJ0OE7KVR1EuPExYW5hDQ19aiRYvQrVs3dO7cudq2v/zyCywWC2JjbcMvp6SkICcnBz///DN69uwJANi5cydycnLQu3dvl/vAgJ6I6KoHxvfBnbd3Q4vEJhXum/LgrSgoMiPi6rCaDY1YVUBfx2PtExFpIT8/H8ePH7cvnzx5Eunp6YiIiECzZs0A2OrxP//8c/z973+vsP1vv/2GpUuXYujQoWjSpAkOHTqEadOm4frrr8eNN94IAGjfvj2GDBmChx56CAsXLgRgG7Zy2LBhLl8QCzCgJ6J6TFVVyFYZVosM2SLDGGRAcYkVsqJCVVSoqoqAAD0CA21lKq2bVz56TnONL8bVmq6KGnorS26IqIa0qKF31e7du9G/f3/78tSpUwEAEydOxMcffwwAWL58OVRVxd13311he4PBgE2bNuGdd95Bfn4+EhIScNttt+Gll16CJF0rx1y6dCkmTZqEQYMGAQCGDx+Od999162+MqAnqqEjB84i70ohBFFAtxvbVL8Bed28yYvx7adb7csvL3sSry/6AVdyCu3rHnu4P8aO6alF9/yKWEUNvcKAnojqoX79+jmUWTrz8MMP4+GHH3Z6X0JCArZs2VLtcSIiIrBkyZIa9bEUA3qiGvrgja/xy95TCAwyYNWul7TuDjlRfsQXq1Wu82Ek66uqauitflRDv/P7w/jorW+gqipaJTXFX966q/qNiKjOKBCheKmG3lvH0QIDeqIaEiXbG4Oi+E8w09AktI1Fl77tIOkl6HQSwpuEVhiakq+fa6rM0PtRDX1BbhFOHbONKBESFlhNayIi/8CAnnyeoqiY9tK/oaq2mmhFUa/9rqp4btIQJMZ7v75ZKg3oZf8JZhqakY8MwMhHBjisE4X1DsvVfZ1KNlVn6P3o30CZExOezBFpT1YFyF6qoffWcbTAgJ40l3XmIgrziqE3SIhvHVPhfkEA9uw/Xen2hUXmuuxepcSrpRsyA3r/Uu79nPG8a6oa5Ubx0Scx68xFFBeUILRRMCKiTQAcJw9TFAXnfsuCqqowNQ5FaAMdwYiI/B8DetLcP/+yDLs2HkRCmxh8sG1mhfurmylN1SjLJumuldy4O6Mb1Y2Nq/fAXGyFqtq+xbm+d2s0be44BGX50hFfDUZ9jT+OcvP25E+w/8cjaBQVhj7Drsfjc+52+HdaUmTBAz1eAADc/9IdGDs5VauuEhHVCgN6jaQfOotzWVegKCpkWYGiKJAVWzmJoii29YqK1olN0LtbK627W6dKL1Ks6utvURQqvV+reEwsE+AoslLnM4JS9T6Y/RXyrlwbwebZv99VIaAvf1GsVieE/safR7m5nJ2L08cyK95R5s2D53VE2vDlYSv9CQN6jaxevx8bth6utt1t/TvU+4C+tHRFreLCOltWzfknrlYZ1tIMfagpELKiguG89sq/VTv70xDKtWINvWuqrKHX4KTowrnLyM8pROOYcIRFOC+VcfjWTK24jq88EdUXDOg1Irk4dF5DmIHRnqGvIrASBQFyJfdpFZCVZuhnvpsGg4H/lHyCC2VPYvkMfQ3+fvJzi/Dvj360X5xt24V6dX9X96kCsQkRGH5XL7f374ukKkpuvJmhX/Xh98g8fRHH/3cah3afRPtuLfD0P9IQ52RSsMimEYhvHY323Vvi5jt6ALj2+guCAEEQ7KNVsWKOSBuqKkJRvTOcpOql42iBUYhGXB0LuyGMwqA36GEM1MNg1FfapqoPW62eI0kqLRWq/ydd/qL834nTYL38sJU1COgLC0rw74+2VtuuY7fE+hPQV/GP0Js19Fu/SsehXSfsy4f3nMQfGVecBvRT/zGhwrqbUjvhm9ROddpHIiJvY0CvEVcz9A0hoJ/+4YPVtrmxZ2tYZQWiKNhqeQVb1l4QBYSbgrzQy4pKM3uylQF9WRazFa+OexuyVcZDr9+D5skJXjt2hQuTnfzzSYiPgNGgg3D1bymiUUjtj1OJ+lTNoxMqLyrzZtkbM+lE9YsMAXKFgsm6O1Z9xYBeI2IVX1+XxSERbV5+drjWXaigtAShIZx0uUMUBZw9eh6SXoLZy0OK3nZ3LxQVlACCAAECmrWOqtDm9b/dWevjBATqccttnQHYAkwBtpPM0kBfEGz/i0/0/vwIdWVg/HW4p3VX7Mw+BVEQoRNEiIIAnShCL3rvCpL+o3ogqUfLa881gMi4Rl47PhGRL2JAr5EhNychqU0sJEmAJIq2zLMoQBRFSFd/iqKAJjXIHpJ3dOrZEoYAHSKiQrXuik+RdBL+9cvbmhz7T08O9MpxQk1B+Mus0V45li95pfsQrbuA2yb00boLRORBiuq90Wfqc/6NAb1GOrePR+f28Vp3g2ph0KiuGDSqq9bdICIiogau/l7uS0RERETUADBDT0RERESaULw4bKW3jqOF+vvIiIiIiIgaAGboiYg0cPhYBg4dzXC5fUR4MPrf2LYOe0RE5H0KBCheGk7SW8fRAgN6IiIN7Nh7Ev9a9pPL7ZOui2VAT0RETjGgJ01YrTLufXaJfVktNwPQiFs7YexQjiCjtRXvbsD3q/dAUVSoqgpVUW2/KyreXfcMgkMDtO4ikcu+TT+KDzf+DFlRYFUUvDx2ILq2bKp1t4gaNFkVIHtp2EpvHUcLDOhJMyfO/FHpfZdzC73YE6rMpexcnDqa6fQ+hZOekY/69Vw2snPyceB0JrJzCmBVFMiygt8vXMaR8xfs7QpKvDvxGRFRXWFAT+TE1tW7cPrXc7jjySEICG64WWhRqvy6eVWtxzN0+IhZz43ETb1aa90Nv/Pplr1Yu/twte1khSelRFrjKDee4VZAv2DBAixYsAC///47ACA5ORkvvvgiUlNTAQBZWVl49tlnsX79ely5cgV9+/bF/Pnz0aZNmyr3u3LlSsyYMQO//fYbWrVqhVmzZmHUqFH2+61WK2bOnImlS5ciMzMTsbGxuPfee/HCCy9AFG0vTn5+Pp577jmsXr0aFy9eRPPmzTFp0iQ89thj7jxE8hFax4qb/70NP/73Z6Te19+vA/r3/7YWW77abw++77i/L8Y+3M/l7f80NRXjnhgAocxMxqW/GwMNddTrhqFD2zjcNaJ7lW3iY8MhCPX3K+K6Uvq5UB1Z5kkpEdUPbgX08fHxeP3119G6tS1jtHjxYowYMQL79u1DUlISRo4cCb1ejzVr1iAsLAxz587FgAEDcOjQIQQHBzvd5/bt2zFu3Di8+uqrGDVqFFatWoWxY8di69at6NWrFwBgzpw5eP/997F48WIkJydj9+7duO+++2AymTB58mQAwFNPPYXvv/8eS5YsQfPmzbF+/Xo8/vjjiIuLw4gRI2rzHFFd8PUg5Wr/FD/P4BXmF+PKxXz7cnFhiVvbB4cGAKyTrxM9ujRHjy7Nte5GvSSJrr2/WP383zcRUSm3Avrbb7/dYXnWrFlYsGABduzYAb1ejx07duDgwYNITk4GALz33nuIiorCsmXL8OCDDzrd57x58zBw4EBMnz4dADB9+nRs2bIF8+bNw7JlywDYgv4RI0bgtttuAwA0b94cy5Ytw+7du+372b59OyZOnIh+/foBAB5++GEsXLgQu3fvZkBPbut2Swe07twcwaYgrbtCRG7SuZqhZ0BPpDkFAhQvXaxan4etrHExkSzLWL58OQoKCpCSkoKSElvmLyDgWjZPkiQYDAZs3bq10v1s374dgwYNclg3ePBgbNu2zb7cp08fbNq0CUePHgUA7N+/H1u3bsXQoUMd2qxduxbnzp2Dqqr4/vvvcfToUQwePLjSY5eUlCA3N9fhRgQAO75Jx/kTWQgIMmrdldop902I1qVMRN4gMaAnogbG7YtiDxw4gJSUFBQXFyMkJASrVq1CUlISLBYLEhMTMX36dCxcuBDBwcGYO3cuMjMzkZFR+eQpmZmZiI6OdlgXHR2NzMxrI2s8++yzyMnJQbt27SBJEmRZxqxZs3D33Xfb2/zjH//AQw89hPj4eOh0OoiiiP/7v/9Dnz59Kj327Nmz8fLLL7v7FJAHCALQr+e1aysy/8jFryeyrjXQOPIURbFejuLCC1mpIZCquJi7LKvCfw9EWlO9OLGUWo8z9G4H9G3btkV6ejquXLmClStXYuLEidiyZQuSkpKwcuVKPPDAA4iIiIAkSRgwYID9gtmqlL/oS1VVh3UrVqzAkiVL8NlnnyE5ORnp6emYMmUK4uLiMHHiRAC2gH7Hjh1Yu3YtEhMT8cMPP+Dxxx9HbGwsBgwY4PS406dPx9SpU+3Lubm5SEhIcPcpoRqQRBGvPT3cvvzT3hP41+fb7cuRESFadMtOlEQo9eCCOV+/VIGoLuhcrKH392tkiIhKuR3QGwwG+0Wx3bt3x65du/DOO+9g4cKF6NatG9LT05GTkwOz2YzIyEj06tUL3btXPpJDTEyMQzYeALKzsx2y9s888wyee+453HXXXQCAjh074tSpU5g9ezYmTpyIoqIiPP/881i1apW9zr5Tp05IT0/HW2+9VWlAbzQaYTT6eUmFxi5n52DDpz9AtsgYmNYXTZpG1Gg/N3ZtiRu7tvRw72pOlETI9SBD//iLI/HI88Ptgb1OL2nbISIvuC4uEoO7XAdJFKETBUiiaLtJjsttYpto3VWiBk9RvVhDz4mlKqeqqr1+vpTJZAIAHDt2DLt378arr75a6fYpKSnYsGEDnnrqKfu69evXo3fv3vblwsLCCsOQSZJkz65YLBZYLJYq21DduJKVg/+bbrt4uXO/pBoH9L5m0IS+WnfBI4wBeq27QOR1w7q1x7Bu7bXuBhGR17gV0D///PNITU1FQkIC8vLysHz5cmzevBnr1q0DAHz++eeIjIxEs2bNcODAAUyePBkjR450uOh1woQJaNq0KWbPng0AmDx5Mvr27Ys5c+ZgxIgRWLNmDTZu3OhwIe3tt9+OWbNmoVmzZkhOTsa+ffswd+5c3H///QCAsLAw3HzzzXjmmWcQGBiIxMREbNmyBZ988gnmzp1b6yeJKte0TQze3/06dHoJ0c0jte6Ox/QY1EnrLhCRj7mcU4hjv2fDKttmnrXKClo1a4JmcfUjkUGkBU4s5RluBfRZWVlIS0tDRkYGTCYTOnXqhHXr1mHgwIEAgIyMDEydOhVZWVmIjY3FhAkTMGPGDId9nD592iGT3rt3byxfvhwvvPACZsyYgVatWmHFihX2MegBYP78+ZgxYwYef/xxZGdnIy4uDo888ghefPFFe5vly5dj+vTpuOeee3Dp0iUkJiZi1qxZePTRR2v0xJBrDAEGtOzUTOtu+JVCswVbjpyAitJrf1WoKqBe/Rmg12FQctWTsXnL6z/+gBOXL0NWFSiKCllVHX6fftNNuD42Tutu1htZ5y7jcPppAEC/2zpr3Bsqb/+v5/DXt9Y6rHsirS/GD2dAT0TaciugX7RoUZX3T5o0CZMmTaqyzebNmyusGzNmDMaMGVPpNqGhoZg3bx7mzZtXaZuYmBh89NFHVR6bam7O5KX4df9px5VORkxZuP4ZGIyOZR6Pp7yA/CuFAICHXrsLN43qWWf9rEvn/8hBidlqf9ilAbh9SQUCjXrER4VXuZ9LBYWY+u+vK70/1hTqMwH9vswM7Dp3rtL7c0vcm6jKm5SLowHrCQCC85tQ+rt49SfK3C+WaYNr63StITZaUGd9Prz/NOY8swIAcPPQTpwl1sc4m7DKWg+utSHSEmvoPaPWNfTUMFzMzkHm6YvVtnM2KmLWmYvIv1wAACgu8N0AsDrPvfclDv+eVWWb7u0SsOAvd1bZRuu3k0KLBVZZBgCUvlwqrr1wZV9DnVDx68leTeMRoNdBJ4poHOTDE28phYBaUPn9NRnESHA+43VtZZ2/AqtFRkFesX1d+dG+SHs6XcWLyuvDxfNE5P8Y0JNLxj1+K4aM6+XkHseAQ6erGAC++NkkyFZbAJnYvmlddM9nqK5EidUEaXU9Vvzkb77CphMnarz9O0OHIipY22FFXVMXz2PdBNjPP/oxzp26iPYd4+3rOGWA79E5Gd/eamVAT0TaY0BPLul2U9sab9u5b/0YbcKVZKkrQVh1u6nrOE6qZdZX0Pw7Bld5P6C/ciEXly/kQhAENHfj5NVpJp4Rvc9xNmEVS26Iakfx4sRS3jqOFhjQk4OfvtiDfz69BIqsQrbKaNK0ERb89EqFdr/+fBzZp/9A3zE3aNBLbXgqkK2ujKKuM/SikzIaclXVr826JVvx8azV0Bt0+OL8ey7vlfG8f3CWoWfJDRH5Agb05CC0UQjadW+J4gIz9n7/C4yBhgptCnIKMXPM33Ep4zK+Hb2swdT5upahrz4K8/sMvd+83nXxTNZN8ObsOa3rEztyHzP0RJ7Hi2I9gwE9OejUpy069WmL3w+fw4tj5yEiOrxCG1Enou/oXtDppQZ14Z6nHqfWGfpGgYGIDQmBIAiQBBGiYOuTeHVZEGD//VTOFeSbzXXan7pTF4FWHQdvZV760r+D/T/+iv/9dBTGQD3GTk6t2+NTlZxn6GUNekJE5IgBPTnVvH1TfHLgTaf3BQYH4PG37/Vuh+qRajP0dZyYfeWWW/HKLbe61PahtasdLqBtEhSEMKOxrrrmYX6YoS/zx1H6d5D+w69Y9vevEBYRwoBeY7wolsjzmKH3DAb0RC6aNr4/CorMEIQyo5aXWRAgICTQhWC3mvcTl0bK8RKd6BjA/KlTZxikikP3+aY6eB7VqoO3+DYx6HN7V6elGeUdOXgWyz7cAgC4kJlT8VBXI3rx6r4UlnZoztnrKit8XYhIewzoiVyU3CLGI/up7uJaXyqd1ovXgneDJGF8p4Y+e2nVwVufYV3RZ1hXl/Z05WIBdmz+tfIGV/8OGND7DmboiTyPGXrPYEBP5GXVleL7Uob+3i7XY2CrVgCAiMAgNPHliaTKERp9AKhm2CLjMjf7GZPisF7NmQ7Ip7zYwarvLs3QS5KtITPB2gsJNiLCFIRLOYX2dRHh/vNvgojqLwb0RF4WqNfj4b49bJl6wZaxL63cEQQBgQa91l206xoXh66I07obNSLoWru3QZOvYAvulatBf+kNZX73XHanbXJTvPJumu21FwTbhcmSAMgqIAAGo+3teUjaTbghtQskJ7OUkneFhwVh9jMjUFBkhiQJ0EkSmsaEa90tIiIG9ETeFmw04KmBfbTuBpUjCGWGaPXCt7LhjUPQ86brqm8XGYbwyLC67xC5pENb/zzBJfJVLLnxDM4wQ0RERETkx5ihJyIiIiJN2AodvZM5950r1DyPAT0RERE1eGazFSVFFphLLCgptiAo2IjwxiFad4vIJQzoiYiISFPFJRbkF5TAapVhsciwlPlptSowW6xo2yoGprDAavf17fe/YO23+2GxyDBbrFe3l2GxWNH+uli89vwop9vNemoZdpYZSnbMfTfhwaeHeOwxknOsofcMBvREREQNWIHZjO2nz6DYakWx1YqSqz/LLiuqihf696t2X5mX8zB50VqYrVaYLTJKyvy0WGV89+ojCA92DMoLCktw/5TFyMiqOMFaWX9/+U706NK82j78cSkfBw6fc3rflTJDjpZnDHAcYcxcYqn2WES+ggE9ERFRA/bMunX49tjxKtsYJMmlgF6Fil/PZld6v9kqV1iXl19cbTAPAFYn2zqj11c+xKvFUvk+SoeKLVXCgN4rmKH3DAb0BMCWiSgsMMNqkWG1yhAEIDquEbLOXMSujQfRJDYcNwxxnCU0L6cQeTlF9mWdXkJUbLiXe649WVaw/dv/QbYqMBj1SBncscr2ly7l491/boSq2iYPst2AP93TG9dd5/pstOd+/wNH9p+GLCtQFRUWsxWyVYEsK5BlGcPG90ZAkAElxRb8673voCi2Y4WoVuRm5dh+jzQh90ohVEUFdDrIVgVhMWHItFqgqioUxdbHGAB/HM1EYGgA8iQ9FFWFqqh4dd54jo9eQ3kFxRjzl4/sr7+q2qYUK/2bMIUEYvXcB7XuJn7+5RT+vvR7WGUFsqJi7IAuGD+4m9O2D9wxH6Xj9Uc0CcGbH9zn1b5SzeQUl1TbxiK7FkwbdFWHFWaLtcK6qgJwAAgw6vHIhL5ITGjsUh/0VbwnWao4KaiYoa/YVyJfxYCeAABff74L77/xtX25aWITLPpiCn4/fA7//MsyNGsbi7zLBUi4LhbturUAAKxeuh1L3//evk3z1tF4/79Per3vWlMVFbMe+QgAEJ0QUW1AX1RsweYydZqlUod0cljOvJSHn389DVmxBelWRbX9lBXbuhNX8N93NgEAIpqE4FJ2rsP2/W+/HgFBBlitClYt32lf375JAI78bMvGte51HX47dB4AEBAeguJCM9r3aok9F6847Kt7o2Ac2vgLIuMjkFXmbUNWVDCcrxkVwJW8okrvN+h94+252GzFyfOX7Ms5+cWVtj13+qJ9htviInOd962+UhQVsmo7gZIVBYpq+xliNEIneX60ab1Y/T5V2GYrlqppa6wmOC9xElBXFYADgE4nYvSwrtX20b6/GmboGzUJQUhYIPJzbf8uzcUVA/qszBzcf9+HkGUFq9ZMQWCgoUIbcg8z9J7hG58YpDlduTdA69UsinB1KKnTRzIwd9InGP5gf3tAX17pB3lDI+mufcBZq/iwKCVUMjyXUu75O3ImGzMXr690P7fHxdt/1+kqfsjKsgIAEEXX3sBK/wacvo6CbR+qojjMXqEqDfM194TqXpXa/nvKPnMROX/kQbbKkGUFrTsnwliD4EMq9/dT+nfljCBcnWQXwC2pnSpt523HDpzBW1OXoqTIgpJiM266rQsef3m01t0CAHyw5Wcs3PKzLXBXVFiVyp/ffz92Nzo0df1bPFfpJddOy62uBPS1zNA/NvFmDL4lGXqdBJ1OhF6vq/A3WJ2qM/SVP79/evxWhIUH471ZX9j66qTkRhAFFBfb1n+2dDskSYAs25ItytXkiyiKeOzxW93qM1FtMaAnAKhQNiFffdMTyr2RVhVkNNTQThAESDrRVu7iQo2nUNlnU7nntrpMnFJmP+LVgF6URDwxcyQkSULI1dEgyr+GlR1f70JAryiqY0DfQE/iPEGo9A/BprZP7aevrcaGpVvty4v2zkbT1tUHg3uPnMXbn222lW4pCgrKZdrlKgJO29+Jeu1XH6HICk4fy7Iv512u/MJIb5MVBUVm12q15To6gda7mPW3KgqM1bTRSaLDiV15zmroywbgAQF6RIQHu9SfylSdoa+6jKZsHX1JccXXRSrzXH22dJvzfRh0DOjJ6xjQE4CKb4ClX0tGNm2EIX+6ETq9DgEhRiT1aFn5ThpwcCdJEmSrAqulimDnqvIBdqnyT191mbCyAb10NcPWsWdLDL07xaGdWC6yim0ZjUYRwRBEEZdzr9XOlp7UOc26X92FKqsO7xoKM/Q1JooCGoUGAoIAQQBy8oocArbaniyV/9atqsx6WUXFZhw5VflFjVXtRxQE2O/1oYi+/DcTxYXeKQdSVbXaE7fq/p2XVf5bPE/Ri65n6KsjCAIMOgkllXxb6azkpmyQXFWNu6samYKQdF0s9HrJdtPZfhr0OoSFBlS5bc++bfH3Tx+GwahDqKniEJmSCyc/igvPE13DkhvPYEBPABzLRgDYM83N2zfF5LfTnG5T/oOqAcfzkPQiUALXMvSVrC//YV3dB4fqENDb2kbGmioer9wJRMqIHug7IAkA8Hza+/b19rIdJ6+jas/QO35QMUNfc0EBBqz752P25T+98CmOnb5gX67tM3vPcyMw6olBkCQRkk5C47hGLm1X3d9dVRn6sm8J7ZKbunQ8bzAGOl7sWFJctwG9oihYsXAzTh/Pwl/euqvKoN6dchJXT8rc5WpdvsXFQNWg01Ua0DsruREEAQa9BPPVQRlq6/qOzfD+m3+q0bYRkaGIiAyt9H5Jqv71qqvXibzvhx9+wJtvvok9e/YgIyMDq1atwsiRI+3333vvvVi8eLHDNr169cKOHTvsyyUlJXj66aexbNkyFBUV4dZbb8V7772H+PhrZbOXL1/GpEmTsHbtWgDA8OHDMX/+fISHh7vcVwb0BADQl7sAz1pFnWFlGnJwF5vYBEX5JWjpQhBT6Yd7+YC+mg/6shn62MTGCA0PQlxiEyfHK7ddmeOUDd6qytBHtYlB6pDOkFUVb//9Wl0/M/SeU/7Vru2/pyZxjQC4FsSXVV3G2CpX0a8yf2y+NOJV+dFL6jJDX5BXhL//5d/YvukQAKBtpwSMnNin0vai4HqGXq6j91iDixn6KsutyjDqJVR2vbezDD0A6HTS1cmfah/Q1yWx3L8PSRIhioLtxPnqTZQEKIrq8vVLDZ2qClC9lDl39zgFBQXo3Lkz7rvvPowe7fy6myFDhuCjjz6yLxsMjt8ITpkyBV988QWWL1+Oxo0bY9q0aRg2bBj27Nlj/3Z9/PjxOHv2LNatWwcAePjhh5GWloYvvvjC5b4yoPcRP286iJf+tADGAD069m5jW6kC0/4xAeFNKs8WeErzNtF44KnB2LB2H07/lu3SxZ1xzSLQ/cY29uXGUWF12UWfNv2fEyGIApq2iKq2bWUBvbslN2VfoTEP9kOHHs4vVi5/PKVM9qhNxwRAsH1IZWfnXe1HxaAhJCoMA+9KQWFBiUNAz4tiPafi9Sra9MNTGXofqrhBaHgwXv7XgzAGGGAMNCDESSmFp2xctdcezAPA/835Cq2Tmlb671PnToa+jko53Lko1hVVDV3pLEMPXKujr+qiVV8QFGTAN98+Yw/kqyupIv+WmpqK1NTUKtsYjUbExDi/PiknJweLFi3Cp59+igEDBgAAlixZgoSEBGzcuBGDBw/G4cOHsW7dOuzYsQO9evUCAHz44YdISUnBkSNH0LZtW5f6yoDex5QUW7D7u2sfBhYvjYMb37wJ7rzvJlx/QysU5BVXqL915pbbuuCW27rUfef8QHyraJfbGgwSOnaMh3C1dtr2U0B4eJBDu2ovii1TlFFlXXO5gKFshn7CtGtvVCv/9QP+b87XToP00kx8Vfui2ik/+pFW33jpqjmRPHQyEwtW/oSEqHAMuynZ4T6Hx+BDgY7eoEPPW5Krb+gB5b8NkK0KXpuyFO+umoQIJ0mP8hnfqmh9UayrY9FXNdKNs4tigWvXcVV30arWBEGAwcDQyZMUCFCqHffLc8cCgNxcx2GejUYjjMbqLvl2bvPmzYiKikJ4eDhuvvlmzJo1C1FRtuTenj17YLFYMGjQIHv7uLg4dOjQAdu2bcPgwYOxfft2mEwmezAPADfccANMJhO2bdvGgN7fVDaUobe1bh+ndRfqPZMpCO/Mq76+s9rShzK/y1V80FbM0DsPCnoPTL4a0Fc8ObCfMFSsC6myj+S68vGvVgF9dTXCl3IKcfhkptN3rPjmjVFSbIGqVj9ZUH01ZGxP/Jp+Gt/+Z5d93eULeTh/+qLTgN6dGnpFraMaelGEAMCgk6AXJRikqzedBIN47XdXM/kDurTBpbxCGPUSDDodDDoJRr3tZ1Iz58mPN160lTNENKrdCDdaKSm2YMN/dsFqtV0HYIoIwcA7umvdLapEQkKCw/JLL72EmTNnur2f1NRU3HnnnUhMTMTJkycxY8YM3HLLLdizZw+MRiMyMzNhMBjQqJFj+WN0dDQyMzMBAJmZmfYTgLKioqLsbVzBgN5HdO5zHZYdeN0hawsAwXX41TD5tupKH3ShBkx9/U5IkojEaoYjFEXBnmWvLFAsPalUFBWCYBuxRBAF28+rf4/lR8xhDb3nNDYFIzoi1P7v36hRFrBl0yZYPPMeSKIISRKgkyToSmuERREBBh1Cg52PFPLeZ485Xd/QPP7SCPx2+DyO/3IOADDmoZvRvkszp21NgQFo3qQRJFGAJNieY+nqvzudeLU+W7D9DA+qm8+DZ/v2xXN9+3qsfOTPQ3u7vU1rF8oVfVlJkRn/nLnKvtyyfZw9oFdV1TYKmlVGACeiqkCLUW7OnDmDsLBrJ9g1zc6PGzfO/nuHDh3QvXt3JCYm4quvvsIdd9xR6XblR8By9m/PlVGyymJA7yMMRj0MRn31DanBiI80Ycn0u+2BlCSJ0F39XSeKCDTqEBpU9RBspdZumQ5RsgXmlV2oFR3fCF8dfs1eAuSM3qDDivVPQ7haOxpSzRBw5Lq/Tx2pdRcAAIFGPdo3d72EjCoyGPV4Yf6f8OQd83HjwGQ88MzQStumdmyL1I6ufaVeV8qfqJP7Ks7lIuO///oBH731jf2atKi4cCze8rwW3aNywsLCHAJ6T4mNjUViYiKOHTsGAIiJiYHZbMbly5cdsvTZ2dno3bu3vU1WVlaFfV24cAHR0a6/FzOgJ/JRgQY9kpp7ZlbIspOlVKaqQL5sm/CIEI/0iag+i46PwF//8SeEN/bPEhJyT4XZ1q9e3Ft2gAlfH8GHau/ixYs4c+YMYmNjAQDdunWDXq/Hhg0bMHbsWABARkYGDh48iDfeeAMAkJKSgpycHPz888/o2bMnAGDnzp3IycmxB/2uYEBPRERUBzrf0ErrLpCX6JzM5aIvVzbnyuhxDZEvD1uZn5+P48eP25dPnjyJ9PR0REREICIiAjNnzsTo0aMRGxuL33//Hc8//zyaNGmCUaNGAQBMJhMeeOABTJs2DY0bN0ZERASefvppdOzY0T7qTfv27TFkyBA89NBDWLhwIQDbsJXDhg1z+YJYgAE9ERERUa2I5a55slrkCll7i9m3R/Chinbv3o3+/fvbl6dOnQoAmDhxIhYsWIADBw7gk08+wZUrVxAbG4v+/ftjxYoVCA29Ntz422+/DZ1Oh7Fjx9onlvr444/tY9ADwNKlSzFp0iT7aDjDhw/Hu+++61ZfGdATERER1YIgCHj4r8MhSgJ0OgnBoQEVSmyYoXdOi4tiXdWvX78qRxz79ttvq91HQEAA5s+fj/nz51faJiIiAkuWLHGrb+UxoCciIiKqpVH33eSwfP7UH5j6+ljo9BJ0egl6Q8McypW8gwE9ERERkYfFJTZBXGITrbvh83y5ht6fuD5FHRERERER+Rxm6ImIiIhIE6oXa+iZoSciIiIiIp/EDD0RERERaUIFUMVAMh4/Vn3FDD0RERERkR9jhp6IiIj8nlVWcPLMHzCbZVisVsRGmRDdJEzrbhF5BQN6IiIi8nv5BcW49+lP7Mu339oRvbu1hCiK6NO9lYY9o6ooECDASxNLeek4WmBAT0RERH5Pr3cMab7YdABfbDqA8LBAfPWvJzTqFZF3MKAnIiIiv2fUO5+J1WKVvdwTcgcnlvIMXhRLREREfk+SRAhO4jWLhQE91X/M0BMREZHfEwQBer0OZrPVYb3ZIkNVVQjOon3SnKIKELyUOffWBFZaYIaeiIiI6gWDjmU31DAxQ09ERET1gsEgAYUV11ssMgx6hjy+SFW9OLFUPZ5Zihl6IiIiqhf0lWTozayjp3qOAT0RERHVC7P/MtLpem+W3OzYdgzj7vgHHn/4X147JhG/fyIiIqJ6oZEpyOn68hfK1iVZUXHxYj4Esf5egOlJHLbSMxjQExERUb1gCgnAS5Nvg0EvwaCXoNfroNdJiIwI8VofjEZbaOXNkwgiBvRERERULxiNegy6qb2mfTAYrgb0JQzoXcEMvWewhp6IiIjIQ+wBvdkKtT4Pq0I+hQE9ERERkQd8+P53eHnGSgCAoqiY/pcVGvfI9ymq4NVbfcWAnoiIiMgDcnKKcOFCnn05P79Yw95QQ8KAnoiIiMgDDAbHcfAv/pGPM6cvatQb/1A6sZS3bvUVA3oiIiIiD9CXm432QnYu/rf/tEa9oYaEAT0RERGRBzSJDK2wLjQ0QIOeUEPDYSuJiIiIPKB1m+gK62LjGmnQE/9hK4Xx1rCVXjmMJtzK0C9YsACdOnVCWFgYwsLCkJKSgm+++cZ+f1ZWFu69917ExcUhKCgIQ4YMwbFjx6rd78qVK5GUlASj0YikpCSsWrXK4X6r1YoXXngBLVq0QGBgIFq2bIlXXnkFiqI4tDt8+DCGDx8Ok8mE0NBQ3HDDDTh9ml91ERERUd3T6a7V0AuCbQhLWVaq2ILIM9zK0MfHx+P1119H69atAQCLFy/GiBEjsG/fPiQlJWHkyJHQ6/VYs2YNwsLCMHfuXAwYMACHDh1CcHCw031u374d48aNw6uvvopRo0Zh1apVGDt2LLZu3YpevXoBAObMmYP3338fixcvRnJyMnbv3o377rsPJpMJkydPBgD89ttv6NOnDx544AG8/PLLMJlMOHz4MAIC+FUXERER1b2kpKb44punoTdIkCQRglB/h0n0FE4s5RmCWstZDyIiIvDmm2/ipptuQtu2bXHw4EEkJycDAGRZRlRUFObMmYMHH3zQ6fbjxo1Dbm6uQ6Z/yJAhaNSoEZYtWwYAGDZsGKKjo7Fo0SJ7m9GjRyMoKAiffvopAOCuu+6CXq+3L9dEbm4uTCYTcnJyEBYWVuP9EBEREfkaX4pzSvvS+tPpkIK8k3yVC4txPG22Tzx+T6vxRbGyLGP58uUoKChASkoKSkpKAMAhIy5JEgwGA7Zu3VrpfrZv345BgwY5rBs8eDC2bdtmX+7Tpw82bdqEo0ePAgD279+PrVu3YujQoQAARVHw1Vdf4brrrsPgwYMRFRWFXr16YfXq1VU+hpKSEuTm5jrciIiIiMg7VC/f6iu3A/oDBw4gJCQERqMRjz76KFatWoWkpCS0a9cOiYmJmD59Oi5fvgyz2YzXX38dmZmZyMjIqHR/mZmZiI52vIgkOjoamZmZ9uVnn30Wd999N9q1awe9Xo/rr78eU6ZMwd133w0AyM7ORn5+Pl5//XUMGTIE69evx6hRo3DHHXdgy5YtlR579uzZMJlM9ltCQoK7TwcRERERkabcDujbtm2L9PR07NixA4899hgmTpyIQ4cOQa/XY+XKlTh69CgiIiIQFBSEzZs3IzU1FZIkVbnP8jVmqqo6rFuxYgWWLFmCzz77DHv37sXixYvx1ltvYfHixQBgvzh2xIgReOqpp9ClSxc899xzGDZsGN5///1Kjzt9+nTk5OTYb2fOnHH36SAiIiKiGiqtoffWrb5ye9hKg8Fgvyi2e/fu2LVrF9555x0sXLgQ3bp1Q3p6OnJycmA2mxEZGYlevXqhe/fule4vJibGIRsP2DLuZbP2zzzzDJ577jncddddAICOHTvi1KlTmD17NiZOnIgmTZpAp9MhKSnJYT/t27evstzHaDTCaDS6+xQQEREREfmMWk8spaqqvX6+lMlkQmRkJI4dO4bdu3djxIgRlW6fkpKCDRs2OKxbv349evfubV8uLCyEKDp2VZIke2beYDCgR48eOHLkiEObo0ePIjExsUaPi4iIiIjqGIvoPcKtDP3zzz+P1NRUJCQkIC8vD8uXL8fmzZuxbt06AMDnn3+OyMhINGvWDAcOHMDkyZMxcuRIh4teJ0yYgKZNm2L27NkAgMmTJ6Nv376YM2cORowYgTVr1mDjxo0OmfXbb78ds2bNQrNmzZCcnIx9+/Zh7ty5uP/+++1tnnnmGYwbNw59+/ZF//79sW7dOnzxxRfYvHlzbZ4fIiIiIiKf5lZAn5WVhbS0NGRkZMBkMqFTp05Yt24dBg4cCADIyMjA1KlTkZWVhdjYWEyYMAEzZsxw2Mfp06cdsu29e/fG8uXL8cILL2DGjBlo1aoVVqxYYR+DHgDmz5+PGTNm4PHHH0d2djbi4uLwyCOP4MUXX7S3GTVqFN5//33Mnj0bkyZNQtu2bbFy5Ur06dOnRk8MEREREZE/qPU49PWJL43PSkRERORJvhTnlPal5cd/heilceiVwmKcuHeWTzx+T3P7olgiIiKqX04eOotj+89AVRUMHn+j1t0hIjcxoCciImrgdm44iMWz10IUBQb05FWqart561j1Va1HuSEiIiL/ptPZ5otRFNU+ghwR+Q9m6ImIiBo4SXctvydbFYgG5vvIO7w54VN9nliK/2KJiIgaOFEqE9BbZA17QkQ1wQw9ERFRA6fTS/bfZZklN+RFqmC7eetY9RQDeiIiogaudadmuPupVEg6ySG4JyL/wICeiIiogWt7fXO0vb651t0gohpiQE9EREREmuCwlZ7Bi2KJiIiIiPwYM/REREREpA316s1bx6qnmKEnIiIiIvJjzNATERERkSY4sZRnMENPREREROTHmKEnIiIiIu3U49p2b2GGnoiIiIjIjzGgJyIiIiJNlNbQe+vmjh9++AG333474uLiIAgCVq9ebb/PYrHg2WefRceOHREcHIy4uDhMmDAB58+fd9hHv379IAiCw+2uu+5yaHP58mWkpaXBZDLBZDIhLS0NV65ccauvDOiJiIiIiMopKChA586d8e6771a4r7CwEHv37sWMGTOwd+9e/Pe//8XRo0cxfPjwCm0feughZGRk2G8LFy50uH/8+PFIT0/HunXrsG7dOqSnpyMtLc2tvrKGnoiIiIionNTUVKSmpjq9z2QyYcOGDQ7r5s+fj549e+L06dNo1qyZfX1QUBBiYmKc7ufw4cNYt24dduzYgV69egEAPvzwQ6SkpODIkSNo27atS31lhp6IiIiItKF6+VaHcnJyIAgCwsPDHdYvXboUTZo0QXJyMp5++mnk5eXZ79u+fTtMJpM9mAeAG264ASaTCdu2bXP52MzQExEREVGDkZub67BsNBphNBprtc/i4mI899xzGD9+PMLCwuzr77nnHrRo0QIxMTE4ePAgpk+fjv3799uz+5mZmYiKiqqwv6ioKGRmZrp8fAb0RERERKQR4erNW8cCEhISHNa+9NJLmDlzZo33arFYcNddd0FRFLz33nsO9z300EP23zt06IA2bdqge/fu2Lt3L7p27WrrlVDx8auq6nR9ZRjQExEREVGDcebMGYcsem2y8xaLBWPHjsXJkyfx3XffOezXma5du0Kv1+PYsWPo2rUrYmJikJWVVaHdhQsXEB0d7XI/WENPRERERNrQoIY+LCzM4VbTgL40mD927Bg2btyIxo0bV7vNL7/8AovFgtjYWABASkoKcnJy8PPPP9vb7Ny5Ezk5Oejdu7fLfWGGnoiIiIionPz8fBw/fty+fPLkSaSnpyMiIgJxcXEYM2YM9u7diy+//BKyLNtr3iMiImAwGPDbb79h6dKlGDp0KJo0aYJDhw5h2rRpuP7663HjjTcCANq3b48hQ4bgoYcesg9n+fDDD2PYsGEuj3ADMKAnIiIiIq14YfQZh2O5Yffu3ejfv799eerUqQCAiRMnYubMmVi7di0AoEuXLg7bff/99+jXrx8MBgM2bdqEd955B/n5+UhISMBtt92Gl156CZIk2dsvXboUkyZNwqBBgwAAw4cPdzr2fVUY0BMRERERldOvXz+oauVnAVXdB9guvt2yZUu1x4mIiMCSJUvc7l9ZrKEnIiIiIvJjzNATERERkTZUwXbz1rHqKWboiYiIiIj8GDP0RERERKQJVbXdvHWs+ooZeiIiIiIiP8YMPRERERFpw4eHrfQnzNATEREREfkxZuiJiIiISBsc5cYjmKEnIiIiIvJjDOiJiIiIiPwYS26IiIiISBOCart561j1FTP0RERERER+jBl6IiIiItIGh630CAb0REREGpKtCjLPX4bFKsNqkWGxyJAtMsyFZlgsVoQnREAnCggJCUB0ZJjW3SUiH8SAnoiogfvhwnc4UXAM6tV50RUoUFUVKlSoqgIVKhTY5mdXoSI+KBHD40Zr3Ov6IyenEPeN/afDuo7t43Box28AgOLrIiCrQP8+bTFhbAqsVhkJcREICjJo0V0iz+KwlR7BgJ6I3DZt6mewWmUoiop57/wJksTLcfzZsfxfsevSdpfbWxRLHfbGtyx4dQ32bj2Glz+8D3HNGtfJMfR6qcK60pMrADDoJBRZZHy/9Qi+33oEAPCP1+5Clw4JddIfIvI/DOiJyG2//HIWFosMAFAUFVLFeIT8iOjm+AhqfS5ELedidi7OnrwAi9laZ8fQOQnolTIBvf5qQF+W2VJ3/SHyKtbQewQDeiJymyhe+9qybCaRHJ09eQE/fLUfsqyg8w2t0KlXK6275JQA976GVlQFVsV6tRBHhaqqEAQRBtH3SkCyzl7CqePZMBh16JLS2u3tDUY9AOCHr/YjIioMkTEm9Ozf3qN9dJahV5QyAb2Tb8CsVsWjfSAi/8aAnojcJgjXAsCygUd1/rMpHZkX86CoKlRFxUOjUhAU4HtBoKecO3kBn76zHoDtJMhXA3pRcC9DfzT/MP687z6HdV3Cu+PRVpM92S2P2PHdYbz/t7WIatoIi79/zu3tk65PxPdr9+Gzf24CAHS/6TrEtYpCUZEZJlMQREmEIiuQZQVhpkAEhwS4fQxJEiEIQNlzY6vsmKEvz1IuY6+1vNwiXLmUD9mqQLYqsFpl+0+rVUFCiyaIjDZp3U3yRczQewQDeiIP2Pzfn3Hh3GWoqoqU1M5IaBOrdZfqVE0z9F9tPYRfTmTal9Nu61GvA3qpTCAmy76bUXU3Q++Mr5bhlP6tqorrz/8zo9/B0fTTEATgrmlDHe5TVBV/e/G/+O1YVoXtnpw2BMPv6O52HwVBgE4vwWK+FqQrZfrrLENfVFiC/JxCqIqK0EbBbh/T0775zy78650Nld7/57/ejmFje3qxR0QNCwN6Ig/48l9bcHDHMQBATLMm9TKgP3EiG8eOZUJVHL/udyegL5vZB4CzWVewbf9J5BeVQFFspRvK1ZuqALJiG21FJ0m493b/CwbKnvi4802Gt5V/XWrCF0uvFr35NY7sPwPAveffUmKFucR24W/5h6UqaqUXgZc/aZvz2L9QmFsERVUx/YMHEVRF9l6v1zkE9NYy+9I5+Qbl2/c2YN7mf6Bpqygs2jWr2sdU1yQn3yKUZfWxbxSI6hsG9EQecOKXM/bffTGw8YTt24/jX4u2QK+X0LdvW2zadAiAe4GSWC5wfPBvy13aLihA7/MBvcVshd7g+JZamF9i/13x6Qx97UcpUuF7j++b5TtRkFcMwL1/lw5ty22nKCp0RufBa/mAfv+PR3D5Qi4AoKTQXGVA3+vGNjCXWKE3SNDpJQToJZw7eA4AoJOcnHBd/bdU9iRAS7pqAnpf/oaKNMaSG49gQE/kAc2TmuLQTtuY0fU1oC8Nxg0GHUaM6GYP6N3L0Dsu39G/EzbtOoqc/OIqt/Ol7LZslXHs4Dlknr2ExlFh2PfTMez96RgK8orw4bfPOLQt+9z4ckBTXzP0gkNpWM32kdgmGg8/PwyiKEIQBDSJMeE/q3Y7bVv+NdYZrgW5VmvVgff0V+5wWD5/6g9888k2AIDk5PUpfWx1OfqOOxpHhSKpSzNIOhE6nQSdToRU+lMSEZ/YROsuEtVrDOiJPGDw+BvRpU87CIKAxHZxWnenTpQGEIqiOgRK7gTbf3vsNpitVtvJgSAgpnEo9h87V21Ar2WwqKoqMk5fxN6tx7Dvp2PYv+O4PesrigLadm6GuMTG6HxDayiKAlG8lu0Wy5Rm+HKG3t1hK53xxRr6st8IqW78nZoah9h/j42PQO9BHR3uX7V2r9Pt5HIjz5QtQ7G6GXiX/bbHWUBfenbs7n7rSp8ByegzIFnrbpA/4sRSHsGAnsgDBt/TR+su1LnS4EhVVcdAyY04LrpxaIV1rmSHtUzQ/5p+GlPLzeJZqk2HeMz99xOVbiuVKZVQZN8LeEsFSoG13ocvBvQ1PfHsOSAZu76zfQPl7JsV0cUa+iax4VBkxek489UxBujRrnMCdHodolpGIaxpOHQ6CQa9BJ1OQqwAtGoeicAajKpDRPUPA3oics3V2Kg2GXpn7hrUFVfyiiAKAgRRgCgIEAVboC+KAgRBgCRql1VpndwUAUEGFBeaAdgmAUru3gJdb2yDrn2uq3JbY6ABUXHhEEURoeG1D5rrSkdTF1w0/wEBtudbhGgb+UYQIEJA08AE6EUDBAgQBdHezv6fICBMF671w6jgubfHQ7bKEEQBOr3rH3c3D++GJjHhECURsU5KRSY80Bcjx/SATidBkgRIkq2sJCrGcVjGN9dMq3HfwxoF4+3lj9d4eyJ/Iai2m7eOVV8xoCcil7RsEYnUoZ2h14kINwXhtmFdIAgCDIbaTRM7vG8HD/WwbugNOgwe0wOiJKJrn+vQoUcLBAS6NtTm9b3bYPGW5+u4h7XXMqQNWoa00bobHleTiaQAIDQ8CDeUK7MpK7ljQk27RERUJxjQE5FLuvdoie49WtqXp05N1bA33vXojBFad4GIqH7iKDceUfsroYiIiIiISDMM6ImIiIiI/BhLboiIiKheOngyE+cv5sBskVFitqLEakWJ2QqzRUZMRChG9PHta3iIXMWAnoiIiGpk16aDyDz1B0IbBaPfqB7Vtv91/2l8s+JnNG3eBGMf7lfn/fvk213YtPe40/u6XRfPgJ7qDQb0REREGrPIMsxWGVZFgSwrsCoKrLICi6xAVhQE6HWIaxQGANj4n59x5WI+VEVBUU4hrGYZgcEGXDx/GQHxjXAmUITFYtufxWKFxarAYr22bLbKmPf0HYiPDq91v7/86Af8vOEAWndKcCmgzz53BetX7kZYo2CvBPSGKoYrNVczey95hwAvDlvpncNowq2AfsGCBViwYAF+//13AEBycjJefPFFpKbaRrvIysrCs88+i/Xr1+PKlSvo27cv5s+fjzZtqh4ObeXKlZgxYwZ+++03tGrVCrNmzcKoUaPs91utVsycORNLly5FZmYmYmNjce+99+KFF15wmJWx1COPPIIPPvgAb7/9NqZMmeLOQyQi8hqL2YoTh84BKtA4xoQmseEV2hQXm5GdmQNFVqEoCoKCjYiJa+TyMXIv5eO3g2ehyAoS2sQgKj7C7X5u/WIPQkxB6NK3/bX95hbhxx9+hcUiw2KRERYWiMFDOgEA1i7ciAPbjiIkPBj5Flv70tl+ew1IxsDR1Qd+1SnILcIbD38IQQAGp92ElKHX13qfq5dsw+Zv/gdVBXLHhOOCvhgqAEVVoUKFqqpXfy+7zvb7F7dPhMlQ80meXv9yC5bv2F/p/Te3bYH37h0JAPj3extx5ng2QkyByM+8DADocEMrHNh6BO1uScL2oOqPV1RiqXFfr1zIxTtTPoHVIqO4xDZTrdVS+UzIh/aewk/rD8BcYsW53/8AACiKd2ZONlYV0FsY0FP94VZAHx8fj9dffx2tW9vG9l28eDFGjBiBffv2ISkpCSNHjoRer8eaNWsQFhaGuXPnYsCAATh06BCCg4Od7nP79u0YN24cXn31VYwaNQqrVq3C2LFjsXXrVvTq1QsAMGfOHLz//vtYvHgxkpOTsXv3btx3330wmUyYPHmyw/5Wr16NnTt3Ii4uribPB1GtqaqKkmILZFmBoqgIDas4oVBubhF+O5EN+Wr2zWpVENEoGO3b8e9WC+uW78D8F/4DVVHxxdE3IOlcH1t/xvOf4+ivGZAV2+utyCoam624mJUD2Wpbp9OJWHPwtQrbXvkjD1OG/R0AMH7KEKQ9PbRCm18PnsOzjy62L980IAkvvD7W5f4d+99pvHDXuwCAx18bh9vvv9nlbUu9/9xytEhq6hDQX7qUj7lvfWNfbt062h7QH9lzEj+u2oX462Jx7mKxw77MJRaPBPQWswU7v0kHAHS+qV2t9wcA2Zk5+PXAWQBAwS1WnNEXuLxtbSdY01UzeZqlTACsN9g+uq3mawFp6YzNilkGgqr/+7XWIjttMVux/et0AEByb9vkanIV+ztzIhv//WirwzrFyQy8daHKgN5q9UofqBqqYLt561j1lFsB/e233+6wPGvWLCxYsAA7duyAXq/Hjh07cPDgQSQnJwMA3nvvPURFRWHZsmV48MEHne5z3rx5GDhwIKZPnw4AmD59OrZs2YJ58+Zh2bJlAGxB/4gRI3DbbbcBAJo3b45ly5Zh9+7dDvs6d+4c/vznP+Pbb7+1tyXytksX8nDPoLcAANclN8U/lj5Soc2vRzPw7F8/d1h3043X4ZUXR1Vo2xCoqoqTh85h57f/w871BzDy4VvQ747aB33uHL80wFDdiMsuXy7AxT/ycPFivsP6UEVFSdG1DKjswmeIWsmBy8+Sq8juBY5lT07cyYqOafoYVFXF8EcHOr1fX+6kpzTIrE7ZILRWyjwNgodmEhaEa/up7PWovDu1DOilqoNw2UlAb7FcC0hL+yubLQCqD+hrU26i01/bf+lzZq0i2x0cWvGbi9qeALnKoK/8uWCGnuqTGg9bKcsyli9fjoKCAqSkpKCkpAQAEBBw7R+uJEkwGAzYunVrZbvB9u3bMWjQIId1gwcPxrZt2+zLffr0waZNm3D06FEAwP79+7F161YMHXotm6UoCtLS0vDMM8/YTyiItCDprv2zkivJQumcZIAra1sf7Dl0Bkd/z8a+w2dx8HhGhfvfm74cT9wyC5/M+QJH9v2Onev/55HjKqqK03lXqm0nlgkIt35TedlDec89vQxHj2RWWF8+wHQ3CHfsm+PbtLulCmUfmztZ0bxL+ci/XICSwhIIglDhREdXLlCKjAy1/24MMiAkPAiBwUaEhAUiJCwQomR7HLXJDJdVNiAsG4jXhsNu3Nyl4uYJQHk6J+WjZVnLvHa6q1lnuUxAql59PuQS17LOlloEs44Bve1nVRn6oJCKAb3qpYDeFzL0Jw+fxzNj38WUkfPwxNC38Hjqm145rt9QvXyrp9y+KPbAgQNISUlBcXExQkJCsGrVKiQlJcFisSAxMRHTp0/HwoULERwcjLlz5yIzMxMZGRU/wEtlZmYiOjraYV10dDQyM699SD777LPIyclBu3btIEkSZFnGrFmzcPfdd9vbzJkzBzqdDpMmTXL5sZSUlNhPRAAgNzfX5W2JKuNKRlSSKn54y16qKfWmomIL3lvxIz5fvw+NwgJxJa8I8dGN8OlraQgw6u3tknq0wpcf/QAAaN0pAR2vfo1fG+fyc/HsT1/j8OULWD/yATQOqLywWCgTTGWfv+zyMZxdw2Nb7xgNqqqt/rp84OlKICpK5U4O3AyERKn6E0xn7n9lLFSoaNejFX78Mr3C/eUz9GUD9UnzJmLSvIkO9z83/j3s3368ysDPHaGNgjFv01+hqkBUQmOP7LPs6+Zuxl2pZaQguRHQ278NEQQ8OHM0DAF6CKKAtl2bQwg2Yt/hE9UezyLX/HUwBhmR9vwI6HQSQhuHoN+YXgiLCKm0fULLSNw3bQh0egmiKMBg1MMYoK+0fU0Vm604mXERxWYLikqsKDZb8HvmJTRtEob4yHAY9ToYDTqczrqMI2cueC1DX1xkxsFdjq+Js/cDotpwO6Bv27Yt0tPTceXKFaxcuRITJ07Eli1bkJSUhJUrV+KBBx5AREQEJEnCgAED7BfMVqX8H3X5P/QVK1ZgyZIl+Oyzz5CcnIz09HRMmTIFcXFxmDhxIvbs2YN33nkHe/fudesfyOzZs/Hyyy+7/uCJXKAvk72SrZUE9E5KBOpbhn7/kXN4deE6nM26AgC4nFsEADiTeRnvf/4Tpvypn71t91uTIUoiFFlB91uSMXTCTTU+rqqq+Pz4Abz68ybkWcwAgBe2f4v3+o2s9P2hpp+rY8b2xCcf/Yhz5xxPAkpPECY8NRgRkWEQJaHagL6yEo8KGXo3/05KTx67909CTLMmLm837hnHEkupXEa+/ElpUZG5yv1Nf3cCzMUWhxOM2tAbdGjXvZVH9lXK4fVwd+NaZv501TwvVuVa8Kk3XvvoTp14k0MG/OS5i1j0XPUBfW2CWYNRj3vK/X1UJTI23Csj2pz/Iwd/mvVZhfWPj+iNB27rZV9e+9MveHnxeli8NMqNs3I0i1mGwciBBgF4N3PODP01BoPBflFs9+7dsWvXLrzzzjtYuHAhunXrhvT0dOTk5MBsNiMyMhK9evVC9+7dK91fTEyMQzYeALKzsx2y9s888wyee+453HXXXQCAjh074tSpU5g9ezYmTpyIH3/8EdnZ2WjWrJl9G1mWMW3aNMybN88+Kk9506dPx9SpU+3Lubm5SEhIcPcpIXLgSsmN0wx9LcoyfEmx2YIPPv8Jy77ZU2k9+op1e9CvR2t0aRsPAAgND0bHlDaQrTLiW8fU+NhZhXl4bts6fH/WMaD55tRRrD15GCNaJjndrmwg16hJqNM2ztw6sAPWffO/CgF9UvfmuK5jPAaP6YGIqDCX9lXZc1U+Q+/uiV9oeDB63JqMGR894nKde3mCAASXu7j711/POyyn7ztVZdbRVEUG11cIYs0D+jovuSnzzUyvW5MRl9gEOoNU4QQpKiIUs/58G/Q6CQa9BL3OdjPoJOj1Egw6HfR6CY2cXKzvr/7342HMue99mFpFAom2ATgCjXq8cv8QxEaEol2zKIf2wQEGAECJlzL0zgN6KwN68qha/zWpqupQtgIAJpMJAHDs2DHs3r0br776aqXbp6SkYMOGDXjqqafs69avX4/evXvblwsLCytkqSRJspczpKWlYcCAAQ73Dx48GGlpabjvvvsqPbbRaITRaKzmERK5R+dQclPJhY5OA/r6kaF/Yf6X2Lq36gyhqgJ/W/gtlsyeYC+9mbXiSbdGl3Hcn4o1Jw7hpZ0bkWMudtrmxR0bkBLTDFFBFQPLskHozbe7N/xhx04J2Lfnd4d1ox/uj5atopxvUEawKRBPvj4OANCqQ7zTNuXf+66cu4ihjR6wjcjzxwf2eurKxLeOxitLn6i2L1UZfM+NKMovwv9+OIxOV0e6KX8diKrarhWQdP5bRiDWIqCv/UWx1ZXcXAs+b0u7sdJ2wYEGDOjVtlZ98TeKrOCPc5cgBurx5FODEWjQIzjQgFuub+20fem3qLJiG2WsunKn2tIbKr6vWS0cYYc8y62A/vnnn0dqaioSEhKQl5eH5cuXY/PmzVi3bh0A4PPPP0dkZCSaNWuGAwcOYPLkyRg5cqTDRa8TJkxA06ZNMXv2bADA5MmT0bdvX8yZMwcjRozAmjVrsHHjRocLaW+//XbMmjULzZo1Q3JyMvbt24e5c+fi/vvvBwA0btwYjRs71lDq9XrExMSgbduG9cZG2hMEwV4+UmnJTT0O6NOG9cRP+05UO1rM2awr+GHPcQzqbQsQaxrM/1FUgL9uX49vTx+tsl2OuRiv79mCuTdVHAFL0onQG3QQBPdHN7m5Xzt8/WU6rBYZV64UAnD9tQwINGDonyoPzgAno9wo18puFFkFPF+KXEFO9hWseW89sk9dsAf0eiejh5gtVgTqDHXfISeWvPofGIOMuHOa66Ug5dWm5Ka213i6c1EsOdIb9DAE6BGgk3DvkOpHxwoLMqJ5TAQMOglWue4D+qimEfj4xxegN+igN0jQG3QwBmrz78QXCaoXJ5aqH1+EO+VWQJ+VlYW0tDRkZGTAZDKhU6dOWLduHQYOtA1rlpGRgalTpyIrKwuxsbGYMGECZsyY4bCP06dPO2ScevfujeXLl+OFF17AjBkz0KpVK6xYscI+Bj0AzJ8/HzNmzMDjjz+O7OxsxMXF4ZFHHsGLL75Ym8dOVGekqwF9ZfXOZQP65PZxuK5NDGKdTCrkjzq3bYq7hnTDsm/2VNqmSXgwpj84CDde37JWx/rm9yP46/ZvcamkyKX2mYV5Ttf3G94V/YZ3rVEfEptHYvl/nsTePSfx4vP/wc392yPKxTIbV4SEBaLvwGSIogBJFKFXrDi12TYKkCv19IV5RVj5j2+gqkB0QmMMnuj+OPSlgW7Zkx1nIzV5agSbmpB0UoWLkcv7bvlPWPPet2jXsw0eeyutwv0OGXo3P/lrm6Gv9qLYenjRvKck974OX1z+l8vtu7RuipWvTKy+oYfo9BKiazChG5E73AroFy1aVOX9kyZNqnaUmc2bN1dYN2bMGIwZM6bSbUJDQzFv3jzMmzfPlW4CQKV180TeIEkiLHCthv6mG6/DuDt7OW3nrx4ZeyN+Sj+B0xkVR4wZcmN7TJ14C8KCaz6rZk5JMWbsWI+1Jw+7tV1dBkVdu7XAl98+4/H9NokKw19n32lfPrLnBHYs2wpBEFwawrIovwRLXlsNAOhwY9saBfSPvpWGx/4+wWFd+WErgdoNhVhbd0+vfg6HnD/y8OvPvyEotJL68asnLoIgIEDSIVCnt01LDwGCUDqSZenvZX56oP+Gq3XuOlG03STbTbq6HGNy/doOcvTl4h9hDDRg4Nj69T5bb/CiWI/gFRlEdaA0YHdl2ErZS+Mxe1OAQY8XHh6MR15Zbi+9aRQWhOceGICbu7ep9f4FQcCOzDNub1d2pBB/tOKNNbCarVhx4h8ubyPpRETGR0AQBDSq4TcHzi50bZ7YBIuXPAq9ToJOL0GnExFaWaDsIzr2aYdH3vgTYlpEOr2/bXJT3H5XLyS2isKwcd4N/u7s2RF39uzo1WM2BIX5xfj3exthMOqRn1tk++ZUsU0kN/aJAV4fOvLLhRsQGhGCm+9M8epxqf5jQE9UBySdaK+ld3p/DccG9yedrmuK8UO7Y+lXu3Frr+vwzH23Ijy08rHg3RFmMOKlXrfiic1r3NpOruVIJFobdG+/astKyguPDMOSI/M83heDUYd4PysjaN2lOVp3aV7p/d37XIfufWo/BwLVzoGzmTh/ORcWWUHLyEYQRREWWbbdrAossgyzLCM5LhqNggOgqEBAJReH514qwIXzVwAAH7y8yuG+MY/dCknybkAfFBqAICcz5zZozNB7BAN6ojqw/Lu/VDrpEAA0aRyCZYsfhSQJCA6uvyMtPTSmNzq3bYq+3ZyPNlEbQxPbol/Tlth8rvoxt0v5ex1yoyiT1l0gqnOLftiN9QePudy+z3XN8cG9zkuuyg4jXFZgiBGKrDgdoKAu3TK+5nNsEFWFAT1RHagqmAdsGfqYmPofnAUY9HUSzAO2MpBXbxiIgasXoVh2bQg4fy+5IWoIDG6OeFXVBFGNIsOw8LvpECURO9YfRMapCxj354GIjGvEmVp9BEe58QwG9EQe9M6ra7F14y+Qrw5Z2a13a7w4926tu1VvRQWFoFOTWBy+lI0XevTH58cPYHf2OadtOzaOwZwbq5+5moi0ZZDcDOjlygN6nV5Csza2yerGPHpLrfpF5MsY0BN5yOkTF7Drx6PIy7k2hOKlP5wPk0ieIQkiOjSOxjt9hyE2OAy9Ypohde1HKLJa7G2Mkg5PdemDB5N7VDvWNxFpT+92QO/fpXREnsBPNyIPMRh1CAlzvNjpYjYD+rqkE0W82PNWxAbbRm9pHtYIz3W7Nixjz+h4fDP8PjzasReDeSI/4XbJTRUZevIDquDdWz3FDD2Rh8Q0bYQOXZvj9+PZ9nWde7TQsEcNU1q7rvgp4xRuimuBe9p2gcg6WSK/8mj/XujSLBYbfjmObcdPIbeopMr2Zg0nNCPyFQzoiTzkyqUCHNz7u8O63T+5PlIDeYYoCPjglju07gYR1VCj4ECkdmqL1E5tcfeC5dh/JqPK9szQ+zkOW+kRDOiJPKS4yOyQnQdsQb6iKNWOekNERBXFR5iQV1wCvU6CQZKgl0ToJclhOTI0WOtuEmmOAT2Rh4SEBqDHjW2QlXEFoiRCkkTodCIUWQXjeSIi9705jiNT1XccttIzGNATeUhIWCBe/Wea1t0gIqIqFBeUoLCgGKqiIjgsCAFBBqftNq3dh+ULv4dsVaAoqn044tvG9cQ9T9zq5V6TFn744Qe8+eab2LNnDzIyMrBq1SqMHDnSfr+qqnj55ZfxwQcf4PLly+jVqxf++c9/Ijk52d6mpKQETz/9NJYtW4aioiLceuuteO+99xAfH29vc/nyZUyaNAlr164FAAwfPhzz589HeHi4y31l3pCIiIgajP8u3IR7Ok3Hn7o8jy2rd1faLj+3CGdP/oGMM5eQde4y/sjMweU/8lCQX+zF3jYAqpdvbigoKEDnzp3x7rvvOr3/jTfewNy5c/Huu+9i165diImJwcCBA5GXd22EuylTpmDVqlVYvnw5tm7divz8fAwbNgxymWs/xo8fj/T0dKxbtw7r1q1Deno60tLcSxAyQ09EREQNhuMMsZVHeJLOec5TtnLc+4YiNTUVqanOy75UVcW8efPw17/+FXfcYRuIYfHixYiOjsZnn32GRx55BDk5OVi0aBE+/fRTDBgwAACwZMkSJCQkYOPGjRg8eDAOHz6MdevWYceOHejVqxcA4MMPP0RKSgqOHDmCtm3butRXBvRERETUYAjitYBerSJjK1UywZWiVB7Q79p9Ehs3HYTVqsBsscJqUWC1yrBYZNtPqwyrRbH9tMr4eNFDMBgaeCjmxRr60vO33Nxch9VGoxFGo9GtXZ08eRKZmZkYNGiQw35uvvlmbNu2DY888gj27NkDi8Xi0CYuLg4dOnTAtm3bMHjwYGzfvh0mk8kezAPADTfcAJPJhG3btjGgJyIiIiqvbIJeUSqPJBtHhaFzr5YQRdsgB6JOhCQKaN4mptJtzp69hA0bf3G5L1arzIBeAwkJCQ7LL730EmbOnOnWPjIzMwEA0dHRDuujo6Nx6tQpexuDwYBGjRpVaFO6fWZmJqKioirsPyoqyt7GFfwrIiIiogZDp9fBGGiAIAiQpMovJex5c1v0vNm17Ggpvd7NWW5ZvqOJM2fOICwszL7sbna+LKHc5IWqqlZYV175Ns7au7KfshjQExERUYMx+rEBGP3YgDrZt7sBvdXCSbG0mFgqLCzMIaCviZgY2zc1mZmZiI2Nta/Pzs62Z+1jYmJgNptx+fJlhyx9dnY2evfubW+TlZVVYf8XLlyokP2vCgN6IiIiIg9o1zYWTzx2K3R62+RXOp0EvV6CTide/Wlbr9dL0OklhIUFat1lqqEWLVogJiYGGzZswPXXXw8AMJvN2LJlC+bMmQMA6NatG/R6PTZs2ICxY8cCADIyMnDw4EG88cYbAICUlBTk5OTg559/Rs+ePQEAO3fuRE5Ojj3odwUDeiIiIiIPSExsgsTEJlp3w79okKF3VX5+Po4fP25fPnnyJNLT0xEREYFmzZphypQpeO2119CmTRu0adMGr732GoKCgjB+/HgAgMlkwgMPPIBp06ahcePGiIiIwNNPP42OHTvaR71p3749hgwZgoceeggLFy4EADz88MMYNmyYyxfEAgzoiYiIiIgq2L17N/r3729fnjp1KgBg4sSJ+Pjjj/GXv/wFRUVFePzxx+0TS61fvx6hoaH2bd5++23odDqMHTvWPrHUxx9/7DCK0tKlSzFp0iT7aDjDhw+vdOz7ygiqWtWgTQ1Lbm4uTCYTcnJyal1bRURERORLfCnOKe1Lq+dfgxQQ4JVjysXF+O21533i8XsaZ4olIiIin7Lu481484H3te4Gkd9gQE9EREQ+5dyxTOzfckjrbhD5DQb0RERE5FN0eglWi1XrbhD5DV4US0RERD6lXc/WsJr9P6DfseEAcv7Ih9Uqw2qRcVtaH+jcHKueyBUM6ImIiMin9Bp6PXoNvV7rbtTap299jRO/nLMvD7izJ3R6jj3vwIeHrfQnLLkhIiIiqgMP/HUEmraItC/LVkXD3lB9xoCeiIiIqA507dsO4ZHXxiS3WmQNe+ObBNW7t/qKAT0RERFRHdHprtXMy1YG9FQ3WENPREREVEdE3bXcKTP0lajHmXNvYUBPREREVEdm/uthCKIASSdCFFkYQXWDAT0RERFRHTEE6LXugm/jKDcewVNFIiIiIiI/xoCeiIiIiMiPseSGiIiIiDThzeEkOWwlERERERH5JAb0RD6gILcIsswZBImIqIFRvXyrp1hyQ6SxXev3Y96TH2P0k4Nxx5+HaN0dIiJyUda5yzj/+wVYrQpkq4LENtGIbda4QrsFr3+JX/93BuYSK8xmK8zFFox/tD9SR/fQoNdUHzGgJ9LQ5awcvDJ+PszFFnz88kr0Sr0eTVtFa90tIiJywZYv0/HRm1/blx+dMQIjJvap0O7MyQs4cuCsw7q8K0V13j9/wBp6z2DJDZGGGkWb8I/NLyEi2oSSIjO2fblH6y4REZGLdDrHMKqymWCNxopj0ZeUWOqkT9QwMUNPpLEWHRIw9f0HocgKeg3ponV3qIE6eCYTv56/AFlRoKgqrIoCRVEhKypkRUHf9i3QNjZS624SaSLvSgFKCs2wmK2wlFhhtViR2L4pJL3k0E62Og/oDcaK4ZalxFonffU7nFjKIxjQE9WRH1bvxpU/8tB3RDeER4ZV2bbHwE5e6hWRcxsPHsf/fb+r0vsbhwYxoKcG64Ux7+DI3pMO6/59/G3odI4BvbXSgJ4ZeqpbDOiJ6siKeetw4pezaNu1ebUBPVFtvPryahz/LQuyrNhvd911A+5w44I7Say6AlNR6nFqi6gaemcZdrPVSUDvfLSyiMhQxMZHQG/UwWjUwRCgR3Rcozrpq99hht4jGNAT1RFRsgVIciVv8ESekpWdg7NnLjmsKygocWsfkihUef+u387ixusSEduIJ6fU8Nz/0mgU5hVBb9RDb9BBb9QhLCIEMQkRuHFwB+j0Ouh0Ilq2i3W6/X2TB+G+yYO83GtqSBjQE9UR4WqAxICe6pokVcyuuzuvQXUZ+q/Sf0Vql7YM6KlBSurZyun6zimt0TmltZd7Q1QRA3qiOiKWBvSy85pKIk9xHtC7991ydRl6AFDUevx9NRFpgsNWegaHrSSqI/YMfSXDmBF5iicy9KJQ/ceBrPDbJiIiX8SAnqiOiIKA4LBANI4Nr9H2RQUlWPOvLVg487+VDoVGBHiq5Kb6DD0DeiLyONXLt3qKJTdEdSTlti549oMHERUf4fa2sqzgmdHv4LeDtpkFG0ebMOaxWz3dRaonvFFDDwAyR7ohIvJJzNAT1ZExTwyqUTCvqipenPC+PZgHgP+8v8ntAI0aDmboichvMUPvEczQE/kYQRBwY2pn7N3yq31dzsV8/PufG3D3pMEa9ox8VerQTrj++kRIkmi/NUts7NY+RKFiQC+JAkRBhE4UIIqiS1l8IiLyPgb0RD4o9Z7eOH0sE98s2Yb23ZojtFEwzh7PQn5OIUJMQVp3j3xMSkqbWu/jjp4dMLxbEnSiCFEUIAoCBCdBPhGRJ3GUG89gQE/kgwRBwKMvj8b9zw93OmU4kafpJQl6Saq+IRER+Rx+f0rkwxjMExERUXWYoSciIiIibXjzYtV6XHLDDD0RERERkR9jhp6IiIiINMGLYj2DGXoiIiIiIj/GDD0RERERaYM19B7BDD0RERERkR9zK6BfsGABOnXqhLCwMISFhSElJQXffPON/f6srCzce++9iIuLQ1BQEIYMGYJjx45Vu9+VK1ciKSkJRqMRSUlJWLVqlcP9VqsVL7zwAlq0aIHAwEC0bNkSr7zyCpSr05BbLBY8++yz6NixI4KDgxEXF4cJEybg/Pnz7jw8IiIiIvIm1cu3esqtgD4+Ph6vv/46du/ejd27d+OWW27BiBEj8Msvv0BVVYwcORInTpzAmjVrsG/fPiQmJmLAgAEoKCiodJ/bt2/HuHHjkJaWhv379yMtLQ1jx47Fzp077W3mzJmD999/H++++y4OHz6MN954A2+++Sbmz58PACgsLMTevXsxY8YM7N27F//9739x9OhRDB8+vIZPCxERERGRfxBUVa3V+UpERATefPNN3HTTTWjbti0OHjyI5ORkAIAsy4iKisKcOXPw4IMPOt1+3LhxyM3Ndcj0DxkyBI0aNcKyZcsAAMOGDUN0dDQWLVpkbzN69GgEBQXh008/dbrfXbt2oWfPnjh16hSaNWvm0mPJzc2FyWRCTk4OwsLCXNqGiIiIyB/4UpxT2pekx1+DZAzwyjHlkmIceu95n3j8nlbjGnpZlrF8+XIUFBQgJSUFJSUlAICAgGsviiRJMBgM2Lp1a6X72b59OwYNGuSwbvDgwdi2bZt9uU+fPti0aROOHj0KANi/fz+2bt2KoUOHVrrfnJwcCIKA8PDwmjw8IiIiIiK/4PYoNwcOHEBKSgqKi4sREhKCVatWISkpCRaLBYmJiZg+fToWLlyI4OBgzJ07F5mZmcjIyKh0f5mZmYiOjnZYFx0djczMTPvys88+i5ycHLRr1w6SJEGWZcyaNQt33323030WFxfjueeew/jx46s8AyspKbGfiAC2s0UiIiIiIn/idoa+bdu2SE9Px44dO/DYY49h4sSJOHToEPR6PVauXImjR48iIiICQUFB2Lx5M1JTUyFJUpX7FATBYVlVVYd1K1aswJIlS/DZZ59h7969WLx4Md566y0sXry4wr4sFgvuuusuKIqC9957r8rjzp49GyaTyX5LSEhw45kgIiIicmRVFFzIL0BGbh7OXslBidWqdZd8Gy+K9Qi3M/QGgwGtW7cGAHTv3h27du3CO++8g4ULF6Jbt25IT09HTk4OzGYzIiMj0atXL3Tv3r3S/cXExDhk4wEgOzvbIWv/zDPP4LnnnsNdd90FAOjYsSNOnTqF2bNnY+LEifZ2FosFY8eOxcmTJ/Hdd99VWx81ffp0TJ061b6cm5vLoJ6IiIhq7Lc/LmHYomvX962+bzySY6Kr2IKo9mo9Dr2qqg5lKwBgMpkQGRmJY8eOYffu3RgxYkSl26ekpGDDhg0O69avX4/evXvblwsLCyGKjl2VJMk+bCVwLZg/duwYNm7ciMaNG1fbd6PRaB+Cs/RGVFxk1roLRETkpyTRserAqtTjtLAHCKp3b/WVWxn6559/HqmpqUhISEBeXh6WL1+OzZs3Y926dQCAzz//HJGRkWjWrBkOHDiAyZMnY+TIkQ4XvU6YMAFNmzbF7NmzAQCTJ09G3759MWfOHIwYMQJr1qzBxo0bHS6kvf322zFr1iw0a9YMycnJ2LdvH+bOnYv7778fgG2c+jFjxmDv3r348ssvIcuyPesfEREBg8FQu2eJGoTiIjM+m/ctvlu1GwvWP4vQ8CCtu0RERH5GVy4BWTb5SFRX3Aros7KykJaWhoyMDJhMJnTq1Anr1q3DwIEDAQAZGRmYOnUqsrKyEBsbiwkTJmDGjBkO+zh9+rRDtr13795Yvnw5XnjhBcyYMQOtWrXCihUr0KtXL3ub+fPnY8aMGXj88ceRnZ2NuLg4PPLII3jxxRcBAGfPnsXatWsBAF26dHE43vfff49+/fq58zCpgTq48zd8vmATAODjOV/iydlj3d7H8QNnsGvTQSiyitvv64uwiBBPd5OIiHyYWO66QKvKgL5K3qxtr8cZ+lqPQ1+f+NL4rKSNVx5ahO3fHoAgCJi7ejLaXd/cpe3yrhTip6/24eCO49j0uW1StA+3voT41qybJKqJ7zccxG9HMiHLiv32yKRBMBgr5qGO7/sdv+w4goT2CZB0EoJCA5BfZEVBfrG9TdmBFpKvT0R4RLBXHgc1POdzcnHze9fmzfl0/BjckOgb1+f5UpxT2pfkR7w7Dv0vC+vnOPRuXxRLVJ89OvMO7PvxCIoLzZj//Of4xxdTIemqHqUJAPIu5+OdaUsd1slWua66ST7kxO8XYLHIUFQVqqI6/lRVKIoKVQWu79QMYrnaWqrcjh+P4vv1Bx3W3ffoLU4D+jNHzmPn1+lY8Nf/AABadWoGNbIRThzJrNAWAOYsuh/hES0932kiAFK5khsrS26qx9RyrTGgJyojqmkj3PPUECyatRYnfjmHtR//iFEP9qt2u4AgY4V1rJtsGKbPXInM7OrnsNi4ZhpEsfqTQ7KRpIpjNsiy839TFrMFOv21jzNJJ8LCAIE0Uv6iWKWSi2IVVYWsKJAVFYIAGHUMyajm+NdDVM7I+2/GppW78PuvGfj079/gpmFd0CQmvMptwiNDsezgHJw9noWDO49DkkSER9avr/PIOcHFrLvC6ka3uBPQm0us0BuunSyVHxWNyJtCjUbMHzUMOlGEJAr4+5afMHnNV1AUFVZFsQfyZd8RejdvhsV3j9asz+T/GNATlaPTS/jzrDvx9Oh/oKigBDvWH8SwCX2q3EYURYQ3CUV4k1B0uKG1l3pKvqD8BXCVUTl0nVtENwL68CahSGgXh+v+KIAiK0hoE4OzuWYEBdu+OSt/qVj5kggiTzLqdBjSro19+d2tO5FfUvVwyA35hN+bw0ly2EqiBia5R0ukTUtFco+W6Ny7TfUbUINVfqbryjTkD+yacJqhtzoP6PuM6ok+o3ri3jruE1FNlC/BcUZmiSbVEtMU1CDs2vEbcnMK3dpm/OTBDOapWjHRJjSNC0dC00ZolhCB5s0ao0ViE7RuGQVTWKC9HQcUc487JTdEvsyVb4QadECvevnmhubNm0MQhAq3J554AgBw7733VrjvhhtucNhHSUkJnnzySTRp0gTBwcEYPnw4zp49615HXMAMPdVrF7JzseAfG/Dj5l8xeGgnPP387Vp3ieqZv8+qfL6CErMVslWGIAoIMOq92Cv/J0kVs5oM6MkfyIqCYqsVsqLAqqgunczLPOH3Sbt27YIsXxux7uDBgxg4cCDuvPNO+7ohQ4bgo48+si+Xn8x0ypQp+OKLL7B8+XI0btwY06ZNw7Bhw7Bnzx5IkucGSmBAT/WSbFWw6j+78MmiH1BUZKtd/Pbr/2HgkE7o3DXRo8dSVdVhrGxZtl3wFBxkhF7PUU0aMqNBBxj4NlsTg27rgo7XJ0KSRPstMpoXmpPvsFqs+HTOFyjILUJBbhFad2qG0Y8PxP7zmRj36Qq39lXZSDgNgS/X0EdGRjosv/7662jVqhVuvvlm+zqj0YiYmBin2+fk5GDRokX49NNPMWDAAADAkiVLkJCQgI0bN2Lw4MHudagK/KShemnGsyuwa+eJCuvnvfk1Fn78kNOxrN1VWFiC28b+o9I34vlvjken5PhaH4eoIWrZJhot23BiNvJdoiTi3++ss2fgC3OLMPrxgTW66JoZeu/KzXUcathoNMJorDj8dFlmsxlLlizB1KlTHa6d2rx5M6KiohAeHo6bb74Zs2bNQlRUFABgz549sFgsGDRokL19XFwcOnTogG3btjGgJ6rOgMEdnQb0Z89cwrIlP2HiAzc72co9kiRWmVVheQARUf0liiICQ4wozLPNSFyQWwQASGwUjndGDoUoiNCJIkRBwM9nzuJSQSGkq0NZSoII6ep9OlFEdGiIlg9FWzWoba/VsQAkJDjO3PvSSy9h5syZVW66evVqXLlyBffee699XWpqKu68804kJibi5MmTmDFjBm655Rbs2bMHRqMRmZmZMBgMaNSokcO+oqOjkZnpfOK7mmJAT/VS/4HJ+PqLdOzfd6rCfcs/3YZ+tyYjsXmTWh3D2UV7ZTGgJyKq324Y0hlWs4zgsEAkXGcruwgPDMDQ9m0d2t3ShjMT+5IzZ84gLOxaCV912XkAWLRoEVJTUxEXF2dfN27cOPvvHTp0QPfu3ZGYmIivvvoKd9xxR6X7UlXV5RHSXMWAXiM/bj+GWXO/si0IpT9sv5S+xqUv9vL/exihIQHe7qJfEwQBf35qMB697/8qBNZWq4J33vwab81Pg+jipEDOVDd5DQN6IqL67S8LHtC6C1QDYWFhDgF9dU6dOoWNGzfiv//9b5XtYmNjkZiYiGPHjgEAYmJiYDabcfnyZYcsfXZ2Nnr37l2zzleCAb1GZEVBUbFF627Ua81bRmLStCHYt+d35FwpRE5Ooe3nlUIc2H8Gm749gIGpnWq8f1EUIIpCpWU3Vgb0RJo7eu4CXlvxHRRFhawqUBQVbeMjMfOeQdVvTER1zpcvii310UcfISoqCrfddluV7S5evIgzZ84gNjYWANCtWzfo9Xps2LABY8faRkTLyMjAwYMH8cYbb9SsM5VgQK8RXv/iHUOHX4+hw693WKeqKgoLzR4ZgUYniTArstP7mKEn0l5hiQXpJ847rDNw9CkicpGiKPjoo48wceJE6HTXwub8/HzMnDkTo0ePRmxsLH7//Xc8//zzaNKkCUaNGgUAMJlMeOCBBzBt2jQ0btwYERERePrpp9GxY0f7qDeewoCeGhxBEBAcXH29nCskSQQstoC+ebPGiGgUDJ1OgiQKaGQK8sgxiKjmnM3S2ZCHCCTyORpcFOuOjRs34vTp07j//vsd1kuShAMHDuCTTz7BlStXEBsbi/79+2PFihUIDQ21t3v77beh0+kwduxYFBUV4dZbb8XHH3/s0THoAQb0fsHTF06Q58x9bRwEQYAkCYiNCee1Dg3Q+OaPozC3CGkvjsHoKVV/HUveJzp5/5QZ0BORiwYNGuR0crDAwEB8++231W4fEBCA+fPnY/78+XXRPTsG9Brp3aMl/vPxo9dKb67+UrqsljmNDAo0gHxTUru46htRvTbgTzdBlES07d5K666QE87GBGeGnsiH+HiG3l8woNeI0ahHJKeCJ/J79//tbq27QFVwNpKVotb/61t+/ukYTp+8ALPZCkuJFWazFSUlVljMVphLrJjy19thDOBnEFF9wYCeiIjqLWcBfUMouVn/RTp+3HSo0vsfnTaEAT35BH8Y5cYfMKAn8pD8nEJcyrwCRVGhKipUVUVk00YIbdSAZwAkv6aqKqwWGbJFhizLkK0KJL2E4NBArbvmMklomCU3BkPVH+/mEquXekJE3sCAnshDfly9G+9M+cRh3dR/3odB42/UqEdEtXPq1/N49KaXHdYNvLs3ps2/V5sO1YDzDH39L7mJaFJ1IsFcwnlQyEewht4jGNATeYikl2AMMkAQbBNOCYIAnY7jXZP/knQVs9tWi/N5F3yVs1FulAYwEciYtN4wGHUwGHQwGHX4Zf8ZbP3uMABg4mP90TjK9Vkyicj3MaAn8pBB429kNp7qlfDIMDz08hhIOgmSXoIkiWjaKlrrbrmlodbQhzcKxoRH+tuX9XqdPaBvmtAYAayf19TBn45gxVtrocgKgk1BeP7TJ7XuEvk5BvRERORUaHgwRj8xSOtu1IrzYSvrf8lNebekdsINfdvCYNQhOMQzE+tRzeX8kYv9Ww5D0omIiA7XujuaElQVgpe+NfPWcbTAgJ6IiOotpyU3DSBDX15wiJGBvA+5cUQPrL3UQ+tuUD3CgJ6IiOotyek49A0voCfPKrJYUGS1wKIoMMtWlMgyzBVuVphlGSWKDIuz+xXbSENTe/bR+NFojBfFegQDeiIiqrdEZyU3DOiplh74ehW2nTtd6/3oRZEBPXkEA3oiIqq3nGXo3Rm2UlVVyLICVQX0eo5aVROyoqCkNFt9NXNdcvX3IL0eLUyNtO6i2wySZ/4WLIoCRVWdloY1FJxYyjMY0PuxD/+1BZlZOVBVFapq++ABbD//MnUogoNZL0lEDVtIgBGbX38UoihAEkXbz2qCp/0HzuDp5/8NWVHs9fY33tAas166wxtd9ht7s85jzs4fKgTqtmWr/adcxTciAxJb4f+GjPJirz3DUwE9AFgUGUaJ4RjVDv+C/NjOXSfw24lsp/c99eRgL/eGiMj3iKKA8BD3ZrYVBMBidRxvv7qs/s6v9uDE/05j9FO3wRBgcLufzuQVFOPCpXxYrDLMFhkxTcIQGeE7M0/nmUuwM+NsrfZRIvvnjLWeDOjNcgMP6FlD7xEN+C/I/1WVZFLr819tPXMk+w9cKSpCaRKr9LUrm9QqXdciohHiTJ6fEOb9Iz/gXOHlin0o20h1XFe+n2X/5ia1vwXxwf73NTq5rqjYjH98tBnK1Sy2rCiQZRXhYYF46sFbte5erUhSxbp7Wa76PTWqWRMoigrRybY1tWnHUcz5vw325SkT+mNcaleP7b+29GLtg9pia8MO6OfemopAHecEoNpjQO/HhHIR/ZBBHRFg1EMQAKOBL62/+Pv3W7H5+EmX2r4wqB8m9Lje4334PvMIDlw+57H9TWydgngwoK/PZFnFFxv/V2F9bFSY/wf0Ti6kleWqM/QtOiaiRcfEWh33dMYl/HXelzBbrDBbZOQXljjcb7b4VvBr9EBQ67cZ+lqezNyU0BzxoWHoEdsUOid/b0TuYtTnx8oH9A8/cDMahQdr1Buia1SOIlLvOZuBFagfs7A6z9DX/WRUiqLi+OkLld5vtsiV3qcFvQcCen/N0A9v0x5JTaJgkKQyN539d6PofL1BkmC8uq4hXwhbFi+K9QwG9H6s/HsBYyj/5M5bel0FyvxYqZ9OHc9CYV4xIAj294tQUxCaNm9S6307Gz0GqB+TNmkV0Bv0VX8kl6/r15qrWerY4FAE6HQwShKMOh0CJN3VZR3iQ0113Mu60Tu+GXrHN9O6G0R2DOj9mFA+DGNE75fGXt8RN7ZMxLrDx3BbUlu8veUn5BaXVL+hB1X4W/JRpSM6CULFb6ioogWzvsD+Hb85rEu5NQkvvptW6307G98dABQ3hoT0VZJOq4C+6gDZbPatbHZlGfq4kFAE6fQI1hsQERiIj1JHe7ln5Fd4UaxHMKD3Z+XiGU6W4p9uva4VAGDAda0QZwrDB9t3VRrQ/5KZjS9/OYKwACP6tmruxV66pyZ/iWu/+x/e/uR72OJBW+Cu2MZjhXI1kC+1efFkn7xO5GLGFXzy+lrojToEhwXivhf8bzg+VznLYgPVXzzqD5w9Nm9881DdOPdmn8vQV3yeBAA/jX+YJ9xEXuZ7n4jkMpbc1C+ujF6z+sBhrD5wGG2jmng0oPeFz16rrKK4xLUMpK+O4pRzMQ/fLt0KADA1DtE+oHfypuCpQKvyGvp6kKHXqOTGWF3JjY/V0BucDLWoAjBzXHVyA2voPYP/4vxYhQ9mRvT1ghax9cDYJCSZYh16UPrnVbYcp/S30r+98veUbtPY6P7F2ZUFiM6oPhozWsqUROh94BsEZ28Jnjx5k0ShwkWw9aKG3tkoN144Uak2Q+9jo9wE6vQY2Lw1DGLphZ62Gz+KiLxP+08cqrF77krBlZxCCLAFWCGhAVp3ibzk2IWLeHrNN3hrRKpH9ndfm94e2U9tuHVxsI9m6MsG9DofCOi73tgGkTGma7NIA7iuQ7zH9i+KImTFMWtcH2roG4UHYeE/JkCSxKs3AUZD3Y8VLoki+nZvDUkSYdBJ0OslGHQSdDoJBr2Eti2i67wP7ggzGvHh4JFad4P8HWvoPUL7Txyqsd43tNa6C1QHXCmJCDUa0Ckuxgu98R7BrQy9b74rN20ZjWnv3gurWYYx0DOzhdbGuIf71en+JUlA+aRxfaih1+sltG2jzb+vOdNGaHJcIvJvDOiJfEyI0YCwACOAiiUupdpENq6TCaa0VFU4Lwi20h9BtBUA+WrI2CgqDAPv0v7bDm+ZN3MsAFvpjSiKEEXBabkKEVFV6nNtu7cwoCfyMV88VPshBf1Rat9kDO7THoIgXL0B4tXfyTd1uC5O6y4QEREY0BORj9BJIlDJUIhERERUOQb0RERE5HMu/ZGHC5k5sFpkNIkOQ3RcI627RHXh6nwjXjtWPcWAnoiIiHzOhjV78dE7GwAA9zzaH2mP36pxj4h8FwN6IiIiquD8mYs49/tFWK0yQsMC0aFbcwDAF//ajLWLNkO2KmgUFYa/f/F0nRxfp7s2Lr9s9f/hUMk5TizlGQzoiYiIqILvv/4fPv3nJgBA8vWJ+PvihwAAuZcLcPZ4FgDAXGKps+Pryky05Wuz5BL5Ggb0ROSTth09hZzCYqiqCllRoagKmoSG4Ma2iVp3jahB0OmuXaQuW+Uy68tkzusw0HbM0DOgr7c4sZRHMKAnIs39kVeAjCt5UFUViqJCUVX8bdV3OP3HFYd2KW2a1Tigf+yGF5B56gKmLngQN43s4dI2s5/8FKePZUFRVaiKCkVWoKgqXvrgPvxnzmqkvTQGUQlNatQfX5RXVIK8ohLIqgJFUdE4NAghV+dEoIZHksoE1PK1khepTOa8To9f5oTCygw9UZUY0BOR5r7edwRvfLGl2nZqLUYoKMovRmFuESxulAicP/UHfj+aWWG9pcQK2SrXu7reRd/twqLvdtmXpauz9w7o2AZvTbhNq26RRspm6K1l/tav79sOU95OQ0CQASHhQXV3/DInDlY3M/QlRWY8P+x1NImLwPRP/+zprpEHCYrt5q1j1VcM6IlIc6Lo2uRRtfm2tDTb506JQGWTWimKClESocj169Oh/OsgK7ZnPP3385j2yZcYm9IJvdo006JrpAGpkpKXVh0S0KpDQp0fv2zJjbsZelEScfCnI4htGV1mH1asfG8DZKuC5u3jUJhXDEuxBRazFYPuuREBQfw2ivwXA3oi0pzo4mywSi0y9E9/8DCsZivi28S6vE3ztrGQdBJEUYAoChCu/gwMNiIiNhxiPZsIq7LXISsnH+v3H0PKdYno1cbLnSLNVJah997xy2bo3Tu+Ti+hTdcWiIxvbF9XmFeMj/+2GqqqIqlHKxzaedx+X8rQLgzoya8xoCcizbka0Nek5OarZTuwd+tRqIoKFcBNqZ1wy/DrXdp26hvjKr3v/r/d7XZffJ1Uzeuw5Ie9+Db9KFRVRdMIE14eN9BLPSMthDcOsf8uaXDy6lBy42aGXhAEvLvtbw7rnh011/4eIkqOf+us0dcQL4r1CAb0RKQ5VwP60hIQd5w4fB7bNvxiX27RNsbtfTQUolh10PZb1iX8lnUJANA6ptAbXSINJbSItP9uDNB7/fhBIUbEJkRAr5fQOCrMpW22rd2NAz8dBQC07pKIW+++0X6fMdBg/z0mMRJBIQFo2SEBoeHBdXotAJE3MKAnIs25WkNfo5KbcicLSg1OChoKV0+sgNqVP5F/KDvKjKzB9SKdurfAR19NdWub/VsOY/V76wEA/e68wSGgb9UhAaqiQBBFDJ3YF0k9W3m0v1QznFjKMxjQE5HmCkssaNYkHM0ah0MQbF+XHzyThUv55bLAtY/n6/VXrrXl6okVwBOjhqBxZCje/vQRSDoRAYHez9DXxIB7+qBdz9ZQVRVRCY0d7nvyrXs06hVR3WNAT0Sau1RQiBHdkvDIgF72dU9+tAbfHzrh0K4mWeHyWefaDH1Z31VXQ1+WrNSvEX6oIoNRj/ad6340G09q07UF2nRtoXU3yB2qart561j1FAN6ItKcKAgVgvU59wyFoigQBQHC1Zs7Aadd+ZKbevyGXlsDO7fBdXGREAXbaD6iIKDQbMET/7e6Qls+j0REvoMBPRFpTkDFgD7I4Jmv+G8bfwN69W8HCLYANTq+kUf2Wx81jTChaYTJYZ3FKmPuxGG2IF8QIIm2k6tAD70+RNSwsYbeMxjQE5HmujR3fWx4dyW2jkZi6+jqG/qArNx85BQXQ1FVqKoKWVGhqGVuigJFBWRVsY2lHRuN8MAAjx1fURTs/ukYZKsCWVagyAoURcXA1E4eO0ZdO5VxCb9nXIJVVmCVbY/DKitIjG2Ezm2aat09IqI6wYCeiDR3UzvWvALAGxt/wJcHj7jc/uM/jUZKS8/N3KrIKl584lOHdYIgoJ8fBfTrdx7Bh6u3V1h/R/9ODOiJfBHHofcIt2aKWLBgATp16oSwsDCEhYUhJSUF33zzjf3+rKws3HvvvYiLi0NQUBCGDBmCY8eOVbvflStXIikpCUajEUlJSVi1apXD/VarFS+88AJatGiBwMBAtGzZEq+88gqUMhdlqaqKmTNnIi4uDoGBgejXrx9++eWX8ociIvJZgpvXCKge/nQqP9kOYHtvVfzoAlipkrH0tRh2kYjIW9wK6OPj4/H6669j9+7d2L17N2655RaMGDECv/zyC1RVxciRI3HixAmsWbMG+/btQ2JiIgYMGICCgoJK97l9+3aMGzcOaWlp2L9/P9LS0jB27Fjs3LnT3mbOnDl4//338e677+Lw4cN444038Oabb2L+/Pn2Nm+88Qbmzp2Ld999F7t27UJMTAwGDhyIvLy8GjwtRETe58448EDNJtqq8vii6HToSkX2n7SWTuf8Y83KgJ6I6jG3Sm5uv/12h+VZs2ZhwYIF2LFjB/R6PXbs2IGDBw8iOTkZAPDee+8hKioKy5Ytw4MPPuh0n/PmzcPAgQMxffp0AMD06dOxZcsWzJs3D8uWLQNgC/pHjBiB2267DQDQvHlzLFu2DLt37wZgyyDNmzcPf/3rX3HHHXcAABYvXozo6Gh89tlneOSRR9x5mEREmrjz+g7o1TwBogCIguhwEeqzq9ehxOo4PX1djDQjSSIUxfE4VqsMnV7y+LHqQqUZej/6loGoIeFFsZ7hVoa+LFmWsXz5chQUFCAlJQUlJSUAgICAaxdoSZIEg8GArVu3Vrqf7du3Y9CgQQ7rBg8ejG3bttmX+/Tpg02bNuHoUdt0zvv378fWrVsxdOhQAMDJkyeRmZnpsB+j0Yibb77ZYT/llZSUIDc31+FGRKSVHonxGN0lGaM6J2NEp/a4vWM7DE1ui9Sk66CXKgbUdRHQi1LFjwV/mkRK56T/ADP0RFS/uX1R7IEDB5CSkoLi4mKEhIRg1apVSEpKgsViQWJiIqZPn46FCxciODgYc+fORWZmJjIyMirdX2ZmJqKjHUegiI6ORmZmpn352WefRU5ODtq1awdJkiDLMmbNmoW7777bvo/S7crv59SpU5Uee/bs2Xj55ZfdfQqISCOlk0K5W2teHzgbg78uAvoBt18PWZYhSRIknQhJcl6G46ukSgJ61tAT+ShOLOURbgf0bdu2RXp6Oq5cuYKVK1di4sSJ2LJlC5KSkrBy5Uo88MADiIiIgCRJGDBgAFJTU6vdZ/kPZ1VVHdatWLECS5YswWeffYbk5GSkp6djypQpiIuLw8SJE13eT3nTp0/H1KlT7cu5ublISPCvWfGIGpIJrf+M2eteQHybuhvm0lc5ey+ri4D+yRnDK71PURTs2fIrREmEKIrQG3To0LOlx/tQG8zQE1FD5HZAbzAY0Lp1awBA9+7dsWvXLrzzzjtYuHAhunXrhvT0dOT8f3v3Hh5Vde9//DOZJJMEk0BCriQgIiiSUNpgI/GCJECkKlrlgNiDcOQg/jRFK9QqFaWKcFALaixFW4VyKQheHuyR8kCKeqRcipFWiIJRRAIkxCokYCEhmfX7I2SaIZMbjLNnhvfrefZTZs+avfd8u118+c7aa1VVqba2VgkJCcrOztbAgQNbPF5ycrJbNV6SKisr3artP//5z/XQQw/ptttukyRlZmbqyy+/1Jw5czR+/HglJydLaqjUp6SktHicMzkcDjkcjo6GAIBFBv/HIIWFn5+z7XoaG+7r1Vrr65x6dPxLrtfRnaO06qPZPr2GtoQyhh4IKIyh946zHkPfyBjjGj/fKDY2VgkJCSotLdUHH3ygm266qcXPDxo0SBs2bHDbt379euXk5Lhe/+tf/1LIGZ203W53TaXWs2dPJScnux2ntrZW7733nttxAAS2//6f/1RSjwSrL8MSnka9+DpJPXP6Sk/j7a3WUoW+PoBm6gGAjupQqWv69OkaMWKE0tPTdezYMa1cuVLvvvuu1q1bJ0lavXq1EhIS1L17d+3cuVP33Xefbr75ZreHVe+44w5169ZNc+bMkSTdd999uuaaazR37lzddNNNWrNmjYqKitwepL3xxhv15JNPqnv37urXr5927NihefPm6c4775TU8FP0/fffr9mzZ6t3797q3bu3Zs+eraioKN1+++3nHCQA8KUvvj6ibfvKVO9sWDHWaYxOnKpr1s7Xw0Hr685I6FuohlsprnMn/eCSNNntIQq1h2h/xREd/KpKdfX1bX8YgO+xsJRXdCihP3z4sMaNG6fy8nLFxsaqf//+WrdunYYNGyZJKi8v1wMPPKDDhw8rJSVFd9xxh2bMmOF2jP3797v9JZCTk6OVK1fqkUce0YwZM9SrVy+9+uqrys7OdrUpLCzUjBkzdM8996iyslKpqamaPHmyHn30UVebBx98UCdOnNA999yjI0eOKDs7W+vXr1d0dPRZBQYArPKPg+V67O2/tNnO10NunGeMQ7e3MOf7d2Hjqq3a/PYO1Z2qV1ZuP8k49fbvNqqurk7xyV00d+1DkqQfXtZdP7zs36vnLnhtkxb/799UF0Az9QDwDzNnzmw2eUrTiVuMMfrVr36ll156yZV7/uY3v3FN3y41zKg4bdo0rVixQidOnFBeXp4WLFigtLQ0r15rhxL6l19+udX3p0yZoilTprTa5t133222b9SoURo1alSLn4mOjtazzz6rZ599tsU2NptNM2fO1MyZM1s9PwD4O7utfYmyrxP60DC7fvSfOXLWN6wee0FslM/O/eXuQ9r0VrEkqUtijOKTYvRFSZkkqfbEqRY/Zz89Vqm+jgo9gI7r16+fioqKXK/tTaYQblzUdPHixerTp49mzZqlYcOGac+ePa6C8v33368//elPWrlypeLj4zV16lTdcMMNKi4udjvWuTo/ny4DAD+WcjxEt37VWSG2ENnUsIKszSYlJMVqwtQRCrHZFGKztfgA6HclIsqhn84e7dNzNmq6sFXdqTqNvCtPuWMGyR5qb/VB6S4xUeqe3EWJcfxaC/gjf38oNjQ01DX5SlPtWdS0qqpKL7/8spYuXaqhQ4dKkpYtW6b09HQVFRUpPz//nL6P23V67UgAAK+oqjimHX/+uNn+Hr2T1OnhlicZCGY9Lk3VVTdlyW6369KsixQTH62Y+LaT9NFDv6/RQ7/vgysEEIxKS0uVmpoqh8Oh7OxszZ49WxdddFGbi5pOnjxZxcXFOnXqlFub1NRUZWRkaPPmzST0ABDMTAtPbp2PC2o1uubHl+uaH19u9WUA8Danadh8dS41rDvUVEvTmGdnZ2vJkiXq06ePDh8+rFmzZiknJ0clJSXtWtS0oqJC4eHh6tKlS7M2Z07Zfq78b4oCADjPtTg0/vzN5wHAa9LT0xUbG+vaGmdePNOIESN06623KjMzU0OHDtXbb78tqWFoTaOOLmra3jYdRYUeQEDYV3pYez+tkCMizLUlpnRWUmpnqy/N6wZfP0BX5F4mY8zpVdEbMvwQT5PRA0Ags2DayrKyMsXExLh2t3eR0U6dOikzM1OlpaW6+eabJbW+qGlycrJqa2t15MgRtyp9ZWWl19dJIqEHEBC2vrdbiwuL3Pb9x4SrNPFn3huD6C/CHaEKd7Svez7wTZUm/+FNOU/PV9+4GWPkdBrVm3/PZe88vW/GyFzdOKDvd/wtAMA/xcTEuCX07VVTU6NPPvlEV199tduipt//fsNzOo2Lms6dO1eSlJWVpbCwMG3YsEGjRzdMKFBeXq5du3bpqaee8t4XEgk9gABx8mTzqQkdkWEWXIl/qXM69cU/j3ToM7VM4QjAT9jkw1luOth+2rRpuvHGG9W9e3dVVlZq1qxZqq6u1vjx49u1qGlsbKwmTpyoqVOnKj4+XnFxcZo2bZprCI83kdADCAi1nhJ6Bwl9yFmMwzS+XmIWAALQgQMHNHbsWP3zn/9UQkKCrrjiCm3dulU9evSQ1L5FTefPn6/Q0FCNHj3atbDU4sWLvToHvURCDyBA1HhK6CNI6O1nMa7e1wtSAUAgWrlyZavvt2dR04iICBUWFqqwsNDLV+eOhB5AQKipqXP92RERpvjEaMV09t1Kpf7qbGZKIKEH4Dcanvz33bmCFAk9gIBQc7LW9edLM9M09/d3Wng1/uPshtx8BxcCALAMCT2AgHDLuCt1Ze5lqq2pU2yXTlZfjt84m4SeCj0Af2EzPnwoNoi7PhJ6AAGhb/909e2fbvVl+J3I8DDdktVPITabbDabQk5vNptN9hBbk/1y/blft0SrLxsA4EUk9AD8wv9tK1VNbV3Duh/GKCzMriGDLmnXZ7ft+lJ/en+XZE6vUdK4INPpHY3zshtJmRenaPz1P/wOv4lvRUc49Oj1QyRjFGIPUWhYQ7f+7bGTOnzoqOqdTjnrnKqvd8rpbPjf+oP/Ul1KvULDvDvLAgB0mAULSwUjEnoAfuGphet1tPqE63XnmMh2J/T7K45o/dY97Wr79z0HdWvu93RBZPtWBgwE/++Hv9SBT8uVNSxTs996UJJUvOUzzX5wVYufWf1/Dys6LNJXl9hhn3zwhf68/K9y1jsVG3+B0nsnq98PL1L6xclWXxoA+B0SegB+qSPDvDsyjHz6fw1VlCO84xfkzzwEyx4a0upHnPXO7+pqvKJ8/z+1YdU2t31TnrqNhB4IMjZjZPPRcz2+Oo8VSOgB+IWQM+ZT79jiR+3P6CMdYc3OFejueHSUvq3+lxK6xbn29R/YU4V/vFsH93+t/3lodbPP1PtxQr/w0df1p0XvNdv/7bGTFlwNAPg/EnoAfsF2RlLekZlYOlKhD8YZXgaPym62LzomUtGXRbZYqffnhP7y3MvUJSFaqxcUKfeWgeqa0lmJ6fHq0ZvqPBB0nKc3X50rSJHQA/APZyblHci7r8i8UE9NGXl6FpeGxZbe+/AzrXlvV7O2QZjPtyoyKlx9+nWT3R4ie2iI7PYQhYTYFBrqvw/EZl3bV1nX9lW3ixKVPSxDYeH8VQUAraGXBOAXzlzx1HQgo++WEKtuCbFu+8oOH/XYtmNDeQJfSlqcnl8+2erLOCtXXT/A6ksAgIBAQg/AL5y5QNK55t3J8dG6/LLuroq97fQ87J2j/XdmFwA43/BQrHeQ0APwD2cMuTnXSvqQgb01ZGDvczoGAACBgIQegF9oNoQ+iCspAIDTWFjKK1qfqBgAfKTZGPog7ngBAPAmKvQA/ELzh2IBAEHPGN9VcIK4UkRCD8AvXHtFbx2tPtEwp7zNpjA/nlYRAAB/QkIPwC/cO/5aqy8BAOBjNtOw+epcwYox9AAAAEAAI6EHAAAAAhhDbgAAAGANHor1Cir0AAAAQACjQg8AAABL2JwNm6/OFayo0AMAAAABjAo9AAAArMEYeq+gQg8AAAAEMCr0AAAAsIY5vfnqXEGKCj0AAAAQwKjQAwAAwBI2Y2Tz0dh2X53HClToAQAAgABGQg8AAAAEMIbcAAAAwBpMW+kVVOgBAACAAEaFHgAAANYwkpw+PFeQIqFvwpz+Kaa6utriKwEAAPCuxvzGBPHQk/MVCX0Tx44dkySlp6dbfCUAAADfjWPHjik2Ntbqy5DEtJXeQkLfRGpqqsrKyhQdHS2bzdZq2+rqaqWnp6usrEwxMTE+ukL/R1yaIyaeERfPiEtzxMQz4tIcMfGsMS779++XzWZTamqq1ZcELyOhbyIkJERpaWkd+kxMTAydhgfEpTli4hlx8Yy4NEdMPCMuzRETz2JjY/0vLkY+nOXGN6exArPcAAAAAAGMhB4AAAAIYAy5OUsOh0OPPfaYHA6H1ZfiV4hLc8TEM+LiGXFpjph4RlyaIyae+XVcWFjKK2yGuYsAAADgQ9XV1YqNjVXu936hULtv/qFRV1+jjf+Yq6qqKv97luAcUaEHAACANZySWp9Y0LvnClKMoQcAAAACGBV6AAAAWIKFpbzjvKjQf/jhhxo2bJg6d+6s+Ph43XXXXTp+/LjHtl9//bXS0tJks9l09OjRNo+9ZcsW5ebmqlOnTurcubOuvfZanThxolm7mpoaDRgwQDabTX//+9/d3rPZbM22hQsXurXZuXOnBg8erMjISHXr1k2PP/74OS/d7M9x+cc//qGxY8cqPT1dkZGR6tu3r5577jm3z+7bt89j7NatW9ehODTlzzGRpP379+vGG29Up06d1LVrV02ZMkW1tbVubYLtXhk5cqS6d++uiIgIpaSkaNy4cTp06JDr/cWLF3u8D2w2myorKyUF373SVkyk87NfaSsuVvUrkn/HRbKmb7EqJvv27dPEiRPVs2dPRUZGqlevXnrsscfcvq9V/Yq/x0Wyrm9B64I+oT906JCGDh2qiy++WNu2bdO6detUUlKiCRMmeGw/ceJE9e/fv13H3rJli6677joNHz5cf/vb37R9+3YVFBQoJKR5WB988MFWV2ZbtGiRysvLXdv48eNd71VXV2vYsGFKTU3V9u3bVVhYqGeeeUbz5s1r13V64u9xKS4uVkJCgpYtW6aSkhL98pe/1MMPP6wXXnihWduioiK32OXm5rbrOs/k7zGpr6/X9ddfr2+//VabNm3SypUr9frrr2vq1KmuNsF4rwwZMkSrVq3Snj179Prrr+vzzz/XqFGjXO+PGTPG7f//8vJy5efna/DgwUpMTHQ7X7DcK23FpNH51q+0FRcr+hXJ/+NiRd9iZUx2794tp9OpF198USUlJZo/f74WLlyo6dOnu45hRb8i+X9cGnm1b2mc5cZXW7AyQe7FF180iYmJpr6+3rVvx44dRpIpLS11a7tgwQIzePBg85e//MVIMkeOHGn12NnZ2eaRRx5p8xrWrl1rLr30UlNSUmIkmR07dri9L8m8+eabLX5+wYIFJjY21pw8edK1b86cOSY1NdU4nc42z+9JIMTlTPfcc48ZMmSI6/UXX3zRrs+1l7/HZO3atSYkJMQcPHjQtW/FihXG4XCYqqoq13UF473S1Jo1a4zNZjO1tbUe36+srDRhYWFmyZIlrn3BeK805Skm52u/0lRb94ox332/Yoz/x8WKvsXfYvLUU0+Znj17tvi+L/oVYwIjLt7qW6qqqowkk9fv5ya//yM+2fL6/dxIct3XwSToK/Q1NTUKDw93q1ZERkZKkjZt2uTa9/HHH+vxxx/XkiVLPFZNz1RZWalt27YpMTFROTk5SkpK0uDBg92OKUmHDx/WpEmTtHTpUkVFRbV4vIKCAnXt2lWXX365Fi5cKKfz349ib9myRYMHD3abPzY/P1+HDh3Svn372rxWTwIlLk1VVVUpLi6u2f6RI0cqMTFRV155pV577bV2HcsTf4/Jli1blJGR4Va9z8/PV01NjYqLi11tgu1eaeqbb77R8uXLlZOTo7CwMI9tlixZoqioKI8V62C5V5pqLSbnW7/SVHvuFem771ck/4+LFX2LP8VEavk+aOSLfkUKnLj4um9B24I+oc/NzVVFRYWefvpp1dbW6siRI66fj8rLyyU1/Ac0duxYPf300+revXu7jrt3715J0syZMzVp0iStW7dOP/jBD5SXl6fS0lJJkjFGEyZM0N13362BAwe2eKwnnnhCq1evVlFRkW677TZNnTpVs2fPdr1fUVGhpKQkt880vq6oqGhnJNwFQlya2rJli1atWqXJkye79l1wwQWaN2+eXnvtNa1du1Z5eXkaM2aMli1b1u44NOXvMfF0H3Tp0kXh4eGu+yDY7pVGv/jFL9SpUyfFx8dr//79WrNmTYvHfeWVV3T77be7/hKUguteadRWTM63fqVRR+4VX/Qrkv/HxYq+xR9i0ujzzz9XYWGh7r777haP64t+RQqMuHi9b2HIjXdY/AvBWXvssceMpFa37du3G2OMWb58uUlKSjJ2u92Eh4ebadOmmaSkJDN37lxjjDE/+9nPzJgxY1zHfuedd9r8+eqvf/2rkWQefvhht/2ZmZnmoYceMsYY89xzz5mcnBxTV1dnjGn/z3PPPPOMiYmJcb0eNmyYueuuu9zaHDhwwEgyW7ZsCfq47Nq1yyQkJJgnnniilag1KCgoMJmZmUEZk0mTJpnhw4c3O35YWJhZsWKFMSb47pVGX331ldmzZ49Zv369ufLKK82PfvQjjz/zb9682UgyH3zwQYvnbRSo90pHY9Io2PuVjsblXPuVYIqLN/uWQIqJMcYcPHjQXHzxxWbixIktHvNc+xVjgjMujc62b3ENublsmsnP/KVPtrzLphkpOIfcBGxC/9VXX5lPPvmk1e3EiRNun6moqDDHjh0zx48fNyEhIWbVqlXGGGO+973vmZCQEGO3243dbjchISFGkrHb7ebRRx/1eP69e/caSWbp0qVu+0ePHm1uv/12Y4wxN910k9tx7Xa767h33HFHi99t06ZNRpKpqKgwxhgzbtw4M3LkSLc2H374oZFk9u7dG9RxKSkpMYmJiWb69OktxqupZcuWmYiIiKCMyYwZM0z//v3djvHNN98YSWbjxo3GmOC7VzwpKyszkszmzZubvXfnnXeaAQMGtPjZpgL1XuloTBoFe7/Skbh4o18Jprh4s28JpJgcPHjQ9OnTx4wbN85tzPqZzrVfCda4NDrbvsWV0PedavIzpvtky+s7NWgT+oCdh75r167q2rVrhz7T+JPPK6+8ooiICA0bNkyS9Prrr7tN8bV9+3bdeeedev/999WrVy+Px7rwwguVmpqqPXv2uO3/9NNPNWLECEnS888/r1mzZrneO3TokPLz8/Xqq68qOzu7xevcsWOHIiIi1LlzZ0nSoEGDNH36dNXW1io8PFyStH79eqWmpurCCy90+2wwxaWkpES5ubkaP368nnzyyXZ9lx07diglJcVtX7DEZNCgQXryySdVXl7u+o7r16+Xw+FQVlaWq00w3SuemNM/mdbU1LjtP378uFatWqU5c+a067sE6r3iSUsxaSrY+xVPPMXFW/2KFDxx8WbfEigxOXjwoIYMGaKsrCwtWrSoxXHo3uhXpOCLS1Pn0rfAi6z994RvFBYWmuLiYrNnzx7zwgsvmMjISPPcc8+12N7Tz1cHDhwwl1xyidm2bZtr3/z5801MTIxZvXq1KS0tNY888oiJiIgwn332mcfjehpG8dZbb5mXXnrJ7Ny503z22Wfmd7/7nYmJiTFTpkxxtTl69KhJSkoyY8eONTt37jRvvPGGiYmJMc8888zZB8X4d1wafw7/yU9+YsrLy11bZWWlq83ixYvN8uXLzccff2x2795tnn76aRMWFmbmzZsXlDGpq6szGRkZJi8vz3z44YemqKjIpKWlmYKCAlebYLtXtm3bZgoLC82OHTvMvn37zMaNG81VV11levXq5TaDgjHG/P73vzcRERHmm2++aXY9wXSvtCcm52O/0p64WNWv+HtcrOpbrIpJ43CS3Nxcc+DAAbd74Uy+7leM8e+4eLNvcVXoL5lq8i+b7pMt75LgrdCfFwn9uHHjTFxcnAkPDzf9+/d3m3bKE0//cTQmWO+8845b2zlz5pi0tDQTFRVlBg0aZN5///0Wj+spSfvzn/9sBgwYYC644AITFRVlMjIyzLPPPmtOnTrl9tmPPvrIXH311cbhcJjk5GQzc+bMs55arpE/x6Wl8YY9evRwtVm8eLHp27eviYqKMtHR0SYrK6vZz4kd5c8xMcaYL7/80lx//fUmMjLSxMXFmYKCgmaJbTDdKx999JEZMmSIiYuLMw6Hw1x44YXm7rvvNgcOHGh2zkGDBrU4zCCY7pX2xOR87FfaExer+hVj/DsuxljTt1gVk0WLFrU4jv1Mvu5XjPHvuHizbyGh9y6bMcH8yC8AAAD8TXV1tWJjYzW0zwMKtTva/oAX1NXXqOjTeaqqqlJMTEyrbefMmaM33nhDu3fvVmRkpHJycjR37lxdcsklrjYTJkzQH/7wB7fPZWdna+vWra7XNTU1mjZtmlasWKETJ04oLy9PCxYsUFpamle/W9BPWwkAAAB0xHvvvad7771XW7du1YYNG1RXV6fhw4fr22+/dWt33XXXua2au3btWrf377//fr355ptauXKlNm3apOPHj+uGG25QfX29V683YB+KBQAAQIDz5fzwHTjPunXr3F4vWrRIiYmJKi4u1jXXXOPa73A4lJyc7PEYVVVVevnll7V06VINHTpUkrRs2TKlp6erqKhI+fn5Z/ElPKNCDwAAgPNGdXW129baDGGNqqqqJKnZyrnvvvuuEhMT1adPH02aNEmVlZWu94qLi3Xq1CkNHz7ctS81NVUZGRnavHmzl75NAxJ6AAAAnDfS09MVGxvr2tqaltQYowceeEBXXXWVMjIyXPtHjBih5cuXa+PGjfr1r3+t7du3Kzc31/UPhIqKCoWHh6tLly5ux0tKSjrrFblbwpAbAAAAWMNpJJuPhtw4G85TVlbm9lCsw9H6Q7kFBQX66KOPtGnTJrf9Y8aMcf05IyNDAwcOVI8ePfT222/rlltuafF4xhjZbLaz+QYtokIPAACA80ZMTIzb1lpC/9Of/lRvvfWW3nnnnTZnpklJSVGPHj1UWloqSUpOTlZtba2OHDni1q6ystK1cJi3kNADAADAGo0Pxfpqa/dlGRUUFOiNN97Qxo0b1bNnzzY/8/XXX6usrMy1WnBWVpbCwsK0YcMGV5vy8nLt2rVLOTk5HY9VKxhyAwAAADRx77336o9//KPWrFmj6Oho15j32NhYRUZG6vjx45o5c6ZuvfVWpaSkaN++fZo+fbq6du2qH//4x662EydO1NSpUxUfH6+4uDhNmzZNmZmZrllvvIWEHgAAABbx4bSVav95fvvb30qSrr32Wrf9ixYt0oQJE2S327Vz504tWbJER48eVUpKioYMGaJXX31V0dHRrvbz589XaGioRo8e7VpYavHixbLb7V75Ro1YKRYAAAA+5Vop9qIpCg3x0UqxzhoV7X2+XSvFBhoq9AAAALCGny4sFWh4KBYAAAAIYCT0AAAAQABjyA0AAACs4TTqyMOq536u4ESFHgAAAAhgVOgBAABgDeNs2Hx1riBFhR4AAAAIYFToAQAAYA2mrfQKKvQAAABAAKNCDwAAAGswy41XUKEHAAAAAhgVegAAAFiDMfReQYUeAAAACGAk9AAAAEAAY8gNAAAArGHkwyE3vjmNFajQAwAAAAGMCj0AAACswUOxXkGFHgAAAAhgVOgBAABgDadTktOH5wpOVOgBAACAAEaFHgAAANZgDL1XUKEHAAAAAhgJPQAAABDAGHIDAAAAazDkxiuo0AMAAAABjAo9AAAArOE0knxUOXdSoQcAAADgh6jQAwAAwBLGOGWMbxZ88tV5rECFHgAAAAhgVOgBAABgDWN8N7adWW4AAAAA+CMSegAAACCAMeQGAAAA1jA+nLaSITcAAAAA/BEVegAAAFjD6ZRsPppOkmkrAQAAAPgjKvQAAACwBmPovYIKPQAAABDAqNADAADAEsbplPHRGHrDGHoAAAAA/ogKPQAAAKzBGHqvoEIPAAAABDASegAAACCAMeQGAAAA1nAaycaQm3NFhR4AAAAIYFToAQAAYA1jJPloOkkq9AAAAAD8ERV6AAAAWMI4jYyPxtAbKvQAAAAA/BEVegAAAFjDOOW7MfQ+Oo8FqNADAAAAAYyEHgAAAAhgDLkBAACAJXgo1juo0AMAAAABjAo9AAAArMFDsV5BQg8AAABL1OmU5KORMHU65ZsTWYCEHgAAAD4VHh6u5ORkbapY69PzJicnKzw83Kfn9AWbCeYnBAAAAOCXTp48qdraWp+eMzw8XBERET49py+Q0AMAAAABjFluAAAAgABGQg8AAAAEMBJ6AAAAIICR0AMAAAABjIQeAAAACGAk9AAAAEAAI6EHAAAAAtj/B8N31qs93UjdAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Commercial and Retail'])].plot(ax=ax, column='building_area', legend=True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
+       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
+       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
+       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry',\n",
+       "       'region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
+       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
+       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
+       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
+       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY_right',\n",
+       "       'WARD', 'building_area', 'area_fraction'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NC    135\n",
+       "Name: THEME3, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Commercial and Retail'])].loc[:, 'THEME3'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get vacant parcels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1.294453e+06\n",
+       "1        3.424272e+06\n",
+       "2        7.356163e+06\n",
+       "3        2.914559e+04\n",
+       "4        2.778234e+06\n",
+       "             ...     \n",
+       "67781    6.116406e+03\n",
+       "67782    6.036308e+03\n",
+       "67783    1.027241e+06\n",
+       "67784    1.085022e+04\n",
+       "67785    8.179494e+04\n",
+       "Name: Shape_Area, Length: 67786, dtype: float64"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parcels['Shape_Area']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS', 'CITY',\n",
+       "       'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y', 'NUMSTORY',\n",
+       "       'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY', 'DATE_MOD',\n",
+       "       'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "buildings.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parcels = gpd.read_file('../data/spatial_data/armourdale/parcels.gpkg')\n",
+    "vacant_parcels = gpd.read_file('../data/spatial_data/armourdale/vacant_parcels.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "non_vacant_df = parcels.loc[~parcels['PARCEL'].isin(vacant_parcels['PARCEL'].values)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "non_vacant_df = non_vacant_df.sjoin(armourdale, predicate='within').drop(columns=['index_right'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.drop(columns=['index_right'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>Shape__Are</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21858</th>\n",
+       "      <td>134121.623419</td>\n",
+       "      <td>1292.407908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21867</th>\n",
+       "      <td>121119.563452</td>\n",
+       "      <td>808.705081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21889</th>\n",
+       "      <td>12249.901201</td>\n",
+       "      <td>59.783524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21951</th>\n",
+       "      <td>12249.901201</td>\n",
+       "      <td>1302.073732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22093</th>\n",
+       "      <td>5383.474243</td>\n",
+       "      <td>995.018923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88124</th>\n",
+       "      <td>11723.714630</td>\n",
+       "      <td>122.811691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88126</th>\n",
+       "      <td>9428.960315</td>\n",
+       "      <td>579.409618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88128</th>\n",
+       "      <td>13849.235090</td>\n",
+       "      <td>501.120281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88130</th>\n",
+       "      <td>4778.174101</td>\n",
+       "      <td>182.988778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88131</th>\n",
+       "      <td>5903.793314</td>\n",
+       "      <td>95.217654</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>972 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Shape_Area   Shape__Are\n",
+       "21858  134121.623419  1292.407908\n",
+       "21867  121119.563452   808.705081\n",
+       "21889   12249.901201    59.783524\n",
+       "21951   12249.901201  1302.073732\n",
+       "22093    5383.474243   995.018923\n",
+       "...              ...          ...\n",
+       "88124   11723.714630   122.811691\n",
+       "88126    9428.960315   579.409618\n",
+       "88128   13849.235090   501.120281\n",
+       "88130    4778.174101   182.988778\n",
+       "88131    5903.793314    95.217654\n",
+       "\n",
+       "[972 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inhabited[['Shape_Area', 'Shape__Are']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inhabited = core_buildings.loc[core_buildings['FEATURECOD'].isin(['Building General'])].sjoin(non_vacant_df, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assumption\n",
+    "\n",
+    "1. Rooftop potential is distributed proportionally across all rooftop sectors.\n",
+    "2. There is some factor, E, that represents the fraction of actual suitable rooftop area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(6079.45687907, 'kW')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inhabited.area_fraction.sum() * kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "# core_buildings.loc[core_buildings['FEATURECOD'].isin(['Building General'])].plot(ax=ax, column='FEATURECOD')\n",
+    "inhabited.plot(ax=ax, )\n",
+    "# armourdale.plot(ax=ax, fc='lightgray', ec='k', zorder=0, alpha=0.5)\n",
+    "sunroof.plot(ax=ax, fc='lightgray', ec='b', lw=1, zorder=0)\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(972, 68)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inhabited.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01895735, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    972.000000\n",
+       "mean      17.432131\n",
+       "std       12.997489\n",
+       "min        0.515257\n",
+       "10%        1.928423\n",
+       "16.5%      2.992695\n",
+       "25%        6.192480\n",
+       "50%       18.375387\n",
+       "75%       24.719141\n",
+       "95%       36.167172\n",
+       "max      145.933488\n",
+       "Name: Shape__Are, dtype: float64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(inhabited['Shape__Are'] * unit_area).describe(percentiles=[.1, 0.165, .25, .5, .75, .95])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.5675675675675675"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2.8 / 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.108108108108109"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 / 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64.86486486486487"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "24 / 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.25"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "25 / (1/0.37)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.8733677860005695"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(inhabited.loc[((inhabited['Shape__Are'] * unit_area) > 25), 'Shape__Are']*unit_area).sum() / 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unyt import acre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(3.40283517, 'acre')"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2810*kW) / unit_area.to(kW/acre)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Estimated kW per Roof')"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(inhabited['Shape__Are'] * unit_area).plot.hist(ax=ax, bins=100)\n",
+    "ax.set_xlabel(\"Estimated kW per Roof\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Estimated kW per Roof')"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(inhabited['Shape__Are'] * unit_area / (1/0.37)).plot.hist(ax=ax, bins=100)\n",
+    "ax.set_xlabel(\"Estimated kW per Roof\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Park Areas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parks = gpd.read_file('../data/spatial_data/armourdale/parks.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PARKS_ID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>DATEMOD</th>\n",
+       "      <th>DATEADDED</th>\n",
+       "      <th>DEV_ACRE</th>\n",
+       "      <th>UNDEV_ACRE</th>\n",
+       "      <th>TOTAL_ACRE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TRACK</th>\n",
+       "      <th>SPRAY_PARK</th>\n",
+       "      <th>PLAY_PAD</th>\n",
+       "      <th>DOG_RUN</th>\n",
+       "      <th>PIC_TABLE</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>TYPE</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Wyandotte Co Lake</td>\n",
+       "      <td>46695.764713</td>\n",
+       "      <td>6.147028e+07</td>\n",
+       "      <td>66109</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1402.811279</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Regional Park</td>\n",
+       "      <td>400 acre lake</td>\n",
+       "      <td>Leavenworth Rd &amp; N 91st St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.76616 39.16852, -94.76616 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Wyandotte Co Lake</td>\n",
+       "      <td>7911.596242</td>\n",
+       "      <td>1.099166e+06</td>\n",
+       "      <td>66109</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.233259</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Regional Park</td>\n",
+       "      <td>spillway slough</td>\n",
+       "      <td>8124r Wolcott Dr</td>\n",
+       "      <td>MULTIPOLYGON (((-94.76524 39.17592, -94.76277 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Quindaro</td>\n",
+       "      <td>3859.471645</td>\n",
+       "      <td>7.730992e+05</td>\n",
+       "      <td>66104</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.633940</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Sewell Ave &amp; N 34th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.66803 39.14613, -94.66845 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Roswell</td>\n",
+       "      <td>5811.686684</td>\n",
+       "      <td>1.168415e+06</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2011-03-23</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.923965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Manorcrest Dr &amp; N 7th St Trfwy</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62444 39.14172, -94.62435 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Garland</td>\n",
+       "      <td>8904.375882</td>\n",
+       "      <td>1.884229e+06</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>43.255947</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>contaminated &amp; closed</td>\n",
+       "      <td>301 Roswell Ave</td>\n",
+       "      <td>MULTIPOLYGON (((-94.61536 39.13298, -94.61536 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0</td>\n",
+       "      <td>City</td>\n",
+       "      <td>13119.185729</td>\n",
+       "      <td>3.596500e+06</td>\n",
+       "      <td>66102</td>\n",
+       "      <td>2011-03-22</td>\n",
+       "      <td>2011-03-22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Park Dr &amp; S 26th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.66789 39.1039, -94.66789 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Northrup</td>\n",
+       "      <td>3226.782966</td>\n",
+       "      <td>3.234217e+05</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2011-03-23</td>\n",
+       "      <td>2011-03-23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Grandview Blvd &amp; N 10th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.63527 39.11125, -94.6352 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Jersey Creek</td>\n",
+       "      <td>1235.601197</td>\n",
+       "      <td>8.141766e+04</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.869092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Parallel Pkwy &amp; N 13th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62922 39.12424, -94.62922 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Jersey Creek</td>\n",
+       "      <td>5527.890780</td>\n",
+       "      <td>8.759639e+05</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.869092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Parallel Pkwy &amp; N 13th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62896 39.12539, -94.62669 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Jersey Creek</td>\n",
+       "      <td>8545.961583</td>\n",
+       "      <td>1.122098e+06</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.869092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Parallel Pkwy &amp; N 13th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.63906 39.12935, -94.63903 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>69 rows × 71 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    PARKS_ID               NAME    Shape_Leng    Shape_Area    ZIP    DATEMOD  \\\n",
+       "0          1  Wyandotte Co Lake  46695.764713  6.147028e+07  66109 2010-05-04   \n",
+       "1          2  Wyandotte Co Lake   7911.596242  1.099166e+06  66109 2010-05-04   \n",
+       "2          4           Quindaro   3859.471645  7.730992e+05  66104 2010-05-04   \n",
+       "3          6            Roswell   5811.686684  1.168415e+06  66101 2011-03-23   \n",
+       "4          7            Garland   8904.375882  1.884229e+06  66101 2010-05-04   \n",
+       "..       ...                ...           ...           ...    ...        ...   \n",
+       "64         0               City  13119.185729  3.596500e+06  66102 2011-03-22   \n",
+       "65         0           Northrup   3226.782966  3.234217e+05  66101 2011-03-23   \n",
+       "66        24       Jersey Creek   1235.601197  8.141766e+04  66101 2010-05-04   \n",
+       "67        24       Jersey Creek   5527.890780  8.759639e+05  66101 2010-05-04   \n",
+       "68        24       Jersey Creek   8545.961583  1.122098e+06  66101 2010-05-04   \n",
+       "\n",
+       "    DATEADDED  DEV_ACRE  UNDEV_ACRE   TOTAL_ACRE  ... TRACK SPRAY_PARK  \\\n",
+       "0  2010-05-04       0.0         0.0  1402.811279  ...  None       None   \n",
+       "1  2010-05-04       0.0         0.0    25.233259  ...  None       None   \n",
+       "2  2010-05-04       0.0         0.0    17.633940  ...  None       None   \n",
+       "3  2010-05-04       0.0         0.0    13.923965  ...  None       None   \n",
+       "4  2010-05-04       0.0         0.0    43.255947  ...  None       None   \n",
+       "..        ...       ...         ...          ...  ...   ...        ...   \n",
+       "64 2011-03-22       0.0         0.0     0.000000  ...  None       None   \n",
+       "65 2011-03-23       0.0         0.0     0.000000  ...  None       None   \n",
+       "66 2010-05-04       0.0         0.0     1.869092  ...  None       None   \n",
+       "67 2010-05-04       0.0         0.0     1.869092  ...  None       None   \n",
+       "68 2010-05-04       0.0         0.0     1.869092  ...  None       None   \n",
+       "\n",
+       "   PLAY_PAD DOG_RUN PIC_TABLE         CITY               TYPE  \\\n",
+       "0      None       Y      None  Kansas City      Regional Park   \n",
+       "1      None    None      None  Kansas City      Regional Park   \n",
+       "2      None    None         Y  Kansas City  Neighborhood Park   \n",
+       "3      None    None      None  Kansas City  Neighborhood Park   \n",
+       "4      None    None      None  Kansas City  Neighborhood Park   \n",
+       "..      ...     ...       ...          ...                ...   \n",
+       "64     None    None      None  Kansas City               None   \n",
+       "65     None    None      None  Kansas City  Neighborhood Park   \n",
+       "66     None    None      None  Kansas City  Neighborhood Park   \n",
+       "67     None    None      None  Kansas City  Neighborhood Park   \n",
+       "68     None    None      None  Kansas City  Neighborhood Park   \n",
+       "\n",
+       "                  COMMENT                         ADDRESS  \\\n",
+       "0           400 acre lake      Leavenworth Rd & N 91st St   \n",
+       "1         spillway slough                8124r Wolcott Dr   \n",
+       "2                    None          Sewell Ave & N 34th St   \n",
+       "3                    None  Manorcrest Dr & N 7th St Trfwy   \n",
+       "4   contaminated & closed                 301 Roswell Ave   \n",
+       "..                    ...                             ...   \n",
+       "64                   None             Park Dr & S 26th St   \n",
+       "65                   None      Grandview Blvd & N 10th St   \n",
+       "66                   None       Parallel Pkwy & N 13th St   \n",
+       "67                   None       Parallel Pkwy & N 13th St   \n",
+       "68                   None       Parallel Pkwy & N 13th St   \n",
+       "\n",
+       "                                             geometry  \n",
+       "0   MULTIPOLYGON (((-94.76616 39.16852, -94.76616 ...  \n",
+       "1   MULTIPOLYGON (((-94.76524 39.17592, -94.76277 ...  \n",
+       "2   MULTIPOLYGON (((-94.66803 39.14613, -94.66845 ...  \n",
+       "3   MULTIPOLYGON (((-94.62444 39.14172, -94.62435 ...  \n",
+       "4   MULTIPOLYGON (((-94.61536 39.13298, -94.61536 ...  \n",
+       "..                                                ...  \n",
+       "64  MULTIPOLYGON (((-94.66789 39.1039, -94.66789 3...  \n",
+       "65  MULTIPOLYGON (((-94.63527 39.11125, -94.6352 3...  \n",
+       "66  MULTIPOLYGON (((-94.62922 39.12424, -94.62922 ...  \n",
+       "67  MULTIPOLYGON (((-94.62896 39.12539, -94.62669 ...  \n",
+       "68  MULTIPOLYGON (((-94.63906 39.12935, -94.63903 ...  \n",
+       "\n",
+       "[69 rows x 71 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_parks = parks.sjoin(armourdale, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "armourdale_parks.plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unyt import acre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(152.4475, 'ft**2')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1/unit_area)*(2.89*kW)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01895735, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(1379.05592417, 'kW')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1.67*acre).to(foot**2)*unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PARKS_ID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>DATEMOD</th>\n",
+       "      <th>DATEADDED</th>\n",
+       "      <th>DEV_ACRE</th>\n",
+       "      <th>UNDEV_ACRE</th>\n",
+       "      <th>TOTAL_ACRE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DOG_RUN</th>\n",
+       "      <th>PIC_TABLE</th>\n",
+       "      <th>CITY_left</th>\n",
+       "      <th>TYPE</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>index_right</th>\n",
+       "      <th>CITY_right</th>\n",
+       "      <th>WARD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>64</td>\n",
+       "      <td>Bill Clem</td>\n",
+       "      <td>1212.905396</td>\n",
+       "      <td>72983.420466</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.675469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas Ave &amp; S 10th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.63561 39.08731, -94.63561 ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>67</td>\n",
+       "      <td>Shawnee</td>\n",
+       "      <td>3865.413812</td>\n",
+       "      <td>295406.668890</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.719724</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>Armourdale rec center</td>\n",
+       "      <td>730 Osage Ave</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62713 39.08444, -94.6281 3...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 74 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    PARKS_ID       NAME   Shape_Leng     Shape_Area    ZIP    DATEMOD  \\\n",
+       "20        64  Bill Clem  1212.905396   72983.420466  66105 2010-05-04   \n",
+       "21        67    Shawnee  3865.413812  295406.668890  66105 2010-05-04   \n",
+       "\n",
+       "    DATEADDED  DEV_ACRE  UNDEV_ACRE  TOTAL_ACRE  ... DOG_RUN PIC_TABLE  \\\n",
+       "20 2010-05-04       0.0         0.0    1.675469  ...    None         Y   \n",
+       "21 2010-05-04       0.0         0.0    0.719724  ...    None      None   \n",
+       "\n",
+       "      CITY_left               TYPE                COMMENT  \\\n",
+       "20  Kansas City  Neighborhood Park                   None   \n",
+       "21  Kansas City  Neighborhood Park  Armourdale rec center   \n",
+       "\n",
+       "                   ADDRESS                                           geometry  \\\n",
+       "20  Kansas Ave & S 10th St  MULTIPOLYGON (((-94.63561 39.08731, -94.63561 ...   \n",
+       "21           730 Osage Ave  MULTIPOLYGON (((-94.62713 39.08444, -94.6281 3...   \n",
+       "\n",
+       "   index_right   CITY_right WARD  \n",
+       "20           0  Kansas City   06  \n",
+       "21           0  Kansas City   06  \n",
+       "\n",
+       "[2 rows x 74 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale_parks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Impervious land"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impervious = gpd.read_file(\"../data/spatial_data/armourdale/impervious_land_cover.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_impervious = impervious.sjoin(armourdale, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pervious                    2641\n",
+       "Buildings                   2337\n",
+       "Sidewalks                   1072\n",
+       "Driveways                    662\n",
+       "Decks/Patios                 381\n",
+       "Parking Lots                 289\n",
+       "Concrete Pads                271\n",
+       "Miscellaneous Structures     203\n",
+       "Railroad Ballast              93\n",
+       "Roads                         28\n",
+       "Parking Lots-Dirt             10\n",
+       "Pools-Above-Ground             9\n",
+       "Driveways-Dirt                 8\n",
+       "Bridges                        8\n",
+       "Roads-Dirt                     6\n",
+       "Athletic Facilities            5\n",
+       "Name: impervio_1, dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale_impervious['impervio_1'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "armourdale_impervious.loc[armourdale_impervious['impervio_1'].isin([\n",
+    "                                                                    'Parking Lots',\n",
+    "                                                                    'Concrete Pads',\n",
+    "                                                                    'Parking Lots-Dirt'\n",
+    "                                                                    ])].plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
+       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
+       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
+       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
+       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY', 'WARD',\n",
+       "       'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sunroof.columns"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/11-pypsa-model-more-buses.ipynb b/notebooks/11-pypsa-model-more-buses.ipynb
new file mode 100644
index 0000000..bade541
--- /dev/null
+++ b/notebooks/11-pypsa-model-more-buses.ipynb
@@ -0,0 +1,7371 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pypsa\n",
+    "import matplotlib.pyplot as plt\n",
+    "from unyt import MWh, kWh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PyPSA Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 1: Create the network and timesteps (snapshots)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = pypsa.Network(name='Armourdale')\n",
+    "\n",
+    "N_days=365\n",
+    "N_hours=24\n",
+    "\n",
+    "index = pd.date_range(start=\"2018-01-01\", \n",
+    "                      periods=N_days*N_hours, \n",
+    "                      freq='h')\n",
+    "\n",
+    "n.set_snapshots(index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 2: Add energy carriers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(class_name=\"Carrier\", name=\"grid\")\n",
+    "n.add(class_name=\"Carrier\", name=\"solar\")\n",
+    "n.add(class_name=\"Carrier\", name=\"battery\")\n",
+    "n.add(class_name=\"Carrier\", name='net metering')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 3: Add buses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bus_name = 'Residential'\n",
+    "bus_names = ['Residential','CommunitySolar']\n",
+    "for bus in bus_names:\n",
+    "      n.add(class_name=\"Bus\",\n",
+    "            name=bus,\n",
+    "            carrier='AC')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 4: Add lines connecting the buses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(class_name='Line',\n",
+    "      name=f\"CommSol-Res\",\n",
+    "      bus0=\"CommunitySolar\",\n",
+    "      bus1=\"Residential\",\n",
+    "      )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 5: Add load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load = pd.read_csv(\"../data/timeseries/residential_elec_load_rescaled.csv\", parse_dates=True, index_col='timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "load_resampled = load.loc['2018'].resample('h').mean().sum(axis=1)\n",
+    "load_resampled = load_resampled / 1e3  # kW --> MW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(\n",
+    "    class_name=\"Load\",\n",
+    "    name=f\"Load {bus_name}\",\n",
+    "    bus=bus_name,\n",
+    "    p_set=load_resampled\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 6: Add weather data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date_time</th>\n",
+       "      <th>temp_db</th>\n",
+       "      <th>rel_humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "      <th>wind_direction</th>\n",
+       "      <th>ghi</th>\n",
+       "      <th>dni</th>\n",
+       "      <th>dhi</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 00:00:00</th>\n",
+       "      <td>2005-01-01 01:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 01:00:00</th>\n",
+       "      <td>2005-01-01 02:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 02:00:00</th>\n",
+       "      <td>2005-01-01 03:00:00</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>57</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:00:00</th>\n",
+       "      <td>2005-01-01 04:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:00:00</th>\n",
+       "      <td>2005-01-01 05:00:00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               date_time  temp_db  rel_humidity  wind_speed  \\\n",
+       "2018-01-01 00:00:00  2005-01-01 01:00:00      8.0            61         5.7   \n",
+       "2018-01-01 01:00:00  2005-01-01 02:00:00      8.0            57         5.1   \n",
+       "2018-01-01 02:00:00  2005-01-01 03:00:00      8.0            57         5.1   \n",
+       "2018-01-01 03:00:00  2005-01-01 04:00:00      7.0            56         6.2   \n",
+       "2018-01-01 04:00:00  2005-01-01 05:00:00      7.0            56         5.1   \n",
+       "\n",
+       "                     wind_direction  ghi  dni  dhi  \n",
+       "2018-01-01 00:00:00              80    0    0    0  \n",
+       "2018-01-01 01:00:00              90    0    0    0  \n",
+       "2018-01-01 02:00:00              90    0    0    0  \n",
+       "2018-01-01 03:00:00              80    0    0    0  \n",
+       "2018-01-01 04:00:00              90    0    0    0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weather = pd.read_csv(\"../data/timeseries/weather_year.csv\", parse_dates=True, index_col=0)\n",
+    "weather.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# normalize GHI\n",
+    "ghi = weather['ghi'] / weather['ghi'].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 4: Upload cost data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "costs = pd.read_csv(\"../data/technology_costs.csv\", index_col='technology')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DistributedWind</th>\n",
+       "      <td>35912.100000</td>\n",
+       "      <td>5.678577e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ResPV</th>\n",
+       "      <td>28108.825392</td>\n",
+       "      <td>2.630889e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>78943.789878</td>\n",
+       "      <td>3.157752e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Fixed O&M           OCC\n",
+       "technology                                             \n",
+       "DistributedWind              35912.100000  5.678577e+06\n",
+       "ResPV                        28108.825392  2.630889e+06\n",
+       "Residential Battery Storage  78943.789878  3.157752e+06"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "costs *= 1e3  # convert /kW to /MW\n",
+    "costs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>OffShoreWind</th>\n",
+       "      <td>98936.957353</td>\n",
+       "      <td>2.149057e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pumped Storage Hydropower</th>\n",
+       "      <td>18660.000000</td>\n",
+       "      <td>3.215894e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utility-Scale Battery Storage</th>\n",
+       "      <td>53840.076037</td>\n",
+       "      <td>2.153603e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utility-Scale PV-Plus-Battery</th>\n",
+       "      <td>57635.123527</td>\n",
+       "      <td>1.906395e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtilityPV</th>\n",
+       "      <td>20483.036808</td>\n",
+       "      <td>1.204175e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Fixed O&M           OCC\n",
+       "technology                                               \n",
+       "OffShoreWind                   98936.957353  2.149057e+06\n",
+       "Pumped Storage Hydropower      18660.000000  3.215894e+06\n",
+       "Utility-Scale Battery Storage  53840.076037  2.153603e+06\n",
+       "Utility-Scale PV-Plus-Battery  57635.123527  1.906395e+06\n",
+       "UtilityPV                      20483.036808  1.204175e+06"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utility_costs = pd.read_csv(\"../data/utility_technology_costs.csv\", index_col='technology')\n",
+    "utility_costs *= 1e3\n",
+    "utility_costs.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 7: Add generators to network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def annuity(r, n):\n",
+    "    return r / (1 - 1 / (1 + r)**n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.09439292574325567"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "annuity_adj = annuity(0.07, 20)\n",
+    "annuity_adj"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "      <th>annualized_cost</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>OffShoreWind</th>\n",
+       "      <td>98936.957353</td>\n",
+       "      <td>2.149057e+06</td>\n",
+       "      <td>301792.765867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pumped Storage Hydropower</th>\n",
+       "      <td>18660.000000</td>\n",
+       "      <td>3.215894e+06</td>\n",
+       "      <td>322217.636737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utility-Scale Battery Storage</th>\n",
+       "      <td>53840.076037</td>\n",
+       "      <td>2.153603e+06</td>\n",
+       "      <td>257124.968010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utility-Scale PV-Plus-Battery</th>\n",
+       "      <td>57635.123527</td>\n",
+       "      <td>1.906395e+06</td>\n",
+       "      <td>237585.318179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>UtilityPV</th>\n",
+       "      <td>20483.036808</td>\n",
+       "      <td>1.204175e+06</td>\n",
+       "      <td>134148.605782</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Fixed O&M           OCC  annualized_cost\n",
+       "technology                                                                \n",
+       "OffShoreWind                   98936.957353  2.149057e+06    301792.765867\n",
+       "Pumped Storage Hydropower      18660.000000  3.215894e+06    322217.636737\n",
+       "Utility-Scale Battery Storage  53840.076037  2.153603e+06    257124.968010\n",
+       "Utility-Scale PV-Plus-Battery  57635.123527  1.906395e+06    237585.318179\n",
+       "UtilityPV                      20483.036808  1.204175e+06    134148.605782"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <th>OCC</th>\n",
+       "      <th>annualized_cost</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>technology</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>DistributedWind</th>\n",
+       "      <td>35912.100000</td>\n",
+       "      <td>5.678577e+06</td>\n",
+       "      <td>571929.570926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ResPV</th>\n",
+       "      <td>28108.825392</td>\n",
+       "      <td>2.630889e+06</td>\n",
+       "      <td>276446.116985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>78943.789878</td>\n",
+       "      <td>3.157752e+06</td>\n",
+       "      <td>377013.201712</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Fixed O&M           OCC  annualized_cost\n",
+       "technology                                                              \n",
+       "DistributedWind              35912.100000  5.678577e+06    571929.570926\n",
+       "ResPV                        28108.825392  2.630889e+06    276446.116985\n",
+       "Residential Battery Storage  78943.789878  3.157752e+06    377013.201712"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "costs = costs.assign(annualized_cost = costs['OCC']*annuity_adj + costs['Fixed O&M'])\n",
+    "utility_costs = utility_costs.assign(annualized_cost = utility_costs['OCC']*annuity_adj + utility_costs['Fixed O&M'])\n",
+    "display(utility_costs.tail())\n",
+    "display(costs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResPV\n",
+      "276446.11698458303\n",
+      "Residential Battery Storage\n",
+      "377013.20171200635\n"
+     ]
+    }
+   ],
+   "source": [
+    "# residential generators\n",
+    "for generator in costs.index:\n",
+    "    if generator == 'DistributedWind':\n",
+    "        pass\n",
+    "    else:\n",
+    "        print(generator)\n",
+    "        annualized_cost = costs.at[generator, 'annualized_cost']\n",
+    "        print(annualized_cost)\n",
+    "        \n",
+    "        if generator=='ResPV':\n",
+    "            n.add(class_name='Generator',\n",
+    "                    name=generator,\n",
+    "                    bus=bus_name,\n",
+    "                    carrier=\"solar\",\n",
+    "                    capital_cost=annualized_cost,  # $/kW\n",
+    "                    p_min_pu=ghi,\n",
+    "                    p_max_pu=ghi,\n",
+    "                    p_nom_extendable=True,\n",
+    "                    p_nom_max = 2.807,\n",
+    "                    )\n",
+    "        elif generator=='Residential Battery Storage':\n",
+    "            pass\n",
+    "            n.add(class_name=\"StorageUnit\",\n",
+    "                    name=generator,\n",
+    "                    bus=bus_name,\n",
+    "                    carrier=\"battery\",\n",
+    "                    capital_cost=annualized_cost,  # $/kW\n",
+    "                    p_nom_extendable=True,\n",
+    "                    max_hours=2.5,\n",
+    "                    cyclic_state_of_charge=False,\n",
+    "                    )\n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# community solar generator\n",
+    "\n",
+    "n.add(class_name=\"Generator\",\n",
+    "      name=\"Community Solar\",\n",
+    "      bus=\"CommunitySolar\",\n",
+    "      carrier=\"solar\",\n",
+    "      capital_cost=utility_costs.at['UtilityPV','annualized_cost'],\n",
+    "    #   p_min_pu=ghi,\n",
+    "      p_max_pu=ghi,\n",
+    "      p_nom_extendable=True,\n",
+    "      p_nom_max=10\n",
+    "      )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Add a \"net metering\" technology to capture the excess energy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retail_price = 112.9  # $/MWh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(class_name=\"Generator\",\n",
+    "      name=f\"Net metering {bus_name}\",\n",
+    "      bus=bus_name,\n",
+    "      carrier='net metering',\n",
+    "      p_min_pu=-1,\n",
+    "      p_max_pu=0.0,\n",
+    "      marginal_cost=retail_price*0.0,\n",
+    "      capital_cost=0.0,\n",
+    "      p_nom_extendable=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.add(class_name='Generator',\n",
+    "      name='Evergy Import',\n",
+    "      bus=bus_name,\n",
+    "      carrier='grid',\n",
+    "      capital_cost=0,\n",
+    "      marginal_cost=retail_price,\n",
+    "      p_nom_extendable=True,\n",
+    "      # p_nom_max=1.3315\n",
+    "      )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>attribute</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>control</th>\n",
+       "      <th>type</th>\n",
+       "      <th>p_nom</th>\n",
+       "      <th>p_nom_mod</th>\n",
+       "      <th>p_nom_extendable</th>\n",
+       "      <th>p_nom_min</th>\n",
+       "      <th>p_nom_max</th>\n",
+       "      <th>p_min_pu</th>\n",
+       "      <th>p_max_pu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>min_up_time</th>\n",
+       "      <th>min_down_time</th>\n",
+       "      <th>up_time_before</th>\n",
+       "      <th>down_time_before</th>\n",
+       "      <th>ramp_limit_up</th>\n",
+       "      <th>ramp_limit_down</th>\n",
+       "      <th>ramp_limit_start_up</th>\n",
+       "      <th>ramp_limit_shut_down</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>p_nom_opt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ResPV</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Net metering Residential</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Evergy Import</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "attribute                         bus control type  p_nom  p_nom_mod  \\\n",
+       "Generator                                                              \n",
+       "ResPV                     Residential      PQ         0.0        0.0   \n",
+       "Net metering Residential  Residential      PQ         0.0        0.0   \n",
+       "Evergy Import             Residential      PQ         0.0        0.0   \n",
+       "\n",
+       "attribute                 p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
+       "Generator                                                                    \n",
+       "ResPV                                 True        0.0      2.807       0.0   \n",
+       "Net metering Residential              True        0.0        inf      -1.0   \n",
+       "Evergy Import                         True        0.0        inf       0.0   \n",
+       "\n",
+       "attribute                 p_max_pu  ...  min_up_time  min_down_time  \\\n",
+       "Generator                           ...                               \n",
+       "ResPV                          1.0  ...            0              0   \n",
+       "Net metering Residential       0.0  ...            0              0   \n",
+       "Evergy Import                  1.0  ...            0              0   \n",
+       "\n",
+       "attribute                 up_time_before down_time_before  ramp_limit_up  \\\n",
+       "Generator                                                                  \n",
+       "ResPV                                  1                0            NaN   \n",
+       "Net metering Residential               1                0            NaN   \n",
+       "Evergy Import                          1                0            NaN   \n",
+       "\n",
+       "attribute                 ramp_limit_down  ramp_limit_start_up  \\\n",
+       "Generator                                                        \n",
+       "ResPV                                 NaN                  1.0   \n",
+       "Net metering Residential              NaN                  1.0   \n",
+       "Evergy Import                         NaN                  1.0   \n",
+       "\n",
+       "attribute                 ramp_limit_shut_down  weight  p_nom_opt  \n",
+       "Generator                                                          \n",
+       "ResPV                                      1.0     1.0        0.0  \n",
+       "Net metering Residential                   1.0     1.0        0.0  \n",
+       "Evergy Import                              1.0     1.0        0.0  \n",
+       "\n",
+       "[3 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Baseline\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* uses the sticker price for rooftop solar from NREL's ATB\n",
+    "* does NOT pay for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 6: Run the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 29.39it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.63it/s]\n",
+      "INFO:linopy.io: Writing time: 0.58s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.430806</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.329983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
+       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_1 = n.statistics().copy()\n",
+    "results_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "StorageUnit\n",
+       "Residential Battery Storage   -0.0\n",
+       "Name: p_nom_opt, dtype: float64"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.storage_units.p_nom_opt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 7: Calculate the LCOE from the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "112.9000000000018"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_1 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe that the model has an LCOE of $112.9/MWh, which makes sense because it only uses electricity purchased from the grid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Step 8: Plot some data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Policy Simulation\n",
+    "\n",
+    "The purpose of this simulation is to test the effect of price reductions and net metering on solar penetration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.11it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.69it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.49it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.94s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.3s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.95s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.95it/s]\n",
+      "INFO:linopy.io: Writing time: 1.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.44it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 49.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.95s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.96it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.00it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.84s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.9s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.11it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.83it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.75it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.44it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.9s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.02it/s]\n",
+      "INFO:linopy.io: Writing time: 0.85s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.91s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.75it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.43it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.90it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.90it/s]\n",
+      "INFO:linopy.io: Writing time: 0.92s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.91s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 22.34it/s]\n",
+      "INFO:linopy.io: Writing time: 2.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.85s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.02it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.04it/s]\n",
+      "INFO:linopy.io: Writing time: 2.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 18.77it/s]\n",
+      "INFO:linopy.io: Writing time: 2.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.51it/s]\n",
+      "INFO:linopy.io: Writing time: 0.96s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.53it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.95it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.27it/s]\n",
+      "INFO:linopy.io: Writing time: 0.86s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.72it/s]\n",
+      "INFO:linopy.io: Writing time: 0.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 62.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.80it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.21it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.65s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.55it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.93it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.89it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 53.13it/s]\n",
+      "INFO:linopy.io: Writing time: 0.82s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.18it/s]\n",
+      "INFO:linopy.io: Writing time: 0.78s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.25it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 68.73it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 67.66it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.72s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 59.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.40it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 64.24it/s]\n",
+      "INFO:linopy.io: Writing time: 0.81s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.91it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 54.19it/s]\n",
+      "INFO:linopy.io: Writing time: 0.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.01it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.97it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.69it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 50.05it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 22.57it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.12it/s]\n",
+      "INFO:linopy.io: Writing time: 0.79s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.78it/s]\n",
+      "INFO:linopy.io: Writing time: 0.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 66.07it/s]\n",
+      "INFO:linopy.io: Writing time: 0.84s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 21.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.67it/s]\n",
+      "INFO:linopy.io: Writing time: 0.81s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.48it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.75it/s]\n",
+      "INFO:linopy.io: Writing time: 2.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.99it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 30.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.25it/s]\n",
+      "INFO:linopy.io: Writing time: 1.45s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 28.66it/s]\n",
+      "INFO:linopy.io: Writing time: 1.91s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 24.17it/s]\n",
+      "INFO:linopy.io: Writing time: 1.77s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.91it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.73s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  8.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.81it/s]\n",
+      "INFO:linopy.io: Writing time: 2.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 25.00it/s]\n",
+      "INFO:linopy.io: Writing time: 1.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.83it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.24it/s]\n",
+      "INFO:linopy.io: Writing time: 1.75s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.73it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.76s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  9.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.89s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 35.82it/s]\n",
+      "INFO:linopy.io: Writing time: 1.63s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.14it/s]\n",
+      "INFO:linopy.io: Writing time: 1.69s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.47it/s]\n",
+      "INFO:linopy.io: Writing time: 1.48s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.56it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.53it/s]\n",
+      "INFO:linopy.io: Writing time: 1.19s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00,  7.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 21.77it/s]\n",
+      "INFO:linopy.io: Writing time: 2.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.44s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.44s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.28it/s]\n",
+      "INFO:linopy.io: Writing time: 1.4s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.48it/s]\n",
+      "INFO:linopy.io: Writing time: 1.48s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.94it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.40it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.4s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 11.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 30.98it/s]\n",
+      "INFO:linopy.io: Writing time: 1.48s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.59s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.51it/s]\n",
+      "INFO:linopy.io: Writing time: 1.38s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.83it/s]\n",
+      "INFO:linopy.io: Writing time: 1.34s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.20it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.48it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 10.59it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.58s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.93it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.71it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.82it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 31.52it/s]\n",
+      "INFO:linopy.io: Writing time: 1.19s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.25it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.85it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.10it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.09it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.36s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.65e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.73it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.84it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.63e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.03it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.44it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.63e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.22it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.62e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.69it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.61e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.97it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.47it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.35e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.66it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.78it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.60e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.60e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.02it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.59e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.08it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.19e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.17it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.09it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.34it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.56e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.95it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.52e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.31it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.04e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.70it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.55e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.15it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.41it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.54e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.40it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.52e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.85it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.45e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.25it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.88e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.32it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.52e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.28it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.74it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.47e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.92it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.59it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.73e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 20.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 58.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.83s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.39it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.47e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.29it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.17it/s]\n",
+      "INFO:linopy.io: Writing time: 1.14s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.42e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.48it/s]\n",
+      "INFO:linopy.io: Writing time: 1.13s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.72it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.37it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.45e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.74it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.42e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.66it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.43it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.69it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.12it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.41e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.62it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.38e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.11it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.31e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.2s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.93e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.26e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.91it/s]\n",
+      "INFO:linopy.io: Writing time: 1.1s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.15it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.92it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 41.28it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.77e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.54it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.21it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.10e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.41it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.18it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.28e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.2s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.17e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.05it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.61e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.33it/s]\n",
+      "INFO:linopy.io: Writing time: 1.16s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.95e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.36it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.64it/s]\n",
+      "INFO:linopy.io: Writing time: 1.15s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.28e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 35.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.22e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.21s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.91it/s]\n",
+      "INFO:linopy.io: Writing time: 1.2s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.46e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.85it/s]\n",
+      "INFO:linopy.io: Writing time: 1.29s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.79e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.56it/s]\n",
+      "INFO:linopy.io: Writing time: 1.21s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.23e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 23.79it/s]\n",
+      "INFO:linopy.io: Writing time: 1.33s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.16e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.82it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.96e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.86it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.30e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.48it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.83it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.36it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.18e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.94it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.10e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.22it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.81e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.10it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.15e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.62it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.76it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.48e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.52it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.42it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.13e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.50it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.03e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.62it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.99e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.49it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.84it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.31it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.29it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.96e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.73it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.50e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.27it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.35it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.84e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.69it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.17e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.42it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.02e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.84it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.88e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.26it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.78it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.35e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.75it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.68e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.44it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.99it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.02e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.99it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.96e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 51.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.87s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.78e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 18.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 55.34it/s]\n",
+      "INFO:linopy.io: Writing time: 0.94s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.19e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 19.19it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 56.38it/s]\n",
+      "INFO:linopy.io: Writing time: 0.88s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.53e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.07it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 63.79it/s]\n",
+      "INFO:linopy.io: Writing time: 0.74s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.86e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 65.74it/s]\n",
+      "INFO:linopy.io: Writing time: 0.68s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.89e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.33it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.65it/s]\n",
+      "INFO:linopy.io: Writing time: 1.11s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.68e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.04it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.04e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.49it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.37e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.75it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.42it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.71e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.23it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.14it/s]\n",
+      "INFO:linopy.io: Writing time: 0.97s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.82e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.49it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.61it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.55e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.88e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.24it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.51it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.22e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.71it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.55e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.40it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 36.84it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.75e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.68it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.30it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.39e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.46it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.60it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.73e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.27it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.06e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.61it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.40e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.04it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.65it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.39it/s]\n",
+      "INFO:linopy.io: Writing time: 0.97s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.33it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.57e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.04it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.91e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.89it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.24e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.50it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.64it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.58e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.98it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.52it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.08e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.42it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.98s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.42e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.18it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 42.39it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.75e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.51it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 48.04it/s]\n",
+      "INFO:linopy.io: Writing time: 0.98s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.09e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.63it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.93e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.71it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.19it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.26e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.09it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 47.05it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.60e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 37.16it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 9.32e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.30it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.59it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.39e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.88it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.97it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.77e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.80it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.01it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.11e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.34it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.11it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.44e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.57it/s]\n",
+      "INFO:linopy.io: Writing time: 1.02s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 7.77e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.70it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.32it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.27e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.62e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.60it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.92it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.95e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.95it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.50it/s]\n",
+      "INFO:linopy.io: Writing time: 1.01s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.29e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.98it/s]\n",
+      "INFO:linopy.io: Writing time: 0.99s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 6.21e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.58it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.36it/s]\n",
+      "INFO:linopy.io: Writing time: 1.0s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.13e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.45it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.63it/s]\n",
+      "INFO:linopy.io: Writing time: 1.09s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.46e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.77it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 34.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.33s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.80e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 26.54it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.13e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.64it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 32.46it/s]\n",
+      "INFO:linopy.io: Writing time: 1.23s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.66e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.41it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 43.56it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.97e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.76it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 27.00it/s]\n",
+      "INFO:linopy.io: Writing time: 1.31s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.31e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 13.20it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.13it/s]\n",
+      "INFO:linopy.io: Writing time: 1.29s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.64e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.61it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.95it/s]\n",
+      "INFO:linopy.io: Writing time: 1.08s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 9.77e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.06it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.33it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.11e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:01<00:00, 12.32it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 38.45it/s]\n",
+      "INFO:linopy.io: Writing time: 1.34s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.82e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.43it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 29.35it/s]\n",
+      "INFO:linopy.io: Writing time: 1.17s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.15e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.14it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.59it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.49e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.15it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 46.72it/s]\n",
+      "INFO:linopy.io: Writing time: 1.05s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 8.21e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.86it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.38it/s]\n",
+      "INFO:linopy.io: Writing time: 1.07s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.56e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 14.92it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.08it/s]\n",
+      "INFO:linopy.io: Writing time: 1.12s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.66e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.01it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 44.98it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.00e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 17.16it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 39.12it/s]\n",
+      "INFO:linopy.io: Writing time: 1.03s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 1.33e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 16.67it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 40.93it/s]\n",
+      "INFO:linopy.io: Writing time: 1.04s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 6.66e+04\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n",
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 15.90it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 45.98it/s]\n",
+      "INFO:linopy.io: Writing time: 1.06s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 6.61e+01\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {'discount':[],\n",
+    "        'percent_retail_price':[],\n",
+    "        'solar_capacity':[],\n",
+    "        'battery_capacity':[],\n",
+    "        'objective_value':[],\n",
+    "        }\n",
+    "\n",
+    "\n",
+    "delta = 0.02\n",
+    "discounts = np.arange(0, 1+delta, delta)\n",
+    "retail_prices = np.linspace(0, 1, 5)\n",
+    "for discount in discounts:\n",
+    "    for pct_retail in retail_prices:\n",
+    "        n.generators.loc['ResPV', 'capital_cost'] = costs.at['ResPV','annualized_cost'] * (1-discount)\n",
+    "        n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*pct_retail\n",
+    "        n.optimize(solver_name='highs')\n",
+    "        \n",
+    "        data['discount'].append(discount)\n",
+    "        data['solar_capacity'].append(np.abs(n.generators.p_nom_opt['ResPV']))\n",
+    "        data['battery_capacity'].append(np.abs(n.storage_units.p_nom_opt['Residential Battery Storage']))\n",
+    "        data['percent_retail_price'].append(pct_retail)\n",
+    "        data['objective_value'].append(n.objective)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>discount</th>\n",
+       "      <th>percent_retail_price</th>\n",
+       "      <th>solar_capacity</th>\n",
+       "      <th>battery_capacity</th>\n",
+       "      <th>objective_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>266273.015698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>199721.286719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>133169.557739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66617.828760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.807</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66.099781</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>255 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     discount  percent_retail_price  solar_capacity  battery_capacity  \\\n",
+       "0         0.0                  0.00           0.000               0.0   \n",
+       "1         0.0                  0.25           0.000               0.0   \n",
+       "2         0.0                  0.50           0.000               0.0   \n",
+       "3         0.0                  0.75           0.000               0.0   \n",
+       "4         0.0                  1.00           0.000               0.0   \n",
+       "..        ...                   ...             ...               ...   \n",
+       "250       1.0                  0.00           2.807               0.0   \n",
+       "251       1.0                  0.25           2.807               0.0   \n",
+       "252       1.0                  0.50           2.807               0.0   \n",
+       "253       1.0                  0.75           2.807               0.0   \n",
+       "254       1.0                  1.00           2.807               0.0   \n",
+       "\n",
+       "     objective_value  \n",
+       "0      466950.487985  \n",
+       "1      466950.487985  \n",
+       "2      466950.487985  \n",
+       "3      466950.487985  \n",
+       "4      466950.487985  \n",
+       "..               ...  \n",
+       "250    266273.015698  \n",
+       "251    199721.286719  \n",
+       "252    133169.557739  \n",
+       "253     66617.828760  \n",
+       "254        66.099781  \n",
+       "\n",
+       "[255 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df_large = pd.DataFrame(data)\n",
+    "results_df_large"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df_large = results_df_large.assign(solar_penetration=results_df_large['solar_capacity'] / n.generators.p_nom_max.ResPV)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df_large = pd.read_csv('simulation_data.csv', index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x2aa82cfd1e0>"
+      ]
+     },
+     "execution_count": 355,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large[['discount','solar_penetration','percent_retail_price']]*100,\n",
+    "            x='discount',\n",
+    "            y='solar_penetration',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis')\n",
+    "\n",
+    "ax.set_ylabel(\"% of total electricity covered by solar\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost\\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "ax.set_ylim(0,1.05*100)\n",
+    "ax.set_xlim(0,1.0*100)\n",
+    "\n",
+    "ax.axvline(x=50, color='tab:red', linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df_large['solar_penetration'] = results_df_large['solar_penetration'].round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = results_df_large.loc[results_df_large['discount']==0.5, ['percent_retail_price','solar_penetration']].plot.bar(x='percent_retail_price',\n",
+    "                                                                                                               y='solar_penetration',\n",
+    "                                                                                                               legend=False,\n",
+    "                                                                                                               zorder=3,\n",
+    "                                                                                                               color='gold')\n",
+    "ax.set_xlabel('Net Metering Price \\n (fraction of retail price)')\n",
+    "ax.set_ylabel(\"Solar penetration \\n (fraction)\")\n",
+    "ax.set_title(\"Solar potential with 50% Tax Credits\")\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major', zorder=0)\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "\n",
+    "ax.patches[0].set_edgecolor('red')\n",
+    "ax.patches[0].set_linewidth(3)\n",
+    "ax.bar_label(ax.containers[0])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>discount</th>\n",
+       "      <th>percent_retail_price</th>\n",
+       "      <th>solar_capacity</th>\n",
+       "      <th>battery_capacity</th>\n",
+       "      <th>objective_value</th>\n",
+       "      <th>solar_penetration</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>255.000000</td>\n",
+       "      <td>255.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1.217456</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>382392.224079</td>\n",
+       "      <td>0.433721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.294971</td>\n",
+       "      <td>0.354249</td>\n",
+       "      <td>1.236635</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>117536.626041</td>\n",
+       "      <td>0.440554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>66.099781</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>324929.125859</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.757254</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>448625.587086</td>\n",
+       "      <td>0.269774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.760000</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>2.807000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.807000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         discount  percent_retail_price  solar_capacity  battery_capacity  \\\n",
+       "count  255.000000            255.000000      255.000000             255.0   \n",
+       "mean     0.500000              0.500000        1.217456               0.0   \n",
+       "std      0.294971              0.354249        1.236635               0.0   \n",
+       "min      0.000000              0.000000        0.000000               0.0   \n",
+       "25%      0.240000              0.250000        0.000000               0.0   \n",
+       "50%      0.500000              0.500000        0.757254               0.0   \n",
+       "75%      0.760000              0.750000        2.807000               0.0   \n",
+       "max      1.000000              1.000000        2.807000               0.0   \n",
+       "\n",
+       "       objective_value  solar_penetration  \n",
+       "count       255.000000         255.000000  \n",
+       "mean     382392.224079           0.433721  \n",
+       "std      117536.626041           0.440554  \n",
+       "min          66.099781           0.000000  \n",
+       "25%      324929.125859           0.000000  \n",
+       "50%      448625.587086           0.269774  \n",
+       "75%      466950.487985           1.000000  \n",
+       "max      466950.487985           1.000000  "
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_df_large.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df_large = results_df_large.assign(lcoe=results_df_large['objective_value'] / n.loads_t.p_set.sum().values[0] / 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 281,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large,\n",
+    "            x='discount',\n",
+    "            y='lcoe',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis'\n",
+    "            )\n",
+    "\n",
+    "ax.set_ylabel(\"Retail Price of Electricity \\n ($ / kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "# ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "results_df_large.to_csv(\"simulation_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.18761865362970692"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(costs.at['ResPV','annualized_cost'] * 2.807) / load_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* uses the sticker price for rooftop solar from NREL's ATB\n",
+    "* applies 100% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 28.82it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 71.10it/s]\n",
+      "INFO:linopy.io: Writing time: 0.62s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.67e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.430806</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.329983</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>466950.487985</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.96535</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Optimal Capacity  Installed Capacity      Supply  Withdrawal  \\\n",
+       "Generator grid          1.430806                 0.0  4135.96535     0.00000   \n",
+       "Load      -             0.000000                 0.0     0.00000  4135.96535   \n",
+       "\n",
+       "                  Dispatch  Transmission  Capacity Factor  Curtailment  \\\n",
+       "Generator grid  4135.96535           0.0         0.329983          0.0   \n",
+       "Load      -    -4135.96535           0.0              NaN          0.0   \n",
+       "\n",
+       "                Capital Expenditure  Operational Expenditure        Revenue  \\\n",
+       "Generator grid                  0.0            466950.487985  466950.487985   \n",
+       "Load      -                     0.0                 0.000000 -466950.487985   \n",
+       "\n",
+       "                Market Value  \n",
+       "Generator grid         112.9  \n",
+       "Load      -              NaN  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Load\n",
+       "Load Residential    112.9\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_2 = n.objective / n.loads_t.p_set.sum()\n",
+    "model_lcoe_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* does NOT pay for net metering\n",
+    "* does NOT include residential storage\n",
+    "\n",
+    "Apply the Residential Renewable Energy Tax Credit\n",
+    "\n",
+    "[DSIRE Data on the RRETC](https://programs.dsireusa.org/system/program/detail/1235/residential-renewable-energy-tax-credit) -- solar and storage each get a 30% tax credit. \n",
+    "\n",
+    "Apply the Investment Tax Credit (ITC)\n",
+    "\n",
+    "[EPA Data on ITC](https://www.epa.gov/green-power-markets/summary-inflation-reduction-act-provisions-related-renewable-energy) -- qualified residential units in a low-income area recieve +20%.\n",
+    "\n",
+    "[Homeowner's Guide to Federal Tax Credits](https://www.energy.gov/eere/solar/homeowners-guide-federal-tax-credit-solar-photovoltaics).\n",
+    "\n",
+    "This will be implemented as a direct 50% cost reduction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reset the price of net metering\n",
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rretc_credit = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>attribute</th>\n",
+       "      <th>bus</th>\n",
+       "      <th>control</th>\n",
+       "      <th>type</th>\n",
+       "      <th>p_nom</th>\n",
+       "      <th>p_nom_mod</th>\n",
+       "      <th>p_nom_extendable</th>\n",
+       "      <th>p_nom_min</th>\n",
+       "      <th>p_nom_max</th>\n",
+       "      <th>p_min_pu</th>\n",
+       "      <th>p_max_pu</th>\n",
+       "      <th>...</th>\n",
+       "      <th>state_of_charge_initial_per_period</th>\n",
+       "      <th>state_of_charge_set</th>\n",
+       "      <th>cyclic_state_of_charge</th>\n",
+       "      <th>cyclic_state_of_charge_per_period</th>\n",
+       "      <th>max_hours</th>\n",
+       "      <th>efficiency_store</th>\n",
+       "      <th>efficiency_dispatch</th>\n",
+       "      <th>standing_loss</th>\n",
+       "      <th>inflow</th>\n",
+       "      <th>p_nom_opt</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>StorageUnit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Residential Battery Storage</th>\n",
+       "      <td>Residential</td>\n",
+       "      <td>PQ</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>inf</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "attribute                            bus control type  p_nom  p_nom_mod  \\\n",
+       "StorageUnit                                                               \n",
+       "Residential Battery Storage  Residential      PQ         0.0        0.0   \n",
+       "\n",
+       "attribute                    p_nom_extendable  p_nom_min  p_nom_max  p_min_pu  \\\n",
+       "StorageUnit                                                                     \n",
+       "Residential Battery Storage              True        0.0        inf      -1.0   \n",
+       "\n",
+       "attribute                    p_max_pu  ...  \\\n",
+       "StorageUnit                            ...   \n",
+       "Residential Battery Storage       1.0  ...   \n",
+       "\n",
+       "attribute                    state_of_charge_initial_per_period  \\\n",
+       "StorageUnit                                                       \n",
+       "Residential Battery Storage                               False   \n",
+       "\n",
+       "attribute                    state_of_charge_set  cyclic_state_of_charge  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                  NaN                   False   \n",
+       "\n",
+       "attribute                   cyclic_state_of_charge_per_period  max_hours  \\\n",
+       "StorageUnit                                                                \n",
+       "Residential Battery Storage                              True        2.5   \n",
+       "\n",
+       "attribute                    efficiency_store  efficiency_dispatch  \\\n",
+       "StorageUnit                                                          \n",
+       "Residential Battery Storage               1.0                  1.0   \n",
+       "\n",
+       "attribute                    standing_loss  inflow  p_nom_opt  \n",
+       "StorageUnit                                                    \n",
+       "Residential Battery Storage            0.0     0.0       -0.0  \n",
+       "\n",
+       "[1 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.storage_units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['ResPV', 'capital_cost'] *= (1-rretc_credit)\n",
+    "n.storage_units.loc['Residential Battery Storage', 'capital_cost'] *= (1-rretc_credit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 23.35it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 73.29it/s]\n",
+      "INFO:linopy.io: Writing time: 0.7s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 4.17e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.342852</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2964.049410</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>2964.049410</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.251973</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>334641.178424</td>\n",
+       "      <td>334641.178424</td>\n",
+       "      <td>112.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>0.669086</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>224.13732</td>\n",
+       "      <td>-224.137320</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.038241</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar</th>\n",
+       "      <td>0.947609</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1396.053259</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>1396.053259</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>3.914040e-10</td>\n",
+       "      <td>82394.7361</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>82394.736100</td>\n",
+       "      <td>59.019766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.96535</td>\n",
+       "      <td>-4135.965350</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-417035.914525</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.342852                 0.0  2964.049410   \n",
+       "          net metering          0.669086                 0.0     0.000000   \n",
+       "          solar                 0.947609                 0.0  1396.053259   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
+       "\n",
+       "                        Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.00000  2964.049410           0.0   \n",
+       "          net metering   224.13732  -224.137320           0.0   \n",
+       "          solar            0.00000  1396.053259           0.0   \n",
+       "Load      -             4135.96535 -4135.965350           0.0   \n",
+       "\n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.251973  0.000000e+00               0.0000   \n",
+       "          net metering         0.038241  0.000000e+00               0.0000   \n",
+       "          solar                0.168178  3.914040e-10           82394.7361   \n",
+       "Load      -                         NaN  0.000000e+00               0.0000   \n",
+       "\n",
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    334641.178424  334641.178424    112.900000  \n",
+       "          net metering                 0.000000       0.000000      0.000000  \n",
+       "          solar                        0.000000   82394.736100     59.019766  \n",
+       "Load      -                            0.000000 -417035.914525           NaN  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the LCOE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100.83157842499818"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_3 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the electricity price reduction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.689479185119586"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.abs((100.831578 - 112.9)/112.9)*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like almost a 10.7% reduction in electricity cost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering + Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* Applies 50% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.0\n",
+       "Net metering Residential      0.0\n",
+       "Evergy Import               112.9\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators.marginal_cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.00\n",
+       "Net metering Residential     56.45\n",
+       "Evergy Import               112.90\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators.marginal_cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 24.79it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 60.33it/s]\n",
+      "INFO:linopy.io: Writing time: 0.71s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 3.77e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.331491</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2358.485524</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2358.485524</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.202205</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>112.900000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>2.505756</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2357.900052</td>\n",
+       "      <td>-2357.900052</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.107419</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-133103.457958</td>\n",
+       "      <td>-133103.457958</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar</th>\n",
+       "      <td>2.807000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>1.159408e-09</td>\n",
+       "      <td>244069.150984</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>255898.612865</td>\n",
+       "      <td>61.880316</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.965350</td>\n",
+       "      <td>-4135.965350</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-389068.170605</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.331491                 0.0  2358.485524   \n",
+       "          net metering          2.505756                 0.0     0.000000   \n",
+       "          solar                 2.807000                 0.0  4135.379878   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
+       "\n",
+       "                         Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.000000  2358.485524           0.0   \n",
+       "          net metering  2357.900052 -2357.900052           0.0   \n",
+       "          solar            0.000000  4135.379878           0.0   \n",
+       "Load      -             4135.965350 -4135.965350           0.0   \n",
+       "\n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.202205  0.000000e+00             0.000000   \n",
+       "          net metering         0.107419  0.000000e+00             0.000000   \n",
+       "          solar                0.168178  1.159408e-09        244069.150984   \n",
+       "Load      -                         NaN  0.000000e+00             0.000000   \n",
+       "\n",
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    266273.015698  266273.015698    112.900000  \n",
+       "          net metering           -133103.457958 -133103.457958           NaN  \n",
+       "          solar                        0.000000  255898.612865     61.880316  \n",
+       "Load      -                            0.000000 -389068.170605           NaN  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "91.20934940819072"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_4 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Version: Net Metering + Tax Credits\n",
+    "\n",
+    "At this moment, the model\n",
+    "\n",
+    "* reduces the price for rooftop solar by applying federal tax credits.\n",
+    "* Applies 99% retail price for net metering\n",
+    "* does NOT include residential storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['Net metering Residential', 'marginal_cost'] = retail_price*1.00"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Generator\n",
+       "ResPV                         0.0\n",
+       "Net metering Residential    112.9\n",
+       "Evergy Import               112.9\n",
+       "Name: marginal_cost, dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.generators.marginal_cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n.generators.loc['ResPV', 'p_nom_max'] = 2.807"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:linopy.model: Solve problem using Highs solver\n",
+      "INFO:linopy.io:Writing objective.\n",
+      "Writing constraints.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 14/14 [00:00<00:00, 26.47it/s]\n",
+      "Writing continuous variables.: 100%|\u001b[38;2;128;191;255m██████████\u001b[0m| 6/6 [00:00<00:00, 69.62it/s]\n",
+      "INFO:linopy.io: Writing time: 0.63s\n",
+      "INFO:linopy.solvers:Log file at C:\\Users\\sdotson\\AppData\\Local\\Temp\\highs.log\n",
+      "INFO:linopy.constants: Optimization successful: \n",
+      "Status: ok\n",
+      "Termination condition: optimal\n",
+      "Solution: 52564 primals, 122645 duals\n",
+      "Objective: 2.44e+05\n",
+      "Solver model: available\n",
+      "Solver message: optimal\n",
+      "\n",
+      "INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, StorageUnit-ext-p_dispatch-lower, StorageUnit-ext-p_dispatch-upper, StorageUnit-ext-p_store-lower, StorageUnit-ext-p_store-upper, StorageUnit-ext-state_of_charge-lower, StorageUnit-ext-state_of_charge-upper, StorageUnit-energy_balance were not assigned to the network.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('ok', 'optimal')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.optimize(solver_name='highs')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\sdotson\\AppData\\Local\\miniforge3\\envs\\kansas-city\\lib\\site-packages\\pypsa\\statistics.py:308: FutureWarning:\n",
+      "\n",
+      "The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Optimal Capacity</th>\n",
+       "      <th>Installed Capacity</th>\n",
+       "      <th>Supply</th>\n",
+       "      <th>Withdrawal</th>\n",
+       "      <th>Dispatch</th>\n",
+       "      <th>Transmission</th>\n",
+       "      <th>Capacity Factor</th>\n",
+       "      <th>Curtailment</th>\n",
+       "      <th>Capital Expenditure</th>\n",
+       "      <th>Operational Expenditure</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Market Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Generator</th>\n",
+       "      <th>grid</th>\n",
+       "      <td>1.331491</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2358.485524</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2358.485524</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.202205</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>266273.015698</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>net metering</th>\n",
+       "      <td>2.505756</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2357.900052</td>\n",
+       "      <td>-2357.900052</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.107419</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-266206.915917</td>\n",
+       "      <td>-266206.915917</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>solar</th>\n",
+       "      <td>2.807000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.379878</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.168178</td>\n",
+       "      <td>1.159408e-09</td>\n",
+       "      <td>244069.150984</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>466884.388204</td>\n",
+       "      <td>112.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Load</th>\n",
+       "      <th>-</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4135.965350</td>\n",
+       "      <td>-4135.965350</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-466950.487985</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        Optimal Capacity  Installed Capacity       Supply  \\\n",
+       "Generator grid                  1.331491                 0.0  2358.485524   \n",
+       "          net metering          2.505756                 0.0     0.000000   \n",
+       "          solar                 2.807000                 0.0  4135.379878   \n",
+       "Load      -                     0.000000                 0.0     0.000000   \n",
+       "\n",
+       "                         Withdrawal     Dispatch  Transmission  \\\n",
+       "Generator grid             0.000000  2358.485524           0.0   \n",
+       "          net metering  2357.900052 -2357.900052           0.0   \n",
+       "          solar            0.000000  4135.379878           0.0   \n",
+       "Load      -             4135.965350 -4135.965350           0.0   \n",
+       "\n",
+       "                        Capacity Factor   Curtailment  Capital Expenditure  \\\n",
+       "Generator grid                 0.202205  0.000000e+00             0.000000   \n",
+       "          net metering         0.107419  0.000000e+00             0.000000   \n",
+       "          solar                0.168178  1.159408e-09        244069.150984   \n",
+       "Load      -                         NaN  0.000000e+00             0.000000   \n",
+       "\n",
+       "                        Operational Expenditure        Revenue  Market Value  \n",
+       "Generator grid                    266273.015698  266273.015698         112.9  \n",
+       "          net metering           -266206.915917 -266206.915917           NaN  \n",
+       "          solar                        0.000000  466884.388204         112.9  \n",
+       "Load      -                            0.000000 -466950.487985           NaN  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n.statistics()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "59.02739266934207"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lcoe_5 = n.objective / n.loads_t.p_set.sum().values[0]\n",
+    "model_lcoe_5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='snapshot'>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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hSXBGJMbVgzPghNcXOxABaHqGD7GvzigjIkyPQCSVjIivc0aEpmakxqXFe2fLd7ZINBLSHd9ARCtLeCkQ4UHohncAEAozCIa7vTkHHPD4DXHvQ4EId2J1VW3H902ZdGX3ds+ICJ2aoUBEask2veuO6kTkw2dqBtDOEl4KRHgQmg0BgFBID2/Y2+VnroArYUZkW4NT8Hl7C5qaUZfuNR2prJzpPJ1DUzPS47vZ45p9rVQnIhO+j41WuqtSIMKDGG+CwZAevlDXmg9XwIdQJP5DQRkR7mhqRl3axM6ICGiKRrjhU6wKAL4g1YnIhevOu+2oRiQNCd3wDogGIt5Q14yIJ04PkXbbG7SznbPSxJ6aoYyIMN2nZlLZb6Zz8BIIRXgVUxL+Ugm+qU5EHlSsStAiUUYkWSDS5PLTZl8ciT41I2B/FNJzk7qUpma63Yd6iUjLm0JdFNWJyIN3jQgFIulHaHt3APAHYmRE4jQz62x7I9WJcCH61AxdfQsiytRMt/uI8Tok8aVSoE11IvLgmxGhPiJpSIwaEX9ID1+YX0YEoDoRrsRuaOaivWZSxrJsz1UzAhuaAbRyRmqpTH35ghGs22eXYDSkM5qaISJlRJieUzNcMiIUiHBCLd7Vw+kPIdxtB8KUGpp1y4h079ZKxBMIRTjvZdIdvUdJj+/UTIs70OM1qEYUiPDQIsKVmD/YMyPii9PevTPahZcbWjWjHrHqmlLJiNipRkQ2QgqBGzXSs0LL+E4VR1hx2k5IjQIRHsTIiLAs02MHXsqIiIdWzahHrCkUoct3AaBNA2+sWiWkgZ9WpgG0jO/yXUAbjwsFIjyI1Uype5t3H4dA5ECbN6Vq9t5G7KkZvj0VyCGxplBSa2jWrViVMiKS4dtVtTPKiEgvlQsjCkTSjFjV+oFw90Ak+ZOLZYEdjZQVSUbsqZlwhKUAMEViZEQiEbbHmy/ViEhHSEZEK/uaaFkq7QQoEEkzYvQRAYBgt6t2X4jbk4sCkeTEzogAND2TqlhdUD2BMEIxdqCOx+kPge1Wa0erZqRDNSLqlkrNWpNT/YE7BSIc+YJh+ESqPwh0eyPmMjUDALua20Q5fzoTu0YEoILVVNnjBO58Vs7EKm6l/Wak4xGwXF0LV95axrJsSn2NtPC4UCDCkZiVx51rRALhAAIhhtP9Djioe2Ey3YM8MVBGJDXxMhd8Vs7EmsqhLsPSEdLAzxMI02tFQu5AGKmsxNXClBkFIhyJGYgEI4cCEUfAgVDIyOl+dc420caQrqTo7kgZkdTE26COT+V/rOJWyohIR2hxNk3PSCfV9yEtdFelQIQjMd/8OteIOANOBEMmTvdrdLsRDNPVYCKSTM1Qm/eUxM2I8ChYjZkR8QYR0UCTJi0SGnRTICKdVJbuAjQ1k1bEXDLYefrAFXAhEOCWEWl1mLDbsVu0caQjaYpVadVMKuxxVrfwmpqJcdsIm1o/EpIcZUTUK9VpLwpE0oiYG20FwoeeUM6AE/6AgdP9mu0m7GrbJdo40hFNzaiHOBmR2P/2tHJGGkKzf41OX/IbkZSkuhN4sysAtvvSM5WhQIQjUWtEOmVEHEEHfBwDkVBEh32ORtHGkY6kyIhQIJKaeDUifJqaxcueUJ2INISsmgG0URipValmREIRVvWBOwUiHIlaI9KpoZkr4ILHxy0QAYB6V5to40hHUtSI0EqA1HTfebed0BoRgDIiUqGpGfUS8j6k9ukZCkQ4kqpGxBlw8gpEGigQSYimZtTBEwghECc7xa9GJPa/PWVEpCGkoRkANGlghYZWuQTURak9U0WBCEdi1oiEOi3fdQe9CIS4PwxtfjdCEfpgjCfeh58QVKzKX6KMBZ+GZvGyKpQRkYaQFu8AZUSkJGxDQnUHiBSIcCRqQ7NOGRGuXVXbRYI27HXuFW0s6YZqRNQhYSAisKFZ9PjqfmPVKiGb3gEUiEgp1eW7ANCk8seFAhGOxEwFd95rg+s+Mx33DWRhZ9tO0caSbigQUYdEG9PxqhGJW6xKGREpCM2INLv9ql+hoVUuf+rPeaoRSQMsy6JZzKmZMIsIG/3A9PMMRLy+TOxo2yHaWNKNFDUiVKzKX6I27HxWzcS7CqQaEWkIrREJhlkKEiWS6vJdQP3dVSkQ4aDR5Re19iASMcAXiq635xuIOFxW7LBTIBJLJMIiGBb/aow6q/IXb+kuIE5GJF7tCBHGLUI9FE3PSENIrRplRNLAgVavqMeLhA3whqLH5BuItDisNDUThxTTMoA4b869TaIaEa5z3ZEIC1ecIJAyItLwihB0UyAiDZqa6eVq28TtFhgOG+ALR4/ZucsqF20uIw64ajumdsghUkzLADQ1k4pENSLuQAhhDnvFOH0hxCs3aHVTRkRsLMvCGxQhI+Ki7qpSENZHRN2BOwUiHNS2iZsRCYX1naZm+L3wWZaBzZCJ/c79oo4pHUiXEaFAhK9ENSIsCzg5TM8kmsKhVTPi8wZT22a+O8qISENIZpb6iKSBA2IHIqFojUg4Eu6ygoarbGMBFazGIEVXVSDabZJ2e+UnWQ0Hl4LVRMdwB8Jdtkogwok1BUmBiDSELN8NhCKq3iiSAhEORA9Ewnp4Q164gi6ANfO+v1WXRwWrMQTC0tVyUMEqP4mmZgBuBavJbkN1IqmLVXwvdMVMOwpEpCGkRgRQdy8RCkQ4EHtqJhCM1og4A06wERPv++vZXCpYjcEnUUYEoIJVvpJNvXBpapYsa0LdVVO3bn9bj5+JlhFR+TSAGiXrvRIKRwS/v6m5ToQCEQ7EzogEQzr4QqkHIghlY6edApHupKoRAahglS+nL/GHkSgZERF7+/Q2P+9q6fEzsTIiTU56XPhauLkh4e/FCBLVvHKGApEkPIGQ6FdeweChqZlwiP/UTMCfiV32XdTBsBupVs0AVLDKV/KMSPJ/z2RZk20NLl5jIof8sqe1x8oloV1V21FGhL//rqtFvSP+aqMWEaYhKRDRMLGnZQDA/3sg4gg4EA4Zed/f48mAJ+TBQfdB0cemZVJmRCgQ4S4UCcHlS/xYcMqIJAlEXli4HV6RPjx7mzqHD3tbPF1+JkYPESBau5NKEX5vVtvmw5e/xn8///LXWsHnoKkZDdsvcjMzAPAHmI6pmWCI/9SM3W1DyB7C4l8Wiz42LZNq1QxAUzN8OHxeBMJM4ttwqRFJskqgzuHDq9/TFGUqWt0BbKt3dvmZWDUiLKvuDz01qrV78UWcYINlWby3ap/gc+xtqxd8DKlQIJKE2M3MgGhGxBf2wRVwIRDknxGp3+/F9ru346LjLsKECRPw73//G36/etNucpF0aoZWzXBmT1IfAiQPMgBuwcorS3agwUkNtPhq8QSwvbHr1JZYNSIArZzhIxJhUe/wYc2+tpj1iD9sb8K+FuEXxHsd+xCKqPN9jAKRJKSYmglFdAiEgnAGnPAFDLzuy7Is9n3+CkKO6BNq2bJluOiii1BZWYm7774bDQ2Ji57SmbTFqjQFwBWXaZctdU74knTx5HIcdyCMp+dv5Tw2AngDYfiCEWyv7xqIiFUjAlB3VT6aXH4EwyxYFvgqxvTMuz8Lz4YAQItLj18bfxXlWGKjQCQJsVfMtAtGInAGnfDzDETcGxfBu205GD2Dqv+rQvGZxTDlm9DQ0ICHHnoIVVVVuOGGG7Bnzx5Jxq1mYm5M2B3ViHDHpWvqsp3NmPrMUvy4vSnubbju0vv+qv3YUudMfkMC4FD/lR4ZERGf45QR4e6g/VDQ1n16ptnlx/zfxJlSaWyxYNnBZaIcS2wUiCQhWSASDsMZcMLr4x6IhBxNaFnwCgBg6AUjkXV4FopnFGPgYwNRMbsCtn42+Hw+vPDCCxgwYACuuOIKuFy9Z2UBFauqg4PD1AwA7G724KLXVmDO+2tjLsXl2gkyHGHx16828Rpjb9by+7/1jgZXl5V3omZEKBDh7KD90GfMuv127G0+VET84er9CIhU+Ov2G7C2Yb0oxxIbBSJJSDE1A/yeEQk44fbpOd2eZVk0f/0sWL8bpj6DMOKsYzp+xxgY5ByRg5q7a1B9azUyh2UiFArhjTfewL333ivJ+NVIyhoRKlbljktGpLOPfzmAE59ago9Wd90/iUuNSLulWxuxdGsjr/P2Vu0ZEXcgjNpOV+NUI6KM7nWIX6w/lBV5b6U40zLt6lwNcAXUd3FKgUgCkQiLOrs0c52hcATuoIdzjYhr3Tfw7V4DxmBC4fSbYWDyetyGYRhkDs1E9V+qUX5NOQDgtX++1muyIlKumqGMCHeOJO3dY2lxB3DLB+vwx9dWYE+zO3ocnntrLNhCK2i4aOmUfeq8csYjao0IBSJcdc6IAOhYxrt8ZzN2NrlFPZdNn4ef634W9ZhikDQQWbp0KU4//XSUlZWBYRh8+umnUp5OdPVOH0ISbXYWDEcQCHO74gvU70Drwn8CAHKPvRTGggpEgtkJ75NzZA5MJSY47A68/fbbgserBdJOzVCxKldC9sT4YXsTTnlmKV5ctJ33SqW9DuG9FnqDztNg2zs1hRPzOU4ZEe4OdrvY3VjrwK4mN979ea/o5zIhH8sPLhf9uEJJGoi43W6MHDkSL7zwgpSnkYxU0zJAtEYkyGGTtmBbHeo/uBds0AdL1ShkjTsdAOD3Zya8H6NjUHBSAQDg2WefRSSS/g2GaGpGHZx+YT0kfMEIHv/fFvBtHNzodsLutws6d2/Q0qlTdOdARMypGeojwl33QAQA/rV8D77eUCf6uZhQLpbVqq9gVdJAZNq0aXjooYdw1llnSXkayUjRzKxdKBJBMJL4gzPssaPh/XsQcbfBWFSNojPngmGiD5nLbUt6jtxjcqGz6LBlyxYsWLBAlHGrGRWrqgPfGhGxtDqsql2eqCZtnjgZESpWVcTBGBe8r/+4S5L3s4A/G7sdu1HnFj/IEUJVNSJ+vx8Oh6PLl5KkaGYGACwbQUttM8IJqqEjQR8aPnwAodZa6LOLUHzu/dCZMzp+3+ZMHojorXrkTYrWkjz33HPCB65y1FlVHYRuV56qJrsF6xrXKXJuLelcI9J5Ca+Yy3dd/hC13+cgEmHRECNok6giAB5PNJOutqyIqgKRRx55BDk5OR1fFRUVio5HiqmZSMCLhvfuxrxLn8b619fG3LiODQfR9PljCBzcAp0lEyXnPgBDVkGX2zS3mcEgcRttAMg/MR9ggC+//BLbtm0T7e9QI7GWucVCnVW5cwmcmklVIKjD9tbdipxbS1o7ZUTaPMGO7IWYxaoAZUW4aHD6JatDjKXNGb2YVVs/EVUFInPnzoXdbu/42rdP3KVLfIndQyTsdaL+3bvg2xO9atv+xWa0LnilSzASdreh/t274N3+MxiDCUVn3wtjYc+ALBDWIduUk/Sc5lIzskZkAQBefPFFkf4SdfIn6dQpRLJCvv379+P111/H/fffj6uuugpTp07FWWedha1be1/XT1dAmYwIANQ66xFh078eSogWd9fHZ1tDdOWM2ME2dVdNrtYu3fR/LA2tFgDAioMrVLV7O7+2nhIzm80wm81KD6ODmBmRsKsV9e/fjWDjbugsWRh62lBs+GgFnL98AZZlkX/yNQg27ETDxw8h7GgEY7KiaMbtsJQPiXvMXFMh7IG2pOfOPzkfznVO/PP1f+LBBx9EVlaWaH+Xmkjb4j3+m/SaNWtw/PHHw27vWSi5ZMkSfP7555g4caJkY1Mbtz8IgP8eSmJgWDO2t23HoLxBipxfC7o3j9vR4MKE/oXwiLwyjDIiyR2UaPo/Hl9Aj0JTDpq9zfjm52/Qsr0Fq1atwqpVq9DU1ISrr74as2fPhsnEfzNWIVQViKiNWBmRkL0B9e/diVDrQegz81F83gMYffp+ZPXJxbLnv4VrzZcIOxrg2/Mr2JAfhrwyFJ91d8xMSGcZ+nxO588clglzHzNcB12YN28ebrjhBjH+LNWRctVMIBRBMByBUd81ifjbb79hypQpsNvtKKopgrGfEeHsMAx5BrQsbkHLzhaceOKJ+Ne//oWzzz5bsvGpRSAcgFvBzx8Tk4d1jesoEEmg89QMAGxrcCEUjog+tUmBSHLde4jIIUdXiHVPr8Opv57a43dz5szB3//+dzz11FM49dRTwTDJp//FIOnUjMvlwtq1a7F27VoAwK5du7B27Vrs3Sv++mixOXxBOHk2VIqFDQfR8OH90SAkpwQlF/4NpqJqgDVgwAlHoODUmwAw8O5YCTbkh6VmDEoveSppEAJE33S5YBgG+SdFg5annn4KYQ7LhrVIyowI0HPlzM6dO3HyySejqakJZYPLUHhrIfIvzkfRjCLkHZuHmttqkDU6C36/H+eeey6efvppScenBna/HW6vctc3TCgH6xqoYDUeTyDU43WyvcEl6oqZdjta99A0WRKxlu5KiWVZbHx5I1y/usAYGJQOK0X5tHL0vaov+lzSB8ZsI7Zu3YrTTjsNU6dOlW3PMkkDkVWrVmH06NEYPXo0gGi0NXr0aNxzzz1SnlYUYk3LOFZ8jGDTHuhsOSi98G8w5vWJ/oI1gY2YkTn8RBScNgf6zHxkH30Ois+5F3pL4h4h7Zhw4qZmneUdkwd9ph67d+3WXGM5rqRcNQN0nZ7Zv38/TjzxRNTW1qKkfwmyr8+G3tq1Xb/OrEPlDZXIPzEfLMtizpw5ePTRRyUdo9IcAQdcHuUCkWAgm1bOJNASY0+fbQ0uUXuItNvqWIMr/ncF6tx1YFlWVTUJaiF3RqRt6VvYs2Q7oAcqb6pE4V8KkXt+LvIm5qHghAIM+NsAFJ5aCJ1Bh2+//RYnnHAC6uqkX+oraSAyefLkjidg56958+ZJeVpRHBChh0iw5QDafnoXAJB/4lUwZBd2/I5lDUA4Og+XOex4lM9+C3nHXQZGx23vGQAIBbgHIjqzDvknRLMijz3+WFq+KUg5NQMcKlhdsmQJJk+ejN27d6Ooogi5N+TCkBn7w5fRMejzxz4oOacEQLQgO10DQQBw+nwIRZSrgfd4M7HHsYcam8XR6u5ZSNzo9EtyZe7z27C6fjVmfjQTE6ZMQGlpKV577bW0fO9JlVQtImJxrvkKjuUfAAD6XtYXWYf3rBXUW/UoPa8U/R/uD2ORETt37sS0adNi1r+JSVWrZtREaEaEZVk0/+9FIByEpXo0bEOO6/r7iAHhsLCCIJ+PW+akXcGJBWAMDH5e8TN+/PFHQedWI6mnZvbV1uGyyy7D5MmTsWPHDuSW5iLvz3kw5iYuzGQYBkWnFUWXUgP44x//iHXr0vOqXWhXVaEcThtYsJQViaPFE/vxWbevTfRzeTw2sBEWm1/ejOULlqOhoQFXXXUVjj/+eGzZskX082mRVHuZdefZvgIt8/8OABh21qkd/aXiMRebUf2Xauiz9Vi7di1mzJgBn0+6sVIgEscBgZGqe8N38O/9FYzBjPxTZvco+omEjQiFhAUiDlfypmadGXIMyJ2YCwB44oknBJ1bjaQKRFiWhXPd/3DG5NF48803AQbIPz4ffe7uA1Mh98ewz4V9kDEsA263G6effjrq6+slGa+SlOoh0q6pLbo8kQKR2NriBCK/7hf/itfuMqP+w3rYl9sBPVBwcgF0Zh2WLFmCESNG4MEHH0Qo1Hv784TCETQ4pQ9Ewh47mv77JMBGkDH8ZFRNOZfT/czFZlTfUg2dJfqYXXTRRZLVF1IgEoeQFTNhd1vHJnU5x1wIY25pj9tEIgaEQsKWOLbYrbzvUzg1Oj30+eefp91VSUCCQISNhNEy/2W0fPM8vE43LJUW9LurH8ouLYM+g/s0GgAwegaV11XCVGrCvn37cOaZZ0p6laEEpTvQOr1G2Aw2CkTiiFUjAkiTEdnx3QI0fdUEAOh7RV/0uagPBjw0AJnDMxEIBHDPPffgnHPOSbvXAFf1Tr9kHVQ7c6z4CGzAA2NxPxScMhutzozkd/qdtcqKyj9XgjEw+Pjjj3HttddKMrVGgUgcQqZmWhe+hojPCWNxP2QfMTPmbSJhAwJBYYFIq8sIo47fMcx9zMganQWWZdNuFYfYNSJsKIimzx+Ha81XABjkHncZjnv8RNj688tEdabP0KPqpirobDosW7YM48ePx1dffZU28+ZKNjNrl2cuxIamDbRiI4buPUTa7WoWd7t5z9af0Pi/VwEAxWcXI29idCrAVGRC1ZwqlF9dDsbI4LPPPsP06dPhdDpFPb8W1MlQqBpyNsP5yxcAgLxjLwajN6Cp1cKpK3e7zCGZKP9TOcAAr776Km677TbR368oEIkj1UAk0LAT7t8WA2BQMPX6uMWnobAegYCwQIRlGeSbC5PfsJv2rMi8N+ehoaFB0BjURMyMSMTvQcOH98Gz5QdAZ0DhjNuQc/Q5yI+cLPjY5lIzKq+vhM6iw9q1azF9+nQcc8wxWLx4sfCBK8yt8NQMAGQaCuAOurG9bTsAIBwOw+12w+FwwO/3p03Ql4p4NSJi/pMEm/dFpwLAovzkChSdVtTl9wzDIHdCLqrmVEFn0WHhwoU46aST0NLSIt4gNECOQlX7svfBhgIw9x0CS79xAKJduXPN3Fo/tMs5Igdll5UBAB5//HHRV/9RIBJDKBxBvSO1J4l9WbQq2Tb4GJj7xG+qFArr4RMYiABAtpF/IGIbZIO1nxV+nz9t2r4HQhFR0pwsy8J/YDPq370Dvj3rwJisKD73PmQMPgYAsHpjDe8XcSyZQzMx6LFBKJxaCJ1Jh59++gnHH388brzxRsHHVpIadik2Iw8HXj+AEeUjYDAYYDAYkJmZiZycHFgsFuh0OlgsFpSXl+Ptt99WeriyirVqRkxsOIim/z4R7YlUNQrjrj4mblOszCGZqL61GvoMPX7++Wcce+yxsiwVVQupl+6G7PVwrfsfACD32Iu7PA55pmLex8s/Lh+l50fLDO644w68/PLL4gwUFIjEVOfwpfShFmzaB8/mHwAAOePPS3jbUMgAr49fjUEsFh237qqdMQzTkRX5+yt/T4uCMaHTMmGPHY6Vn+Lg67NR987/IVC3HTprNkoueBjW6lGHzhPUo8Z0ksDRRhmyDSi9oBQD/zawY3PC559/Hr/88osox1eCWwVTMweW1aF1aSv8Hn/c4jq/348DBw7gkksuwfPPPy/zCJUTr0ZELG3fv4NA/Q7orNkomH4zrMbchLe39bOh5o4aGHIN2LhxIy6++OJek7GSOiPS9sN/gEgIlqpRsFSO6PI7m74gzr0SK5xWiKLToxmu2bNn4z//+Y/gcQLU4j2mVHuI2Je/D4CFdeDRMBXXJLxtMKSH1y/8n9/A5qZ0v6wxWdBn6dFQ34D58+dj2rRpgseiJCErZly/fovmb18CwtGAjDGYYRs8ETkT/nCoAV0nv205HMbyTxGMiPOha8wzouziMoTdYdiX23HHHXfgm2++EeXYcotmRJR7W4kEfFj51rcAgIKpBSg8pRCMkYHOqAMYgA2zYIMsIsEImv/XjOZvm3HjjTfCbrfjzjvvlK2ltVK6t3cXk3fPOjhWfAwAKJh2IwxZBTCwyTfmtPS1oOa2Gmy/ZzsWLFiA119/HbNmzZJsnGoh5dLdYPM+uDcuBBDNhnRnZPlfwLYrPqsYYU8YLd+14JprrsHMmTNhtfJfONEZZURiSGVHxGDrQbh/WwIAyBl/ftLbB4J6uP3CMyJskHtTs850Bh1yjoq+Sbz55puCx6G0VAMR3/7ffu/3EoKppD/yp1yH8uvfQuH0OTGDEABospsxNPtYIcONqfisYkAP/O9//8OSJUtEP74cPAFla0Tsy96Fp7kNxgIjSs4sgTHPCEOmATqzDjqTDnqrHoZsA0wFJpT+oRTFM6Mp6rvvvhu33npr2l+NSxWIhL0ONH/xFAAWmaOmwjbwaAAAG+bW68jcx4ySs6JN/+bMmYMDBw5IMk41kXJqpu2HfwNsBNYBR8FcdliP37Oh5AFiPAzDoM9FfWAsMMLhcODLL78UMlQAFIjElErKzLH8A4CNwFIzFuY+A5Pe3uk2g2WFX335/anvpJt3TLTW4ZNPP0FbW5vgsSjJH+Q/NRNytaDp00eASBi2wZNQeukzyBp9KnTm5Mvb6vYdmcowEzIXm5F/XPRKZe7cuZr8UJSiVThXweZ9cPz8KQCgz0V9oDMnfntjGAbFM4tR+ofovPcTTzyB6dOnY8OGDVIPVTGtHvGnzliWRcs3LyDsaoYhvxx5x1/Z8btwgPv7U8EpBbD2s8LhcOCaa67R5POfj1qJMiL+uu3wbP4eAJA76Y+xb+NL7QK2HaNjOi5kxZieoUAkBr49REKOBrg2RNNgORMu4HSfNpc46Wu3h/ua8O4sVRaY+5oR8Afw/vvvizIepfDdOZQNB9H06aMIu1thLKxEwbQbeaXlt+/PwaCcUTxHmVzRGUVgTAyWLVuGL774QvTjS80dVCYQYVk22jkyEkLOYWORNZr7B2DhKYXoO6svGD2Dr7/+GiNHjsQVV1yBffv2SThi+bn8IUl67Th+/gSerT9FV5ed/n/QmSwdv/P5uafsGR3T8Th88cUXotUfqFEwHEGzS/zdiVmWRet3/wAAZAydHLdEwOXm15U7lpyjo4HIF19+IbgFPAUiMfBduutY8REQCcFcOQKW8iGc7hNhxfmnb3WkPjfHMExHp9U35r0hyniUwnfDu9ZFr8N/4DcwJhuKzrwTOhP/f0drcETyG/FkzDWi4KRoIdkdd96BSEQ7vTA8QQ+8fmVqLDybv4+ucjKYkHX8n2DU81uRljcpDwMeGoDscdmIRCJ44403MHDgwLRZVQbE7yEihGf7CrQtjr535J0wC+bSAV1+73Lz67lj6WtB0YxoMeQNN96QVu0FOquzp7YgIhnPlh/h3/8bGIMZucddGvd2LW3CajoAwFJhgbkseiH7ySefCDoWBSIx8AlEwh47nOuixXG5HLMhYmqyW5LfKIHcCbkAAyxfthxbt24VZ1AK4FMj4v5tMZyr/wsAKDztFhjz+6Z0ztZW/kvguCiaXgSdVYcN6zdo6qrQEXDA5zfLft5I0I/Wha8BALKPPhf6nLKU+uuY+0T7u/S7qx8yDsuA3+/HzTffLNtW6FITuz4k0LgbTf99Au11IVljTutxG7uL//tT0alFsFRa0NLcgjPOOAN79+4VYbTqIsUmg2wogNbfg8Lso86CIbso7m0b7RboGWE1igzDdGRF/v3vfws6FgUiMfCpEXFvXASEgzCVDoC5criEo4otENQhx5Sb8v2NuUZkHh5N07311lsijUp+XJfvspEwWhfPAwBkjz8ftoFHpXzOXQfyBL+YY9Fn6FE0Pfomcvc9d0u2v4PY7H67KCvB+HL9+i3Crhbos4uRc9TZAIBsY2rLEwHANsCG6turkTEkA8FgEA8//LBYQ1WUmEt3w+42NHz4ANiAF5aqEcg/6ZqYU5utDhOvLp4AwBgY9L2yL3RWHVasWIFRo0bhs88+E2voqiBFoapj1WcI2+uhzyxA9pFnJ7xtOMIgz5L6a6Rde53Id999J2jvLApEurF7g5z3y2BZFq5f5wMAMkdMUWzpX545fuTLRfv0zJtvvampqYDOuE7NeHeuQtjZBJ01W3AGy+PXoyyjUtAx4ik4uQD6TD127dyF//73v5KcQ2yOgAMur/AmfXyw4WB0ahRAztHngDFENyG06oS9ybYXsgLA66+/nhZZEbEyImwoiMZPHkbY0QBDXh8UzpgLRh87AA2Edcgy8S+MtFZaMeD+AbDWWNHa2oqZM2fipptugt8vfl2FEsTOiIRdrbAvi9b55U6+rEudTjw5RmGfGwBgLjHDWmNFJBLBBx98kPJxKBDphs+0TKBuO4JNe8AYTMgYIv5yTq5s+tTXhANA9phs6Kw67N+3X7NtxrlOzbjWRvtzZA4/CYxB+IdmvqG/4GPEojPrkHdcdFXTc88/J8k5xOYMOOH2ypsRcW9chLCzCfrMfGQOP9RoLtX+Op1lHJaBjKEZCIVC+Otf/yr4eEprEaGrKstG0PTV09H6KnMGis++B3pr4sLgbGNqnYhNxSbU3FmDglOiQeWzzz6LM844Iy1W0xwUsJdZLG3fvw024IWpz0BkDD2O031sAoP1du3TM//6979SPgYFIt3wCURc6xcAAKwDx0NnEV6FnCoThLUc15l0yDlS2z1FuEzNhOwN8O5YBQDIHHmKKOdl/eWiHCeW/BOi3VYXLVyEjRs3SnYesbj8foQj8mUF2UgY9uUfAgCyj5jZkQ0BUu+v0117VuSNN97A7t27RTmmUsQoVm1b8iY8m5YCOj2KZs6FsaAi6X0yDKm/P+kMOvT5Qx9U3lQJnUmHb7/9Fp9++mnKx1OL1XtbRTuW/8Dmjsx83glXg2G4fawbWOFbVQCIfnb8XmeY6muEApFuuAYibCgAz2+LAQCZI4RvhCYEE069OU273GNyAQDvvf+eJpctcsmIRPddYGGpGplygWp3zc3SFKwCgKnAhOwx0Q9ULbQhl7u9u2fzDwi11kJnyULmqK6dgf1+cQKRjEEZyBgWzYpovVZE6NSM85cvO6bBCqbe2GXrg0RMjPDHIntUdkdm5NbbbkUwqPxWAqna2+zBhgMOUY4Vdrei8dNHALDIGDqZ86pNAIgEhX9uANHO0BmDo20k3n333ZSOQYFINwc4Fqp6ti5DxO+GPrsIlirxl3HyERLhTdc2wAbbIBv8Pj9uu+02EUYlr2QNzdhwCK5fo6ubun9oCbGrNgcGnXTTEfknRafd3nr7LbS2incVJQWPjIEIy0ZgXx6dk84ad0aP5dduD79lo4l0zors2rVLtOPKTUgg4tm2Ai0LXgEA5Ez6IzKHn8j5vvqIOEFh4amF0GfpsX3bdvzjH/8Q5ZhK+HL9QVGOw4ZDaPzsbx2N5PKnXMfr/n5f6s0wu2ufnnnn3++kdH8KRLrhmhFpn5bJPPxEzqkwqXi8qTc1a9fethdMtFPeDz/8IMLI5JOsoZl3+88Iu1uhy8gVtFKmO39Qj762xPsKCZExOAPmcjO8Hi/eeEPdvV48MjYz8+5YiWDjbjAmK7LGnt7j960O8QKRjIHpkRVJddWMv247mj5/DGAjyBwxhdMWFl2ExQlE9FZ9R1B43/33weEQJ6sgt69ECkRaF78B/74N0R3Cz7oTOjO/53xLmziPCwDkjMsBY2Cwcf1GLFu2jPf9KRDphksgEnI0wLd7LQAgY7g4O7EK4XSJ86ZrrbIib1J03vCmm27S1AqaZKtmnGu/BgBkDj8ZDM9mV8nkGfqJerzOGIbpaHD2/AvPq3opr1wZEZZlYf8pukIga/R06GPUZzXZLbyXjSZSMjO6D8q8efOwc+dO0Y4rp9YUilVZNoKW/70ANuSHpd9Y5J8ym/fqwGBA+IVSu/zj8mEqNaGpsQmPPfaYaMeVy74WD9YfENaFFPi9F9Kq6JLmwuk3c6rV6e5AY6ZorxF9hh4546NZkb/97W+870+BSDdcllVF27mzMFcOhzG3VPpBJdFoF94lr13J2SXQWXVYvXq1pgpXE9WIBFsPwrd7DQBGtCLVzsJe6QpWASB3fC70GXrs3rUbX331laTnEsIr0z4z7o0LETi4BYzBhOwjZsS8TSCoQ445V7Rz2gbakHl4pqazIi0pTM24NyxCoG47GJMVhafeDEbHv2+OzydedooxMCg9N/qe++RTT2puc7wvfhWeDfHXbUfz19Gasezx58E2aEJKx/EF9Ciyivf5VTStCGCAzz77DL/99huv+1Ig0kk4wqLekTgQYdkI3O3TMirIhgCAw22EWS+sw2o7Q44BxWdE05+3z71dM+nPRKtmXOuiS3YtNWMkCRwbm4Wvx09EZ9Z1ZKrUvJTXI0MBYaBxD1q+fQnA711UM+JX/uea+HdXTaR9WuDNN9/UZFakjWcgEgl40bY0ejGSM+EC6DNyUzqvyyPehRIAZI3Jgm2gDT6vD/fcc4+ox5aa0GkZ/4HNqH/3zmiGqmYMco+5SNDxCkziXUSZy8wdxfV8s1UUiHRS7/AhlGQDAP++jQi11YExWWEbNFGmkSWXSkvruMc6OR+mEhMa6hs00z8hXkYk7LHD9XsL/qxRUyU59+7abJh0puQ3FCD/xOhS3gXzF2D58uWSnitV3oC000aRgBeNnz4CNuiHpXo0csafl/D2Np2w/jo9jjfgUFZEK6+Ldk5fEMEwv/4bjhUfIexqgSG3FNljz0j53HanuG3/GYZB6fnRC4o33nhDM7slC52W8e1dj/r37wbrd8NcPhRFM25PKUPVmRniXpgVTo9+Dv3rX//itfqSApFOuNSHtK+8yBg8iVP3OrlkCWhp3Z3OoEPpBdEn6NNPP421a9eKdmypxKsRaV30OiI+J4xF1bAOOFKScwfDOpRnSNPYrJ2pyNTRAffKK69EICD+BmZCeSXMiLAsi+b/vYBQy37oMwtQePr/JX0TNjHi9EnoTKtZEb71ISFHAxw/fwwAyJt8haDmf61OE3QiF/TbBtiQPS4bLMvirrvuEvXYUhGyWsa7czUaPrj395b6o1B87gO8i1NjifjFzeba+tmQMSRa2M2n5QAFIp0cSBKIRPxueLb8BCDa0l1NLIy4V39Zo7KQNToLwWAQ559/PpxOp6jHF1usqRnfnl/h3vAdAAYFp8wWfPWQSLauWrJjtyu9oBT6LD02btyYUkGYlFiWhS/JEmohXGu/hue3JQCjQ+GMW6G3Je+BwITE6ZPQWXtWJBwOayorwrc+pHXxm2BDAZgrDod10HhB5w5FdMg2if9YlJxV0lGTkMpKDbmlOi3j3vwDGj5+EGwoAGv/I1B8zj2iXQQ7HLmiHKezotOiwc28N+dxvg8FIp0k2+zO/dsSsCE/jAWVMJUdJtOouNFHxH2hMwyDvrP6wpBvwNatW3HdddepurVy96kZNhRA87fRLdwzR0+DuS/3Rj+pCElcsAoAhkxDdIk1gIceegibNm2S/JxcuYIuhELi7zMTCfrhWPkZWr6L9o3IPe4yWMqHcbpvwC/+hx/QNSuyY8cOSc4hNj5dVf0HNsGzaQkABvknXiXKHlrZRnEvlIBoTUJ77dTcuXNV/f60r8WDX/fzm5Zh2QjafvgXmj57FAiHYBs0AUVn3tGlg7BQB5vEW8LbLmNoBizVFvh93PcFokCkk2RTM4c2uDtZsQ3u4omExH9CGTINqLimAtAB77zzjqpX0QS6BSL25R8g1HIA+ow85B17ieTnb2iStmC1Xc5ROcgckYlAIICrrrpKNUusHQEHAsHU3yDZULDLB0kk6Idj1WeofeVKtC58FQiHYB00HtlHnsn5mAfqSlIeTyK2ATZkDtdWVoRrD5GQswmN/30CQLQ1galEnClHmz5XlON0VzSjCIyBwZIlS/Dtt99Kcg4x8J2Waa+Hsv/4HwBA1tgzUDjjNtFbDzTZLcg0irs9CcMwHbuHc0WBSCeJApFAwy4E6rYBOgMyDj9BxlFxI2aXvM4yBmWg+MzoFeDs2bN5L8uSGsuyWL1uNXbu2AA2FH2zDTbv6+i6mXfi1bLsA7T7YBYsIq1cSoRhGJRdWgadWYcff/wRr7zyiuTn5MLutyMQ4F+UyIaDaPriSex98izsfXwG9j13IQ68ciUOvHw5Wr97FWF3K/TZxcg/5XoUnXEbrwuAfQ0Z6GOTJlNVPCP6mnjrrbc0sQ8Ql66qYY8d9e/ehbC9HobcPsibfJlo5zcz0mSnTAWmju7Dt8+9XTWBeXefr63lfNtgay3q3vkLvFuXAXoDCqb9GfknXS3Z1HKxhX8PkmSyx2bDWMw9aKJApJNENSLtRaq2AUdymp+Wm9MtXtOg7oqmFyFjWAY8Hg/OP/98uFwuyc7V3YEDB/D444/jiy++QEtLS8fPHQ4HXnrpJQwbPgzjRo3D4oeuwN4nz8b+ly5D/bt3Rq+g+42DbfAxsowzHGFQnjFAlnOZCkwoOSd6tX/bbbepYjM2R8ABr5/f1Vok6EfDxw/BvXERABZgI4h4HQi11SHidXQEIH2vfgVZo6bG3Wo+kRLjSN734cI2wIas0VkIh8OYNWuWqhvNAckDkYjPhYb374kWA2cVouSCv4r6PqeLSHOhBERrEnRWHdauWStoK3qpLPitHr8dTN4GIdCwC03/fQK1r16DYONu6DJyUfqHRyTfyyxLXyb6MRkdg5IzuWck5d2zO0Xvv/8+rrzySsnPEy8jwoaCcG9cDEB9Rart2uxWQNzl+h0YHYOKqyuw/e7t2LBhA6ZNm4avvvoKWVnSvbkAgNfrxbRp07B+/fqOnw0ePBiHHXYYFixYALfbDQDQGY1gGQPYgBdhZ1N0zEYz8qdcK+sUWiZTA0CepYT5J+ajbUUbnNudOP300/Hjjz8iO1v86TmuHH4HPF7ugUjE70HDxw/Cv3c9GIMZRTNvh7G4HyJ+NyI+NxAJwdx3sOBUtLttAERssNpF2cVl2LZ5G1asWIHnnnsON998szQnEkFLglUzkYAPDR/ej0D9DuhsuSi54K8w5Ii7mSMblu69wpBpQOG0QjR83IA777oTZ555JkwmaZfTc8WyLJ6avzXhbfwHt8H+w7/g3bmq42eW6tEomPZnGLLF7YUTCxOSZuPOrFHcH3NNZERuvPFGyacEXP4QHL7YnSE925Yh4nNCn1UIS81oSceRqia7WfQlcp0ZcgzRrbhtOvzwww845ZRTJG92dsMNN2D9+vXIzMlERt9oxmfz5s347LPP4Ha7YSmzYPDlQ1E++01U3PQ+ym/4F0r/+AQKTrsFpRc9DkOONDUC8QQ94l9ZxMPoGFRcWwFDrgEbNmzAH/7wB4RC8u310p0j4IDLy+26Juxzof79u6NBiMmK4vPuh7X/ETBkFcBUWAlL+RBYKoeLMh++aWexZD1ejPnGjn4Wd955p2oLV1mWxZb62IWSkYAPjR8/AP+BTdCZM1By/oOi7UzdmZht3mMpmFIAfbYeO7bvwO233y7pufj4ekNdwmxIsPUg6v99ezQIYXSwHXYMSi95GiXnPyhLEAIAXrf4hcR8aSIQ8Xq9OPvssyVdQpqoPqSjSPXwEyVdAipEKKJDrkn8vgmd2frZUHNrDfQZeixbtgxTpkxBW1ubJOd688038c9//hM6nQ6l15ai5q81GPz8YFT+uRIl55SgZm4N+v+1P0ad/Ccw5mwwDAO9LQfmvoOROex4mEqk2/8lnvpGed442pkKTKj6cxUYE4OvvvoKt9xyi6zn78zhd8DtS/7aiAS8aHjvbgRqt0BnyUTJBX+FpeJwycblDejRL2u4ZMfPOy4PGUMy4PV6ceWVV6qyRuG9Nevxy56egUjE70HDB/fAt+fX3wPCB2AqlmYDRzHbvMeit+jR97JoAPX000+rYoomEmHxdIJsCMuyaPnfi2BDfpjLBqPsqr+jaObtMPcZKOMogabWXFnPF4smAhF9rh6bN2/G1VdfLdkSrXj1ISF7pw3uJJ6rEypH5JbWsVirrai+tRr6DD1WrFiBk08+GQcPirObZLsNGzbg2muvBQD0O68fLIOjRaCGLAOyR2ej6LQiZByWgWJbKVZtkG7nW7721GXIUrDambXGivKrogWZzz33HF566SVZz9/OEwwgwiZ+O2EjYTR9/hgCddugs2aj5A+PwNxnkORjMweHSnZshmFQdnkZGBODxYsX49VXX5XsXKnY07YfLy7q2Xgt7HWi/r074d//2++ZkIdglrAlgdMt0bxxJ9ljslF4avQ98IorrsCWLVskP2cin6+rxbaG+PV07o0L4duzFozBhILT5sCYJ19GtbPahgwYGGWrNDQRiJRfWQ7ogXfffRcvvviiJOeIlxFxrPwUAAtL1QhVbHCXiE0vT4rNWmVF9W3V0GfpsWrVKhx22GF4+umnERShs6bL5cK5554Lr9eLinEVME+JvxKjDFMRDKvnKRxhdbIVrHaWc0ROR/HqjTfeiA8//FD2MfiCiaeFWJZF63f/gHfHSjAGE4rPvluyq+/u9tdWSXp8c7EZJWdH//3/8pe/YNu2bZKej6twJIwXf1qCfY1dp7iiq2PuQOBge0D4sKRBCAC0OsRt8x5PydklsB1mg8vlwtlnn91RRya3cITFs9/Ffx6EPXa0LvwnACBn4h8UC0IAIBDWodjWR7HzAxoJRGz9bSg9LxoEzJkzB0uXLhX9HLECkWDzPjjXfAkAyD7qHNHPKTYjpJ2a6cxaaUXN3BpY+1nhdDoxZ84cjB49GosWLUo5a9XS0oJzzjkHmzdvRl5xHjIuyQCji11pmGvOw+oN0l9N85XJSPuhF0/h9ELkHpOLcDiM8847D08++aSsDZ68SQIR56rP4PzlSwAMCk67RfIGc53tPpiJYqu0b7QFJxfAOiD6Wpg0aRLWrVsn6fkSaX/c//3bJ/jq565L1/21W1D3zl8QbNgFXUYuSv7wiGi9QhJpcxmhZ6Sf1mb0v9dO5RiwceNGXHPNNYo0Ovvol/3Y1RQ/CGpd+BoiXgeMRdXIPoJ7bxyp5BrErwviQxOrZoBoMZJnmweOVQ5Mnz4d//vf/zBhQmrbH8cSq6tqy8LXgEgY1gFHwlozRrRzSUWKltaJWMos6HdXP7R+34qGDxqwceNGnHBCtMeK0WiEyWSC2WzGuHHjcMkll+DMM8+EzRZ7rnjFihU4//zzsWfPHpjMJhRcVQBDdvynZ41xKvYF1FevE/Iq84JmGAZ9L+8LnVmHlu9a8H//93/YsWMHnnvuORgMqb/Mt23bhg8//BAHDx5EfX096uvr4XQ6MXDgQIwYMQIjRozAqFGj4Euw+7F7y48dV395x1+OjMPk3yyyzDwSDV5xpxA7Y3QMqm6owu4nd6N+bz2OO+44fPHFFzjmGGmXj7Msi3Xr1mHFihVYuXIlVq5cic2bN2P4mOGwjR8Dp+E0MLroyr+2H/8Fx4qPATYCfVYRSi54SJLC1FgirA45ply0+JslP5cx14iK6yqw62+78M4772DixIm45pprJD9vO38ojOcSZEO8u9b8vmSdQcHUG1Jali42M6tstp9hVdwX1+FwICcnB0NeHgK9VY9IIII9z+yB+zc3srKyMH/+fBx11FGinOu8V5bh512H+lR4d6xEw4f3AzoDyma9KNsLVoijR+zExuA/FDl3yBVCw8cNaF3cCjbODsaZmZk499xzMW3aNNTU1KCmpgb5+fl47rnn8Je//AXBYBCFFYXIvTIXlqr4tRYGxoDI3nthd4nfUlyo/mUONOQ8rNj5WZZF87fNqHu3DmCBadOm4V//+hfy8vhly1iWxd///nfMmTMHPl/irQ8AYNgpY+EccU+PYm7v7rVo/OgBsKEAMkdPR/7J1yjSlXjskAPYCu6bcKUq7A5jz7N74NnqgdVqxYcffohTTz1VknM5HA5cccUV+Oijj+LexpBTgsyRp8D922IEm/YCADKGTkbeSX+C3irt8vvuhh/5T+x2yjdt1fR1E+req4PZbMayZcswerQ8Kx6fXbANTy+IFqmy4RD8B35DsKUWIXt9tOZwzzpEPG3IGns68k/6kyxjSmb8yO3YEHhN1GOGvWFsunYT7HZ70tYCmgpEACDij2DP03vg3uxGdnY2FixYgCOOOELwuY7520Lsb41Oz7DhEGpfvx6hlv3IPvIs5B1/heDjy2HEwAbsMjyl6Bgi/ggi/gjYEAs2xCLsDcPxiwPOZU74Gnp+oFmtVni90X/36mOqYb3QCr0tcaZjYPZw/LLiIknGL5RexyJv6P3wh5N/eEvJsdqB/a/sRyQQgdVqxTnnnINZs2bh2GOPTRoINDY2YtasWfjvf/8LACgbUYZwZRhMFgNDjgE6sw7+g34E9gUQrg3Duc8JNsLCNuRYFJ52S0cw4tu3AQ3v3ws25Id14NEomjlXsVVnmZYQjP3vQygi/RLniD+CfS/tg3OdEwaDAc888wyuu+46UQOwTZs24ayzzsLmzZthMBpQPLwYTAUDfaUepmITsGwwdi5cgojv0EpDnS0XBadcB9sg8TLJfBx59EfYZF8p2/lYlsXeZ/fCudaJAQMGYPXq1ZL32tnT7MbJTy2GY89vcP+2BJ7N3yPi7bl815BTgj6XPy/KDrpiGFrTjH2Wx0U9ZloHIkD0hb77yd3wbPUgNzcX7733HqZMSb3RWCTC4rC7v0YwHP2ncKz8DK0LX4XOlou+V78CnVnaNfBi6VvkgaPwAaWHERPLsvBs88C+zI7IgQgCTQF4WjwAAL1Rj5qLamA+zszpzXp05h+wdKU0HTPFMOqot7DDoXwrfM9OD2pfr4Vv/6GgaMCAATjrrLMwadIkTJw4sSNTEggE8Ntvv+Hnn3/Gvffei7q6OhhNRlReUAnLZEvcWh3g96Dn5f2IhCKwDZqAwjNuRaBuG+rfvye6bXm/sSg+8y5BW8mLYezR72Krfa0s52JDLPb/cz/sy6LLZs844wz885//RGGh8JVtH330ES677DK4XC7kFeeh4E8FsPQ/lEGsyhyADSuvRCTog3vjIrjW/Q/GggrknXClol2hjxm/AOvaFsh6zpArhB337kCwOYjzzjsP7777rqQZuVNufRnf/eN+hO31HT/T2XJg7jMIhpxi6LNLYMgphrVmtKo+V3Iz/QhX3CvqMdM+EAGif+SeJ/fAsz36YTZ16lQ89thjGD6cf8+AOrsPRz/yXfS4HjsO/ONqsH438qfegKyRpwj/Q2Ri0EWQNfgehFjlGlvxEQlEEGwKQp+lhyGL+zxpqfN2bNufK93ABJp09BKstX+t9DAARANA7w4vWr9vhXOFE6FOTfsYhsHhhx8OgyFa2BcIHGoDXlxTjOzLs2Gp5LYcuXDLMCx+7CMgHIKlaiT8B7eBDXhgqRqForPvhs4oz6qJRCaN+xVr3f+W7Xwsy6J5fjPq368HG2JRVlaGt99+u6OOiq9IJIK7774bDz8cnforH1WOzMszYcjp+toZbrkcP61R1+7gAHDsUSuwxvGJ7Of1bPdg5yM7gTDw0ksvdbQGENsTb36KW6+6AGzQD8ZkhW3QeGQMOQ6W6lGq7T/VWfnIR2AP8NshOBE+gYgmVs3EorfqUXVLFQpOLgCjZ/DNN99g1KhRmDVrFvbt28frWLX236dkQkE0f/UMWL8bppL+yBx+khRDl0wookOhVd5uokLoTDqYy8y8gpA8cwG2H1DfXj+dhb3SbLSWCoZhYBtgQ9/L+2LQs4NQfnU58o7NQ0ZZBliWxfr167FmzRoEAgFYMi0oG1mGynMqUTi3kHMQAgDlY0ai+My7AL0Rvj3rwAY8MFccjqKz71JFEAIAdfWVsp6PYRgUTilEv7v7wdzHjNraWpx00kmYNWsW7x4XXq8XF1xwQUcQ0n9Gf+TckNMjCLEZbPh1k/SrYFIRCclbk9LONsCG0nOjxZg33XQTvvrqK9HPsfTHZbj9TxeBDfphqRmD8tlvo3D6HFj7jdVEEAIARRZ5Xx+daTYQAaLBSJ+L+mDAwwOQPS4bkUgEr7/+Ovr164eLL74Ya9euTXqMUCSET7Z90rEBV3ufg/yTr9XME6izXKO6e50IVWkdBZaVv9iRj4ZmeTuscqUz65A7IRd9r+iLmodrMPjZwai8oRIV11dg0OOD0P/5/si/OR/Zp2VDZ+L31hAJW2DtPw7F59wLnSUTlqoR0f83ytvgLZH99VlgpNp4JgFrlRX97+uPvMl5YFkWr7/+OoYMGYKzzz4bP//8c9L7NzQ04IQTTsAHH3wAg9GAgdcOhPVMKxh9z79lUOYkuHzKr8KIJeCXfhfseApOKUDW6CwEAgFMnz4d06ZNw4YN4uwLtW7dOkydOhVhvwfmyuEoOvMO6Ezqed5zlcEo10tEs1MzsXi2e1D/UT3cmw6t3z7ppJNwyy23YMqUKdDper653vfTfdi8K4xP7/gc/r2/gjGaUXT2PbBWqbcGIRE1TQtIYYj+Wvy8QZleHVwZ9RFkD74PgUjyrdfTxVD9tVjx++PChkOATq/I6phkakY/hSZfg2Ln92zzoPHLRjjXHioiHTx4MMaOHYsxY8ZgzJgxKCkpgdPphMvlQnNzM2699Vbs3r0bmTmZKJ1dCsug+B9ypa7bsW1frgx/CX8leT54Su9T7PwRfwT1H9WjZWEL2BALnU6HWbNm4Y477kB1dXVKx9y0aROOmTQJLc3NMJcNRvF5D6imAJWvSeM2YK37HdGO1ytqRBLx7vai6esmOFY6OpaSDhw4EDfccAMuvfTSjn+UNze8iUcXPoqmFwNo2rozut/CuffBUj5Mkr9HDmI/mdREx+jA7L0PbS517KyZyMgj52Gnc7PSw5DNgMifsWaLst0ZuRh39HvYYl+j9DDgO+BD01dNsC+3gw0nfwsuLC9E3uw8mPvEn+bqlzUY636+TMRRim/4ka9jtzPxbrRS89f7Uf9BPRyrDq1mOfbYY3HxxRfjnHPOQW5uLqfjrFq1ClOnTUVzUzNMJf1RcsFfobMol/URaszgWmxjnhPteHwCEXXm8ASyVltRcW0FAucE0Dy/GfYf7Ni2bRtuvPFG3HHHHTjyyCOxfe927N+3HxF/dJMqnTkDxec9IHmrY6l5PblKD0EyNVmDsVYDQQgAZOurAfSeQMQf0MbjYoU6pi4tfS0ov6ocpReUwrvLC+9uLwJ7AwjuDSLsDcNkM8FgNUBv0cNaZoXtTFvSWqqMgLTN08SQh9HYDWUDEXOJGZXXV8K9zY2GTxrg3uTG0qVLsXTpUlx//fW45JJL8PDDDydc4fTdd99h5syZcLlcyO/fB7ZTH9B0EAIAjS05QIEy507LQKSdqciEPhf2QfFZxWj7sQ2t37XCVevCwoULu96uoAwFp98mS6tjqbXaswFpl8orJpuVbhdVsYUV6rCqFJ9fG4EIGyxSeghdGLIMyBqRhawRwgo5M4yZWLtJPRtAxlNb2w9Qpma1h4yBGai5tQaB5gDsy+1wLHPAu9+LV199FR999BEee+wxXH755T2m9D/66CNceOGFCAQCyBiSgRP+7yas3KruAnouDjRaUVhihTcUfyd6qaR1INJOb9Gj4MQC5J+QD/dmN4ItQRjzjDDmG1FUWoIDm25XeoiiqWuyQZemgUh9fbXSQ+CsqbkI0PYFEmcZxkzUN0u/u6oY3K5cjZfoxzbQNgnf+9VfXL99fw76jSlBo7c++Y1lYiowoWh6EQpPLYRnmwcH3z6Iln0tuPLKK/HGG2/ghhtuQGtrK+rq6rBnzx689dZbiEQiyBmXg75/6gt3UB1ZNqEirA5ltn7Y4dgo+7l7RSDSjmEYZA7p+ulQkl2BAwqNRwpuvwGV5gK0yrCng5xyTLnYtlk7Vx2767KRMcggSydPpQ2ynYClfm28lTS1ZiuWfpZS7T552peLodw0Do3eL5UeRg8MwyBjUAb639cfzfOb0fBJA3788Uf8+OOPPW7b54Q+yP9jPhgdg9q69LniyGIqAVAgIrsMnfoL7PgqMPdJu0CkyjYK+1ntXMoGgjoMstVgj0sdW8JLRc/osX37KKWHwdnBJhuyCg2aafrHxWE5o7Fqk3bSoPaWQYBefYFIO0bPoHBqIbKPyEbDxw1gGhlY8ixAFhDODMNSYUHWmCwwDIMcUw72u9TRJ0cMYb8yn4e9PhBBUJ09H4TI0JUAEGeNvBKyjNE3VWfwUFV72K29IuI8Qw32IL0DkaE54/HTb9pZrhiK6FBs7YNaD7+mh2oWbD5e6SHwsmlHEfKHZcIddCk9lIRMBSaUX5W4OWGxpRL7ZRqPHFpaCgEFXs7aucSUiNfDb1dSLdCFtR1c9c+YgIYNt6MmNAejc6ajyFqCbbvLlB4WbxFf+hesttWNV3oIvOUatfdciqdf1hCs21as9DB4CYR16JcxRulhiMKmS4/6kHZ7DuZAx8gfFvT6jEhLW67SQxBdwJer9BAECXkqEIro8Ou2YmBbMYBJSg8pJU3NxYB69rUSXb+sIVi3SXsFF2ZoZxuEZIyuE5UeQkrCrqEAlio9DMGYYPo8lwDAG9Cj0laOWvdeWc/bqzMiDBgcaNROWpkrh1M7RZ2x1DVq6wovnh37c2A1aGM1SSosnuOUHkJKQn5tZwzblWdUY/UmbWbdNu3sCwOj/etgtytf6SGIrsBYLfs5e3UgUmQtgT+o/iVvfNU3a7eK22bIwJ6D6ZFGCEV0qMwYovQwJFFoKcYvm5TbJEsIpyNX6SGIIicwRfX7LsXjcBvRP1s7fYHiaWzRTpEwV/qQ/MFtrw5E8k3avJpIptlhgs2gzUxPRcYgRDS0OiYZW2Sg0kOQRLnhJATD2nyc6pu1/+FRbO2DVRuqlR6GIJagtgMRo86I2qb0uGjqzOWQPyOtzXcSkViQHlMAsRRZtFmQZ2OrlR6CqFqb0y/YtRky8Osm7WZ6Gu1mzU+Z9cEpCEW0/fa9c0+10kMQpMTaF+GINjNSieyvl38Bh7afyQKprd2zmLIM2iyi8jjT64N7274CGHVGpYchqoHGc2B3afdvYlkGxRbtPs9yzXlYtUH7mbbaJhv6ZWk3oM01avc5lEhjmwW5ZnmDkV4diLhd6bd0t52R1WaQtadWe6swEvEF9KjKHKT0METTP3soflqjvZ4u3WUbtNvIsMYyAb5AetS2ZYfHKj2ElBkj6bV0t7NSSz9Zz9erA5GmVpXsviSBsF971dyFlmI0tmk7ZR5LFrT/wQ0ABsYA576ZaVHDYwhrM2MIAC0Ng5Uegmi27+oPBtqc3gh402P1VSxWNnEjN7Fp/x0lRQbGgINN2izo5MLt1t4S3lKL9tPNsTjb5H1RS2V41hnYUav9Qk8ACPi0mXnLNxfgt13aHHssB1us6J89VOlhpKTVnqv0ECTj98ib7em1gUixtY/mi70SadRgtscQrFZ6CJLYvrdIkW6FYupjK8eKX7SbRu+uza69QB0AqixHpUVGqrOMkDafV7UN2m2TkExjs7zZnvR6RvOQTm2eY6lvtmiuYVBba3rOuTq9RlRkyDvnKjar47y06rlT26S9QB0AWhq1W9wZz9Yd/TQ3PZNvLoDTq92C7WT21mXApDPJdr5eG4ikU5vnWEIRHYqs2vlg1zE67NyfvsXDeXrt1omMyD1ec/uZJONwG5Fj0lZWJN9cgN92ps+0TLuGNgsGaKy5WaGlQukhSCoU0aFvRrVs5+u1gUjIn34v6O5yjdoJRMozquH2ayuDw4ffWa30EFJiM2Rg62/abOWeTKFFW7U76Tgt084a1NYmeDZod9UVV9n6KtnOlZ7Pag5czvS9+m5nhnaW8OYZ+is9BEnt3K/NjMIg85lotFuUHoYkMjW2c2prGk7LtNuyo0ZTdVTp3IOqg1++8gVZHvmXXnoJNTU1sFgsGDt2LL7//ns5TptQQ7M254h5CWkn6xPxanPfEq6a7GaUZWgrnds3owrL12hzRQMXTEg7HyZ55gJsTMNpmXZNdjMGZo9UehicuZzaa4/AV1ubfK8PyQOR9957DzfddBPuvPNOrFmzBpMmTcK0adOwd6+82wx3ZtZbUN+Wnld5nXk9uUoPgbP6Jm1mDPgoMmqr/4PFcZZm95PhwufRzodJleXItJ2WaWfya2d6Jh32K0pmz0H5aqgkf2Y/9dRTmDVrFq688koMGTIEzzzzDCoqKvDyyy9Lfeq4Sqx9NbtrJR+tdm28WCx6C3anSX+KRELuGqWHwNnhuROxdmt6F3S3tGmnWLWtMX0zU+02ba9Cvln9WR+z3oK6lvRrvNid02vEINyAikzp37ckDUQCgQBWr16NKVOmdPn5lClT8NNPP/W4vd/vh8Ph6PIlBS23d+Zjz8FMDNZdh1G5U9HHpt7CvIqMgWm5eVR3e2tLUWgphp5R9zJYs96C3VtPUnoYkjvQmIlhuePRx1au6sck3adl2rU6Tdj/6/+hf/gmjMidDLPerPSQYiq1lveKC1kAWL2pLzavugrDjH9CiVW6z01Jlyk0NTUhHA6jpKTrlVVJSQnq6up63P6RRx7B/fff3+Pnz0x+BhlZ4m23bHCF8HzDM9CFA6Idky9WZwQTCQo6RmPVdNT1P5fz7U2WNrD6RrBgBZ1XbHo2B/5h/K6+M9q2oGzrv2BxyT/Fx+qMaKw4BY1VpyJi4NuddzJ0TAQGoxuMwYEI41Hh45EJv4dfodqg5bfD7On5mlZSc98TUHvYJUluNR4AoNOFYDA3g9U51Pl4jOH3eFRs/Dty65dLNKLU1Pc7Cw3VZ3C89RkwGHxgzPvU93iELcjM24ri3Z/DEJDmYlkObaXjsW/onzje+mjomHOgt+0Ciwine7idbpyCUzjdVpb1kgzTNXpkWbbHzwBg7ty5mDNnTsf3DocDFRUVOLrsaGRni5y6jziA9y8BWG7/qKLL7gs4Dgg6RP6YmThsEJ+CoiIA6dJGvQgYNx7Y9i2w4hVg5yLpT5lXDYy9HBh1EQoyi6Ctig+JLd4B1K1RehRd5I+YioG8Xh9plCnNPA34xxNKj+IQczbyJ76FIRl8MzsqLfCuHAsEZwKbvwR+eQvYtUTpEfGWf/jJ6Mfr9QEA3Fea8ZnRkHRqprCwEHq9vkf2o6GhoUeWBADMZjOys7O7fElmyOnAGc9Ld/xkLLmA0NRjnnZqDiSh0wOHTQMu+RTof4L05zvjBeCYm4BM7ay2kI1FhfUWRb04VCwbDQzhmn2QwcQ/A7yDEJUzWoHh5wCXfg5c+l+gSGPLq8tGKz2CDpIGIiaTCWPHjsX8+fO7/Hz+/PmYMGGClKfmZvQfgVMeVubcRitgERho5cnXcEb1DDIUj5nTd28JwSy5So+gp8JBSo9AWcffCaihdXpWH+Do65QehbRqjgWu+R6Y+ihg1kjhfdkopUfQQfJVM3PmzMFrr72G119/HZs2bcLNN9+MvXv34pprrpH61NyMnx2N1uVmtAJmIb1MGCBHpWlLJRhkKGwzUSASl9oyIkYbvT6KBwMjL1B6FMDkuYApfXc676A3AkdfC9ywGigYoPRoEisYoKrXrOSByPnnn49nnnkGDzzwAEaNGoWlS5fiq6++QlWViq7mR5wv/zmNVmGRc3YZYEz/XiicyRKIiFcwnXZU9KYGIJoN0aV33w1OJt8O6BTcnK3wMGDURcqdXwmZxcBpzyg9isTK1NWzRZZX6nXXXYfdu3fD7/dj9erVOPbYY+U4LXe5CgRFQqdm8qpFG0paoIyIstQWiBRpd5NBUeVVA2MvVe78J90L6NN3D6m4aiapOwBTUX0I0Iv3munCnAlkyFyAaBCYEaFApCuDDNkhyojEZ81VegRdUSByyLF/kaeGqruKo4HDTpX/vGpx8oOAVaXde/v2woyIJsj9wS50akaJLI6aSZ0RMdqiq3RIbGorVu3NK2a6yyoFDpsq/3kHnQLEaNPQa2QUAKf8VelR9MTogNIRSo+iCwpE2skeiNhoakZMUmdEKBuSmNqmZgopI9JFpgLt+jMK5T+n2oz8A1A9SelRdFU0RHXFwxSItJM7wyA0I0KBSFdSZ0SoPiQxNQUiehO9PrqzKRAUyD3drUYMA5z2dPQ5qRZ91VUfAlAgcojsGRELZUTEJHlGhAKRhNQ0NVMwoHcWSCaiRDMxJYIfNSocCAw7S+lRHKKyQlWAApFDlJiaSbWPiMEaXSJGDpH6ioOmZhJTU0aEClV7UiQjkmadVIUYfo7SIzhEZUt3AQpEDtFSsWpede8uAotF6owIdVVNTFWBCBWq9qBEvQZlRA7pN1kdK2j0JqBkmNKj6IECkXbZZfI2/jEI6CNCrd17omJVZRktwvdOEktvb+0ei9xBgd4ksHN0mtEbgWEzlR5FNAiRo+cSTxSItNPpgVwZW0IbrYA5xatIqg/pSfJiVXpTTUotvUQoI9KT3BkRWyFlbbs7XAXTMyqclgEoEOlKzg94ITUiFIj0RBkR5alheobRAQX9lR6F+lhyo/82cqH6kJ4qxwNZZcqOQYWFqgAFIl3JGogIWDVDgUhPUmdEqEYkOTUEIvn9VJl6VpxOJ2+NAi3d7UmnAw5XePWMyjqqtqNApDPZMyIUiIhG8qkZyogkpYZAhKZl4pNzeoYKVWM7/Gzlzm20qbbRHwUinckaiFijH26ppEtzK8Ufj9ZRjYjy1NBLhApV45MzOKCuqrGVjY5m7ZRQOkK1/XUoEOlMzu6qRmu0mItvnUhGMV2dx0I1IsqjjIi6yVm3YaMakZgYRrmi1T7q2l+mMwpEOpMzI9K+GybflTM0LRMbTc0oTxWBiDpTz6pAGRF1UGp6JqtUmfNyQIFIZ9Zc+dLLxt8DEb4FqxSIxEYNzZSnhkCkcKDSI1AvqhFRh+LBQMnh8p9XDQ3V4qBApDu5PuiNv+9+yLdglQKR2GivGeUp3Uckt5IyV4lQRkQ9qo+R/5wqni6jQKQ7OT7oGV200x7Av0aEuqrGRrvvKk/pjIhKVwSohpw1IrR8NzElLihtlBHRDjk+6I22Q10HaWpGHFK3F6cr7eSUDkSoPiQxOTMiKr76VoW8GvnPqeLHhAKR7uT4oG+vDwFoakYsegPA6KU7Pu2bkZzSgUj5EcqeX+3k+iDSGZV/LqhdvgKBCNWIaIjcgQifjIjeBGT1EX886ULKOhHKiCSndB+R6knKnl/t5KrbsBXQPjPJ5FYBkPnfiKZmNESOQMTQOSPC40o7tzK6OR+JTao6EUYnfTFsOlAyECkZTvubJCNXRoQKVZMzWqI7vsvFnH2oLlGFKBDpLqdC+s2hUp2aoWmZxKQKFkxZdIXHRap7J4mh5ljlzq0V+gRTJmJOa6q4FkFV5KwTUXE2BKBApCe9Ecgul/Yc7Ut3AX5zqXJ2ftUiqTIiNC3Djd4IGBX6t+p3nDLn1Zp4BatiFvpSRoSb/Gr5zqXi+hCAApHYpF45Y+x05c4rI0KBSEJSBSLUzIw7JXqJMProFuskuXhBQulwEc9BS3c5kTUjou4sFQUisUg9BdI5I8KnRkSJJV9aQhkR5SmxWqLvGGWnhbQkXkZEzECEuqpyI+fKGZqa0SDJA5EUV80oseRLSySrEaGMCGdKBCJUH8JdvIJeUTMi6r76Vg3KiHSgQCQWqQMRQ6pTM9WiDyWtSJYRoUCEMwpE1C1WtkJvErcrLWVEuJHzwpJqRDRIzqkZrhmRjCJqqpWMZBkRmprhTO4lvHoTUHGUvOfUslg1InnV4k5tUbEqN9Y8+QJ3mprRIKlTZp2nZkwcgwuqD0mOilWVJ3dGpOKorq8nklisFH1+v98vjkRaok4ZEe7kel+nqRkNsuVLe2XXOSOiN3Bb8kj1IclRjYjy5A5EaFqGn1hBQn6/aJ8csZ7nlBHhTq73dcqIaBDDAAUDpDu+sdsHJpe0KGVEkpNq4zsKRLijQETdYhWS5veL/leMzB+jV77Vv5ZQRgQABSLxSRqI2Lp+z6X2gwpVk6Plu8qTs4+IMQMoGyPf+dJBzIzI7x+GYjzPbQWAjj5WOIuXESkeJu55qFhVowqlDES6zWlzWTlDUzPJSTU1QzUi3ImRETFlASfem/x2VeMBg0n4+XqTWNMm7RkRMTJ/NC3DT7yMyITrgZEXincemprRKCkzIt0/MGlqRhy0fFd5ggIRBhh7GXDjL8CkOckLuWuorTtvRmvXmjSdAcipjP6/GM9zlU8BqE7MC0wGGHAycNK94jwmpkzp3htFQoFIPLJOzSQJRIwZQGaxdONJF1SsqrxUA5GCAcA13wOnP3vouZ5smqd4aGrn6u0614nkVEQL5gFxMn+UEeEnq6xnbVvfMUBmEZBVChx3q/BzqDwbAlAgEl97ulIKPaZmklz55VXT7q9cUI2I8lItVJzxUs/unsmCGiX2tUkHnetEOr/PiVIjQoEILzpdzz3EBp5y6P+PuhbI7y/sHCqvDwEoEInPlAFk95Xm2N0zIsnecKk+hBvKiCgvlYzIuFlAZYymZMmCGmse/3ORrlmLLoEIZUQU0X3afdCUQ/9vMAFTHxF2fA1Ml1EgkohU0zPdl+8mm5qhFTPcSFW4SMWq3JmzwasxVmZpdC48lmRBDS0TTU3cjAjViCii84VmZglQOrLr7wedEq0ZSRVNzWicZIFI94xIkkCEMiLcUIt35el0/PZPmva3+AFHsqkXJfa1SQedP5g6ByKi1IgUCT9Gb9P5QnPgybGXP594d+rH10BwSIFIIpIFIinUiJDkaNWMOnANEAZNA4bOSHCc3Pi/M2UdKrIk/MSdmhEh4KapGf46T810rg/pLKci9eNTjYjGSRWIGHj2EaGlu9xQjYg6WDkEIsYM4NTHExdhJwpoqFA1dR1TM0zXQkkqVlVGe8ZbZwT6TY59GyEbntLUjMYVCKxWjqd7RiTR1AyjB3IrpRlHupEiI2Kw0JU3X1xqN4aeAeQmucpLFGxQfUjq2rMWORVdXzNcN+DkcmzCXW4VACbaoC/eZ4He2HNKnysKRDQutyoapYqt+5V7ooxITnn0SUiSkyIjQvUh/HGZmuGy4oUyItJoz1p0rz0T/FxnaCVTKowWILss/rRMu1SzIlQjonF6g/iFogZLz2KkRIEIFapyJ8WmdxSI8MclEOHyppoo60GFqqlrb2jW/b1FaLGqLR/Q6YUdo7fKq4mujkmETxF4ZxSIpAGx60S6T8sAiadmqD6EOymmZsRIV/c2XKZNuLypUkZEGh0ZkW5NG4XWQlF9SOoGnJj8sybVjAgVq6YBsetEYs3zUUZEHDQ1ow5cshVc9leiGhFpmLMAvUn8QISW7qZuzKXJu2dzec3EQjUiaUCOjIjREn1jiIUyItxJkRGhZmb8cZqa4ZIRyY3/O6pFSB3DRLMXPQIRgUF3hvqnAFSLy79dKhkRoy32Z47KUCCSTMFAcY/Xfeluu3hPMuohwh1lRNRBrIwITc1IJ6Ow53uL4BoRmpqRVCo1IhqoDwEoEElOjowIEP9JRlMz3FGNiDpwCRLMXHqNWONnCmlqRpjS4T2DbMFTMxSISCqVQEQjmUMKRJLJLBb3wyheIBLrCtFWKKyRTW9DGRF1ECsjwjCpt38niZWP6/kzvVHYyjPKiEgrlc8CyoikCYYRt2CVT0aEsiH8xLt6FoJqRPgTa/kuED/zYdHGlZ5qlR8Z++dCAu+sktTvS5JLpVhVA4WqAAUi3Ig5PcMnEKFCVX50OvGDEcqI8CdWsSoQP/NBGRFhiofE/rmQwLvk8NTvS5KjjEgvVyhiwWq8Nr2xol3KiPAn9vQM7TPDX7L6DZ2BeyV/vKCGakSEidd4LNXnuymLLpykllKNCGVE0gdlRLRD7IJVCkT4M2VE90iKx5ydvGdCu7hTM9RZVRKpPt9Lh8fevp6Ih1bN9HJi1ojEu2KPlREpGSbeeXsL0TMiNDXDW6IiU4DfXHes45iyaCNCqaT6fO8zUtxxkJ6oRqSXyxezWDXO1Ez3+b/SEdGrDMKP2BkRWrWUmkSBCJ8ru1i1IFQfIp1Ua0T6jBB3HKSnlGpEKBBJH5Zs8ZamcZ2aOWIW9/Q1OUTsje8oI5KahO3ZeUyrxJqaofoQ6aQ8NUOBiOSoRoQgq1Sc43DpI2LKAg4/R5zz9TZUI6IOCTMiPK7sYh2HMiLSSeX5rjcDRYeJPxbSFa2aIcgsFuc4cTMind5wR15A/StSRTUi6pCooyNNzahXKs/34iHRZmhEWqZMADyz5BSIpJlMsTIiHGpExl0hzrl6I8qIqEOilLDQYlWampFOKhdAVB8iD52OX1bEYAFMcT5vVIYCEa7E6hqYbNVM5XigZKg45+qNxM6IUGYqNYmK5PhkRGIFHZQRkU4qgTetmJEPn0BEI9kQgAIR7jJFCkTiZkR+f3OmbIgwomZEmPiPF0ks0Zsgn4xIrKCDMiLSSSUQKaVARDa8pjW1UagKUCDCnWiBSIJiVVsBMHSGOOfprcTMiJgyaeVSqhK9CfLKiFCxqqz41ogwOup3JCdeGREKRNKP1IGIMQMYc4k0W9n3JgYR95qhQtXUiZURMeegR4EeZUSkw3dlRsFAzdQhpAU+rx0KRKL++te/YsKECbDZbMjNzZXyVNKTevmuTgccdY045+jNxMyIUH1I6mwirZrR6XrenjIi0uEbfFOhqryoRoS/QCCAc889F9dee62Up5GH1BkRQLxgpzcTM6NEGZHUiTU1AwDWbtMzlgRBDhGG73OeGpnJK01rRCTdsOH+++8HAMybN0/K08jDnBmdPgm6hR2Hih+lJWqNCLV3T5lYUzPA71Mxew99TxkR6fAtVqUVM/KijAgRZQmv2MtLSVeUEVEHcxagi3Odwzcj0r1gNVGzNCIM30CE9sOSF5/tETJE2pZEBqrawtLv98Pv93d873A4FBxNDJmlQMtOYcdINDVDhBM1I0KBSMoYJpoadjf0/B3fjEj3DAifN2PCD5+6qJxKTRVEpgU+GZGcCunGITLeGZH77rsPDMMk/Fq1alVKg3nkkUeQk5PT8VVRobJ/SKFt3nVGaoUsNTE3vaNiVWFipoYZ/lfdnQMPczag0wsaFknAaAPnNuJUqCo/XoFIuXTjEBnvjMj111+PCy64IOFtqqurUxrM3LlzMWfOnI7vHQ6HuoIRocWklA2RnqhTMxSICBLratmczb83S+flurR0V1rM74FiwJn8tlSoKj+u05qMXlOLH3gHIoWFhSgslGbuyWw2w2xWcR8NoStnKBCRntgNzUjqYgUifKdlgK5TM91X0BDxmTK4BSKUEZEf14xIdl9NZQ4lrRHZu3cvWlpasHfvXoTDYaxduxYAMGDAAGRmavBNngIR9aNiVfWItXyQb6EqQBkRuZkzAReH25WNkXwopBuugXxOX2nHITJJA5F77rkHb775Zsf3o0ePBgAsWrQIkydPlvLU0hC6aoaW7kqPGpqph1gZkc7BBy3dlR6XADy/v3gbgRLuuAbyGqoPASRevjtv3jywLNvjS5NBCBBdNSMELd2VHtWIqEesYtWUMiKdpmMoIyI9Lv1zqsZLPw7SEwUiRPjUDGVEJEfLd9Uj1tSM4BqR3Hi3ImLh8ryvnCD9OEhPfGpENIQCET5sBfGbNHFBNSLSE3XTO8qICBJv1QxfVCMiLy5TkpQRUYbRyu0zSEM9RAAKRPjR6YAMAb1EjDQ1IzlaNaMeMadmUmib33lqhrqqSi9ZRiSzFMirkWcspCuG4fYaoqmZNCekqRlNzUiPilXVQ6ypmS6BSG7KwyEcJasRqRrPvxcMEQ+XrCIFImlOSJMYmpqRHhWrqodYUzNGy6EAk6ZmpJcsI0L1IcpK9hoyZWpuGwQKRPiijIi6iZURySjS3FWF6lhyAabbW0yqb5DtAQhlRKSXLBNI9SHKSjY1k1OuuYwVBSJ8CVnCS8t3pSdWRqTmWM29mFVHp+uZwUglIwIcCkAoIyK9RBkRcw5QPFS+sZCekk1vavACigIRvoQ08aGMiPTE2vSu32RxjtPbdS9YTaVGBDiUSaFiVeklqhGpPEpTrcPTEpeMiMZQIMKXkF4iVCMiPb0RnHcPTaTmOOHHID3rRFLNiLRnQjQ2961JiTIilTQto7hkr6FsCkTSn5CpGVq+Kz2GET4FllcN5FWJMpxer/vKmVSW7wLRAMScTVfjckhUI1JFhaqKo4wIoakZDRBaJ0LZEPGINTVjzaX6ELnEWy2mNwNlo+UdC+mJakSIsIZmNDUjC6EZkX4UiIjG1q2mQ8jUjJWmZWQRLxApHyfu8niSmmSvIY3tvAtQIMKf0ZL6lZmBAhFZUEZEPTpnREyZqU+tWHKoUFUu8WpEqD5EHZLWiFAg0juk2tSMMiLyEJIRKTkcyCgUbyy9XecakVSzIQBNzcgpXo0I9Q9Rh0Q1IpklmsxaUSCSilSbmlGNiDyEbHxH2RBxdV41k2p9CPB7RiRX8HAIB8YYGRFGB5QfKf9YSE+JXkcarA8BKBBJTaorZygjIg8hGRGqDxFX56kZIRkRSy5lRORiMAH6bsF86XBhgSQRT6KMiAanZQAKRFKT6soZCkTkkWogojPQ8kSxdZmaSXHpLhDNhlBGRD7dC1Zpfxn1SBTQ51TINw4RUSCSilSbmlEgIo9U50j7jhX2YUl6EnNqhjIi8ukeiFCArh4JAxGamuk9aGpG3VLNiFB9iPg6r3QROjVDGRH5dC9YpRUz6pHoYokCkV4k1akZWr4rj1QzIlQfIj69MbpRGiAsI2LKBGy0mkk2nZfwFgwEMouUGwvpymCKf7GlwR4iAAUiqUlpaobR5LIqTUolI2KwAuVHiD8Wcmh6xiygIZlOR2335dR5aoaW7apPvKwI1Yj0IqkEIkYbbSsvl+4V/1xUHEmBolTaAxGhqy40uJmXZnXOiFChqvrEmubUmzWbNaRAJBWWHP7bzVN9iHxSyYhUHyP+OEhU+8oZoYXAeoPwsRBuOj9WVKiqPrFeS9ll0cyhBmlz1EpjGP51IhSIyCeVzEbVRPHHQaLae4kIKVYl8mrPiGT3BXIrlR0L6SlWdlGjhaoABSKp49s4hgIR+fDNiOjN0aW7RBpiTc0Q+bTXiFSOpyllNYoV1Gu0PgSgQCR1ZWP43Z4CEfnwzYiUHxHdzJBIo2NqhgIRzWgPRKhQVZ1iBiKUEel9KniusKClu/LhmxGppmkZSVFGRHva+4hQoao6xaoR0ejSXYACkdTx3QCKdnSVD99N76g+RFpiLN8l8jJlRJvRFQ1WeiQkFqoRIQCi0SefObmSYdKNhXTFJyOiM1L/EKl1FKtS+3zNMGVG60M0ugoj7XV/LRkzNF3nRs8yIfh8gFEgIh8+gUjfsYDJJt1YSLRGxGDhn6kiymkPRIg6dQ9ERv+x63YKGkOBiBAVR3G/bcnh0o2DdMWnWJXqQ6Rny6dCVa0xZ1L/EDXr/HpidMDR1yo3FhFQhyAhKjjWiZgygbwaacdCDuGTEaH6EOlZ86lQVWsyiqmlvpp1DkSGnAHka/vzhTIiQpQO57YapngozbXKiWtGhNHzy2qR1Bgt0a6PRDsK+kc3LCTq1Dmwn3CDcuMQCX06CqE3An059BOh+hB5cc2IlI3uud05kUZetdIjIHxQEzN1a68RqRwPlI9TdiwioEBEKC7TM6VUHyIrrpveUX2IfCgQIUQ87VMzaZANASgQEY5Lar9kuPTjIIdwzYhQfYh8KBAhRDzmLCC/PzBomtIjEQUFIkJxWcJbMlT6cZBDuNSIMDqg8mjpx0KiKBAhRDzmLGDC9WlTe5gef4WSMgqjkWk8edXUyEluXDIifUYBFur0KRtaNUaIeHR6YNRFSo9CNBSIiCHR9Az1D5Efl4zIqAulHwc5RMPNlghRJb6be6oYBSJiSLQBHgUi8kuWETFYgeHnyjMWEkWrMAghcVAgIoZEGRFaMSO/ZFcKw2YC1lw5RkIIISQJCkTEUDQ4fgtryojIT6cHdAmaBo+5VL6xEEIISYgCETHo9LF3PjRlArnUJlkR8aZnCg+j1TKEEKIiFIiIZcwlPX9WMixtlldpTrzpmbGXUr0CIYSoCH1KimXYmUD1pK4/o2kZ5cTKiOhNwIgL5B8LIYSQuCgQEQvDAKc+Ht1IrR3tMaOcWBmRIacDGQXyj4UQQkhcFIiIqXgIcNQ1h74vpdbuiomVERl7mezDIIQQkhgFImKbfBuQURz9/2Jq7a6Y7hvf5ffrOXVGCCFEcRSIiM2SA5z8QLSlNW0xr5zuGZGJN1GRKiGEqFCCZgskZSPOBzzNSo+id+tcI1J+BDD6YuXGQgghJC7KiEhBpwPGz1Z6FL1be0aE0QHTn6Rl1IQQolL07iwVmgZQVntG5IirgD4jlR0LIYSQuCgQIenJYIkWDZ9wp9IjIYQQkgAFIiQ9GUzAlIeixcOEEEJUi4pVSXoacBIwdKbSoyCEEJIEZURIeho6k+p0CCFEAygQIemJghBCCNEECkQIIYQQohgKRAghhBCiGApECCGEEKIYCkQIIYQQohgKRAghhBCiGApECCGEEKIYCkQIIYQQohgKRAghhBCiGApECCGEEKIYyQKR3bt3Y9asWaipqYHVakX//v1x7733IhAISHVKQgghhGiMZJvebd68GZFIBK+88goGDBiADRs24KqrroLb7cYTTzwh1WkJIYQQoiEMy7KsXCd7/PHH8fLLL2Pnzp2cbu9wOJCTkwO73Y7s7GyJR0cIIYQQMfD5/Ja1RsRutyM/P1/OUxJCCCFExSSbmulux44deP755/Hkk0/GvY3f74ff7+/43m63A4hGVoQQQgjRhvbPbU6TLixP9957Lwsg4dfKlSu73OfAgQPsgAED2FmzZgk+Nn3RF33RF33RF31p42vHjh1J4wreNSJNTU1oampKeJvq6mpYLBYAQG1tLY4//ngcddRRmDdvHnS6+LNB3TMibW1tqKqqwt69e5GTk8NnmEQiRxxxBFauXKn0MMjv6PFQF3o81IUeD+XY7XZUVlaitbUVubm5CW/Le2qmsLAQhYWFnG574MABHH/88Rg7dizeeOONhEEIAJjNZpjN5h4/z8nJoWJVldDr9fRYqAg9HupCj4e60OOhvGSf+4CENSK1tbWYPHkyKisr8cQTT6CxsbHjd6WlpVKdlkhs9uzZSg+BdEKPh7rQ46Eu9Hhog2TLd+fNm4fLL7885u+4npKW7xJCCCHao4rlu5dddhlYlo35xZXZbMa9994bc7qGEEIIIerE5/Nb1oZmhBBCCCGd0aZ3hBBCCFEMBSKEEEIIUQwFIiSml156CTU1NbBYLBg7diy+//77jt/V19fjsssuQ1lZGWw2G6ZOnYpt27YpONr0tnTpUpx++ukoKysDwzD49NNPu/yeZVncd999KCsrg9VqxeTJk7Fx40ZlBtsLJHs8Pv74Y5xyyikoLCwEwzBYu3atIuPsLRI9HsFgELfddhuGDx+OjIwMlJWV4ZJLLkFtba1yAyY9UCBCenjvvfdw00034c4778SaNWswadIkTJs2DXv37gXLspg5cyZ27tyJzz77DGvWrEFVVRVOOukkuN1upYeeltxuN0aOHIkXXngh5u8fe+wxPPXUU3jhhRewcuVKlJaW4uSTT4bT6ZR5pL1DssfD7XZj4sSJePTRR2UeWe+U6PHweDz45ZdfcPfdd+OXX37Bxx9/jK1bt+KMM85QYKQkLn4N3qXz4osvstXV1azZbGbHjBnDLl26tON3TqeTnT17Ntu3b1/WYrGwgwcPZl966SUFR5vejjzySPaaa67p8rPBgwezt99+O7tlyxYWALthw4aO34VCITY/P5999dVX5R5qrwOA/eSTTzq+j0QibGlpKfvoo492/Mzn87E5OTns3//+dwVG2Lt0fzw627VrFwuAXbNmjaxj6s0SPR7tfv75ZxYAu2fPHnkGRZJSRUYk0RU4ANx888345ptv8M4772DTpk24+eabccMNN+Czzz5TeOTpJxAIYPXq1ZgyZUqXn0+ZMgU//fRTRwv+9hb+QLR7oclkwg8//CDrWAmwa9cu1NXVdXm8zGYzjjvuOPz0008KjowQdbLb7WAYJmnbcSIfVQQiTz31FGbNmoUrr7wSQ4YMwTPPPIOKigq8/PLLAIBly5bh0ksvxeTJk1FdXY2rr74aI0eOxKpVqxQeefppampCOBxGSUlJl5+XlJSgrq4OgwcPRlVVFebOnYvW1lYEAgE8+uijqKurw8GDBxUade9VV1cHAHEfL0LIIT6fD7fffjsuvPBCapKpIooHIsmuwAHgmGOOweeff44DBw6AZVksWrQIW7duxSmnnKLEkHsFhmG6fM+yLBiGgdFoxEcffYStW7ciPz8fNpsNixcvxrRp06DX6xUaLYn3eBFCooLBIC644AJEIhG89NJLSg+HdCLZXjNcJbsCB4DnnnsOV111FcrLy2EwGKDT6fDaa6/hmGOOUWLIaa2wsBB6vb7H1XRDQ0PHYzR27FisXbsWdrsdgUAARUVFOOqoozBu3Dglhtyrte/bVFdXhz59+nT8vPPjRUhvFwwGcd5552HXrl1YuHAhZUNURvGMSLtEV3TPPfccli9fjs8//xyrV6/Gk08+ieuuuw4LFixQYqhpzWQyYezYsZg/f36Xn8+fPx8TJkzo8rOcnBwUFRVh27ZtWLVqFWbMmCHnUAmAmpoalJaWdnm8AoEAlixZ0uPxIqQ3ag9Ctm3bhgULFqCgoEDpIZFuFM+IJLsC93q9uOOOO/DJJ59g+vTpAIARI0Zg7dq1eOKJJ3DSSScpMey0NmfOHFx88cUYN24cxo8fj3/84x/Yu3cvrrnmGgDABx98gKKiIlRWVmL9+vX485//jJkzZ/aYXiPicLlc2L59e8f3u3btwtq1a5Gfn4/KykrcdNNNePjhhzFw4EAMHDgQDz/8MGw2Gy688EIFR52+kj0eLS0t2Lt3b0evii1btgCIZq9o53HxJXo8ysrKcM455+CXX37BF198gXA43PFZk5+fD5PJpNSwSWfKLtqJOvLII9lrr722y8+GDBnC3n777azdbmcBsF999VWX31999dXsySefLOcwe5UXX3yRraqqYk0mEztmzBh2yZIlHb979tln2fLyctZoNLKVlZXsXXfdxfr9fgVHm94WLVrEAujxdemll7IsG13Ce++997KlpaWs2Wxmjz32WHb9+vXKDjqNJXs83njjjZi/v/feexUdd7pK9Hi0L6GO9bVo0SKlh05+p4pN79577z1cfPHF+Pvf/95xBf7qq69i48aNqKqqwuTJk9HU1IQXXngBVVVVWLJkCa699lo89dRTuPbaa5UePiGEEEJSpIpABIi2FH/sscdw8OBBHH744Xj66adx7LHHAogW4s2dOxfffvstWlpaUFVVhauvvho333wzrQwghBBCNEw1gQghhBBCeh/VrJohhBBCSO9DgQghhBBCFEOBCCGEEEIUQ4EIIYQQQhRDgQghhBBCFKPJQIRhGHz66adKD4MQQgghAqkiELnsssswc+ZMpYdBCCGEEJmpIhAhhBBCSO+kukCkuroazzzzTJefjRo1Cvfdd58i4yGEEEKIdFQXiBBCCCGk96BAhBBCCCGKoUCEEEIIIYpRXSCi0+nQfR++YDCo0GgIIYQQIiXVBSJFRUU4ePBgx/cOhwO7du1ScESEEEIIkYrqApETTjgBb7/9Nr7//nts2LABl156KfR6vdLDIoQQQogEDEoPAAAikQgMhuhQ5s6di507d+K0005DTk4OHnzwQcqIEEIIIWmKYbsXZChg6tSpGDBgAF544QWlh0IIIYQQGSk6NdPa2oovv/wSixcvxkknnaTkUAghhBCiAEWnZq644gqsXLkSt9xyC2bMmKHkUAghhBCiAFVMzRBCCCGkd1LdqhlCCCGE9B4UiBBCCCFEMZIGIo888giOOOIIZGVlobi4GDNnzsSWLVu63IZlWdx3330oKyuD1WrF5MmTsXHjxi63+cc//oHJkycjOzsbDMOgra2tx7m2bt2KGTNmoLCwENnZ2Zg4cSIWLVok5Z9HCCGEEIEkDUSWLFmC2bNnY/ny5Zg/fz5CoRCmTJkCt9vdcZvHHnsMTz31FF544QWsXLkSpaWlOPnkk+F0Ojtu4/F4MHXqVNxxxx1xzzV9+nSEQiEsXLgQq1evxqhRo3Daaaehrq5Oyj+REEIIIQLIWqza2NiI4uJiLFmyBMceeyxYlkVZWRluuukm3HbbbQAAv9+PkpIS/O1vf8Of/vSnLvdfvHgxjj/+eLS2tiI3N7fj501NTSgqKsLSpUsxadIkAIDT6UR2djYWLFiAE088Ua4/kRBCCCE8yFojYrfbAQD5+fkAgF27dqGurg5TpkzpuI3ZbMZxxx2Hn376ifNxCwoKMGTIELz11ltwu90IhUJ45ZVXUFJSgrFjx4r7RxBCCCFENLL1EWFZFnPmzMExxxyDww8/HAA6pk1KSkq63LakpAR79uzhfGyGYTB//nzMmDEDWVlZ0Ol0KCkpwTfffNMlc0IIIYQQdZEtI3L99dfj119/xX/+858ev2MYpsv3LMv2+FkiLMviuuuuQ3FxMb7//nv8/PPPmDFjBk477bQuO/kSQgghRF1kCURuuOEGfP7551i0aBHKy8s7fl5aWgoAPQpKGxoaemRJElm4cCG++OILvPvuu5g4cSLGjBmDl156CVarFW+++aY4fwQhhBBCRCdpIMKyLK6//np8/PHHWLhwIWpqarr8vqamBqWlpZg/f37HzwKBAJYsWYIJEyZwPo/H4wEA6HRd/xydTodIJCLgLyCEEEKIlCStEZk9ezb+/e9/47PPPkNWVlZH5iMnJwdWqxUMw+Cmm27Cww8/jIEDB2LgwIF4+OGHYbPZcOGFF3Ycp66uDnV1ddi+fTsAYP369cjKykJlZSXy8/Mxfvx45OXl4dJLL8U999wDq9WKV199Fbt27cL06dOl/BMJIYQQIoCky3fj1Xm88cYbuOyyywBEsyb3338/XnnlFbS2tuKoo47Ciy++2FHQCgD33Xcf7r///oTHWbVqFe68806sWrUKwWAQw4YNwz333INp06aJ/ncRQgghRBy06R0hhBBCFEN7zRBCCCFEMRSIEEIIIUQxFIgQQgghRDEUiBBCCCFEMRSIEEIIIUQxFIgQQgghRDEUiBBCCCFEMRSIEEJUbfHixWAYBm1tbUoPhRAiAQpECCG9AsMw+PTTT5UeBiGkGwpECCGEEKIYCkQIIfjwww8xfPhwWK1WFBQU4KSTToLb7cZll12GmTNn4oknnkCfPn1QUFCA2bNnIxgMdtz3nXfewbhx45CVlYXS0lJceOGFaGho6Ph9+9TKl19+iZEjR8JiseCoo47C+vXrO26zZ88enH766cjLy0NGRgaGDRuGr776qssYV69ejXHjxsFms2HChAnYsmVLl9+//PLL6N+/P0wmEw477DC8/fbbHb+rrq4GAJx55plgGKbje0KI8igQIaSXO3jwIP7whz/giiuuwKZNm7B48WKcddZZaN+GatGiRdixYwcWLVqEN998E/PmzcO8efM67h8IBPDggw9i3bp1+PTTT7Fr166OzSg7+8tf/oInnngCK1euRHFxMc4444yOgGb27Nnw+/1YunQp1q9fj7/97W/IzMzscv8777wTTz75JFatWgWDwYArrrii43effPIJ/vznP+OWW27Bhg0b8Kc//QmXX345Fi1aBABYuXIlgOhGmQcPHuz4nhCiAiwhpFdbvXo1C4DdvXt3j99deumlbFVVFRsKhTp+du6557Lnn39+3OP9/PPPLADW6XSyLMuyixYtYgGw7777bsdtmpubWavVyr733nssy7Ls8OHD2fvuuy/m8drvv2DBgo6fffnllywA1uv1sizLshMmTGCvuuqqLvc799xz2VNPPbXjewDsJ598EnfchBBlUEaEkF5u5MiROPHEEzF8+HCce+65ePXVV9Ha2trx+2HDhkGv13d836dPny5TL2vWrMGMGTNQVVWFrKwsTJ48GQCwd+/eLucZP358x//n5+fjsMMOw6ZNmwAAN954Ix566CFMnDgR9957L3799dce4xwxYkSXMQDoGMemTZswceLELrefOHFix/EJIepFgQghvZxer8f8+fPx9ddfY+jQoXj++edx2GGHYdeuXQAAo9HY5fYMwyASiQAA3G43pkyZgszMTLzzzjtYuXIlPvnkEwDRKZtkGIYBAFx55ZXYuXMnLr74Yqxfvx7jxo3D888/3+W2ncfRfr/2cXT+WTuWZXv8jBCiPhSIEELAMAwmTpyI+++/H2vWrIHJZOoIKBLZvHkzmpqa8Oijj2LSpEkYPHhwl2xJZ8uXL+/4/9bWVmzduhWDBw/u+FlFRQWuueYafPzxx7jlllvw6quvch7/kCFD8MMPP3T52U8//YQhQ4Z0fG80GhEOhzkfkxAiD4PSAyCEKGvFihX47rvvMGXKFBQXF2PFihVobGzEkCFDYk6RdFZZWQmTyYTnn38e11xzDTZs2IAHH3ww5m0feOABFBQUoKSkBHfeeScKCwsxc+ZMAMBNN92EadOmYdCgQWhtbcXChQu7BBHJ/OUvf8F5552HMWPG4MQTT8R///tffPzxx1iwYEHHbaqrq/Hdd99h4sSJMJvNyMvL43x8Qoh0KCNCSC+XnZ2NpUuX4tRTT8WgQYNw11134cknn8S0adOS3reoqAjz5s3DBx98gKFDh+LRRx/FE088EfO2jz76KP785z9j7NixOHjwID7//HOYTCYAQDgcxuzZszFkyBBMnToVhx12GF566SXOf8PMmTPx7LPP4vHHH8ewYcPwyiuv4I033uioVwGAJ598EvPnz0dFRQVGjx7N+diEEGkxLPv7Gj1CCJHA4sWLcfzxx6O1tRW5ublKD4cQojKUESGEEEKIYigQIYQQQohiaGqGEEIIIYqhjAghhBBCFEOBCCGEEEIUQ4EIIYQQQhRDgQghhBBCFEOBCCGEEEIUQ4EIIYQQQhRDgQghhBBCFEOBCCGEEEIUQ4EIIYQQQhTz/2qe2bOsgC3YAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "time = '2018-07-08'\n",
+    "n.generators_t.p.loc[time:'2018-07-12'].plot.area(ax=ax, lw=0, legend=False)\n",
+    "n.loads_t.p_set.loc[time:'2018-07-12'].plot(ax=ax, color='k', legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic Analysis: How much solar capacity is required to meet 100% demand?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "solar_100 = ghi / ghi.sum() * load_resampled.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net_load = load_resampled - solar_100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8073974047750987"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solar_cap = solar_100.max()\n",
+    "solar_cap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_unmet_load = net_load.where(net_load>0).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "266266.65710289485"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_unmet_load*retail_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounts = np.linspace(0,1,20)\n",
+    "full_solar_cost_data = np.array([(costs.at['ResPV','annualized_cost'] * (1-discount) * solar_cap \n",
+    "                                  + total_unmet_load*retail_price) \n",
+    "                                 / load_resampled.sum() \n",
+    "                                 for discount in discounts])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1129"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retail_price/1e3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x2aa83eda5c0>"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "y1 = ax.plot(discounts, full_solar_cost_data/1e3, label='100% Solar Power')\n",
+    "y2 = ax.axhline(y=retail_price/1e3, xmax=0.74, color='tab:red', linestyle='--', label='Retail Price')\n",
+    "y2 = ax.axvline(x=0.74, ymax=0.1129*2.5, linestyle=':', color='tab:purple', label='Subsidy needed to breakeven')\n",
+    "ax.set_xlim(0,1)\n",
+    "ax.set_ylabel(\"Levelized Cost of Electricity \\n ($/kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost\\n (%)\")\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor',alpha=0.2)\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0b9+eRo0aERkZyfnz52nQoAHW1tb069ePgIAARo0aVeZ5DxYcL3oXZdExKe1x0WOFl/5eeukl5s6dS3R0NIsWLQIKkrLCy7ilFR4vzW5ZsSlNu2fPnnTr1o2xY8fy1Vdf0ahRI8LCwpDL5QwYMKDUuBUuuF++fHmpunK5HJlMVuL1b6j3Qq2+G9HExAQ/P78S0+CBgYF07ty5zPPWrl3LpEmTWLNmDYMHD36ojiRJhIaGlqgO/iioTEzxHTScKct/osfzU7GwtSM14Q57vlvG6jkvc+nQPrQP2exNIBAIBI/HRx99VGxdEUDbtm05dOgQYWFhdO3aFR8fH95///1in/0LFiwgKiqKJk2alFhjVR5169bVFUB+EJlMxl9//UXXrl2ZPHkyzZs35+mnnyYqKko3cTB69GgGDBhAz549cXR0ZO3atchkMnbs2EG3bt3KPE9f2NjYsG3bNkJDQ/H29uadd97h/fffB6j0nfqPwsaNG+nQoQMTJkygbdu2vPXWW7qNUUuL23vvvaeX72y9oZ919tXHn3/+KalUKmnlypXS5cuXpdmzZ0uWlpZSVFSUJEmSNHfuXGnixIm69mvWrJGUSqW0YsUKKS4uTvd37949XZsPP/xQ2rVrlxQeHi6FhIRI//d//ycplUrpxIkTFfKp8G7EpKSkh7bNy8mWTv29UVoxdYL05bjB0pfjBksrX5smXTq0T9Lk55d5nrHcEWUMd10ZyzgZQywkSdyNWFGMIRZFNcq7G/Fx7T9495u+0Wg00t27dyWNRmMwDUP04/fff5dUKpXuLsqqGCt9aYi7ESvJ+PHjSU5O1u174uXlxY4dO3B3dwcgLi6u2J5b33//Pfn5+cycOZOZM2fqjr/wwgu6TeIKN0+Lj4/H1tYWHx8fDh8+TMeOHfXuv8rUjPZDR9Gu7yBC92zn1N8buRsXy84Vizi+aR0Bo5+mRZduyOViIb1AIBAIqo9ff/2Vxo0bU79+fc6dO8dbb73FuHHjypyxE/xLrU+2AGbMmMGMGTNKfe7B2lMHDx58qL3FixezePFiPXhWcVRmZnQYNpp2/QYRsusfTm/bxN242+z4+iuOb1qH/5hnaBHwlEi6BAKBQFAtxMfH8/777xMfH4+rqytjx47l448/rm63agVGkWwZEyZm5nQaMRaf/oN1SVdKbAw7ln3BiU3rCBjzDM07dQEDL5oVCAQCgaAob775Jm+++WZ1u1ErEclWDcXE3IJOI8fh3X8IITv/5vT2zSTHRPPPks9wcHPHf/QzOLXwrG43BQKBQCAQPIRafTfik4CphQX+o59m2ter6Dz2WUwtLEm6dZN/lnzKlo/fJexkEFIFdjIWCAQCgUBQPYhkq5ZgamFJwJhnmPr1SvxHP4OJuQUpMdFsW7SQ3+bNJuxUcInbmAUCgUBQNhMnTuSTTz6pbjf0yqRJkxgxYoTucY8ePZg9e7bedT788MNKbSL6ML7++muGDRumN3s1DZFs1TLMLK3oMu5Zpiz/Ce9BwzExNycxKoK/v/yY3+fNJvzMCZF0CQQCwUM4f/4827dv59VXX9Ud+/LLL3F2dsbZ2bnETVInTpzAz89Pt7dTWRw8eLDYxp5169alV69eHDt2rFL+PWqStHTp0hI3hpXH6tWri/nr6urKuHHjiIyMLPe8//znP+zbt6/S/pXFtGnTOHXqFEePHtWbzZqESLZqKeZW1vgNH8uUZT/RaeQ4VGbmJESGs+Xzj/jj7TlEnD0lki6BQCAog6+//pqxY8fqyuBcuHCB999/n7Vr17JmzRrefvttLl68CBSUNnr55Zf57rvvipWJKY9r164RFxfHwYMHcXR0ZPDgwSQkJBisP4XY2tpiZ2dXqXNsbGyIi4sjNjaWNWvWEBoayrBhw0pNLKX7hb+trKxKFMB+HExNTZkwYUKZO77XdkSyZUCkB6qTG+LPzMqaLuMnMnXZj3QYNhqVqRl3IsLY/Nl81rzzBhFnT+mqmz/Jf1UVD/EnYlGb/owtFhXtj0ajYf369QwdOlR37PLly7Rt25aePXvSq1cv2rZty+XLlwH46quv6Nq1K+3bt6+wH46Ojjg7O+Pl5cU777xDamoqx48f17W5dOkSgwYNwsrKCldXV1588UWSkpKQJIlJkyZx6NAhli5dqptxioyMJD8/n8mTJ9OoUSPMzc1p0aIFS5YsKaZdeBnxwTEp77tJJpPh7OyMi4sLPXr04P333+fixYuEhYVx4MABZDIZu3bton379piamnL48GE++OADvL29i9lZtWoV7dq1w8zMDFdXV2bOnKl77t69e0ybNg0nJydsbGzo1asXoaGhxc4fOnQoW7ZsISsrq9yx1dd3bFk2DIG4G1GPrFixghUrVuh+DaSnp6OsZIHpiiJJEhkZGcC/hbjbDh5Js269ubBnO1cO7iU+/DqbP5uPY6Om+A4dRX3PNpWqT1aahr6pCo3Ciu5paWkGiYexjJMxxAIM3w8Ri4pTlWOVl5eHVqtFo9E89FJfaGgo9+7dw8fHR9fW09OT69evExkZiSRJXL9+nVatWnHt2jV++eUXTp48+VC7ANr7NywV+pGVlcWqVauAghqCGo2GuLg4evTowZQpU/j888/Jyspi7ty5jB8/nsDAQL766iuuXbtG69at+fDDD4GC5E2tVlO/fn3WrFmDg4MDwcHBvPzyyzg7OzN27FidfmFCWTg+hY+1pdxMVdTfQgpr/ebk5Oief/PNN/n8889p1KgRdnZ2HDx4sJjOd999x3//+1/+97//MXDgQFJTUwkKCkKj0SBJEoMHD8be3p6///4bW1tbfvzxR/r06cPly5ext7cHwMfHB7VazfHjx+nWrdtDx/hxKByP9PR0cnNzdcfT09Mf23ZpiGRLjxTuSp+WloatrS3W1tbY2toaRKsw+7a1tS32IWZra4vL5JfoMvppTv29kXOBO0iMvMHuZZ9Tr3krAsZOoKFXuwp98JWlURX90CdqtRoomCo3VCFqqP3jZAyxAMP3Q8Si4lTlWJmampKSkoJCoXjopb5bt26hUChwdXXV+eXl5cXHH3/MwIEDAfjkk0/w8vKib9++fPrpp+zdu5f58+ejUqlYsmRJmclAYUFlDw8PAN0sjZ+fH3379kWhUPDDDz/g6+vLwoULdX1Yvnw5Xl5ehIeH07x5c0xNTbG0tKR+/frF7C9YsED3/6ZNm3L8+HE2btzI008/rdOXyWS6MSicGSt8/ODYFPpbeDwmJoZFixbRoEEDWrVqRVBQkE63f//+uvMetLtw4ULmzJnDa6+9pjvm7+8PwP79+7l48SJ37tzRJXJfffUVf//9N5s3b2b69OlAwWvRzs6O6Ojoh8awopdzyztfLpdjbW1drLZj4Y8QfSOSLQPyYOVzQ9kvTcPSrg49np9Kh2GjOfX3Bs7t2Uns9Sts/Pg96rf0pPPYZ3Fr3fah/pWnoS8MrfFg1XtDadT2caoKjaqIRVH7It7l2y+qYyiqcqwe/H9Z5OTkYGpqqks0Cnn55Zd5+eWXdY9Xr16NtbU1/v7+tG7dmlOnThETE8MzzzxDZGSkLnF40BeAI0eOYGlpSUhICG+99RarV6/GxMQEgLNnz3LgwAHderGiRERE0KJFizL78t133/HTTz9x8+ZNsrOzycvLw9vbu0S7oo8f9lxqairW1tZIkkRWVha+vr5s2rQJU1NTXdsOHTqUalMmk5GQkEBsbCy9e/cuVePs2bNkZGTg4OBQzI/s7GwiIiKKtTU3Nyc7O7vMGBa9zPc4r6myXi+Gep2KZMvIKUi6ptF+6GhObl3P+b27uH31Mus/eocGrbzoPO5Z3DzbVLebAoFAUGU4ODiQlZVFXl6eLgF6kKSkJBYsWMChQ4c4efIkzZs3p1mzZjRr1gy1Ws3169dp06bsz87Cy23NmzcnJyeHkSNHcvHiRUxNTdFqtQwdOpTPPvsMKLgslpGRgZWVVYmZrKL89ddfvP7663z11VcEBARgbW3NF198wYkTJx5rPKytrTl79ixyuRxnZ2csLS1LtCntWCEPq42o1WpxdXUttVzeg4v5U1JScHR0rJDftQmxQP4JwaqOPb0mvciUZT/i3X8ICqWSmCsX+Wv+PP5a8DYxVy5Wt4sCgUBQJRTuD1W4AL40Zs+ezeuvv06DBg3QaDS6y65QcKmpIuu3Cpk4cSJarZZvvvkGAF9fXy5duoSHhwdNmzaladOmNG7cmKZNm+qSGhMTkxIaR44coXPnzsyYMQMfHx+aNm1KeHh4hf0oC7lcrvOhvKSqLKytrfHw8ChzKwhfX1/i4+NRKpW6/hb+FZ3tCg8PJycnBx8fn0fuS01FJFtPGNb2DvSe/BJTlv1Eu36DUSiV3Lp0nnUfzmX9R+9w+2rZHz4CgUBgDDg6OuLr61vmnk6BgYGEhYUxc+ZMoOAS2tWrV9m5cyc//PADCoVCd6mvIsjlcmbPns2nn35KVlYWM2fOJCUlhWeeeYaTJ08SERHB/v37mTJlii7B8vDw4MSJE0RFRZGUlIRWq6Vp06acPn2a3bt3c/36dd577z1OnTr1+AOiBz788EMWLVrE8uXLCQsL4+zZs7ptHPr06UNAQAAjRoxg9+7dREVFERQUxLvvvsvp06d1No4cOULjxo1p0qRJdXXDYIhk6wnFuq4Dfaa8zOSlP9Ku70DkCiXRF8/x5wdvsuHj94i9fqW6XRQIBAKDMX36dP74448Sx7Ozs3nllVf4/vvvdWu66tevz7Jly/i///s/Pv74Y3755ZeHXjp7kMmTJ6NWq/n666+pV68ex44dQ6PR0L9/f9q2bcu8efOwtbXVaf7nP/9BoVDg6emJo6Mj0dHRvPTSS4waNYrx48fTqVMnkpOTmTFjxuMPhh544YUXWLx4Md9//z1eXl4MGTKEsLAwoGAd1I4dO+jWrRuTJ0+mefPmPP3000RFReHs7KyzsXbtWqZNm1ZdXTAoMslQm0o8wRTejZiUlKTXTd+KIkkSqampervLJy0xgeOb13Hp4F60939Zubfzpe3AYTTz9jPonUT67EdpqNVqduzYwaBBgwx2N6Kh+2AsGoaOBRi+HyIWFacqx8rU1JSoqCgaNWpU7O6yssjJyaFFixb8+eefBAQElGtfo9GgUCgM1getVktaWho2NjYlFu3ri6rox+NoXLx4kd69e3P9+vVy7+LXVz9ycnKIjIws8XpJTk7GwcGB1NRUbGxsHtn+g4gF8gIAbByd6Df9VTqNGMvxTX9x6dBebp47y81zZ/Hw9qPL2Gdxadq8ut0UCAQCvWBmZsavv/5KUlJSdbsiAGJjY/n1118Ntl1SdSOSLUExbJ1c6P/SLDqNHMfxTX9y+fB+okLPEBV6hsa+Heg89lmcGzetbjcFAoHgsenevXt1uyC4T79+/arbBYMiki1Bqdg5u9D/pdfw7D2AS4E7uHLkIBFnTxFx9hRN2nciYMwEnBsZ3yJGgUAgEAj0jVggLygXGycXBsx4nUmLvqVV157IZHLCT5/g97mvsfXL/5EQFVHdLgoEAoFAUKMRM1sGxJBFLQ1dNPNBjTqu9Rg4c07B5cWNf3I16DA3Th3nxqnjNO0YQMCYCTg29HgsDUPxYMFRQ9ivyljUdo2iWobSqM32q1KjqJahNKp6rAzdH0MjNKpGo6zXiyhEXQuo7kLUVaGhsLSmy/PTaN13ECHbNxNx+gQ3TgZz42Qwjfw64jNkFHXqNXgsDX0jClHXHA1jKH4sYlFxamoh6sqij8LH5VH4Ba/RaAxa1sjQ/ahNGqIQdS2mJhSirioNW1tb3Ft6knTrJsc3/sn140eJPHOSyLOnaOH/FP5jnqFufbfH0tAXohB1zdEwhuLHIhYVp6YWon4UDGGzkMI+GHJbhkIM2Y/apCEKURsRxlTgtSwNx4YeDH19LonRUQRvWEPYiSCuBR/h2vGjtOzcDf/RTz806TKGgrs1IRa1QcNYih+LWFROp6YVoq4M+ip8LDRqloYoRC2olTg29GDYnLdJiIogeMNabpwK5uqxQ1wLOkLLp7rjP+pp7OuVXWBVIBAIBAJjRSRbAr3i5NGY4f95h4SoCILWryH89HGuHDnA1aOHaNW1B/6jn6aOS73qdlMgEAgEgipDbP0gMAhOHo0Z8d93eW7hEhr7dUSStFw+vJ+fX3+JXd8s4d6d+Op2USAQCJ5ovvnmG125Gj8/P44cOVJu+0OHDuHn54eZmRmNGzfmu+++qyJPaz8i2RIYFOfGTRn55vs8+/EiGvm0R9JquXRoL6tmT2f3d0tJTRBJl0AgEFQ169atY/bs2bzzzjuEhITQtWtXBg4cSHR0dKntIyMjGTRoEF27diUkJIS3336bWbNmsXHjxir2vHYiki1BleDStDmj5n7IhP99hYe3H5JWy8UDgfz8+ksc/W0laYkJ1e2iQCAQPDEsWrSIKVOmMHXqVFq1asWSJUtwc3Pj22+/LbX9d999R8OGDVmyZAmtWrVi6tSpTJ48mS+//LKKPa+diDVbgirFtVkLRs+bT+z1KwStX8PN8yFcO3qAsOAjePXsQ6eR47BxcKpuNwUCgeCRkCSJnKzchzcseo5WS05mLiaKHGTyR5sDMbMwrfCddHl5eZw5c4a5c+cWO96vXz+CgoJKPSc4OLhE/cL+/fuzcuVK1Gq1wbYPMRZEsiWoFuo1b8WYdz4i5uoljvz5G7FXLnJ+7y4uHthLm1796DRyHNZ1HarbTYFAIKgUOVm5DLOeWOW6f6f/hrml2cMbAklJSWg0GpydnYsdd3Z2Jj6+9KUd8fHxpbbPz88nKSkJV1fXR3P8CUEkW4JqpX4LTwbOnkt6bAzBG9Zw69J5zgXu4OKBPbTp3Z+OI8ZibS+SLoFAINA3D86ESZJU7uxYae1LOy4oiUi2BDWCBq1aM+79T7h1+QJB6/8g5vJFQndv58L+PbTtM4COw8diVce+ut0UCASCcjGzMOXv9N8qdY6k1ZKWlo6NjfVjXUasKA4ODigUihKzWAkJCSVmrwpxcXEptb1SqaRu3bqVd/gJQyRbBsTYCrxWhUaDVl6Me38h0ZfOE7x+DbevXiJk5zbO791Nuz4D6DB8DJZ2dSqtUVTL0H0wBMakUVTLUBq12X5VahTVMpTGk1iIujKJT6HdPE0eZpZmjzVLVFH/VCoVfn5+7NmzhxEjRuiOBwYGMmzYsFLt+Pv7888//xR7bvfu3bRv3x6lUlniHFGIujgi2dIjT0Ih6qrSsG3gTv/Zc4m9eomz2zaSEB7G2Z1/c27vLlp1703b/kMwt6lY3UlRiLrmaBhD8WMRi4ojClFXjOooRP3aa68xadIkfH198ff356effiI6Oppp06ah0Wh45513uH37NqtXrwZg2rRprFixgtdff50pU6Zw/PhxVq1axe+//15svEUh6tIRyZYeeZIKUVeVhp1/F1p16szN8yEEb1hDXNg1Lu7dydUj+2nXdxAdho3G4iFJlyhEXXM0jKH4sYhFxRGFqCtGdRSifuaZZ7h79y4ff/wxcXFxeHl5sX37dho3bgzAnTt3iImJ0bVv2rQp27dvZ86cOXz77bfUq1ePpUuXMnbs2DI1qqIfj3O+KERtJBhTgdfq1JDJZDTy9sOjnS9R584StP4P4m9c58w/mzkfuBPvAUNoP2RkmUmXKERdczSMpfixiEXldEQh6pqpUThBUBqFM1pF6dGjB2fPnq2Uhr4RhagFAgNTNOmKDD1N0F9ruBMRxqmtGwjdvR2f+0mXubVNdbsqEAgEAoEOkWwJah0ymYzGPh1o5N2eiLMnCVq/hoTIcE5uWU/o7n/wGTAMvyEjMLeyrm5XBQKBQCAQyZag9iKTyWji14nGvh0JP32CoA1rSIyK4MTmdYTs2obvoGH4DRqBwrRydwYJBAKBQKBPRLIlqPXIZDKadvCniV9Hbpw+TvD6NSRGR3F845+E7NyG94ChaOQi4RIIBAJB9SCSLYHRIJPLadaxM03b+xN2Kpjg9WtIunWTE5v+RK4y4aSUR/shIzG1sKhuVwUCgUDwBCGSLYHRIZPLad6pC806BHD9xDGC1q8h5fYtjm9cS+iubfgNGYnvwKGYmIukSyAQCASG59HqAggEtQCZXE6LgK5MWLgY5869qFOvPjmZGRxb9xs/vjKFE5v/Ii8nu7rdFAgEAoGRI5ItgdEjlyuw9mjCs58uZdCr/6FOvQbkZKRz9M9f+emVKZzcugF1Tk51uykQCAQCI0VcRhQ8McjlClo91YMWnbty9dhhjm9cy924WI6sWc3pfzbTYdhovPsOQlVkN2GBQCAQCB4XMbMleOKQyxV4du3JpK++ZcCM17FzdiU7LZXDv6/ip1lTOf3PZtS5YqZLIBAIBPpBJFsGpGg1evFXvX+lxUMml+PZrReTFn1Lv5dew9bJmazUexz6bSUrZ03jzPYt5OXmVLvvxvYn3hs158/YYmGI/lTFZ3p1aaxYsYJGjRphZmaGn58fhw8fLvP8jRs30rdvXxwdHbGxsSEgIIBdu3YVa7N69WpUKhVyubxYmabs7OwaOVZl2TAE4jKiHlmxYgUrVqzQVUBPT09HqTTMEEtSQaV7MGwNKmPQKCwsmpaWVmY83Hw6UL+tD2HBRwndsZWM5EQO/voTJ7duoO2AobTo2hOlyqTa+mAsGhWJxeNi6H6IWFScqhyrvLw8tFotGo1G9xmsL7RarV7tPUjhF7xGozFoDcmi/fjrr794/fXXWb58OZ07d+bHH39k0KBBnD9/noYNG5Y499ChQ/Tu3ZsFCxZgZ2fHL7/8wrBhwzh27Bg+Pj46+zY2Nly6dKnYuSqVSq8x0Uc8NBoNWq2W9PR0cnNzdcfT09Mf23ZpiGRLjxQW9UxLS8PW1hZra2tsbUsvjvy4FL45bW1tDV7ws7ZrqNVqAGxsbFCpVOW27Th4OH79B3Hp0H5ObF5HelIix9f9xoU92+k0YhxevfqhfMCGsYxTTYvFo2LofohYVJyqHCtTU1NSUlJQKBQoFAq96xjCZiGFfVAoFAZNtgo1AJYuXcrkyZOZPn267nFgYCA//PADCxcuLHHe0qVLiz1euHAh27ZtY8eOHbRv3x5AN6NVv359g/YBHj8eCoUCuVyOtbU1ZkXW6Rb+CNE3ItkyIFVV6V5oPNx+UZ2HoVSZ0K7PALx69Obigb0c37yOjOQk9v/8HSf/3lCQdPXsWyzpMoZxqgqNysbicXQM3Q9j6ENRHUNRlWP14P/1QdHLShWxK0kSObnqSmlotQXnqHLUyOWP5ruZqapc/4r2Q61Wc+bMGebOnVvsnH79+hEcHFyhfhbOCtWtW7dY+4yMDDw8PNBoNHh7e/PRRx/pZr70QWXjURZlvV4M9ToVyZZAUAYKpYp2fQfSukcfLu7fw4ktf5GRnMS+ld9wcst6Oo0ch1fPPsgV4m0kEAgKyMlV0/fZZVWuG/jHLMzNSl/q8CBJSUloNBqcnZ2LHXd2diY+Pr5CNr766isyMzMZN26c7ljLli1ZuXIl7dq1Iz09naVLl9KlSxfOnTtHs2bNKt4ZI0R8SwgED0GpUuHdfzBePftyYf9uTm5ZT3pyInt/WsHJrevpOGIcbj7tq9tNgUAgqBQPzuJIklShmZ21a9fy4YcfsnXrVpycnHTH/f396dChg+5yaJcuXfD19WX58uUsW1b1CWhNQiRbAkEFUZqY4DNgKG169ef8vl2c3LKetMQE9v74NVZ1HQkY/TStu/dGYaDFxgKBoOZjZqoi8I9ZlTpHq5VIT0/D2trmsS4jVhQHBwcUCkWJWayEhIQSs10Psm7dOqZMmcL69evp06dPuW3lcjkdOnQgLCyswr4ZK+JbQSCoJEoTE3wHDqNN7/6cD9zFya3ryUhOJPCH5Zzcuh7/kePx7NYLuQEX1AoEgpqJTCar8OW8QrRaLeo8FeZmBdsmGBoTExP8/PwIDAxk5MiRuuOBgYEMHz68zPPWrl3L5MmTWbt2LYMHD36ojiRJhIaG0qZNG734XZsRyZZA8IioTEzxGzycNr37cWLbZi7s2U7qnXh2f7eUE5v/wn/007R6qodIugQCQY1jzpw5TJw4kfbt2xMQEMAPP/xAdHQ0L730EgDz5s3j9u3b/Prrr0BBovX888+zdOlS/P39dbNi5ubmurvu58+fT8eOHWnRogXp6eksW7aM0NBQVqxYUT2drEGIZEsgeExUpma06TuITkNGci5wB6f+3si9O3Hs+mYxJzavw3/0M7Ts0g25XCRdAoGgZjB+/HiSk5NZsGABcXFxeHl5sWPHDtzd3QGIi4sjOjpa1/77778nPz9ft8VRIS+88AKrV68G4N69e7z88svEx8dja2uLj48Phw8fpmPHjlXat5qIUewg/8033xTbBffIkSNltt20aVOJXXB3795dot3GjRvx9PTE1NQUT09PNm/ebMguCIwAlZkZHYaNZurXK+k6YRJm1jbcjYtl59dfsfqNmVw5ehCtVr+bLQoEAsGjMmPGDKKiosjNzeXMmTN069ZN99zq1as5ePCg7vHBgwdL3W29MNECWLx4MeHh4eTk5JCQkMDu3bsJCAiowh7VXGp9srVu3Tpmz57NO++8Q0hICF27dmXgwIHFMvKiHD58mL59+7Jjxw7OnDlDz549GTp0KCEhIbo2wcHBjB8/nokTJ3Lu3DkmTpzIuHHjOHHiRFV1S1CLMTEzp+PwMUxb/hNPPf08ZpZW3I2NYcfyL/nlP69w9dghJAPvSC0QCASCmkOtT7YWLVrElClTmDp1Kq1atWLJkiW4ubnx7bffltp+yZIlvPnmm3To0IFmzZrxySef0KxZM7Zt21asTd++fZk3bx4tW7Zk3rx59O7dmyVLllRRrwTGgIm5BZ1GjmPq16voMu45TC0tSbl9i+3LvuCX/77CteCjIukSCASCJ4BavWYrLy9PtwtuUfr160dQUFCFbBTugmtvb687FhwczOuvv16sXf/+/ctMtnJzc4vVVkpLSwMKduktLImhbyRJIj8/H7VabdAyGMagURiD6oqFXKXCb9hovPoMIHT3P4Ts/JvkmGj+WfIpdd3c6TRyPE3ad0JWzl1IIhYVx9D9ELGoOFU5VnK5HEmS0Gq1eq9laOgixUWLIhuyDqOh+1GbNLRaLZIkoVari5X+MdT7oVYnW4baBTc+Pr5SNhcuXMj8+fNLHD9w4AAWFhYV8kNgeAIDA6vbBTC1pv6gMdy7epHUaxdIvnWTHcs+x8TOHvs2vlg28DB4bbSaQI2IhQAwnlgolUpcXFx0RalrI4YqgiwoSV5eHtnZ2Rw+fLhYPcSsrCyD6NXqZKsQfe+CW1mb8+bNY86cObrHaWlpuLm50bNnT+rWrVvRblQKSZJIS0vDxsbGoL8YjUFDrVYTGBhI3759DVJw99H6MJKczAxCdvxN6O5/yLuXQvyRvTi4N6LTqPE09u1YzJaIRcUxdD9ELCpOVY6ViYkJMTExWFlZFSssrA80Go3BC1Gnp6djbW1t0B9bhu5HbdLIycnB3Nycbt26FXu9JCcnP657pVKrky1D7YLr4uJSKZumpqaYmpqWOK5SqQz6IaZUKlGpyi8+KjT+xVDxeNQ+qOzq0G3CC3QYOpIz27dwduc2km5Gsn3xpzg3bkrAmAk09u2ATCYTsagEhu6HiEXFqY6xksvlet0YtPCHtiGLaRdeOiz03xBURT9qk4ZcLkcmk5V4/RvqvVCrF8gX3QW3KIGBgXTu3LnM89auXcukSZNYs2ZNqbvgBgQElLC5Z8+ecm0KBI+KubUNTz39PFOX/0TH4WNQmZpxJ+IGWz5fwJp35hARcsqg6x8EAoFAYFhq9cwWGGYX3Ndee41u3brx2WefMXz4cLZu3crevXs5evRo9XRS8ERgYWNL1wmT8BsyklN/byR0z3biw8PY/Ol8XJq2oN3AYdh07vpErOkSCAQCY6JWz2xBwS64S5YsYcGCBXh7e3P48OEK74Lr6uqq+3vttdd0bTp37syff/7Jzz//TNu2bVm9ejXr1q2jU6dOVd4/wZOHhY0t3Z+bzLTlK/EbMhKliSnxN66xe/kX/Pn+f4k6d1bMdAkEAkEtotbPbEHBLrgzZswo9bmiu9sCxXbELY8xY8YwZsyYx/RMIHh0LGzt6DFxCh2GjuLk1g2cC9xBXNg1Nn7yPvVaeNJ5zAQatmknZroEAoGghlPrZ7YEAmPH0q4OPZ6fyriPF+EzcBgKlYrYa5fZ8PG7rPtwLtEXz1e3iwKBoBZSmVJ3Bw8eLLYwvfDv6tWrVehx7UUkWwJBLcHCtg49X5jGlGU/4t1/CAqlkttXL7H+o7dZN38uty5fqG4XBQJBLaGype4KuXbtGnFxcbq/Zs2aVZHHtRuRbAkEtQxrewd6T36JKct+ol2/wSiUSmIuX+Sv+fP4a8HbxFy5WN0uCgSCGk5lS90V4uTkhIuLi+7P0HtqGQtGsWZLIHgSsa7rQJ8pL9Nx+BhObvmLC/sDuXXpPOsunadhG286j32W+i1aVbebAsEThSRJZBfZkbwiaLVastVqlGr1I++zZa5UVnj95uOUuvPx8SEnJwdPT0/effddevbs+Uj+PmmIZEsgqOXYODjSZ+pMOg4fy4nNf3HxYCDRF0KJvhCKe1sfOo99lnrNW1a3mwLBE0F2fj5e3y6rct2LL8/CooIbcj5KqTtXV1d++OEH/Pz8yM3N5bfffqN3794cPHiQbt26Pbb/xo5ItgyIoQuX1oZinzVFo6iWIezXhHGydnCkz7SZdBg+hhNb/uLSwb3cPB/CzfMheLTzJWDss7g2bf5YGo+LoWNR1HZttV+VGkW1DKVR1WNVFQWjH7eNIahMv4u2K/p/rVarq1jxIM2bN6d5838/P/z9/bl16xZffvklXbt2LVfDUDyORlmvF0P5LZItPbJixQpWrFiBRqMBCoqKKpWGGWJJksjIyABK1nEUGsUpLDKalpZmkHjUuHEyNaPT+Odp1WsAoTu2cOP4UaLOnSXq3FkaeLXDd+hoHD0aP57GI2LoWIDh+1Hj4v2IGEMsimrk5eWh1WrRaDS6z2B9UVhOpyKYyGSETit9K6KykCSJrMxMLCwtH3mcTGSyh/a7sB916tRBoVAQGxtb7Jw7d+7g5ORU4fHr2LEja9asKda+MmP1qOhDQ6PRoNVqSU9PJzc3V3fcUMXARbKlR2bOnMnMmTNJS0vD1tYWa2tr3a70+qYw+7a1tTXoh5gxaKjVagBsbGwMVhsRat442dra4jbrv9wd9ywnNq3jypGDxFw8R8zFczT260jAmGdwbtT0sTQqi6FjAYbvR02Nd2UxhlgU1TA1NSUlJQWFQmGQRduVsWldyeRVkiSkvDyszcwMvm+eQqHA3NwcPz8/9u3bx+jRo3XP7du3j2HDhlW4r+fOncPV1bVE+6pYNP+4GgqFArlcjrW1dbFC1PmVXG9XUUSyZUAMWYyzqH2h8XD7RXUMpVFTx8netT4DZ87Bf9R4jm/8kytHDxFx5iQRZ07SpL0/ncdOwOn+TJcxxKKo/Scx3pWxX1THUFTlWD34f31Q9LKSoRPGqtQoLHXXoUOHYqXuXn75ZWQyWYlSd0uWLMHDw4PWrVuTl5fH77//zsaNG9m4caPO59o0VmW9Xgzlt0i2BIInhDqu9Rn4yht0Kky6jh0i/PRxwk8fp2mHAALGPIOJnX11uykQCKqA8ePHk5yczIIFC4iLi8PLy6vcUnd5eXn85z//4fbt25ibm9O6dWu2b9/OoEGDqqsLtQqRbAkETxj29Row6NX/6JKuq0GHuXEqmBungvHw7UC3Z17AsaFHdbspEAgMTGVK3b355pu8+eabVeCVcVLpDT0mTZrE4cOHDeGLQCCoQurWd2PwrP8y6csVNA/oCjIZUWdP8eubr7JtyWckx5S/k7RAIBAIKkalk6309HT69etHs2bN+OSTT7h9+7Yh/BIIBFVE3QYNGTr7LZ7/fDkevh1BkrgefITV/5nJP0s/JznmVnW7KBAIBLWaSidbGzdu5Pbt27zyyiusX78eDw8PBg4cyIYNG3R3twgEgtqHg5s7vV+cxcTPltGsY2eQJK4FHWb1f2awfdkXpMTGVLeLAoFAUCt5pLoAdevW5bXXXiMkJISTJ0/StGlTJk6cSL169Xj99dcJCwvTt58CgaCKcHRvxLA33mbiZ8to2sEfJImrxw6xes4Mdnz9FXfjxGy2QCAQVIbHKkQdFxfHnj172LNnDwqFgkGDBnHp0iU8PT1ZvHixvnwUCATVgJNHY4b/512e+3QpTdp3QpK0XDlygJ/nvMyubxZzLz6uul0UCASCWkGlky21Ws3GjRsZMmQI7u7urF+/ntdff524uDh++eUX9uzZw2+//caCBQsM4a9AIKhinBs1YcR/3+O5hUto7NsBSavl0qF9rHr9RXZ9u4R7d0qvpSYQCASCAiq99YOrqytarZZnnnmGkydP4u3tXaJN//79sbOz04N7AoGgpuDcuCkj3/qAuBvXCF6/hsjQM1w6uJcrRw7g2a03/qPGY+vk/HBDAoFA8IRR6WRr8eLFjB07ttj29g9Sp04dIiMjH8sxgUBQM3Ft2oJR8+YTe/0qwRvWEHXuLBcP7OHy4X149ehLp1HjsHFwqm43BQKBoMZQ6WTrwIEDjBgxokSylZmZyauvvsqqVav05lxtx9DV5w1p39g0imoZwr6xjFNlNFybFUm61v/BzQuhnN+3i4sH9+LVsy+dRozF2sGxhEZRLUNg6LGqibF4VI2iWobSqOqxMnR/DI3QqBqNsl4vhvK70snWL7/8wqeffoq1tXWx49nZ2fz6669PdLK1YsUKVqxYoauAnp6ejrKSBUkrSmGlezBsDSpj0CgsLJqWlmaQeBjLOD2qhqWzK31e+Q/xN64Rsm0TsVcvcX7vTi4eDKRFlx60GzgUyzp1AcPHAgw/VjU5FpXBGGJRVCMvLw+tVotGo9F9BusLrVarV3sPUvgFr9FoDFpD8sF+fPvttyxatIi4uDg8PT1ZtGgRTz31VKnnTp48md9++63EcU9PT86dOwcU5AdTp04t0SY9Pb3cq2GVRR/x0Gg0aLVa0tPTyc3N1R1PT09/bNulUeF3WFpami4DfHDgNBoNO3bswMnpyb50MHPmTGbOnElaWhq2trZYW1tja2trEK3CN6etra3BC37Wdo3C/d9sbGxQqVR6t28s4/S4GrZ+HWnh15GYKxcJWr+GmMsXuHJoL9ePHaRN7wF0HD4GCxsbwHCxAMOPVW2IRUUw9PsCqnasTE1NSUlJQaFQoFAo9K5jCJuFFPZBoVAYNNkq1ABYt24db7zxBitWrKBLly58//33DBkyhEuXLtGwYcMS5y1btozPPvtM9zg/Px9vb2/GjBmjsymXy7GxseHq1avFzrW0tDRYPx7nfLlcjrW1dbF8pvBHiL6pcLJlZ2enq47dvHnzEs/LZDLmz5+vV+dqO1VV6V5oPNx+UR1DadT2cdKXhptnG8Z/sJBbl84XJF1XLhK6+x8u7N9Nm179ybewrRX9qE77VaFRFe+LovarQuPB/+uDopeVDJ0wVqXG4sWLmTJlCtOmTQNg6dKl7Nmzh++++46FCxeWON/Ozq7YjW9btmzh7t27TJ48uZjPMpkMFxeXGj9WZb1eDOV3hZOtAwcOIEkSvXr1YuPGjdjb2+ueMzExwd3dnXr16hnESYFAUPtwa92WcZ5t7iddf3D76mVCd/+DTKHgcHYa/iPHYWlXp7rdFAieOPLy8jhz5gxz584tdrxfv34EBQVVyMbKlSvp06cP7u7uxY5nZGTg4eGBRqPB29ubjz76CB8fH735XlupcLLVvXt3ACIjI2nYsKHBpzoFAkHtRyaT0dCrHW6t23LzQijH/vqd+LBrhO7axsX9e2jXbxAdh43Gwtauul0VCPSCJEnkafMqdY5WqyVXm0uuJhe59Gh7jZvITSr8vZyUlIRGo8HZufhWLc7OzsTHP3zfvLi4OHbu3MmaNWuKHW/ZsiUrV66kXbt2pKens3TpUrp06cK5c+do1qxZxTtjhFQo2Tp//jxeXl7I5XJSU1O5cOFCmW3btm2rN+cEAoFxIJPJ8GjrQ72Wrdmw8ge0tyK4E36dM/9s5lzgDrz7DabDsNFY2BhmjaNAUFXkafOYfmZGlev+4PcNpgrTSp3zYHImSVKFErbVq1djZ2fHiBEjih339/enQ4cOurVnXbp0wdfXl+XLl7Ns2bJK+WZsVCjZ8vb2Jj4+HicnJ7y9vZHJZKXeHimTyfR+F4hAIDAeZDIZlvXcGDhlOrfvX16MDw/j9LZNnNuzA+8BQ2g/ZKRIugQCA+Lg4IBCoSgxi5WQkFBitutBJEli1apVTJw4ERMTk3LbyuVyOnToIOolU8FkKzIyEkdHR93/BQKB4HGQyWQ08mmPh7cfkSGnCVr/B3cibnBq6wZCd2/Hd+BQ/IaMxNzK+uHGBIIahInchB/8vqnUOVqtlrT0NGysbZDLH/0yYoXbmpjg5+dHYGAgI0eO1B0PDAxk+PDh5Z576NAhbty4wZQpUx6qI0kSoaGhtGnTpsK+GSsVSraKLoB7cDGcQCAQPCoymYzGvh1o5NOe8DMnCV6/hoSocE5s/ouQXdvwHTgMv8EjMbOyqm5XBYIKIZPJKn05TyvTYio3xVRh+sjJVmWZM2cOEydOpH379gQEBPDDDz8QHR3NSy+9BMC8efO4ffs2v/76a7HzVq5cSadOnfDy8iphc/78+XTs2JEWLVqQnp7OsmXLCA0NZcWKFVXSp5pMpXeyW7hwIc7OzkyePLnY8VWrVpGYmMhbb72lN+cEAsGTgUwmo2n7TjTx68iN08cJXr+GxJuRHN+0jrM7t+E7aDh+g4djZimSLoFAH4wfP57k5GQWLFhAXFwcXl5e7NixQzehEhcXR3R0dLFzUlNT2bhxI0uXLi3V5r1793j55ZeJj4/H1tYWHx8fDh8+TMeOHQ3en5pOpZOt77//vsQdCACtW7fm6aefFsmWQCB4ZGQyGc06BNDUrxM3Th0naMMakqKjOL5xLSE7/8Zv8Ah8Bw3D1EL/myQKBE8aM2bMYMaM0hfzr169usQxW1tbsrKyyrS3ePFivvzyyyrZnLW2UelkKz4+HldX1xLHHR0diYuL04tTAoHgyUYml9OsU2eadvAn7GQQQevXkBwTTdD6PzizYwvtB4/EZ+AwTC0sqttVgUAgeCiVTrbc3Nw4duwYjRo1Knb82LFjYlPTBzC2Aq+1WaOoliHsG8s41bhYyGQ069SFph0CuH7iGMc3riU55hbH/vqdMzu24jd4BD4DhmBi/m/SJQpRV1yjqJahNEQhaqFREzVqfCHqqVOnMnv2bNRqNb169QJg3759vPnmm7zxxht6d7A2IQpR10wNUYi65mg8TixcPNsyrKUXkaePE7J9C6nxsRxb9xun/9lMm36D8OzRD5WZmShEXUFEIeqKY6yFqJ9kjRpbiLqQN998k5SUFGbMmEFeXsEuuWZmZrz11lvMmzdP7w7WJkQh6pqpIQpR1xwNfcSiTt+BePfux7WgIxzf+Cd3425zevNfXNq7i/bDRtOuz0BAFKJ+GKIQdeUwtkLUT7pGjS1EXYhMJuOzzz7jvffe48qVK5ibm9OsWTNMTSt3q+uTgDEVeK3NGqIQdc3R0FcsFAolnl170rJzN64eO0TwxrXci4/jyB8/c+afzXj1HUSnoSMxMTPXl+vFELGonI4oRC00appGjS1E/SBWVlZ06NBBn74IBAJBpZArFHh260XLLt25cvQgwRvXknonnpMb1nAxcAcdh4+hbd+BqEzEj0GBQFB9VCjZGjVqFKtXr8bGxoZRo0aV23bTpk16cUwgEAgqilyhoHX33rTs0p3Lh/cTvHEt6UmJHPz1J079vZGOI8bStvcAlA8pLyIQCASGoELJVtHr7YZagyQQCASPi0KpxKtnX+q38yUm9DQnNv9FWmICB1b/wKmtG+g4YixtevUXSZdAIKhSKpRs/fzzz0DBtdIPP/wQR0dHLMT+NgKBoIYiVyhp06s/rbv35tLBfRzfvI70pET2//w9J7duoNOIcXj16ofSQAvDBQKBoCiVKsIkSRLNmjXj9u3bhvJHIBAI9IZCqaJtnwFMXvIDfabOwKquAxkpyexb9S2rXpvOucAdaPLV1e2mQCAwciqVbMnlcpo1a0ZycrKh/BEIBAK9o1SpaNd3EFOW/kivyS9hVcee9ORE9v70DStfm875fbvQGOiWb4GgJnL48GGGDh1KvXr1kMlkbNmy5aHnHDp0CD8/P8zMzGjcuDHfffed4R01EipdXvzzzz/nv//9LxcvXjSEPwKBQGAwlCoVPv2HMGXZT/Sc9CKWdexJT0ok8IevWTX7RS7s3yOSLsETQWZmJu3atePrr7+uUPvIyEgGDRpE165dCQkJ4e2332bWrFls3LjRwJ4aB5Xe+uG5554jKyuLdu3aYWJigrl58X1sUlJS9OacQCAQGAKliQm+A4fSpnc/LuzdxcmtG0hLvMOe75dxYstf+I8cj2e3XsirYHNGgaA6GDhwIAMHDqxw+++++46GDRuyZMkSAFq1asXp06f58ssvGT16tIG8NB4qnWwtXrxYVPMWCARGgcrEFN9Bw2nTuz/n7yddqXfi2f3dUk5s/gv/0U/T6qkeIukSVBhJkkDKruRJ2oJzJCWSttIXnAqQmRv0uzk4OJh+/foVO9a/f39WrlyJWq02WBUCY6HSydakSZMM4IZxYmwFXmuzRlEtQ9g3lnGq7bEoarsy9pWFSVev/pwL3MGpbZu4dyeOXd8s5vimdfiPfpqWXbohlytELCqp8aQVopakLEjwqbRtG4BEeGTPnUKAiu0SUFo/HjZu8fHxODk5FWvj5OREfn4+iYmJuLq6PlRD3xh1IWqFQkFcXBxOTk7FjicnJ+Pk5KT3AqC1CVGIumZqiELUNUejNhQ/btatNx6dunDl4F7O7/6He/Gx7FqxiOANa/EZMhIPv05kZWU9sv2KIGJReY0aU4ha0lR+MbQe0Go0ICu/3+X1o3DsHkbRNoWvoaLnikLUpVPpd1hZWV9ubi4mT/hGgaIQdc3UEIWoa45G7Sl+bEvXcc/SadgoQnf/w+ltm0m9E8fBld9gv2sbbQcMpV6vfga7vChiUXmNmlKIWpKs7s8yVQJJIi0tHRsba3jEcZJTscuIZfVDLpeX20cXFxfu3LlTrE1ycjJKpRInJ6dix0Uh6pJUONlatmwZUPDr5KeffsLKykr3nEaj4fDhw7Rs2VL/HtZijKnAa23WEIWoa45GbSt+bGpuQacR4/DuN4SQXds4889mUm7f4uDKb7iwexsBYybQvFMXZHL9z2WIWFRe48H/64PKFj4uaGNZKQ2tVguyfJBZIDfAawke3o+HjVtAQADbtm0r1iYwMJD27dvrJlpEIeqyqXCytXjxYqCgo999912xrNLExAQPDw+x54ZAIDBKTC0s8B81Hp8BQzi7429Ob99Mcswt/lnyGQ5u7gSMeYZmHTsbJOkSCAxBRkYGN27c0D2OjIwkNDQUe3t7GjZsyLx587h9+za//vorAC+99BJff/01c+bMYdq0aQQHB7Ny5UrWrl1bXV2oVVQ42YqMjASgZ8+ebNq0iTp16hjMKYFAIKiJmFpY4j/6aRp37kb4sUOc3bGVpFs32bb4UxwbehAwdgJNOwSIO7YFNZ7Tp0/Ts2dP3eM5c+YA8MILL7B69Wri4uKIjo7WPd+oUSN27NjB66+/zooVK6hXrx7Lli0T2z5UkEqv2Tpw4IAh/BAIBIJag6mFJQFjnsF34DDO7NjC2R1bSYyO4u+vPsHRozGdx0ygSftOIukS1Fh69OhR7p13q1evLnGse/funD171oBeGS+VnvMeM2YMn376aYnjX3zxBWPHjtWLUwKBQFAbMLOyosu455j69So6jRyPysycxKgItn75P36fN5vwMyeq5BZ4gUBQs6l0snXo0CEGDx5c4viAAQM4fPiwXpwSCASC2oS5lTVPPT2RaV+vpOOIsahMzUiIDGfL5x/xx9tziDh7SiRdAsETTKWTrYyMjFK3eFCpVKSlpenFKYFAIKiNmFvb0PWZF5j69Uo6DB+D0tSUOxFhbP5sPmvefYPI0DMi6RIInkAqnWx5eXmxbt26Esf//PNPPD099eJUZfnmm29o1KgRZmZm+Pn5ceTIkTLbxsXFMWHCBFq0aIFcLmf27Nkl2qxevbrY7cqFfzk5OQbshUAgMBYsbGzpNmES05avpP3QUShNTIm/cZ1NCz9g7Xv/IercWZF0CQRPEJVeIP/ee+8xevRowsPD6dWrFwD79u1j7dq1rF+/Xu8OPox169Yxe/ZsvvnmG7p06cL333/PwIEDuXz5Mg0bNizRPjc3F0dHR9555x3ddhalYWNjw7Vr14odK7rxmUAgEDwMC1s7uj83mfZDRnLq742c27ODuLBrbPzkfeo1b0Xnsc/SsE07sZBeIDByKj2zNWzYMLZs2cKNGzeYMWMGb7zxBjExMezdu5cRI0YYwMXyWbRoEVOmTGHq1Km0atWKJUuW4Obmxrfffltqew8PD5YuXcrzzz9f7u7uMpkMFxeXYn8CgUDwKFja1aHH81OZ+vVKfAcNR6kyIfb6FTZ8/C7rPpxL9MXz1e2i0SBmDAUVoapfJ4+0A9/gwYM5duwYmZmZJCUlsX//frp3765v3x5KXl4eZ86cKVGJvF+/fgQFBT2W7YyMDNzd3WnQoAFDhgwhJKSS5RcEAoHgASzt6tDzhWlMWf4TPgOHolCpuH31Eus/ept18+dy6/KF6nax1lK40XZeXl41eyKoDRTWNzVUqaoHeaTqo/fu3WPDhg1ERETwn//8B3t7e86ePYuzszP169fXt49lkpSUhEajwdnZudhxZ2dn4uPjH9luy5YtWb16NW3atCEtLY2lS5fSpUsXzp07R7NmzUq0z83NLVbIsvBGAbVaras/pm8kSSI/Px+1Wm3QsgjGoFEYAxGL6tcwdCzA8P3Qh31TK2u6PjsZn4HDOb1tExcP7CHm8kX+mj+PBp5edBz1NNauDUQsKqEhSRJmZmYkJCTo6t7pC41GY9B6f5IkkZeXR3Z2tkEvKRu6H7VBQ5IksrKySExMxMbGBq1WW6ywtaHeD5VOts6fP0+fPn2wtbUlKiqKqVOnYm9vz+bNm7l586Zua//SuHbtGmvXruXIkSNERUWRlZWFo6MjPj4+9O/fn9GjR2NqalrpTjz44pQk6bFesP7+/vj7++sed+nSBV9fX5YvX66rEVmUhQsXMn/+/BLHDxw4gIWFxSP7IdAvgYGB1e2C4D4iFkVwrI/b4LHcvRxKWvg1Yi5fJObyu5g718O+rR/mjoZdwmBMsZDL5Tg6Ooo74wXlotVqSU9PJywsrMRzhTNe+qbSydacOXOYNGkSn3/+OdbW1rrjAwcOZMKECaWeExISwptvvsmRI0fo3LkzHTt2ZMSIEZibm5OSksLFixd55513ePXVV3nzzTeZPXt2hZIuBwcHFApFiVmshISEErNdj4NcLqdDhw6lBgZg3rx5ulIHUDCz5ebmRs+ePalbt67e/CiKJEmkpaVhY2Nj0F+MxqChVqsJDAykb9++BpkyNpZxMoZYgOH7YTj740hPSuTU3xu4fGg/2XdiuR0YS8M23nQa/TSuTVvoUcs4YlGahlarRa1W621NjiRJZGZmYmlpabA+5OfnExQUROfOnVEqH+mC00Opin7UBg2ZTIZSqSxzZiw5OflxXSyVSkf11KlTfP/99yWO169fv8xLdyNGjOC///0v69atw97evkzbwcHBLF68mK+++oq33377ob6YmJjg5+dHYGAgI0eO1B0PDAxk+PDhFehNxZAkidDQUNq0aVPq86ampqUmhyqVyqAfYkqlEpVKZdAXtTFoFGKoeBjLOBlDLMDw/TCkfXvXevR/cRb+I8dzZN3vhAUfJvpCKNEXQvHw9qPz2Al6T7pqcyzK0niUKyTl2ddqtVhbWxv0R0h+fj5WVlYGjYWh+2EMGoYa/0onW2ZmZqVO0V67dg1HR8dSzwkLCyt1I9QHCQgIICAgoFILHOfMmcPEiRNp3749AQEB/PDDD0RHR/PSSy8BlKhcDhAaGgoULIJPTEwkNDQUExMT3T5h8+fPx9/fn2bNmpGWlsayZcsIDQ1lxYoVFfZLIBAIHhUbRyeemjiFp8ZN4MTm9Vw6tJeo0DNEhZ6hkU97Oo99FpcmJdePCgSCmkmlk63hw4ezYMEC/vrrL6BgSi46Opq5c+eWWf27IonWo7YfP348ycnJLFiwgLi4OLy8vNixYwfu7u4AJSqXA/j4+Oj+f+bMGdasWYO7uztRUVFAwQ0A06dPJz4+HltbW3x8fDh8+DAdO3asVD8EAoHgcbB1cqH/S7PoNGIsxzet4/KR/USGnCYy5DSNfTvQeeyzODduWt1uCgSCh1DpZOvLL79k0KBBODk5kZ2dTffu3YmPjycgIICPP/64Qjb27dvHvn37SEhIKHYXAMCqVasq6xIzZsxgxowZpT5XWuXyh13LX7x4cbkbngoEAkFVYufiyoAZs+k0ahzHN/7JlSMHiTh7ioizp2jS3p/OYyfg5NG4ut0UCARlUOlky8bGhqNHj7J//37Onj2LVqvF19eXPn36VOj8+fPns2DBAtq3b4+rq6vYOVkgEAgqSB2XegycOYdOI8dzfNOfXD16iPDTxwk/fZymHQLoPHYCju6NqttNgUDwAI9820OvXr105Xoqw3fffcfq1auZOHHio0oLBALBE419vfoMeuUNOo0smOm6GnSYG6eCuXEqmGadOtN5zAQcGnpUt5sCgeA+FUq2SttbqixmzZpV7vN5eXl07ty5wvYEAoFAUDp167sxeNZ/8R81nuANa7l2/ChhJ4IIOxFEc/+nCBjzDA5u7tXtpkDwxFOhZKui65dkMtlDk62pU6eyZs0a3nvvvQrZFAgEAkH51G3QkCGz38L/1tMEb1jL9eNHC/5OHKNFQFcCRj9D3QZu1e2mQPDEUqFkKzIy8rFEim74qdVq+eGHH9i7dy9t27YtsafFokWLHktLIBAInlQc3NwZ+vpcEqOjCN6whrATQVwLOsy14CO07NyNgDHPYF+vQXW7KRA8cRhmq9oHeLCIs7e3NwAXL14sdlwslhcIBILHx7GhB8PmvE1CVATBG9Zw49Rxrh47xLWgI7R8qjsBo5/GysGput0UCJ4YKpxseXp6cvToUd0O8NOnT+fjjz/WbWSakJCAh4dHqXWFDhw4oCd3axeSJOmtZERZtg1l39g0imoZwr6xjFNtj0VR27XVvj41HN0bMeyNd0iIDCd4w1rCz5zgypEDXD16iBZduqOu4yTGqprtF2oU1TKURm2PRVVoGMpuhZOtq1evkp+fr3v8559/MnfuXF2yJUkSOTk5ZZ7/008/0atXLxo3Nt69YFasWMGKFSvQaDQApKenG7TOVUZGBmC4GUFj0Sh83aalpRkkHsYyTsYQCzB8P2pjLEztHegx/VW8bg7h7LZN3LoQwtWjB0AmY2diLD5DRmLjqP+Zrto4VlVtH4zjfWEsGunp6Xq3CY9xGbG07K+8jr/22mvk5ORQv359evbsSc+ePenVqxcNGzZ8VBdqHDNnzmTmzJmkpaVha2uLtbU1tra2BtEqHH9bW1uDvqiNQUOtVgMFe8QZqjYi1P5xMoZYgOH7UZtjYdvWmyZtvYkPv86xv/7g5rmz3Dh+hIhTQXh260WnkeOxdXLWm15tHquqsg/G8b4wFo2ik0r6pErWbEFBCZzjx49z6NAhDhw4wMyZM8nJycHd3Z1evXrpErB69epVlUsGRyaTGXQdWqF9ofFw+0V1DKVR28epKjSqIhZF7Yt4l45r0xYM/+97bPp1NfK4aKIvhHDxQCCXD++ndY8++I8cr7eZrto+VlVlv6iOoTCGWBhaw1B+VzjZKq1jlXFKpVLRtWtXunbtyrvvvotareb48eMcOHCAgwcPsnbtWnJzcw2WVQoEAoGgOGYOTgx6fhKJkTcIWr+Gm+dDuLBvN5cO7sOrZx86jRyHjVhILxA8NhVOtiRJonfv3rrrydnZ2QwdOlRXNLqySZJGoyEvL4/c3FxdktWokSgzIRAIBFVNveatGPPOR9y+epmg9X8QffEc5/fu4uKBvbTp3Z9OI8ZiXdehut0UCGotFU62Pvjgg2KPhw8fXqLN6NGjyzw/JyeHoKAgDh48yP79+zl9+jSNGzemW7duvPLKK3Tv3t2oLiEKBAJBbaN+S0/GvvcxMZcvErRhDbcunefcnu1c3L+bNr0H0GnEWKzs61a3mwJBreORk63KYmdnh7OzM8OGDeO1116je/fuODmJ6WmBQCCoaTTw9GLc+59w69J5gtavIebKRUJ3/8OF/btp22cAHYePxaqOfXW7KRDUGqpsgXy7du0IDQ3l0KFDyGQy5HI5PXr0oG5d8StJIBAIaiJurdsyzrPN/aTrD25fvUzIzm1c2Lubtn0H0nH4GCzt6lS3mwJBjafKkq0TJ06QmZnJkSNHOHDgAJ9//jnPPPMMzZs3p0ePHnTv3l3MdgkEAkENQyaT0dCrHW6t2xJ94RxB6/8g9voVzu7Yyvm9u2jXbxAdh43Gwtauul0VCGosVZZsAVhaWjJgwAAGDBgAFGweduTIEQIDA5k2bRoZGRnibkSBQCCogchkMtzbetOwTTtung8h6K8/iLtxjTP/bOZc4A68+w2mw7DRWNgYZm9BgaA2U6XJViFarZZTp05x8OBBDhw4wLFjx8jMzMTd3b063BEIBAJBBZHJZHi088W9rQ9RoWcIWv8H8eFhnN62iXN7duA9YAjth4wUSZdAUAR5RRrZ29uTlJQEwOTJkx9pO/tTp07x+eefM2jQIOzs7AgICGDFihU4OTmxbNkyIiIiiIyMrLRdgUAgEFQ9MpmMRj7tmfDxIka+9QHOjZuizs3h1NYN/PTqVI6s/YXs9LTqdlMgqBFUaGYrLy+PtLQ0HBwc+OWXX/jss8+wtraulFCnTp1wdXWlR48eLFq0iB49etC0adNHcrq2IIqK1hyNolqGsG8s41TbY1HUdm21X5UaRbUeh0Y+7fHw9iPi7CmC1/9BQlQEJ7esJ3T3P7Tq2Y/OI8djXsnvjIpiLPEuqmUoDWN53RptIeqAgABGjBiBn58fkiQxa9YszM3NS227atWqUo9fuXKFFi1alKsjSZJBt/g3NKIQdc3UEIWoa46GMRTcFbEoG4emLRgydz7R585ydttGUmKiObdjK5f378Gr9wBa9xmAqYWlXrQKMYZ4G8P7wlg0qrUQ9e+//87ixYsJDw9HJpORmppKTk5OpYQKE62FCxcyb968Es9rNBqee+451q5dWym7NQlRiLpmaohC1DVHwxgK7opYPBy77r1o07UHYaeCOfbXH9y9fYuQ7Zu5fGAPvoOG4ztomN6SLmOItzG8L4xFo1oLUTs7O/Ppp58C0KhRI3777bdH3h9ryZIl1K1bl+nTp+uOaTQann76aS5evPhINmsqtbkYpzFpiELUNUfDWAruilhUwL5CQfNOXXBq7knCtcsEb1hDckw0wRvWcHbnVtoPHonPwGGYWlg8vlYtj7exvC+MQaPaC1EX8riL2Hfs2EGfPn2ws7Nj3LhxqNVqxo8fz9WrVzlw4MBj2RYIBAJBzUIml9PcvwvNO3Xm+oljBG9YS3JMNMf++p0zO7bSfshIfAYMwcT88ZMugaCm8kgXhw8dOsSXX37JlStXkMlktGrViv/+97907dr1oef6+fmxefNmhg8fjqmpKStXriQ8PJwDBw7g7Oz8KO4IBAKBoIYjk8tpEdCVZp06cz34KMEb1pISG8PRP3/l9PYt/yZdZqWvBxYIajMV2vqhKL///jt9+vTBwsKCWbNm8corr2Bubk7v3r1Zs2ZNhWz06NGD3377jTFjxhAVFcWhQ4dEoiUQCARPAHK5gpZduvPCVysY9Mob1HGtT056GkfX/sJPr0zh5NYNqCu5JlggqOlUembr448/5vPPP+f111/XHXvttddYtGgRH330ERMmTChxzqhRo0q15ejoiJ2dXbH1W5s2baqsSwKBQCCoZcjlClp17UmLzt24euwQwRvXci8+jiNrVnNm+xY6DB1Fu36DUJmaVberAsFjU+lkKyIigqFDh5Y4PmzYMN5+++1Szynrjrz+/ftXVl4gEAgERoRcocCzWy9adunOlaMHOb7xT+7diePQ76s4tW0THYePoW3fgahMTKvbVYHgkal0suXm5sa+fftKbEi6b98+3NzcSj3n559/fjTvBAKBQPBEIFcoaN29d0HSdeQAxzf9SWrCHQ7++hOn/t5Ix+FjaNNngEi6BLWSSidbb7zxBrNmzSI0NJTOnTsjk8k4evQoq1evZunSpYbwUSAQCARPCAqlEq+efWnVtSeXDu3jxOZ1pCUmcOCXHzn590Y6jRhLm179UZqYVLerAkGFqXSy9fLLL+Pi4sJXX33FX3/9BUCrVq1Yt24dw4cP17uDAoFAIHjyUCiVtO3dn9bde3Hp4D6Ob15HelIi+3/+npNbN9BpxDi8evVDYaAd1wUCffJIr9KRI0cycuRIffsiEAgEAkExFEoVbfsMwLN7by4dDOT45r/ISE5i36pvObl1Ax1HjMXNt2N1uykQlEult34QCAQCgaCqUapUtOs7iClLf6T35JexqmNPenIi+1Z+w/r33uD83l1o8tXV7aZAUCpVNv86YcIERowYwYABA7Cxsakq2WpFVHCvORpFtQxh31jGqbbHoqjt2mq/KjWKahlKQ9/2FUol7foNonWPPlzYv5uTWzaQmZLM3p9WcHLrejqNHI9nt156u7woYvFkaRjKbpUlWy1atOCzzz7j+eefp1u3bgwfPpxhw4aVeQdjbWTFihWsWLECjUYDFFQPFxXcq1+jsLBoWlqaQeJhLONkDLEAw/dDxKLiGLofjQO64ebbifP7dnHtwB7SEhMI/GE5xzetw3vQcJr6P4VcoXgsDRGLJ0sjPT1d7zahCpOtDz74gA8++ICYmBj+/vtvtm7dyhtvvIGnpyfDhg1j+PDh+Pj4VJU7BmHmzJnMnDmTtLQ0bG1tsba2LnOPscfFGKqrV5WGWl1wacHGxgaVSqV3+8YyTsYQCzB8P0QsKk5VjZXvwGE8NXIcF/bt4tTfG0lPSuDIrz9yfvc/+I8aT6unejxy0iVi8WRpFCa++uaRk628vDwiIyNp0qRJpTLxBg0aMGPGDGbMmEF6ejo7d+5k69at9O7dG2tra4YOHcrLL79M69atH9W1GkNtrnxuTBqFdg2tUdvHqSo0qiIWRe2LeJdvv6iOoaiqsTIxM6P9kJG06zOQ0MAdnPp7I6l34tj97RJObvkL/1FP0/Kp7sjllU+6RCyeHA1D+V3pBfJZWVlMmTIFCwsLWrduTXR0NACzZs3i008/rZQta2trxo0bxx9//EFiYiKrVq1CoVAQHBxcWbcEAoFAIEBlZkaHoaOYtnwlXSdMwszahrtxsexcsYjVc2Zw5cgBtFpNdbspeMKodLI1b948zp07x8GDBzEz+7dmVZ8+fVi3bt0jO6JQKOjduzdLly5l6tSpj2xHIBAIBAKVmRkdh49h2tcreeqZFzCzsuZu3G12fP0Vv7wxk6vHDomkS1BlVDrZ2rJlC19//TVPPfVUsek2T09PwsPD9eqcQCAQCASPg4mZOZ1GjGXq8pV0GT8RM0srUmJj2L7sC37976tcCz6CpNVWt5sCI6fSyVZiYiJOTk4ljmdmZhr0Gq1AIBAIBI+KqYUF/qPGM/XrlXQe9yymlpYkx0Tzz5LP+PXNV7l+/KhIugQGo9IL5Dt06MD27dt59dVXgX8Xk/34448EBATo17tajjYrC625ecknFArkpqbF2pWJXI68yOXawraSJKHNzkarUv2b5D7YNjsbytozRCZDXsS30trqNExMUFhY/Ns2JwfK+VCSV6KtrKgPubmgKXtav5jdh7SVmZvrxkXKy0OWl1cQj1Lu9CnaVpuXB+XcjSIzM0Mml+vsSvn5pceijLZl2jU1RXb/bqnS2hbVkJuZ/dtWrUZSl72Ro8zEBNn9G1ge1pYiYyPl5yPl5ZVtV6VCdr99pdpqNOXHQqlEdr/mnaTRIOXmlm23aFutFiknp+D/pcSjrLalolQiL2wrSUjZ2cWeLmZfpSq3bTEq8b6X5MV/Bz/KZ0RF2pYXi4p8RpTZ9v77vqz3RmU+Ix72vi+qobC0LLctgAoZnQYOx7v/EEJ2buPsjq2k3Ixix5ef4ODmTseR42j2VHfde1cg0AcyqZI7eAUFBTFgwACeffZZVq9ezYsvvsilS5cIDg7m0KFD+Pn5GcrXWkPh1g8nmzbDqpTbjeUd5Jh99G8R1azhOVDGd4q8jQyzL/79gM4anwOpZbRtJsNs+b9ts5/PQUoova2soQzzH4q0nZ6LFF36S0HmBOa//vsBnfNqLtqwMl42tmCxrkjb/+aivVBGW1Ow2Fqk7Xt5aE+V/aFrsevftrn/y0NztOy25ltMkZkVfLjnfpmHZm85bf80RWZX0DbvazX5/5SdxJmtNkHuUvAhnPejmvyN5bT9zgS5x/22v6nJ/6PstqZLTVC0KGirXp+PemXZiZnpZyoU7QpeV+q/81F/U07b+SoUnQra5u/JJ29R2W1N3lah7Ha/7WENeZ+UnZiZzFGi7FeQxGlOaMj9oOy2iVNsudPflhytAtMLubT5OLbMtkFdnyK6Sw9cLOvSMiMDl8/eK7Otw8yZOL76CgC5YWFEDB1WZlv7yZNxfvO/AOTF3Ca8T58y29aZ8Awu778PQH5KCmGdu5TZ1nbECOp9uhAoSHKu+Zb9+Wfdvz8Nli7RPb7SslWZbS27dcPm8890t7df9fEtM5Gz6NAB999+1T2+HtAZzd27pbY18/Ki0Yb1QMF2A1e6dUN1916pbU2aNqHJP//oHocPGULejdKXiqjq1aPp/n26x5FjxpJz8WKpbRV16tA8OEj3+ObE58k6darUtjJzc1qGnNU9jn7xRTIPHS61LUCrq1d0/495bTbpu3eX2bbF2TPILSzIycjg6tQpmIae1z13anhfOo+ZQJP2nQBITU01+NYPO3bsYNCgQQbd+sHQ/TAGjeTkZBwcHEhNTdXrBuyVntnq3Lkzx44d48svv6RJkybs2bMHX19fgoODadOmTaUd2Lt3L0eOHKF9+/YMHTq00ufXRswVGhqYZeoeX5fJkSj9RWMq1+JWpO0N5GjKaGvyQNtwmZz8MtqqHmgbKZeTV0ZbhUwq1vamXE5OWW0p3jZaLie7jLayB+zGKORkltEWKNb2tkJORjltG5hmIb+fm8UpZKSVc8W8nlkWyvtt7yhl3CuvrWk2qvttE5Qy7pbT1sU0G9P7bZOUMpLLaetsmo35/bYpKhmJ5bR1MsnB4n7buyoZCeW0dTDJwep+21SVjPiHtLW+3zbdBGIp+xZ5e1UetmYFvxAyTOB2OW1bW93lKacUALLi4FY5bXPq3SLKcz9RQOSNDP6vzJbwx/6jHL2lxcHUkhb5WQwop61AUBZmVlY4eTQmtUiylRgVwdYv/4dToyYEjJlA3SbNq9FDgTFQ6Zmtx2HGjBnUrVuXjz76CICNGzcyfvx4vL29uXz5Mv/73/+YM2dOVbljMApntpb/8ToWlmYlnpfkMiTVv1848txyNlGTydCalN5Wo9GgKDpz9mDbvHwoK7oy0JooH9pWo9GgUCoeaKsp+3ICoDWtXNvCfsjUGmTaitl9aFsTBRT+6lHnk5Z8F1s7u1J/CRVtK8vXINOUY1elAHlhWy0yTcGMWYlYlNO2MnaLUqhRrK1Giyy/HLtKOSjkFW6rQSroh0aLvJy2klKOdN9uYVuFXINKnodSrkYpV2OiUKNS5KFQaTAxzcdEocZCnkUL65so5AVjHJrixMbYJkSqrZCbaMBci9xci0IpIdNKKNVl+6BRyNAq5eTmKsnJVqFJV5CToyI3wwR1mgr1PQWydBkmeaDSKLDEBAeFCfXMLXGztcDZ2Zb6Deri0sAeOydbrGwtkMvlFbqMmJqWhq2NjUEvI6bn5Oh+vRviMqJarWbnli3079ev9NkUPV1G1I2VAS8jFmpU5DKizuUylg9kZ6Rzdt8uQnZuQ51bcLnZwb0RT42fSGPfDgaZTREzWzVHw1AzW5VOtnbs2IFCoaB///7Fju/evRutVsvAgQPLPLdVq1YsW7aMvn37AtClSxeGDx/Om2++yaZNm3jzzTe5cePGI3SjZlGYbCUlJVG3bl2DaBjDi7qqNAz9QWYs41RVsTi47zd6drqGPPdv4P6XoWkPZFazkKm8AMjTqknOTeFG8m2u3blJTOodkvNSyJTSyVNmI6lykZupkcvL//hSqxXk5KrIzjEhK9uUrCxTsrJNyclQociVocgFRbaEIlODMiMfsywtVrky7CUlda3NcahjjatLQVJWz60u9i51qONsi1ahwa6M5F0fGMP7Amr36zYrLZXT/2wmZNc28u+vHXRp2pzOYybg4e2nVy0Ri5qjUWMuI86dO7fUzUslSWLu3LmlJlvz588HIDo6mq1btxIcHIwkSZw6dYp27dqxYMECcnJyiI6OZsGCBQC8f3/NRG1GFBWtORpFtQxh31jGqSo0snLrorX8CLnVy5D5LeRshdyDSLkHkUx6gtUsVCpPXMyccKnvxFP1Sy/jJUkS6fnpJOYmkZCbyO2sOGKy4ojLukNKXjJqeTYqlQaVSoO1VfEF8RqtjOxsUzLvJ19ZWaZkZptyN9cMkIEkocjNR5FzF0XWXZTno1Acl1DeU6O6m4syVY2FQoaNmQn21mY4O9pSr54d9Rs44FCvDvaudahbrw62jjYFs2WPME61/X1R1HZtfN2aW9vQ9ZkX8B04jGMb/+Tqob3E37jOpk8/xLVZCwLGTMC9rY9evvBFLGqORo0pRB0WFoanp2eJ4y1btixzVmrSpEkAfPfdd/Tt2xdvb2+OHDmCi4sLc+fORZIkMjMzWbZsGZMmTTJooAyJKERdMzVEIeqao1E8FnbAPOQmEzDJX4VKuxtZ3gFIOUCevDu5ymlo5Q9fK+NAXRyUdfG0aQk2//ZDZaEiOT+FpLxkktSJxOfdIS43nvi8OyBXY2WZg5XlA0mYRk5GphnpGWakZ5iTnmlOdq4KkN3/My3400oos0GZDYpsUGZnoIzJQHnlJop7eciz85Bl5yLPUWOlUmBvbY6jvSX1XO1wrG+Pvasd9vULErI6LnaoTIq/Lo3hfQHG8bqVkOHZfyht+g7iwp7tXDm0l7iwa2xa+AFOTZrhO3Q09Vq2fixtEYuao1FjClHb2toSERGBh4dHseM3btzAssj18qK4u7sD4O/vzxdffMHMmTNZvnw5I0eOpGHDhgCcOnWKRo0a6R7XRkQh6pqpIQpR1xyN0mPRFliClB8BmSsgZzsq7SFUeYfAtB9YzkSmallhjaL9cJY5l3heK2lJyk0iJvs2t7NjuZ19m5js28TlxINCg61NFrY2/657UmKGXG1HRoY5iXeVxKTIyctXkG8J+SU+8pTINAoU2ea6ZCw1C+5kQVgWyOPTkUUmIcvKQ5aVW/CXmYu1qRKXutbUq1cHp4YOOLk5YFnXHPcWDXF2d8TO6dFmyMpDFD+uvH3XqTPoMuYZTm3dwLm9u0gID2PXkk+p38KTgHHP0rB120fSELGoORo1phD1sGHDmD17Nps3b6ZJkyZAQaL1xhtvMGxY2bdeAyxevJiJEycyffp0nnrqKT744APdc99//73R3Y1Ym4txGpOGKERdczTKi4VM1QTsFiHlz0DKWAE5OyB3D+TuQTIdgMzqFWSqit0VVl4/FDIFzubOOJs744ev7ni+Np/4nDtEZkYSmRlFRGYk0Vm3yJdyQBWPSR2oXwfqNwZbhR3OKg9M8l3IyLDm1r0sIu/eJSYtFY0C8q0K/h5EngvKLDNUmWYos9D95akhBbis0SK7eAvZiRvIMnOQZ+Yiy8xBlZePc10rnN0ccHZ3wtndEWePgj8XDycc6tujUFauwLIofvxo9q3q2NNz0nQ6DB/Dya3rOb93F7evXWbDR+/QwNOLzmOfxc2zcnfmi1jUHA1D+V3pZOuLL75gwIABtGzZkgYNGgAQExND165d+fLLL8s918PDgyNHjpT63E8//VRZVwQCgREiUzZFZrcYST0DKfNryNkJubuQcncjmQ1AZvUqMmVTvesq5UoaWNSngUV9ujo+BRQs1L+VdYuIzEgiMiKJzIwkLieeVM09UjWhBf6ayWjUxIMhtl60tO6CqdaBW6npRN67S+TdFMLvpnAjJYXErEy0pjLyTCGvTnFtRT4oMiRUmXKUmRaoMixQZoL8/k16eUCEJBGZlYcsLA5ZSCTyjBxkGTnIMrJR5Obj3MAeZw8nnNwdcPVwxqWxE66NnXFt7Iy9i+EW8z+pWNWxp9ekF+kwbDQnt6znwr7dxFy+yF/z5+HWui2dx06gQSuv6nZTUEN4pMuIQUFBBAYGcu7cOczNzWnbti3dunUzhH8CgeAJRaZqhsxuKZJ6JlLG15C7C3J2IuXsQjIbhMxqpkGSrqKYyFU0sWpME6vGcP+KZIY6kwsJF4jMj+Ji2iVuZ8cWJGOZkWxlG+YKc1rZtKRNPS+GtGqHo6kjAKk5OfcTr2RdAhaeksyttFQ0StDYycizK65vqVVili1DczcfWZoWZYYpSktTZE62FNvUQKslKiuXmxk5yM5GIj98BVl6DvL0bMhVY2qmwqXR/eSrUUEC5tiwLsm375GbnWewS1dPAtb2DvSe/DIdho0pSLr27+HWpfOsu3Sehl7t6Dz2Weq3LLnOWfBk8Ugr8WQyGf369aNfv34Vav/pp58ya9YsLIrsl1IWJ06cICkpicGDBz+KawKBwMiQqZojq7MMSX31ftK1B3K2I+XsQDIbcj/palxl/lgqLWhl2RJ/207IZDJS8lK4mHqZi6mXuJR2mYz8DM7eDeHs3RAAPG1a0te5D9527fB1rYeva71i9nLy1YSnpHAtOYmrSYlcS07iWnISCZmZZMrzybQELIH7m/iayBU4ys2xzFHAXQ2ZsVlo7wFW5khWBXtdFUvE1PnkpueQkZ5N+PU45GcjkaVnI8vMRSZJrH3tHxzq21OvqQv1mrhQv6lLwf/vP7awLqXkmKAENg6O9Jk6g44jxnBi819cPLCX6IvniL54Dve2PnQeO4F6zcuuGiAwbiqUbC1btozp06djZmbGsmXLym07a9asEscuX75Mw4YNGTt2LMOGDaN9+/Y4Ohb82svPz+fy5cscPXqU33//nbi4OH799dcSNgQCwZONTNUSWZ2vkdSX7yddeyFnG1LOdiSzofeTLo8q98vexJ5ujk/RzfEptJKWm5nRXEi7yMXUS1xPD+Ny2lUup13F0dSBPk696OrYFUvlvz88zZQqWjs509rp38X8kiQRdecOcepcricnF0vCcvLzua3NKPj0diz4M1UocbOwxUlhgUWOHCkln9RbGcQnpKFVKZHsrdDYF19EJpMkZJk5kJpFXFo2d8LiCT0bWXBpssgd4XZOttRv5kKDZvWo38yVBs0L/uo1dcHU3BRBcWwcnOg77RU6jRjH8c3ruHRwLzfPh3DzfAge3n50HjsB16YtqttNQRVToU1NGzVqxOnTp6lbty6NGjUq25hMRkRERKnPnT9/nhUrVrB+/XpSU1NRKBSYmpqSdX+3Yx8fH6ZPn84LL7yAqWntfgOLTU1rlobY1LTmaOgzFgVJ13LILazJpwCzYWA5g7RMW4PenVbRcUrOTWZfwgEOJhwmU1NQbspUbspTDp3p49ybeuauldLI12qJvHuXiwl3uJh4h0sJCVxOTCBDXbIQuLlSSWtHZxpb2uEgmWGWLiM5PoObMclEx6aQU0blChlgqtbAvQzyE9OQp2cjS8suSMIeaOvoVpcGzevRoJkrDZrXo35zVxq2rI+TuwMKhcIoXrePaz81IZ7jm9Zx6dA+pPs75TfyaU/nsc/i0qQZIDY1rUkaNWYH+cdFkiTOnz9PVFQU2dnZODg44O3tjYODQ1W6YVBEslWzNESyVXM0DBELSX3hftJ18P4RBXmKQZjUeQ25Uv9byTzKOOVqcglOPk7gnX3EZN/WHfeyaU0/lz60sfVCLvt3a4fKaGgliZup97iUcIeLiQlcTLjDhTt3SM8rWd2+vrUNPi6utHN2pYHKkguHz+Baryk3Y+8SeSuJqFvJZOeUXlRcKZNhKQNlejY5t1NQ37mHPC0bWV7JpE1lqqJBc1fcWtTD0cOBZu0a07BVfRo0d8XcSr+XJWt6slXIvfg4jm9ax+Uj+3VJV2PfDnQe+yz2bu4i2aohGjUi2VKr1bRo0YJ//vmn1I1Nq4tvvvmGL774gri4OFq3bs2SJUvo2rVrqW3j4uJ44403OHPmDGFhYcyaNYslS5aUaLdx40bee+89wsPDadKkCR9//DEjR46skD8i2apZGiLZqjkahoyFlHeuIOnKO3z/iBLMRyKzfBmZsoH+dB5jnCRJ4kr6VQLj9xJy7xzS/YKkzqbODK03iM4OAShkjz8jpJUkIu6mEBIfR0hcLCHxcVxPTipR/lQlk+HrWh9/Nzf867vR1smF1HtZRN5KJvJWUrF/c0tJqgCszVTYKuQoM3NRx9/l3rVYNClll4l3bFAXt1b1cW/VAHfPgr+Gng2wsbeudD+h9iRbhdyNj+X4xj+5cuQgknR/psu3A/lODRj53PMi2apmjRpRrkelUpGbm1ujbiFet24ds2fP5ptvvqFLly58//33DBw4ULdO7EFyc3NxdHTknXfeYfHixaXaDA4OZvz48Xz00UeMHDmSzZs3M27cOI4ePUqnTp0M3SWBQPAIyEzaIbP/CW1uCPmpi1Fqj0P2eqTszUjmo5FZvYxMUe/hhgzpo0yGp00rPG1akZCTyL6E/RxOPMKd3Dv8FPkz22K3M7z+UDrZd3wsHblMRlP7ujS1r8tYz4LtBzLy8jh/J56Q+DhC42M5GxfL3ZwcTsTGcCI2hqUEY6JQ4OPiSqf6bnTya8CoIT6YKVVoNFpu37lH+M1EwqMSCb+ZRHh0IrF3UknPUaPbc9ulDrjUwVSlwLmOJbZyOdxNJz8+lZTLMaQmpJIYk0xiTDJnA88X87mOs21B4tWqAe6ebjRsVR8PLzfsHA2zMXR1UcelHgNnzqHTyPEc3/QnV48eIvLsKeAU25Ni6TLuORzdy16uI6idVPoy4qeffsrVq1f56aefDFZWoDJ06tQJX19fvv32W92xVq1aMWLECBYuXFjuuT169MDb27vEzNb48eNJS0tj586dumMDBgygTp06rF279qE+iZmtmqUhZrZqjkZVrk2xMQ+HzOWQF3T/GRWYj0Fm9RIyRelrpSpjX1/jlKPJYV/CAXbG7SI9v6AMiYuZM33tetOzQQ8U8sptVlpR8vLyWP33VsybNuFUfCwnb8eQkJlZrI2JXEE7Fxc6uzWkh0dj2jg5Iy/S56zsPCKik7hxM5EbUQnciCr4t7T1YAqFnIaudjjbWGADyFIyyIy8w63LMSREJ5Xpp52TLR5ebni0dqORV0M8vNxwb+2GpU3BTQa1bWbrQZJv3yJo/RquB/+7B2WzTp0JGDMBx4YeetMxls+QJ2JmCwq2Zti3bx979uyhTZs2JUr0bNq0SW/OPYy8vDzOnDnD3Llzix3v168fQUFBZZz1cIKDg3n99deLHevfv3+plxuhYLYsN/ff9RFpaWlAwRdLYRkGfSNJEvn5+ajVaoO+qI1BozAGIhbVr2HoWMC//ciXtUFm/SOozyDPWoEs/wRkr0WbvQHJdAyS+TRQlCznU1H7+honBQr6OfShW52n2J94kN0JgcTn3OG3+DUE3t3PUJfBdKjjV2xNlz7Iz8/HWWVC3+Ytebp1m4I7IFPvcSr2Nidib3MytiD5OhV7m1Oxt1l6IhgHcwu6uXvQo6EHXdzcsDIxpUVjR1o0dgQKlpZotFpux93jemQCYVGJXL0RR1TMXdIycoiMSSGSlH/7bmVO49H+dG9YF0czE8xycsmOSSHmaiw3L8dwJyqRewmphO5PJXT/xWL+OzZ0wKO1G+6tG+Dc2AHPjs1xa1kfpUq/kwCGfl/YOLnQe/qr5Ng7Y5IYy41TwYSdCCLsRBBNO3am06jx1G3w+GsPjeUzxNAahvpsqvSr0s7OjtGjRxvCl0qTlJSERqPB2bn4B6azszPx8fGPbDc+Pr5SNhcuXMj8+fNLHD9w4ECF9hYTVA2BgYHV7YLgPlUfizHYW/vSosEeHGzCkeWuRZO9npsJnbgR25Ncdc25VNVL1p1wiwjCrMKJz43nx5sr+fPGX7TKaE79nHrIylwN9Wg8GAsroDfQq64LSbZqwnOyuZKTxbXsLJKys9h09TKbrl5GATQ2M8fT3AJPc0sclaoSX37u9uDe0QqpgyXpmfncSckt+EvOIT45l6wcDWFRiYRFJerOUSpkONW3xrWdDy1sVVjk5ZF7J42UW6mkRN8j+eY9MlOySYxOIjE6iVM7Q3TnypVy7BvYUtfDjrrudXDwsMPBow4WdjV/rzAT2zpgWwc3B1fuXjxLRnQkN04GceNkEFYNG2PfxregjcCgFO6QoG8qnWz9/PPPhvDjsXjwDS5J0mNnvJWxOW/ePObMmaN7nJaWhpubGz179jToZcS0tDRsbGwM+gvCGDTUajWBgYH07dvXYJcRjWGcjCEW8LB+vIJGfQJ51goU+Wdo7HKMRi6nkczGIplPBbnjY9rXXx8S7iVwMuc0gYn7SCedk3XOUM8sjmEug/G183nsma7KxiJPo+FMXCwHb0Zy8GYUUan3CMvJJiwnm613k2loY0ufRk0Y3qIlLes66PpR1lhJksSdpHSuhd/havgdrkXc4VpEAlnZecQm5hCbmKNra29nQetOjekx0ZXWzVxwtbMk/kY8Ny/dIvLiLcLPRXLrSixZadkkRd0lKeouEKk7387JBg+vhjRp507jdu408fagfvN6KBQPH8PqeV88R1J0FCc2ryP81HEyoiPIuBVJi4CudBwxjjr16ldaw1g+QwytkZycrHebUIlkS6vV8tVXX7FlyxbUajV9+vTh/fffx8zMzCCOVQQHh4K9XB6ccUpISCgxM1UZXFxcKmXT1NS01L3BVCqVQb9QlEolKlXJX5NCo3QMFQ9jGSdjiAVUoB+qp5DMu0BeMFLGMmTqs8hyfoec9WDxDDLL6cgUZW9FU1WxsDKxYqTjcAbU68eeO3vZHb+H2JxYvov6ETfzBoyoPwzfOo+fdFU0FiqVim6NGtOtUWPeByLv3eVAZAQHoiI4eTuG6LRUVp07y6pzZ2nl4MjIlp4Ma94Sk3LGyq1eXdzq1aVP14JLkFqtxK3YFK7ciOfKjXguXY8lLCqRlHtZHDkVzpFT4UDB+q/mjZxo3dwVr2e7MuTNwTTxqEdCdBIR528SeT6aiAs3iTx/k5jrcdxLSCN0/8VilyJNzU1o1KYhTbwb0dSnEU28PWjUpiFmFsU/y6vrfeHapBkj/vMuCVERBG9Yw41Tx7kWdJjrwUdp+VR3/Ec9jX0lki5j+QwxtIahPpcqnGx99tlnvPvuu/Tu3Rtzc3MWLVpEUlISP/zwg0EcqwgmJib4+fkRGBhYbFuGwMBAhg8f/sh2AwICCAwMLLZua8+ePXTu3Pmx/BUIBDUDmUwGpp3BJADygpAyloE6BLJWI2X9iWQxAZnlNGQKw8xMVwYLpQUj6g+jr3Mf9sQHsvtOILeyY1h+4xsaWrgVJF12PlV+l3gjuzo08vFjso8fGXl5HImO4u9rV9kfGc6VpESuHD3EZ8cO41+vPmO92tKvSVPMlOV/kcnlMtwb1MW9QV0G9GgNQE6ummvhd7h4PZaL12K5dD2WlHtZuoRsw46Cy4iuTja082yAdys3uk3uxQTXguLbOVm53Lx0i/BzNwkPjeRGaBQR56LIyczl6skbXD15o5i+W8v6NPVtRHO/JjT3a0zjdh4GG8OK4OTRmOH/eZc7keEEb1hD+OkTXDlygKtHD9Gqaw/8Rz9NHZfqvctW8HAqnGytXr2a5cuXM2PGDAB27drFiBEj+P7776t1K4g5c+YwceJE2rdvT0BAAD/88APR0dG89NJLQMElvtu3bxcrARQaGgpARkYGiYmJhIaGYmJiots77LXXXqNbt2589tlnDB8+nK1bt7J3716OHj1a5f0TCASGoyDp6gImnSHv6P2k6xxkrULKXotk8RwyyynI5PbV7SqWSgtGNhhOP5c+7Irfw574vURn3WJZ2ArcLRoyov5wfOzaVcvnsZWJCQObNmdg0+bczc5me9g1Nl+9TEh8HMdux3DsdgxWJiYMatqcUa1a075e/WJ3NZaHmamKdp4NaOdZsFeaJEnEJ6bpEq8LV2MJi0wgLiGNuITL7Dp4GSi49NiuVQO8PRvQztONAZN7IZcXaGo0GmJvxBMeGsWNkIIELDwkknuJady8HMPNyzHs+73g7kCZTEa9Zi606tiMZn6Nae7XmCbeHnrfnPVhODdqwoj/vsediBsErf+DiLOnuHx4P1eOHsSzay/8Rz+NnbNLlfokqDgV3vrBzMyM69ev6/aukiQJMzMzIiIiqF+/8teP9ck333zD559/TlxcHF5eXixevJhu3boBMGnSJKKiojh48KCufWkfRu7u7kRFRekeb9iwgXfffZeIiAjdpqajRo2qkD9i64eapSG2fqg5GjW9LIkkSZB3+H7SdaHgoMwCdElXnRoTiwx1Brvi9xB4Zy852oK7oT0s3BlZfzjt7No+1LeqiEVESgrrzoewIzKC2+lpuuNN6tgzo30nhrZoiVL+eJdBJUkiLj6R6PhMzl2O4fyV21wOi0OdX6wcN1aWpvi0dqNDO3fat3XHzbVOsTGSJInkuLvcOBtJ2JkIrp8NJ+xMBMmxd0toymQyGraqT4uOTWnZoSktOzWjUZuGj3wn5KPEIu7GNYLXryEy9EyBT3I5rbv3wX/UeGydSi55qSmv25quUe07yMvlcu7cuaMrIA1gbW3NuXPnaNy4sd4cMgZEslWzNESyVXM0anqyVdQGuQcLkq78SwUHZZZgMREs/o+0dFmNiUW6Op2d8bvZe2c/ufeTrkaWjRhZfxhtbduUeX5VxsLaxoYzcbFsunKJHWHXdbUc3WxsedGvA6Nbtcb0EfdtLG2scvPyuRIWR+iVGM5fvs2Fa7dLlCFycrCmfduCxKt9m4bY21mWZp7kuLuEHrlA7NU7XD9TdgJmYqaiqW9jXfLVsmNTXBo5Veg18jixiL1+leANa4g6dxYAuUJB6x598B85HhtHJ107Y/kMeSKSrenTpxfbymDFihU899xz2Nr+e9v0okWL9OZcbUUkWzVLQyRbNUejtiRbRW2Ru7+gDFB+weUpZJbkyp/G1P5F5Aq7x3e4DN3K9iFNnc7OuF3sTdhPnrYgmWli2ZgR9YfRxtarhJ3qikV6bi6/XwhlVcgZkrOzAXC2tGKqb3ue8WqLRSV9qchY5Wu0XAu/w5kLNzl9/iYXrsaWmPlq4u5I+7YNad/WHd/Wbpiaqsq0nxJ/l+unI7h6MoyrJ29w7eQNMu5lltC1dbCmRcemePq3oFVAc1p2bIqFdcnLj/qIRez1KwStX8PN8wVr2OQKJV49+9Bp5DhsHJyM5jPE6JOtHj16PLRjMpmM/fv368Wx2oxItmqWhki2ao5GbUu2itokd+/9pOtqwUGZNVi8gMxyEjK5/j6UC/UetQ9p6jS2x+1kf8JBXdLV1KoJI+oPw8umtc5edcciW61m3aUL/Hj2FHEZBTvn25uZ838+vkxs64NNKXd4V1ajLHJy1Zy7HMPp8zc5fSGasMiEYs+bmijp0M6dLu2bEODbGKU8v1z7kiRxOyyuYMH9iTCunbpBeGgU6gfqScrlMjzaNKR1QEHy5RnQnHpNXMjPz9dbLGKuXiJ4/RqiL54r0FQoadOrHx1HjEWrVNX6zxCjT7YEFUckWzVLQyRbNUejur/gH9+2FiknEE3aEhRSwTYEyGyQWU4qSLzkj1ZMuaTO4/fhXl4qO+4nXWqp4BJaM6umjKw/HE+bVnr9gi+LivQjNz+fLVcv8+3pk0SnpQIFC+5faOfD/3n7Ym9e/sbQ+hiru6lZnL0YzalzNzkZGkVCcnqx55s1cqRbx2Y81bEpTd0dK6STl6sm4lwUV46Hcfn4da4EX+fOzcQS7ewcbWjZqRmyOlpGTR6Gp38LTEwfPx4xly8StGENty4V1KBUKJW0eKonT417Fuu6ZW9t8jgYw+eUSLZqESLZqlkaItmqORq1PdnS2b93Fxuz45C5AvLDCp6Q2SKz/D+weB6Z3OrxNfTUh3t599get5MDCQdRSwUzLc2tmzHMeQgRR8JrTCzytVr+uX6Nb0+fICylYGNJS5WK59v5MNWnPXXMS7/7T9/xliSJG1GJHDsdzrHT4Vy5UXzPRScHa7q0b8JT7Zvg16YhSmXFa1cmxaZwJfg6l4Ovczn4GmFnIkrMfpmYqWjRsSltnmqFV9dWeAY019WBfBRuXTpP0Po1xFwp2GNMoVLRts8AOg4fi1Ud/d5lawyfUyLZqkWIZKtmaYhkq+ZoGE2ydd8+SJCzEynja9AUznTZ3U+6Jj5y0mWIPtzNu8v2uJ0cTDikS7occuvyf61fwMu+tV40HuRR+qGVJAIjbrDi5HEuJhZc3rNSmfCCtw9TfPywMyuedBk63okp6ew/eomQS3GcOn+T3CLJkY2VGT07t6DPUy1p16qBbmuJipKXq+ZGSCQXjlxm/+bDJIenkpqYVqyNXC6jibcHrbu0pE3XVrTp2oo6znaV0pEkieiL5zjy56/cuXEdAKXKhLZ9B9Jx+Bgs7epUyl55OrX9c0okW7UIkWzVLA2RbNUcDWNLtgrtS5KmSNIVUdBQZofMckrBthHy0u90q4yGvkjJu8s/sTs4lHiY/PtJl6dNS0bWH0Fz62Z61XrcbTj2RoSz9EQQl5MKLr9ZmZjwf96+TPb2w/Z+9ZKqjHdeXj5nLkZz7HQER06GkXLv3zp6jvZW9OrSgr5PtaJFE+dK+VL4vhg4cCB3IhO5cOQqF49e4cKRK8Q/sJ4MwN2zAd49vWjX04t23T2xqfvwy9eSJHHv3j1Sb0URvH4NsdevAKA0MaVd34F0GDb6sZMuY/icEslWLaIw2UpMTBTJVg3QUKvV7Ny5k4EDB4pkq5o1DB0LqJ5k69/nNJCzveDyoiaq4KCsDlhOAYtnkckqdjmoKmJxJ/MO3536kWirW2ikgjvzWtu0YkS94TSzbqoXDX1tw7En4gbLTgRzNTkJAGsTU/7Pu2BNl7WJabXEW6PREnLpFnuPXuXQ8TAysnJ1zzVwrUOfpwpmvNzrP/w7oLz3RdLtFC4cucLFo1e4ePQqkReiiz0vk8lo3M6ddj1a493TizZdW2FpW/J1VnxGFm6eDyFo/Rrib1wDQGlqine/wbQfOgoLm0crzG4Mn1PJyck4OjqKZKsms2LFClasWIFGo+H69etERERQp45+pmcfRJIkMjIysLKyMuiL2hg08vPzOXbsGF26dEH5iHv5lIexjJMxxAIM348K2ZfyUWn3YJK/EoV0CwAtdchTPk+eYjTIyq8pW5Wx8Ozkyf7UQxxPPYEWLQAtLZozsO4APMzdH0tDn/3QShIHoqP4/lwIN+4V7HNlbWLChFatGebmjksd+2qLt1qt4czFGA6duMHJc9Hk5v27rUSThnXp360FPQOaYW5W+g+Myrwv0lMyuHzsOheOXOXSkavEXIsr9rxMLqOxtztturWiXU9PWnRqispEWWo/JEki5tJ5zm7bSFJUwYys0tQUzx59adNvMGZWlbvhwxg+p+7evUvjxo2rJ9k6f/58hQ22bdv2sRwyBsTMVs3SEDNbNUfD2Ge2SrbNh5xtkPkNaO7PSMgdwGIaWDyNrIykqzpikZibxLbY7RxLDtLNdLWx9WJkvWE0tnq0jasN0Q+tJLHzxnWWnzyuW0hvY2LCVN/2vNDOFysTE73oFFLZPmRl53H0VDiBR69w6txNNJqCBNbC3IQBPTwZ2d8bjwbFvxce532REn+PcwcvEXrgIucOXiL2gQX9ZpamtOvRGr9+7WgR0JgWPs2QP7BrvyRJRIacJnjDGu5EFNSKVJmZ49N/CH5DRmBuXbGkwxg+p6p1ZksulyOTySiraeFzMpkMjUZTapsnCbFmq2ZpiDVbNUfDWNdsPfwcNWT/jZS5AjQxBQfljsgsp4PF+BJJV3XGIiEnkW2x/3A0KUg309XOtg0j64+gkZVHpTQM2Q+NVsuOG9dZdiKY8LspANiZmTHVpz3Pt/PRW9L1OH24l5bFnsNX2LQrlJi4f3ed92vTkFEDvOnSoSlKhVyv74uEW0mcO3CJM3vPcWbPee4lpBZ73qWRE+37taN9f2+8e3kVu9NRkqT/b+++w6K4vgaOf2fZZekoghUbsWFXVERjiTX2rmlGjZoYTTSaXo1pphjrazQaezT23o29d+wtsQuoCEive98/UH4SiQHdZZf1fJ7H55Fh9p4z9yzsYXZ2LhePHGDPwnncupz+gQ9HZ2dqPN8+ven6jzNd9vB7yqrXbF25ciXbA5Ys+WSnne2BNFu2FUOaLduJ8bQ2W/97bAokLEPFTgLTjfSNuoIPNF3GJ46RXf9Vi5uJt1gZsprd4XtQpL9MVM9XjU7FOlDKNXu/53PjOFLT0lh0LJhpJ49x8d7bi/mdnOhfszY9q1bH9QmbLnMcg8mkOHT8CkvXH2XP4YuYTOnzWbCAO+1bVKVVI3/27dlu9p8Lk8nExWNXOLTxGIc2BnNy11nSUv53QkTnoKNS/fIEtatNUPta+JYtAty7/cWhfexdNI/bVy4B4OjsQs3WHQho0wEn16w/ZWsPv6fkAvk8RJot24ohzZbtxHjam63/jZEMCUtRsZPBFJK+UVcIzW0AOHcDDDZTi7DEm6y4sYq9d/ZlNF0181WnY7EOlHQt8cgYufm8dXN3Z/WFc4zbv5crd6OA9DvSv1GrNi9XqZ7jZYD+Ob65jiHs1l2WbzzG6s0niIpOX65I76CjbAlX3h/YnnJ+RZ44RlaUUoTduMnlo9c5vPEYhzYe48aFzNd7lfAvRlC7WgS1r02FwDLoNI2/Du5jz+J5hF+9DIDRxTWj6TK6uD4UI6//nrJqs7Vy5cqM95JXrlz5yH3bt29vtuTyKmm2bCuGNFu2E0OarX+OlQwJS+6d6bp3rY2uCLi+QXRyczzzedtMLUITwlgRsop9d/ZnNF0B+WvSsVh7SrgUz/Ix1njepppMrDh3hv87sC+j6Srg7MIbAbV5uUo1nC2w9uLjSEpOZeuecyxdH8zpB5qexkHl6NMtiGdK+pgtFmR9HKGXbrJ/9RH2rjrIsW2nSXtgvch8Ph4EtgmgXofaVG9SieunjrBn0TzuXE+/9tDo6kpAm47UbNUB4701k+3h95RVmy2dTkdYWBgFCxZ86MK6TIPJNVuANFu2FkOaLduJIc3Wv42ZDAmL7jVd6fdVMlEYnftANJfOaJp5L/qGx69FSEIoK26sYn/EgYymq3b+ADoWa4+vi2+mfa35vE01mVh29jT/d2Af1+4tA+Tt4sKAgDq8VKUqTvrsHXNuHMPJc9cZ8+tqzl2JzdjWuG5ZenerR5lS5mm6/us44u7GcWBdMHtXHeTA2qPE3f3fPcQcnQwEtKhGo25B+BRJ4ciaJRlNl5OrGwFtO1GzVTsMTs55/veUvI2Yh0izZVsxpNmynRjSbP3X2EkQvwAV9yuY7q2j5+CL5joAnDuhaeabsyetxY2EEFbcWMmBiEMoFBoatb1q0bFYe4o5F713PNZ/3qakpbH07GkmHtzH9ej0u7MXdHVlQEAdXqxcFeN/3GohN38uKlSuw+/LDrJt33nuvzI3CixL7+5BlC1V8Ili5OQ4UlNSObHzDHtWHGTfqkOEXf7fmo6OTgYC29Sgct38RF07SGRo+rWHTu4e1GrbEb+6DfEulLObulrqOB6HNFt5iDRbthVDmi3biSHNVvaYTAkkRszCyTTnH03XIHDugKY9+T3KzFWL6/HXWX5jJQcjDwOgoVHHqzYdi7WjiFMRm3neJqelsfTMKf7v4D5CYtIXmi7k6sabterQo1KVf226rPFzcfHqbWYu2sfWvecymq6GgWXp0y2IsqUfr+l63ONQSnHpxFV2LNrLtoV7Ml3n5eTqSP22vri7hpEYk34bDic3d2q370KNlm0xOD36fnK5eRzZZVPNVlxcHNu3b+fq1askJydn+t7gwYPNllxeJc2WbcWQZst2YkizlbMYHh6OaAkLUHFTwJT+YoZDCTS3geDU/omaLnPX4mr8NVbcWMmhyCNAetMV6FWHJh6NKedT1maet8lpaSw+fZKJB/cTGpvedBV2dePN2oF0r1j5oabLmj8XF6+GM2vxXrbs+UfT9RhnuszzwQ7FX0cvsW3BHrYv3MPNK+l/CGiaomQ5B8pXU2gq/e1HZw9P6rTvQrUWrTEYzdd0PTXN1tGjR2ndujXx8fHExcXh5eVFeHg4Li4uFCxYkIsXL5otubxKmi3biiHNlu3EkGbr8WIolQDx81BxU8GUfk8pHErda7raoWkOOY5hqVpcibvK8pCVHIk8CqQ3XXULBNKxaDsKOxc2W5z7HrceSampLDp9kkmH9hMam36tVBE3dwbWDqRbxco4Ojg80fg58V+1uHQtnJmL9rFlz9lMby++1qNeti+kN/dxKKU4e+Avts3fzY7Fewm/EYGmKYqUSKN8VRNGp/Trt10881GnQ1eqNm+FwdFolrh5sdn696vd/8XQoUNp164dERERODs7s2/fPq5cuUJAQACjRo0yW2JCCCHSaZozmmtfNO8taG7vp6+3mHYZdfcDVHhrVMIqlLKNDyeVdC3BkLJvMaLSF1TPVw2FYu+dfXx04jOm/D2Nm4k3rZ0iAEa9nleqVmfLq335slETCrm6ERobw+db/6TJ7GnMP3mcFBv5wFfp4t6MGNaW2WN607R+BTQNtu+/QK9hs/h81EouXg3P9Zw0TcM/sCxvjunN3CuTGL39K9q80ZK7UR5sW23gxEED8bEa8Xej2Db7N357qy9H1q4gJTnpvwe3Qzk+s5UvXz72799P+fLlyZcvH3v37sXf35/9+/fTq1cvzp49a6lc8wxZrse2YshyPbYT42lbrsdSMZQpDhLmQtw0UFHpGx2eAbdBYHw+W2e6cqsWJ26eZHP0Fo7dPQGADh31vOvSvkg7Cjo9+SftzFWPpNRU5p86waRDB7gdHweAr4cHA2sF0qRIMbzz57eZn4tL18KZsXAvW/eeB0DT4Lmg8vTuFkTp4lm/5uTW8/b2zXBObj3HhhlbCd5ynGKl0njGPxVn1/RWw+jmQb2uL1ClSUv0j3HD2dw4s2WJ5Xpy/Ia/wWDIOMBChQpx9epV/P398fT05OrVq//xaPv24ELUADExMRZfbBew6A+OPcRITU0F0ptgSy5EDXl7nuyhFmD547CdWvQAx7Y4pi3EmDoXLe1vuDuMNG0CSfp+pOqagvbvb17kVi28UvPTp2AvrnpcY92dDZyJP8uu8D3sCd9HbY9atCzQjAKGx/+j1Jz16FjKj+d9S7Dk/FlmnjzO9ehoPtmyiaKurvSvWoM2z5RF/4jbHz2unNbCy8PAu/0a0uX5ysxbeYQ9hy+zZc85tu49R8M6z/BCuxoUL5Iv02Ny63mbnJpEzVaVCWhdhVtXw9k6dzfb/tiNk+E2z/inAtFsnTmFHfPmUrN9Jyo1bopDDpp9Sx9HzL0PT5hbjs9stWjRgt69e/PSSy8xYMAAjh49yuDBg5kzZw6RkZHs37/fIonmJXJmy7ZiyJkt24khZ7YsE0OZYiF+FsTPBJV+ewP05cB1EBhboGXRdFmrFn/F/s3yGys5GX0KAAfNgWe969OuSBu8jTn/fWmpeiSkpDDv5HF+PXyQOwnpF32X8PTk7dpBtC9fwaxN15PW4q/Lt5ixcC87DqQvIq3TaTStX4He3epSoqgXYN3nbVqaiaObT7Bh+p9cObaXkmWTcXZJbz10ehcCO/egTvt2OGTj3md59cxWjputQ4cOERMTw3PPPcft27fp1asXu3btokyZMkyfPp3q1aubLbm8Si6Qt60YcoG87cSQC+QtG0OZoiF+FipuJqh7f6Hry6O5vQXG5pmaLmvX4kLMXyy7sYJT0aeB9KarofeztCvahgI5aLosXY+45GSmHdzP7NMniEhIX16nVL78DK5Tl3blKuBghqbLXLW4cOkW0xfsYefB/zVdLRr607trEMUK57OJ5230nRg2zNjMrkWLKVgoCifne9/QnAlo15kGPbo+sunKqxfIy322LECaLduKIc2W7cSw9gt8XhjfHDGUKRoVNyP9bJe6d1dyvf+9pqsZmqbZTC3Ox1xg2Y0VnI4+A6Q3XY18GtCuaBu8HL3MEuNJ3B9f7+zM3JPHmHL4IJGJiQD45c/P23WCaFu2/BM1XeauxbmLN5m+YA+7D/0NgINOo0WjinRuUYkKZYvbxPM2LTWN7Qt3sWn677i5hGY0XWkmI5WbtKVFv1fQZzEXebXZyvGzo0mTJkRFRT20PTo6miZNmpgjJyGEEE9A03mgcx+C5rMFXN8EzRVSz6CiBqHudEIlbgYb+Tu7nHtZPqzwHh9X+AB/9wqkqTS23NrG+8c+Zs7luUQmR1o7RQBcHR15I6AOO3r3572gZ8nn5MTFyEiGblhLq7mzWHX+LCYbmdPyfoX44eNO/PbDK9QL8CPNpFi39RRvfLqI73/ZQMjNKGuniIPegSYvNeK7TVNo/c63JKT6kZgADrokzmxbwqge3Vn8wyRSkpL/e7A8IMfN1rZt2x66kSlAYmIiO3fuNEtSQgghnpymy4fOfei9puuNe03XaVTUm+ju9qBgvtM203RV8CjPR/7v81GF9ynvXo5Ulcqft7bw/rGP+P3KPKKSo6ydIpDedA2sHcj2Xv14N6g+nkYn/oqMYMj6NbSaO4s158/ZTNNVoUxhfvykM1O+f5nA6qUwmRRrtpzkxben88OkDYTeumvtFNE0jRpNqvHZkvG89O0ENOdKJCdpGAwpXDmyhp+6d2fBt/+X55uubH8E5fjx4xn/P336NGFhYRlfp6WlsX79eooVK2be7IQQQjwxTZcfzf1dlGsfVNw0iJ+LlnaKwPKnUHcPojwGg2Mji729lBP+HhWo4F6eszHnWHpjOedjLrDp5ma23drBcwUb06ZIK/I5elo7TdyNRgbVrkvPqjWYdewI044e5kLEHd5ev5pyB70ZEhhEy2fKorOBOa1YtgijPuvCvsPnWbj2OAePXWHVnydYt+0UrZ+rzKtd6lLYx3xvmT2u0lVKM2zmD9y+fpOlP/xC5PUjGJ1SuX58PT9130yZus3p+G4/a6f5WLJ9zZZOp8v4QczqIc7OzkyYMIHXXnvNvBnmQXLNlm3FkGu2bCeGrVwnZMvj50YMZYogLfpXTHG/o3dISd9oqIbm9jY4NjBbzCe+9kwpTkefYemNFfwVm37Rt6POkSYFG9O6yPN4Gjxtpt7RSYnMCE5vumLvvftTwduHIYFBtPAr88jH5vbPxclzIUxbsIdDx68AoNfraNukCj27BFLI+/GbLnPXIvpOJAu+Gc+dy4cwOKb3HUmJBkrWaky3D9/E8THu0/VfLHXNVrbPbF26dAmlFH5+fhw4cAAfn//djM7R0ZGCBQvi4JDzJSOEEELkLk3nhXJ9j827S9C8/hV0ifMh5Rgqsh8YaoDbYHCsZ/UzXZqmUcmzIhU9/DkZfYpl11fwd9xF1odtZMutbTQt+BytCj9v1Rzv8zA6MSSwHr2r1WR68GFmHD3C2fDbvLlmJRW9fRgSWI9mfs9YfU4BqlQoxtjh3Th25jrTF+zh8ImrLN94jDVbTtKuWRV6dg7Ep4C7tdPEo0B++o8Zzt3bESz4djwRVw9jdEoh7OQmfuq2jQrPtqL9O70xGMzfdJlbtputkiVLAmAymSyWjBBCiNyTnOqGcn0fzb1/+rqL8X9AylFUZB8w1LzXdAVZvUHQNI0qnpWp7FGJ43dPsuzGCi7FXWJd2AY239pKA8/6dHRpj4ej9d8K83RyYmjd+vSpXpNpRw8zM/gIp8Nv88aaFVT2KciQwHo0Ke1n9TkFqObvy7gvu3P01DWmL9jD0VPXWLo+mNWbT9C+eVVe6RSIt5ebtdPE08eL18d+SeStOyz8dhyR147i5JTC5UMr+anreio0bE2HIb1xsNDNec3hsT6rOmfOHOrXr0/RokW5ciX9NOSYMWNYsWKFWZMTQghheZqDDzqPT9B8NoPLq4AjpBxBRfZGRbyMSrKNm1Vrmka1fFUYXvFThpYbTCmXkiSbktkcuZX3j3/EomtLiE2JtXaaAORzcubdoGfZ0bs/b9aqg4vBwMnbt+i/ejkdF8xly6WLWV6SYw01KhVnwlc9GP9ld6r5+5KcksbitUfpPug3xk3fwp3IOGunCED+ggV4fewIXvh+LK4Fq5OaomF0SubSgeV836U7y8ZMJ+3e3fhtTY6brUmTJjFs2DBat25NVFRUxtI0+fPnZ+zYsebOTwghRC7RHAqi8/gMzedPcHkFMEDKIVRkT0wRPVHJB62dIpDedFXPV40vK33OkLJv4WssRqIpidWha3n32Icsvr6U2FTbaLryOzvzfr0G7OjdjzcCauOs13Pi1k36rVpG54Xz2Hb5ks00XTWrlOD/vu7B2OHdqFKhGMnJqSxac4RuA6cyYcZWIqJso+nKV7AAb4z/mn7/Nw1Xn2qkpmg4OSVzcd9Svu/SgxXjZ9pc05Xjm5pWrFiR7777jo4dO+Lu7s6xY8fw8/Pj5MmTNG7cmPDw3F993NbIcj22FUOW67GdGLJcj+3EyE4tVFoYxP0KCYuAexfSO9YF17fRHGv9Z4zcmquoqCguqsssD1nJtYRrADg7ONOiUDNaFGqOq97licY35zGEx8cz9chBfj9xjMR7DUG1QoWpq3S806mzRS76hsdYAkopDh2/wvQFezh5PhQAo6OeTi2r8VLHOuT3fHhOrfWzcSfkFgu/HUdM2An0hvSWJjHRSJVm7Wkz8BV0ObjhrM0s1+Ps7MzZs2cpWbJkpmbrwoULVK1alYR7yxk8jR5ciPr8+fNcvHiR/PnzWyTW/cU43dzcLL4Ybl6PkZqayu7du6lfv75FF6LO6/NkD7UAyx/H01gLTd3EmDoDQ9pKNNIbhFRdHZL0r5Omq/qvj8vtuVIoTsSeZN2djYQmpzcIzjonGuVvSON8DXF2cP6P0R49vjmP4U5CArNOHWfRuTMk3XuHqKq3D29WD6BOkaJmn6/HPQ6lFEdO3WDeisOcu3gbSG+62japSOfnq+Lp7vTEMXLiUTHCb9xi7dgpJESc5f5TOjHRSIVGrXmud6dsNV2RkZH4+flZv9mqWLEiI0eOpEOHDpmarfHjxzNr1iwOHz5stuTyKjmzZVsx5MyW7cSQM1u2E+NxaqHSbtw707UE7jVdOD5770xX9Yf3t9JcmZSJw5FHWBGyiusJNwBwcXChZeHmtCjULEdNl6WP4XZcHJMO7mfeyWOk3Hs5rlWkGO/UDSLIt4TZ4pjjNhz7jl5i+oI9nP37JgDOTgY6t6rBi+1r4enubDM/G7euhLBo5Djiw0/jkNF0OVH9+c48/3qPRzZdljqzleM/Ld9//30GDRpEYmIiSikOHDjAH3/8wciRI/ntt9/Mlpg90DTNop84uT++xPjv8R+MY6kYeX2eciNGbtTiwfGl3o8e/8E42XqM3hc8v0a5voGKmwQJSyF5FyTvQjk2QHMbjOZY7aE4uT1XDpoDdQrUppZXAAcjDrM8ZCUhCSEsu7GCjWF/0qpIS5oVapLtpsuSx1DQzY1Pnm1I6Ygo/vbyZMHpkxwKvcEryxYTWMyXdwLrEehb3CyxnuQ4NE2jXsAzBNX0Y8/hi0xbsIfzF28yd9kBlq47StfWNXmhXYBN/GwUKlWMt379kZuXr7Pou3HE3zmDk1MiZ7fN49iGZdRs05UWfbtl2XRZKu8cN1t9+vQhNTWVDz74gPj4eF566SWKFSvGuHHjeOGFFyyRoxBCCBui6X3RPL99oOlaDsk7URE7UcbGaG5voxmqWDtNdJqOwAK1qe0VwIGIgyy/sYrQxFAWX1/K+rCNtCqc3nQ5OTj992AW5qnX89mzjXizdiCTDx1g/skT7L9xnReXLiTItzhDAutRp5ivtdNE0zTq13qGegF+7D70N9MW7OHCpVvMWbqfJeuO0q5pRXp1rY+He87fsjW3QqV8eWvKT4RevMbikeNIiDiL0ZjAqT/ncGTNEgLadaN5ny45uqbrcT1WhP79+3PlyhVu3bpFWFgY165do2/fvty4ccPc+QkhhLBRmr4EOs+RaN7rwakToIOkbag7XTBFDkClnLJ2ikB601W3QCDfVfmKAX79KexUiNjUWBZdX8J7xz5kTeg6ktKSrJ0mAIXd3PmycVO29HqNl6tUw6DTsff6NV5YsoBXli3iUIhtvM5qmsaztcsw/aeefPtBB54p6UN8QjILVgfTbeBUpi3YTUxcorXTBKCIX3HenjqKV374BYNbOdLSwGiM5+TGWXzf+QU2zVhi8XuIPlE75+3tTcGCBQkLC+Ptt9+mTJky5spLCCFEHqHpS6LL98O9pqsD6U3XFojojHPye6iUM9ZOEUhvuoK86/Jdla953a8vhYwFiUmNZeG1xbx77EPWhq63maarqLsHXz/XjC29+vLSvaZrz7WrdF88n1eXLeZIaIi1UwTSm65GgWWZMepVvn6vHSWL5ScuPpkZC/fSbcBUZizcQ2ycjcxpmRIMnjaal76bgN61TEbTdXz9DL7v8iJ/zl5msaYr281WVFQUL7/8Mj4+PhQtWpTx48djMpn44osv8PPzY9++fUyfPt0iSQohhLB9mr4Uunw/oXmvBad2gIbBtAMiOmKKfAuVctbaKQLp13TV967HyKrf0L/0axQ0FiQmNYYF1xbx3rGPWB+6wWaarmLuHnzzXDO2vNqXFypVQa/TsevaFbou+oM+K5ZwLCzU2ikCoNNpNK5bjglfduarYW0pXbwAsfFJTFuwh24DpzJz8V7i4m1jTn3LlWbI9LG8+M0EHFyeSW+6HOM4tmYaE/oMsEjMbDdbn3zyCTt27KBXr154eXkxdOhQ2rZty65du1i3bh0HDx7kxRdftEiSQggh8g5N74cu389QYA0puhaABkkbUXfaY4ocjEo5b+0UgfSm61mf+oys8jV9S/fBx+hNdGo0f1xbyPvHP2Zj2CaSTcnWThOAYh4efNe0BZt7vkb3ipVx0DS2X7lMp4XzeG3lUo7dDLN2ikB60/VcvfLMGt2bEcPaUsrXi5jYRH77Yzfd3pzK7CX7iE+wjTktXqE078wYR4+vxuLg7IcpDYyOlrkZbrabrTVr1jBjxgxGjRrFypUrUUpRrlw5tmzZQqNGjSySnBBCiLxL0z9DguM3UGAVOLVK35i0HnWnHaaoIajUv6yb4D16nZ6GPs/yfZVv6VOqF96OBbibcpe5V+fzwfGP2RG5k2RTirXTBKC4pyffN2vJ5ldfo2vFSjhoGtsuX6LTgrn0W7mME7duWjtFIL3palq/ArNG92b4O20oUdSL6NhEpszbRbc3pzJn6X6babpKVizDOzPH0234aDTHkhaJke1mKyQkhIoVKwLg5+eHk5MT/fr1s0hSQggh7IemL4su3zi0AqvB+DygIHEdKrwNpqihNtV0NS7YkB+qfkfvUq9SwNGLqJS7LLm9nA+Pf8KfN7eQYiNNVwnPfPzY7Hk29exDF/9K6DSNLZcv0mH+77y+ajmnbKTpcnDQ0byBP3PG9uaLIa0pXjQ/d2MS+HXuTroPnMrc5QdISLSNpqtUlXK8MXGkRcbOdrNlMpky3fjOwcEBV1dXiyQlhBDC/miGcujyj0crsBKMzUlvutbca7reRaVetHaKQHrT9VzBRvxQ9TteLfkK+fSeRKZEMufKXD44/jGbb261maarVL78/NT8eTa+0puO5f3RaRp/XvqbdvN/Z8DqFZy5fcvaKQLpTVeLhhWZM7YPn77dCt/C+YiKTmDSnB10H/gbf6w8SGKSbcypJWT7PltKKXr37o3RaAQgMTGRAQMGPNRwLV261LwZCiGEsCuaoQJa/omolNOo2AmQtBkSV6ES16Cc2qK5vYWmL2XtNDHoDDQp2JhqhioEpxxndchaIpIjmX3ld1aHrqV90TY08H4Wvc4yS0/lhF9+L0a3bM1bdeoy/sBeVp07y8aLf7Hx4l+0fKYsQ+rUpbDBMusu5oTeQUerxpVo3sCfjdtPM3PxXkJu3mXirO38seIgL3esQ8cW1TAaLbPChLVk+xnSq1evTF+/8sorZk9GCCHE00MzVETLPwmVcupe07UFEleiElejnNqjuQ1C01vmGpqc0Ov0NC34HA19GrD99g5Wh6whIjmCmZfnsDpkLe2KtuFZ7/o203SNbdmGQbXqMuHAXtZcOMeGvy+w4e8LNC9ZmmH1G1De28faaaJ30NG6SWVaNPRn/fbTzFq8l9Bb0UyYuY15yw/ySudA2jevitHR+nNqDtk+ihkzZlgyD7uklCKHS0/meGxLjW9vMR6MZYnx7WWe8notHhw7r46fmzEejGWpGNkaX18RLd8kVMoJiP0/SN4GictRiatQTh3AdSCaPusla3Kz3gZNT7OCTWjo/Szbbu1gTdg6wpPvMOPybFbda7rqFwjKcdNliVqU8fJi3PNtGFQ7kAkH9rH2r/NsunKJP69conXZ8rxdpy5lvcy7du/j1MLBQUebJpVp2dCfddtOMXvJfsJuRzNu+hbmLj/AK53q0LZplYymKzfqbQk5Xoha/LuJEycyceJE0tLSOH/+PBcvXiR//vwWiWXt1dXzUozU1FR2795N/fr10evN/1eSvcyTPdQCLH8cUovse9zj0JlOYUydisG0J30cHEhxaEOSw2soXVGzxMiuR42fbEph9909bI7YSkxaDAAFDAVo6dWMWh4BOGgO2YqRG7U4H3GHSUcOsT3kOgAa0LKUH/2r1aC0Zz6zxDBHLVJS09i8+wILVgdzOyL9NgwF8rvQvXV1WjQoj16vs2i9IyMj8fPzM/tC1NJsWUB0dDSenp7cvn2bAgXM+5fDfbayunpeiJGSksK6deto1apVpg95mIu9zJM91AIsfxxSi+x70uNQycEQNyF9sWsA9ODcGVwHoDkUM0uM/8whG+MnpSWx5fY21oauJyY1vekqZCxI+6JtqVsg8D+brtysRUhKMhMO7GPjxfRPgOo0jXblyvN27SBKP+HJAXPWIjkllTVbTvL70v3cupPedBX0dqdnpzrUDyiOdwEvi9T7zp07+Pj4mL3Zso83Q22UtVc+lxj/G//BOJaKkdfnKTdi5EYtHhxf6v3o8R+MYylPchyasQYYp6OSj6Bix0PyHkhYCAnLUM5d0NwGoOmKWL3eTnonWhd5nqYFn2Pzra2sDV3HzaRbTL00nZUha+hQrB1BBQLRaVnfACA3a1HRpyCT23bg1K2bjNu/lz8v/c2Kc2dZdf4cHcr781adupTO9/hNl7lqYXQ00Pn5GrRtWoVVf55gztL93AqP4eepm5m9xI3e3YJo/VxlDIbsnT3MLkvNv+WXuhZCCCGegOZYE53XTDSveeBYF0iBhPmo281R0V+iKdu4p5TRwUjrIs8zqtoPdPftgpvejZtJN5ly8Tc+OfE5e8P3YVKWXfA4uyoVLMSUdh1Z8cIrNCnlh0kplp09TYs5M3h/03quREVZO0UAHA16urSqwYKJ/XinbxMK5HfldkQsP/26iZcGT2P1nydITU2zdpr/SZotIYQQeYLmWAud12w0r7ngGEh60/UHbkmdUdFfo9Jso+lycnCiTdHWjKr2PV19O+Pq4EpoYhiTL07l0xNfsP/OAZtpuqoULMRv7TuxrMfLNC5VmjSlWHLmFM3mTOfDPzdw7e5da6cIgNFRT9fWNVnwf33p/0JdvPK5Enormu8nbeDFt6ezZssJUtNsY06zIs2WEEKIPEVzrI3Oaw5a/jlgqI1GCiT8jrrdFFP0N6g027iRp7ODM+2KtuHn6j/QxbcTrg4uhCSG8svfv/LZyeEciDhoM01XtUKFmd6+M0u7v0SjkqVIU4pFp0/SdM50Pt68kevRNtJ0GQ10aF6ZBRP78nbvxuT3dCH01l1GTtzAy4Ons27rSZtsuqTZEkIIkSdpxkDIP4c4w0QwBADJED/7XtP1HSrttrVTBNKbrvZF2zKq2g90KtYBFwdnbiSEMPGvyXx+cgSHo46gsI3PqlUvXIQZHbqwuNuLNChRklSTiQWnTtB09nQ+3bKJGzHR1k4RACejgR7tarHwl34MfLUR+TycuREWxbf/t55Xhsxg/bZTpNlQ0yXNlhBCiDxL0zTSHGpD/rlo+WeAoQaQBPEz7zVd36PS7lg7TQBc9C50LNaeUdV+pGPR9jg7OHM94TqTLk1hi/d2jkYFW/TeajlRs0hRZnXsyqJuL1C/eAlSTCb+OHmcJrOm8fnWPwmNibF2igA4OznyUofaLJrUnzd7NsTT3ZnroZF8M2Edr7wzg407TttE0yXNlhBCiDxP0zQ0Y300r/lo+aeBoRqQCPHTUeFNMMX8iDJFWDtNAFz1LnTy7cDP1X6gQ9F2OOmcuGuIZuKlyQw/9RVHIm2n6QooUow5nboxv0sPgnzTm665J47x3KxpDN+2mbBY22m6Xu5Yh0WT+vPGyw3wcHPiWkgkX41by6tDZ/LnrrNWbbrsotn65ZdfKF26NE5OTgQEBLBz585H7r99+3YCAgJwcnLCz8+PyZMnZ/r+zJkzM3189f6/xMRESx6GEEKIJ5TedDVA81qIln8qGKqASoC431C3m2CK+cmGmi5XOvt25PtK31I+pixGnZEr8VcZd2ECX576muDIYzbTdNUp5svczt34o3N3Aov5kmxKY87xYBrPmsaI7Vu4GRtr7RQBcHF2pGfnQBZPfp3XX3oWdzcnrtyI4Msxq+k1bBabd5/FZMr9Oc3zzdaCBQt45513+PTTTzl69CgNGjSgVatWXL16Ncv9L126ROvWrWnQoAFHjx7lk08+YfDgwSxZsiTTfh4eHoSGhmb65+TklBuHJIQQ4gmlN12N0LwWo+X7FfSVQcVD3NT0txdjfkaZIq2dJgBuelcqxfrzfaVvaVOkNUadkcvxVxhzYTwjTn/LsajjNtN0BfoW548uPZjbqRu1ixYjOS2NWceO0njWNL7esZXwhHhrpwikN12vdqnL4kn96fdifdxcjVy+fofho1fT+91ZbN17LlebrjzfbI0ePZq+ffvSr18//P39GTt2LMWLF2fSpElZ7j958mRKlCjB2LFj8ff3p1+/frz22muMGjUq036aplG4cOFM/4QQQuQtmqahOT2HVmAJWr7JoK8IKg7ifr13pmssymQbn7Rz17vRvXgXRlX7gdZFnsdR58iluEuMPj+Or09/x/GoEzbTdAUVL8H8Lj2Y3bErAUWKkpSWysxjR2m3dCHf7tzG7fg4a6cIgKuLkd5dg1g86XVe61EPNxcjF6+G8/moVfR5bzbb953PlaYrT99BPjk5mcOHD/PRRx9l2t6iRQv27NmT5WP27t1LixYtMm1r2bIl06ZNIyUlJWOphNjYWEqWLElaWhrVq1fn66+/pkaNGlmOmZSURFJSUsbX0dHpn9ZISUkhJSXlsY/vUZRSpKamkpKSYtElQ+whxv0aSC2sH8PStQDLH4fUIvtsbq4cGoDHs5CyBV38RLS0cxD3C6b42SinniinV0GXeYkWa9TCGSc6F+5IswJN2HBrE1tvb+PvuIv8fH4sz7j60b5wWyq6++coH0sdR2CRoszt0IXd168y/uB+jt0MY3rwEeadPM5LlarSr3pNCri4mC3e4x6H0VFHz0616dSiKovWHmHJ2mD+vnKbT39aSZlSPvTqEkj9Wn4W+3nI02sjhoSEUKxYMXbv3k29evUytn/33XfMmjWLc+fOPfSYcuXK0bt3bz755JOMbXv27KF+/fqEhIRQpEgR9u3bx19//UWVKlWIjo5m3LhxrF27lmPHjlG2bNmHxvzyyy8ZMWLEQ9vnzZuHixmfZEIIIczFROH8pyjvuxEPlzAAUlKduBjWgIthDUhNc7Zyfv+TqEvkvOtfXHS9jElLv8i7QLIX/jHl8Un2RsNyS/zkhFKKs4nxrI+K4Gpy+gkIR03jWXdPnvPIj5uDeZfWeRIJSWkcPh3F4bNRJKekz2lBLyMB5Zz47IPXZW3ErPyzu1VKPbLjzWr/B7fXrVuXunXrZny/fv361KxZkwkTJjB+/PiHxvv4448ZNmxYxtfR0dEUL16c5557zqILUUdHR+Ph4WHRvxjtIUZKSgqbNm2iefPmFluI2h7myR5qAZY/DqlF9tn+XLUF9T5pyZvQJfyCgb8o77uJcsX3o5x6oZxeQWmuNlOLqJS7rL+5gW3hO7jjGMGuAnsp61qGDkXaUcG9/CNj5FYtno2OZqi7OzuvXWX8wX2cvH2LLdFR7IuP45UqVXmtek3yOz1+I2vO4+jSCe7GJLBozVGWrg/mVkQSq3Za5i3lPN1seXt74+DgQFhYWKbtt27dolChQlk+pnDhwlnur9fr/7Ux0ul01K5dmwsXLmT5faPRiNFofGi7wWCw6C8xvV6PwWCw6A+OPcS4z1L1sJd5sodagOWPQ2qRfXlmrhzbolxbQ+J6VOwEtLS/0RImQOIccO2D3qG9TdTCx+BNz9Iv07ZYa9aErmPbre1ciPuLUX+NoYJ7eToV60AFj6ybrtyshaOjI83KlKXpM2XYcvki4/bt4eTtW0w5epi5J4/Tq1pN+tUMIN9jNF3mPg5vLwNv9mzEix1q88eKQyxYufeJx8xKnr5A3tHRkYCAADZt2pRp+6ZNmzK9rfigoKCgh/bfuHEjtWrV+tcnuVKK4OBgihQpYp7EhRBC2BRN06E5t0bzXo3mORocSoOKgtgxuCV1RMVNQZls46Lv/I75eaXkS/xY7XuaFmyCXtNzNuYcI8/+yPdnfuJczHlrpwikv1vUtPQzrHjhFaa07UBFbx/iUlL45dB+Gs74jdF7d3PXRm6plM/DhTd7NmT6Ty9ZZPw83WwBDBs2jN9++43p06dz5swZhg4dytWrVxkwYACQ/hbfq6++mrH/gAEDuHLlCsOGDePMmTNMnz6dadOm8d5772XsM2LECDZs2MDFixcJDg6mb9++BAcHZ4wphBDCPmmaA5pzWzTvtWieo8ChFDruQuzPqNtNULFTUSbbuL2Bl2N+Xi31Mj9WHUmTgo1x0Bw4E3OW7878wA9nR3E+Jut3Y3Kbpmk08yvDqhd7MqlNeyp4+xCbksz/HdxHg5lTGbNvN9FJttF0ebpb5jrrPP02IkCPHj24c+cOX331FaGhoVSuXJm1a9dSsmRJAEJDQzPdc6t06dKsXbuWoUOHMnHiRIoWLcr48ePp0qVLxj5RUVG8/vrrhIWF4enpSY0aNdixYwd16tTJ9eMTQgiR+zTNAZzbo4ytSIhchLNpBqRdQcX+BPHTwbUfuLyEpln/QvoCRi96lepJmyKtWBWylp3huzgdfYbT0Weo7FGJTr4deMbVz9ppomkaLZ8pS3O/Mmz4+wLj9u/l/J1wJhzYx8zgo/StEUDv6jXxyOKynLwuT38a0VZFR0fj6elJeHi4RS+Qv3v3Lp6enhZ9/90eYqSkpLB27Vpat25tsWu27GGe7KEWYPnjkFpknz3M1f3xPTxc0ZJWoWInQtq19G/qvNFc+4PLi2ja49/02ty1uJ0UzqqQ1ewK30OaSgOgimdlmns0pWrhKjZTC5NSrP/rPOP37+V8RPr6lZ5GJ/rWCKBXtRq4Z9F0Wbred+7cwdvb2+yfRszzbyMKIYQQlqZpejTnzmje69E8vgMHXzCFo2JGom43RcXNQinbeCvMx+jNa6V7832Vb2no/Sw6dJy4e5LR18Yx+vw4LsVetnaKAOg0jdZly7P25V6Mf74NZfJ7cTcpkdH7dtNo1m9MPLif2ORka6dpFtJsCSGEENmkaQY0l65o3hvQPL4BXTEw3UbFfIu63QwVNxulkv57oFxQ0MmHvn59+L7qtzzrXQ8dOo7fPcGXp79mzPnxXI67Yu0UgfSmq225Cqx7uRdjW7bGL39+ohIT+XnvLhrNnMrkQweIy+NNlzRbQgghRA6lN13d0Xw2oHl8BbqiYLqFivnmXtP1O0rZRoNQyKkg/Uq/xielPqR+gSA0NIKjjjH81FeMOz+BK3FZryWc2xx0OtqX92fDy70Z3aI1pfLlJzIxkR/37KTRzN+Ycvgg8RZc8cCSpNkSQgghHpOmOaK5vIDmsxHNYwToCoPpJirmq/SmK36ezTRdPo7e9Pfry/dVvqFegbpoaByJCuaLUyMYf2EiV+OvWTtFIL3p6ljBn42v9GZU8+cp6ZmPiMQEvt+9g8azpjHn1AkS8ljTJc2WEEII8YTSm64X0Xz+RPMYDrqCYApDRX+Jut0cFT/fZpquws6FeeOZ/nxX5WuCCgSioXE48gifn/ySCRd+4Vr8dWunCIBep6OzfyU29ezDj81aUsLDkzsJ8Yw5fIDGs6cx/ehhElPzRtOV52/9YMuUUhZbof3+2Jb8MKk9xXgwliXGt5d5yuu1eHDsvDp+bsZ4MJalYuT1ucr5+AZwfgmcukDCQoj7FUyhqOgvIPZXlOub4NwRTfvfpw6tVYsiToV5w68/bYu0YUXIKg5GHOJQ5GEORR6mdv5adCzWjmLOxZ4ohjk4aBpd/CvRvlwFlp09zYT9ewmJi+WbnduYcuQgbwTU5sVKVTHqn7ylsdT8S7NlRhMnTmTixImkpaV/1DYmJga9GYqfFaUUsbGxwMNrPUqMzFJTU4H0W3JYoh72Mk/2UAuw/HFILbLPHubqycZvD4YWOKYtxzF1FjrTDYj5DFPsLyQ5vEaKQ2vQ9FavhRuuvOz9Ak3cG7P+zkaCY49xMDK98arhXo2WXi0obMx6CbzsxjCXFsWKU6eZJ1tvhvLbiWDC4uL4esc2Jh88QJ8q1ehUthxGh8efw5iYGDNm+z/SbJnRoEGDGDRoUMZ9ttzd3fH09LRIrPvdt6XvX2MPMVLuvbfv4eFhsftsQd6fJ3uoBVj+OKQW2WcPc/Xk43sCb6BUL4ifD/FT0ZlCcE79Bmc1C1wHkurcCrB+LTzxpELB8lyLv8bykFUcjjzCkZhgjsYcI9CrDh2KtqOIc+EnivGk7sfoXbw4L9esxeIzp/jl4H5CY2P48cBeZp86wZu16tCtUuXHarruN77mJs2WBWmaZtGFS++PLzH+e/wH41gqRl6fp9yIkRu1eHB8qfejx38wjqXYy1w96fia5gxufVCuL0D8PFTc1PSbo0Z/jINuEr7e9dBogaY5mjHzf+aQveMo4VqCwWUHcSXuKstDVnIk8ij7IvazP+IAQQUC6VCsPYWdsj7TlZv1Nur1vFylGl39K7Ho9EkmHdpPaGwsw7dvYfLhgwysHUi3ipVxdHDI0diWIBfICyGEELlE05zRXPuieW9Bc/8AtPxopqvUeGY+uqgOqISVqHt3fbe2kq4lGFL2Lb6q9AU18lVHodhzZx8fHf+UKX9P42biTWunCIBRr+eVqtXZ8mpfvmzUhEKuboTGxvD51j9pMnsa808eJyXNunMqzZYQQgiRyzSdC5prPzSfLZhchpKc4oJmuoy6+x4qvA0qYbUNNV0leafc23xZ6XOq56uGQrH7zh4+Ov4ZUy9O51bibWunCKQ3Xa9Wq8G2Xn0Z3ug5Crq6EhITwydbNtF0znQWnDphtaZLmi0hhBDCSjSdK8q5H38Gf4zJeQhonpB2EXV3GCq8LSphDUqZrJ0mAKVdSzG03GCGV/yMap5VMGFiV/huPjz+CdMuzuB2ku00Xb2q1WRbr7583vA5fFxcuR4dzcebN9J8zgwWnT5Jqil351SaLSGEEMLK0kxOKJfX0Xy2orm9A5oHpP2NujsUdacdKnGdzTRdfm6lGVb+Hb6o+ClVPCtjwsSO8F18dOIz5oct5HZSuLVTBMBJb6BP9fSm69MGjSng7MLV6Lt8+OcGms+ZwZIzp3Kt6ZJmSwghhLARms4NzW3gvaZrMGjukHoBFTUEdacDKnGDzTRdz7j58V75oXzu/zGVPSqRptLYG72fj058ysxLs7mTdMfaKQLgbDDQt0YA23v34+NnG1LA2Zkrd6N4f9N6Wvw+k2VnTpNm4aZLmi0hhBDCxmg6dzS3t9B8toLrW6C5Qeo5VNTbqDsdUYmbLHqz2Jwo416G9ysM49MKH1LOpSxpKo2tt7fz/vGPmXV5DhHJEdZOEQAXg4H+NWuzvXd/PqzfAC8nZy5HRfLupnW0+H0mK86dsVjTJbd+EEIIIWyUpvNAcx+Mcu2FipsJ8TMh9SwqahDoK4Lb22BsYtFbLWRXWfeyDPIdQJjuJstvrORMzFm23NrGjtu7aOTTkLZFW+PlmN/aaeJiMPBGQB1eqVKd2cePMvXIIS5FRTJ0w1pKOjlZJKac2RJCCCFsnKbzROc+5N6ZrgGguULqaVTUm6g7XVCJW23mTFd593J85P8+H1f4gPLu5UhVqWy+tYUPjn3E71fmEZkcae0UAXB1dOTNWoHs6N2fd4OexdPoxKWoKIvEkmZLCCGEyCM0XT507sPQfDaD6+uguUDqSVTUG6iIrqik7TbTdFXwKM/HFT7gwwrvUc6tLCkqlU03N/P+sY+Ze+UPopLvWjtFANwcHRlUO5AdvfvxZkBti8SQZksIIYTIYzSdFzr399B8toBrf9CcIeUEKrI/KqI7KmmnTTRdmqZR0cOfT/w/5IPy71LGrQwpKoWNN//kvWMf8sfVBdxNsY2my91opF/1AIuMLddsWZBtrUT/dMd4MJYlxreXecrrtXhw7Lw6fm7GeDCWpWLk9bmy+Vpo+dHc3kO59IG43yB+HqQcQ0X2BUMNlOvb4Fjv8cfPgf+aq4oe/vi7V+BU9GmW3VjB33EXWR+2kS23ttGk4HO0LtwSD4PHE8V4UpYaV5otM5o4cSITJ04k7d4damNiYqyygrvEyOz+wqLR0dEWqYe9zJM91AIsfxxSi+yzh7nKO7XQAwPQjF1xTJ2DY9pStJSjEPUaqVo1EvX9iY2vAFi/FsXx5e2iAzkTf5Z14Ru4mnSN9WEb2HJzKw3y1aeJV2PcHNyeKMbjiomJMfuYIM2WWQ0aNIhBgwYRHR2Np6cn7u7ueHp6WiRWbq6untdjpKSkAODh4YHBYDD7+PYyT/ZQC7D8cUgtss8e5irv1cIT+BKVNhDip0L8fPTqGG4pb+HkWB298ztoxqAnzjkrOZ2roHx1qVskkGN3T7DixkouxV9mc+RWdt3dQ7OCTWhVpCVu+sxNl6Xrcb/xNTdptiwor690by8x7o9r6Rh5fZ5yI0Zu1OLB8aXejx7/wTiWYi9zlddqoekLgcdnKNd+qLgpEL8AvQqGqN4oQx0098FojnXMEitT3BzOlaZp1Mhfjer5qhIcdYxlN1ZyJf4Ka8LWsfnWFpoXasbzRVpkarosWQ9L1VgukBdCCCHslOZQGJ3HF+D9J8kOXQEDpBxARbyCKeJVVPJBa6cI3G+6qjOi0ucMKfsWJVyKk2hKYlXoGt4N/pCl15cTlxpn7TQfmzRbQgghhJ3THAqTaPgAvDeB84uAAZL3oSJexhTRG5V8xNopAulNV838Nfiq0nAGlx10r+lKZEXIKt479iHLbqwgPi3B2mnmmDRbQgghxFNCcyiCznMEms9GcO4B6CF5DyriBUwRr6GSg62dIpDedAXkr8mISl/wVpk38XUuRnxaAitCVvHVpW9ZcWMV8anx1k4z26TZEkIIIZ4ymkMxdJ5fo3lvBOdugAMk70JFdMcU0ReVfMzaKQKg03TU9qrF15W/ZFCZARR1KkqCKYFlISt479iHrAxZTUIeONMlzZYQQgjxlNL0vug8v0Xz3gDOXUhvunaiIrphinwdlXLS2ikC6U1XHa/afFP5S3oVfoUiToWJS4tnyfVlvBv8IatC1th00yXNlhBCCPGU0/Ql0HmORPNeD06dAB0kbUPd6YwpcgAq5ZS1UwTSm66aHjX4tvJXDPDrT2GnQsSlxbH4+lLeO/YRa0LWkpiWaO00HyLNlhBCCCEA0PQl0eX74V7T1YH0pmsL6k4nTJFvolJOWztFIL3pCvKuy3dVvuZ1v74UMhYkNjWWhdeX8N6xD1kbup6ktCRrp5lBmi0hhBBCZKLpS6HL9xOa91pwag9okLQZdacjpshBqJSz1k4RAAfNgfre9RhZ9Rv6l36NgsaCxKTGsuDaIt479hHrQzfYRNMlzZYQQgghsqTp/dDlG3Wv6WpLetO1CXWnPabIt1Ep56ydIpDedD3rU5/vq35D39J98DF6E50azR/XFvL+8Y/YELaJZFOy1fKTO8hbkD0sjmovMR6MZYnx7WWe8notHhw7r46fmzEejGWpGHl9rqQW9zj4oXn+jHIdALETIWk9JG1AJW1AGZ8Ht7fAoYzV661DRwPv+gR5BbL7zl5WhawhPDmceVfnsyZ0HW2LtKKRTyMcdVkviyQLUecBshC1bcaQhahtJ4Y9LH4stcg+e5grqcU/FQRtBDrHVzGmTsVg2gJJ61FJG0jRNSUx6SWgkk3Uu7pjVSqXrMiBu4fYGPEnkSmRzL06n9U31tLMqylBnoEY/tF0yULUeYAsRG2bMWQhatuJYQ+LH0stss8e5kpq8W9qApPSr92Km4iWtBFH058UNGxGow24DkLT+5khTmaPcxyt8rWkefGm7AjfxeqQNUSkRLLk9jK2RG2jbdHWNPR+NqPpkoWo86C8vviqvcSQhahtJ4a9LH4stchZnLweQ2rxiDEd/cHx/1App1Gx/4eW9CckrYaktSintmhub6HpS5ktHjzecRgcDDQt9BwNfZ5lx+2drApZS2RKJHOuzGVN6DraF21DA+9nLTb/0mwJIYQQ4olohoqQbyIxkQdw02ZA0hZIXIlKXI1y6oDmNghNX8LaaWLQGWhaqAkNfBqw/fYOVoesJSI5gpmX57A6ZC2NXBpYJK58GlEIIYQQZmHSlUfLNwmtwBIwNgZMkLgMFd4S092PUalXrZ0iAI46A80LNeWnat/zcokX8TR4Ep58hz+uL7RIPGm2hBBCCGFWmqEKuvxT0LwWgWNDIA0SlqDCn8d09xNU6nVrpwikN10tCjdjVLXveanEC3jo3S0SR5otIYQQQliE5lgNnddvaF4LwbEBkAoJi1HhLTDd/QyVdsPaKQLgqHOkZeHmDPf/3CLjS7MlhBBCCIvSHKuj85qG5jUfHOuT3nQtRN1ugenuF6i0EGunCIBR52iRcaXZEkIIIUSu0BxrovOageY1DxyDgBRImI+63RzT3S9RaWHWTtEipNkSQgghRK7SHGuh85qF5jUXHANJb7rmoW43xRQ9wu6aLmm2hBBCCGEVmmNtdF5z0PLPAUNtIAXi56JuN8MU/TUq7Za1UzQLabaEEEIIYVWaMRDN63e0/LPAEAAkQ/yce2e6vkOl3bZ2ik9EbmpqQTa9qOhTFuPBWJYY317mKa/X4sGx8+r4uRnjwViWipHX50pqkcsxHOtC/kBI3gtx4yHlKMTPRMXPR7m8CC59Ucpg0XmyBGm2zEgWorbNGLIQte3EyFsL7lpn/NyKYQ+1yI0YUgtrxagEusk4GPZhTJ2KXp2E+Bmo+D/QpbUnWr0GOq8nT/ofZCHqPEAWorbNGLIQte3EyJsL7ubu+LkVwx5qkRsxpBbWjvE8SrWE5B0QOwEt9QTuDgshZTU4vwyur6GZsemShajzoLy4qKg9xpCFqG0nRl5ecDc3x8+NGPZSi9yIIbWwbgxN08CpMcrYCJW0jbS7Y3BQZyF+avonGF1eQXN9DU2X3zyxLEAukBdCCCGEzdM0Dc3YmDjHWZBvEugrgoqDuF9Rt5tgihmDMkVZO80sSbMlhBBCiLxD09CMTdAKLEPL9wvoK9xruibda7rGoUzR1s4yE2m2hBBCCJHnaJqG5tQMrcBytHwTQF8eVCzETUTdfg4VOwFlsswF7zklzZYQQggh8ixN06E5tUQrsAIt33jQlwUVk95s3X4OFTsRZYq1ao7SbAkhhBAiz0tvup5HK7AKzXMs6MuAikbFjrvXdE2yWtMlzZYQQggh7Iam6dCcW99rukaDgx+ou6jYMajbTVCxk3O96bKLZuuXX36hdOnSODk5ERAQwM6dOx+5//bt2wkICMDJyQk/Pz8mT5780D5LliyhYsWKGI1GKlasyLJlyyyVvhBCCCHMTNMc0JzbonmvQfP8GRxKg4pCxY6+13RNQZniciWXPN9sLViwgHfeeYdPP/2Uo0eP0qBBA1q1asXVq1ez3P/SpUu0bt2aBg0acPToUT755BMGDx7MkiVLMvbZu3cvPXr0oGfPnhw7doyePXvSvXt39u/fn1uHJYQQQggzSG+62t1run4Eh5L3mq5RqPCmqLhpKJVg0RzyfLM1evRo+vbtS79+/fD392fs2LEUL16cSZMmZbn/5MmTKVGiBGPHjsXf359+/frx2muvMWrUqIx9xo4dS/Pmzfn444+pUKECH3/8MU2bNmXs2LG5dFRCCCGEMCdN06M5d0TzXofm+T04lABTBCrmh/QzXXEzQCVaJHaebraSk5M5fPgwLVq0yLS9RYsW7NmzJ8vH7N2796H9W7ZsyaFDhzKWTPi3ff5tTCGEEELkDelNV+f0pstjJDj4gukOKmYkuqhOFomZp5frCQ8PJy0tjUKFCmXaXqhQIcLCwrJ8TFhYWJb7p6amEh4eTpEiRf51n38bMykpiaSkpIyvo6PTb6aWkpKS0cCZm1KK1NRUUlJSLLrOlT3EuF8DqYX1Y1i6FmD545BaZJ89zJXUws5jGNqDZyu0pJVoCZPR1LUnHzMLebrZuu+fE66UemQRstr/n9tzMubIkSMZMWLEQ9u3bt2Ki4vLo5MXuWbTpk3WTkHcI7WwHVIL2yG1sCZnNO1t8jvvBsaYffQ83Wx5e3vj4ODw0BmnW7duPXRm6r7ChQtnub9er6dAgQKP3Offxvz4448ZNmxYxtfR0dEUL16c5557LmNMc1NKER0djYeHh0X/grCHGCkpKWzatInmzZtjMBjMPr69zJM91AIsfxxSi+yzh7mSWjxdMe7cqY80W//g6OhIQEAAmzZtolOn/73PumnTJjp06JDlY4KCgli1alWmbRs3bqRWrVoZT/KgoCA2bdrE0KFDM+1Tr169LMc0Go0YjcaHthsMBov+4Oj1egwGg0Wf1PYQ4z5L1cNe5skeagGWPw6pRfbZw1xJLZ6uGJaa/zzdbAEMGzaMnj17UqtWLYKCgpgyZQpXr15lwIABQPpZpxs3bjB79mwABgwYwP/93/8xbNgw+vfvz969e5k2bRp//PFHxphDhgyhYcOG/PDDD3To0IEVK1bw559/smvXLqscoxBCCCHyrjzfbPXo0YM7d+7w1VdfERoaSuXKlVm7di0lS5YEIDQ0NNM9t0qXLs3atWsZOnQoEydOpGjRoowfP54uXbpk7FOvXj3mz5/PZ599xueff84zzzzDggULCAwMzPXjE0IIIUTeluebLYCBAwcycODALL83c+bMh7Y1atSII0eOPHLMrl270rVrV3OkJ4QQQoinWJ6+z5YQQgghhK2TZksIIYQQwoKk2RJCCCGEsCBptoQQQgghLEiaLSGEEEIIC5JmSwghhBDCguzi1g+2SimVse6ipca21Pj2FuPBWJYY317mKa/X4sGx8+r4uRnjwViWipHX50pq8XTFsNS40myZ0cSJE5k4cSJpaWkAxMTEoNdbZoqVUsTGxgIPL5otMTJLTU0F0testEQ97GWe7KEWYPnjkFpknz3MldTi6YoRExNj9jFBmi2zGjRoEIMGDSI6OhpPT0/c3d3x9PS0SKz73benp6dFn9T2ECMlJQUADw8Pi62NCHl/nuyhFmD545BaZJ89zJXU4umKcb/xNTdptixI0zSLLlx6f3yJ8d/jPxjHUjHy+jzlRozcqMWD40u9Hz3+g3EsxV7mSmrxdMSwVN7SbFnA/c47JibGon+lREdHW/RJbS8xUlJSiI+PJzo62mJntuxhnuyhFmD545BaZJ89zJXU4umKcf9tRHNfuyXNlgXcuXMHSF/0WgghhBB5y507d8x6GZA0Wxbg5eUFwNWrVy12zRZA7dq1OXjwoMXGt5cY0dHRFC9enGvXruHh4WGRGPYwT7kRIzdqAZY/DqlF9tnDXEktnp4Yd+/epUSJEhmv4+YizZYF6HTpty/z9PS06A+Og4ODRce3pxiQfvGppeLYyzzZQy3A8schtcg+e5grqcXTF+P+67jZxjPraCJXDRo0SGLYCHuZJ3uoBVj+OKQW2WcPcyW1ePpimJumLHn3safU/Vs/3L17N1f+GhKPJvWwHVIL2yG1sB1SC9thqVrImS0LMBqNDB8+HKPRaO1UBFIPWyK1sB1SC9shtbAdlqqFnNkSQgghhLAgObMlhBBCCGFB0mwJIYQQQliQNFtCCCGEEBYkzdZj+uWXXyhdujROTk4EBASwc+fOR+6/fft2AgICcHJyws/Pj8mTJ+dSpvYvJ7VYunQpzZs3x8fHBw8PD4KCgtiwYUMuZmv/cvqzcd/u3bvR6/VUr17dsgk+RXJai6SkJD799FNKliyJ0WjkmWeeYfr06bmUrX3LaS3mzp1LtWrVcHFxoUiRIvTp0ydjdRLx+Hbs2EG7du0oWrQomqaxfPny/3yMWV6/lcix+fPnK4PBoKZOnapOnz6thgwZolxdXdWVK1ey3P/ixYvKxcVFDRkyRJ0+fVpNnTpVGQwGtXjx4lzO3P7ktBZDhgxRP/zwgzpw4IA6f/68+vjjj5XBYFBHjhzJ5cztU07rcV9UVJTy8/NTLVq0UNWqVcudZO3c49Siffv2KjAwUG3atEldunRJ7d+/X+3evTsXs7ZPOa3Fzp07lU6nU+PGjVMXL15UO3fuVJUqVVIdO3bM5cztz9q1a9Wnn36qlixZogC1bNmyR+5vrtdvabYeQ506ddSAAQMybatQoYL66KOPstz/gw8+UBUqVMi07Y033lB169a1WI5Pi5zWIisVK1ZUI0aMMHdqT6XHrUePHj3UZ599poYPHy7NlpnktBbr1q1Tnp6e6s6dO7mR3lMlp7X46aeflJ+fX6Zt48ePV76+vhbL8WmUnWbLXK/f8jZiDiUnJ3P48GFatGiRaXuLFi3Ys2dPlo/Zu3fvQ/u3bNmSQ4cOkZKSYrFc7d3j1OKfTCYTMTExZl8H62n0uPWYMWMGf//9N8OHD7d0ik+Nx6nFypUrqVWrFj/++CPFihWjXLlyvPfeeyQkJORGynbrcWpRr149rl+/ztq1a1FKcfPmTRYvXkybNm1yI2XxAHO9fsvaiDkUHh5OWloahQoVyrS9UKFChIWFZfmYsLCwLPdPTU0lPDycIkWKWCxfe/Y4tfinn3/+mbi4OLp3726JFJ8qj1OPCxcu8NFHH7Fz5070evl1ZC6PU4uLFy+ya9cunJycWLZsGeHh4QwcOJCIiAi5busJPE4t6tWrx9y5c+nRoweJiYmkpqbSvn17JkyYkBspiweY6/Vbzmw9Jk3TMn2tlHpo23/tn9V2kXM5rcV9f/zxB19++SULFiygYMGClkrvqZPdeqSlpfHSSy8xYsQIypUrl1vpPVVy8rNhMpnQNI25c+dSp04dWrduzejRo5k5c6ac3TKDnNTi9OnTDB48mC+++ILDhw+zfv16Ll26xIABA3IjVfEP5nj9lj8lc8jb2xsHB4eH/iK5devWQ93vfYULF85yf71eT4ECBSyWq717nFrct2DBAvr27cuiRYto1qyZJdN8auS0HjExMRw6dIijR4/y1ltvAekv+Eop9Ho9GzdupEmTJrmSu715nJ+NIkWKUKxYMTw9PTO2+fv7o5Ti+vXrlC1b1qI526vHqcXIkSOpX78+77//PgBVq1bF1dWVBg0a8M0338i7IbnIXK/fcmYrhxwdHQkICGDTpk2Ztm/atIl69epl+ZigoKCH9t+4cSO1atXCYDBYLFd79zi1gPQzWr1792bevHlyDYQZ5bQeHh4enDhxguDg4Ix/AwYMoHz58gQHBxMYGJhbqdudx/nZqF+/PiEhIcTGxmZsO3/+PDqdDl9fX4vma88epxbx8fHodJlfnh0cHID/nVURucNsr985upxeKKX+9zHeadOmqdOnT6t33nlHubq6qsuXLyullProo49Uz549M/a//9HRoUOHqtOnT6tp06bJrR/MJKe1mDdvntLr9WrixIkqNDQ0419UVJS1DsGu5LQe/ySfRjSfnNYiJiZG+fr6qq5du6pTp06p7du3q7Jly6p+/fpZ6xDsRk5rMWPGDKXX69Uvv/yi/v77b7Vr1y5Vq1YtVadOHWsdgt2IiYlRR48eVUePHlWAGj16tDp69GjGbTgs9fotzdZjmjhxoipZsqRydHRUNWvWVNu3b8/4Xq9evVSjRo0y7b9t2zZVo0YN5ejoqEqVKqUmTZqUyxnbr5zUolGjRgp46F+vXr1yP3E7ldOfjQdJs2VeOa3FmTNnVLNmzZSzs7Py9fVVw4YNU/Hx8bmctX3KaS3Gjx+vKlasqJydnVWRIkXUyy+/rK5fv57LWdufrVu3PvI1wFKv35pSck5SCCGEEMJS5JotIYQQQggLkmZLCCGEEMKCpNkSQgghhLAgabaEEEIIISxImi0hhBBCCAuSZksIIYQQwoKk2RJCCCGEsCBptoQQQgghLEiaLSFEtl2+fBlN0wgODrZonJkzZ5IvXz6LxgAoVaoUY8eOtXgcc+nduzcdO3a0mXGEENkjzZYQdqh3795omoamaej1ekqUKMGbb75JZGSktVN7SFYNT48ePTh//rzFYx88eJDXX3/9icf566+/6NOnD76+vhiNRkqXLs2LL77IoUOHzJDl/4wbN46ZM2dmfN24cWPeeecds8a4TynFlClTCAwMxM3NjXz58lGrVi3Gjh1LfHy8WWJomsby5cvNMpYQtkyaLSHs1PPPP09oaCiXL1/mt99+Y9WqVQwcONDaaWWLs7MzBQsWtHgcHx8fXFxcnmiMQ4cOERAQwPnz5/n11185ffo0y5Yto0KFCrz77rtmyjSdp6dnrpzxA+jZsyfvvPMOHTp0YOvWrQQHB/P555+zYsUKNm7cmCs5CGE3nnRRRyGE7enVq5fq0KFDpm3Dhg1TXl5embZNnz5dVahQQRmNRlW+fHk1ceLETN/fv3+/ql69ujIajSogIEAtXbpUAero0aNKKaVmzJihPD09Mz1m2bJl6p+/WlasWKECAgKU0WhUBQoUUJ06dVJKZb0w+L+N+8svvyg/Pz9lMBhUuXLl1OzZszN9H1BTp05VHTt2VM7OzqpMmTJqxYoVj5ynkiVLqjFjxjz2GCaTSVWqVEkFBASotLS0h74fGRmZ8f8PPvhAlS1bVjk7O6vSpUurzz77TCUnJ2d8//4i3JMnT1a+vr7K2dlZde3aNdMYD9a1V69eD83dpUuXVGpqqnrttddUqVKllJOTkypXrpwaO3Zspryyen48aMGCBQpQy5cvz/KYo6KilFJKpaWlqREjRqhixYopR0dHVa1aNbVu3bqMfZOSktSgQYNU4cKFldFoVCVLllTfffedUip97h/MvWTJkv+ajxB5nZzZEuIpcPHiRdavX4/BYMjYNnXqVD799FO+/fZbzpw5w3fffcfnn3/OrFmzAIiLi6Nt27aUL1+ew4cP8+WXX/Lee+/lOPaaNWvo3Lkzbdq04ejRo2zevJlatWoBsHTpUnx9ffnqq68IDQ0lNDQ0yzGWLVvGkCFDePfddzl58iRvvPEGffr0YevWrZn2GzFiBN27d+f48eO0bt2al19+mYiIiBzlm5MxgoODOXXqFO+++y463cO/Th88C+Xu7s7MmTM5ffo048aNY+rUqYwZMybT/n/99RcLFy5k1apVrF+/nuDgYAYNGpRl7HHjxhEUFET//v0z5q548eKYTCZ8fX1ZuHAhp0+f5osvvuCTTz5h4cKF2Z6DuXPnUr58eTp06PDQ9zRNw9PTMyOHn3/+mVGjRnH8+HFatmxJ+/btuXDhAgDjx49n5cqVLFy4kHPnzvH7779TqlQpIP0tXIAZM2YQGhqa8bUQdsna3Z4Qwvx69eqlHBwclKurq3Jycso4ezB69OiMfYoXL67mzZuX6XFff/21CgoKUkop9euvvyovLy8VFxeX8f1Jkybl+MxWUFCQevnll/8113+eXcpq3Hr16qn+/ftn2qdbt26qdevWGV8D6rPPPsv4OjY2VmmalulMy3/FzukY988AHTly5F9j/Jsff/xRBQQEZHw9fPhw5eDgoK5du5axbd26dUqn06nQ0FCl1MNnpBo1aqSGDBnyn7EGDhyounTpkvH1f53Z8vf3V+3bt//PcYsWLaq+/fbbTNtq166tBg4cqJRS6u2331ZNmjRRJpMpy8cDatmyZf8ZR4i8Ts5sCWGnnnvuOYKDg9m/fz9vv/02LVu25O233wbg9u3bXLt2jb59++Lm5pbx75tvvuHvv/8G4MyZM1SrVi3TNU1BQUE5ziM4OJimTZs+0bGcOXOG+vXrZ9pWv359zpw5k2lb1apVM/7v6uqKu7s7t27dylGsnIyhlALSz/b8l8WLF/Pss89SuHBh3Nzc+Pzzz7l69WqmfUqUKIGvr2/G10FBQZhMJs6dO5ejY5g8eTK1atXCx8cHNzc3pk6d+lCsR1FK/ecxRUdHExIS8si69O7dm+DgYMqXL8/gwYPlWi/x1JJmSwg75erqSpkyZahatSrjx48nKSmJESNGAGAymYD0txKDg4Mz/p08eZJ9+/YB/2skHkWn0z20X0pKSqavnZ2dzXE4D734Z9UQPPg26f3H3D/W7MrJGOXKlQN4qOn7p3379vHCCy/QqlUrVq9ezdGjR/n0009JTk5+5OPuH192mrn7Fi5cyNChQ3nttdfYuHEjwcHB9OnT5z9jPahcuXL/eUz/zPG+B+tSs2ZNLl26xNdff01CQgLdu3ena9eu2c5DCHshzZYQT4nhw4czatQoQkJCKFSoEMWKFePixYuUKVMm07/SpUsDULFiRY4dO0ZCQkLGGPcbsft8fHyIiYkhLi4uY9s/78FVtWpVNm/e/K95OTo6kpaW9sjc/f392bVrV6Zte/bswd/f/5GPs7Tq1atTsWJFfv755ywbsqioKAB2795NyZIl+fTTT6lVqxZly5blypUrD+1/9epVQkJCMr7eu3cvOp0uo6n7p6zmbufOndSrV4+BAwdSo0YNypQpk3G2Mrteeuklzp8/z4oVKx76nlKKu3fv4uHhQdGiRf+zLh4eHvTo0YOpU6eyYMEClixZknENnMFg+M/aC2EPpNkS4inRuHFjKlWqxHfffQfAl19+yciRIxk3bhznz5/nxIkTzJgxg9GjRwPpL7g6nY6+ffty+vRp1q5dy6hRozKNGRgYiIuLC5988gl//fUX8+bNy3QfKEhv8v744w+GDx/OmTNnOHHiBD/++GPG90uVKsWOHTu4ceMG4eHhWeb+/vvvM3PmTCZPnsyFCxcYPXo0S5cufawL9s1J0zRmzJjB+fPnadiwIWvXruXixYscP36cb7/9NuMC8zJlynD16lXmz5/P33//zfjx41m2bNlD4zk5OdGrVy+OHTvGzp07GTx4MN27d6dw4cJZxi9VqhT79+/n8uXLhIeHYzKZKFOmDIcOHWLDhg2cP3+ezz//PMcXn3fv3p0ePXrw4osvMnLkSA4dOsSVK1dYvXo1zZo1y/hgwvvvv88PP/zAggULOHfuHB999BHBwcEMGTIEgDFjxjB//nzOnj3L+fPnWbRoEYULF8744ECpUqXYvHkzYWFhNnkPOCHMxorXiwkhLOTfLoCeO3eucnR0VFevXs34unr16srR0VHlz59fNWzYUC1dujRj/71796pq1aopR0dHVb16dbVkyZJMF8grlX5BfJkyZZSTk5Nq27atmjJlykO3fliyZElGHG9vb9W5c+dMMapWraqMRuMT3/rhnxdbe3p6qhkzZvzrPGV1gXxOx1BKqXPnzqlXX31VFS1aVDk6OqqSJUuqF198MdOF8++//74qUKCAcnNzUz169FBjxozJdIz3b/3wyy+/qKJFiyonJyfVuXNnFRERkbHPP+t67tw5VbduXeXs7Jxx64fExETVu3dv5enpqfLly6fefPNN9dFHH6lq1ar96zhZSUtLU5MmTVK1a9dWLi4uysPDQwUEBKhx48ap+Pj4jH3u3/rBYDA8dOuHKVOmqOrVqytXV1fl4eGhmjZtmmlOVq5cqcqUKaP0er3c+kHYNU2pbFyYIYQQwqK+/PJLli9fbvGlkIQQuU/eRhRCCCGEsCBptoQQQgghLEjeRhRCCCGEsCA5syWEEEIIYUHSbAkhhBBCWJA0W0IIkYWePXtm3JPscXXt2jXjvmVCiKeXNFtCCPEPx48fZ82aNRlrSQKMGjWKQoUKUahQIcaMGZNp//379xMQEPDQ3dC/+OILvv32W6Kjo3MlbyGEbZIL5IUQ4h9ef/11NE3j119/BeDEiRMEBgayevVqlFK0bduWgwcPUrlyZVJSUqhTpw5Tpkyhdu3aD40VEBBAv379ePPNN3P7MIQQNkLObAkhxANMJhOLFi2iffv2GdvOnDlD1apVadKkCU2bNqVq1aoZCzX/9NNPNGzYMMtGC6B9+/b88ccfuZK7EMI26a2dgBBC2JLjx48TFRVFrVq1MrZVqVKF8+fPc/XqVZRSnD9/nsqVK/PXX38xc+ZMDh8+/K/j1alTh5EjR5KUlITRaMyNQxBC2Bg5syWEEA+4fPkyDg4OFCxYMGObv78/3333Hc2bN6dFixaMHDkSf39/BgwYwI8//siGDRuoXLkyNWrUYMeOHZnGK1asGElJSYSFheX2oQghbISc2RJCiAckJCRgNBrRNC3T9gEDBjBgwICMr2fOnIm7uztBQUGUL1+egwcPcv36dV544QUuXbqUcRbL2dkZgPj4+Nw7CCGETZFmSwghHuDt7U18fDzJyck4OjpmuU94eDhfffUVO3bsYP/+/ZQrV46yZctStmxZUlJSOH/+PFWqVAEgIiICAB8fn1w7BiGEbZG3EYUQ4gHVq1cH4PTp0/+6zzvvvMPQoUPx9fUlLS2NlJSUjO+lpqZmugXEyZMn8fX1xdvb22I5CyFsm5zZEkKIB/j4+FCzZk127dqV0Xg9aNOmTVy4cIHZs2cD6RfAnz17lnXr1nHt2jUcHBwoX758xv47d+6kRYsWuZW+EMIGSbMlhBD/8PrrrzNz5kzeeuutTNsTEhJ46623WLBgATpd+hsDxYoVY8KECfTp0wej0cisWbMyrtNKTExk2bJlbNiwIdePQQhhO+SmpkII8Q+JiYmUL1+e+fPnExQU9NjjTJw4kRUrVrBx40YzZieEyGvkmi0hhPgHJycnZs+eTXh4+BONYzAYmDBhgpmyEkLkVXJmSwghhBDCguTMlhBCCCGEBUmzJYQQQghhQdJsCSGEEEJYkDRbQgghhBAWJM2WEEIIIYQFSbMlhBBCCGFB0mwJIYQQQliQNFtCCCGEEBYkzZYQQgghhAX9P4uHHwqNRrFDAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large,\n",
+    "            x='discount',\n",
+    "            y='lcoe',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis'\n",
+    "            )\n",
+    "\n",
+    "y1 = ax.plot(discounts, full_solar_cost_data/1000)\n",
+    "y2 = ax.axhline(y=retail_price/1000, xmax=0.74, color='tab:red', linestyle='--')\n",
+    "\n",
+    "ax.set_ylabel(\"Retail Price of Electricity \\n ($ / kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "# ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lcoe_costs = full_solar_cost_data.copy()\n",
+    "lcoe_costs[lcoe_costs>=retail_price] = retail_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=results_df_large,\n",
+    "            x='discount',\n",
+    "            y='lcoe',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis'\n",
+    "            )\n",
+    "\n",
+    "y1 = ax.plot(discounts, lcoe_costs/1000)\n",
+    "y2 = ax.axhline(y=retail_price/1000, xmax=0.74, color='tab:red', linestyle='--')\n",
+    "\n",
+    "ax.set_ylabel(\"Retail Price of Electricity \\n ($ / kWh)\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost \\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "# ax.set_ylim(0,1.005)\n",
+    "ax.set_xlim(0,1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.660796450820109"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((lcoe_costs/1000) - results_df_large.loc[results_df_large['percent_retail_price']==0].lcoe.values).sum()*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 1.0)"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "y1 = ax.plot(discounts, full_solar_cost_data)\n",
+    "y2 = ax.axhline(y=retail_price, xmax=0.74, color='tab:red', linestyle='--')\n",
+    "ax.set_xlim(0,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.51968500751449"
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "costs.at['ResPV', 'annualized_cost'] * 2.807 * 20 / 1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.384904875130196"
+      ]
+     },
+     "execution_count": 248,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "costs.at['ResPV', 'OCC'] * 2.807 /  1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/11-results-analysis.ipynb b/notebooks/11-results-analysis.ipynb
new file mode 100644
index 0000000..a3c42ce
--- /dev/null
+++ b/notebooks/11-results-analysis.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sb\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from glob import glob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['.\\\\simulation_data.csv',\n",
+       " '.\\\\simulation_data_detailed.csv',\n",
+       " '.\\\\simulation_data_sparse.csv']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files = glob('./*.csv')\n",
+    "files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frames = []\n",
+    "for f in files:\n",
+    "    frames.append(pd.read_csv(f, index_col=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat(frames, axis=0).drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x2273270e140>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sb.lineplot(data=df[['discount','solar_penetration','percent_retail_price']]*100,\n",
+    "            x='discount',\n",
+    "            y='solar_penetration',\n",
+    "            ax=ax,\n",
+    "            hue='percent_retail_price',\n",
+    "            palette='viridis')\n",
+    "\n",
+    "ax.set_ylabel(\"% of total electricity covered by solar\")\n",
+    "ax.set_xlabel(\"Reduction in Capital Cost\\n (%)\")\n",
+    "ax.legend(title='Net Metering Price\\n (% Retail Price)')\n",
+    "\n",
+    "ax.minorticks_on()\n",
+    "ax.grid(which='major')\n",
+    "ax.grid(which='minor', alpha=0.2)\n",
+    "ax.set_ylim(0,1.05*100)\n",
+    "ax.set_xlim(0,1.0*100)\n",
+    "\n",
+    "ax.axvline(x=50, color='tab:red', linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/12-cashflow-analysis.ipynb b/notebooks/12-cashflow-analysis.ipynb
new file mode 100644
index 0000000..2d0d1c5
--- /dev/null
+++ b/notebooks/12-cashflow-analysis.ipynb
@@ -0,0 +1,44 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy as sp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/simulation_data.csv b/notebooks/simulation_data.csv
new file mode 100644
index 0000000..d786d5f
--- /dev/null
+++ b/notebooks/simulation_data.csv
@@ -0,0 +1,256 @@
+,discount,percent_retail_price,solar_capacity,battery_capacity,objective_value,solar_penetration,lcoe
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diff --git a/notebooks/simulation_data_detailed.csv b/notebooks/simulation_data_detailed.csv
new file mode 100644
index 0000000..9d4f615
--- /dev/null
+++ b/notebooks/simulation_data_detailed.csv
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diff --git a/notebooks/simulation_data_sparse.csv b/notebooks/simulation_data_sparse.csv
new file mode 100644
index 0000000..650849d
--- /dev/null
+++ b/notebooks/simulation_data_sparse.csv
@@ -0,0 +1,101 @@
+,discount,percent_retail_price,solar_capacity,battery_capacity,objective_value,solar_penetration,lcoe
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diff --git a/scripts/retrieve_renewables.py b/scripts/retrieve_renewables.py
new file mode 100644
index 0000000..1486031
--- /dev/null
+++ b/scripts/retrieve_renewables.py
@@ -0,0 +1,102 @@
+import numpy as np
+import sys
+import pandas as pd
+import geopandas as gpd
+from tqdm import tqdm
+from unyt import m, s, MW, W, kg, g
+
+sys.path.append("functions")
+
+from nrel_data_api import parameters, make_csv_url
+
+model_years = np.array(snakemake.config['model_years']).astype('int')
+
+def handle_datetime(dataframe):
+    """
+    Combines time columns into a single timestamp column.
+    Expects columns ['year','month','day','hour'].
+
+    Parameters
+    ----------
+    dataframe : :class:`pd.DataFrame`
+        A pandas dataframe.
+    """
+    frame = dataframe.copy()
+    timestamps = pd.DatetimeIndex([])
+    for year in model_years:
+        period = pd.date_range(start=f"{year}-01-01",
+                                        freq=f"1h",
+                                        periods=8760)
+        timestamps = timestamps.append(period)
+    
+    frame.index = timestamps
+    try:
+        frame.set_index(timestamps,inplace=True)
+        frame.drop(columns=['Year','Month','Day','Hour','Minute'],inplace=True)
+    except:
+        raise ValueError
+    
+    return frame
+    
+
+def retrieve_solar_timeseries(region):
+    """
+    Retrieves data from NREL's national solar radiation database (NSRDB).
+
+    Parameters
+    ----------
+    region : :class:`gpd.GeoDataFrame`
+        A geopandas dataframe containing modeled bus regions.
+    """
+   
+    parameters['attr_list'] = ['ghi']
+    outer_pbar = tqdm(snakemake.config['solar_years'], position=0, leave=True)
+    all_frames = []
+    for year in outer_pbar:
+        outer_pbar.set_description(f"Processing {year}")
+        parameters['year'] = int(year)
+        frames = []
+        inner_pbar = tqdm(region[['name','x','y']].values, position=1, leave=True)
+        for n, i, j in inner_pbar:
+            inner_pbar.set_description(f"Processing {n}")
+            parameters['lon'] = i
+            parameters['lat'] = j
+            URL = make_csv_url(parameters=parameters, 
+                            kind='solar')
+            df = pd.read_csv(URL, skiprows=2)[:8760]
+            df.rename(columns={'GHI':f"{n}"}, inplace=True)
+            frames.append(df)
+    
+            solar_df = pd.concat(frames, axis=1)
+    
+        all_frames.append(solar_df)
+    full_df = pd.concat(all_frames, axis=0)
+    full_df = handle_datetime(full_df)
+    
+    
+def process_solar_timeseries(df, normalize=True):
+    """
+    Converts solar radiation timeseries to a
+    hypothetical power production.
+
+    Parameters
+    ----------
+    df : _type_
+        _description_
+    normalize : bool, optional
+        Whether the data should be normalized. Default is true.
+    """
+    frame = df.copy()
+    if normalize:
+            frame = frame.divide(frame.max(axis=0), axis=1)
+        
+    return frame 
+
+if __name__ == "__main__":
+    
+    regions = gpd.read_file(snakemake.input.supply_regions)
+    
+    # solar data
+    df = retrieve_solar_timeseries(regions)
+    df = process_solar_timeseries(df)
+    df.to_csv(snakemake.output.solar)
\ No newline at end of file

From e54f7acba73323f4e791e058de7bffbee3ccff96 Mon Sep 17 00:00:00 2001
From: Samuel Dotson <samgdotson@gmail.com>
Date: Tue, 19 Nov 2024 19:30:45 -0500
Subject: [PATCH 48/52] adds notebooks

---
 notebooks/08-data_download_test.ipynb       | 1004 ++++++-
 notebooks/11-pypsa-model-more-buses.ipynb   |    5 +-
 notebooks/13-primary-school.ipynb           |  177 ++
 notebooks/14-solar-options-comparison.ipynb |   99 +
 notebooks/15-community-solar-options.ipynb  | 2784 +++++++++++++++++++
 5 files changed, 3985 insertions(+), 84 deletions(-)
 create mode 100644 notebooks/13-primary-school.ipynb
 create mode 100644 notebooks/14-solar-options-comparison.ipynb
 create mode 100644 notebooks/15-community-solar-options.ipynb

diff --git a/notebooks/08-data_download_test.ipynb b/notebooks/08-data_download_test.ipynb
index dccaa88..49cd3f1 100644
--- a/notebooks/08-data_download_test.ipynb
+++ b/notebooks/08-data_download_test.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -50,13 +50,13 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "core_buildings = buildings.sjoin(sunroof, predicate='within')"
+    "core_buildings = buildings.drop(columns=\"index_right\").sjoin(sunroof, predicate='within')"
    ]
   },
   {
@@ -100,9 +100,9 @@
        "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
        "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
        "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
-       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry',\n",
-       "       'index_right', 'region_name', 'state_name', 'lat_max', 'lat_min',\n",
-       "       'lng_max', 'lng_min', 'lat_avg', 'lng_avg',\n",
+       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'CITY_right',\n",
+       "       'WARD_left', 'geometry', 'index_right', 'region_name', 'state_name',\n",
+       "       'lat_max', 'lat_min', 'lng_max', 'lng_min', 'lat_avg', 'lng_avg',\n",
        "       'yearly_sunlight_kwh_kw_threshold_avg', 'count_qualified',\n",
        "       'percent_covered', 'percent_qualified', 'number_of_panels_n',\n",
        "       'number_of_panels_s', 'number_of_panels_e', 'number_of_panels_w',\n",
@@ -112,8 +112,8 @@
        "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
        "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
        "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
-       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY_right',\n",
-       "       'WARD'],\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY',\n",
+       "       'WARD_right'],\n",
        "      dtype='object')"
       ]
      },
@@ -133,7 +133,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -187,7 +187,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area_fraction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Building General</th>\n",
+       "      <td>9963.985320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Commercial and Retail</th>\n",
+       "      <td>5193.889979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Industry</th>\n",
+       "      <td>6815.730465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       area_fraction\n",
+       "FEATURECOD                          \n",
+       "Building General         9963.985320\n",
+       "Commercial and Retail    5193.889979\n",
+       "Industry                 6815.730465"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.groupby(['FEATURECOD']).sum(numeric_only=True)['area_fraction'].to_frame().loc[['Building General',\n",
+    "                                                                                               'Commercial and Retail',\n",
+    "                                                                                               'Industry']]*kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(15984.407, dtype=float32, units='ft**2')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1485*meter**2).to(foot**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -211,52 +300,796 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>THEME1</th>\n",
+       "      <th>THEME2</th>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>AGENCY</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>CITY_left</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>CHNG_TYPE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>yearly_sunlight_kwh_f</th>\n",
+       "      <th>yearly_sunlight_kwh_median</th>\n",
+       "      <th>yearly_sunlight_kwh_total</th>\n",
+       "      <th>install_size_kw_buckets_json</th>\n",
+       "      <th>carbon_offset_metric_tons</th>\n",
+       "      <th>existing_installs_count</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>WARD_right</th>\n",
+       "      <th>building_area</th>\n",
        "      <th>area_fraction</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1320</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>Morse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>912 S Baltimore St</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>1485.397201</td>\n",
+       "      <td>0.004762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2356</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>4044.709008</td>\n",
+       "      <td>0.012968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 63 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     THEME1 THEME2 FEATURECOD   NAME  AGENCY             ADDRESS    CITY_left  \\\n",
+       "1320   None   None  Education  Morse       0  912 S Baltimore St  Kansas City   \n",
+       "2356   None   None  Education   None       0                None  Kansas City   \n",
+       "\n",
+       "        ZIP COMMENT  CHNG_TYPE  ...  yearly_sunlight_kwh_f  \\\n",
+       "1320  66105    None          1  ...           2.222399e+07   \n",
+       "2356   None    2020          3  ...           2.222399e+07   \n",
+       "\n",
+       "      yearly_sunlight_kwh_median  yearly_sunlight_kwh_total  \\\n",
+       "1320                 8223.137817               2.861974e+07   \n",
+       "2356                 8223.137817               2.861974e+07   \n",
+       "\n",
+       "                           install_size_kw_buckets_json  \\\n",
+       "1320  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "2356  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "\n",
+       "      carbon_offset_metric_tons existing_installs_count         CITY  \\\n",
+       "1320               19866.275546                     0.0  Kansas City   \n",
+       "2356               19866.275546                     0.0  Kansas City   \n",
+       "\n",
+       "      WARD_right building_area area_fraction  \n",
+       "1320          06   1485.397201      0.004762  \n",
+       "2356          06   4044.709008      0.012968  \n",
+       "\n",
+       "[2 rows x 63 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Education'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ttddwzz33WD7qLrHmF6EZov63t2zPQugzuq1nRETk2BRCWDYRHxsbi6FDh2L9+vXSvoiICEyZMgWZmZlt4hctWoTdu3ejsrJS2peWloajR49Cp9MBAFJSUtDQ0IA9e/ZIMYmJiejTpw/effddAMDvf/97/Pvf/75jFW5OQ0MDVCoV9Ho9fH19LTrW2LAc+OGdLr+3w3J/BC7+ObbuBRHJwJrPREvfY2v5EPTyce1yOz9cMWDGI0dl7astWVQZNzU1oaysDPHx8Sb74+PjcfDgwXaP0el0beITEhJw5MgR3Lx502zMrW3u3r0bMTEx+NWvfoXAwEA88sgjePPNN832t7GxEQ0NDSabTSjHQuH3dyj8//fHLRcK//cBdP0Xk4jIkRiFi9WbM7NodBcvXoTBYEBQUJDJ/qCgINTW1rZ7TG1tbbvxzc3NuHjxotmYW9v85ptvsH79egwYMAAffvgh0tLSMG/ePGzbtq3D/mZmZkKlUklbaGioJcO9jRXT1C59oPAYAoX74B+3QVC4R1nXZo/hOWMiIrl1aTW14rbH/wkh2uy7U/zt++/UptFoRExMDFauXAkAeOSRR3DixAmsX78eTz/9dLvvu3jxYmRk/HRutqGhwYqELEfiZDImoruDwcqbfhic/LPIomQcEBAAV1fXNlVwXV1dm8q2lVqtbjfezc0N/v7+ZmNubTM4OBiRkZEmMRERESYLwW6nVCqhVCrvPDAiIpKVEbBqRbSx+7pilyz6M8XDwwPR0dEoLCw02V9YWIi4uLh2j9FoNG3i9+7di5iYGLi7u5uNubXNX/ziFzh50vQmFl9++SXCwsIsGYIVHOwOXN3GkftOROQYLJ6mzsjIgFarRUxMDDQaDTZt2oTTp08jLS0NQMvU8NmzZ6VzuWlpacjKykJGRgZSU1Oh0+mwefNmaZU0AMyfPx8jRozAqlWrMHnyZOzatQv79u1DcXGxFLNgwQLExcVh5cqVSE5OxuHDh7Fp0yZs2rTJ2p9BpyhcfwbhPhg/JWXFbV/jtq9/el3hGt5+ox7/AaD5Dsd38B6Kjvphvi8mXytuj20nxvXn7fediMgC1t/0w7kXcFmcjFNSUnDp0iWsWLECNTU1iIqKQn5+vlSh1tTUmFxzHB4ejvz8fCxYsABr165FSEgI1qxZg2nTpkkxcXFxyM7OxrJly/DSSy+hf//+yMnJQWxsrBTz6KOPIi8vD4sXL8aKFSsQHh6O119/HU899ZQ14+80hfezUHg/261tuvht6db2iIjslfW3w3TuZGzxdcaOrCeuqSMichQ9eZ1xVlksvHp3/Q7M1682Y050idN+fvPe1EREJDs+z9g8JmMiIpIdp6nNYzImIiLZWX+dsXMnY+ceHRERkQNgZUxERLIzCgWM1tz0w8kfochkTEREsjNaOU3t7NcZO/foiIiIHAArYyIikp21j0F09kcoMhkTEZHsDFDAYMW1wtYc6wic+08NIiIiB8DKmIiIZMdpavOYjImISHYGWDfVbOi+rtgl5/5Tg4iIyAGwMiYiItlxmto8JmMiIpIdHxRhHpMxERHJTlj5CEXBS5uIiIhITqyMiYhIdpymNo/JmIiIZMenNpnn3H9qEBHRXWn9+vUYPHgwfH194evrC41Ggz179kivz5gxAwqFwmQbNmyYSRuNjY2YO3cuAgIC4O3tjUmTJuHMmTMmMfX19dBqtVCpVFCpVNBqtbh8+bLF/WUyJiIi2Rl+fISiNZsl+vbti1deeQVHjhzBkSNHMGbMGEyePBknTpyQYhITE1FTUyNt+fn5Jm2kp6cjLy8P2dnZKC4uxtWrVzFx4kQYDD/dgmT69OmoqKhAQUEBCgoKUFFRAa1Wa/HPh9PUREQku56epv7lL39p8v2f/vQnrF+/HocOHcJDDz0EAFAqlVCr1e0er9frsXnzZmzfvh3jxo0DAOzYsQOhoaHYt28fEhISUFlZiYKCAhw6dAixsbEAgDfffBMajQYnT57EwIEDO91fVsZEROQwGhoaTLbGxsY7HmMwGJCdnY1r165Bo9FI+/fv34/AwEA88MADSE1NRV1dnfRaWVkZbt68ifj4eGlfSEgIoqKicPDgQQCATqeDSqWSEjEADBs2DCqVSorpLCZjIiKSnREuVm8AEBoaKp2fValUyMzM7PA9jx07ht69e0OpVCItLQ15eXmIjIwEACQlJWHnzp346KOP8Nprr6G0tBRjxoyRknttbS08PDzQp08fkzaDgoJQW1srxQQGBrZ538DAQCmmszhNTUREsjMIBQxWTFO3HltdXQ1fX19pv1Kp7PCYgQMHoqKiApcvX0Zubi6eeeYZFBUVITIyEikpKVJcVFQUYmJiEBYWhg8++ABTp07tsE0hBBSKn8Zx69cdxXQGK2MiInIYraujWzdzydjDwwP3338/YmJikJmZiSFDhuCNN95oNzY4OBhhYWE4deoUAECtVqOpqQn19fUmcXV1dQgKCpJizp8/36atCxcuSDGdxWRMRESya13AZc1mLSFEh+eYL126hOrqagQHBwMAoqOj4e7ujsLCQimmpqYGx48fR1xcHABAo9FAr9fj8OHDUkxJSQn0er0U01mcpiYiItkJK5/aJCw8dsmSJUhKSkJoaCiuXLmC7Oxs7N+/HwUFBbh69SqWL1+OadOmITg4GN9++y2WLFmCgIAAPPHEEwAAlUqFmTNn4oUXXoC/vz/8/PywcOFCDBo0SFpdHRERgcTERKSmpmLjxo0AgFmzZmHixIkWraQGmIyJiKgHGKCAwYqHPVh67Pnz56HValFTUwOVSoXBgwejoKAA48ePx/Xr13Hs2DFs27YNly9fRnBwMEaPHo2cnBz4+PhIbaxevRpubm5ITk7G9evXMXbsWGzduhWurq5SzM6dOzFv3jxp1fWkSZOQlZVl8fgUQghh8VEOqqGhASqVCnq93mQBABHR3agnPhNb32NmUTI8ert3uZ2mqzexeeR7Tvv5zcqYiIhkZxTW3V/a6ORlI5MxERHJzmjlOWNrjnUEzj06IiIiB8DKmIiIZGeEAkYrFnBZc6wjYDImIiLZddcduJwVp6mJiIhsjJUxERHJjgu4zGMyJiIi2Rlh5fOMnfycsXP/qUFEROQAWBkTEZHshJWrqYWTV8ZMxkREJDtrn7zUHU9tsmdMxkREJDsu4DLPuUdHRETkAFgZExGR7DhNbR6TMRERyY63wzSP09REREQ2xsqYiIhkx2lq85iMiYhIdkzG5nGamoiIyMZYGRMRkexYGZvHZExERLJjMjaP09REREQ2xsqYiIhkJ2DdtcKi+7pil5iMiYhIdpymNo/JmIiIZMdkbB7PGRMREdlYl5LxunXrEB4eDk9PT0RHR+PAgQNm44uKihAdHQ1PT0/069cPGzZsaBOTm5uLyMhIKJVKREZGIi8vz+T15cuXQ6FQmGxqtbor3Scioh7WWhlbszkzi5NxTk4O0tPTsXTpUpSXl2P48OFISkrC6dOn242vqqrChAkTMHz4cJSXl2PJkiWYN28ecnNzpRidToeUlBRotVocPXoUWq0WycnJKCkpMWnroYceQk1NjbQdO3bM0u4TEZENMBmbpxBCWLRILTY2FkOHDsX69eulfREREZgyZQoyMzPbxC9atAi7d+9GZWWltC8tLQ1Hjx6FTqcDAKSkpKChoQF79uyRYhITE9GnTx+8++67AFoq43/84x+oqKiwaIC3amhogEqlgl6vh6+vb5fbISJyBj3xmdj6Ho/tng03b2WX22m+1ojiSWud9vPbosq4qakJZWVliI+PN9kfHx+PgwcPtnuMTqdrE5+QkIAjR47g5s2bZmNub/PUqVMICQlBeHg4/vM//xPffPON2f42NjaioaHBZCMiop4nhMLqzZlZlIwvXrwIg8GAoKAgk/1BQUGora1t95ja2tp245ubm3Hx4kWzMbe2GRsbi23btuHDDz/Em2++idraWsTFxeHSpUsd9jczMxMqlUraQkNDLRkuERF1k9bnGVuzObMuLeBSKEx/KEKINvvuFH/7/ju1mZSUhGnTpmHQoEEYN24cPvjgAwDA3/72tw7fd/HixdDr9dJWXV19h5ERERH1PIuuMw4ICICrq2ubKriurq5NZdtKrVa3G+/m5gZ/f3+zMR21CQDe3t4YNGgQTp061WGMUqmEUtn1cxRERNQ9eJ2xeRZVxh4eHoiOjkZhYaHJ/sLCQsTFxbV7jEajaRO/d+9exMTEwN3d3WxMR20CLeeDKysrERwcbMkQiIjIBnjO2DyLp6kzMjLw1ltv4e2330ZlZSUWLFiA06dPIy0tDUDL1PDTTz8txaelpeG7775DRkYGKisr8fbbb2Pz5s1YuHChFDN//nzs3bsXq1atwhdffIFVq1Zh3759SE9Pl2IWLlyIoqIiVFVVoaSkBE8++SQaGhrwzDPPWDF8IiIi27P4dpgpKSm4dOkSVqxYgZqaGkRFRSE/Px9hYWEAgJqaGpNrjsPDw5Gfn48FCxZg7dq1CAkJwZo1azBt2jQpJi4uDtnZ2Vi2bBleeukl9O/fHzk5OYiNjZVizpw5g1//+te4ePEi7r33XgwbNgyHDh2S3peIiOwXp6nNs/g6Y0fG64yJiH7Sk9cZR+cusPo647Jpq53285sPiiAiItkJKytjnjMmIiIiWbEyJiIi2QkA1pwUdfbzqUzGREQkOyMUUFhxFy3egYuIiIhkxcqYiIhkZ+2NO5x9AReTMRERyc4oFFDwOuMOcZqaiIjIxlgZExGR7ISwcjW1ky+nZjImIiLZ8ZyxeZymJiIisjFWxkREJDtWxuYxGRMRkey4mto8JmMiIpIdF3CZx3PGRERENsbKmIiIZNdSGVtzzrgbO2OHmIyJiEh2XMBlHqepiYiIbIyVMRERyU7AumcSO/ksNZMxERHJj9PU5nGamoiIyMZYGRMRkfw4T20WkzEREcnPymlqOPk0NZMxERHJjnfgMo/njImIiGyMyZiIiGTXuprams0S69evx+DBg+Hr6wtfX19oNBrs2bPnlv4ILF++HCEhIfDy8sKoUaNw4sQJkzYaGxsxd+5cBAQEwNvbG5MmTcKZM2dMYurr66HVaqFSqaBSqaDVanH58mWLfz5MxkREJD+hsH6zQN++ffHKK6/gyJEjOHLkCMaMGYPJkydLCffVV1/FX/7yF2RlZaG0tBRqtRrjx4/HlStXpDbS09ORl5eH7OxsFBcX4+rVq5g4cSIMBoMUM336dFRUVKCgoAAFBQWoqKiAVqu1+MejEMLZZ+J/0tDQAJVKBb1eD19fX1t3h4jIpnriM7H1Pe7b/BJcenl2uR3jDzfw7cw/WNVXPz8//PnPf8azzz6LkJAQpKenY9GiRQBaquCgoCCsWrUKzz33HPR6Pe69915s374dKSkpAIBz584hNDQU+fn5SEhIQGVlJSIjI3Ho0CHExsYCAA4dOgSNRoMvvvgCAwcO7HTfWBkTEZHsWhdwWbMBLcn91q2xsfGO720wGJCdnY1r165Bo9GgqqoKtbW1iI+Pl2KUSiVGjhyJgwcPAgDKyspw8+ZNk5iQkBBERUVJMTqdDiqVSkrEADBs2DCoVCopprOYjImISH6iGzYAoaGh0vlZlUqFzMzMDt/y2LFj6N27N5RKJdLS0pCXl4fIyEjU1tYCAIKCgkzig4KCpNdqa2vh4eGBPn36mI0JDAxs876BgYFSTGfx0iYiInIY1dXVJtPUSqWyw9iBAweioqICly9fRm5uLp555hkUFRVJrysUpuehhRBt9t3u9pj24jvTzu1YGRMRkey6azV16+ro1s1cMvbw8MD999+PmJgYZGZmYsiQIXjjjTegVqsBoE31WldXJ1XLarUaTU1NqK+vNxtz/vz5Nu974cKFNlX3nTAZExFRz7ByitrqtxcCjY2NCA8Ph1qtRmFhofRaU1MTioqKEBcXBwCIjo6Gu7u7SUxNTQ2OHz8uxWg0Guj1ehw+fFiKKSkpgV6vl2I6i9PURETkdJYsWYKkpCSEhobiypUryM7Oxv79+1FQUACFQoH09HSsXLkSAwYMwIABA7By5Ur06tUL06dPBwCoVCrMnDkTL7zwAvz9/eHn54eFCxdi0KBBGDduHAAgIiICiYmJSE1NxcaNGwEAs2bNwsSJEy1aSQ0wGRMRUQ/o6Uconj9/HlqtFjU1NVCpVBg8eDAKCgowfvx4AMCLL76I69ev4/nnn0d9fT1iY2Oxd+9e+Pj4SG2sXr0abm5uSE5OxvXr1zF27Fhs3boVrq6uUszOnTsxb948adX1pEmTkJWVZfH4eJ0xEdFdqievMw7d8DJcvKy4zvj6DVSn/bfTfn6zMiYioh6g+HGz5njnxQVcRERENsbKmIiI5GftqmgnP6HKZExERPJjMjaL09REREQ2xsqYiIjk14XHILY53okxGRMRkexuffJSV493ZpymJiIisjFWxkREJD8u4DKLyZiIiOTHc8ZmcZqaiIjIxlgZExGR7BSiZbPmeGfGZExERPLjOWOzmIyJiEh+PGdsFs8ZExER2RgrYyIikh+nqc1iMiYiIvkxGZvFaWoiIiIbY2VMRETyY2VsVpcq43Xr1iE8PByenp6Ijo7GgQMHzMYXFRUhOjoanp6e6NevHzZs2NAmJjc3F5GRkVAqlYiMjEReXl6H7WVmZkKhUCA9Pb0r3Sciop7Wuprams2JWZyMc3JykJ6ejqVLl6K8vBzDhw9HUlISTp8+3W58VVUVJkyYgOHDh6O8vBxLlizBvHnzkJubK8XodDqkpKRAq9Xi6NGj0Gq1SE5ORklJSZv2SktLsWnTJgwePNjSrhMREdklhRCWPZgqNjYWQ4cOxfr166V9ERERmDJlCjIzM9vEL1q0CLt370ZlZaW0Ly0tDUePHoVOpwMApKSkoKGhAXv27JFiEhMT0adPH7z77rvSvqtXr2Lo0KFYt24d/vjHP+Lhhx/G66+/3um+NzQ0QKVSQa/Xw9fX15JhExE5nZ74TGx9j5+/+ke4eHl2uR3j9Rs4/eIyp/38tqgybmpqQllZGeLj4032x8fH4+DBg+0eo9Pp2sQnJCTgyJEjuHnzptmY29ucPXs2Hn/8cYwbN65T/W1sbERDQ4PJRkRENiC6YXNiFiXjixcvwmAwICgoyGR/UFAQamtr2z2mtra23fjm5mZcvHjRbMytbWZnZ+PTTz9tt/ruSGZmJlQqlbSFhoZ2+lgiIqKe0qUFXAqF6Yl0IUSbfXeKv32/uTarq6sxf/587NixA56enZ/mWLx4MfR6vbRVV1d3+lgiIqKeYtGlTQEBAXB1dW1TBdfV1bWpbFup1ep2493c3ODv7282prXNsrIy1NXVITo6WnrdYDDgk08+QVZWFhobG+Hq6trmvZVKJZRKpSVDJCIiGShg5VObuq0n9smiytjDwwPR0dEoLCw02V9YWIi4uLh2j9FoNG3i9+7di5iYGLi7u5uNaW1z7NixOHbsGCoqKqQtJiYGTz31FCoqKtpNxEREZEd4aZNZFt/0IyMjA1qtFjExMdBoNNi0aRNOnz6NtLQ0AC1Tw2fPnsW2bdsAtKyczsrKQkZGBlJTU6HT6bB582aTVdLz58/HiBEjsGrVKkyePBm7du3Cvn37UFxcDADw8fFBVFSUST+8vb3h7+/fZj8REZGjsTgZp6Sk4NKlS1ixYgVqamoQFRWF/Px8hIWFAQBqampMrjkODw9Hfn4+FixYgLVr1yIkJARr1qzBtGnTpJi4uDhkZ2dj2bJleOmll9C/f3/k5OQgNja2G4ZIREQ2xztwmWXxdcaOjNcZExH9pCevMw5b+Se4WLAA93bGGzfw3ZKlTvv5zQdFEBER2RgfFEFERLJTCCtXUzv5HC6TMRERyY/njM3iNDUREZGNsTImIiL5sTI2i8mYiIhkx3PG5nGamoiIyMZYGRMRkfysvaUlb4dJRERkJZ4zNovJmIiIZMdzxubxnDEREZGNsTImIiL5cZraLCZjIiKSn5XT1M6ejDlNTUREZGOsjImISH6cpjaLyZiIiOTHZGwWp6mJiIhsjJUxERHJjtcZm8fKmIiIyMaYjImIiGyM09RERCQ/LuAyi8mYiIhkx3PG5jEZExFRz3DyhGoNnjMmIiKyMVbGREQkP54zNovJmIiIZMdzxuZxmpqIiMjGWBkTEZH8OE1tFpMxERHJjtPU5nGamoiIyMZYGRMRkfw4TW0WK2MiIpKf6IbNApmZmXj00Ufh4+ODwMBATJkyBSdPnjSJmTFjBhQKhck2bNgwk5jGxkbMnTsXAQEB8Pb2xqRJk3DmzBmTmPr6emi1WqhUKqhUKmi1Wly+fNmi/jIZExGR0ykqKsLs2bNx6NAhFBYWorm5GfHx8bh27ZpJXGJiImpqaqQtPz/f5PX09HTk5eUhOzsbxcXFuHr1KiZOnAiDwSDFTJ8+HRUVFSgoKEBBQQEqKiqg1Wot6i+nqYmISHY9vYCroKDA5PstW7YgMDAQZWVlGDFihLRfqVRCrVa324Zer8fmzZuxfft2jBs3DgCwY8cOhIaGYt++fUhISEBlZSUKCgpw6NAhxMbGAgDefPNNaDQanDx5EgMHDuxUf1kZExGR/LppmrqhocFka2xs7NTb6/V6AICfn5/J/v379yMwMBAPPPAAUlNTUVdXJ71WVlaGmzdvIj4+XtoXEhKCqKgoHDx4EACg0+mgUqmkRAwAw4YNg0qlkmI6g8mYiIjk103JODQ0VDo3q1KpkJmZeee3FgIZGRl47LHHEBUVJe1PSkrCzp078dFHH+G1115DaWkpxowZIyX42tpaeHh4oE+fPibtBQUFoba2VooJDAxs856BgYFSTGdwmpqIiBxGdXU1fH19pe+VSuUdj5kzZw4+++wzFBcXm+xPSUmRvo6KikJMTAzCwsLwwQcfYOrUqR22J4SAQqGQvr/1645i7oSVMRERya71nLE1GwD4+vqabHdKxnPnzsXu3bvx8ccfo2/fvmZjg4ODERYWhlOnTgEA1Go1mpqaUF9fbxJXV1eHoKAgKeb8+fNt2rpw4YIU0xlMxkREJL8evrRJCIE5c+bg/fffx0cffYTw8PA7HnPp0iVUV1cjODgYABAdHQ13d3cUFhZKMTU1NTh+/Dji4uIAABqNBnq9HocPH5ZiSkpKoNfrpZjO4DQ1ERE5ndmzZ+Odd97Brl274OPjI52/ValU8PLywtWrV7F8+XJMmzYNwcHB+Pbbb7FkyRIEBATgiSeekGJnzpyJF154Af7+/vDz88PChQsxaNAgaXV1REQEEhMTkZqaio0bNwIAZs2ahYkTJ3Z6JTXAZExERD2gpy9tWr9+PQBg1KhRJvu3bNmCGTNmwNXVFceOHcO2bdtw+fJlBAcHY/To0cjJyYGPj48Uv3r1ari5uSE5ORnXr1/H2LFjsXXrVri6ukoxO3fuxLx586RV15MmTUJWVpaF4xPCyW8y9pOGhgaoVCro9XqTBQBERHejnvhMbH2PiNkr4ar07HI7hsYbqFy7xGk/v3nOmIiIyMY4TU1ERPLjgyLMYjImIiLZKX7crDnemXGamoiIyMZYGRMRkfw4TW0WkzEREcmupy9tcjRMxkREJD9WxmbxnDEREZGNsTImIqKe4eTVrTWYjImISHY8Z2wep6mJiIhsjJUxERHJjwu4zGIyJiIi2XGa2rwuTVOvW7cO4eHh8PT0RHR0NA4cOGA2vqioCNHR0fD09ES/fv2wYcOGNjG5ubmIjIyEUqlEZGQk8vLyTF5fv349Bg8eDF9fX/j6+kKj0WDPnj1d6T4REZFdsTgZ5+TkID09HUuXLkV5eTmGDx+OpKQknD59ut34qqoqTJgwAcOHD0d5eTmWLFmCefPmITc3V4rR6XRISUmBVqvF0aNHodVqkZycjJKSEimmb9++eOWVV3DkyBEcOXIEY8aMweTJk3HixIkuDJuIiHqU6IbNiVn8POPY2FgMHTpUenAzAERERGDKlCnIzMxsE79o0SLs3r0blZWV0r60tDQcPXoUOp0OAJCSkoKGhgaTSjcxMRF9+vTBu+++22Ff/Pz88Oc//xkzZ87sVN/5PGMiop/05POMBz+7Eq4eVjzPuOkGPnubzzMGADQ1NaGsrAzx8fEm++Pj43Hw4MF2j9HpdG3iExIScOTIEdy8edNsTEdtGgwGZGdn49q1a9BoNB32t7GxEQ0NDSYbERGRvbEoGV+8eBEGgwFBQUEm+4OCglBbW9vuMbW1te3GNzc34+LFi2Zjbm/z2LFj6N27N5RKJdLS0pCXl4fIyMgO+5uZmQmVSiVtoaGhnR4rERF1I05Tm9WlBVwKhemTJYUQbfbdKf72/Z1pc+DAgaioqMChQ4fwu9/9Ds888ww+//zzDt938eLF0Ov10lZdXW1+YEREJA8mY7MsurQpICAArq6ubSrWurq6NpVtK7Va3W68m5sb/P39zcbc3qaHhwfuv/9+AEBMTAxKS0vxxhtvYOPGje2+t1KphFKp7PwAiYhIFry0yTyLKmMPDw9ER0ejsLDQZH9hYSHi4uLaPUaj0bSJ37t3L2JiYuDu7m42pqM2Wwkh0NjYaMkQiIiI7I7FN/3IyMiAVqtFTEwMNBoNNm3ahNOnTyMtLQ1Ay9Tw2bNnsW3bNgAtK6ezsrKQkZGB1NRU6HQ6bN682WSV9Pz58zFixAisWrUKkydPxq5du7Bv3z4UFxdLMUuWLEFSUhJCQ0Nx5coVZGdnY//+/SgoKLD2Z0BERHLjHbjMsjgZp6Sk4NKlS1ixYgVqamoQFRWF/Px8hIWFAQBqampMrjkODw9Hfn4+FixYgLVr1yIkJARr1qzBtGnTpJi4uDhkZ2dj2bJleOmll9C/f3/k5OQgNjZWijl//jy0Wi1qampalskPHoyCggKMHz/emvETEVEPUAgBhWVX0rY53plZfJ2xI+N1xkREP+nJ64wf1v7J6uuMK7YvddrPb96bmoiI5MdparOYjImISHZcTW0en2dMRERkY6yMiYhIfpymNovJmIiIZMdpavM4TU1ERGRjrIyJiEh+nKY2i8mYiIhkx2lq85iMiYhIfqyMzeI5YyIiIhtjZUxERD3C2aearcFkTERE8hOiZbPmeCfGaWoiIiIbY2VMRESy42pq85iMiYhIflxNbRanqYmIiGyMlTEREclOYWzZrDnemTEZExGR/DhNbRanqYmIiGyMlTEREcmOq6nNYzImIiL58aYfZjEZExGR7FgZm8dzxkRERDbGypiIiOTH1dRmMRkTEZHsOE1tHqepiYiIbIyVMRERyY+rqc1iMiYiItlxmto8TlMTERHZGCtjIiKSH1dTm8VkTEREsuM0tXmcpiYiIrIxVsZERCQ/o2jZrDneiTEZExGR/HjO2CwmYyIikp0CVp4z7rae2CeeMyYiIrIxVsZERCQ/3oHLLFbGREQku9ZLm6zZLJGZmYlHH30UPj4+CAwMxJQpU3Dy5EmTGCEEli9fjpCQEHh5eWHUqFE4ceKESUxjYyPmzp2LgIAAeHt7Y9KkSThz5oxJTH19PbRaLVQqFVQqFbRaLS5fvmxRf5mMiYjI6RQVFWH27Nk4dOgQCgsL0dzcjPj4eFy7dk2KefXVV/GXv/wFWVlZKC0thVqtxvjx43HlyhUpJj09HXl5ecjOzkZxcTGuXr2KiRMnwmAwSDHTp09HRUUFCgoKUFBQgIqKCmi1Wov6qxDCyWv/WzQ0NEClUkGv18PX19fW3SEisqme+ExsfY/HRi+Hm5tnl9tpbr6B4o+Xd7mvFy5cQGBgIIqKijBixAgIIRASEoL09HQsWrQIQEsVHBQUhFWrVuG5556DXq/Hvffei+3btyMlJQUAcO7cOYSGhiI/Px8JCQmorKxEZGQkDh06hNjYWADAoUOHoNFo8MUXX2DgwIGd6h8rYyIikp1CCKs3oCW537o1NjZ26v31ej0AwM/PDwBQVVWF2tpaxMfHSzFKpRIjR47EwYMHAQBlZWW4efOmSUxISAiioqKkGJ1OB5VKJSViABg2bBhUKpUU0xlMxkRE5DBCQ0Olc7MqlQqZmZl3PEYIgYyMDDz22GOIiooCANTW1gIAgoKCTGKDgoKk12pra+Hh4YE+ffqYjQkMDGzznoGBgVJMZ3A1NRERyc/442bN8QCqq6tNpqmVSuUdD50zZw4+++wzFBcXt3lNoTC9glkI0Wbf7W6PaS++M+3cipUxERHJrrumqX19fU22OyXjuXPnYvfu3fj444/Rt29fab9arQaANtVrXV2dVC2r1Wo0NTWhvr7ebMz58+fbvO+FCxfaVN3mMBkTEZHTEUJgzpw5eP/99/HRRx8hPDzc5PXw8HCo1WoUFhZK+5qamlBUVIS4uDgAQHR0NNzd3U1iampqcPz4cSlGo9FAr9fj8OHDUkxJSQn0er0U0xmcpiYiIvn18L2pZ8+ejXfeeQe7du2Cj4+PVAGrVCp4eXlBoVAgPT0dK1euxIABAzBgwACsXLkSvXr1wvTp06XYmTNn4oUXXoC/vz/8/PywcOFCDBo0COPGjQMAREREIDExEampqdi4cSMAYNasWZg4cWKnV1IDTMZERNQTevgOXOvXrwcAjBo1ymT/li1bMGPGDADAiy++iOvXr+P5559HfX09YmNjsXfvXvj4+Ejxq1evhpubG5KTk3H9+nWMHTsWW7duhaurqxSzc+dOzJs3T1p1PWnSJGRlZVnUX15nTER0l+rJ64xHxr1k9XXGRQf/4LSf3zxnTEREZGOcpiYiIvnxQRFmMRkTEdmZyxf0+KLkKxgNRhgMRhibDTAYjHB1dcHI5M6v0LUnCmPLZs3xzozJmIjIznxz9Du8NOmVNvu9ens6bDIm85iMiYhsqLLkFN7NfB+GZgOMBiOMBiP0F6+0G2s0OHB5yGlqs5iMiYhsqP78Zeh2H+lUrNHowAmph68zdjRcTU1EZEOurp3/GHboypjM6lIyXrduHcLDw+Hp6Yno6GgcOHDAbHxRURGio6Ph6emJfv36YcOGDW1icnNzERkZCaVSicjISOTl5Zm8npmZiUcffRQ+Pj4IDAzElClTcPLkya50n4jIbrjcJcm4u+5N7awsTsY5OTlIT0/H0qVLUV5ejuHDhyMpKQmnT59uN76qqgoTJkzA8OHDUV5ejiVLlmDevHnIzc2VYnQ6HVJSUqDVanH06FFotVokJyejpKREiikqKsLs2bNx6NAhFBYWorm5GfHx8bh27VoXhk1EZB8sScZCCDjsfZpazxlbszkxi+/AFRsbi6FDh0q3GgNa7s05ZcqUdp8ruWjRIuzevRuVlZXSvrS0NBw9ehQ6nQ4AkJKSgoaGBuzZs0eKSUxMRJ8+ffDuu++2248LFy4gMDAQRUVFGDFiRKf6zjtwEZG9Kf/oGF4ct6LT8QVN2XB1c71zYCf05B24RkcvtvoOXB+XZTrt57dFlXFTUxPKysqk+2+2io+Px8GDB9s9RqfTtYlPSEjAkSNHcPPmTbMxHbUJAHq9HgDg5+fXYUxjYyMaGhpMNiIie2JJZQwABkedqhb46ZnGXdmcuzC2LBlfvHgRBoOhzTMag4KC2jwTslVtbW278c3Nzbh48aLZmI7aFEIgIyMDjz32GKKiojrsb2ZmJlQqlbSFhobecYxERD3JkgVcgOOeN+Y5Y/O6tIBLoVCYfC+EaLPvTvG377ekzTlz5uCzzz7rcAq71eLFi6HX66WturrabDwRUU+ztDJ21GTccmmTNeeMbT0AeVl0nXFAQABcXV3bVKx1dXVtKttWarW63Xg3Nzf4+/ubjWmvzblz52L37t345JNP0LdvX7P9VSqVUCqVdxwXEZGtWJqM18x+Ey6uLi23ymw2wt3DDS9unSNT76inWPRb4OHhgejoaBQWFprsLywsRFxc+7do02g0beL37t2LmJgYuLu7m425tU0hBObMmYP3338fH330EcLDwy3pOhGRXbI0Gf9rxwEU/q0I/9pxAPuz/41P/q6TqWfdjKupzbL4DlwZGRnQarWIiYmBRqPBpk2bcPr0aaSlpQFomRo+e/Ystm3bBqBl5XRWVhYyMjKQmpoKnU6HzZs3m0wxz58/HyNGjMCqVaswefJk7Nq1C/v27UNxcbEUM3v2bLzzzjvYtWsXfHx8pEpapVLBy8vLqh8CEZGtqO8LxAubn4ermwtcXV3g4uqCT/5XhwO5JW1i//P3TyDht6OlOFc3F4uTuc0YAXR8NrNzxzsxi5NxSkoKLl26hBUrVqCmpgZRUVHIz89HWFgYAKCmpsbkmuPw8HDk5+djwYIFWLt2LUJCQrBmzRpMmzZNiomLi0N2djaWLVuGl156Cf3790dOTg5iY2OlmNZLqUaNGmXSny1btmDGjBmWDoOIyC74+vsg8bejTfadrjzbbjL29fdB3wHBPdU16kFdujf1888/j+eff77d17Zu3dpm38iRI/Hpp5+abfPJJ5/Ek08+2eHrDnuhOxGRhTqqdh128RZg9YpoZ19NzQdFEBHZGWdMxnxqk3kOcrKBiOju4ZTJmMxiZUxEZAeabzaj8YdGGAxG3Lxxs92Ys1/VoPTDCum5x/dFhSI4vP3LSu0OK2OzmIyJiOzA3q37sfq5jWZjCrcVoXBbkfT97DeexZS5SXJ3rXswGZvFaWoiIjvQlUuUOG3tPFgZExHZga4kY4d6aASvMzaLyZiIyA44e2XMS5vMYzImIrIDzp6Mec7YPJ4zJiKyA5Y+ShFwsGRMZrEyJiKyA05fGRsFoLCiujU6d2XMZExEZAe6toDLIENPZMJparM4TU1EZAecvjIms1gZExHZAedPxtY+k9i5K2MmYyIiO+D0yZjT1GZxmpqIyA5wNfXdjZUxEZEdcP47cAlYNdXM1dRERCQ355+mNrZs1hzvxDhNTURkB5w+GZNZrIyJiOyA0ydjLuAyi8mYiMgOOP0CLp4zNovJmIjIDnRpAZfRgZIxK2OzeM6YiMgOOP00NZnFypiIyA44fTIWsLIy7rae2CUmYyIiO+D8yZjT1OZwmpqIyA44/QIuMouVMRGRHXD6ythoBGBFfx1psVoXMBkTEdkBp78dJqepzeI0NRGRHXD6ypjMYmVMRGQHOkrGwyZGY9aftXBxdWnZXFykr5VeHj3cSyuwMjaLyZiIyA50tIDLs7cnQgf+rId7IwPegcssTlMTEdmBjipjTkXfHVgZExHZAWdPxkIYIax4DKI1xzoCJmMiIjug7KXEzJXTfzo3/OMW0l9t6651DyGsm2rmOWMiIpKbh9Id//n7J2zdDfkIK88ZO3ky5jljIiIiG2NlTERE8jMaAYUV5315zpiIiMhKnKY2i9PURERENsbKmIiIZCeMRggrpql5aRMREZG1OE1tFqepiYiIbIyVMRERyc8oAAUr444wGRMRkfyEAGDNpU3OnYw5TU1ERE7nk08+wS9/+UuEhIRAoVDgH//4h8nrM2bMgEKhMNmGDRtmEtPY2Ii5c+ciICAA3t7emDRpEs6cOWMSU19fD61WC5VKBZVKBa1Wi8uXL1vcXyZjIiKSnTAKqzdLXLt2DUOGDEFWVlaHMYmJiaipqZG2/Px8k9fT09ORl5eH7OxsFBcX4+rVq5g4cSIMBoMUM336dFRUVKCgoAAFBQWoqKiAVqu17IcDTlMTEVFPEEZYN01t2bFJSUlISkoyG6NUKqFWt/8gDr1ej82bN2P79u0YN24cAGDHjh0IDQ3Fvn37kJCQgMrKShQUFODQoUOIjY0FALz55pvQaDQ4efIkBg4c2On+sjImIiLZdVdl3NDQYLI1NjZ2uU/79+9HYGAgHnjgAaSmpqKurk56raysDDdv3kR8fLy0LyQkBFFRUTh48CAAQKfTQaVSSYkYAIYNGwaVSiXFdBaTMREROYzQ0FDp/KxKpUJmZmaX2klKSsLOnTvx0Ucf4bXXXkNpaSnGjBkjJffa2lp4eHigT58+JscFBQWhtrZWigkMDGzTdmBgoBTTWXfVNLUQP/1lRUR0t2v9LBQ9sFK5WTRa9bCHZtwEAFRXV8PX11far1Qqu9ReSkqK9HVUVBRiYmIQFhaGDz74AFOnTu3wOCEEFAqF9P2tX3cU0xl3VTK+cuUKgJa/rIiIqMWVK1egUqlkadvDwwNqtRrFtfl3Dr4DtVqNgIAAeHp6dkPPTAUHByMsLAynTp2S3qupqQn19fUm1XFdXR3i4uKkmPPnz7dp68KFCwgKCrLo/e+qZBwSEoLq6mr4+PhY/FeLpRoaGhAaGtrmrzhH5Uzj4VjslzONxxHGIoTAlStXEBISItt7eHp6oqqqCk1NTVa35eHhIUsiBoBLly6huroawcHBAIDo6Gi4u7ujsLAQycnJAICamhocP34cr776KgBAo9FAr9fj8OHD+I//+A8AQElJCfR6vZSwO+uuSsYuLi7o27dvj76nr6+v3f6P2BXONB6OxX4503jsfSxyVcS38vT0lC2JduTq1av46quvpO+rqqpQUVEBPz8/+Pn5Yfny5Zg2bRqCg4Px7bffYsmSJQgICMATTzwBoOXnMnPmTLzwwgvw9/eHn58fFi5ciEGDBkmrqyMiIpCYmIjU1FRs3LgRADBr1ixMnDjRopXUwF2WjImI6O5w5MgRjB49Wvo+IyMDAPDMM89g/fr1OHbsGLZt24bLly8jODgYo0ePRk5ODnx8fKRjVq9eDTc3NyQnJ+P69esYO3Ystm7dCldXVylm586dmDdvnrTqetKkSWavbe6IQvTEmfu7UENDA1QqFfR6vV3/VdxZzjQejsV+OdN4nGksJD9e2iQTpVKJl19+ucsr/eyNM42HY7FfzjQeZxoLyY+VMRERkY2xMiYiIrIxJmMiIiIbYzImIiKyMSZjIiIiG2My7sCnn36K8ePH45577oG/vz9mzZqFq1evtht76dIl9O3bFwqFolMPldbpdBgzZgy8vb1xzz33YNSoUbh+/XqbuMbGRjz88MNQKBSoqKiQ9h89ehS//vWvERoaCi8vL0REROCNN95wyLEAwPz58xEdHQ2lUomHH37Y7PvZ+1hOnz6NX/7yl/D29kZAQADmzZtn9s5DthzPpEmT8POf/xyenp4IDg6GVqvFuXPnTNr417/+hbi4OPj4+CA4OBiLFi1Cc3OzQ46ltLQUY8eOxT333IM+ffogPj6+zb+fI4xl69atUCgU7W63PnWIHAuTcTvOnTuHcePG4f7770dJSQkKCgpw4sQJzJgxo934mTNnYvDgwZ1qW6fTITExEfHx8Th8+DBKS0sxZ84cuLi0/ad48cUX271NXVlZGe69917s2LEDJ06cwNKlS7F48eJ2LzS397EALbfke/bZZ01u3N4eex+LwWDA448/jmvXrqG4uBjZ2dnIzc3FCy+8YJfjGT16NN577z2cPHkSubm5+Prrr/Hkk09Kr3/22WeYMGECEhMTUV5ejuzsbOzevRu///3vHW4sV65cQUJCAn7+85+jpKQExcXF8PX1RUJCAm7evOlQY0lJSUFNTY3JlpCQgJEjR7b7BCFyEILa2LhxowgMDBQGg0HaV15eLgCIU6dOmcSuW7dOjBw5UvzrX/8SAER9fb3ZtmNjY8WyZcvu2If8/Hzx4IMPihMnTggAory83Gz8888/L0aPHu3QY3n55ZfFkCFDOmzH3seSn58vXFxcxNmzZ6V97777rlAqlUKv19vleG61a9cuoVAoRFNTkxBCiMWLF4uYmBiTmLy8POHp6SkaGhocaiylpaUCgDh9+rQU89lnnwkA4quvvnKosdyurq5OuLu7i23btlnULtkXVsbtaGxshIeHh8lfq15eXgCA4uJiad/nn3+OFStWYNu2be1WULerq6tDSUkJAgMDERcXh6CgIIwcOdKkTQA4f/48UlNTsX37dvTq1atTfdbr9fDz83OKsXTE3sei0+kQFRVlUjUnJCSgsbERZWVldjeeW33//ffYuXMn4uLi4O7uLvXv9vsJe3l54caNG23GY+9jGThwIAICArB582Y0NTXh+vXr2Lx5Mx566CGEhYU51Fhut23bNvTq1cukeiYHZOu/BuzR8ePHhZubm3j11VdFY2Oj+P7778XUqVMFALFy5UohhBA3btwQgwcPFtu3bxdCCPHxxx/f8S9jnU4nAAg/Pz/x9ttvi08//VSkp6cLDw8P8eWXXwohhDAajSIxMVH84Q9/EEIIUVVVdcfK+ODBg8Ld3V3s3bvXocdyp8rY3seSmpoqxo8f36Z9Dw8P8c4779jVeFq9+OKLolevXgKAGDZsmLh48aL02ocffihcXFzEO++8I5qbm8WZM2fEY489JgC0GY+9j6W1j/379xcuLi7CxcVFPPjgg+K7775zuH+X20VGRorf/e53Hb5OjuGuSsYvv/yyAGB2Ky0tFUIIsXPnThEUFCRcXV2Fh4eHWLhwoQgKChKrVq0SQgixYMECkZKSIrXdmf8Z//3vfwsAYvHixSb7Bw0aJH7/+98LIYR44403RFxcnGhubhZCdJzAOBb7G0tqaqqIj493iPG0unDhgjh58qTYu3ev+MUvfiEmTJggjEaj9Hp8fLxTjOWHH34QISEhTjGWWx08eFAAEEeOHOnwfckx3FXJ+MKFC6KystLsdv36dZNjamtrxZUrV8TVq1eFi4uLeO+994QQQgwZMkS4uLgIV1dX4erqKlxcXAQA4erqKv7f//t/7b7/N998IwBIf023Sk5OFtOnTxdCCDF58mSTdl1dXaV2n376aZOx/N///Z/w8/MTs2bNcvixVFZWitmzZ4uBAwc67FheeuklMXjwYJPfs0OHDgkAYuvWrXY1nvZUV1cLAOLgwYMm/zaff/65KCoqEuXl5eKf//ynACDee+89hxrLW2+9JQICAsSJEyekfh89elR4enqK//mf/3Gosdzq2WefFQ8//HCHx5LjuKseoRgQEICAgACLjgkKCgIAvP322/D09MT48eMBALm5uSaXI5SWluLZZ5/FgQMH0L9//3bbuu+++xASEoKTJ0+a7P/yyy+RlJQEAFizZg3++Mc/Sq+dO3cOCQkJyMnJQWxsrLT//PnzmDlzJmbOnCk96NpRx9L67xIQEABPT088+OCDDjkWjUaDP/3pT7h586Y0hpycHCiVSjzxxBNmn9zT0+Npj/jxNvWNjY3SvtZ/l4iICADA+++/j9DQUEydOtXkMXL2PpYffvgBbm5uiIiIgEKhAAA0NzfD1dUVwcHBHf7O2eNYWl29ehXvvfceMjMzOzyWHIiN/xiwW3/9619FWVmZOHnypMjKyhJeXl7ijTfe6DC+vWmqM2fOiIEDB4qSkhJp3+rVq4Wvr6/4+9//Lk6dOiWWLVsmPD0926zobNXedOjx48fFvffeK5566ilRU1MjbXV1dQ43FiGEOHXqlCgvLxfPPfeceOCBB0R5ebkoLy8XjY2NDjWW5uZmERUVJcaOHSs+/fRTsW/fPtG3b18xZ86cDvtnq/GUlJSIv/71r6K8vFx8++234qOPPhKPPfaY6N+/v7hx44bUzquvvio+++wzcfz4cbFixQrh7u4u8vLyHG4slZWVQqlUit/97nfi888/F8ePHxe/+c1vhEqlEufOnXOosbR66623hKenp/j+++877Bc5DibjDmi1WuHn5yc8PDzE4MGD73jZQHv/M7Z+YH/88ccmsZmZmaJv376iV69eQqPRiAMHDnTYbnsf+h2dkwwLC3O4sQghxMiRI9sdT1VVlcON5bvvvhOPP/648PLyEn5+fmLOnDltPkTtYTyfffaZGD16tPDz8xNKpVLcd999Ii0tTZw5c8akjdGjRwuVSiU8PT1FbGysyM/Pd9ixtJ5/ValUok+fPmLMmDFCp9M55FiEEEKj0Zid3ibHwkcoEhER2RivMyYiIrIxJmMiIiIbYzImIiKyMSZjIiIiG2MyJiIisjEmYyIiIhtjMiYiIrIxJmMiIiIbYzImIiKyMSZjIiIiG2MyJiIisjEmYyIiIhv7/5czJN3z356BAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Education'])].plot(column='building_area', legend=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FEATURECOD\n",
+       "Building General                             1526\n",
+       "Commercial and Retail                         202\n",
+       "Industry                                       57\n",
+       "Public Attractions and Landmark Buildings      16\n",
+       "Government and Military                         3\n",
+       "Information and Communication                   2\n",
+       "Education                                       2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings['FEATURECOD'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.fillna('-999')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12,8))\n",
+    "core_buildings.loc[core_buildings['THEME1']=='Church'].plot(ax=ax, color='brown')\n",
+    "core_buildings.loc[core_buildings['NAME']=='Armourdale Community Ctr'].plot(ax=ax, color='green')\n",
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Education'])].plot(ax=ax, color='blue')\n",
+    "# armourdale.plot(ax=ax, fc='None')\n",
+    "# ax.legend()\n",
+    "sunroof.plot(ax=ax, fc='lightgray', ec='b', lw=1, zorder=0)\n",
+    "ax.grid()\n",
+    "ax.set_axis_off()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>THEME1</th>\n",
+       "      <th>THEME2</th>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>AGENCY</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>CITY_left</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>CHNG_TYPE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>yearly_sunlight_kwh_f</th>\n",
+       "      <th>yearly_sunlight_kwh_median</th>\n",
+       "      <th>yearly_sunlight_kwh_total</th>\n",
+       "      <th>install_size_kw_buckets_json</th>\n",
+       "      <th>carbon_offset_metric_tons</th>\n",
+       "      <th>existing_installs_count</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>WARD_right</th>\n",
+       "      <th>building_area</th>\n",
+       "      <th>area_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>923 S Bethany St</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>383.093978</td>\n",
+       "      <td>0.001228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>131.098731</td>\n",
+       "      <td>0.000420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1101 Argentine Blvd.</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>590.512130</td>\n",
+       "      <td>0.001893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>First Christian Church</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000 Argentine Blvd</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>570.662440</td>\n",
+       "      <td>0.001830</td>\n",
+       "    </tr>\n",
        "    <tr>\n",
-       "      <th>FEATURECOD</th>\n",
-       "      <th></th>\n",
+       "      <th>604</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>9.399597</td>\n",
+       "      <td>0.000030</td>\n",
        "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Building General</th>\n",
-       "      <td>9963.985320</td>\n",
+       "      <th>653</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>14.522235</td>\n",
+       "      <td>0.000047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Commercial and Retail</th>\n",
-       "      <td>5193.889979</td>\n",
+       "      <th>664</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>67.705655</td>\n",
+       "      <td>0.000217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Industry</th>\n",
-       "      <td>6815.730465</td>\n",
+       "      <th>668</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>79.574002</td>\n",
+       "      <td>0.000255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>46.630836</td>\n",
+       "      <td>0.000150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>933 Argentine Blvd</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>519.316511</td>\n",
+       "      <td>0.001665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>933</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>58.644385</td>\n",
+       "      <td>0.000188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1577</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>599.008449</td>\n",
+       "      <td>0.001921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1672</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>665.004464</td>\n",
+       "      <td>0.002132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1709</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>Armourdale Community Ctr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>720.998486</td>\n",
+       "      <td>0.002312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>184.993241</td>\n",
+       "      <td>0.000593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2263</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>432.634655</td>\n",
+       "      <td>0.001387</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>16 rows × 63 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                       area_fraction\n",
-       "FEATURECOD                          \n",
-       "Building General         9963.985320\n",
-       "Commercial and Retail    5193.889979\n",
-       "Industry                 6815.730465"
+       "      THEME1 THEME2                                 FEATURECOD  \\\n",
+       "124   Church   None  Public Attractions and Landmark Buildings   \n",
+       "237     None   None  Public Attractions and Landmark Buildings   \n",
+       "361   Church   None  Public Attractions and Landmark Buildings   \n",
+       "576   Church   None  Public Attractions and Landmark Buildings   \n",
+       "604     None   None  Public Attractions and Landmark Buildings   \n",
+       "653     None   None  Public Attractions and Landmark Buildings   \n",
+       "664     None   None  Public Attractions and Landmark Buildings   \n",
+       "668     None   None  Public Attractions and Landmark Buildings   \n",
+       "717     None   None  Public Attractions and Landmark Buildings   \n",
+       "718   Church   None  Public Attractions and Landmark Buildings   \n",
+       "933     None   None  Public Attractions and Landmark Buildings   \n",
+       "1577    None   None  Public Attractions and Landmark Buildings   \n",
+       "1672    None   None  Public Attractions and Landmark Buildings   \n",
+       "1709    None   None  Public Attractions and Landmark Buildings   \n",
+       "2182    None   None  Public Attractions and Landmark Buildings   \n",
+       "2263    None   None  Public Attractions and Landmark Buildings   \n",
+       "\n",
+       "                          NAME  AGENCY               ADDRESS    CITY_left  \\\n",
+       "124                       None       0      923 S Bethany St  Kansas City   \n",
+       "237                       None       0                  None  Kansas City   \n",
+       "361                       None       0  1101 Argentine Blvd.  Kansas City   \n",
+       "576     First Christian Church       0   1000 Argentine Blvd  Kansas City   \n",
+       "604                       None       0                  None  Kansas City   \n",
+       "653                       None       0                  None  Kansas City   \n",
+       "664                       None       0                  None  Kansas City   \n",
+       "668                       None       0                  None  Kansas City   \n",
+       "717                       None       0                  None  Kansas City   \n",
+       "718                       None       0    933 Argentine Blvd  Kansas City   \n",
+       "933                       None       0                  None  Kansas City   \n",
+       "1577                      None       0                  None  Kansas City   \n",
+       "1672                      None       0                  None  Kansas City   \n",
+       "1709  Armourdale Community Ctr       0                  None  Kansas City   \n",
+       "2182                      None       0                  None  Kansas City   \n",
+       "2263                      None       0                  None  Kansas City   \n",
+       "\n",
+       "        ZIP COMMENT  CHNG_TYPE  ...  yearly_sunlight_kwh_f  \\\n",
+       "124   66105    None          4  ...           2.222399e+07   \n",
+       "237    None    None          4  ...           2.222399e+07   \n",
+       "361   66105    None          4  ...           2.222399e+07   \n",
+       "576   66105    None          4  ...           2.222399e+07   \n",
+       "604    None    None          4  ...           2.222399e+07   \n",
+       "653    None    None          4  ...           2.222399e+07   \n",
+       "664    None    None          4  ...           2.222399e+07   \n",
+       "668    None    None          4  ...           2.222399e+07   \n",
+       "717    None    None          4  ...           2.222399e+07   \n",
+       "718   66105    None          4  ...           2.222399e+07   \n",
+       "933    None    None          4  ...           2.222399e+07   \n",
+       "1577   None    None          1  ...           2.222399e+07   \n",
+       "1672   None    None          1  ...           2.222399e+07   \n",
+       "1709   None    None          1  ...           2.222399e+07   \n",
+       "2182   None    None          1  ...           2.222399e+07   \n",
+       "2263   None    None          1  ...           2.222399e+07   \n",
+       "\n",
+       "      yearly_sunlight_kwh_median  yearly_sunlight_kwh_total  \\\n",
+       "124                  8223.137817               2.861974e+07   \n",
+       "237                  8223.137817               2.861974e+07   \n",
+       "361                  8223.137817               2.861974e+07   \n",
+       "576                  8223.137817               2.861974e+07   \n",
+       "604                  8223.137817               2.861974e+07   \n",
+       "653                  8223.137817               2.861974e+07   \n",
+       "664                  8223.137817               2.861974e+07   \n",
+       "668                  8223.137817               2.861974e+07   \n",
+       "717                  8223.137817               2.861974e+07   \n",
+       "718                  8223.137817               2.861974e+07   \n",
+       "933                  8223.137817               2.861974e+07   \n",
+       "1577                 8223.137817               2.861974e+07   \n",
+       "1672                 8223.137817               2.861974e+07   \n",
+       "1709                 8223.137817               2.861974e+07   \n",
+       "2182                 8223.137817               2.861974e+07   \n",
+       "2263                 8223.137817               2.861974e+07   \n",
+       "\n",
+       "                           install_size_kw_buckets_json  \\\n",
+       "124   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "237   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "361   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "576   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "604   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "653   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "664   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "668   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "717   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "718   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "933   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "1577  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "1672  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "1709  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "2182  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "2263  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "\n",
+       "      carbon_offset_metric_tons existing_installs_count         CITY  \\\n",
+       "124                19866.275546                     0.0  Kansas City   \n",
+       "237                19866.275546                     0.0  Kansas City   \n",
+       "361                19866.275546                     0.0  Kansas City   \n",
+       "576                19866.275546                     0.0  Kansas City   \n",
+       "604                19866.275546                     0.0  Kansas City   \n",
+       "653                19866.275546                     0.0  Kansas City   \n",
+       "664                19866.275546                     0.0  Kansas City   \n",
+       "668                19866.275546                     0.0  Kansas City   \n",
+       "717                19866.275546                     0.0  Kansas City   \n",
+       "718                19866.275546                     0.0  Kansas City   \n",
+       "933                19866.275546                     0.0  Kansas City   \n",
+       "1577               19866.275546                     0.0  Kansas City   \n",
+       "1672               19866.275546                     0.0  Kansas City   \n",
+       "1709               19866.275546                     0.0  Kansas City   \n",
+       "2182               19866.275546                     0.0  Kansas City   \n",
+       "2263               19866.275546                     0.0  Kansas City   \n",
+       "\n",
+       "      WARD_right building_area area_fraction  \n",
+       "124           06    383.093978      0.001228  \n",
+       "237           06    131.098731      0.000420  \n",
+       "361           06    590.512130      0.001893  \n",
+       "576           06    570.662440      0.001830  \n",
+       "604           06      9.399597      0.000030  \n",
+       "653           06     14.522235      0.000047  \n",
+       "664           06     67.705655      0.000217  \n",
+       "668           06     79.574002      0.000255  \n",
+       "717           06     46.630836      0.000150  \n",
+       "718           06    519.316511      0.001665  \n",
+       "933           06     58.644385      0.000188  \n",
+       "1577          06    599.008449      0.001921  \n",
+       "1672          06    665.004464      0.002132  \n",
+       "1709          06    720.998486      0.002312  \n",
+       "2182          06    184.993241      0.000593  \n",
+       "2263          06    432.634655      0.001387  \n",
+       "\n",
+       "[16 rows x 63 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "core_buildings.groupby(['FEATURECOD']).sum(numeric_only=True)['area_fraction'].to_frame().loc[['Building General',\n",
-    "                                                                                               'Commercial and Retail',\n",
-    "                                                                                               'Industry']]*kw_total"
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Public Attractions and Landmark Buildings'])]#.plot(column='building_area', legend=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -265,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -286,14 +1119,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -313,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -335,7 +1168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -357,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +1217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -406,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -442,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +1317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +1348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -539,7 +1372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -549,7 +1382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -558,7 +1391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -567,7 +1400,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -576,7 +1416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -693,7 +1533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -712,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -732,7 +1572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -758,7 +1598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +1618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -798,7 +1638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -829,7 +1669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -849,7 +1689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -869,7 +1709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -889,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -909,7 +1749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +1769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -938,7 +1778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -958,7 +1798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -990,7 +1830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1029,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1038,7 +1878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1435,7 +2275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1444,7 +2284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1476,7 +2316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1485,7 +2325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1512,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1532,7 +2372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1552,7 +2392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1695,7 +2535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1704,7 +2544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1713,7 +2553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1749,7 +2589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1785,7 +2625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1818,7 +2658,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "kansas-city",
+   "display_name": "pypsa-illinois02",
    "language": "python",
    "name": "python3"
   },
@@ -1832,7 +2672,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/11-pypsa-model-more-buses.ipynb b/notebooks/11-pypsa-model-more-buses.ipynb
index bade541..71a7464 100644
--- a/notebooks/11-pypsa-model-more-buses.ipynb
+++ b/notebooks/11-pypsa-model-more-buses.ipynb
@@ -61,7 +61,8 @@
     "n.add(class_name=\"Carrier\", name=\"grid\")\n",
     "n.add(class_name=\"Carrier\", name=\"solar\")\n",
     "n.add(class_name=\"Carrier\", name=\"battery\")\n",
-    "n.add(class_name=\"Carrier\", name='net metering')"
+    "n.add(class_name=\"Carrier\", name='net metering')\n",
+    "n.add(class_name=\"Carrier\", name='efficiency')"
    ]
   },
   {
@@ -78,7 +79,7 @@
    "outputs": [],
    "source": [
     "bus_name = 'Residential'\n",
-    "bus_names = ['Residential','CommunitySolar']\n",
+    "bus_names = ['Residential','CommunitySolar']#, 'CommunityCenter', 'FiskeElementary','MorseElementary']\n",
     "for bus in bus_names:\n",
     "      n.add(class_name=\"Bus\",\n",
     "            name=bus,\n",
diff --git a/notebooks/13-primary-school.ipynb b/notebooks/13-primary-school.ipynb
new file mode 100644
index 0000000..70338a5
--- /dev/null
+++ b/notebooks/13-primary-school.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import geopandas as gpd\n",
+    "import us\n",
+    "import unyt as u\n",
+    "import pypsa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = \"https://oedi-data-lake.s3.amazonaws.com/nrel-pds-building-stock/end-use-load-profiles-for-us-building-stock/2021/comstock_tmy3_release_1/timeseries_aggregates/by_puma/state=KS/g20000500-primaryschool.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(url, parse_dates=True, index_col='timestamp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='timestamp'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.loc['2018-09-21','out.electricity.total.energy_consumption'].resample('h').mean().plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(23291, 'ft**2')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "area_avail = 23291*u.foot**2\n",
+    "area_avail"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01894737, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kw_per_sqft = ((7.2*u.kW) / (380*u.foot**2))\n",
+    "kw_per_sqft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(32.63164421, 'W/m**2')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kw_per_sqft.to(u.W/u.meter**2) * 0.16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(4.5630845, 'W/m**2')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/(2500 * u.hectare/(u.TWh/u.year)).to(u.meter**2/u.W)  # this includes capacity factor!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kansas-city",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/14-solar-options-comparison.ipynb b/notebooks/14-solar-options-comparison.ipynb
new file mode 100644
index 0000000..d7b4cb5
--- /dev/null
+++ b/notebooks/14-solar-options-comparison.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import scipy as sp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "community_solar_cost = 1744.25  # $/kW\n",
+    "rooftop_solar_cost = 2519  # $/MW\n",
+    "needed_capacity = 3.00  # MW\n",
+    "\n",
+    "comm_sol_capacity = np.arange(0, needed_capacity, 20)\n",
+    "roof_sol_capacity = np.arange(0, needed_capacity, 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x2_intercept = (0 ,community_solar_cost*needed_capacity)\n",
+    "x1_intercept = (rooftop_solar_cost*needed_capacity, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.AxLine at 0x2961b4e2ed0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.axline(xy1=x1_intercept, xy2=x2_intercept)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pypsa-illinois02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/15-community-solar-options.ipynb b/notebooks/15-community-solar-options.ipynb
new file mode 100644
index 0000000..2ab72cf
--- /dev/null
+++ b/notebooks/15-community-solar-options.ipynb
@@ -0,0 +1,2784 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "from unyt import foot, meter, kW"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Retrieve all buildings within the Armourdale core."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sunroof = gpd.read_file(\"../data/spatial_data/armourdale_rooftop_potential.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = gpd.read_file(\"../data/spatial_data/armourdale_shape.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "sunroof.plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None', ec='k')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "land_bank = gpd.read_file(\"../data/spatial_data/armourdale/land_bank_parcels.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VACANT\n",
+       "Y    72\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "land_bank['VACANT'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.11031194057364"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "land_bank['ACRE'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12,8))\n",
+    "sunroof.plot(ax=ax, fc='None', ec='k')\n",
+    "land_bank.plot(ax=ax, column='ACRE', legend=True)\n",
+    "# armourdale.plot(ax=ax, fc='None', ec='k')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PARCEL', 'PARCEL_NBR', 'ACRE', 'STATE_ID', 'SID_COUNTY', 'SID_TWNSHP',\n",
+       "       'SID_QUAD', 'SID_SEC_NO', 'SID_SHEET', 'SID_QSEC', 'SID_BLOCK',\n",
+       "       'SID_PARCEL', 'SID_PRCL_S', 'SID_OWNER', 'OWNER_NAME', 'NUMB',\n",
+       "       'ADDR_EXT', 'DIR', 'ST_NAME', 'SUFX', 'MISC', 'CITY_left', 'STATE',\n",
+       "       'ZIP', 'DELQ_CODE', 'VACANT', 'LAND_USE', 'ORIONID', 'TotDue',\n",
+       "       'Shape_Leng', 'Shape_Area', 'index_right', 'CITY_right', 'WARD',\n",
+       "       'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "land_bank.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buildings = gpd.read_file('../data/spatial_data/armourdale/building_footprints.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = buildings.drop(columns=\"index_right\").sjoin(sunroof, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
+       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
+       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
+       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'CITY_right',\n",
+       "       'WARD_left', 'geometry', 'index_right', 'region_name', 'state_name',\n",
+       "       'lat_max', 'lat_min', 'lng_max', 'lng_min', 'lat_avg', 'lng_avg',\n",
+       "       'yearly_sunlight_kwh_kw_threshold_avg', 'count_qualified',\n",
+       "       'percent_covered', 'percent_qualified', 'number_of_panels_n',\n",
+       "       'number_of_panels_s', 'number_of_panels_e', 'number_of_panels_w',\n",
+       "       'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY',\n",
+       "       'WARD_right'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "core_buildings.plot(ax=ax, column='FEATURECOD', legend=True, legend_kwds=dict(loc=(0.05,-0.15), ncol=3))\n",
+    "# armourdale.plot(ax=ax, fc='lightgray', ec='k', zorder=0, alpha=0.5)\n",
+    "sunroof.plot(ax=ax, fc='lightgray', ec='b', lw=1, zorder=0)\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate the area of each building"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.assign(building_area=core_buildings.to_crs(epsg=5070).area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.assign(area_fraction = core_buildings['building_area'] / core_buildings['building_area'].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kw_total = core_buildings['kw_total'].unique()[0] * kW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area_fraction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Building General</th>\n",
+       "      <td>9963.985320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Commercial and Retail</th>\n",
+       "      <td>5193.889979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Industry</th>\n",
+       "      <td>6815.730465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       area_fraction\n",
+       "FEATURECOD                          \n",
+       "Building General         9963.985320\n",
+       "Commercial and Retail    5193.889979\n",
+       "Industry                 6815.730465"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.groupby(['FEATURECOD']).sum(numeric_only=True)['area_fraction'].to_frame().loc[['Building General',\n",
+    "                                                                                               'Commercial and Retail',\n",
+    "                                                                                               'Industry']]*kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(15984.407, dtype=float32, units='ft**2')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1485*meter**2).to(foot**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>THEME1</th>\n",
+       "      <th>THEME2</th>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>AGENCY</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>CITY_left</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>CHNG_TYPE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>yearly_sunlight_kwh_f</th>\n",
+       "      <th>yearly_sunlight_kwh_median</th>\n",
+       "      <th>yearly_sunlight_kwh_total</th>\n",
+       "      <th>install_size_kw_buckets_json</th>\n",
+       "      <th>carbon_offset_metric_tons</th>\n",
+       "      <th>existing_installs_count</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>WARD_right</th>\n",
+       "      <th>building_area</th>\n",
+       "      <th>area_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1320</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>Morse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>912 S Baltimore St</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>1485.397201</td>\n",
+       "      <td>0.004762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2356</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Education</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>4044.709008</td>\n",
+       "      <td>0.012968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 63 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     THEME1 THEME2 FEATURECOD   NAME  AGENCY             ADDRESS    CITY_left  \\\n",
+       "1320   None   None  Education  Morse       0  912 S Baltimore St  Kansas City   \n",
+       "2356   None   None  Education   None       0                None  Kansas City   \n",
+       "\n",
+       "        ZIP COMMENT  CHNG_TYPE  ...  yearly_sunlight_kwh_f  \\\n",
+       "1320  66105    None          1  ...           2.222399e+07   \n",
+       "2356   None    2020          3  ...           2.222399e+07   \n",
+       "\n",
+       "      yearly_sunlight_kwh_median  yearly_sunlight_kwh_total  \\\n",
+       "1320                 8223.137817               2.861974e+07   \n",
+       "2356                 8223.137817               2.861974e+07   \n",
+       "\n",
+       "                           install_size_kw_buckets_json  \\\n",
+       "1320  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "2356  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "\n",
+       "      carbon_offset_metric_tons existing_installs_count         CITY  \\\n",
+       "1320               19866.275546                     0.0  Kansas City   \n",
+       "2356               19866.275546                     0.0  Kansas City   \n",
+       "\n",
+       "      WARD_right building_area area_fraction  \n",
+       "1320          06   1485.397201      0.004762  \n",
+       "2356          06   4044.709008      0.012968  \n",
+       "\n",
+       "[2 rows x 63 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Education'])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Education'])].plot(column='building_area', legend=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FEATURECOD\n",
+       "Building General                             1526\n",
+       "Commercial and Retail                         202\n",
+       "Industry                                       57\n",
+       "Public Attractions and Landmark Buildings      16\n",
+       "Government and Military                         3\n",
+       "Information and Communication                   2\n",
+       "Education                                       2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings['FEATURECOD'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.fillna('-999')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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g83RsIXE6tgAAVN3kpGALSdOxBQCg6nRsIXE6tgAAVN3ERB7tdm6OLaRKxxYAgKrLsojJyaGOLaRKxxYAgDqYmhoItpAqHVsAAOpgampgKDKkSscWAIA6mJzsx6FDedll7DjBFkLHFgCAepiaGsSTT5Zdxc4TbCEiGo2IPL0PtgAAqJlijm16J7aCLUSxglyeG4oMAEC1mWMLCdOxBQCgDopgm17DRrCFKDq2w2HZVQAAwNZMTg7j8OEsufVjWmUXALuBjm21felL3fjgBw9ERPF9HA0rv+WWR6Pd9o0FANIxNVUk2sOHIw4eLLmYHSTYQujY7na//MsXxmc/uyeazYhWK49mM49mM6LZzKPVyuPJJ1txzz0TK1736lc/Gu12CQUDAJRkFGyfekqwheQ0GhER6c1FqIoHHujEffetDK6nowsPAKRmcrIItocOlVzIDhNsIXRsd6uvf70dDzzQicFgsx86ZBEh3QIA6Rh1bAVbSJA5trvTa197aXz+8xOxb19iqx8AAGzS4qHIKbEqMoSO7W51/HjxX9SxY81Nvd6HFQBAavbtG0aW5cl1bAVbiFHH1hzbuhFsAYDUNBrFlj+pBVtDkSGKjm1EEYQy+bZUw2HE7//+2RERcfTo5jq1AAApm5jI48SJsqvYWYItxGhV5CJUNWWpUuV5xK/92oXb9l4AAKnJsvROggxFhlgItoIQAABUj44txOKhyLaH2S533TUZhw4V/8WMPjBY/MFBnmen7j/72cfjGc+YiYjtGwr+a792f+zda0UwAIAUCLYQOrbj8M53nhf33TexoWN/7uce2vZge845/VPfVwAA6s1pH8RCmLLlTzkWr0i9XcHWhxQAAOkQbCF0bMfhTAKqv3cAALZCsIVYPseW7VB+sPW9BABIhWALoWO722zHEvW+lwAA6bB4FIQ5tmUbRwjN84jHH2/Fm998waJVmbM1Vmgurm+++bF4+tNntr8YAADGSrCF0LEdh60sApVl2/O9OHGiER/+8IENH//ylz8ZEYItAEDVCLYQ5tiOw1aGE2/Hysi/9VvnR6+nBQ8AkALBFkLHtmzj+Hv/y7/csyvqAABg/ARbCHNsx+FMuq5PPdWKBx9slx4sy/76AABsjmALoWM7DmcSbH/3d8+N3/3dc8dXDAAAtSbYQphjW7Zv+7ZjsX//ILIsjyyL+PjHp2JmpozdyHz/AQCqSLCFWOjYGoq8fc6kY/ua1zwS11wzfer+C17wjHjssZ0Ptjr2AADVVEZLBHYdQ5EBAKC6BFsIQ5HLtls+UNgtdQAAbFVq5zWGIkPo2I7Dc55zPA4e7EeWLXxwMJpDW9xeuJ6aWjoG/Prrj8Thw61Te+EuHta89P1im44p6rrggrmt/aEBAHaBZjNiMCi7ip0l2ELY7mccXvvaRzf92je84eFtrAQAIC2tVh5ziX1ebygyhI4tAAD10W4LtpAkc2wBAKiLViuP2dmyq9hZgi2Eji0AAPVhKDIkyhxbAADqwlBkSNSoYyvYAgBQdSkORbYqMsRCsI0wxxYAqKbj994bT33yk5EPBtHaty8u+Ef/qOySKEmzmV7HVrCFMBQZAKi+6fvvjyc+8pGIiGife65gmzBDkSFRFo8CACqv2Vy47dP6pBVDkdM6sRVsIcyxBQCqL1sUbPN+v8RKKFuKc2wFW4jFHVtzbAGAaloSbH1anzRDkSFRozm2hiIDAFWlY8uIji0kylBkALZT/8iRmH7oobLLIDE6towUHdu0OjaCLYShyABsn+P33htffN3r4qtvfnMMTpwouxxSsnjxqMGgvDooXYodW9v9QOjYArB1eZ7H43feGQ+/732nQsWD73pXXHrLLZFlPjhl/PZefXVc8frXR9ZoLOnekp4U59gKthDm2AKwOYPp6Zh74oloHzgQD7zznXHkL/5iyfOHP/nJePKaa+LsF76wpApJSWv//pi89tqyy2AXaLUEW0iSfWwBOFPTDz4YX3vrW2M4PR1ZqxWzjz666nEPvec9MXHVVbHnyit3uEIgVe12ekORzbGFWDwU2VAxAE4v7/fjgbe/PWa+/vWYe+KJNUPt6NgHb789cp+eAjuk1YqYm0vrvFbHFsIcWwA2bvaJJ+L+3/iNOPmVr2zo+N6ll8ZlP/Mz5tkCO8ZQZEiUObYAbNTRz342TnzhCxs69qzrr4+Lf+InotHtjrkqgAUWj4JE2e4HgI06+F3fFcfvvjue+l//a+2DGo245Kab4uB3fufOFQYwL8WOrTm2EIYiA7BxWZbFxa98ZXQuvHDtg4bDyFr6B0A5BFtIlFWRATgTzYmJuOy1r42s3V7zmIfe+94YzszsYFUAhWJV5Cypc1vBFmLxHFtDkQHYmIlLL42Lb7xx1ec6F14YV77hDebWAqVotYpEOxiUXMgOEmwhDEUGYHPOuv76OOs7vmPJY/u//dvj6l/5lZi49NKSqgJSNwq2KQ1HNvkDQrAFYHOyLIuLf+In4sRf/3XMPvxwXPhjPxZnf+/32toHKFW7XQTb2dmIiYmSi9khgi3EQrAFgDPV6PXiste8JobT07HnqqvKLgdAxxZSNfpgfTj0CTsAZ653ySVllwBwyqhjm1Kw1aeCMBQZAID6GHVsZ2dLLmQHCbYQtvsBAKA+RjuR6dhCYhaCraHIAABUm44tJGphjm25dQAAwFaZYwuJMscWAIC6SHFVZMEWwnY/AADUh6HIkKiFjq05tgAAVJuhyJAoc2wBAKgLQ5EhUVZFBgCgLgxFhkTZxxYAgLowFBkSZVVkAADqwlBkSNRojq2OLQAAVWcoMiRqIdiaYwsAQLUZigyJyrKILMsNRQYAoPJareJasIUENRrm2AIAUH1ZVgxHNhQZEpRlhiIDAFAP7XauYwsp0rEFAKAuWi3BFpLUaFgVGWAj8uEwcv9hAuxqrVZaqyK3yi4AdotiKHLZVQCU5xt33BFzTz4Z+dxcHPnMZyIGg8gHg8j7/eJ6/nbkeVz2sz8bk9ddF41Op+yyAVhFp5NWx1awhXlFx9YcWyBdh/77f4/p++/f0LFfe8tb4hm/+ZvRveCCMVcFwGZYPAoSlWXm2AJpy1pn9nl3PhiMqRIAtsocW0iUObZA6rLGmZ0WCLYAu5dVkSFRgi2QvDPt2Pb7YyoEgK1KbSiyObYwrxiKbI4tkK6s2TztMc29e6PR68UFP/zD0TnvvB2oCoDNSG0osmAL83RsgdRtJNhe+jM/E5PXXrsD1QCwFakNRRZsYZ7tfoAU5cNhHLv77ojBIAbHji157uALXhCNXi+yZvPURZcWoBoMRYZEFcHWUGQgLXm/H1/55V9e9blGpxMX/diP7XBFAGyH1IYiWzwK5jUatvsB0rPeFj+5/xQBKiu1YKtjC/PsYwukKGs0VszFuOAf/+M4+4UvjKzTKbEyALaiGIqcR0QaIxIFW5h3hts3AtTG3mc+MyLi1Dza7sUXR3PPnpKrAhi/QT6IRjQiy+oX/tptc2whSVmW2+4HSNLfeOMbyy4BYOx+4S9+IT7x0Cein/ejP+zHIB9EHnl8+B98OC7Ze0nZ5W27ViuP48fLrmLnCLYwz3Y/AADVdKJ/Iv7yib+MQT6IYT6Mft6PYT6Mi/ZcFNecdU1ERMwMZuJY/9iK1/aH/Z0ud0dYFRkSZfEoAIBqevjEw3HT/7xpxeMvuOgF8evf9uuRZVm0GqtHn35ez2Bb7GObzhxbswphnu1+AACqqZGtHmvueuiuePPn3hyPTz8erWz1YDsYDsZZWml0bCFRhiIDAFRTM2uu+dzvf+n3Y244F0/MPLHq8//xi/8xJtuT8ZPP/Mk40D0wpgp3XtGxLbuKnSPYwjxDkQEAqmm9YBsR8Ydf/sM1n7vj/jsiIuIVf+MVgm2FCbYwr4arvAMAJOF0wXY93UY3eq1eDPJ6DUkuhiKnc4Ir2MK8omObzg8/AEBdrDXHduT7Lv2+uHLyynjb3W9b8dyvfOuvxAsveeG4SitNqxVJdWwtHgXzssxQZACAKlqvY/v9l35//Ktr/1Wc2zt31ednh/VcYclQZEiUxaMAAKpprWD7Tfu/KX7pOb8UrUYrOs3OqsfMDeuZ/lotwRaSJNgCAFTTntaeeOO3vDGaWXPJ5ZzeOaf2r+00Vg+2s4N6dmwFW0hUMRTZHFsAgKrpNDvxg1f84LrHPG3f0+LlV7482o12dJvd6DQ60Wl04psPfvMOVbmzDEWGROnYAgDU1zP2PyP+9bP/ddll7JiiY5tFnqex+4fFo2BelmWCLQAAtdBqFSe2/X7JhewQwRbmNRq5YAsAQC2028WJbSrDkQVbmGcfWwAA6mLUsZ2t59pYKwi2MM9QZAAA6mIUbHVsITEWjwIAoC5GQ5F1bCExtvsBAKAuBFtIlI4tAAB1IdhCogRbAADqotMRbCFJqUysBwCg/nRsIUF5HnHvvRFXXDFTdikAALBlgi0k6Gtfizh8OIunP3267FIAAGDLBFtI0Gc/W1x/0zcJtgAAVN8o2M4kMiBRsIWIuPvuiAMHBnHuuf2ySwEAgC2zeBQk6N5787j88pnIbGMLAEANGIoMCbr33ojLL09knAYAALUn2EJi8jzivvusiAwAQH0ItpCYRx6JOHo007EFAKA2Wi3BFpLy1a8W15dckshPPQAAtZdlRdfWqsiQiMGguG61yq0DAAC2U6eT69hCKkYrIed5uXUAAMB2arcFW0iGYAsAQB0JtpAQe9cCAFBHgi0kRMcWAIA6EmwhIYItAAB1ZFVkSJBgCwBAnejYQkIW5tiabAsAQH0ItpAQQ5EBAKijdnso2EIqBFsAAOpIxxYSItgCAFBHrZZgC8kQbAEAqKNOJ4+ZmTROcgVbkpdZMwoAgBqy3Q8kRLAFAKCOzLGFhCwMRZZwAQCoD8EWEmKOLQAAdWS7H0iQYAsAQJ0UHds0TnIFW5KnYwsAQB0VqyKXXcXOEGxJnmALAEAdFfvYprGOjGBL8qyKDABAHVk8ChJiVWQAAOqo3c5jbq7sKnaGYEvydGwBAKgjHVtIiDm2AADUUacj2EIyBFsAAOqo3c5jZiZL4jxXsCV5gi0AAHXUbhcnuP1+yYXsAMGW5Am2AADU0SjYpjAcWbCFU6wiBQBAfQi2kBAdWwAA6kiwhYQItgAA1FGnI9hCMgRbAADqaNSxnZkpuZAdINiSvMzUWkhCPhxGPhyWXQYA7Jh2u/i9p2MLCdCxhfqbfeyx+PIv/mI88bGPlV0KAOyYlObYtsouAMom2EJ95Xkehz7xiXjoPe+J4fR0nPjyl2Py2muje9FFZZcGAGPXagm2kIyFocjGJEOdzD31VDz4rnfF0f/7f089NnH55ZF1uyVWBQA7R8cWEqJjC/Vz+M//PB5897tjcPTokscHx45Fa3KypKoAYGdZFRkSIthCfQxOnIgH3vGO+Npb37oi1EZEzDz0UDzyB39QQmUAsPNSWhVZx5bkCbZQD8f+6q/igdtui7knnlj3uMc/9KGYeu5zY9811+xQZQBQjpSGIuvYwjzBFqrr+Be+EF++9dbThtqIiMjzeOC222Jw8uT4CwOAEgm2kBD72EL17bn66pj8lm/Z8PFz3/hGPPze946xIgAon2ALCRFsofqyLItLbropmvv2bfg1x+6+O4b9/hirAoByCbaQkIU5thIuVFn7rLPi4htv3PDxs48+Go+9//1jrAgAytVsRjSbuWALKbB4FNTHgec9L/Y/73lLHmtOTa15/GN/8idx4stfHndZAFCadju3KjKkQLCFern4xhvj+D33RN7vxyWvfGXsveaaOPxnfxZf/53fWXHseT/wAzFx6aUlVAkAO6PdTqNjK9iSPMEW6qW1b19c/rM/G+1zz432gQMREXHwu787jnzqU3H8nnti8rrr4vCf/3lERHTPOy+yll+FANRXu53GHFu/zUmeYAv1s+fqq5fcz7IsLnnVq2I4MxOd886LL996a7TPPjumnvvckioEgJ2hYwuJsCoypKF91lmnbl/x8z8fjU6nxGoAYGd0OsMkgq3Fo0ieVZEhPUItAKlIpWMr2AIAANRUKqsiC7YkzxxbAADqSscWEiHYAgBQV4ItJEKwBQCgrlotwRaSINgCAFBXVkWGRNjuBwCAuiqGIte/gyPYkjzb/QAAUFftdh7T02VXMX6CLckzFBkAgLoyxxYSIdgCAFBXVkWGRAi2AADUVadjji0kQbAFAKCudGwhEY1TPwUWjwIAoF4EW0jEqGM7HJZbBwAAbLd2O4+ZmbKrGD/BluQZigwAQF3p2EIiBFsAAOqqCLb1n3In2JK8rP4/5wAAJKrTGcbcXNlVjJ9gS/IWOrYSLgAA9WIoMiTCUGQAAOpKsIXECLYAANRNu51Hv5/VfgcQwRYiIstywRYAgNpptYqT3Lp3bQVbiGI4smALAEDddDqCLSSj4ScBAIAaarcFW0hGlkUMh1ZFBgCgXgRbSIihyAAA1JFgCwkRbAEAqKNRsJ2ZKbmQMRNsIQRbAADqSccWEpKZXgsAQA1ZFRkSUnRspVsAAOpFxxYSYigyAAB1JNhCQgRbAADqSLCFhAi2AADUkVWRISEWjwIAoI50bCEhjYaOLQAA9ZNlxUlu3c91BVuIomM7HGrbAgBQL435xDccllvHuAm2EObYAgBQT4ItJEawBQCgbkZDkQVbSEDDTwIAADWkYwuJyXNzbAEAqJfR7h+CLSTAHFsAAOrIUGRIiGALAEAdGYoMCRFsAQCoI8EWEjIaogEAAHUymmNb9yaOYAvz6v7DDgBAmhqNXMcWUpBlVkQGAKCessxQZAAAACqs0ah/sG2VXQAAEPH44624995eDIcRg0EWw2HEt3/78dizp+ZnIgCMXZblMRzWe4SiYAuxMKkeYCc98kg7+v2I4TCL//E/JuNXf/XCJc9/8INfiCuumC2pOgDqQscWABibG254evT7a3+yNhj41A2ArUsh2JpjC/Py3AkksLMajfWXYx8MdqgQAGpNsIVE2McWKEOzuf7zdZ8PBcDOyLK89ltbCrYwr+4/7MDuc7qObd0/XQdgZ9juBwAYm8ZpfgubYwvAdjAUGRJhVWSgDM2mji0A45dCx9aqyDDPUGRgp52uY/t7v3dOfOQjczEYZHHxxbPxT/7JEztTGAC10mjkgi2kQMcWKMMFF8zFxMQwGo08Dh9uxpEjC7+W9+0bxPHjjZie7kSjEbFnT83PSAAYmxSGIgu2AFCSP/zDvz51++1vPy9++7fPO3X/sstm4vbbv1ZGWQDUTArB1hxbmGcoMlCm5fNtbfUDwHYSbCEBhiIDZVs+33YwKKcOAOqn3c5jdrbsKsbLUGSYp2MLlOHYsUacPNmIEyeWJtvjx5vxf/7P3hgMIg4eHMQznjFdUoUAVF2vN4yTJ8uuYrwEW4iICC1boBxvecsF8f73H1zx+Ne/3okbb7wiIiK+53sOx1vf+sBOlwZATXS79Q+2hiJDRGSZdi1QjtPtZRtR/3lRAIxXrzeMEyfKrmK8BFsAKNHp9rKNiBgMjCoBYPNSGIos2EJYPAooj44tAOPW7eZx4kS9RygKtjAvz6VbYOfp2AIwboYiAwBjtbGOrWALwOYJtpAIQ5GBsmykY2soMgBb0evlcfKkociQBPvYAmVoNE7/n4+hyABsRberYwsAjFGzefpjdGwB2IqJiWGcPFnvD0kFWwhDkYHy6NgCMG7dbm67H0iFochAGXRsARg3+9hCInRsgbLo2AIwbr1eHjMzWQwGZVcyPoItzNOxBcqgYwvAuPV6xS+S6emSCxkjwRYASqRjC8C4dbtFsK3zysiCLYShyEB5dGwBGLeJieJD1DrPsxVsAaBEzaaOLQDjpWMLAIzVRoKtji0AWzGaY6tjCzVnKDJQlsYGfhPr2AKwFb1e8SFqnTu2rbILgN3Cqsiwcx7+T/8pHv/oR4sfvNEP3/ztfNHt0eOT110XV/z8z5dV7lgt79hec83J+PVfvz8ajTyazWJxqVbLf1AAbF4KHVvBFoAdl/f7kc/MlF3GrrC8Y9tq5XHRRXPlFANALZljC4nIsog8N9QPdswZjv/PazykYvl2P+bTArDdUlgVWccWgN2vxsH2ec87Fu95z5ej0cij0YjYu1eyBWB7pdCxFWwhLB4FO84P3Slnnz2Is8+u8ZkGAKVrtSLa7TxOnqzv71/BFubVuCEEu0774MHoXX55RERkWbYQdOdvZ4tuR0T0LrmklDoBoC56vWGcONEsu4yxEWwB2HHnvuhFce6LXlR2GQCQjF4vr/UcW4tHQYwWjyq7CgAAGI+iY1t2FeMj2AIAANScji0AAACVpmMLCTAUGQCAOut2hzq2AAAAVFe3q2MLtWdLTQAA6qzXG8bJk/UdoijYAgAA1Fyvl8fx42VXMT6CLQAAQM0VHduyqxgfwRbC4lEAANRbsSpyfU94BVsAAICa63Zzi0cBAABQXcVQ5PqumCrYQoyGItf3Bx0AgLT1erk5tgAAAFSXxaMgAfaxBQCgznq9YczOZjEYlF3JeAi2MM+qyAAA1FWvV5zs1rVrK9gCAADUXLc7jIio7crIgi2EfWwBAKi3Xq8Itjq2AAAAVNLERNHF0bEFAACgkkZDkXVsocYMRQYAoM5GQ5F1bAEAAKgkqyIDAABQaTq2kIAsy8ouAQAAxkbHFgAAgEqzjy0kwOJRAADUWbMZ0enkOrYAAABUV6831LEFAACguno9HVuotSzLI88tIAUAQH3p2AIAAFBp3W4e09NlVzEegi3Ms3gUAAB11m7nMTNTdhXjIdhCFKsiAwBAnbXbw9oG21bZBVBPeb5wGQ6X3t/Kc2sdv9XHjx+XbAEAqLdOp74d29oG2/e9L+K++yIGg+IyHC7cXu/+5sNSvuS55cdErHz/9d8zX/RctuT+mQfD5a/PVn1dxHaE0KoGxCz+zt8Zll0EAACMTbudx+xs2VWMRy2D7X/+zxGveEXE+ef3o9nMo9GIJddZFssez6PZLFbGzbJYdMlPDVEdPdZo5IvuLxwfEdForPXY8tetfP1aX7PRWFnDRl47+rpZtvLrrl7L6f/8oz/f5t8vIiJf9LrV3+90f77l77fwZ136NVb7O1jv7+XKK2v68RUAAEREpzOMmZk8IqrajFpbLYPt174WcdZZg/iv//XesksBAADYFdptqyJXytGjEXv3GlYKAAAwUuc5trUMtkeOROzZI9gCAACMCLYVU3RsB2WXAQAAsGsUc2zLrmI8ahps89izR7AFAAAYabfz+cWj6qeWwdZQZAAAgKWKYFt2FeNRy2Br8SgAAIClOp367mNb02Cbm2MLAACwSLF4VP32sI2obbA1FBkAAGCxTmeoY1slx45lhiIDAAAsYo5theR50bHdt0+wBQAAGBntY5vXcGHk2gXb6emIfj+z3Q8AAMAi7XYeeZ5Fv192JduvdsH26NHi2lBkAACABZ1O0aqt43BkwRYAACABgm2FCLYAAAArdTpFRhJsK2AUbM2xBQAAWNBuFx3bOm75U9tgq2MLAACwwFDkChFsAQAAVhJsK2RhKLJgCwAAMCLYVsjRo0WobdTuTwYAALB5ozm2gm0FHDliGDIAAMBy7bZVkSvj6FHBFgAAYDlDkSukCLa2+gEAAFhMsK2Q0RxbAAAAFoyCrX1sK+Do0VzHFgAAYBkd2wo5ckTHFgAAYLlWS7CtDItHAQAArJRlEZ3OULCtAkORAQAAVtfp6NhWwtGjmaHIAAAAq2i3c8G2CgaDiFar7CoAAAB2n05HsK2ERqMItwAAACxljm1FNJsReV52FQAAALuPjm1FNJt5DAZZ2WUAAADsOp1OHrOzZVex/WoYbCOG1o4CAABYweJRFdFsho4tAADAKtptc2wrQccWAABgdTq2FaFjCwAAsLpiVeT6rbZby2CrYwsAALBSp5PH9HTZVWy/WgZbHVsAAICVDEWuiGYz07EFAABYhX1sK8I+tgAAAKsr9rE1x3bX63SymJ0VbAEAAJYrFo8qu4rtV7tg+/SnR3z1q72yywAAANh1zLGtiGuvzeKLX+zGYFB2JQAAALuLYFsRz3pWxPR0I+6/v1N2KQAAALtKsXhU/aZu1i7YXnttcX3ffYYjAwAALFYsHlV2FduvdsH2nHMiLrwwjy98QbAFAABYzHY/FXLttSHYAgAALGNV5Ap57nOz+Nzn9lpACgAAYJF2O4/hMIt+v+xKtlctg+33f3/EoUPN+Oxn95RdCgAAwK7R6eQREbWbZ1vLYPut3xpxwQV53HXXVNmlAAAA7BrtdhFs6zYcuZbBttGIeMlLIj7xianI87KrAQAA2B1GHVvBtiJe+tIsHnywE1/4QrfsUgAAAHYFwbZirr8+Ymoqj49/3HBkAACAiIh2exgRgm1ldDoRL3pRxMc+tt9wZAAAgNCxraRXvjKLL32pF5/4xGTZpQAAAJROsK2g7/zOiO/4jjxuu+38GA7LrgYAAKBcgm1F3XprFvfd17P1DwAAkLzRdj/2sa2Yv/f3Ir77u4uu7WBQdjUAAADl0bGtsFtvzeJLX+rGxz62v+xSAAAASmNV5Ar7tm+LuOGGPN75zvOi3y+7GgAAgHLo2Fbcrbdm8ZWvdOPDHz5QdikAAAClEGwr7jnPiXjxi/O4/fbzYm6u7GoAAAB23mjxKMG2wn7pl7K4//5O3HHHgbJLAQAA2HGNRhFuBdsKe9azIn7wB/O4/fbzY24uK7scAACAHdfpCLaV96Y3ZfHww614//vPKrsUAACAHadjWwN/829G3HhjxFveckH81V9NlF0OAADAjmq389rtFpNcsI2I+K3fyuK667K45ZbL4pFHWmWXAwAAsGOazbx2C+omGWx7vYgPfCCLTqcZP/3Tl8eJE+bbAgAAaWi1Qse2Ls4/P+LOO7O4//5uvP71l8RwWHZFAAAA49dqGYpcK9deG/G+92Vx111T8fa3n1d2OQAAAGNnKHINvfjFEb/6q1m8+93nxZ/+6YGyywEAABirZrN+Q5GtnBQR//JfRnz+83m86U0Xx9OeNhPXXXey7JIAAADGwlDkmsqyiNtvz+Jv/+2IW265LB56qF12SQAAAGNhKHKNdbsRf/zHWUxNNeOnfuqyOH7cXw0AAFA/9rGtuXPOKVZKfvTRbtx882Xx1a92yi4JAABgWzWbgm3tXXNNEW4PHdoTP/ADV8ftt58bs7P2uQUAAOrBUOREPP/5EXffncVrXhPx2799XvzQD10Vn/rUnrLLAgAA2DId24Ts2RPxb/9tFp/+dBbnntuJf/pPr4w3vvGiOHy4WXZpAAAAm9Zq5TE3l5ddxrYSbE/jm7854n//7yze+c6Iu+46K1784qvjT/90f+T1+ncAAAAkoo772Aq2G9BoRLzqVRH33JPF93xPM37hF54WN910eXztaxaXAgAAqqXo2JZdxfYSbM/AhRdG/MEfZPGhD0U8/PDeeOlLr453vevcmJuzuBQAAFANFo8iIiJuuCHi85/P4pZbIm67rVhc6tOftrgUAACw+9nHllP27Il485uLxaXOPrsTP/7jV8ab3mRxKQAAYHczx5YVrr22WFzqttsi/st/OSte8pKr4847LS4FAADsTsVQ5HoFFsF2GzSbETffXCwu9V3f1Yyf+7mnxT//55fHAw9YXAoAANhdWi1DkVnHRRdF/NEfZXHHHREPPbQ3/uE/vCre/W6LSwEAALuHVZHZkBe9KOLuu7P4qZ/K4h3vsLgUAACwe5hjy4bt3Rvx7/5dFp/6VBYHDxaLS/3iL14Uhw/7KwcAAMpjKDJn7LrrIj75ySze/vaIj370rHjJS54eH/qQxaUAAIBy2MeWTWk2I1796oh7783i+uub8brXPS1uvvmyeOCBdtmlAQAAiSk6tvVaByjLc73DnXbHHRE/+ZN5PPZYHq961WPx4z/+eLRlXAAASEKeF3Nc5+YaMTubLbksf2xuLovZ2fUfK+6v/djo+NFjTzzRim63EY8+Wp9wK9iW5NixiDe+MY/f/M2IK6+ciX/zb74e1113suyyAACgdobD2ER43GzAXBkkV7vk+eZCZaORR7cb0e1GdDoR3e7C/eKSnbrd6y29v/hy3XURL3vZ9v49l0mwLdlnPhNx0015fOpTWbzsZU/ELbc8GlNTw7LLAgCATSm6kSuD3Di6kysD5OphcivDbjud9YLkxkLkdl5arW38ZtWIYLsLDAYRt90W8frX59HtDuJ1r3sovvd7j0RWn5EBAADsoFGHcmYmi5mZRszMFKFvenrp9eLnl94evSaL6enGouvG/Ps2YmZm9Pji4BkxNze+bmSvly0KkeMPkp1OOCevCMF2F3nwwYif/uk8/viPs3j+84/G61//UFxySc2WKwMASMRoHuXmAuXo2IVgufiY5e8xuj89nZ0KnGdqYiKPXi/mL8XtiYkiTE5MLL09Om5iQjeS3UGw3YU++MGIV786j8cfz+Pmmx+LH/1Ri0sBAJzOxz8+GV/8Yi8iii7bqNOWZQunu6PHFz+3uCO38PjCnMzloXNpwByF0MayEFqEzOHwzAJmu700XC6EyNXD5fKQufz2Rh/TmaTqBNtd6tixiDe8IY+3vS3iqquKxaWe9SyLSwEALJfnEW972/nxO79zbpxzzkJQXXyWm+cL95ffXuv5URdxcSdzYiKLXi9iz54iYJ4uMJ7J871esU0kcOYE213u05+OeOUr8/jMZyJe9rIn45ZbHo3JSYtLwenkeTG/aDiMyPPs1P08z+av139udHu1+xHZGu8dMRyu/36n+1oRxXusXsfCe/f7xeIZ/X5xGQyy6Pfj1P3iseX3i2OK6+L5wSCLdjuPTic/dd3pDKPdXnx/9ccWXjdc9vqF50fPje437J4ObLO5uSze8IaL4847D8Rb3hLxmtfoPEKKBNsKGAwi3vGOYnGpiYlicakXvrCei0udSRg5k8Cx1TCyWjhZK3Cs/9zi168eYFY+t36tqz238r3Xey5b4/UrX7Py9tLXRqz9upXfk6Vfa/lzC++18v7y7+1q9+suy/LodIqhY63W0ku7nc9fx6LrLNrt4nazOXpdFo1GxOxsHjMzseiSx+zs4vtZzM0t3N/K32+rtVr4XR6ehxsM20uPXe345YF7+fGj5+v4/ymk4OjRRrzmNZfGZz6zN9773ixe/vKyKwLKIthWyIMPRvyLf5HHn/xJFs9+9vE455z+uqElYuXjxWMrw9Z6t1cPG4vfa/VAtP7zEaOgufjrpaLRKDpXjUbxqfLo9mr3i+OL12RZdkavLe4vvKZ4/erHj4Y+LX5+4ZJt8Gtt7v4437uOXyvLyhuqtrAQSiwJw8vvr/bYmd/PY3p6rcBdrLy5+P5WrN6JXh6ol4fr4SrhfL3u9pmFbYEb1jY3F/HJT+6L3/iNC+Ib3+jGBz6Qxd//+2VXBZRJsK2gD3wg4vbb8xgMipPbpSfS2bon2GuddJd97PIT+N0aKLb6tRYvVgFsnzwvTnS3GqY3GrhnZmI+dK/f3d7MqqSLrRW2lwbqpeF7XGF7dEyrJXBTjjyP+NznJuLOOw/ERz+6P558shV/62/l8Ud/lMUzn1l2dUDZBFsAGJNR4N7+bvZq9zceuOfmthq4h9HpxDrd682F7bXmZm/k+FbLB4d19dWvduLOOw/Ehz50IO6/vxMXXJDHK16RxSteEXHddb7vQEGwBYDEDIcrA/f4u9vL53MvH05eLHa2WcW88zza7VgRthd3qIt53qs9t/1hu9PJ7cm5SY8/3oyPfKQIs//v/03E1FQeL31pxI/8SBbXX2/lYGAlwRYA2BWGw3F1s9efv718AbViX9KF+4PB5gN3o7Ey+C7vbhcBeK3u9uLnNz6U/HTDy3djMDxxohF33TUVd965P/7sz/ZFoxFxww1FmP2+7yu2xAFYi2ALALCOwWBpQB7f4mn5ou72wqJpC/eXBu7RQo2b0Wyu3mkez8Jpa28L1moN4/Ofn4g77jgQ/+2/TcXJk414/vPz+JEfyeKHfiji4MFt/EYCtSbYAgBU0GCwE93tMw/cm9nl4JnPzONHfzSLH/7hiMsuG8NfFlB7gi0AANum3z+z8Hz55RHXXmsRKGBrBFsAAAAqrVF2AQAAALAVgi0AAACVJtgCAABQaYItAAAAlSbYAgAAUGmCLQAAAJUm2AIAAFBpgi0AAACVJtgCAABQaYItAAAAlSbYAgAAUGmCLQAAAJUm2AIAAFBpgi0AAACVJtgCAABQaYItAAAAlSbYAgAAUGmCLQAAAJUm2AIAAFBpgi0AAACVJtgCAABQaYItAAAAlSbYAgAAUGmCLQAAAJUm2AIAAFBpgi0AAACVJtgCAABQaYItAAAAlSbYAgAAUGmCLQAAAJUm2AIAAFBpgi0AAACVJtgCAABQaf8fq4G+eRgu3pcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12,8))\n",
+    "core_buildings.loc[core_buildings['THEME1']=='Church'].plot(ax=ax, color='brown')\n",
+    "core_buildings.loc[core_buildings['NAME']=='Armourdale Community Ctr'].plot(ax=ax, color='green')\n",
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Education'])].plot(ax=ax, color='blue')\n",
+    "# armourdale.plot(ax=ax, fc='None')\n",
+    "# ax.legend()\n",
+    "sunroof.plot(ax=ax, fc='lightgray', ec='b', lw=1, zorder=0)\n",
+    "ax.grid()\n",
+    "ax.set_axis_off()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>THEME1</th>\n",
+       "      <th>THEME2</th>\n",
+       "      <th>FEATURECOD</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>AGENCY</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>CITY_left</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>CHNG_TYPE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>yearly_sunlight_kwh_f</th>\n",
+       "      <th>yearly_sunlight_kwh_median</th>\n",
+       "      <th>yearly_sunlight_kwh_total</th>\n",
+       "      <th>install_size_kw_buckets_json</th>\n",
+       "      <th>carbon_offset_metric_tons</th>\n",
+       "      <th>existing_installs_count</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>WARD_right</th>\n",
+       "      <th>building_area</th>\n",
+       "      <th>area_fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>923 S Bethany St</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>383.093978</td>\n",
+       "      <td>0.001228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>131.098731</td>\n",
+       "      <td>0.000420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1101 Argentine Blvd.</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>590.512130</td>\n",
+       "      <td>0.001893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>576</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>First Christian Church</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000 Argentine Blvd</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>570.662440</td>\n",
+       "      <td>0.001830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>604</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>9.399597</td>\n",
+       "      <td>0.000030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>653</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>14.522235</td>\n",
+       "      <td>0.000047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>67.705655</td>\n",
+       "      <td>0.000217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>79.574002</td>\n",
+       "      <td>0.000255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>46.630836</td>\n",
+       "      <td>0.000150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>718</th>\n",
+       "      <td>Church</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>933 Argentine Blvd</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>519.316511</td>\n",
+       "      <td>0.001665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>933</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>58.644385</td>\n",
+       "      <td>0.000188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1577</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>599.008449</td>\n",
+       "      <td>0.001921</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1672</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>665.004464</td>\n",
+       "      <td>0.002132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1709</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>Armourdale Community Ctr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>720.998486</td>\n",
+       "      <td>0.002312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>184.993241</td>\n",
+       "      <td>0.000593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2263</th>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Public Attractions and Landmark Buildings</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.222399e+07</td>\n",
+       "      <td>8223.137817</td>\n",
+       "      <td>2.861974e+07</td>\n",
+       "      <td>[[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...</td>\n",
+       "      <td>19866.275546</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "      <td>432.634655</td>\n",
+       "      <td>0.001387</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>16 rows × 63 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      THEME1 THEME2                                 FEATURECOD  \\\n",
+       "124   Church   None  Public Attractions and Landmark Buildings   \n",
+       "237     None   None  Public Attractions and Landmark Buildings   \n",
+       "361   Church   None  Public Attractions and Landmark Buildings   \n",
+       "576   Church   None  Public Attractions and Landmark Buildings   \n",
+       "604     None   None  Public Attractions and Landmark Buildings   \n",
+       "653     None   None  Public Attractions and Landmark Buildings   \n",
+       "664     None   None  Public Attractions and Landmark Buildings   \n",
+       "668     None   None  Public Attractions and Landmark Buildings   \n",
+       "717     None   None  Public Attractions and Landmark Buildings   \n",
+       "718   Church   None  Public Attractions and Landmark Buildings   \n",
+       "933     None   None  Public Attractions and Landmark Buildings   \n",
+       "1577    None   None  Public Attractions and Landmark Buildings   \n",
+       "1672    None   None  Public Attractions and Landmark Buildings   \n",
+       "1709    None   None  Public Attractions and Landmark Buildings   \n",
+       "2182    None   None  Public Attractions and Landmark Buildings   \n",
+       "2263    None   None  Public Attractions and Landmark Buildings   \n",
+       "\n",
+       "                          NAME  AGENCY               ADDRESS    CITY_left  \\\n",
+       "124                       None       0      923 S Bethany St  Kansas City   \n",
+       "237                       None       0                  None  Kansas City   \n",
+       "361                       None       0  1101 Argentine Blvd.  Kansas City   \n",
+       "576     First Christian Church       0   1000 Argentine Blvd  Kansas City   \n",
+       "604                       None       0                  None  Kansas City   \n",
+       "653                       None       0                  None  Kansas City   \n",
+       "664                       None       0                  None  Kansas City   \n",
+       "668                       None       0                  None  Kansas City   \n",
+       "717                       None       0                  None  Kansas City   \n",
+       "718                       None       0    933 Argentine Blvd  Kansas City   \n",
+       "933                       None       0                  None  Kansas City   \n",
+       "1577                      None       0                  None  Kansas City   \n",
+       "1672                      None       0                  None  Kansas City   \n",
+       "1709  Armourdale Community Ctr       0                  None  Kansas City   \n",
+       "2182                      None       0                  None  Kansas City   \n",
+       "2263                      None       0                  None  Kansas City   \n",
+       "\n",
+       "        ZIP COMMENT  CHNG_TYPE  ...  yearly_sunlight_kwh_f  \\\n",
+       "124   66105    None          4  ...           2.222399e+07   \n",
+       "237    None    None          4  ...           2.222399e+07   \n",
+       "361   66105    None          4  ...           2.222399e+07   \n",
+       "576   66105    None          4  ...           2.222399e+07   \n",
+       "604    None    None          4  ...           2.222399e+07   \n",
+       "653    None    None          4  ...           2.222399e+07   \n",
+       "664    None    None          4  ...           2.222399e+07   \n",
+       "668    None    None          4  ...           2.222399e+07   \n",
+       "717    None    None          4  ...           2.222399e+07   \n",
+       "718   66105    None          4  ...           2.222399e+07   \n",
+       "933    None    None          4  ...           2.222399e+07   \n",
+       "1577   None    None          1  ...           2.222399e+07   \n",
+       "1672   None    None          1  ...           2.222399e+07   \n",
+       "1709   None    None          1  ...           2.222399e+07   \n",
+       "2182   None    None          1  ...           2.222399e+07   \n",
+       "2263   None    None          1  ...           2.222399e+07   \n",
+       "\n",
+       "      yearly_sunlight_kwh_median  yearly_sunlight_kwh_total  \\\n",
+       "124                  8223.137817               2.861974e+07   \n",
+       "237                  8223.137817               2.861974e+07   \n",
+       "361                  8223.137817               2.861974e+07   \n",
+       "576                  8223.137817               2.861974e+07   \n",
+       "604                  8223.137817               2.861974e+07   \n",
+       "653                  8223.137817               2.861974e+07   \n",
+       "664                  8223.137817               2.861974e+07   \n",
+       "668                  8223.137817               2.861974e+07   \n",
+       "717                  8223.137817               2.861974e+07   \n",
+       "718                  8223.137817               2.861974e+07   \n",
+       "933                  8223.137817               2.861974e+07   \n",
+       "1577                 8223.137817               2.861974e+07   \n",
+       "1672                 8223.137817               2.861974e+07   \n",
+       "1709                 8223.137817               2.861974e+07   \n",
+       "2182                 8223.137817               2.861974e+07   \n",
+       "2263                 8223.137817               2.861974e+07   \n",
+       "\n",
+       "                           install_size_kw_buckets_json  \\\n",
+       "124   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "237   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "361   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "576   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "604   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "653   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "664   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "668   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "717   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "718   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "933   [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "1577  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "1672  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "1709  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "2182  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "2263  [[0,280],[5,238],[10,73],[15,46],[20,29],[25,1...   \n",
+       "\n",
+       "      carbon_offset_metric_tons existing_installs_count         CITY  \\\n",
+       "124                19866.275546                     0.0  Kansas City   \n",
+       "237                19866.275546                     0.0  Kansas City   \n",
+       "361                19866.275546                     0.0  Kansas City   \n",
+       "576                19866.275546                     0.0  Kansas City   \n",
+       "604                19866.275546                     0.0  Kansas City   \n",
+       "653                19866.275546                     0.0  Kansas City   \n",
+       "664                19866.275546                     0.0  Kansas City   \n",
+       "668                19866.275546                     0.0  Kansas City   \n",
+       "717                19866.275546                     0.0  Kansas City   \n",
+       "718                19866.275546                     0.0  Kansas City   \n",
+       "933                19866.275546                     0.0  Kansas City   \n",
+       "1577               19866.275546                     0.0  Kansas City   \n",
+       "1672               19866.275546                     0.0  Kansas City   \n",
+       "1709               19866.275546                     0.0  Kansas City   \n",
+       "2182               19866.275546                     0.0  Kansas City   \n",
+       "2263               19866.275546                     0.0  Kansas City   \n",
+       "\n",
+       "      WARD_right building_area area_fraction  \n",
+       "124           06    383.093978      0.001228  \n",
+       "237           06    131.098731      0.000420  \n",
+       "361           06    590.512130      0.001893  \n",
+       "576           06    570.662440      0.001830  \n",
+       "604           06      9.399597      0.000030  \n",
+       "653           06     14.522235      0.000047  \n",
+       "664           06     67.705655      0.000217  \n",
+       "668           06     79.574002      0.000255  \n",
+       "717           06     46.630836      0.000150  \n",
+       "718           06    519.316511      0.001665  \n",
+       "933           06     58.644385      0.000188  \n",
+       "1577          06    599.008449      0.001921  \n",
+       "1672          06    665.004464      0.002132  \n",
+       "1709          06    720.998486      0.002312  \n",
+       "2182          06    184.993241      0.000593  \n",
+       "2263          06    432.634655      0.001387  \n",
+       "\n",
+       "[16 rows x 63 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Public Attractions and Landmark Buildings'])]#.plot(column='building_area', legend=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_area = core_buildings['building_area'].sum()*meter**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01895735, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unit_area = (4*kW) / (211*foot**2)\n",
+    "unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(22833.75, 'kW')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "core_buildings.plot(ax=ax, column='building_area', legend=True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Building General'])].plot(ax=ax, column='building_area', legend=True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Building General                             1526\n",
+       "Commercial and Retail                         202\n",
+       "Industry                                       57\n",
+       "Public Attractions and Landmark Buildings      16\n",
+       "Government and Military                         3\n",
+       "Information and Communication                   2\n",
+       "Education                                       2\n",
+       "Name: FEATURECOD, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings['FEATURECOD'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8,6))\n",
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Commercial and Retail'])].plot(ax=ax, column='building_area', legend=True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
+       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
+       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
+       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry',\n",
+       "       'region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
+       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
+       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
+       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
+       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY_right',\n",
+       "       'WARD', 'building_area', 'area_fraction'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NC    135\n",
+       "Name: THEME3, dtype: int64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Commercial and Retail'])].loc[:, 'THEME3'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get vacant parcels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1.294453e+06\n",
+       "1        3.424272e+06\n",
+       "2        7.356163e+06\n",
+       "3        2.914559e+04\n",
+       "4        2.778234e+06\n",
+       "             ...     \n",
+       "67781    6.116406e+03\n",
+       "67782    6.036308e+03\n",
+       "67783    1.027241e+06\n",
+       "67784    1.085022e+04\n",
+       "67785    8.179494e+04\n",
+       "Name: Shape_Area, Length: 67786, dtype: float64"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parcels['Shape_Area']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS', 'CITY',\n",
+       "       'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y', 'NUMSTORY',\n",
+       "       'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY', 'DATE_MOD',\n",
+       "       'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "buildings.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parcels = gpd.read_file('../data/spatial_data/armourdale/parcels.gpkg')\n",
+    "vacant_parcels = gpd.read_file('../data/spatial_data/armourdale/vacant_parcels.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "non_vacant_df = parcels.loc[~parcels['PARCEL'].isin(vacant_parcels['PARCEL'].values)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "non_vacant_df = non_vacant_df.sjoin(armourdale, predicate='within').drop(columns=['index_right'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "core_buildings = core_buildings.drop(columns=['index_right'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>Shape__Are</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21858</th>\n",
+       "      <td>134121.623419</td>\n",
+       "      <td>1292.407908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21867</th>\n",
+       "      <td>121119.563452</td>\n",
+       "      <td>808.705081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21889</th>\n",
+       "      <td>12249.901201</td>\n",
+       "      <td>59.783524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21951</th>\n",
+       "      <td>12249.901201</td>\n",
+       "      <td>1302.073732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22093</th>\n",
+       "      <td>5383.474243</td>\n",
+       "      <td>995.018923</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88124</th>\n",
+       "      <td>11723.714630</td>\n",
+       "      <td>122.811691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88126</th>\n",
+       "      <td>9428.960315</td>\n",
+       "      <td>579.409618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88128</th>\n",
+       "      <td>13849.235090</td>\n",
+       "      <td>501.120281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88130</th>\n",
+       "      <td>4778.174101</td>\n",
+       "      <td>182.988778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88131</th>\n",
+       "      <td>5903.793314</td>\n",
+       "      <td>95.217654</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>972 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Shape_Area   Shape__Are\n",
+       "21858  134121.623419  1292.407908\n",
+       "21867  121119.563452   808.705081\n",
+       "21889   12249.901201    59.783524\n",
+       "21951   12249.901201  1302.073732\n",
+       "22093    5383.474243   995.018923\n",
+       "...              ...          ...\n",
+       "88124   11723.714630   122.811691\n",
+       "88126    9428.960315   579.409618\n",
+       "88128   13849.235090   501.120281\n",
+       "88130    4778.174101   182.988778\n",
+       "88131    5903.793314    95.217654\n",
+       "\n",
+       "[972 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inhabited[['Shape_Area', 'Shape__Are']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inhabited = core_buildings.loc[core_buildings['FEATURECOD'].isin(['Building General'])].sjoin(non_vacant_df, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assumption\n",
+    "\n",
+    "1. Rooftop potential is distributed proportionally across all rooftop sectors.\n",
+    "2. There is some factor, E, that represents the fraction of actual suitable rooftop area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(6079.45687907, 'kW')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inhabited.area_fraction.sum() * kw_total"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "# core_buildings.loc[core_buildings['FEATURECOD'].isin(['Building General'])].plot(ax=ax, column='FEATURECOD')\n",
+    "inhabited.plot(ax=ax, )\n",
+    "# armourdale.plot(ax=ax, fc='lightgray', ec='k', zorder=0, alpha=0.5)\n",
+    "sunroof.plot(ax=ax, fc='lightgray', ec='b', lw=1, zorder=0)\n",
+    "plt.tight_layout()\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(972, 68)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inhabited.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01895735, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    972.000000\n",
+       "mean      17.432131\n",
+       "std       12.997489\n",
+       "min        0.515257\n",
+       "10%        1.928423\n",
+       "16.5%      2.992695\n",
+       "25%        6.192480\n",
+       "50%       18.375387\n",
+       "75%       24.719141\n",
+       "95%       36.167172\n",
+       "max      145.933488\n",
+       "Name: Shape__Are, dtype: float64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(inhabited['Shape__Are'] * unit_area).describe(percentiles=[.1, 0.165, .25, .5, .75, .95])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.5675675675675675"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2.8 / 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8.108108108108109"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 / 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64.86486486486487"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "24 / 0.37"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.25"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "25 / (1/0.37)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.8733677860005695"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(inhabited.loc[((inhabited['Shape__Are'] * unit_area) > 25), 'Shape__Are']*unit_area).sum() / 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unyt import acre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(3.40283517, 'acre')"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2810*kW) / unit_area.to(kW/acre)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Estimated kW per Roof')"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(inhabited['Shape__Are'] * unit_area).plot.hist(ax=ax, bins=100)\n",
+    "ax.set_xlabel(\"Estimated kW per Roof\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Estimated kW per Roof')"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "(inhabited['Shape__Are'] * unit_area / (1/0.37)).plot.hist(ax=ax, bins=100)\n",
+    "ax.set_xlabel(\"Estimated kW per Roof\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Park Areas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parks = gpd.read_file('../data/spatial_data/armourdale/parks.gpkg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PARKS_ID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>DATEMOD</th>\n",
+       "      <th>DATEADDED</th>\n",
+       "      <th>DEV_ACRE</th>\n",
+       "      <th>UNDEV_ACRE</th>\n",
+       "      <th>TOTAL_ACRE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TRACK</th>\n",
+       "      <th>SPRAY_PARK</th>\n",
+       "      <th>PLAY_PAD</th>\n",
+       "      <th>DOG_RUN</th>\n",
+       "      <th>PIC_TABLE</th>\n",
+       "      <th>CITY</th>\n",
+       "      <th>TYPE</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Wyandotte Co Lake</td>\n",
+       "      <td>46695.764713</td>\n",
+       "      <td>6.147028e+07</td>\n",
+       "      <td>66109</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1402.811279</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Regional Park</td>\n",
+       "      <td>400 acre lake</td>\n",
+       "      <td>Leavenworth Rd &amp; N 91st St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.76616 39.16852, -94.76616 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Wyandotte Co Lake</td>\n",
+       "      <td>7911.596242</td>\n",
+       "      <td>1.099166e+06</td>\n",
+       "      <td>66109</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.233259</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Regional Park</td>\n",
+       "      <td>spillway slough</td>\n",
+       "      <td>8124r Wolcott Dr</td>\n",
+       "      <td>MULTIPOLYGON (((-94.76524 39.17592, -94.76277 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Quindaro</td>\n",
+       "      <td>3859.471645</td>\n",
+       "      <td>7.730992e+05</td>\n",
+       "      <td>66104</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17.633940</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Sewell Ave &amp; N 34th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.66803 39.14613, -94.66845 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Roswell</td>\n",
+       "      <td>5811.686684</td>\n",
+       "      <td>1.168415e+06</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2011-03-23</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.923965</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Manorcrest Dr &amp; N 7th St Trfwy</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62444 39.14172, -94.62435 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Garland</td>\n",
+       "      <td>8904.375882</td>\n",
+       "      <td>1.884229e+06</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>43.255947</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>contaminated &amp; closed</td>\n",
+       "      <td>301 Roswell Ave</td>\n",
+       "      <td>MULTIPOLYGON (((-94.61536 39.13298, -94.61536 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>0</td>\n",
+       "      <td>City</td>\n",
+       "      <td>13119.185729</td>\n",
+       "      <td>3.596500e+06</td>\n",
+       "      <td>66102</td>\n",
+       "      <td>2011-03-22</td>\n",
+       "      <td>2011-03-22</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Park Dr &amp; S 26th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.66789 39.1039, -94.66789 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Northrup</td>\n",
+       "      <td>3226.782966</td>\n",
+       "      <td>3.234217e+05</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2011-03-23</td>\n",
+       "      <td>2011-03-23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Grandview Blvd &amp; N 10th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.63527 39.11125, -94.6352 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Jersey Creek</td>\n",
+       "      <td>1235.601197</td>\n",
+       "      <td>8.141766e+04</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.869092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Parallel Pkwy &amp; N 13th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62922 39.12424, -94.62922 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Jersey Creek</td>\n",
+       "      <td>5527.890780</td>\n",
+       "      <td>8.759639e+05</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.869092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Parallel Pkwy &amp; N 13th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62896 39.12539, -94.62669 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Jersey Creek</td>\n",
+       "      <td>8545.961583</td>\n",
+       "      <td>1.122098e+06</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.869092</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Parallel Pkwy &amp; N 13th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.63906 39.12935, -94.63903 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>69 rows × 71 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    PARKS_ID               NAME    Shape_Leng    Shape_Area    ZIP    DATEMOD  \\\n",
+       "0          1  Wyandotte Co Lake  46695.764713  6.147028e+07  66109 2010-05-04   \n",
+       "1          2  Wyandotte Co Lake   7911.596242  1.099166e+06  66109 2010-05-04   \n",
+       "2          4           Quindaro   3859.471645  7.730992e+05  66104 2010-05-04   \n",
+       "3          6            Roswell   5811.686684  1.168415e+06  66101 2011-03-23   \n",
+       "4          7            Garland   8904.375882  1.884229e+06  66101 2010-05-04   \n",
+       "..       ...                ...           ...           ...    ...        ...   \n",
+       "64         0               City  13119.185729  3.596500e+06  66102 2011-03-22   \n",
+       "65         0           Northrup   3226.782966  3.234217e+05  66101 2011-03-23   \n",
+       "66        24       Jersey Creek   1235.601197  8.141766e+04  66101 2010-05-04   \n",
+       "67        24       Jersey Creek   5527.890780  8.759639e+05  66101 2010-05-04   \n",
+       "68        24       Jersey Creek   8545.961583  1.122098e+06  66101 2010-05-04   \n",
+       "\n",
+       "    DATEADDED  DEV_ACRE  UNDEV_ACRE   TOTAL_ACRE  ... TRACK SPRAY_PARK  \\\n",
+       "0  2010-05-04       0.0         0.0  1402.811279  ...  None       None   \n",
+       "1  2010-05-04       0.0         0.0    25.233259  ...  None       None   \n",
+       "2  2010-05-04       0.0         0.0    17.633940  ...  None       None   \n",
+       "3  2010-05-04       0.0         0.0    13.923965  ...  None       None   \n",
+       "4  2010-05-04       0.0         0.0    43.255947  ...  None       None   \n",
+       "..        ...       ...         ...          ...  ...   ...        ...   \n",
+       "64 2011-03-22       0.0         0.0     0.000000  ...  None       None   \n",
+       "65 2011-03-23       0.0         0.0     0.000000  ...  None       None   \n",
+       "66 2010-05-04       0.0         0.0     1.869092  ...  None       None   \n",
+       "67 2010-05-04       0.0         0.0     1.869092  ...  None       None   \n",
+       "68 2010-05-04       0.0         0.0     1.869092  ...  None       None   \n",
+       "\n",
+       "   PLAY_PAD DOG_RUN PIC_TABLE         CITY               TYPE  \\\n",
+       "0      None       Y      None  Kansas City      Regional Park   \n",
+       "1      None    None      None  Kansas City      Regional Park   \n",
+       "2      None    None         Y  Kansas City  Neighborhood Park   \n",
+       "3      None    None      None  Kansas City  Neighborhood Park   \n",
+       "4      None    None      None  Kansas City  Neighborhood Park   \n",
+       "..      ...     ...       ...          ...                ...   \n",
+       "64     None    None      None  Kansas City               None   \n",
+       "65     None    None      None  Kansas City  Neighborhood Park   \n",
+       "66     None    None      None  Kansas City  Neighborhood Park   \n",
+       "67     None    None      None  Kansas City  Neighborhood Park   \n",
+       "68     None    None      None  Kansas City  Neighborhood Park   \n",
+       "\n",
+       "                  COMMENT                         ADDRESS  \\\n",
+       "0           400 acre lake      Leavenworth Rd & N 91st St   \n",
+       "1         spillway slough                8124r Wolcott Dr   \n",
+       "2                    None          Sewell Ave & N 34th St   \n",
+       "3                    None  Manorcrest Dr & N 7th St Trfwy   \n",
+       "4   contaminated & closed                 301 Roswell Ave   \n",
+       "..                    ...                             ...   \n",
+       "64                   None             Park Dr & S 26th St   \n",
+       "65                   None      Grandview Blvd & N 10th St   \n",
+       "66                   None       Parallel Pkwy & N 13th St   \n",
+       "67                   None       Parallel Pkwy & N 13th St   \n",
+       "68                   None       Parallel Pkwy & N 13th St   \n",
+       "\n",
+       "                                             geometry  \n",
+       "0   MULTIPOLYGON (((-94.76616 39.16852, -94.76616 ...  \n",
+       "1   MULTIPOLYGON (((-94.76524 39.17592, -94.76277 ...  \n",
+       "2   MULTIPOLYGON (((-94.66803 39.14613, -94.66845 ...  \n",
+       "3   MULTIPOLYGON (((-94.62444 39.14172, -94.62435 ...  \n",
+       "4   MULTIPOLYGON (((-94.61536 39.13298, -94.61536 ...  \n",
+       "..                                                ...  \n",
+       "64  MULTIPOLYGON (((-94.66789 39.1039, -94.66789 3...  \n",
+       "65  MULTIPOLYGON (((-94.63527 39.11125, -94.6352 3...  \n",
+       "66  MULTIPOLYGON (((-94.62922 39.12424, -94.62922 ...  \n",
+       "67  MULTIPOLYGON (((-94.62896 39.12539, -94.62669 ...  \n",
+       "68  MULTIPOLYGON (((-94.63906 39.12935, -94.63903 ...  \n",
+       "\n",
+       "[69 rows x 71 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_parks = parks.sjoin(armourdale, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "armourdale_parks.plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unyt import acre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(152.4475, 'ft**2')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1/unit_area)*(2.89*kW)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(0.01895735, 'kW/ft**2')"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "unyt_quantity(1379.05592417, 'kW')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1.67*acre).to(foot**2)*unit_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PARKS_ID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>Shape_Leng</th>\n",
+       "      <th>Shape_Area</th>\n",
+       "      <th>ZIP</th>\n",
+       "      <th>DATEMOD</th>\n",
+       "      <th>DATEADDED</th>\n",
+       "      <th>DEV_ACRE</th>\n",
+       "      <th>UNDEV_ACRE</th>\n",
+       "      <th>TOTAL_ACRE</th>\n",
+       "      <th>...</th>\n",
+       "      <th>DOG_RUN</th>\n",
+       "      <th>PIC_TABLE</th>\n",
+       "      <th>CITY_left</th>\n",
+       "      <th>TYPE</th>\n",
+       "      <th>COMMENT</th>\n",
+       "      <th>ADDRESS</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>index_right</th>\n",
+       "      <th>CITY_right</th>\n",
+       "      <th>WARD</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>64</td>\n",
+       "      <td>Bill Clem</td>\n",
+       "      <td>1212.905396</td>\n",
+       "      <td>72983.420466</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.675469</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas Ave &amp; S 10th St</td>\n",
+       "      <td>MULTIPOLYGON (((-94.63561 39.08731, -94.63561 ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>67</td>\n",
+       "      <td>Shawnee</td>\n",
+       "      <td>3865.413812</td>\n",
+       "      <td>295406.668890</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>2010-05-04</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.719724</td>\n",
+       "      <td>...</td>\n",
+       "      <td>None</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>Neighborhood Park</td>\n",
+       "      <td>Armourdale rec center</td>\n",
+       "      <td>730 Osage Ave</td>\n",
+       "      <td>MULTIPOLYGON (((-94.62713 39.08444, -94.6281 3...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kansas City</td>\n",
+       "      <td>06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 74 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    PARKS_ID       NAME   Shape_Leng     Shape_Area    ZIP    DATEMOD  \\\n",
+       "20        64  Bill Clem  1212.905396   72983.420466  66105 2010-05-04   \n",
+       "21        67    Shawnee  3865.413812  295406.668890  66105 2010-05-04   \n",
+       "\n",
+       "    DATEADDED  DEV_ACRE  UNDEV_ACRE  TOTAL_ACRE  ... DOG_RUN PIC_TABLE  \\\n",
+       "20 2010-05-04       0.0         0.0    1.675469  ...    None         Y   \n",
+       "21 2010-05-04       0.0         0.0    0.719724  ...    None      None   \n",
+       "\n",
+       "      CITY_left               TYPE                COMMENT  \\\n",
+       "20  Kansas City  Neighborhood Park                   None   \n",
+       "21  Kansas City  Neighborhood Park  Armourdale rec center   \n",
+       "\n",
+       "                   ADDRESS                                           geometry  \\\n",
+       "20  Kansas Ave & S 10th St  MULTIPOLYGON (((-94.63561 39.08731, -94.63561 ...   \n",
+       "21           730 Osage Ave  MULTIPOLYGON (((-94.62713 39.08444, -94.6281 3...   \n",
+       "\n",
+       "   index_right   CITY_right WARD  \n",
+       "20           0  Kansas City   06  \n",
+       "21           0  Kansas City   06  \n",
+       "\n",
+       "[2 rows x 74 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale_parks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Impervious land"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impervious = gpd.read_file(\"../data/spatial_data/armourdale/impervious_land_cover.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_impervious = impervious.sjoin(armourdale, predicate='within')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pervious                    2641\n",
+       "Buildings                   2337\n",
+       "Sidewalks                   1072\n",
+       "Driveways                    662\n",
+       "Decks/Patios                 381\n",
+       "Parking Lots                 289\n",
+       "Concrete Pads                271\n",
+       "Miscellaneous Structures     203\n",
+       "Railroad Ballast              93\n",
+       "Roads                         28\n",
+       "Parking Lots-Dirt             10\n",
+       "Pools-Above-Ground             9\n",
+       "Driveways-Dirt                 8\n",
+       "Bridges                        8\n",
+       "Roads-Dirt                     6\n",
+       "Athletic Facilities            5\n",
+       "Name: impervio_1, dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale_impervious['impervio_1'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+35UGwOAOHoR7OzZo29HXibcndWfpjP7sSCrk7i8PcNNHf7E6NhuzuenGPqVSjvN51p563N3d+e233/jmm29YuXIl3bt3Z9u2bS0/QQkJCQkJiWuEJHyuI9nZ2RQVFV2x8HF30LDtqRuYPiAEhfys5eb+gSGX3OdUYSWPj+gAwMncCmYti+X2RXs5nlV2RWOpRyaTMX36dI4dO0ZoaCgjRozgscceo6rq6leVl5CQkJCQaCmS8LmO1Ds2X6nwAXC2VfHSuM6sf3wQ/ULdaOdhzw3hXhdtq9UZOXqmjMLzkhjGZpYx/tM9PLviODX6puf2aYzQ0FC2bt3Kxx9/zDfffEP37t3ZtWvXVelbQkJCQkLiSpGEz3Xk6NGjuLm5ERgYeNX6jPRxYtlD/Zl7U0c+3naK5PzKC9qkFFTR2d+JMC8HbojwbLBNCNiTWoRaefW+CnK5nMcee4xjx47h5+fHDTfcwBNPPIFWq71qx5CQkJCQkGgJzbrbLVq0iG7duuHk5ISTkxPR0dFs2LDBuj0/P5/p06fj5+eHnZ0dY8aMISUlpdE+T5w4wcSJEwkJCUEmk/HBBx9ctN1nn31GaGgoNjY2REVFXVA6QQjBSy+9hJ+fH7a2ttxwww2cOHGiOad3zan377naGZtlMhkjO3kzY3Aoq2OzmfHdITKKtdbkhGFeDvRv587EXgF8e39fnh0byV19A61iZ/qA0AZTZleLsLAwduzYwbvvvsvnn39O9+492Lt371U/joSEhISERFNplvAJCAhg4cKFxMTEEBMTw/Dhwxk/fjwnTpxACMGECRNIS0tj9erVHD16lODgYG688Uaqq6sv2adWq6Vdu3YsXLgQHx+fi7b55ZdfeOKJJ5g/fz5Hjx5l8ODBjB07tkFI+FtvvcV7773HJ598wqFDh/Dx8WHkyJFUVl5oAWktroZjc2PYa5Q8PTqS+Td34q0/Exn9wS5+PpjBhE/38NyqeI5mlnEyt4JBYR68NK4zzrYqHDRKJvdues2w00XVTPv6IIYmVoBXKBQ8+eSTHIw5jFFtz+DBg5k1a5ZU8FRCQkJConUQV4irq6tYsmSJSEpKEoCIj4+3bjMajcLNzU18+eWXTeorODhYvP/++xes79u3r5g5c2aDdZGRkeLZZ58VQghhNpuFj4+PWLhwoXV7bW2tcHZ2FosXL27yuZSXlwtAlJeXN3mfplJcXCwAsXTp0qve96XYkpAnBr+5TQQ/s866TP/6gBBCiN9iMkXwM+vEK2tPNKvP7FKtCH5mndiemN/s8RiNRvHOO+8Ie3t74eXlJb799lthMpma3Y+EhISEhMS5NOf+3WLHDpPJxLJly6iuriY6OhqdTgeAjc3ZUgkKhQK1Ws1ff/3VYmGm1+s5fPgwo0aNarB+1KhR1mmT06dPk5eX16CNRqNh6NChjU6t6HQ6KioqGizXitjYWODqODY3lREdvdn05BCeGhWOjcryr3axU3P4TClL/jqNWiHnP4PbNatPPxdbbu7qS4i7fYP1aYVVl3WQVigUzJkzh6SkJIYPH8706dMZPHiw9bORkJCQkJC41jRb+MTFxeHg4IBGo2HmzJmsXLmSTp06ERkZSXBwMHPnzqW0tBS9Xs/ChQvJy8sjNze3xQMsKirCZDLh7e3dYL23tzd5eXkA1tfG2lyMBQsW4OzsbF2uptPx+dRHdPn7+1+zY1wMG5WCx4Z3YOucG7irbyDr43KZuGgvJ3MruKd/MD7Ojdf0uhgLJnYlxKOh8Jn542GOZJQ2aX9/f39+/vlntm3bRllZGVFRUTz22GOUljZtfwkJCQkJiZbSbOETERFBbGws+/fv55FHHmHatGkkJCSgUqlYsWIFycnJuLm5YWdnx44dOxg7diwKheKKB3q+Q7AQ4qL1py7X5lzmzp1LeXm5dcnMzLzicV6K48ePA9CxY0f27NlzzY5zKfxdbFlwezf+emYY703uzmdTezHvpsgW9eVko7L+/UdcLvvTivni3t4XzfjcGMOGDSM2Npa3336b77//noiICL7++mvM5qb5D0lISEhISDSXZgsftVpNWFgYvXv3ZsGCBXTv3p0PP/wQgKioKGJjYykrKyM3N5eNGzdSXFxMaGhoiwfo4eGBQqG4wHJTUFBgtfDUO0U31uZiaDQaa4Ra/XKtePrpp/n+++9p164dN9xwA++//7416up64uVow+29Aripq+8li5A2B5VCzktrThDgatuy/VUqZs+eTVJSEqNGjeLBBx9kwIABbNq0qVU+HwkJCQmJfzZXfOcTQlj9e+pxdnbG09OTlJQUYmJiGD9+fIv7V6vVREVFsXnz5gbrN2/ezIABAwBL0jwfH58GbfR6PTt37rS2aW26dOnCvffey7Zt25g1axazZ89m8uTJ19Sv6HrQv50bZ4q1LD1wZUVXfX19+fHHH9m5cydGo5HRo0cTFRXFL7/8gtF4YY0wCQkJCQmJltAs4TNv3jx2795Neno6cXFxzJ8/nx07djB16lQAli9fzo4dO6wh7SNHjmTChAkNnI7vu+8+5s6da32v1+uJjY0lNjYWvV5PdnY2sbGxnDp1ytpm9uzZLFmyhK+//pqTJ0/y5JNPkpGRwcyZMwHLFNcTTzzBG2+8wcqVK4mPj2f69OnY2dlx9913X9EHdLVRqVS88847rFixgj///JM+ffoQHx/f2sNqMY42Kh4YFMLaYzkYmxji3hhDhgzh0KFDbN68GXd3d+68804iIiJYtGgRNTU1V2HEEhISEhL/apoTLvbAAw+I4OBgoVarhaenpxgxYoTYtGmTdfuHH34oAgIChEqlEkFBQeK5554TOp2uQR9Dhw4V06ZNs74/ffq0AC5Yhg4d2mC/Tz/91HrsXr16iZ07dzbYbjabxYsvvih8fHyERqMRQ4YMEXFxcc05vWsazn4xkpKSRNeuXYWdnZ344Ycfrssx/27ExMSISZMmCblcLry8vMTrr78uSktLW3tYEhISEhJtiObcv2VCSI4U9VRUVODs7Ex5efk19fc5F61WyyOPPML333/PI488wvvvv49Go7kux/47cerUKd555x2+/fZbVCoV06ZN4z//+Q/du3dv7aFJSEhISLQyzbl/S7W6Whk7Ozu+/fZbPv/8c7766isGDx7MmTNnWntYbY6wsDAWL15Meno6s2bNYsWKFfTo0YN+/fqxZMkSqQq8hISEhESTkIRPG0Amk/HQQw+xd+9eCgsL6dWrV4MaaBJn8fHx4bXXXiMjI4Pff/8dd3d3HnroIXx9fXnooYc4dOiQFA0mISEhIXFJJOHThoiKiuLw4cNER0dz880388ILL2AyNZ4N+d+KSqXitttu448//iA9PZ05c+awYcMG+vbtS+fOnZk/fz4xMTGSCJKQkJCQaIDk43MOreHjczHMZjMLFy7k+eefZ8SIEfz00094eDQvOeC/EZPJxMaNG/n1119Zu3YtpaWlBAQEMGHCBCZMmMCQIUNQqVSX70hCQkJC4m9Fc+7fkvA5h7YifOrZunUrd911FxqNhuXLl9O/f//WHtLfBqPRyO7du1m5ciWrVq0iMzMTFxcXbrnlFsaPH8+oUaPaxP9YQkJCQuLKkYRPC2lrwgcgKyuLKVOmcOjQId59910ee+yxRstwtAVi0kuoNZgZGOZ+0bGmF1VfUOvrWiKE4OjRo6xatcqa50mlUjFkyBBuueUWbrnlFsLCwq7beCQkJCQkri6S8GkhbVH4ABgMBv73v//xwQcfcOedd/Lll1/i4ODQ2sO6JO9tTuajrSl08HJgYlQAN3b0IszL0bq9oLIWTweNVRRV64wo5DJsVFde060ppKens379etatW8f27dvR6XSEh4dbRdCgQYOkKTEJCQmJvxGS8GkhbVX41LN8+XIeeOABAgMDWbFiBR07dmztIV1AVqmW6d8cokZvIrvMkmnZ20nDC7d0JjGvggBXW6KCXQlwtWPryQJMQlBarUelkGOjkqOQy/B00ODnYouPs801F0PV1dVs3bqVdevWsW7dOnJzc3FycmLMmDFMnDiRm2++GXv762edkpCQkJBoPpLwaSFtXfgAJCUlMXHiRNLT01myZAl33nlnaw/popjNgoTcCjJKtBhMZoqq9Hy8LYWu/s48MCiUYRFel9yvsEpHVmkNOWU11BhMCCEwmUEmAxlgr1GiUsjoHuiCl6MNCvnVmfqrnxJbt24da9as4fDhw9ja2nLTTTcxefJkSQRJSEhItFEk4dNC/g7CByxWiocffpilS5fy2GOP8e6776JWq1t7WJclKa+SCJ9zprwqavFysml2P9U6I3kVteSU1ZBfocNkNlNfJkwuA7lMhqu9mkA3W8I8HRpUoU/OryTc2/ESPTckLS2N3377jV9//bWBCJo0aRI333xzm55ulJCQkGgJRqOR3377jerqah588MHWHk6TkYRPC/m7CB+wWCcWL17ME088Qa9evfj1118JDAxs7WG1CYQQVNQYOVNSTXJ+FXIZmMwClULOgPbuLRJb9SJo+fLlxMTESCJIQkLiH4VWq+Xrr7/m3XffJT09HZlMxqlTp2jXrl1rD61JSMKnhfydhE89hw4d4o477kCr1fLTTz8xcuTI63Zsk1lcMM1U/3Vqa5FnZrPgRE4Fx7PLkMtkqBVyugY408HLodljvZgIuvHGG7n11lu55ZZb8PX1vUZnISEhIXF1KS4u5tNPP+Xjjz+mpKSEyZMn83//93+MGzeOe++9l/fff7+1h9gkJOHTQv6OwgcsX9x77rmHP//8k5deeonnnnsOufzaJuWu1hmp1hkvsJ4YTWaMZoEQYKOStzkBVI/JLDiRU86pAkuNL6VCTvcAZ4Lc7Jo15tOnT7NixQrWrFnDnj17MJvN9O7dm1tvvZVbb72VHj16tNnPQEJC4t/LmTNneO+991iyZAlCCB544AHmzJlDaGgoAPPnz+fjjz8mKyvrb3E/lIRPC/m7Ch+wZHt+7bXXeOmllxgzZgw//PAD7u7u1+x43+9L597+wZe8qWeX1eCgVuJs9/cICzeYzBzPKiOjRAuAWqGgT4hrs6bFiouL2bBhA2vXrmXjxo1UVFQQEBDAzTffzNChQ4mOjiY4+NKfmYSEhMS15syZM7z44ov8+OOPODs789hjj/HYY4/h6enZoF12djYhISG88847zJo1q5VG23Qk4dNC/s7Cp55NmzZx9913Y29vz2+//UafPn2u+jGOZJRiq1LQ0bfhZySEoExrwNW+7TtaX47KWgO7kotIzKsgxN2eiVEBzdpfr9eze/du1q5dy4YNG0hOTgbA19eX6OhooqOjGTBgAL169cLGpvk+RxISEhLNobi4mAULFvDxxx/j6urKs88+y4wZMxr1T5w6dSr79+8nOTkZheL65FlrKZLwaSH/BOEDkJGRweTJkzl69CgffPABM2fOvKpWBr3RjFop1bdtDoWFhezfv5+9e/eyb98+Dh06hFarRSaTERgYSFhYGO3bt7cuYWFhhIeHY2dn19pDl5CQ+BtTU1PDRx99xIIFCzCZTDz99NPMnj27SQEZBw8epF+/fqxatYrx48dfh9G2HEn4tJB/ivABi8Vhzpw5fPLJJ9xzzz0sXrz4muegKarS4eGguabH+KdgMBiIi4vjyJEjpKamkpqayqlTp0hNTaWiosLaLigoiMjISCIiIoiMjCQ8PBxnZ2fs7OywtbVt8Cplm5aQkDiXvXv3MmXKFPLy8pg5cybPP/88Xl4Xz6F2KQYOHIharWb79u3XaJRXB0n4tJB/kvCpZ9myZcyYMYOQkBBWrFhBRETENTvW+uO53NxNimi6EoQQFBcXk5KSQlJSEomJidbXU6dOYTQaL7mvQqFArVaj0Wgu+Wpra4ujoyOOjo44OTk1+NvV1RVvb2/r4unpiVKpvI5nLyEhcbX44YcfmDFjBv369ePrr79ucT3C5cuXW2cQevTocXUHeRWRhE8L+ScKH4CEhAQmTpxIVlYW33zzDXfcccc1Oc7G+FzGdJGEz7XCYDCQkZFBZWUlNTU1aLXaC171ej06nQ69Xt/g7/pXrVZLZWUllZWVVFRUWP+urKxEq9VecEx3d3ciIiLo27cvffv2pU+fPrRv315y0JaQaKOYzWaee+45FixYwP3338/ixYuvKMGt0WikXbt2jBgxgm+++eYqjvTqIgmfFvJPFT4AlZWV/Oc//+GXX37hiSee4K233rrqUyOZJVoC3SSflL8rtbW1FBQUkJ+fT15envU1ISGBgwcPkpqaCoCbmxt9+vShb9++9OvXj379+uHh4dHKo5eQkIiJieGxxx7j4MGDvP3228yePfuiDykx6SUs3plG7xBX/jO4HdmlNQS5X/ra/fbbb/Pcc89x5swZfHx8ruUptBhJ+LSQf7LwAcs0yieffMKcOXPo06cPv/76K/7+/q09LIm/CcXFxRw6dIiDBw9y6NAhDhw4QGFhIQAdOnSgf//+REdH079/f7p27SpNk0lIXCcKCwuZN28eX331FV27duWTTz5h8ODBF20rhOC3w1k8/dtxXO1UmIUl/9rh50desih0aWkpwcHBPPzww7z99tvX8lRajCR8Wsg/XfjUs3//fiZNmoROp+Pnn39mxIgRrT0kCSwXpJ8OZlCtM2KvUdK/nTvtPOzb7LSSEIL09HT279/Pvn372LdvH7GxsRiNRuzs7Bg2bBiTJ09m3LhxuLi4tPZwJST+cRiNRhYvXszzzz8PwKuvvsrMmTMbfeiYtzKOnw5kXLD+83ujGN350tac559/nnfffZfTp0/j7e195YO/ykjCp4X8W4QPWJ4Qpk6dytatW3n11Vd59tlnr3m2Z4mmU6bVsze1mFMFVRjNAn8XG/q3c292ZulLYTSZGxRvvVrU1NRw+PBh9u7da81mrVKpGDVqFJMmTWL8+PGSCJKQuArs2rWL//u//yMuLo4HH3yQN95444IkhOcjhGDgwm3klNdesG1cdz8+uqvnJfctLS0lJCSEGTNm8O67717x+K82kvBpIf8m4QNgMpl45ZVXeOWVV7jlllv4/vvvcXV1be1hSVyEjGIt+9KKOFOsxWgW9ApyYUi4J3bqlk0nxWeX08Xf+SqP8kKysrJYsWIFy5cvZ8+ePWg0GiZPnsyjjz5K375926w1S0KirZKVlcX//vc/fv75Z/r168fHH3/c5ES1ZrOg+yubqKy9MDrUQaPk8PM3olFeOlHhiy++yNtvv01aWlqb8/WRhE8L+bcJn3o2bNjAPffcg5OTE7/99htRUVGtPSSJRjCZBbGZpexOKUJnNBPqYc+ISC/cm5FD6UROOZ39Li58zGZBiVZ/1XMyZWVlsXTpUhYvXkx6ejq9evXi0Ucf5c4775QSNUpIXIaqqirefvtt3n77bRwdHZn/0qs89vCMZlvqZ/5wmI0n8i667cv7ejOy06WnscrKyggJCeH+++9vc8VLm3P/luY2JBg7dixHjhzBw8ODgQMH8uWXXyLp4baLQi4jKtiNJ24M55kxkfQJceP3I9ks3JDI13+dJrPkwrD085Fx1tJiMJk5lF7CzuRCag0mYrPKuPG9naw6mn1VvgcZxVpOFVQSEBDAM888w6lTp1i3bh0+Pj7MmDEDf39/Zs+ezenTp6/4WBIS/zTMZjPffPMN4eHhvPnmm4yfOoPXf97GhClTyS67cMrqcgwOv3QE5u9Hshrd18XFhSeffJLFixeTm5vb7GO3FSSLzzn8Wy0+9eh0Op588kkWLVrEtGnT+Oyzz6Qn8b8Z+RW1bErIJ7u0Bhc7FTd29CbMq2FqeiEE25MKcdAo2Xoyn19iMrmxozd9Qlz5Yf8Z7NRKDp4uAWBMZx9MQjBrRIcrnho7U1xNtc6Iu4MGlUKOm72atLQ0Fi9ezFdffUVZWRm33347c+bMoX///ld0LAmJfwI7duxg9uzZHD16lFsmTMTlhumM6NOZlPwqfo3JRCGX8er4LkzqHdjkPr/Zc5qX1yZcdJtaIWff3OGNWo/Ly8sJCQlh2rRpfPDBB809pWuGZPGRaBEajYbPPvuMH374geXLlxMdHc3Jkydbe1gSzcDbyYZ7+wfz7NhI7uoTRFx2Ge9tSmLRjlTissoRQiCTyRBCMPnzfXy+K40yrYFBYR4MCfckOa/KKnoANp7IY3NCPqVa/RWPTS6XWcdor7H4EbRr14633nqLzMxMPvnkE44dO0Z0dDQDBw7k999/x2QyXfFxJST+bqSkpHDbbbcxbNgwVCoVsz5chhg+i6jO4SzckMjSAxkYTIJag5lPt59qVt+hHpcuXaQ3mfntcONWH2dnZ2bPns3ixYvJyclp1rHbCpLwkbiAe+65hwMHDqDX6+nVqxfvv/8+ZrO5tYcl0Uyc7VTc1jOA2aMimNo/iOT8Sl5em8Dr6xNIK6xq0NZOraBGb6JnkMsF/SjlMvqEuF3xeJQyGfI6Z+bzHSjt7Ox45JFHSExMZNWqVSgUCiZOnEh4eDhff/21JIAk/hWUlpYye/ZsOnfuzJEjR3jjoy9xmvImta5hdPR14oMtKdQazl6LO/k68eKtnZt1jH6h7qgbieg898HnUsyaNQs7OzsWLlzYrGO3FZolfBYtWkS3bt1wcnLCycmJ6OhoNmzYYN2en5/P9OnT8fPzw87OjjFjxpCSknLZflesWEGnTp3QaDR06tSJlStXNtheWVnJE088QXBwMLa2tgwYMIBDhw41aDN9+nRkMlmDRTKXt5wuXbpw5MgRZs6cyezZsxk+fLjkg/E3xslGxcSoAF4a15knR4YTn11OfUDV5/dGMbiDB+08HVj2UH++f6Avd/cLom+IGx4OGoxmwYmc8iseg0Iuw2hufGZdLpczfvx4du3axcGDB+nVqxcPPvggvXr1YsuWLVc8BgmJtojBYOCjjz4iLCyML7/8khdffJHZX/zBN/n+DI/0JrWwkuUxFktMkJsdPYNcWHxPL9Y/Pohhkc0rOmqrVlgfcJRyGUq5jAHt3a3vXewuX97CycmJOXPm8MUXX5Cdnd28k20LiGawZs0asX79epGUlCSSkpLEvHnzhEqlEvHx8cJsNov+/fuLwYMHi4MHD4rExETx0EMPiaCgIFFVVXXJPvfu3SsUCoV44403xMmTJ8Ubb7whlEql2L9/v7XN5MmTRadOncTOnTtFSkqKePHFF4WTk5PIysqytpk2bZoYM2aMyM3NtS7FxcXNOT1RXl4uAFFeXt6s/f7pbN++XQQHBwsHBwfxxRdfCLPZ3NpDkrgKFFfpxLpjOeJEdplYfzxH/HE8Rxw+UyJMprP/X7PZLI6cKRF55TVXfrxKnTiWWdrs/fbt2yeio6MFIG6++WaRkJBwxWORkGgLmM1msWbNGhEeHi5kMpmYMWOGiD+VLr7anSaeXxUn3t2UKDo+v0EEP7NOBD+zTjy/Kk5U6wxXfNwPtySL4GfWiTc3nBSVtQZhMpnF9sR8UVXb9L4rKiqEm5ubePTRR694PFeD5ty/myV8Loarq6tYsmSJSEpKEoCIj4+3bjMajcLNzU18+eWXl9x/8uTJYsyYMQ3WjR49Wtx5551CCCG0Wq1QKBRi3bp1Ddp0795dzJ8/3/p+2rRpYvz48Vd0LpLwuTTl5eXiwQcfFIC46aabRHZ2dmsPSeIakF9eI/6MzxUb4nLEnpRCoTOYrlrfZdU6EZtR0qJ9zWaz+PXXX0VoaKhQKBTikUceafaDjYREWyI2NlaMGDFCAGLEiBEiNjZWmM1msTwmUyzccFI889sxq+Dp89pmsSu54Kod+9DpYhH8zDox6r2dV9TPG2+8IdRqdQMjRGvRnPt3i318TCYTy5Yto7q6mujoaHQ6HQA2NjbWNgqFArVazV9//XXJfvbt28eoUaMarBs9ejR79+4FLCm5TSZTg34BbG1tL+h3x44deHl5ER4ezn/+8x8KCgoaPQedTkdFRUWDReLiODk5sWTJEtauXcuRI0fo0qULy5Yta+1hSVxlvJxsGNXZhzFdfOnk58TulEL+PJHHrrpQ9ytBAJeZ6bokMpmMSZMmcfLkSd58802WLl1KREQE33zzjeR/JvG3Ijs7mxkzZtCzZ0+ysrJYu3Ytmzdvpnv37hzJKCWvvIYDacUsO5QJwIhILzY+MYTBHRrPytwcOvo44mSjJCm/kg+3pFBYqWtRP4899hgODg5/P1+f5qqq48ePC3t7e6FQKISzs7NYv369EEIIvV4vgoODxaRJk0RJSYnQ6XRiwYIFAhCjRo26ZH8qlUosXbq0wbqlS5cKtVptfR8dHS2GDh0qsrOzhdFoFD/88IOQyWQiPDzc2mbZsmVi3bp1Ii4uTqxZs0Z0795ddO7cWdTW1l7y2C+++KLAcj1usEgWn8YpKioSkydPFoCYPHmyKCwsbO0hNYmvdqeJ1bHZQqsztvZQ/nZodUaxPTFf/BmfK9KLLj113Rhl1TpxIK3oqownNzdXTJ06VQBi4MCB4vjx41elXwmJa0VJSYn43//+J2xsbIS7u7v46KOPhF6vF0IIYTCaxHd7T4vPtp8SvV7ZJIKfWSc6zPtDfP1X2jVzLXjgm4NWi9LvRzJb3E9bsfpcU4tPREQEsbGx7N+/n0ceeYRp06aRkJCASqVixYoVJCcn4+bmhp2dHTt27GDs2LEoFJdOgQ1ckLZe1IXc1vPDDz8ghMDf3x+NRsNHH33E3Xff3aDfKVOmcPPNN9OlSxduvfVWNmzYQHJyMuvXr7/kcefOnUt5ebl1yczMbO7H8a/E3d2dX375hWXLlrFlyxa6dOnCunXrWntYl2VERy8+2JJM79c2M+fXY/yVUoSppSaIfxm2agW2KgVLD2SwLbGATSfy2JFUgFZ/Yer7S/eh5HJZw/LKa8kq1aIzNm5d8vHx4ccff2Tbtm0UFxfTs2dP5syZQ3V1dZPHIyFxPaipqeHNN9+kXbt2fPrppzz99NMcjU8iYvgkVCoVmSVa3tmURGaxlrf+TKS4Wk+Iux2//3cA9w8MvWZlXaLrHJoB/kopbnE/f0erT7OFj1qtJiwsjN69e7NgwQK6d+/Ohx9+CEBUVBSxsbGUlZWRm5vLxo0bKS4uJjQ09JL9+fj4kJfXMH12QUFBg+qv7du3Z+fOnVRVVZGZmcnBgwcxGAyN9uvr60twcHCjUWUajcYaoVa/SDSdKVOmEB8fT+/evbn11lt54YUX2vS0Q7C7PStmDiDM25EVR7K456sDRC/YymvrEjieVSZlq74M+9Ms2Z1fXpvAi2tOcDSjjG0nLSLoZO7lp4nVSjmNXcPLtHoe+iGG7/edISa9tEljGjZsGMeOHeO1115j0aJF9OrVi5iYmKaekoTENcNoNPLll18SFhbGc889x9SpU0lMTiFo5HTe2ZFB7xA31h7L4ft9ZziZW8GXf51GCJjQw491jw++5rX0zp0623Oq6KLXv4paw2UzwTs6OvLUU0/xxRdfkJXVeA6gtsIV5/ERQlj9e+pxdnbG09OTlJQUYmJiGD9+/CX3j46OZvPmzQ3Wbdq0iQEDBlzQ1t7eHl9fX0pLS/nzzz8b7be4uJjMzEx8fX2beUYSzcHX15e1a9eyYMECXnvtNe644w6qqqouv2Mr4Wqv5qcZ/RjcwZK2vaBSx5K/TjPukz0Me2cH721KIiW/spVH2TaZ2j+I6QNCUMplPD06gnae9vwRn8uG+DyOZZbx0uoTrI5tPLTV18nW+veKw1msO57D9sQCPtmWwugPdpGSX0W1zkiN3sT+tGLOFF/egqNWq3n22WeJjY21ptl44403pNw/Eq2CEIIVK1bQpUsXHnroIYYOHUpiYiKffPIJHp5eZJXW8MDAUN7amEhSXgWbEvLYmVyEg0bJB1N68MGdPXHQXFh82GC6ug+V4d4O1np8eRW1pBZe+Fv7aEsK0785eFnL+N/O6tOcObS5c+eKXbt2idOnT4vjx4+LefPmCblcLjZt2iSEEOLXX38V27dvF6mpqWLVqlUiODhY3H777Q36uPfee8Wzzz5rfb9nzx6hUCjEwoULxcmTJ8XChQsvCGffuHGj2LBhg0hLSxObNm0S3bt3F3379rXOj1ZWVoo5c+aIvXv3itOnT4vt27eL6Oho4e/vLyoqKpp8flJU15WxZs0a4eDgILp16ybS09NbeziNojOYxOM/H7HOcZ+/jH5/p/hoS7JIK2yZP8s/mcySavHsimNi0uK9YkNcrijT6sWqo1nixVXx4r6vDoitJ/OaFA22PTHf+nkv2nFKHD5TIoznhNKbTGZRXqNv1tj0er2YN2+ekMlkYvDgwW3+eyjxz2Lbtm2ib9++AhCjR48WR44cabBdbzSJ7i//Kbq8sFG8tylJdHlxo/V6syEuRyTmNrxfpeRXivc2JYkXVsWJlPzKJo/jjT8SRE6Z9rLtZtVdA2f+ECPyKxqmrCiqrBXTvz4ggp9ZJ1Ydvbz/Tr2vT2Zmy/2FroRrFs7+wAMPiODgYKFWq4Wnp6cYMWKEVfQIIcSHH34oAgIChEqlEkFBQeK5554TOp2uQR9Dhw4V06ZNa7Bu+fLlIiIiQqhUKhEZGSlWrFjRYPsvv/wi2rVrJ9RqtfDx8RGPPvqoKCsrs27XarVi1KhRwtPT03rsadOmiYyMjOacniR8rgJxcXEiNDRUeHp6it27d7f2cBrFbDaLdzclXVL81C9jP9glPtmWIk5LIqgBKfmVYsZ3h0Tf1zeLFYczhdFkFlqdUaw6miWGvrVNPPjtIbH2WENn8vzyGnEi2/L7MpvNYs+pQrEnpVAczyy71GGEEBahevhMidgYnysyiqsv6/C5c+dOERQUJFxcXC64nkhIXG2qq6utzvZ9+vQR27Ztu6BNYWWtWLo/Xby+PkEs+CNBhD67ToQ+u068sT5B1OiNIqO4WgghRHJehTh0uljcs2S/ePj7GJGS3/SH93r2pRaJGv3lgzh+PZQhgp9ZJx789uAF2+KyyqzXwGHvbG/wUHIxWjuvT3Pu31KR0nP4txcpvVoUFRUxadIk9uzZw2effcaMGTNae0iNsjE+l//9dpyK2ss76nbydeLmbr7c3NWXkEZq3vybiEkv4Y0/TlKqNXBfdDBejjZ8ty/dmvreXq1gZCdvXry1M+U1Bo5llRHdzh2zAJVCxvKYLA6cLuaraX2Qy2XUGkykFVZTUWtABqgUcmoMJpLzK+kd7EYXf6cmOXyWlZUxY8YMVqxYwaOPPso777xzQVoMCYkrJSMjg9tuu43ExEQWL17MPffc0+D7qTeaWX44ExdbNa+uS+C9Kd25+8sDRHg78uYd3egR6ALA3lNFfLA1hYOnS+ge6MILt3QkKvjKS8U0xqmCSm58bxc+Tjbc0s2XeTd1tNbUM5sFUa9tplRrAODDO3swvod/o/0tWLCAl156idTUVAICAq7p2M+nOfdvSficgyR8rh4Gg4HHH3+cxYsX8/jjj/Puu++iVF44b91WyCrV8tbGJNYca3rRvS7+Ttzc1Y+bu/oS5P7vrmIvhODPE/m8vzmZpHN8pCJ9HJnQ05+47HIeGdoeT0cN9y45wO29/Lm9VwBOtioAjmaUkl1aQ3ZZDQAejhocNJYoMLlcRjd/5xYJTSEEixcv5sknn6Rjx4788ssvhIeHX52Tlmg1hBC88847DBgwgIEDB7baOA4cOMC4ceOwtbVl1apV9OjRo8H2jfF57Est4s8T+eRV1ALwxI0d6BnkSnQ7d9RKOQk5FSzcmMiu5EKC3OyYMyqcW7v5WQVISzmUXsKeU0U8NKQdduqLX3s/23GKtzYmWd+v+79BDZyqH116hPVxuQBEeDuyYdbgRsdVWVlJSEgId911F5988skVjb+5NOf+3XbvRBJ/a1QqlbW22//93/9x8uRJfvnlF1xdXVt7aBflsx2pONgo+WxqL37cf4a9qZcP74zPriA+u4I3NybS1d+ZW7r5clNXXwLd/n0iSCaTMaaLD6M7e7MrpYglu9PYnVJErcGEg0bJwPYeVOmMtPO0J6NUy9AILx5ZeoSoYFdMZsHG+Dw6eDvw/uQeuNpfvlZQc8b1yCOPMGDAAKZMmUKvXr346KOPuP/++69ZmLDEteeNN97gueee48knn2w14bNixQruueceoqKiWLlyJZ6eZ6OkcstreGH1CTYn5FvX+bvYMmdUOBN6+COXy8gtr+G9Tcn8diQLd3s1r47vzJQ+QaiVzY85SsyrwM1ejZfjWYtmZa2BNbE5DAzzoKu/Mxql/ILvvAwZ3k4aCit1mAXsSy1uIHwGhnlYhU9SfiVbTuYzqrPPJcfh6OjInDlzeOWVV3jppZfw8PBo9rlcD6Tq7BLXlEceeYTNmzdz+PBh+vXrR2JiYmsP6aK8Or4LbnZqnvgllmB3Oz68swcPDAzF01HTpP3jsstZsCGRwW9tZ8Kne1iyO4288tprPOq2h0wmY2i4Jz882I/1jw8iyN2e51bF8/G2FGoMJmxVCh4a3I7fDmeRV16Ls62K0Z192DJ7KI+P6MBvh7P4fGcq+9OKr2oUS/fu3YmJiWHy5Mk8+OCDjBgxokkFlCXaHt9//z3PPfdcqwnXrKwsZs6cyaRJkxg/fjxbtmyxip4qnZH3Nycz4t2dVtHjbKti/k0d2TpnKLf3CqCi1sDymEyW7D7N6mM5/PeG9ux4ehj3Roe0SPT8lVLEuE/2cMeifWSVng091xnMPDg4lIoaA+uP5/LkL7EUV+lYfzzX2mZAe3dqDWZrRvW9qUUN+h4U1lC4fLL91GXTfjz88MPIZDK++uqrZp/L9UKa6joHaarr2pGamsq4cePIzs5m2bJljBkzprWHdFE2xucy59djVOtNdPV3ZkJPf/xdbNiZXMTG+FzrfHdTkMmgT4gb47r7MbaLD+4OTRNR/zQOpBWzKjabm7r6MriDJyXVega9uQ2t/my4uVohp4u/E31C3Ojk58ScX49hq1YwpIMngzt40L+dO8HudlflZrd582ZmzpxJdnY2zz//PE8//TRq9dWzMklcOzZv3sxNN93EtGnTrOWO3n///ety7Pz8fBYuXMiiRYtwcHBg/vz5zJo1C7lcjt5oZtmhDD7amkJRlR6w5K26f2AI/x0ahrOdCp3RxPd7z/DxthSq9SbWPDoQV3s1fi62lznypUkrrOLWj/+iWm+ib6gbi6b24ovdabjbq8kqraGrvzM9g1ywVSsxmwVpRdXoDCar1cZoMtPz1c1U1vk32qsVxL44CpXirAAb9OY2skprrO+/e6AvQ8MbL5/xwAMPsHXrVtLS0i6bwPhqIfn4tBBJ+FxbKioqmDp1Kn/88Qdvv/02Tz75ZJucbkjJr+SRpUc4VWDJR3RzV1/CvBy4IyqA9OJq/ojLZWN8XrNEkEIuY0B7d27t5sfozj4426mu1fDbPFU6I4m5FWj1JrR6E7UGExW1BrR6E8ezykgrrCatqBq90WLxcbRR0i3AmQhvJ7ycNET4ODKgvTsaZcsvqFqtlldeeYV33nmHyMhIvvjii4vmDpNoOxw7dozBgwczaNAgVq9eTc+ePRk5cuQ1Fz5FRUW8++67fPTRRyiVSp566ilmzZqFk5MTZrPgj/hc3v4ziTPFZ60tt3Tz5ZkxkQS62WEyC1Ydzea9zclkl9UwtosPc0ZFEOblcEXjqjWYuO2zvZzMraBvqBvfTO+DjUrB8awySqr1FFfpKa7WU6M3YhZgo5Jbkogio7BKxy3dfOkW4MLDP8SQkFNBXkUtM4e259FhYdioLL+t4iod81fGs/HE2STDgzt48MOD/Rod25EjR4iKimLVqlWN5tu7mkjCp4VIwufaYzKZmD9/Pm+++SbTp09n8eLFaDRtzxJSrTPy36VH2JlcyKvjO/Pq+pMYTGb6hrgxvoc/Y7r4kJBTwfq4XP48kUdJtb7JfasUMgZ38OSWbr6M7OSNo82/VwRdCqPJzJkSLcl5lZzMrSAxr5KUgirsNQoyirW093LAzU7NwDAPBnXwoIOXQ4tE9LFjx3jooYc4dOgQDz/8MC+88IKU9LQNkpGRQXR0ND4+PuzcuRMHBwe6dOnCjTfeyAcffHBNjnny5Ek++OADvv/+e+RyObNmzeKpp57Czc0SabU3tYiFGxI5nlVu3aervzMv3NqJPiGWNrtTCnnjj0RO5lbQv50bz47taI3iuhKEEMxbGc/PBzOsosf+IkkPL4XZLFi8K5X/3hDGd3vT2Z9azIYTeTwytD2DOniwK6WQv1KKOJFTgVIuw2gWdPJ1orzGQHZZDVtmDyHMy7HRY0RHR+Pg4HBBguJrhSR8WogkfK4fS5cu5cEHH6RXr178/vvv+Phc2mGutdieVEB+eS3f7k3nTHE1bvaas1FHDmqeGhXBzd18sVUp2J9Wwvq4XDYn5FlN3U1BrZQzItKL8T38uCHCy/qkJXFxqnRGCit1hLjbsSuliOUxmRzNKMNkFgzu4MGQcE8GtHdv0rTimeJqZMjwd9Hw2Wef8fzzz1NbW8v999/P008/Tbt27a7DGUlcjtLSUgYPHkx1dTX79u2zXiuioqLo27cvixYtumrHEkKwZcsW3nvvPTZu3IiPjw+PPfYYDz/8sNVR92RuBQs3JLIzudC6n5ejhqdGR3BHrwDkchnx2eW8uTGR3SlFBLnZMf/mjozq5N1AnO9KLiQht4KZQ9s3a4xGk5m3NyWhN5oRAp4eHdEs0VOPySxQyGWkFVbx7uZk1h/PpZOvEwnnlJ9xs1cT3d6dQWEeDI/04o0/TrI6Noe7+wXxxm1dG+1/6dKl3HPPPSQkJNCxY8dmj6+5SMKnhUjC5/py8OBBJkyYgEKhYPXq1fTq1au1h2TlVEEVX/2VRpiXI8cyy8gtr+HGjt54OGg4U1xNUn4lp4uqya/QcVNXXyb08KN7oAsqhZzYzFJ2Jhdx+EwJxzPLqdQ1rZCno0bJ6C4+3NzVlwFhVzaV828jr7yWfWlFbIjLY3tSAYFudvQOdqVXkCtRwa6EXcQitCu5kA1xuXQNcGZcD39MtdUsWrSI9957j5KSEu68806eeeYZunZt/AIvce2ora1l9OjRxMfHs2fPHiIjI63bhg4dSmBgID/++ONVOVZMTAz/+c9/iI2NpUePHjz55JNMmTLFapHOKtXy3uZkVh7NthbbtVHJeXhIex4eagkZP1Nczfubk1l9LAcvBw03dvKms58Td/cLth4nNrOMRTtOEexuz2PDw3BqhsW3pFrPMyuOc/+AEAaEXZ2IKSEE0785RFGVjhM5FTw0pB0ncsrxdbbF3V5NYZWOwkodxVV6+oS48t2+M5aHvXkjcLa99Nh1Oh1BQUFMnjyZjz/++KqMtTEk4dNCJOFz/cnOzmbChAmcOHGC7777jkmTJrX2kNifVswLq+NZ+3+DGoiP0mo9DjbKBo5/ZrMgu6yGY1llnMipwGA0I5fLcNQoCXK3Y2i4J2VaA4l5FSTnV3GqoIq0oipSC6qpMVy6lpSdWkEXf2civB0J93YgzMuRCB9H3K5iqPc/laK6yJW1x3LIr6wl3MsRpUKGvUZJpI+jtT5RVmkNAa62BLvbs+VkPt38nRkW6YXZoOPrr7/m7bffJiMjg8jISG655RZuueUWBgwYgEolTU1eD0wmE3feeSfr1q1j69atF/hg3XLLLSiVSlatWnVFx9Hr9bz++uu8/vrrdO/enXfeeYcbbrjBKpTLtHo+3X6K7/adsfqdyWUwpU8gs0aE4+NsQ3GVjg+3pvDTgQxsVApu7uZLXnkt/i42aA0m3p7YjSMZZXy2IxVnWxVPjYpoUe6v7UkF9ApybVRwtIQ5vx6juFrHjqRCnrwxnO1JBcRmll3Qrp2nPWl1Nb1eHteZaQNCGu137ty5fP755+Tl5V3zAAJJ+LQQSfi0DjU1NcyYMYOffvqJF154gRdffBG5/PpnWjCazHy95zRZpTXMGNTuggvTy2tPkFGsZfG9UQ3ED8DERXvJKbNEUdhrlKw8ms1jw8LQGU14OGi4IyoAdwcNn24/xYD27nQPcCGnvIaU/CqS8itJqvNlSS2swmC69E/S01FTJ4YcifRxJNzHkQ5eDi0ydf8byC2vIT67giqdgdSCamLOlJBepCWvopZHbmjPM2POWhDKawy8ui6BCG9H7uwbiI0C1q9fz7p161i/fj15eXk4OzszZswYRo4cSb9+/ejYseN1i1r5NyGEYNasWXz66af8/vvvFzjIxmaW8crshygvKWbr1q0tPk58fDz33XcfcXFxPPfcc8ybN88qbGsNJr7dm85n2081yOo+urM3T4+OJMzLgRq9id+PZrHwj0Sq9UZGd/bBaBa42auoNZjpFuBMUaWOPxPy8XGy4dmxkXQLcGnxeK8Vy2My+WT7Kc4Ua5HL4DI1SQHo6OvEhlmDG20TFxdHt27dWLt2LbfccstVGu3FkYRPC5GET+shhODNN99k3rx53HbbbXz33Xc4OFxZ1ENLMNf94i+WnVSrN3LnF/uJzy5ncu9AXhnfBbVSzu6UQu796uBF+5PJQAjLa5inA/3buzG6kw+DOlw8HNRgMpNWWE1iXgVJeZUk51eSnF9FZqmWxn6pgW62VkFUv7TztJd8hi5Btc6I0SQuiK6rqDXw2E9HOZZZxvgefkyKCqSLvxNCCI4cOcK6detYt24dR44cQQiBvb09vXv3pm/fvtYlMDCwTUYr/l0QQlgT4C1evJiHH364wfYTOeXc/eUBOqX+TGriCQ4evPhvrzGqq6t5//33efXVV+nQoQPfffcdUVFRgMX35fcjWby3OZncc3Jx9Qlx5dmxHa1JN7cnFlCpM6CQy7FRyjmUXkKZ1kC1zkigmx16k5lfDmUS4GrLvJs6MjTcs81+L04XVVucnNOKScyrvPwOdZyf6flidOnShe7du7N06dIrHWajSMKnhUjCp/VZs2YNU6dOpV27dqxZs4bg4ODL73QdKazUMfnzfZwuqibIzY6Ovo5sSyxo1EpzPjIZTI4KZN7NHa0ma5NZ8OXuNJLzKnn2psgGGVgBavQmUgurSM63RDelNEEQyWUQ4m5PB28HOng5Wl8lQXR5KmsNHM8qJzazjPIaA/4utoR7O9Ir2AWNUkFlZSWHDx/m4MGD1iUzMxMALy8vevfubV369OnTJp332yLFxcVMnz6ddevW8dprr/HYk//jmRXHeXxEBzr5OXEko5T7vzlEeY2B4WUb2L9rGwkJCU3uX6/X8+WXX/Lqq69SUlLCk08+ycsvv4yNjQ1CCHYkFfLmxsQGN/9wbweeGRPJ8EgvAHYmF3K6qBoPBw2ONkoGd/BEIZdxIqecX2My0RnMrIrNxk6tZPbIcO7sE4hS0bZzBQshuO/rg0T6OPLl7tNN3u+BgaG8cGunRtu8/vrrLFiwgIKCAuzsrl1We0n4tBBJ+LQN4uPjGTduHFVVVfz+++8MGjSotYfUgJyyGqZ8sY/MkprLN24EfxdbPrqrB1HBbvx+JIvZvx6jX6gbvzwc3eQ+tHojpwqqSMytJCG3goScCk7mVjTqUC2XQZCbHe08HQh2tyPE3Z4egS50C3Bu8ERqNguKqnWUVOsRAmxUClQKGUq5HIVchlIuQ6GQIQOMJoHBbEZvNKMzmtEZzOhNZhQyGWqlHDd7NW72ahRXWH+oNSnXGjhdXI1KIUOtkKNRKtCo5Hg5apDJZOTm5nLo0CFiYmKIiYnh0KFDFBVZMuH6+/tbhVBUVBRdu3bF39+/zVoAWoM9e/Zw5513UlNTw3fffUdEnyE89MNhdAYzi+7pRW55LbOWHUUhkzEk3BP10V9Y8/tyTp++/I3aZDLx448/8tJLL5GRkcG9997Liy++SGhoKADx2eW8vv4k+9LOlqrxdbZh9shwbu8VgEIuIymvktfWJ7A7pYjBHTz44t7e2KotDxBCCDYl5PPp9lPoDGZ6h7jyzNjIZjkutzbTvj7IgPbuLNyY2Kh1+Vw8HTXsnzui0d91amoqYWFhLFu2jClTplyl0V6IJHxaiCR82g5tvcJ7XnktU5fsJ7XO0a+lKOQyFt8ThY1Kzr1fHcRerWDFfwcQ6dO871+51kBSfiU+Tjb4u9oSm1nG7F9jub1nADllNeRW1FJSrcNoEng72SCXwZ7UYv43OgJvJxvKagycKaomv1JnTUkvAKVchkohRymXoTeZqdGbqDFYEg+WafUUVOoou0Qix5EdvQhxt6d3qBvlNQa6BTg3+7zaOiazoLhah0X+WW6AJiFwsVVjo5KTkZFhFUL1S1lZGQDOzs506dKFLl260LlzZ+vf59Z8+jdgNpt56623eO6554iOjubnn3/mUCHM+z2e4R29eG1CF37cd4afDmbw32FhTOzlz77UYn5Z/A4bV/5CRkbGJfsWQrBu3TqeffZZEhISuP3223n11Vfp1Mlipcgq1fLOn0msij1bnNjZVsWjw9pzX3QINioFxVU63t+SzE8HMnCxU/PYsDB2Jhfyxu1d8Xex5fCZEt74I5GiSh0TowKY0icAb6eWZ2NuLV5Zm0BhlY61zSjUDLB0Rj8GXibCrF+/fvj6+l6xI3pjSEVKJf72eHh4sGnTJh5//HH+85//EB8fzzvvvNNmKrz7ONvw80P9ueuL5osfJxul1VlSIZPx7qYk7osO4aauPvwRl8fEz/Zy+PmRTZqOEkLw1p9JfLPnNLUGS8SJSiFDCOjs78ysGztc0L5Ma6C4WsemhHxe/+MkfYLdGN3Fh94hbng5aVDIZIi6tmARZwq5DI1SgZeT5oKnWJ3RRGGljvwKHelFlszWWxMLyCytQa1UMLqRooZN4au/TjMswpN2ntff5+tyKOSyC6YlwZLxtkRrRunsRf8RN9Fv+FjUCjkeDmrS09M5ceIE8fHxxMfHs2/fPr755hv0ekv+Jy8vL6sYioyMJCIigsjISPz8/P5RFiKTycSvv/7KG2+8QXx8PHPnzuXh2XN5bUMyMWdKeeP2Lgxo587sX2Lxd7Vl05NDrMk+R3T0ZrerPSbTpSMjjxw5wpw5c9ixYwfDhw/n22+/pU+fPoDFkf2zHaf4Zk+6NVJLo5Rz/8BQHhnaHmc7FbUGE1/uSuOjbSnoDGb+M7gdj9aFn3cPdOHF1SdQymUcPlPKLd18mTYgmBCPtvcdbSrB7nbkVVis2N5OGrr6u+Bur6ZEq2dzQj5qpcW66eWowdNRg6udmmWHMtkQn3tZ4XPXXXfxzDPPUFZWhouLy3U4m8aRLD7nIFl82iafffYZjz/+OMOHD29zFd4LKmp57KejVOuN1BhM6AxmZDLLDVEuk2Gu+3kp5TJkMhn5FbUsvL0bX/2VhrOtijAvB/6Iy2P3/4Yhl8tIzq9k7bEccspq6eDtwOjOPoRcpEZVrcHEofQS9qUWczSjjGq9Eb3RTKSPIx19nYjwcWTZwUyC3O0ortKTXlxNUZWO/Ipaq0BqKc62KoLd7Qh2tyfYzY6guumyYHc767TP6aJqfjucia+zLff0vzI/rY+3puDrYssdUQFX1E9rozeaKa7WoVbIL0iwaDAYOHXqVANBdOLECU6dOoXRaBHJDg4OhIeHW8VQ/dK+fXscHRvPotuW0Ov1/PDDDyxcuJBTp04xduxYnp07n3iTN4u2pzKpdyCzbgxjW2IBS/dn8L8xkfQNdWvQR3mNgffefIPPFy8iLy+vwbbMzEzmz5/PDz/8QMeOHXnnnXcYO3YsMpkMo8nMTwczeH9zsrXkjEwGE3sFMHtkOH4uthhNZn4/mk1CTgU/H8zgpq6+zBkVToCrxT8lJb+Sx5fFopCDp4OGx4Z3ICq47VyTWsrhM6V8vec064/n8u6k7kys+73VGkzojGacbJQNrkNCCMKf24CLnZoDc0dcNCCknpycHAICAliyZAkPPPDANRm/NNXVQiTh03bZtm0bkyZNwt3dnTVr1jRIZNaWWXMsh8d/PnrRbSqFJRW8s62KJ28MJ7NEy4Se/jjaKFEq5FTrjGSWaKnSGXHQKLFVKawXHo1KTpCbHZW1Rk7mVhCXXY4My9TLybxKEnIqKKrSWY9lp1Y0KAp6rbBRyVk6ox+BrnYcOF1CtwBngt3tr7hfIcQ/ytrRVAwGA6dPnyYpKYmkpCQSExOtfxcWns0c7OnpSfv27Wnfvj2BgYG4uLg0WNzd3fHx8cHb27vV8hCdOnWK1atX8+GHH5KZmcntt9/OnP89Q6rZiy92p9EtwIVZIzqgkMt4Y/1Juge68PDQdg1yaWWWaDlyphQXOxW7ln/JRx9+YP0cKisrefPNN3n33XdxdHTklVdeYcaMGSiVlgKda4/n8OGWFNKKzlpoh4R7MndsJB19LXW3/jyRx7ubkzlVUMXwSC9mjwy/IGqpRm+iUmcgs6SGnoEujd7w/05U1hp49vc4NsTl8uzYSCb09L+oNfNcohdsJbe8luUzo61lOi7FoEGD8PX1Zfny5Vdz2FakqS6JfxzDhw/n4MGDjBs3jv79+7fpCu/n0tg1sT4STAb8daqII2dKUSnl1twys3+JZVikF+O6+7H1ZAH704rxdrbBy9GGFUfyWHMsx2qmvxzXQ/QA1BrMTPv6EG72asZ08eHA6WLKa4zYquQ8d0snnGxUCCHYn1aCzmiifzv3Jk3pnf+kaTAJ9CazNe2+wz80j5FKpSI8PJzw8HBuvfVW8itqkcnAw15DWVkpycnJpKamNlj27NlDeXk55eXlmM0Xfj88PDzw8fHBz8+vwRIUFESHDh1o167dVUk2V1NTw44dO9iwYQMbNmzg1KlTqFQqJk+ezP/NfopjVY48vf0MfUPVfD2tD56OGhbvTCO/opbnb+lEiIdFMAshSMip4FhWGa52am6I8MLZTsUBtQqj0YjJZOLrr7/m+eefp7y8nNmzZ/PMM8/g5GRJQ7DpRB7vbkomKf9spFagmy0v3dqZER29MZsF647n8NHWFJLzq/B3seWrab0Z0dH7oudlq1Zgq1ZcVhRcbQwmMydzKzCaBUaToLBSR06ZJQmnZ930k6ejBjt1y34L9dOInfycWB2bg61Kwb3RIY3u4+moIbe8lk0n8i4rfPr3789vv/3WorFdbf6ZVwuJfyTt27dn3759TJ06lZtvvrlNV3ivR96EsbnaqTGazBRX6/lyVxprYnNwd1CTWaLl96PZvLA6HrlMRnEzCqG2JlU6I1U6IwUVtZRqDdaaRg8MCsXJR8Vr60+yIS6XADc7Xlh9gv/e0J6JUQGoFHLSi6o5mF5CJ18nOvs5XfR/K5PJUCst0WJgsXKVaw2YhUCllP9jRRCAt5MNtQYT2WU16E1qXEI60dk/knb9TUTrjIR62FurfgshqKqqoqysjMLCQvLz88nNzSUvL4+cnBxyc3NJTExk27Zt5ObmYjBYpn4UCgUhISGEh4fTrl07fH198fHxsb56eXnh6elpLeVgNBopLCwkMTGRhIQETp48yYkTJ9i/fz+1tbUEBQUxduxY3n77bdzDe7HmRClv7q9ibBcn1v7fINzs1ZwuqmZ7UgHTB4Tg42wRFKXVeg6eLqawyuI8flNXX1zPyVyuUCjQarX07NmTuLg4pk6dyhtvvEFQUBAAB0+XsHDDSY5klFn3USvkzLyhPf+9oT02KgVHM0o5fKYUjUpBTlkt0weEtLj21bWiuErH4TOlFFfp2HQiH4VCRmGljuyyGmtdwP8bHkapVm9d3y3AhV5BrjjZKFHIZdiplUS3dye1sIr47HJCPewvmUjRUaNiX1oxL61N4O5+wY1GbHnWTdluOVnA/JsbD2vv27cv7777Lvn5+Xh7X1xUXi/azn9XQqIJODk5sWrVKubPn8+cOXOIi4trsxXewfKUdi4PDgplSLgnZiFIyqtk4YZE7DQKburqy7GschRyGTqjmezSGox1yRRLLxE11dY5N1IG4K+UIiJ9nNhzqojvH+xLe08HNiXk8/APh/lgSwqONkpSCqrwdbbhnv7BrDmWg9Ek6OjrSKSPE2FeDtbw4XNRyGXWRIRCCAxGM8i4ILv2PwUblYJAt8vnQ5HJZDg6OuLo6EhgYGCjbc1mM7m5uaSkpJCcnExycjJJSUns2LGDvLw8iouLL9hHqVRiMpk411ui3kLVsWNHXnvtNYaOGEmljTe7U4rZUa0jKKuGGYPbEeHT0Ccp1MOe0DoLT3x2OaeLqtDqTKgUMiJ9nOgZ5HrBDdje3h69Xo+TkxMHDhygb9++gMUH582NiWw5WdCg/bAIT164tTOhHvZkFGtZfjgTtULO3tRiSrV6vn+wL72C2o6vTk5dQeRSrZ6RnbzJr9Dx44EMTuRUNGhnq1Iwe2S49SHh852pLNiQyE8HLNFuvs42dPR1wtfZhkU7UvntcBYA0weE8NK4zg360ijkuDtYxKXJLCip1uPpeOlra/2200XVZBRrGy3DUf//OXTo0DXP4nw5JOEj8bdDoVCwcOFCunbtyoMPPkhycjK///57qz9FXIzzp5jc7NUEutoil8nQ1dXqqqgxEp9djoNGyZsTuxHd3h2wVIEe++Hu6z7ma8W5F1BnW8vFdV9qMSqFjLyKWmYM7ohZCDYn5PP+5mRGdfZmZCdvnGxUxJwp4bfDmRjMAqVchpONig7eDkT4OBLqYW/1A5HJZKiUMmsGbommIZfL8ff3x9/fnxtuuOGC7Xq9nvz8fPLy8igsLKSwsJDq6mqUSiVyuQJnVzecfIKROXlRY5JhNFlumkcqBB3t5cweFX5ZS5zJLDiZW4HeaCbE3YF2nvbYqRWXtOjec889dOvWjejoaGQyGVmlWj7eeorlhzMblFzoG+rG06Mj6BPiRlGVjpfWnGDpgTMYTIIOXg5M7h3I9IEhbUoo7z1VxN7UYvqEuuGgUbI9qYDc8lq8HDXk2asbWH89HNUNPqOCSl2DvnLLa8ktr+VoRmmD0hvf7k3nzr6BDVJMeDnZ8PuRrHP6qm1U+Hids21rYj73Dwy9ZNvg4GA8PDwk4SMhcSVMnTqVDh06MGHCBHr37s2aNWvo2bNnaw+rAdXnJRJ8+88k3v4zqcG6jBItDjZKRnby5s2NiRjNZoSA/Aodjw0Lw8FGicFoSQhYnyBwY3wewyI9rYnGGt4bGt4omjsTaOlTYDaDWQjMAn4/moUQ8Oiw9ijkchCCKp0RAeiMZkwmS/4ag8lctzT822gyNyiwqqiLcntpXGeeHRtJXHY5aoUcVzs1fUPdcdAo2JZYwJe7TpNaWEUXf2e6+jvTM9CZzn7O+DjbkF5czbHMMlYeyabWYEIADholYV6WDNXtvexb7O/QGpwpruZMsRaZDILd7FtUxPJaoVarCQwMJDAwkFqDidzyWmr0JsxCIASEetqj1RtxslE1OSu4wWQmKa+SGoMJhVyGSi5v1v/MwcHBWrh0c0I+j/x42GolBegW4MxToyIY3MEDndHM0v1n2JZYwNbEAvqEuHJvdAhjOvtYp0xbmyqdkdiMMoqqdBRV6RgW6UVHX8cGn8fUuirvX/91mlfWJeDjpKHrec7X9holnfwccbdXk1pQjQDy6qadO3g5YDQLTtc5eK88ks3cm84KHw8HNQ42Sqt4KqjU0dAm1JBzRdHe1OJGhY9MJiMqKorY2NgmfiLXjr/PVUFC4iL07duXQ4cOMWHCBAYOHNhmKrzXE+HjyNBwT6ufy6XYklDAjzP6IZdZ/IJksjqHXlEvPiwCRNS9PjSknfWmU7/N8r6+ncWxWi6XoZBZQusFdYkJBRjNAj8XGzJKtAS42pFWWIWvsy32GgUp+VUYzcLaP8CQcA9kMhn+LjZ1YfqWMFelXGZ9uu4a4Nwk/xq5TGadtqg1mDiRU0FFjYG3NiZRazRxplgLWEz0YV4OuDu4UarVsyulkP1pxZwqqAIs0yPh3o6093Kgk58TIe72uNmrKajUkZxfyeaT+dQaTIzt4kPPNjSFcSmC3e2tEXA64/VxRm8JNiqFdVrqXJrrW5VRoq2z6lz5bahnUF10lVnQzsOep0ZHMLaLD2YBv8Zk8tHWU9io5NzUxYdnx0bSwbv1wv9PFVSSlFdF90BnzhRrsVHJMdf9XnuHuDZJONZXTnezV+N9npP17JHhzB4ZjhCCSp2R45nl3P/tQQwmQU5ZTQNxuDo2h2fGRFoj01zt1LT3cLBWYC+saGg9Op9zhU/91FxjhIaGsn///su2u9ZIwkfib4+/vz+7du3iwQcfZPLkya1a4f18fj+SfVnRA5CUX0mf17e0+DjDIjwprtZzplhLeU3TfII8HDSU1+jxdbYlo0TLsof6087Tnts+29Ok6sz1PHJDe5bsTmN0Zx/6t3PH01HDn/F5FFTqLE/yChm39Qzg5m6+AMjl8MLqeIqqdMSklxLgaouTrYobO3nRK8iVV9cl4GavRi6TcSyrjIqas1azTU8OIdDVjqT8So5nlXE8q5wNcbmk1IkhABc7FcHu9oS42xHsZsf93x7i6+l92pT/xuU4N4T7n0r7q5iQ0sNBw8wh7Qhws+P2nv4oFXIScirYnVKIq72aZQ/1b5Jf1LWkstbAh1tS8HDUEO7tgL1ayYD27i0KzijVWqa6EnIrLxlNJZNZpoSDz7EcVp839Z5XUUtGidYaQedqr8LLSYODRkmfEFfaeTaeisLzHNGVlFdJeY3BWn/wYgQFBfHrr782fnLXAUn4SPwjsLW1ZenSpXTt2pX58+cTHx/fahXezyXSp3lPlgqZxf+lRNu8CK4ag4nKWmOTRQ9gzfOTUWKxsNz5RcuexGyUCgwmwbrjuRzPKueGCE9+P5rdoE2kj5NV+MiwOKy+uTERtUJGkLsdyXlVjO/hb70ZronNZu3xXIaEe5JZrOVMiRajWTDq/V042iiJCnalb4gbT42KwMfZhjKtnsNnSjmUXkpMegnHs8o5VvdUDHD3l/t5+47u3Nrdr0XnKNH2mT0qosH7Tn5OdPK7PvnYhBCUag0NpnPr0RlNbIzPI7+ilv8b0aFRYdBUhnTwJK2wmoeGtGNYhFejbV3sVIyI9GbjibwLtnk4aPBzOVtew0apoLzGQJXOiLuDht4XEVX1lqSCitoGucKMZkFKfuVF96knKCiIkpISqqursbe/8vxeLUUSPhL/GGQyGXPnzqVz585MnTqVgQMHtnqF94eHtkdnNPPe5uQmtfdy0tDR1xm5TIZGKWd9XG6T9tufVnIlw7TSPdClgWBoCkrF2SdWfxdbXh7XmZVHshsUSq01nH3SlMsgLttyDKNZUFCuw6WuRIBCLqN3iBuvrkvAtq4o6isTulBUpWPWslgAJvTwZ3tiAVq9iU+3n+K/w8KYNiCEER29rblXag0m4rPLrUIo5kwp//fzUQ6cLmb+TZ0uGh0mIdFUyrUGjmSWUq0zolLIEQI8HdW42LqSUaKl1miitNpATlkNOqOZGzt64WKntk43Xyn/GdKOzFItt/eyFLpdvDMVNzs1fUPdGuQ/kslkGEyCA6cvjMoDSxj8uT5OaqWcw2dKAUu2+Q+2JNdlfNdRUKmjsKKWomq9NX/Y+ZF25ztWn099dGFmZmarJqGVhI/EP45x48axb98+xo0bR58+fVq9wvvjIzqgVspZuCHxsm2LKvWcMJdza3c/7Opuzrf19Mdeo6CwUkeJVs+h06XXbKwD27s3W/hozrlwphRYQvRrDA1N6ue+D/d2tIbomwWcyK3AyUZJeJ3fhZONktFdfDiaUYYQkF5cza3d/ege4MyxrHIifR2xVSv4YlcaYHEYX7wjlXE9/Li9VwC9glywUSnoHeJW9/TZHqPJzH+XHmFjfD5VtUZu6xVA3xA3SQBJNIuCylqOZZbjZq8i+pzkm6eLqlkek8mjS4+SV1FLzyAXXp/Qlf7t3NAZzexMLuTngxlM6R3I2K6+V2UswyK9+HBLChN6+luvLXPHRtLF35lgdzv2phYxuXcQRVW6i1qCR3Xy5t7zysmoFHJKqvXcERWATCajtFqPm72GTr7OeDlq8HDUsCEuF53RbK3hJ5fJUMpl2Kjk9AttPIlhfY4lSfhISFwDunTpwsGDB7njjjsYPnw4ixYt4sEHH2y18cwc2h61Qs4r6xIabWcwC/IrdSz567R13cqj2Tw4KJSSaj3eTjb0C3XjdFH1ZZ+uLoVSbglbNZvPOkWbzQI/F1t+jcm6fAfncW4YcKCbHSM7efN5nSip59z6YEFudpRqyxtsl8lk1ptIvc+DWiG3PCWbwclGxbuTu/Pe5mRsVQr6t3Pji11p9AxyITazjEqdkaUHMlh6IIMAV1tu7urLmC4+9Ah0QSaT8cq6BDYl5AOW/EJ7U4vp4u9MtwBLtFhXf2e8nK5vJt5/Cpfz62gqZrOgstaAs92VZ42+Vng52jCy09nvycb4PD7cmoKjRomjjZK8iloA4rLKuferA9iqFZRrDdQaTchkMjJKtAzq4GHNknwlDIvwwslGac3LA7CgTgBF+jji62zDtpOFGM1m7NXKBhbYfqFufHhnzwvKbSgVMoLd7Xj7jm6X9D3qEejS4jH7+lpEX25u0yzZ1wpJ+Ej8Yzm3wvuMGTOIi4tr1QrvDwwKRaWQ8fzqE01qb6eS06POIfevU0V4OWqwUSmwUysIcLW1Ch8nGyUCqKw1XtBHzyAXTuVXNbjoRXg7WZKUyc4GvuuNZnLKahrM2TeVc6NEVAo5vUPcsFHJG4gd0zmlE853sHx4aDv+e0OY9b3ZLCis0GE0m5k5tB1hXhZLUJiXI77OtoyI9Gbmj4eRy+CuPkEcPSczL0BWaQ2f70rj811p+DrbcEOEJ3/E5bH4nihc7VQcPF3Cl7vT2JZYwLbEs0nuvBwtocGd/Z3p4udE1wBnfJxs2nRm8GtNeY3emnPpUlwN0aM3mtmckE8Xf6c2LXzOZ1Qnb4ZHWnxs8sotPjwyLL8JXV1xT73JjNEkMJotqR3WH8/FyVaFq53aWg29fmrb0UaFj7MNQW52TcorFBXsRoCrHZtO5FFcrSfMy4Enbwwnv6KWoio9fi42LNl9mgUTu6JRKiiorKVfqBvtPR0afK8NdeVfALJLa3h2xXFGdfbBz8WWdp72V83ZXqPR4ODgQFFR0VXpr6VIwkfiH41arWbx4sV069aNxx9/nISEhFat8H5vdAgGk7is5QfAxV7N8EgvymsMHD5TSic/J1ILqonwdmJd3NmsyE/cGI7eZL7oVFp9rpVzKdHqOZFbwWsTujC1XxBTPt+PvUZBR19n3Ow1HG3mVJfBZEYptxRcVdRdTG1UigbC51xxdKqgskEY7LpjuXXTWhYLVHGVjvS6kPZZy2KZ2CuA/WnF3NTVl7isMnYkF/DelO5EL9jG70ezcLZVXdKpO7e8lp8PZgIw88fDANzey58v7ut9gTN3QaWOrXV5XupxtlUR7u1AmJcj4d4O+LnY4uGgpkpnIru0BmWduV+pkJGUV0leeS0qpcz6OQhBA68OmUzGzd18GRTmYV0nhKCoSk9qYRWphVWcLqyuE4cCrd5EfkWtJWzZyQZ/F1sifZzo6Ot4QYX3a8HlRM/VQq2UW53f2yJmsyC7rIaUgkpOFVSRVlhNenE1ueW1lFbrqTFYLDqW1BF1aSTkMmSczVkll1kc+y1h6254OmrYGJ+HWQiq63Jiyeva2aoURPo6ERXsysAwD7oHOF9SgHs72bB37ogG66p1Ruw1SowmMzUGE2O7+DZaeiK3rBa9yYSjRsVzt3RkUJjnNYuC8/Dw+HsJn0WLFrFo0SLS09MB6Ny5My+88AJjx44FID8/n2eeeYZNmzZRVlbGkCFD+Pjjj+nQoUOj/a5YsYLnn3+e1NRU2rdvz+uvv85tt91m3V5ZWcnzzz/PypUrKSgooGfPnnz44Yf06dPH2kYIwcsvv8wXX3xBaWkp/fr149NPP6Vz58bSL0n8W/jvf/9LZGQkkyZNol+/fq1a4f2BQaGolHKeXxUPWPLV5JbXXtCupEpPrcFEmdZAUl4lOWU1pBdrae9pj7IJFaE/vLMHXfydkWG54c5fGcfe1GKigl2ZMyqC2rqLtY1awfYkS8h9oJslwsPTUcNZvXQ2/w9YpsbOLaOhN5q5s28gP+7PIKNEy/ubkyk7r8yG0XT29t/Zzxn9OaU8xnTxwdNRUzdOy81hfVwusZllDAn35IFBoTwwyJIY7VB6CWO7+FKjNyGTWaprn8humML/cqQWVpOYW8HDQ9uRX16LnUZJN39nymsM/Hkir0Ftp/IaA4fSLdFi9chk0M7DnkgfJ/xdbbFVKTiZW0FiXiUqhYwxXXzJKavBRiVn/+kSa7I4sORdeXBQCBvictmbWkxqYRUGk5n8unwpoR72vDu5O+72Z7PxVtYa+O1wFntOFbE/rYTCyjRKqnW4O2iI9HGkk68Tkb6O9AmxPP1LtBwhhDUPVFKeZUnOrySloOqShX7fmtitUVEBll+QqMuLJRD4Oluyt4e421lLQ5jqhP/ZsVisuBvj89gYn4dGKWdir4BLJrasNZhYE5uDp6MGXd3U2uAOHtx3mUKjQIM+3Rz8Ka22REkK0XA63GgWmM55NZkFTrZKBoV5NNky6u7uftESKNeTZgmfgIAAFi5cSFiYxSz93XffMX78eI4ePUqnTp2YMGECKpWK1atX4+TkxHvvvceNN95IQkLCJUPX9u3bx5QpU3j11Ve57bbbWLlyJZMnT+avv/6iX79+AMyYMYP4+Hh++OEH/Pz8+PHHH639+vv7A/DWW2/x3nvv8e233xIeHs5rr73GyJEjSUpKwtGx9ZJVSbQd2lKF93v7B6NRynlmxXH6hLhRpTM2mHYBy8XmeFY5fi62yGQQ5G5PfoUOsxAo68zgchm8sympgUWlnkU7UvGvC1UVWPJsABRX61l5NJvZI8MBeP7mjiT08ufdTcmEeztQVKnn0PwbLzn2iloD3V7aZH2vN5qtjskBrrYXdXA8d3w9glxYdjDD+v75Wy4sbjgs0os3/jjJecYqCip0lpIVtkrkMhkx6aUNpvGawrHMsgscuNfbqugZ5IKfiy3tPR3QqOQo5XKOZZVdMJUmBKQVVVOtM7E+Lhf3OsvcvJs6UlKtx2g2MzTck+UxmdZUAfVU1hqYuGgfz46NJD6nnIK6Kb164ZNZquWxn45gqCv54GSrYvWjA7l/YKg1K26twcTq2Gy+2JXG7pQidqcUEeJux+u3dcXX2fayN+F/O5W1BuKzy9HqLcVes0tryCzVklGiJb1IS1Uzv0//W3G82WO4IcITRxsVa4/lXL7xOXyy/RT39g/myRvDrfXp6ll7LIf/rTiOSiGjs58zt/fyb1Y+roKKWuatjGdcDz9WH81uYPm8HBer+3Up9Ho9anXrTmc2S/jceuutDd6//vrrLFq0iP3796NSqdi/fz/x8fFWK8tnn32Gl5cXP//8MzNmzLhonx988AEjR45k7ty5AMydO5edO3fywQcf8PPPP1NTU8OKFStYvXo1Q4YMAeCll15i1apVLFq0iNdeew0hBB988AHz58/n9ttvByyizNvbm59++omHH364eZ+KxD+WtlThfXLvQDRKObN/PcbtPf0Z3MGDoxllqBQySrUG9CZBQk4FCTkV+DjZIJdBF38ni/CRy3h8uCWMO7Wwmilf7GvQt0wGiXmVJOZVsurRgfQIdOFIRim3f7YXg8lMSn4lVTojDholfi62lGkNuNqpmlSV+nwxYhaCXw5ZppNMZnFRq8O5Pj6fbT/FofRSHDRKS9mLurDbcwnzcmBAe3cKK3XU6E3YqOSWqQSFDLlcZvVJ0DSj3ICPk43V+fR8ymsM7EhqmGjyi3ujeGlcZ07klPP+5hS2nMxv8BnU91VcrWf54SyWH27oGP74iA4kvzaWhRtO8uVui7O6wSQorzGwPbGAggodNio5pdqzloRgNztuiPBi28kCymsM5FfU8tMBiyUtwseB23oGYBaC9XF5FJyTVXdS70CmLjnA9AEhjOzkzYG0YnoGudI90OWiuWX+6eiMJjJLaiip1pNXUUtaoWV6Kq2oiqJKPVq9EV1d+ReAEA870ou0l+n1LPXf3ZZSoze1yDfKZBZ8uzedtcdyeGlc5wZ5qXoFu7J/7gjcHdTNqjtmNgvkcpkliWhHL15Ze8Ia7ejvYkt+Re1FH6zOJeZM09NplJSU4ObWePTXtabFPj4mk4nly5dTXV1NdHQ0Op3lR2hjc9bjXaFQoFar+euvvy4pfPbt28eTTz7ZYN3o0aP54IMPADAajZhMpgb9giVh3V9//QXA6dOnycvLY9SoUdbtGo2GoUOHsnfv3ksKH51OZx03QEVF80zmEn9P2lKF9/E9/NEoFSyPyaRHoAsapZy/Tp2d/86qSwOfdU46+G4BLigVMroHuuDuoOGzHakXiJEufs7EZVsip+oNAPVtgt3sUMrlfLX7NLNu7MAdi/fh4aDG39UWg8nc7NpetQazNXOy3mRGa7jwhnDuhbNe5NTfOISAXSmFxKSXEOBqi1ohR28yY6NSIJfLuHvJfn59OBqVQoa6LmeQDMtT8/Gs8guOdTE8HDQYzpleawr1Y+7s58ySab05nlXGa+tOcjC9aRf5fqFuKOQy+oS4WYWPnUpOhI8TrnZqsstquKWbL7UGMzqjCZ3BTFG1jm0nC6jSGdEZTGj1JuatjAPARiXnvc3JlukSAd7ONuhLtOiMZj7ckgJYplMySrR8tO2UdRzB7nb0DHShR6ALPYJc6eTr1GbqU10rNEoFYV6XT14qhEBnNFNVa6Ci1khZjYHSaj0FlZYQ8GqdkcpaI1q9kWq9iapaI1U6o/W1XrRbwrotPjrIsJaJkZ1TggbOTuX6ONsQ6mHPwDB3NEoFyjo/MYVczvk/PydbJW52atzs1bg7aPBzscHX2Rbv86IQm5sJu0pn5IVV8WSUaDmSUcqSab2Z0ieQj7edop2HA5klNcy6sQNZpTV8tDWl0b6a+tMSQlBcXPz3Ez5xcXFER0dTW1uLg4MDK1eupFOnThgMBoKDg5k7dy6ff/459vb2vPfee+Tl5TUaupaXl3dBVW1vb2/y8ixZJh0dHYmOjubVV1+lY8eOeHt78/PPP3PgwAGr71B924v1c+bMmUsee8GCBbz88svN/Qgk/gHUV3jv0qULM2bMaNUK72O6+KBRyvk1JpPeIW4UVOoavaFbHCblfL3nNIl5lRfNu1MvegAe+DYGjVJu9avZlVJEYaWOF2/tZO2v1mDCTq3A3b4J4u88kaXVnxU6GqUcDwcNU3oHUms0UWswWW/sPx/M4K6+QRdc2M1C8Nn2Uxw4bREUUcF1qfIFLD+chZ+z5QKvM5pwslFRozehVsovsNA0hsFkblZW6/p9zqVbgAu9gl2bLHzmrYxjXHc/fj9yNou11mDm1Qld6OLvzGPDw1hxJIt2ng44apTYqRUUVemJOVOCRqlgZKfObE4oYPHOVMAiMDNLalAr5EQFuyIQhHs7sCOpEI1SjsYsx9/VtsEYJkUF0DvEFVldCF9KfiWZJVo2nsgj1N2Odp4ORLd3x9e54X5tGaPJzP60EoLd7Qh0syM+u9zqU9bV3/mCKaDGqE+jYKNS4NGGPSLqsyWXaw141kV3thSDyczLa0+w8kg2BpOwXhfMZsvnUWMwkVk3RZuQU8FToyO4p1/Q2bw9cosj/6TFZ63M4vwnr0ug1Wqpra3F09OzxeO/GjRb+ERERBAbG0tZWRkrVqxg2rRp7Ny5k06dOrFixQoefPBB3NzcUCgU3HjjjVbH58Y438x9vun7hx9+4IEHHsDf3x+FQkGvXr24++67OXLkSLP6OZ+5c+cye/Zs6/uKigprZkmJfwf33HMPHTp04LbbbmvVCu/DIr3QqOT8fDCTWSM68OhPRxpERZ2LQiEDIdhzqpiSagOJeQ0tlR4OGh4YFMLdfYNYeiCD8hoDKfmVGOsKOGqUcuzVCr7+6zTf7k0np6yG/qHuKBVyKmsNFwiTy+Fko7TuI4Tl+G/e0e2S7eff3JGqWiMzvo9Bqzfx2Y5UiqvPlugoqdZjFgIHjRK1Us70gSGoFHJGvb+z7qIvv8DC1RjdApypqDFcUviM7OSNnVpBVa0lC6+NSo6Ai4qB3SlNF1tnirV8fI7lpZ5v96YzurMP7evqII07r4zGudFN9Vl0z0VvMrMv7axzqJ+zDTl1zvG25xX87BvqxqTeF17TPtuRSmxGKd0DXSio1GGrUpBdqiXCx4m+oW4EuNq22TB+pULOoA5no+K6nFed/O9OmVbPje/tpKLWyL39g9kQl0tRtR4hBP83vAN9Q93o3869yf1ZrVo6Ix4OGg6fKeWnAxkX+P/UR39qlHJrnovc8hoqagwNyloA1uSq9VxuKqyeeiPF3074qNVqq3Nz7969OXToEB9++CGff/65teR8eXk5er0eT09P+vXrR+/evS/Zn4+Pj/XDqKegoKDBk3f79u3ZuXMn1dXVVFRU4Ovry5QpUwgNDbX2AZYPtT5B0sX6OR+NRtMq0xsSbYt+/fq1iQrvA9p7YKdW8t3edOaO7ciLay7M9+PhoMG27mnP01FDhLcDiXkVBLnZUWMwIYSlBtfxzHL+e4MamQzm3dSR8Z/u4WRuBUvu642rnZr/fB+DUiGjnYc9Z4q1HMsqw9/FlmqdsVlhrI42StwdNFZTflkTrCrdAlwoqKzFLART+wXh7qBmwe1dWfDHSQZ18MRoMrNop2X6rm+IGw8NaQ/AjR29WbQz1VrJuh6Z7EK/o3MJ83Igt6zWGiJ/Pq+O74KDjZIuL/7JjEGhPHeOs3VRlY7s0hoEFoF3sei7cz+L+sy3jfHb4Sxr0rkFt3e9YPvYD3djMJlRyGToTWbUCjm//3cAt3z810X7MwlBZz8nTuRUXCBa6z+Wgopa4rLLrSU9Zo3owBO/HOWPuDyS8yqJCnbjl5hM634+TjZEhbhap8i6+DtfkZXh30qZVs9jPx0lp7yG9p4ODI/0okZvYlAHD2tAAMBnO06x7GAmNio5DholRVWW75CTjSVVg95oJsLbkcdHXBghLYTgVEHVRSvOp+RX8uhPRyio1BHp48jie6IAy/Tb+Wku6stQBLnZ4e9qS1phNX+eyKdbgAuPDgtr0PZ8B/qaS0S8nc+uXbuQyWT06tWrSe2vFVecx0cI0cBPBsDZ2aLAU1JSiImJ4dVXX73k/tHR0WzevLmBn8+mTZsYMGDABW3t7e2xt7entLSUP//8k7feeguwlLr38fFh8+bN1qd1vV7Pzp07efPNN6/0FCX+BbSVCu89Al2wHdqeT7af4tbufg2iPlzsVLjaqUjJr0KjVKCQydiWWIAQFivJuc6WJrOZnw5kkF9ey0t1Aqp3sCsvrTlBZ39n9EYzbvYajmSU1SXpAzcHtcV/QWe0XgStvgl1/cpkMoxmMz0CXQhxtyPA1ZaDp0ssvg1YImYux8ncCm76aDc2SgUFlTomeFtCsWsMZnycbLi7XxATowL432/HGVaXHO5YZilJeZUIYXHwrDGZsVHJuadfMGuO5TSaxVoIGlhIzkchl1lN9d3Py0q7MT6P5+rSDtzY0dt6np39nEgvqm6QjLGDlwOL7oni9fUnWdPEaB1HGyVphVU42CjxcrQht7yGtMIqq9PtvJsiqaw1sjo2m8eHhzG1zgKw5WQBOqPJmufnf2Mimfb1QWxUigZTdK+vP8l7m5IxmMzc3S/IKny+2XPaalE8VViNwCIyE3IrqNIZyauoZf3xXNYft7gpKOUyIn0d6R7gwv0DQ6xJJf+tFFbqGuSiuhR6o9nqs5dWWM3muuzh70/p3kD4HE4vvSACEMDdQc1Hd/Vkz6liyrR6Mku0DR5MThdV88QvsQS72fH2pG7WRINZpVpm/3KswbTs/rQSMktq6N/Ona+m9+FYZhnVOmNdHio5HbwtPkIymeUBqx7TRaw5ivOsgTpj04TPxo0b6du3L+7uTbdYXQuaJXzmzZvH2LFjCQwMpLKykmXLlrFjxw42btwIwPLly/H09CQoKIi4uDhmzZrFhAkTGjgd33ffffj7+7NgwQIAZs2axZAhQ3jzzTcZP348q1evZsuWLVbHZYA///wTIQQRERGcOnWKp59+moiICO6//37AcjF+4okneOONN+jQoQMdOnTgjTfewM7OjrvvvvuKPySJfwfnV3g/ceIE33333XWvIhzh48ickeG8symJoeGe7Ey2TK+UaQ2UaQ0UVum4saM3uRU1GGosF6VzRU9HXyfmjI5AhoyoYFdkMpjaLwilQo7RZMZoFswYFEqopz1O56XON5jMnC6q5mhGaV3uEUveEQRW07hSIWPh7V35fFcan2xPZXAHD0Lc7UnIrUB5jlA0mwXLDmWSWljFw0PaWUtCdPR1wl6tZEwXH96Z1N3a3sVWRWGdgGnv6UDfUDdGdLQIn+PZFdbwWrMQnMytsH5WBX81nm26/sn2vuhgskprLkgboFLIrOe29WQ+u5ILMQuY2Muf3iGuPH9LJxQyCPawJ6OkmqIqHbUG0wUZqI9klHHrx39d1OpVX2fsfI5nlvPYT0e5rac/70/pgVZvYnRniwU7tbAKlULO6tgcvBw1jOnig61aQV6FroEDfICrLQPbuzO2i4912uuOKH+m9Ani679OsyHeYlE/9wH//Jw0T42KYGxXX277bM8F4ftgmcqIz64gPruCGzt5XyB8Cipq2ZSQTzsPe1zs1FTWGqyWL41KjkIux0Ypp4O3IwqZjNyKGhxtVDiolWhUctQKOQazmWqdidzyGk5kVxDd3r1Z1seDp0tIK6wiq7SGnHKLL5RKYfE5GxbpSbcAlyb3VU+VzsgXO1OZeUN77M6ZRqwv+unrbENU8KUddc8vCVGPEJYkgzllFmtieY0BG5WcQWGebDmZT4CrLS/e2plhEZ4oFXJGdPTmVEElPx/MoFuAC/vTiimp1tPV35kv74vCTq3k5wMZTO4TiJ1aiVIup187N964vSs3f7TbKqRzy2voGuDM0HBPhoZffLopu6yGyedMj55vGbrYeTVlWtRoNLJp0yZmzZp12bbXmmYJn/z8fO69915yc3NxdnamW7dubNy4kZEjRwKW+huzZ88mPz8fX19f7rvvPp5//vkGfWRkZDR4ih4wYADLli3jueee4/nnn6d9+/b88ssv1hw+AOXl5cydO5esrCzc3NyYOHEir7/+OirV2Yv2//73P2pqavjvf/9rTWC4adMmKYePRLO4WIX31atXX/cK7yEe9sy/uSOvrE3gtp7+rDx61kG23pEzs6QGDwfLDfGOqADm/h5HRokWhdxSPXp7UiEZJdXkXWR6pv5CdW7SQGSWKaRqnSU5YHQ7d46cKSUx35L/x91eTVGVHpnMUgvI2UbFnX0Cic0s4z+D2zFn+THAchH/anca+9NKrE+cRVU63p3UvUH+IeM5lgl9nQ9CtwBnThVUWSNyZHX7dvZz4r3J3S2h/5/tpVSrR2c08/PBjAvKY5xPvZjKK69t4EtUj0Iusz7Vroo9a6kp1er5enofIn2crOuGRXgxefE+EvMqcLFTMTzSi8JKHYWVOtzs1Xw9vQ8b4/PYnJDPnyfyGNzBg94hbrjaqbk32uJbZadRWl7VSvafLsLP2cZ6/HYe9rw5sRsCwQ/7zlCtM2IWArVSTmWtkZ6vbKZ3sGuDcZvNlrxOi+qmMXYkFeDlaEOfEDcW7Ui1jv3cyuC2501bLT+cRbXexF19gjCaBIl5FRhMF58/9HW+sKZZiVZvtYw1hkoho72nA4l1OaXOx9tJg7+LLRE+jkzu03R/y6lL9jO6sw/3RYfw2roEDqVbrBv1JOdX8ulUy/RKZa2B5Pwq9EYzBpMZfV1ZCYPJUlKi/uFAAIfTS9iRXMjwjt4NalTN+fUYOqOZGyI8efHWzuxMKqBEa6lZ9mBdsk3gkv5yQlj8t+77+qB1nZu9mk/u7smYD3ZRUq1ncAcP6+8FLCVbjmWVEZddzr39gxke6YVSIaewUseygxks2JDITV19sVMr8XBQ8+SNHTiZV8ljw8I4U6Llt8NZZJfVWCrLZ5RarannU1JlyURt/X5d5GtwvsWnKamjDh48SFlZWZP8fq81zRI+X331VaPbH3/8cR5//PFG2+zYseOCdXfccQd33HHHJfeZPHkykydPbrRfmUzGSy+9xEsvvdRoOwmJpjBu3Dj27t3bqhXefZ1tef22rsxadpQ7+wSyPi7XWo9rZ3IhwyI8yavQEephj95otj6Z2SgV+Lva0tXfmbTCqgaZiJtCiLsdt/X0JyrYjQcH113Ez7v4OdqoWHfckjDtvv7B9AlxxctRg5eThsLK2gbh1ACrY3Pwdbbl2bGWbNlyuYz4nAqKqywZiFUKmbWS9cINJ9EoFaQVVZNaUMWO5EJUcpn1Qp1QZ+3xdNTw+38Hsje1iHKtAaNZUKM3YRKCMC8Hwr0cqdQZuGfJAcDyJDtzaHtCPSwWvMwSLTqjiY+2pqA3mrmnfxAyZJRq9ZzIqWBQmAephVUU1QmnuOxyEvMqCXK3I9jdDnuNknv6B11g/ZjQ058JPf3ZlVzI4A6NZ7TVqGT0CnRFpZRjNgtkMlh6wBKJaq9REu7twMyh7dmXWszp4mpeuKUTMuA/g9vRxd+Zw2dKsFEpyCuvRSGX4emooV+oOzKZDLNZ8P6U7iBkJOVXEnSO9eSZsZH4u9jiYGO5BWQUazmUXsLJ3IoGEYEXY39qMacKqrBRWqKh+rdza1ImcbDkMTpf9EyKCuCufkF09nNqcU2ok7mVdKj7P8weFc7wjl7c/eUB6/ZznXHPFGt5c0MiSoUMdV19LFVdOLlSIbeElsvl3N7Ln3v7B7Pgj5N8sCWZb6b3sf4vbVQKdEZLLqniKh2f7UjFaBZ0C3C2Ch+zWTTqe3amRMuMQaF8uzcdY1325vu/OUReRS21BjPrj+cyMSqgwT5fTeuDXCYjLruMo5ll/LDvDBviczGYBF6OGl5el8BToyLwctSwM7kAVzs1N3fzpazGkv072N2OXSmFvPHHSfadV+aiHrlMxorDWYzu7M0fcXkXjdg638dH3gSLz4YNG3B3d2/U5/d6IdXqkpC4BF27duXQoUOtWuHdzV7N4nuiePSnIwxs74HRbGZXsiUcvUpnxMdJw3ubkxvsozeZKazUMW9lHBVN8Lk5lxB3O+7qG8SdfYIuGxbs52KLEPDdvjOoFTL2PDMMlVLBqbqcPudzrsVBVncsFzs1O5Ly2XKygJT8KhxtVGw5aZmKcrFTWae09Fj8mO7qG8jOpELujQ7h3uhg8spr2HqygNjMMrR6kyXfis5krR+mVMjoG+LOonuiiPRxRCaTse54DkWVOj7adoqSaosF65VxnUEms1rAotu7U6Y1sO5YLhqVnFVHsy9qpfhuXzq3dvNjdGefBtFYmSVazELwR1weS/5qWKn+3FvEqYIqKurE7CvjO3NfdAgdvB2Z9vVBvrg3ivwKHZmlNSgUMl5em3BRf4t6NEo5T4+OYMbgdtZpjM93prE9qZAwT3sm9PQnq1RLR18n/FxseGXdCT65qxdyuYxOfk508nPi15hMltUlo1TKZUwbEMLaYzm42aut559dVkONwcyP+8+QXVbDiZdHX/TG52qnwiwsfqAVFymgW8/yw1nEZZfTtc6BOtjdjkgfpwaRW5fDZBbWRI12aiX7Uhv6dPUOOVubr4u/M19O640QAmdby/fNaDJjMAtM5nqrj+BIRik2KgV6k5kdSYUk5VdarX/1CQINJjNRwa4crMt0LoRg5g+HrYU974u+uKVYYMneXlKt55u96db15/qi/XQw4wLhU60z8sKaE1bfq6hgV3oFuXLgdAkFlTr+iMvlzYndsFUp+GLXaWaN6MD647mM7WqZPtUbzZefPpRZpm3fmdSdrScLLjvV5e9iy119L2+d27hxI6NGjUKhaH0neUn4SEg0Qluo8G6vUfLFvb15ZsVx1h3Pp1eQK442SnRGMwq5jLv7BZFZorU4HpvMqBSWRHdd/Z2xVVuSo5mFwGTm7IXdbMYs/p+9swyP4lDb8D3rG3f3IIHg7hQtUkq9pS2leuquVGh7KtS9pe4CNUoFKBSKuzsJhLi7rc98P2Z3yCYbo9TOt/d15YLsju3sZuedV54HduVUyWPu4b70jQskPSaQ/gnBHdJCcX0fxgQa2J1fw9IDxczsF+uW9vY3aLA7JEw2h5I1qjHZCPXTsyevhjsW7lYyMNeMSuZQUS0hvjoqG6xufl9xwUbeumwAQT461maUUW+2cebL6xicFOxWnvLEzpxKUsP7sje/BpPVwX3f7nPrcfHXa5jtwc9o2f4i6i12LhwUr2R7PJ2DH/cWsjmrguUHZcPJQKOWM9OjyCipZ8meAi5xlmx+3Fvo5vnVHFdfRa+YAIJ8tAxLCeWeb/ayJasCrVrVIugZnBTMpUMT2JVTTXZFA2F++hZ34jmVjZTWmnnn8gF8uDGb5DBfGq0O2RRTatmrEec2tixxsLCGHtEB3DWpG7cv3M31Y1MpqDLx+5FSCqpNxAYZqWq0uvV2ubCLkpKh9Ndr5IhPwqPFiK9eQ5+4QD7cmM1nW2Rvs96xgbw+qz9VjVZ0GhV6jcpjU/Xu3CoarbIUwdasCoamhCrKwy4+3JDNjuwqNGqBWpOdA4U15FQ0MiI1lE3HW298f+HCvko2K7eiUQl8XBUou0OWTHn1t0w+3HiCCT0iuGxYArM/2Mac4YmtZkL25VdzwcA4goxaogMN5FfJZTlBgLHdwrlgYBzxzRTQbQ6RSS+vc5sa/GDOIAIMWlIeXIogyNneerMdX52aPXnVVDdaSQj1adLLJ2BrpxHZdcyfbs7m46uGeBQ0bVrqGpAYzC3j2/bjLC0tZceOHdx6661tLvdX4Q18vHhph+YO74cPH2bhwoV/qcO7TqPixQtl88r3N5xQHp+SHkl5nQWrXeS8AbGsPFRCYbWZh6f3YESXMOwOkdI6i2x54ZxeWry7AD+9hvTYQO7/Zi/1VjultRbWZpRzbv84Bia2/brsDpF1mWW8t04+jnFpERRUmXji50NMcE4+fTBnEMNSQjFo1Xy9I4+53+9X7hwDjVp+u2ssS/YU8PmWHML89TwxsxeCIPDuuiy3L/auEX746DU8dU4vqhtt3P31XopqzJwob8BkczBC3XI6RKMS+Pm2UagEgbfXHGfZgWJqzTZmv7/V40W31uzZNkMC+icEU1RjoqSNMXaQ+4iaTuB9uTWXx89OZ3BSiBJUHSqqY3t2FXHBRsVqoLjGzO9HSxnfPUK5wIQ6JQuWHyxiW3YldodEvcVOsI+WCT0ilVH45DBf2cJCBKNOTUZJnTKN5+KqEUlMSIsgMlCe3DszPQo/g4bqRquyHfkc2FALAkE+Olkc0WlOabLJtg5P/HyIsd3CmTUkAZCDOK1G4PwBcc4Aqs3To5z38/rHMtsZEPgbNFz/2U4yS+vx0anxN2jdDF2fPrc3SWG+JNH2cMGvB0uwOSSeXX6E2CAjscFGCpsonQMcLanjaEnLwLWtoAfkz7qrzybU76T1hys4sDmD0WqTlRqTjUaLg8FJIay4cwy1JptblrMpP+4t5OHpPdE5jUc/3HiCG8amkhDi42ZD0RStWsX03tF8tuWkKK8knZy8HNc9gg+vHOy2zjvrsnj63F7c9fVe53HTrn+XKxbel1/D2owypTzttkyT99unAzIHK1bI3n5nnnlmu8v+FXgDHy9eOojL4f2CCy5g2LBh/Pjjj3Tv3v0v279KJfDwWT0J89fzzLIjgKzmW1hjIqOknrP6xvDb4VJ8dGoGJYVw+8LdLN1fxO0Tuip3ZIIg0C8+iNdXZ/L++hNK83F0oIHXZ/WntgM6PHlVJq7+eIfye2G1iUNFsp/YZ5tzmNY7CqNWrfh+9XYKzDX/wlWrBBqtDj7amM09k7vjq9coGQi9RkVquB86jYp5M3qydH8R768/gdUhMrprGLHBRlYeKmnVk8h1Zx7sq8PmEMmvMjE5PRKbQ8JHp+LyYUlYHWKL/oWssnqKa80MSQpheEoowb463l+fxeDkEAYnh4B0smTnWtVkc+CrUyMIsqrthLQI9uXXkBblz097Cxn69G8YtWrO6hPDxB6RbDhWppQqGq0O6sx2CqpNvL7qGPec2Z0LFmyirM7CntxqqhttCIIczFU12vh5XyH/GZNCTKCBK52mpcnhvkpjeUqYu23BoKQQBiWFUGe2sS+/hhdXHCWztJ5jpfVM6imPtudWNHLego3cOakblw1N5LsbW0qJNOfsvjFuwotFNaYWy3jKdahVAv0TTgbWqeF+FNeYCfPTs2RPARqVgN3Z6xQX3DE16ab2GwXVJgqcQY/LQqKj4nqeqGy0YrGJpIT50rfJVJjLI87uEDlWWs+hwlquG53MgIRg7v9uH4khPhTXmnlgag+P260x2civaiQlXO7hGtkljCHJIZTWmXlw8X7ig2VF6hcv6qvoJ0mSRPco94yX2CRg99SLc6S4DqtDZI9T3V0lCO368TVtqH577XEGJAQx2Tlp6KJpxqcjih/Lly9nwIABf4syvie8gY8XL51g/PjxbN++nRkzZjB06FAWLVr0l9/F3DA2lSCjlrmL97PxWDmJoT5cNjQBo1bF67P6szu3mnUZZRi1aoYmh7aY3tCoBDJL6+kZHUBSuA+DEkOIDTKyv6CGiT3a/2Jq/gX7u9M6wlevYfHufM7qE43FLmK2OdA1+RJ1ZXyOFtdxoryBnIpGGq0O0mMCqGqU75izyuT+IItdZFhKKDFBBq77ZIcyjRVk1JBX2cjN47qw8lAJGnXLy2vToxOQSy6ZJXK2xaUI/cz5ntV+U8L9SHF6HgU7e0Y+2HCCcH89x0vrmd4nmjkjkrj+s51KeaI58cFGFu8u4LKh8eRVNVJSa8FPryGnspEak5X4YLk52tXLBHKzrEtHJau8AbsoKUrTLl+u4loz/eKD+GprLvsfP5Mf9xYyb8kB+sYFMSwlhOzyRqoa3afWXA3ToiRrRF0/JkVpdHaVP8rqLVQ2WNmVU81lQ09terGjjvCOZp8dnUZFTJCRtRllJIX6cOGgODQqFT/uLfTYW+IJnYfPAMCMvjH89+xe3LZwtyIJ0R5ndA9nRGooyw4UE+6nJ7NEzkad0z+WfQU1VNZb3cxNHaLcSL/o+uHKNj7ZnM3evGquHpmMj05FcpivWyZLp1Exo0+Mcs6MOjUDE4NZur+Ijzdmu2nvTOsdrfSO/XqwpMXknOsMJYb6tMgtCQK8f8UgukUGMLlnJN/vLkByHnPzUmBTIgP0yiQkwF1f72XpbQEkhJ4svbm/322/96Io8uuvv/6jzMK9gY8XL50kNTWVLVu2cOmllzJt2rS/xeH9kiGyd8593+3D6hDZnl3FF1tz0agELhkSz8SekUzs6TmIqbc40KhUJIf5sTevmld/y6Sg2sQnVw/pmLFjK4+fKG8gIcSHOrON9ZnlvLIqkydmphNklAMI13VsZ06VYrzZK0Y2zJz40lpGdQln6W2jWZtRxosrMvh40wklS9QvPoiLB8ejUQn89+dDiv+VrlnGJy3Kn7hgH2a+sQEJlJKHXRR5+/KB9IwJoDOsPlJCdaONGX1jqKi3oteoCTBo27zQ51Q0sievmjnDk5RsxK3ju3D92FRWHCymqMbM4z+5q3JPTo8kLSqA/fnVBBm1VDZY3c7zvBk9eeC7fbw2qz9DnlqFKEp0Cfdj3lk9cYgiZ/WJxahTU17vrml0xYfb2HCsnK4RflwxPFHRUgr00SoTVDaHiIRnvZaO0ny8GTwHQ837lHQaFUU1JqICDdSY5M+NyeogNdyXUL+OqeoPTQ5l3lk90WpU6JyTWgBBRh2BPlpuHteFiwbFo1HJHncatYCPTo1eo0arFmiw2DFo1YT66qhotCIg0C3Sn14xgYT565EkiX35NZzz1sYWU1pWu0hhtYm31hzDYhOZ0COCp87tTXKor/PvspKrRybxyJKDxAQauHx4IhcPim/x2j7aeIInfznc4rUtP1isBD4ZHkp1L63MwKBRY7Y5qGq0uZXmQM74DE4OYVrvaL7fXcCJ8nq6R/rzn9EpbZ7PAwUnbXDqLXYeXnKAT646OdXmp9fwy22jEEXw1bdd6tq5cyfl5eVMmTKlzeX+SryBjxcvp0BAQABLlizhwQcf/Nsc3i8cFI9DlHjg+/1EBxqcWR81lY1WDhXWcqysHpPVjt2p9eJqNt2XX8OevGol/X06iQrQU1ht4sONcv+PShD4YY8cpLgurGf1icKok5t17/lmH5EBep48pzfnD4jFbBNZn1nOGd3DlekikO/EZw1JoLzewr3f7lOmX7TN7vbD/PR0jfTjt8Mlbo+nRwe2MPDsCKnhftw5qStGnYahKSGU1Vl4eulh+sQFOYMOifJ6KypBznjVmGyoVQK3T+jK4ORgXr6oH/lVJtl0FUgM9UWrVnH92FQm9oigoNpMoFFLWpQ/Ib46VILAT7eOYsOxcnrFBNAlwh8BWd/nzkndCDBouffM7jgkSZnEOlJcq+g8je0eTr3FzqFC+cI1umsYfeMDCfHVMcY56RXRzNV7aHIIJ+ZP7/S5aYqnIMdXr0EQBLeeraaaUlUNVgqrTdSa7dSa6xmaHEKon44VB+U+teHzVzGpZyR786qxiRJ1ZpviTD8oKZhXL+mPyepQypAF1SaOldazIbOMIB8d5/aPY8xzv1NWZ8HubOp38dk1Qwg06ugW6ccZL6zh9Vn9iQo0csHbmxXfs7cvH8iUXlEIgoCfQeNxND09NpDoQAOjuoTRJy7IzdNKL6gY3TWcQYkhhPsbmNgjwi0oaYrFLjKqS5ibMCWgTDUCblkjF19uzVX+HxNkxGJv0pPkq6O4xowAGLUqAo1aEkJ8SAj1ISE0weNxgCz0+UGTPkKAdRllbDtRSVm9BUk6OdVmF+VhiuQw31Zv/JYtW0ZgYCDDhg1rdZ9/Nd7Ax4uXU0StVvPss8/Su3fvv83h/ZIhCdhFiYd/OMDWrEq6RPiRW9nItNfWd2j9h6f3UKT306I6JvbZVmJAlKC0Tr7Qhfnp6BUbyNFm01CHi+o4t38chdUmnrugD2f3jcGgle9a5y05wI97C7m4HfE6lSBnnpr3+Gw4Vt7i4gFywBFo7Lhrd9P1XF5h5/SL4cutuTRYHYztFqYo9v68r5CMknoqGyz46bUMSgxGAOKCfeQJqKOl+OiiOVSYy4Zj5SSG+OBwSKzLKGfN0VLGpUUwJClEeS3f7crHbHPw9trjlNbKGZxvd+YzIDGYUF89SaG+nPvWRqoa5FKY4OxlGZ4SyqVD5QvakGT52PKrGtlfUIPZJvLZ5hxl+atHJSsmrKcjU6nXqJnaK4r1meWKirjNITIiNZSfnf1MAOYmzdd5VY1uzcXXjU6hX0IQS/cXU1wrBx8V9Vb25tcok346jRwwuzzTKhosxOnkEkxprZmiahNrM8oINMqZnqpGWYyvOSargz15VRwtqaO8zsLXO/JInuLr1i8UH3IyiEkM8aFPXCD7nOrbwT5afHQaogMN2BwS/eKDKa+38OPeQnx0ag4X1aJVq/jvzF4YdWqm9IpqcQzNSQrz4bwBJ5XM9+RVu5XorI7WRToBsssbuPfbfbzlFGqsarRRbbLhp9ew6kgZU9Kj3PrsVh4qYdH2PCRJwiFJXDI4gSm9oogL9jzu/uHGE2jVKrf308XmueM9mvoCisjxXzkJ2x7/nCPx4uVfSlOH98GDB7NkyZK/1OH98mGJWO0i767LQusc++0ICSE+XDY0sc16vyfaKvNY7A4qGuSLtWvy5cxeUXy6OVtZZm1GGVGBBurMdjdp/LfXHlfsHsRmJZGkUDlj4hAl7pjYlTsmdmPjsXJqTTZuG9+F9cfKPVotuKiz2DDq1G4Xts6yO7ea+c6mcpUAAxKCZV2gvUX8driEfvFB7MipYnBSMNuzqzirbwxfbM3lx72F5FQ0Mj4tQrloTO8djY9Ozd78Gvbm18ilD0ni3AFxXOGcAvtuZ4HSqAvQYLVz58SupIT78eLKo27PgaxzdO6AWHrFBvL9rnxqTTa+2ZlPjgdz1nP6xyp2DqcDCYllB4qJDTIqgU9JraXFdFXTcfnm6tENVjv25mrRzo9aVYOVpFAf8qsa8TdolSC2aRD+xdZcIvz1BPvoaLDKx6DXqPCkEe3S5jnh9Fv7fEsuGSX1FFSb8HV+Tppuu8HqQC0I9IgOYO7U7jyz7CgTekRw4xmpbDhWxvi0SOYtOcDKwyWM6x7BFcMTGeM03G0ty9McAYHzBpzU7dFr1GzIbBnEt0ZVo401R0/2jakFgepGK4IgsCWrgrsnd3MaGUtc8eE2ssoa3D5Do7rIuklqlVwKbG5rsvJQCc+c36dF4NMrNqDVoKeyspKtW7fy7rvvdvh1/BV4Ax8vXk4DTR3eR40axSeffNKmGvnp5tKhCaw8VMKao6Ut9D9aw1ev6XTQA7Sp0mu1n2zK1apVHC6qpbrRRkCTbMusIQktRNTMNgcHC2vZnSvr3EjAu7MHcqS4jrUZZbz8WwZ3f7OXhdcNZen+IhqtDvbn1/DcBX2Y2jsak82hBD4hvjoarXZECcZ0DWd7diVVDVYWbsvjjoldeXb5ER6a3pOO4ArAPt6U7dZz88pvmfjo1Eo2SKUSlECvwums7ZqQu2J4Iv3ig0gJ9+NocR2/7C+iV2wgpXUnyz6POJtWz+gegZ9z6qZpIiY60ED/hCA+35LLvBk9WwQNIDelLtqeR2KoD/OWHGzxfFPUKuEPBz0mq0P5/LiC4eb9HiW17j1He/Kq+e9PhxAlSXH0dlkj3L5wj7KcSpAbvl29QxIQH+KDSiWQVdagBPeu4GTjsXJESSI22MjXO/LoGuHH/vwavrx2KHvyqzmzZxSFNWZqTDYq6q2kxwZQa7Jx2bAEgow6vtmRj8Xu4PNrhrDqcClrjpZxqLCWhxbvR5Tk0tjw1FCKasykhPsR6qfj403Z3HhGqhIg3D81jUfO6ql8tnflVpFZUsfFg1svK7mQG9Ddgz4JqVWvr1a30+T/apWgaGFZHaKz9Ai/7C9i0/GKNsUwPSFKsuBm82btyT1bz2atXLkSURT/Uf094A18vHg5bbgc3q+++mouvPBCHn30UebNm/eXOLwbtGpundCF+UsP02DpmFNyUzZklpNT2dCs3CWQXV5PpbOk4hrjbios2ByHJCru4AXVJqa+erLk5hIq9KQcuyWrggh/PVEBBsrrrU4dGYlz+8dSUW8hLTqAxbsKWHWkhLI6C++uk9WQXRfA4amhSrlMp1HxzY58VILA+3MGMejJ34h36qOoBIGZ/WJb7L+i3kKgUUtJnQVfnZogH7khu8hZPsmvamRAQjAvXNCHkV3CsIsSeq287yfO6cUDFjuPLDlAUqgPJpsDjUpgT24VIb46gn10fLUtlzqzXSkr9osPYomz90mtEpg9LJFyZ//EnrxqYgINzD+vNxsyy6kx2wgyahmcFKJoybx8cT8sNhGVCvKrTPy4p5DM0jqlp+OWcamkxwTio1ezL68Guyj7T3WL8GdgUjAR/i39tjrCZ5uzKa410z0qgP7xQXy1LZehKaGMTJX1lML99Ri0auKDfThcXEuAQcvw1FA0KoGtJyo5Ud6g9H+5iPDXc8v4LvSLDyI+xIdjpfX0jw9CEATyq2QrjaIaMx9eOZh312Xx095CzM7ylStYGNkljJFdwli8O5/yeivl9ZXEhxgRJXhueQYXDUog0PmefrUtlw2Z5fjpNYzqEk55vYUgHy1v/p5Deb2Vm85IJSHUh4k9I/nvz7JadpCPjvumnNSziQowoFYJ/H6kjOm95ebj1PCTgwF5lY3c8NlOBieFdCjwkV9Ly8c8NY03J8xPz4dXDkJAcBstV6sEZcov2EfL2owyufzXYCUtyp+DhbUetydJkjK11pyF23K5ZlQKL/92Ui1+cnrrpf1ly5bRu3dvYmNb/s39nXgDHy9eTiNGo5Evv/xScXg/cODAX+bwPiI1jDkjkrnHqenSGX7ZX8jevBqW3j7a7fGMkjo2HS9nTUZZm709LkpqzFhsIlq13ODr75yAKq01K5mgn/cWclYzkbaqRisGjUrJIEjA+LRI+UIuCMwaksDAxGCu+mgbPaID2HS8ghvGpjLp5XXUW+wYNCr8DBq+vG4YqeF+ZJbUK/0YT57TC41apWig9IptOcp+x6I9bDpewdl9YzhWWk+4v55eMQEMTw3j9oW7sYsSV49KZtuJSqICDW59MeH+esL99WhUAktuGUWgUctF72yW/QPPTgdg8e58sitOjps7RAkfnYZf7xiDIEC3SH/Gv7AGmyhy6XtbuGtSN64dncKLKzLcepbum9KdLhH+dIs8GaCmxwSy8Vg5VY16LHaRGpMdQRDIKm8gxFfHi00sTa4dlczM/qd+EfpmZz778muYkh5FVICBt9YcZ3rvaCXj88TMXqSE+2G2Odh8vIJDRbXcPK4LALd+tZsT5Q1o1QLJYb7Ume0MSwllZ04VJbUWftxTiMUuT0L9fKvsi3e0uE7pfTpaXMei7XncMq6LUqJpniUxauVLmr/BqQnltKFoikYlT3K5guYwPz0XDIzj25355FU20jsukPWZ5bK9Q7CRbs7PjSRJ3P3NXp6/oC83nJHKE+f0wqBV4xAlJNG9pGWyOSits2BpRyXZRUygkVwPJcl6i503VmVyqKgWlUpgZl95DN7V46YS5Cm2/QU1ygBDRb2VMd3CUasEyuosPLv8CIXVZlYcLOb+KWmtG48638OcikYcosTblw9geGoYKuFkH9hLKzJQq+RpSqtDpGd0gJuJb1NEUWT58uXMmTOnQ+fgr8Qb+HjxcpoRBIEHH3yQ9PR0Lr/88r/U4f2CgXHkVDTwy/4isspaToE05XBRLYOeXInVLtJgsXP7xG4tlpk1JIFZQxIY+cxqimpMqATB+cUrKE2japVA1wg/esUG0iM6AH+9hqMldVQ32tyk7H939h+8uz6LtZllPH/ByUbOIKNO9soSBAzO/gqHKCIhKRc3SYLYIB+l1GZ3iPjo1Bi0KlSCQFGNmXPe2MjS20cjSRIjnT0LHWksjQqQHdL35sn2D+H+eoKMWqoarZQ6NU2+2JrL/VNaqti6sIsS3+zIw1evoazO4q4n5LxwuEo/jTY7a46WklVejwC8Nqs/+VUmMovruXJEEv5O81BDs5JW8/F9Fy6DUo1KRbCvlqHJoezNr1ayby7+gJaf2/6NOrVyoQwwahGcn4sNx8pZfaSUpFBf7vx6D746jRL4xAQaSAnzpaLBip9eQ5ifngsHxnG0uI6CKhP78quRkLN4FruIQavGT6/hnP6xVDda+XRzNreM68KAxGBFm6p5I25quC/f3TicBotD0Sl6+eJ+iOLJspFLGblpyTY+xIdVd49FJQho1Spunyh/bn+4ZaSbcepLF/Vz7kcWXiyvr+eZZUc4t3+sm6+Wa8sdFU/cm1/dstQlyVnTF5p58bXHRYPiGNMtnLcvH+gUOJRlHTYeq+B4WT31Zjt+ejVJoT5yeVaQM0sur7O9+dUAGHWaFgMBWrXAkj2FnNkrip/2FnLhoLjmuz/5mvbupaSk5B9X5gJv4OPFy5/GzJkz3Rzev/32W8aMGfOn7/euSd04o3sEFpsDq0PE7pCwOUSsDtnBPTbIhy4Rfqw+UsqUXlGoBDkD4atr/etg4wPj3X6vt9j57VAJE3tGYtCoWjRwTuwZSWG1CbPNIZegrHYczotwUqhvC02SHtEBFNWYMGrV9IkLJMCgweYQKaw2y+q7DhGjVs0FA+MI9dPRIzqAM3tFMS4tAlGSqDXZWZtRSmq4H8sPFNMnLpBUD75OrZES7sd9U7pTb7YT5DTX7B8fRLi/nrlT06g22RiW0tIeoylDk0PYm1+DAMzsF0N6E82gp87pxT6nEacoSaSG+zEsNRS785y8tiqTc/vHMiAxiIIak+JJ9eQ5vXhgahqHimoxWx2tlhUm94xibYZcdonwNxDhbyA+xIggwKdXD0ECcisbGeac9jpV3rp8AGuPlnFG9wisDpHHZvRUyndzp6aRHOaLTi1/HpbeNtqtrDl3Wg8emJrmZvYJcv9MiK8OUWrZOD80JZShbZz35s3qXSNbvudndHcX8Kw129Br1BiaXNSXHyiia6Q/FfVWBiUGU9Vo5UR5A1anWGGj1cFvh0uYNSSBIckhPLR4P6O7hHHH13sYkhzKU0sP02i1K/YkrkC3RbN2K/SNC2Kjh2nEzuKv1yjncHjqyfP21u/HKa+3sOlYOSO7hJFb2YjKGeyU11uY1DOSGGeD8iHnCL0nXSdRksgsreeM7nJGqTV7DZCnufz8/Bg5cuQffl2nG2/g48XLn0hTh/exY8dy9dVXM3/+fCIiItpf+RQRBKFdvy2Qs0OniutOvDVCfHXKHSSAQadhorMJ8rVZ8sSbS18lOtBIVKCBSz2oBg9PDVW+wGX9EflCOsGDwnRTd/TOcuMZcpOyJElI0skvfY1axfVj2xd1BJTsVtPsAshBYnK4L0lhvkjOfWjVKp4+t7fH7VwzKln5f1Sg3IvTnrDk8NRQIgP0bhmQxFBfyuosDEsJRa0SsDlEBAEyS+qoarQpCtzdo/yVniZPmG0OCqtNiBLkVjbgq9fg52yMv3JksrKdCT0i2Z5didnmwGqXA22bXSIx1Ef5rAiCwPYTlVQ32ugS4UeYn14R81ML8PLKDH7YU0CfuCBed35OVh4qoU9cIP/9+RAGjRo/vZryBitXj0wiPSYQrVrF51tyKK+3UNVopbLBSr/4IKXxvDk3fb6LHtEBPHFOLwBK68zsyK7i6x35rD5Syt55k/h0cw6vrsoE5CzScWf2tKjGxML/DOdocR1VDVbMNpGiahO3juviZkjqevs72kBstjlaZOMEAQxaFaG+elQquaylFgRFvkCtkjOkKgHl/z2jAzyair556QCeWX5YEap0ZR4fnt6TCT0iWHmoRBmXzyiuI8JfT7iH5ndXAvG99ScY0y28zQb55cuXM2HCBHS61j9bfxfewMeLlz+ZsLAwVq1axbvvvsvDDz/Md999x+OPP85NN92EVtt5bZnOsPpICR9sOIEkyTofNlFy9uq4f8vePK4LV41M9riN9pAkiW0nKimuNRNg1DKue8eCOpUA/oaTr39ffjWfb8mhqtHGW5cNQKtW8eDi/YxMDTuloOaeb/ZSXGMmLtjIM+f3Iaeigf98upPCahNLbx/tscnaLkp8tjmHQ0W1TOsdxfi0zmkyWe0izyw7gkqAh6b3QBAEFm7LZeWhEs7qE01ssBGDRs3nW3N467KBnX5NbTF/2RHC/HTMP68PAJuOlXPlR9ud5UK5oXpyz0iyyhpYk1HK3ZO7c3bfmFbLZy6yKxq495t9BBq1bDhWjkGrYtntY6hqtPL55hxWHi7hmxuG8+KKDPrFB1FRb2VbdgWltRZK6ywMTQ5xC5JfWJFBjcnGG5f256w+7hmDn/bJo/9NL7p78qoI89Pxy74i7p7UjbyqRn4/UsrhwlrenzMInUbF4t0FfH/jCCx2kffXZ7kF3U2pbrRic4jKj1oQCDBolWACoLzeyqiuYei1KrpF+DO6WxgrDpZw61e7Ka21UG+x42fQkBDiw63ju7DpWDmP/3yI1HBfLhsmB++urEudxcaKA8VMbqfcGhVoaGEFc1afmBbnpz2Kaky8vvpYi8cDfbQ0WByIokSYn16ZpDtYWEPXSD+GJoeQ57RgySpvID0mwGMvXNMs0NltZHtqamrYuHEjb7zxRqeO/6/CG/h48fIXoFarufHGG7nooot45JFHuOuuu3jvvfd49dVXmTBhwp+233HdIzhYUOvW4OrijO7hXDUymY82nlB0ck6Ft9dm8exyWd+mS4RfhwMf151+vcWOn17DthOVfL0jn6gAg9IjYbGJ2MW2hdtaY19+NRkl9Ri0Kp45vw8fbczGz6Ah3F/fIo2/8Vg5O3OqOG9ALJ9vyWFq7ygGJ3W+LLTxeDkHCmqoarTywNQ0xUusR3QAs4cnUWe2cfvCPZ0eJe4IZ/WJ5qe9RazPLGNdRhlju0W4id7Vmmz4G7TotSouHBjvpqHUFqIIsUFGgn21xAQaeOOyASSH+RJjN2DUqXlwWg+CjDom94zkwkHxHCutZ+H2XGYNSWBPXrUiqugi2EdLuL++hZM8oPQkldZZmPH6Bqb0isLukDhvwSZA/szEBMkGtWV18hScj05DYbWJlAeXKtu5cGAcFwyMZ3duFZuOV2CxObA4ROrNdlLD/ege5c+767I4VFRLZb2VzNI6zDZ5334GDakRfm7v/7i0CD6YM4g3fz+GXqPizPRIPt6Yw7GyeuW9bJptUwkCeo0Ko1bNGWnt/z2sPlL6hyxDXAgIrQ4hqAQw2USCfHQMSwlh+4kqWQvLbGNAQjDBzqyfKEnkVDRyrLROKbm6uHxYAj2i/Xlo8QEmteHrt2rVKhwOxz+yvwe8gY8XL38poaGhvPXWW1x33XXcdtttTJw4kfPPP58XX3zxT2l+FgSBWyd0RZRwG0EFeZplbLdwxjqtDDrLPd/sJaeiwW283d6OuqwnGq12imtMimv0xJ4npf1D/XRuvQqdQeOc7XXFTef2j+X6sSnsya12a1gFCDRq0WtU/HqwGI1a4M3fj5NbaVLKLR3fp0BiqA9xIUbMdpGP1hynsMZEr9hAahptSEgkhfqSHH76p/xm9otlZr9YMkrqSI8JJD7EyE3OEp7cwyrQPcqfIckhyjh4e+RVNpIS7ktuZSPBvoH8cttoxbxVr1HzVJNynatvJ9RXx63juzIkOZhHzmqpl/TO7EFEBujdLCRcLL99jNI8D3IPT1ZZPUadmiCjlv0F1Vw1MpkXLuzrzD6p0agFRTQRwFenZkdOFRkldWhUKlYdLmFXbjWju4bx5mUDkCT5/X55ZQa/eFAhPl5aT2QzWw8/vYbhqaG8szYLAZg1JJFZQxKZ8OIapQzmKi8X15iJDTKyZ95knl1+hJ/3FboJE+ZUNBDko3NrHB6XFsEPTtuRP4JK8OzSDjAsJZS1GWWYbQ5uHNuFzJI9vDarP5kl9Ww9UUHf+CBA1t/KKmvgYGFti8CnS4Q/KkFgWEoogT6tZ6uXLVtGWloaSUlJf/g1/Rl4Ax8vXv4G+vfvz7p16/jqq6+49957SUtL44EHHuC+++7DaOy8p1R73DahC2X1Zj7fctLb549mHZ6Y2Yu8qkbuaCI819EplqYICHSJ8GdnjixeuHhXAcNSQjmrTwwhPrpTPk6Xj5fLEdz1xR7du+X57RUbSK/YQCrrLfSKDSQqwKCYq3aG0V3DGd01nAsWbMJPr+HWCV1bLDNvRsfEE0+VbpEnx93va2MKrTUOF9WyK7cKP71G0TxqLnPgid5xcmkk2Fen9Ex5onsb1iiu4LcpKeF+3OGcONyRXUmgUUulXuN8jRJ6jTzVlxgqT/wV15hJjwkgLtjIsv3FFFabifDXU1Fvpc9jK1CrBBb9ZxhDkkOYkh6F2e5gQloEg5JCOH/BJiICPPet6NQqyhvkUleQj062emjy2ZQkqDHZ+O/PB3n63N4E+ejQqt3VmAHOfWsTd07qxmxnWUwUJWoabaclCygIQqvb2Z5dRd/4IHblVhEXbKC0zsL93+3DR6dh9ZFSXrxQnrLUqE72DXnC5pDa1O6RJInly5dz4YUX/uHX82fhDXy8ePmbEASBSy+9lLPPPpunnnqKp556io8++oiXXnqJc88997S6vQuCwMPTe7LtRCUZJfXAqQUpTTHq1Bi1alr5fuzEscn/ujIADVYHt361m4p6KxuPlxPmrz+lRmxX1sjhvLC0dYfqws+gJdxPT+IfKP0BitDgv5Ee0QH0iO6ci/3poNZsw9dZtgowaD2+Xxq1iqeXHmb14VLCA/T0jPZn9ZFSBAE3a46l+4uY0TeGQUnB3DWpGzanmaZOreI9Zw9QSrjcXH3noj3MHp6ExS6LX4oSHCiooazegt0hMbFHBIIg8P2uAoKMWuXvxiFKZDfT3jlWWs/S/cU8NL0nQa0IqKsE+Hp7HtnlDQjAnBFJCELbHngdxaXv44lukX7YRYkRqbLMQ9+4QAxaNQXO3h5XmVElCIiSXGb2hEOUODO99Z6lgwcPkp+f/48tcwH8+ZKyXrx4aRM/Pz/mz5/PwYMHSU9P5/zzz2fy5MkcOnTotO7HoFXz6iX9lWbW5sJuHSWnooEDBTUcKKjB5hDdFGCjA09NERjcLRokCR798SCHi2rdAiuHKHkUhVtztNTNp+hYaR1+TewTDhTWdOgYdBoVKeEdm+Jqi3dmD/rD2/i3IkkSZXWW9hdswrHSOn7eW8iiHbkEGD0HPSBn8UprLaTHBhDso6PR5mBAQrDiM+Ui0KhlYo9IEkN9uWhwPJcNTeSiQfGc0z+WmCAjCc7GdqNWjUolH/PhIll7ymIT2Xy8gkXb8tiSVaEEJCO7huFwlskAtykuF6G+OtQqQSk3ebp58dVrmD08kUfO6snDTosLWVTwj0c+KkFo4XPnwkevwSFKbMgs5z+f7mB8WiRrjpZx1Ckt0TTwgZOZ0ub0jAloUQpsyvLlyzEajX+JdMep4s34ePHyD6Fr16788ssv/PLLL9xxxx307duXW2+9lUcffZTAwJYTFqdCj+gA7p+axhM/H6LR6nA2iMpTP6LTJiLC39CqmWdBtYl5Sw4qrtHf3zjc7Q7zVCbDXKl5Tx5g5fVWtwvM1qwKPt+awxndIhjZNYxYp+nlEacDvEuzpUuEP4OTQlibIWujeJpQ6Sg1JhtbsioU+wUXO7IreeP3Yzw4rYebkrKXzvPwDwc4XFSHKEmkRQUwIKGlHEOt2UZ2eSMxQUZC/XSsPVqGIAlEBRo4XFTLef1jiQo0sL+ghjPTo1ot1Rg0KurMNvKqTFywYDNWh8jhojrlhsBsd3CiogG7KPHIWT1psNjZdqKSL7bmUGuyKUrSKpWAv15DnbO/yCW2qRLgRHkDob56BGDqq+vpEuFHbmUjJqsdnVrFu+uyeHvtcSL9DXz1n2GK63xFvQW7KDmVvdVuMgNmm4OSWrOivOz6exXFk/+XszWeA5aDBTVE+OvpFuXHmqNl3Dwu1a3vz+qQMNscJ280TjEOW7ZsGePGjcNgOPWboD8bb+Djxcs/jOnTpzNx4kRefvllnnzySb744gueeeYZ5syZc1p8v64akcS6jDLWHC1j8FO/tXh++R2jPcrQ78uvZsXBEopq5NS4SoDfDpe6LXMqN62u6R5Pd9DgngmyixLrM8s5UFBLca2ZPnGBSBIYNSrCm92FNhVVdPX7dJbMkjpu+XI3R0vq+PTqIcrjZpuDBWuO8+C0HhRWm7yBTxMEQVBEDTvKFcOTuP/bfUjAM0uPMDQlhOvGpCjqywDrM8q5c9EeLh4cz4w+MWzJqmBoSgiVDVa0ahX9E4IwaNVY7bJeUXM9JRcPTu+Bv0GLJJmUqTeL3aFIK1TUy15WRU5bjNzKRq76eDvXjErmcJG78ObQlBC3v4GUcD90ahWzP9jGdzcOB+QR8x5R/uzPr26h1eMK+lWCQG5lI2e+sp7yejlbdsngeOZO7YGELEGx9UQFN3y+q83zmBbl77GHymxzYLI5qDXbUQkCVofIwcI6+sQFKtYuJqud42X19IwJ4EBhLYeKall9pEQJrIJ9de1OOtbX17N+/XpeeumlNpf7u/EGPl68/APR6/U88MADzJ49m/vuu4+rr76at99+mzfeeIPBgwf/oW2rVAIvXtSXs17bQHGtucXzrVXAlu4vpqjGREqYH5EBBsrqLGzOqlCe7xkdcEoBhiuw0bSybtOASBCgzmyne5Q/LzUb0b9+TArTep/U++kVE8jsYYms7aDPmCe6RPjx/pxBFNea3YKbY6X1aNQCCSE+bhNFIGvFrDhYwtju4W2WBP4oi7bnctGg+NPaC2ayOvhuVz7DUkLbFU10UV5vwd+gaTEp1xmm9Y5mbLdwSussLD9QzLPLj/D22uP0jA5gyS2yb5dOI/tDOSSJMH8dNofkFPCT/ape+S2TigYrQT5akkN9uGBgHGazyE/7CtmVW0VRtZk7JnZFoxbIrWgkPsSHMD8d5fVWPtuco5jO1lvsaNUqbA65rGrQyr1sBq1KkVZYeaiY4hqz4swO8ucSTpaIXAOOZpuDjNI6DFq12/JNUQkns5YuftpbyMLtecrvrWVhm3KkuM7j+7Yzp4olewoZ3TVMkYr4dmces4YksC9/PwCFNWbSYwIJ8ZWD1o83ZfPxpmxlG7OHJbYb+KxevRqbzfaP7u8Bb+Djxcs/mtjYWL744gtuuOEGbr31VoYPH84LL7zA7bff/ocueGF+ej69ZggXvbO5hdu6p1S53SE6/aOMOESRhBAflh8sptZkp3dsoNKjYOrgmHRTXPon47pHcN3oZPIrTSw7WKw83zTw0apVdI3wa5EdGtc9vMV49KiuYYzqGsYNn+081aw9giAQH+LTQuzQaheJDjTw7c58+sYFuT0X5KPjosEd08j5I3TU9bszHCqqYW+eLCS5pJlPVWvMfGMjP9066g8FPiD3viTrNU7tHjlrExN0cgLPFVQ3WuyI0smeFLVaVir/YXcBWrXstRXiq0enVlFptbI5q4J9+dXkVZq4X5tG1wg/7A6JQB8tlw1N5NVVmSzeU4BRq0anVhEdaKDGZCOnsoGpr65Hr1HRKzaAnTlVXOGcxNKqVZTWWRiUGEx2eQMXD05gQEIQINuH7MiuJMxPhwQMSgzBR6dGq5anz1ROaYG44JOvLTbIh/MGxKISBASg2mSj1mQjKdQXyfnpFYCjJfXOxnNJUW12raNWqTDZHIT76SiqkZW2XaXggYnBPHlOL6IDDfjqNdw2oQsWm8jw5FAm9Yxk5aESDhfVsvl4BWlR/gQatUzvE60MLxRUm5jYs30xz+XLl5OamkqXLl3+0Gfhz8Yb+Hjx8i9g9OjR7NixgwceeIA777yTbdu28d577/0h1/dukf58eOVgLn1viyLeBq0EPqLE7twqimrMHCysJcRXxxndwrlwYDh3LNpzcrkOehN5IshHR++4IEpr3Rtjm1YrBCCztL7FupuOV7Q6iaVSeX5Nf4QBicEM6IAtyL+RCwbGsT6znP35NQzqgIijq7/kj2KyOjDq1CSH+XqUAegdG8j4tAhCfPVoVQJDnL5jAQYtj5+dzp0Tu+GjV/P6qmPcNakbgiD3/7x56QBsDpFnlx0hxEfnNjJ/Rvdwzh8QR3yIEVGCbScqlT4uV/awtNZMRLPM3RndIzijewQOh8ilQxMI9dMrvT+vXtKPRqsDH52am8d1wV+v8Vhyc3ttcYGKAWprZJbUYRdlHSiHJOFwSOzJr2ZEaqiyb4D8qkZUCGSW1tFosbEtu4pRXcKIDDAQFWDA4AzwXlt1DJPNwX/GpDAiNRSLXeSbHXn8sKcAUYIvt+bSMzqAgYnB9IgKwE+vocFiR6dRue3PhSRJLFu2jLPOOqvN1/FPwBv4ePHyL0Gj0fDCCy8wePBgrrnmGg4cOMD333//h+6uBiQEc8/k7jz5y2Hlsdam3KMDjUzsEUlOZSN3TOzK70dKW5hC/tGqy/O/HmFgs8bW0iYTQq1dXi12sYUT+cljal3N1os7Ef4GjhbX8cV1Q0nt4HTbNaOS8fOgv9NZjLq2M0ahfnpeuaQfOrUKvcbd60wQBEVY8Z4zu7s9DnKG5uEmYoqHCmsprTOzL78GjVogOdQXs92BzS5xrKxe9hpzfqZESaLR6mBXThUSsg6RKErYRYkDBTVY7CJpUf6M7BLG5cMS2Zdfw7vrs7h4UDwjUkM9Bj2uDFRTKxZRlLjy4+2MSA0l0Dk2vzevmtVHSqlqtLb6GX5sRk9GdQ1TxAbzq0wUVJn4eNMJ9hfUui17y7gupMcGckb3CF5YkcHWrEoePzvdrYRV3mBlXUYZr13Sj1A/PSObTMxZ7I5WjyMjI4Ps7Ox/fJkLvIGPFy//Oi6++GLS09M577zzGDRoEF988QXTp08/5e1dOSKJhdvzOObMpLR2995gtfPM8iOsvWcceo2aKb2i+Wlv4Snv1xMCLQXYmpbiWhvVhdYFGQU45QmVfzsF1SY2H6/gzPRIN1+01vBU1muPa0ennOrhdZqADryGpjRY7Fzx4TZlGso1MZUU6sOu3OpTOgaX0GZTTpQ3KMd2zzd70apVXPnRNnY/MplAn5bZEYtdpKrR6vaYBKzLkO1GAO6fkkZ2eQOVDe7LhfrqGJAQTK3ZxtYTlTz20yHGdQ/no6tONt+vzyzj9gnduPbTHcpjkQF6JTjcmy+/9qMldfy8r8jNZb3U2feXFh3Qomm/rXLm8uXL0ev1nHHGGa0u80/BG/h48fIvpFevXmzfvp0rrriCs846i8cee4xHHnnklKa+NGoV887qyRUfbgNal7xvtDg4q0+0m8ZKUw0faD1b1FFSw33JrzopCqfXqNwyOWX1revDbDpewa1f7cYhitgdcvnF5pA4WFir9En8L2FziEx4cS0fXjnYY0PrxmPlvLXmmKxYHBfYocDnfw1fvSyIWFRjRqdWcduELvy4t9Bj0JMS7sulQxLcsp8u4oKN/HjLKG5fuJv1meUe9+W6YVAJAoeKat0eM9scXPj2ZiobrFgdImabgx7RAdSZ7UzqGUmXCL8WQqCLtucyb0ZPyuosShO3WgWR/gZSwv04XFTDnBFJ3PftPre/CwFZpfmVS/pzzahkPthwAoAfbxmlNNv7NMmuNe/LqzXJNxq/HiwmLtiIj65jYcKyZcsYM2bMHyq//1V4Ax8vXv6lBAYGsnjxYp5++mnmzZvH9u3b+eyzzwgO7nzvyZhu4czoG8NPewuZ/cE2tGoVM/vFcPfk7gQY5K+JBqudy9LdG2rvntwNUZKot9j5ZV/RH+71KKoxu5U8LHaRTzZnk1fZiEqAg0W1cj+GKF/47aJEo9WO2SaSW9lIbmWjx+3+L5a6ssoayK1sZENmWYvA56ONJ1hztIxZQxIYmhza7nh5dnkDSWF//wWrwWInq6xBsb/4o5TXW4jw11NUYybcX88t47sSHWhkc1YFWmfJzNUQnRLux4S0CCQJpY9Fq5YnxoJ9dIT46tzO4wdzBtErNhCzzcHY59coQX/Tcq9rwkunVsmWKA0WVIJAkI8WlSDQI9pfEf0UBMFNwfmaUcmMT2u9oTjKud68JQeobrQhSZJS8nMJfT40rQcWu4PPt+S6ZUS7hPszsUcEs4cntfDqG901nEU78nhtVSZhfnpmDWm/id5kMrF27Vqefvrpdpf9J9CpwGfBggUsWLCA7OxsANLT05k3bx5Tp04FoKSkhPvvv58VK1ZQXV3NmDFjeP311+natWWjWlO+++47HnnkEY4fP05qaipPPfUU5557rvK83W7nscce44svvqC4uJjo6GiuvPJKHn74YeUO98orr+STTz5x2+7QoUPZsmVLZ16iFy//KlQqFQ8//DADBw7ksssuY9CgQSxevJg+ffp0eltPzExna1aFs6fGwaebc9iSVcHPt47GIcpaIi65exdatYoHp/VAFCUOFtTQ0Gy0u7MIAi3KUh9dOZhvd+azcHseFw6M43mnp5ALSZJ44dejLNlbSL5Tfr85bcU9Zps8snw6yK1oRKMWaLTaWxg8nm4EAZ45rzfn9I91e7yw2oRGJfDBnEFuWkZtcSpBT0G1iQMFsgbM6K5hLTID+/KriQ40dkrTx1evOW1BD4C/QePMctQomcPzB8ZxfhsWKNeNab10p22SUT3udGY3O3WoPKk1K1kglcD883rTHipBwCFJjO4axmVDO2ZaLAgC+VUmrv9sJ3NGJBHhr2dYSggmqwO9j5onz+nNA1N74NvkhqJ3XCDvz/Esi3H/1DQ0aoFLhyaQHtOx92LNmjWYzeZ/RX8PdNKyIi4ujmeeeYYdO3awY8cOxo8fz8yZMzl48CCSJHHOOeeQlZXFkiVL2L17N4mJiUycOJGGhoZWt7l582YuvvhiZs+ezd69e5k9ezYXXXQRW7duVZZ59tlnFQ2Tw4cP89xzz/H888/z+uuvu21rypQpFBUVKT9Lly7t5Onw4uXfydSpU9mxYwcBAQEMGzaML7/8stPbCPLR8dwF7gFTRkk9NodIjcnGhB4Rrarhmu0OVCqBpftbul13FLPNQUW9tcXj/eKDFO8rTwGKIAjsyKlCAM7oFs747uE0P8rWynfy+h07vtaap92WEUWu+WQ7F7+z5Q8Hge3RLdKfS4YktDgnMUFGZg9P6nDQcypIkkSdyUZUgIHYIKPHz8UdC/ew4VjZn3YMHUGvUStZwD/qTQfy6LyLp5ce4T+f7eS2r3YDJ4McAXkCTaMSaLB0Tt7BdRpVgtDuJFjzdVYcKuGy97dy77f7yKsyubm/++k1HZa/CPHV8dS5vTsc9IDc35OYmEhaWudNcf8OOvWXMWPGDKZNm0a3bt3o1q0bTz31FH5+fmzZsoXMzEy2bNnCggULGDx4MN27d+ett96ivr6er776qtVtvvLKK0yaNIm5c+eSlpbG3LlzmTBhAq+88oqyzObNm5k5cybTp08nKSmJCy64gMmTJ7Njxw63ben1eqKiopSfkJD2RzG9ePlfISUlhY0bN3LBBRdw2WWXcccdd2Cz2dpfsQlndI/g4kHuGjR2h+y9FNWGGJ/FJqJVqf6QlotOrSLcX0dYswyBQ5KIDpT1SFwqz01Zl1HG1hOVdI/y550rBpJZVu/WLxEbZPTo+u2iI8dcVGPinbXH210uNdyP587vy6Nnp6PvgODcX0Hz5lgXBdUmtmdXntI2BUEgLTqAvvFB9IoN9HgONWoB3w72h/yZuN4HT5+dzqJrI5h0hVVPn9ebockhTOgRwZGi2laX94TgDNkbrY4OBdoA6mYBTbi/nqgAA4Ig8NKKo6xoool1OhzgPbFs2TKmTJlyWsU0/0xO+S/T4XCwcOFCGhoaGD58OBaL3FzV1J9DrVaj0+nYsGFDq9vZvHkzkydPdnvszDPPZNOmTcrvo0aNYtWqVWRkyEqte/fuZcOGDUybNs1tvTVr1hAREUG3bt247rrrKC11l9NvjsVioba21u3Hi5d/Mz4+PnzyySe8/vrrvPnmm0yYMIHi4uL2V2zCg9N7EOZ3MviwOkQs7Vw0ZLE5Q4uLfUUbzcjNke9wBXTNFJxPlDXwxupjAOzOq3Kb7KpptPHAd/sA2Jdfw/ylR1pc4AqqTS3UlTtLkFHHhB7tC7iBXEY4u2/Mn5pxaYq9nQtka1m62CBju0q8fwS9Ro3ZLvLuuuOUeFAI/6twKR53NJBoi5vOSCUlzJdAo5ZAoxZ/gwZ/vQZfnZqSWjMNFhsFVSbe33CCXw+WnHKzv0bVMqBpjebBxqHCWi5wlvImp0cxqWck+/KruemLnTzxc9vGx1a7yBM/H2JXbsvJtdY4fvw4mZmZ/5oyF5xCc/P+/fsZPnw4ZrMZPz8/Fi9eTM+ePbHZbCQmJjJ37lzeeecdfH19eemllyguLqaoqPX0d3FxMZGR7l8okZGRbl/W999/PzU1NaSlpaFWq3E4HDz11FPMmjVLWWbq1KlceOGFJCYmcuLECR555BHGjx/Pzp070es915jnz5/P448/3tlT4MXLPxpBELjlllvo378/F1xwAQMHDuTbb79l+PDhHVo/0KjlrkndeHCxLGXvmoiqaCVz4Frnwek9eOSHA26Ph/p1zrPJk1/XzV/uUqw1MkrqsYkiepUaSZJ4cPF+CmvM+OjU9I4N5ER5A2qVQHMdxT/a3GzUqZ2Kuf8sqhutXPfpDr65YUSryzQtefyVXD4sgRl9ov/2LIBLbK95qcshStgcIlaHiM0uEuqnp7LBSmWDBatdkh93PhcVKE9SRQQYCPPTk1XeQI/oABosdqWUtiWrkq0nKlm4PVfZR6eb/Z2nyqBVo1IJbDtRwf6CWtQC9IgJICrAwC/7ixBFiSAfHZcPSyTMX09BtYnukf6E+OowaFWc0V1uWE4M9WHu9/sV64tL2lEUl5D4YMMJPt6Uzfr7xrkpZ7fG8uXL0Wg0TJgwoXOv9W+k04FP9+7d2bNnD9XV1Xz33XfMmTOHtWvX0rNnT7777juuueYaQkJCUKvVTJw4UWl8bovmfxiu7nQXixYt4vPPP+fLL78kPT2dPXv2cMcddxATE8OcOXMAWdvERa9evRg0aBCJiYn88ssvnHfeeR73O3fuXO666y7l99raWuLj/3ypeS9e/gpGjhzJrl27uPDCCxk7diyvvvoqN9xwQ4cuRBcPjmfR9lz25tcoQUONqfWymSAIRPgbWjUa7SgqAaRmHTp+hpNfU009oSQJHjs7HZ1WxW+HSlh1pO0ML8gZoqbj+P92fj9a2mpGpymNVjtGrZpak/0ve/2n20fsVHFlfBxODZ9Gq52+j69okY3JenoaH244wRu/H2uxjfMHxHHr+C6yDo4AN49LpVukP2f1kfVvTDYHV364ja4RfswakqCMvJ/qlGONycb27EreX3+CFYdKAJiSHkVatD+v/JYJQHSggRBfnZIBvXhwPI1WOzP7xSIIAkeL67j20+3kVZ5s+HeVuhyihFolYLY5sDpEjNqTlhoA145K7lDQA3LgM2rUKPz9/z1GvZ0OfHQ6naIUO2jQILZv386rr77KO++8w8CBA9mzZw81NTVYrVbCw8MZOnQogwYNanV7UVFRLVLxpaWlblmge++9lwceeIBLLrkEgN69e5OTk8P8+fOVwKc50dHRJCYmkpmZ2eq+9Xp9q9kgL17+F4iOjmb16tXcc8893HTTTWzdupUFCxZgNLb9paZWCTxyVk8ueHszAmDUqjG1YrB4OlGpBKoarQxICCK30sSMvtFkl58cjujfRNVZpZJdwG12UTGIbA1X1urJXw4xZ0QSvWI7PzlUUG2ist56WqaOJElClFovQ3WUSH8DV49MBuRenmBnUHOivAGNSkVlo5WSWjMfbjhB9yh/TFaH21RcQbWJvMpGBOSenIGJbZe+9ufX0GC1IUkCZrtDdkAXBHrGyJo0rrH6Q4W1iuJxt0g/AgxapVn3x72FBBq0fLo5m2BfHS9c2BdRlHj+1yM4JBiUGMzoruHtKjl3lKb2CrPe3YJdFBmYGIwkyYGJJEk4JBjwxAoabZ7LYd/tyue7XfmkRfkT5KPl2535vHxxP+X989Nr0KpVSJLgFvx76qmpM9sY98JaBicFs+DygQCU1VkI89MpIf+u3GoufHuz23rLDxazvEm/TlGNmZu+2OV8jfJgwX1T0ogIkK9pd3+zxy3oaXo8C7fnctnQRG75cje/HS5hSnoUSWG+OESRlHBf7pzUrd3zCmA2m1m9ejWPPvpoh5b/p/CHO88kSVL6e1wEBspfDJmZmezYsYMnnnii1fWHDx/OypUrufPOO5XHVqxYwYgRJ1O3jY2NLYTZ1Go1Yms20kBFRQV5eXlER0e3uowXL/8f0Ol0vPbaawwZMoT//Oc/7Nu3j++//56kpKQ21xuUFMIVwxPRqlWkRfvz+ZYcimvMin5IU44W15F8GnRgtCoV1Y02qhutRAcaeGhaDx5ZcrJ8dumQlhnZDt1UO5dpPgrvYs6H29CoBBJCfXh0RrrHZWKDjIrpY1v8dqiEF1Yc5bYJXd3c4pvyzPIj/LC7gK0PTgTg+s92MGdEEiNSw/hmRx5Dk0NJCG1fQXlElzAkSeLHvYXMW3KAL64dSlpUALM/2Mbqe8YSEaCnuMbEyxf3I7u8gWd/Peq2fk5FA++ty+L3o2WE+urY+cgkAPbkVfPssiM02mQtGJcv1qIdudSa7IxIDWXh9jzyqxqJC/ZhTNcwAoxaJfBZm1HGs8uPMKlHJCsPl7D0ttH0jJFLhV9uzeHaUcnUmGxKCXN7diVfbM1Fr1Vz0xmppy3oARicFEytSVY53pZdiVoloFOrsNgd+Ok16DRq9BoVgT46zDVmekb7o3f6Wek0KqoarVQ3WukXH0ygUUtxjRmzTeRAgZwNFZCFO7UaFYIAk3tGcsfErlwxPKnVMuMFA+O4YOBJGQJJkgcIZvSNIS5YlgBoamha3mDhWEk9w1JDUQsCErJD/IQekYoY4eGiWoYkh1BeZ2ZPWTXj0yLpGuGPn15DsK8cVN06Xk5aPLf8KI//eAib8xrqCqhUAnx344gOyzts2LCBxsbGf1V/D3Qy8HnwwQeZOnUq8fHx1NXVsXDhQtasWcPy5csB+OabbwgPDychIYH9+/dz++23c84557g1L19xxRXExsYyf/58AG6//XbGjBnDs88+y8yZM1myZAm//fabW0P0jBkzeOqpp0hISCA9PZ3du3fz0ksvcfXVVwNQX1/PY489xvnnn090dDTZ2dk8+OCDhIWFuekBefHy/5nLL7+cXr16ce655zJt2jR2797dbsbzvzN7Kf/XqAVeXpnB6G5hhPvpCfPXU2e2s/JQMdWNNnblVnPNqOQ/dIyiJGG1O6hqtCIIcsrf9SU8JCmEM9OjWqxzOiZVZvSNYV1GGRkldX94W4OSgkkI8cGgbb25+b4z01hz5OSod5cIP3o7s1AHC2vZdLyCa0Ylt5uZqmywsmh7HnvyqrA7JCobrKhVAkU1JnrO+5Uvrh3KlF5y8JVfZWph+TEiNYwGi4Pfj5a5jfWvPlzC5qwKQNbjcQU+ob56njynNzUmGxcNiueppYc5f0AsJXUWfjtUwssrM7hzUjf+MyaFpfuL8DdqiA0yKs7qAE+e04ujxfV8e+MIFm3PRZIkhqaEsvT20QiCQJCPrgNnueP8Z0wq14xKweYQ0aplJXC9RsX27CoGJQYrmSiL3UFhlZmkMB+3Ep1DlIuv7Y2Xj+p6UuPqjomtZ0z8DVoemOo+9u0yQX2hWWAuihKC4N4OYrXL/mHnD4iluNaMr15DdnkDob56Vh0uUXzr4oKNRAbokSRZKgLg403ZANSabYpQo49OzuZa7CLXj011y6q2x7Jly4iJiaF37/Y1iv5JdCrwKSkpYfbs2RQVFREYGEifPn1Yvnw5kybJdwlFRUXcddddlJSUEB0dzRVXXMEjjzzito3c3Fy37M2IESNYuHAhDz/8MI888gipqaksWrSIoUOHKsu8/vrrPPLII9x0002UlpYSExPD9ddfz7x58wA5+7N//34+/fRTqquriY6OZty4cSxatOhfVXf04uXPpl+/fvz000/079+f+fPn89hjj3V43d6xgfx2uIQnfz5MrdmGShDw1aux2EX5wnAa+jmuGpnMrweLSQn3o6rByuasCoalhGJziFw5IqlFz4goSlQ0WOge6c+gJPkLWxDkCa+kUF+iAw2oVUK7GYQBCUFkltZx3oDYNpfrCLtzqzlcXEtUQOvZIbVKcLv4pYT5KZYSU3tFsTuvmrUZZe0GPiabg1Fdwqg12zBq1UT4yxfQR2ekU1BtcstQJYX6cO3oloHpwMRg5p/Xm5QmGbubx3fh6lHJbD1RybDkUOXxcWkRwMmG6VlDEkgM8SE5zA+tSqVMz6lVAj/dOgpo6W6u16iV92pmv5PnOy64cx5hniirs+Bv0GDQqt0CFrVKQO1siN+ZU8XILmFKMNf0uLLK69mdV6X0AwUYtSSE+MhN0HYRUYLFuwuICTIgihIOSWJ7dhXl9bLcwz1ndkcUJYprzRwvbWBwcjBrjpZxRvdwN/FPi92BXqPG7hD5eFM2FQ1W6s12TFY7IvLnWpRkT63IAANBRi0ScmaoqMZM3/ggnjqnFzd+vouPrxrMyyszOFBYy4dzBhPmr0Pt1AHSqAQ+3HCCs/vFEh9iREBWh75yRBJv/H6M99Zl4aNVc8u4LgiCwOXD2ldpdlFZWck333zD1KlT/xG9XJ1BkNpS9vp/Rm1tLYGBgdTU1BAQ8M+b4PDi5XQxb948nnnmGXbv3k16uufSTnNMVrkRsrX0fXGNGbWz76ajuC4AzfdzybubiQ/xYWBiMDP7xaLXqFpo8UiSREW9lRqTlQCju51ATkUDoX76TrmGVzdaT1u2obze4iYJ8GdS1SBnx5ofe63ZRoBBi91p7eHKnFU1WKkz2ztUSusImSV1ipN7W1mRRqud73bmMzw1lMgAAz/sLmBXbjX/nZnewkfsjdWZ2EWJPnGBJIf5ERmgZ9Szv3PpkARum9CVWe9tYXxaBFePTFaC2qX7i/hpbyER/nrSnVYSssO6hNUuYrE7qLfYWXmohGW3j1bO1/3f7iOjtA67Q6KoxsToruFUNlgRJYmeMQHMHpaITqNCp5ZtLD7bkkNJrVn5XatWodUIbr+/uOKo2xTk2G7hfHL1EJqzN6+amW9u5IlzejGuezhzPtzG8TJ3wV+V4NkD7+WL+/LZ5hx8dGpOlDeiVgmc2z+2RX/O66syuXWCu3vCYz8e5Od9hZQ7BUMfnt6jU2azpaWlTJo0iYKCAtavX0+PHj06vO6fRWeu33+/upQXL17+ch566CG++eYbrr32WjZs2IBa3X5N36hTY6T15X7YU8B1nfjyrGywcvYbG5g1JIE6s51Gq53rx6YSG2RkRt8YXv0tk6yyBh7/6RBPnpPO/vxaJe2vEuQM0+asCnx0agY1a8rdklXB0JQQBASMOhX3ntm+ouzpLLF0NOh5/tcjXDsqhd+PlrLsQDHvXTGIrVkVbM6q4I6J3diZU8nNX+yma6Qfb102wC1AKK0zc9Pnu9iVW8U1o5J5aHpP5bns8gbOeWsjP90yinu+2UtpnYWXL+5Hv/ggzHYHr67K5MWLPPc7dYYxz/1ObmUjb1zanyCjzq3c03w69+d9RbzyWyaHi+u4ZlQyjyw5yKwh8R4bvHMrG+kdF6R4VdkcIpUNVvKqGmmw2NmZU4VOrSI13I8pvaKot9idBrUdu4+vMdmU9/twcS378muU50w2B29dNqBV0csbxqa2u/0PNmS5BT52D/2oe/KqufvrPUxJj+LSIQnUmmyE++tbBD4vX9yPygYr//35kNLPNiI1lOm9Y/hkUzZmm0hBtdzE7El/x1MwWlZvUYIe6Fy2rbCwkIkTJ1JVVcXatWv/EUFPZ/EGPl68/D9Er9fz3nvvMXr0aN566y1uvfVW5bnjZfVkFNcxtZXG3Obsy68mo0RepzNDSiG+OoYkhfB8k4bbpfuL+fGWkVw7OoWeMQE8+P1+IgP0xAQaefiHg20cQ02Lx1wO2WF+ulYDn6oGqzyi/Dfw6JIDdIn05+sdeVw4KJ6tWZXkVzVic0hKj8ievBqm9Y7m3jO7tyjXRfgb2FdQgyiBQ5Q9uv770yHenj0QhyRR3WjDbHOgVgmcKG9Q5AjUgsB3u/KJCNBz/5Q/ZjHQdDy6sPrkBFGt2Ub//65k1pB40qICuHxYIhqVQFywketGp/CWc2T8q215HC6q44ebR9JgsfPJ5mwEBObNSHfrC9KoBJ49vzeJob6KbcSD03pwokIOEvz0Gm4Ym0LP6EDeWnOMzJJ6rB4EC4ckhZAS7ut2LpuXaNtT+u4I2mbilepmwzlZZfW88lsGoX56XrmkH1a7yFNLD7Mly11J+6FpPZRyYLi/nuIaMz/vK+Lt2QMVI9XiGjMGrQpzKxNpnmj6iqf3jmZyz46Jc+bk5DBhwgQsFgvr1q1r14fzn4o38PHi5f8po0aN4sYbb2Tu3LnMnDmThAS5vh9g0DKlV8smYk+U11u49pMdTmNTuLoDDblNuWJEErdN6MrjPx3k96NllNdbeP7Xo6gEgXvO7Madk7qxcFveH7oQ1ZntlNSanWaV7hwvq2eQ799jbdM9KoD5yw4zNDmESemRzD+vNyqV4Hb3fdnQBLadqGR7diVjmrloA1w4MI5f9hex7EAR3aP8qGqU7+L99BoGJASh06gY0y0cjVpFkLNEqVGreGxGT+aMSOr0MR8oqGFdZhmNFgcSErUmGyoB8iobuWX8yYtgTaONuVPTMOrUJIfKvUPjukeQHhNIcpgvtzunngxaFX4GDcfL6rlr0R6qTXLvWIPFjlolj4b3jgtgfFokFw+WP58um43XV2cyuUmzuyTB9D7RTO8Tze9HS7HYHGhUKtRquddFo1LRJcKvRSm2uQ3F6VB4Hp8WwZHik43yTcXIRVHitoW7mdk3lkuGxKNRCdzw+U5+O+yuQ3XtqGQ3w9Sz+sSwJ68ao05NgEHL51tyOF7WwBXDE3lvXZa87Q52rrgycX3jAnnhwr4d8gU7fvw448ePR61Ws379+nanQv/JeAMfL17+H/PMM8/w448/cuONN/Lzzz8jCJ3r0XlxxVFK6yzMGZ5IWb2l04Jtq4+Ukl/ZyKuz+tPnsRWA3DwKcLSklu9vHEl1o81jRsfF6K5hTE6P4pd9hS3umAEsdrHVkfdT0fM5XfSODaRffBBzp/UgJczP4zKiJPHfnw8xMjXUY+Dz1Lm9eerckxM1ruAgMsDA9zeNBOTSTNPyTIivjitHntr0XYPFznPL5QzdiNRQVtw1RvFRa0p8iE+LnpFgX52SXYsL9iGu2fDQklvkZujCahPHSusZ0y0ci92BKMLX2/PYm1+NQ5TonxAkb89H52ZP0vQtHtc9okOvZ+OxciVYBFkg83hZfYfWbYu7J3dnQo9I9BqVPCrfpC9OpRIYkhTKU0sP8/vRUqICDC2Cnhl9Y3hwmnsJqbLByu9HSrl9Qlcq6i28vz6LygYrJpuDBqfGlieFF09tvAIQF2zk/TmDOyQdcPjwYSZMmEBAQACrVq0iNvaPDwH8nXgDHy9e/h8TEBDAW2+9xcyZM/nqq6+49NJLO7xuWZ1FCVIuH5bIT3sLO+1NZLE7WH+snIcXH2jx3IGCWr7alsv4tAgufX8Lt43vQkKoL4mhPkgSHCqs4bGfDjG9dzSXDElAr1axJ6+6RcpfEPCoPQSe3d7/KnrHBfLZNUPbXMZHp2FCWgTrM8vlktjAuL91gkbTJHUxIjXUY9BzKtSabcxfeoSpvaIor7fwxu/HKKgyYbGL3DwuFZ1azYpDJXSN8CPR2ZRdY7Kx+kgJlw6Vg71TUUlODvPlkbN6svpIKR9vyqbObGfjsQqu/Ggb145KcetZ6gxqlcDAxJORnd0hKj1Pyw8U8+HGEwBsOi5LBswaksDC7blIknxeX7iwT4ssjNUucvO4LpjtDq7+eDvZFbJVRlNPLwkPQU6T53MqGljjzKx+eOXgDt3k7N27l0mTJhEVFcXKlStbWEz9G/EGPl68/D/n7LPP5qKLLuL2229n8uTJhIV17Mv+400nlCDD9XV7Kpo6ZXUWftxb6PG5H/YUcMXwRML89Nw1ubvbc0OSQ9ieXaUEWxcNjueX/UWszShzW66962FRjYnjpQ0cKa5laHLoaVFmHvPc78w7qycTO9g74cJkdbDhWDkmm4Oz+8p2CHOn9eA+UeJEecPfPjasUf05xqsCsOZoKV9tk32u9BoVFrvIWX2iSQzxZXdeNRvuH8fF72whMkDPs+f3RqNSKeXLhdty2ZJVyfIDRYpuUUeICTISE2RkTLdwLh+WSHZ5AzqNCl+9mj5xQR7Xad603Rpmm4Pfj5Sy8lAJAUYtj52djsXu4NnlR1osG+yj5dVL+vPB+izemT3Qo9t9VKABu0Pkli93s7dJBrRpj5KnP7+mh/r7kVIe//kQH84ZTLfI9qVeduzYweTJk0lOTmbFihWEhoa2u86/AW/g48WLF1577TV69OjBXXfdxaefftqhdUakhvHWmuMnAwtB8JhWb4v2Fj9WUo8gCC36MJT1kXjsx4P89+eDiFLr/RltXawqG6y8vjqT/QU1PDitx2kJfFbcOabVY24NUZQoqDYR7KNlf0a123NqlaCoIv+d+OjUdIv0QyUInTagbQt/g5a4YCNFNbKSs8VZwjpUWEtKmC/Z5Q0MfXoVtSYbDZZYwv31WB2SYs9wVt8YvtiaS2VD635yTXl5ZQbXj03BqFXzzPIj7M+voarRRoPFTlywkeQwX+76eq9zHF7W75ncM5Jnzu/DiysyyK1sJL+qkWtHJ1NaayGjtJ57J3d3a5Q/VlrPjV/sIshHy293jaXeYueN1cc44bRgCfbRUtVoo3dsIDeN64KfXsP03tGt2piIosT93+1ndRNPOkGQs1axQUYKqk0e//6aBkbvrsvi1nFdFD2mtti4cSPTpk0jPT2dpUuXEhQU1KFz+2/AG/h48eKFyMhIXnrpJa666iouu+wyzjzzzHbXGdkljOvHpPL22uOsdBopdibssTlEatswPoWTo7guo8mmHC6qZUNmuTy9046NWFt36OkxgSy6vmPO9R2lsyW0X/YV8cKKo8ydmsbk9CgGJf09Ddft0TXSnxV3jv1Tth3q2zKQyipvoMipD1XdKH9Wnl1+VBFKDPLRsmfeZLLK6onw1ytlr/bIKKlDo1Lx0cZsPt2Ug8l28gOkUQn46jXkOEtJLpr2Ah0vq+fqkcmoBBWP/XQIgFFdwtwsSlwBzIPTehDqq+PKj7azLlPORo5IDeX1Wf15dvkR7puSpuhNtRb0SJLE00sP892u/GaPy/5zvnoNGpXAGR56m8w2B3O/34ckyeXV29tQlXaxevVqZsyYwZAhQ/jpp5/w8/v7g+7TiTfw8eLFCwBz5szh888/5/rrr+fgwYP4+rbvvXX35G7syavicFEtL17Ut1OlkJyKBsV5ujWinb05vrqWX1Up4b5M7xPNV9vyMGhVxAQZyWqmgQLyXXFpnVlRNW7OsdI6HvnhIFWNVv4zJoXzBsR1+DWcLnz0ak6UN3Dvt/tIjw3skCfY/xr2VsqkFrvIRYPjSY8JoLzeysFCuczTNdKfX/bJJVKNSuVxfL01ZCdyeQqxxmQjp6IBg1bN1zvyMNscbtm6MD899Rab0kg9pVcU6zPLOH9gHEv3FynL5VY28ubvxxTV5WUHihiWEsKFA+OwixKldRYkSZZXeHv2QAIMWp67oGNaSu+uy+L9DSdaPP7w9B5M6BFJcpgvsz/YqkgASJLE8bJ6JAkyS+r5ZX8RaVH+fH3D8HaNcZctW8Z5553H2LFj+f777/HxOT1Cl/8kvIGPFy9eADkr8vbbb9OjRw8WLFjAPffc0+46WrWKdy4fxJHiWjJL6kkO8+3w6HlMkJEbxqbw9NKWPQ8uCqtNOERJMWJsiloQqGxwjW9rlXHt5kgS1JrsRLTS0tAlwp9Gq50jxXWU11s8L+SkrM7Sqam3vXnVxAYb2xU0HNc9gvMHxLElq4LSWvPfHvhY7SKfbMp2G6f+M6k12zhaUqv8PiEtgjO6h6NSCSSG+DKqa5jS87TpWDk3fL6TqEADoiSXgIJ8tB4/I62h08iBkkatclM6XnagGImTTdxnpkfy9uUDuffbfQT7aHlo8X4m9Yx066V57vw+nNE9nACjFkmCVUdKkCTZf+uxGT0RBIH31h/nsFNXqrzeymu/ZfLwWT3pCN/uzGf+Ms9/I7/sL1IC9Qh/A0IThZ6JL61T/h8XbOSTq4cQYPD8N+Ji8eLFXHzxxUybNo1Fixa16+X3b8Ub+Hjx4kWhS5cuXH311Tz77LPccMMNHUpxB/poGZrS+aZHH53G7YvaE7VmO4XVJo8jt0U1Zn49WML03tGM7hrG00sPt7qd1qa6XHx69VDsooiPh8xSU4J82r5wNGfx7gIOFdXyxbVDW4jaNefRs3siQAv7hr8DnUbFhYP+usxXgEHLpB5RFFabCDBqOKtPjNv4/r78akpqLVQ1WNl0vByzXWTNUblsZHWINFjsipaUJ0RRwuoQ5R+7iEOUbSx8dPD00sNsOl6OxSZSa7ahVauUzKUkyZmoJ2b2Qq9R0efxFcQFG0mLkqNolQABRi0RAQa2Z1eiEmBHdhUfb8rm7kndSA7z5bavdvNLk8yQVu25JOWJ34+Ucv93+1p9fndutdLXJiGx9UQl141JcSvtxgUb+eq6YR51rJqycOFCLr/8cs4//3w+//xztNq//3P4Z+ENfLx48eLGQw89xEcffcSbb77J/fff/6fuq6k6b2vkVTV67Jlx9XH2jgvkkiEJfLI5h7pi+a5aQM5guabMfNrpuQnsYEDTXvDS8hglukT48dbvx7l5XCpqldBqv1F7d+N/BFGUMNvlEo6mg6/hdLukt8e8Ga1nQOKDjTy8+ADl9RbW3TeOH/YU8vFVg3nz92PYRQm9Rk1htYkHvtvHemffl6sx2WoXPZbR7pjYlaPFdSw7UERe5UnVadnFXX6PsisaUAmy7QnIn1e7Qw6QBj25knP6xeJn0DClVxRatQpfnZqYIAPdIv24fmwqgiBw7ehkt6nFFy/q16Ex+d25Vdz0xS6iAw2cmR7FrweLya8ytVhO+TRJcoa0xmQDSQ7K4kN8+OLaoe1aUnz00Udcc801XHHFFXzwwQcdsrD5N+MNfLx48eJGQkIC11xzDc8//zw33XQT/v7tj72eKjoPY7vNya8yofHQl+DSLHHpmPx0y8gWF/Upr6zjSHEdoiShcl4iduZUuWms/JnMGprAxmMVTOsVxfe7C9BrVG6O5H8VDVY7F7+zBT+DhoXXDeuQUu8/iWBfPSO6hFHdaGVHThUGrYqVh0ow20QcDgmjTo3dIbFwe57H9Y1aNTqNCpPVQWSg3mkoKpAQ6sOIlDA0XQW0ahV1ZjsRAXpGdQnjRHkDs4YkuPXE3HNmd6IDDCSH+zG5ZxRmm4Mas9x0nVPRwNqMMlSCwMsX90OnUbE7t4o5H25T1k8O8yWqncwLyH1nV328HZPNQa3JRnWjjYom3lpuy5bVU9lopdZs41BRLX0fl4VAu0f689k1Q4hoZ39vvfUWN998MzfccANvvvkmqj9JsuCfhDfw8eLFSwsefPBBPvzwQ15//XUefPDBP20/zae1pqRHsfxgsdtjZXWWNhsyXRfx5kHPsdJ6Gp2KtjUmmzJ+/VcFPQBdI/xZe7SMeT8eJNRXx7IDxQxPCW33YnS68TdoiQjQc/Gg+H9d0OMi2EfL5uPlJIX5Mj4tgqRQX+6a1E3J1m17aCIHC2uoNdnx1avpFRuIRiWQX2UiNsiISiVQXm8h1FfnlnV79oI+Hvc3skvLrMxlQxNbPb6Z/WLdglpRlLhj0R5qzfIE2vVjU5g1OIFvd+bjq1OT3opqeEmtmTkfblem2GrN9hbTXE259L2tLR7rnxDER1cObjdr9+KLL3LPPfdw55138uKLL/7tOlF/Fd7Ax4sXLy2Ij4/nuuuu44UXXuCWW24hICDgT9lP81JXn/jAFoGPXqPCYvOQ8XFWL1LDPU+fvbjiKLmV8kiy2f7H/ZdOhXWZZby/4QTn9o9lQEIQI7qE/W2mqNN6R3fYePafyPVjU7neab2RXd5AbLCRr7blcsHAOGe/mDwOPrJLKJuOV6BVyxme0joLm7MqOFHeQLCPFq1ahUOUsIuS/K9DwiGK1FscHC2pJTnMl96xgZw/II7/fLaTNy8doPSYldaZGff8GnY+MolvduYze1jrgdDXO/KUkfjzBsTywJQ0BEHgnjO7c6CghlWHS5jQw13gss5sY86H2xS39c4S7KPliXN6MbFHZJuSCpIk8eSTTzJv3jweeughnnjiif83QQ94Ax8vXry0wty5c3n//fd57bXXePjhh/+UfTTP5Lh8oABSwnwprjVj0KppsLQU6nF1bbQ3Qq9WCX/plJTJ6uDbnXl0jwpgXPcItj808S/bd1tcNCj+7z6EP0RFvYVQPz0HCmqY9e4WEECvUTOuewQ+IRqyKxp44Lv9DE8N5WhxHSO7hLE2o5QbPt/Vqf1sPCbbSAxMDMZkdbB4d4GiD6RXq2mwOjhWWsfCbblM6xXlUchxxcFiHly8H4Azuofz7Pl93AKL5DBfnv/1KEeK67h5XBdAnqS74fOdbuambRHso6VrhB+786qxOeS/hq6R/pzVJ6bN9SRJ4qGHHmL+/Pk8+eSTPPTQQx3a3/8S//vFPC9evJwSsbGxXH/99bz44otUV1f/Kftoq1k4IdQHo1aNUavG02IuldrW4h7XdUbg1Kw0ToUGi50zX1nHI0sOsqJZ5srLH+OTTdlklzdg0Kqos9ipM9uRJAmL3YHJ6uCid7awv6CGd9dlUe0Uxmxr0uu8AbHKiLwntp+oYmduFYt35/P7UVkt+bGfDgJQUG3mYGGtUsZyIUkSL/56lFu+3IUoQd/4IN66bECLz7mvXsM7swcq5TRJkrj/u31K0NURogIMhPrpSQyVM56XDU3goysHt7mOJEnceeedzJ8/n5deeun/ZdAD3oyPFy9e2uCBBx7g3Xff5dVXX+XRRx897dtvS3vFNa6s16ra7EtRtZKid43K20WJAwU19I0POvUD7SAatcDFg+PZklVx2ntpvtmRxwV/s0np38XBwhoKqs38uLcQi93BmemR7MiuwiFJWOwiBq2KW8bJZTCtRkVquCzD4OdBU2p01zB8dRpGpoahUcsqzXqNCp1GhU4t//vp5mxOVDRgtYtsz65iX14N47pHKIKF1Y02xnQLR9+sR81kc7B4TwFWh0RKmC8fXTm4VYkEg1ZNP+dn0mRzKIa/HaXeamfjsXLOGxDLozN6MrpreJvLi6LIjTfeyLvvvsuCBQu44YYbOrW//yW8gY8XL15aJTo6mhtuuIGXX36Z2267jeDg09sY3CcuSDGkbI3WSlmuHM6SPQU0Wu2MT3Pvl0gO80UlyMaNFruDWrPtTx0ZB7n0siWrglBfHX3a8Pwy2xydtrXYnFVBaoQfAxL++Hvg0rJxSBKiJClqwwatig2Z5UQFGiioMmEXJYJ9dAiC7H7e3sX1z6JHVAAvXtQXUZR49tcjXDs6hV8PblZeiyAIXDkyucV6k9OjgL1uj714YV+35nJPU3a/Hix2K8NaHXKpVef8rK7PLKN3bECLCS29Ri03kvuLfHL1EEI62M/1896i9hdqRnmdPNof3ooieVPsdjtXX301X3zxBR9//DFz5szp9P7+l/AGPl68eGmT+++/n3feeYeXX36Z//73v6d124FGLf8Zk8Lrq4+1ukxrWj+u5uavtuVhsYmc0S0Cq0NUAopasw2VICBKEkE+Os5+fQN1ZjvDUkN589IBp+X4t2ZVMDgppEV2p7DazOGi2hb9Fg8t3s83O/Ix6tTsfXRyp/ZlsYsdCnoaLPZ21bMvemcze/KqWzx+5Ygk9uVXM/+8PqRG+HGgoIayejOJob7syK78Q4GP2eagrM6C2eYgKcy3U5pIrvOrUglcPjSRt9Yc49VL+qFWCUqpxxMGDx5vj/10ELVKhdXuwGqXRQ1tdom8qkYsdln3p8FqZ1wTkUGXXYVeo6IO+HmfHKjMHpakiGNKksRDi/dTVmdh4X+GER/SMauHAwU1PLLkQIeWbUpMkBF9B4Jnm83GZZddxvfff8+XX37JxRdf3Ol9/a/hDXy8ePHSJlFRUdx000288sor3HHHHYSEnF4DzcuHJbJgzfFWvZq+2JrbajnLhc3pbH7RO5u5zNmI+u3OfGWbIb46VIJARYOV+mZ9GZ1FkiS+3ZlPv/ggescFcvc3e7l0aAJVDVbyqkykxwSwO7fa4zj0f2f2IjLAQHZFS0+x9rDYWs+Kfbk1l0Xbc4kL8eHGsan0amVU2kVrzvEOUaJ7lD87cip5Z22WMhUX6qvrkOheW2w7UcmevGrWZZTx4kV9WwQsT/1yiPwqEzaHyMQekVwyxLPhqCRBSpgfZ/eNabfsp1GrEISTQTLA0v0d671quumDhbV8viVHCYCuHJHExB6RborgL67IYOH2PFQC5Fc1khru2+7xldaa+c+nOzxmPMP89K1aqKSE+/LVdcPazWCazWYuuugifv31V7777jtmzpzZ5vL/X/AGPl68eGmX++67jwULFvDSSy/x5JNPntZtRwYYeGf2QB74Xr5bbs7qI6VKL0RTEkN9uHtSN15cmYHV7mDV4RKKasy8sCKjxbKiJDF3Wg9MNgcRnfDa8sSSvYV8uOEEyWG+XDUqmQk9Ivhyay7rMkqpaLCx4s4xnNM/ls825zAi1T1YUKsEbpvQ9ZT2axdFakw2jpXWExVocJtUK6huZG9+DXvza7hudPv+WppWsmgOSaJndAAPLXbPQEzvE82wU7AlaUpEgJ71mWUMSwllwZrjPHO+u37OxmMVHHL6WcW0MoWXVVbPp5tz+HhTNjtyKlEJAk+e08vjZJWLUF89t47vwqM/HuzU8TYNwzcdr2DT8ZONx1eNTHIL3JbtL+KN3+WsZd/4IIYmh7Irt4pdOdVcOzrZYwDUaLVzzSc7KKwxK4/1ig1gWu9oxqdFEBfsw8QX11Jca3ZbLy3Kn0+vGdKq6a6y/cZGzj33XNatW8eSJUuYMmVKZ17+/zTeqS4vXry0S0REBLfccguvvvoq5eXlp337E3pEsug/wxiW0vFsklatIsBpTGp3SGSVt55FMdscHC2u5VhpvZLF6CyiKPHJpmye/uUwh4vrWHWklAe+24cowoy+0dw1uTvPnd+HWpONuxbtRZRO7ySZ1S5y9cfbOX/BJhY1UygemBisZHEarW1ntI6V1rdaZnI4JNQeeqr6xgXhb+jcfXJRjYnqRllteOOxcurNdsw2UTZi9RDgaj2UpRyiRINFfj05FQ1MeXU92RUNTO4ZyYGCWvbmVXO4qO3xb7sokl/V+fe8rVxNU4+5WrONec6gqkuEHx/OGYxRp2ZgYgjJYb68uCIDsVk20+4QueXL3ewvqEEQZFPWm85IRa9Rszu3mpyKRoY9vapF0DM4KZhF1w9vN+ipq6tj2rRpbNy4kaVLl3qDnmZ4Mz5evHjpEPfeey9vvvkmL774IvPnzz/t208J9+PW8V3ZktVSiba1ioFrpN3qECmqMXteCLlM5MoEDU8J7bSmTWZJHfd/t49dudUnt2kXOV4mC+k1VYO+9pMdnDcgtl3D084iShI7c6oA6N8sA7Yjuwqr06wyOcyX8S+uAeC/Z/dqUaKqbLCiVau4ckQSm49XcLTkZOBgE0WP9iAOSVIu9vOWHOBAQQ3do/yZkBaJhJx96xbpbm3yztosDFo1V49K4vaFe/juxuH8dOso5fmSWjMR/nolG6JvEox9viWHz7fkKC7oq+8ey8M/HMBqFxndNZxfDxZTUG1yNhu31Hhy0eAce39v/YlWl2mN1j5zfnoNEQEnM0zvrs2irM5CVICBT64e4iZQObFnJH56NSV1ZqIDT2axlh4oZl1GGZN7RmITJdZllrHqSKny/JzhScr76WJKehSvXNKv3ab46upqpk6dyqFDh1ixYgUjRozozMv+f4E38PHixUuHCAsL49Zbb+X111/nrrvuIjz89E/4DEsJxUenVqwmXLQ3wG1ziCSG+tArVlaYliR5mkuSJCQJt+bj1so8zam32DlaXMex0joe+eFgiwuRi6+25fLb4RJEUWJYaijvzxnUoe13lqZ9Tm1lkxqtDoprzDRaHdRb7FQ2WLnnm72YbQ5ig4yc3S+GffnV/Ha4pMW6dofkcQxfFCUlEDhaXMeu3Gp25Vbz1TY583TjGancPyXNbR2tWuDttcf5ZkceFQ1WpT/GRZ3Z7lZ2HNU1jHB/PXqtCoNWjV6jQqMSUKkEMkvq2HS8gm6RfhwqrCUu2IivLpzkMD9FvA9k8cg7Fu1mXUY5N56Ryoy+Maes4RQZoCcx1EdRXwZZO+e1Wf2V4EOSJHIrG0kJ92XBZQNbCGU2Wu3EBPm4BT0AY7qGMa13FD/tK8LTW/n66kwuGhjH51tzAbmn6JGzerZp3QJQXl7O5MmTycnJYfXq1QwcOPBUXvr/PN7Ax4sXLx3mnnvu4Y033uCFF17g2WefPe3bV6sEksN8OVhY26HlXdcMUYQnz+nd6nI2h0h8iJG8ShNHiuu4Y+FuHj6rJ2Gt9IasOFjMoz8e5Mz0KL7ekddq0ANyE7WL/GoTdoeEgOz1ZGyiU1TTaKOo1qRc6LpH+rep9ZNd3sCx0np0GhV+Bo3bBbz5tfzc/rEEGDSoBIEQXx06jYpGq4PlB4rIq2pktTOb0DM6gHP6xyqlJp1GRVqUPxkldZhtIn4GDfvzq1scy3vrs+gRHUCgUcslQ+JJDvN1MwQVJYm8ykZ+2F3AyK5hLNtfpGTHKhrkcld+lYkHvt9PkFHL0+f1pkuEn9s+mvc+HSqsxWJ30D8hWPZa89Xx0kX93Bq3P9+Sg63JeyMhseZoGTFBRqx2z9mrpqgE3PR7tGr5R6dREWDQ8fblA5m35AB1ZjtxwT68cWl/t4zLusxyzugezssX92sRlNgcIj/vLeKiwe7ZxdJaM5e8t4WsstZLs1tPVDKtdxQ6tYr7pnTn2g70bRUXFzNp0iRKS0v5/fff6dPHsweZF2/g48WLl04QGhrK7bffzssvv8zdd99NRERE+yt1knsmd+eqj7cD8PLFfVmyp5C6ViaxOtpGo1WrCDRqycNEWZ2FH/YUcuekbryz9jjXj01VAqDSOjOZJfXc8uVuEOD2CV35eV9hiwxUa/yyr4hfnKPOa+89g7hgH+rNdgJ9tKw6UsJdX5/UlDn83ylugVFzVhwq5uON2ei1suFm08mf5hmfmCAjb645Tp3ZzsSekZzTL5aPN2Xzw55CdubK5bFBicEMSQ5xK+HEBRv58ZZRTHt1PYeKahmcFIxapeKTzTlu2z9e1oBRJx9Hr9hASmrde3Tign3ILK3jzF5RhPvpW5SW/PQagn20Sqmu1mTDahcJ9tV5FBk02xxc/O5mbA6R3+4aS1ywDwatmg82nCDEV4fFOYq+4lAJj81IV9YzaNTcNqErZ6ZHsfpICTFBRu6Z3I3IAAPDUkJPChU6g53mxrae+OaGEZTXW9h4rLxFmWlsN89ZT0mSmL/0CNeNaakt9PbarDaDHgB/g4aUcD+W3j6KLhH+bS4LkJ+fz4QJE6ivr2ft2rWkpaW1u87/Z7zNzV68eOkUd911FxqNhueee+5P2X6v2ECCfLTMGZ5I1wh/JElu7M2vaiS/qpGCahOFzp9as2xNINF+BHTtqBQCjVr89Ro0KoHvdhWwL79GafQVRYlF2/K4/IOtDEwMZlz3cOyihLmNMfK2+GhjNnmVjQx+6jcAzhsQx5Bk9+bt3blVPPbjQR5dckD5KXE2tKoEgXevGMTv95zBozN6ugU+UrPAx+YQuWFsKoFGLQFGrVtAZXNIqARICvOla6SfW8nMlUVSqwT89BoCjdpWy4pik9Owv6DG7bnyOgtqlYpVh0tbmMy69hPeROxvwZrjXPHhNvbmVfPuuuPkVrg3H0sSDE0OJSnUl6+dmaVrRyejUQn46NRE+Bvw02u5dEgCadEnAwOVSuA/Y1JIDfflutEpqFUCt4zvyoWD4okP8SEiwECQjw4fnaZDQY+rOTvMT8/MfrEU1ZiUDJMoSrz5+zGqnBmtpjz/61FGdgltUeL6ekceH286GRSqhJa9RCnhviy5eSSju4Z3KOg5ceIEY8aMwWKxsG7dOm/Q0wEEqflf0P9jamtrCQwMpKam5k9zo/bi5X+BefPm8cILL5CVlUVUVNQpbaPRamfu9/upM9vRa1TEBhl5+KyegGxRkBzmi49Ow66cSqoa5QCn6UVCJQho1XIfSJCPjq4Rfh22icirbMQhivjqNYT7GzhWWsfc7/ezPVvOSAT5aBFFib7xQazPLFcee2S6fHw/7Clw117x8C06//zeRAUYeOW3DO6e3B2LXWTJngIOFNQwNDmUmf1iyKls5KutudgcImqVnIm4cWwqgT5adudWcay0Ho1aQJIgMdRXmXDqER1AeJP+mKoGK7ct3M3Fg+OZ1DOS46UNvPF7JgIC909JY+uJCs7sFUWAQUt2eQOvrZafU6vkvqrt2ZWM7RbBlF5R1JptlNZa0KgE1M4fjUpwln+0nKho4EBBDcE+sjbSifJ6RnQJw0enZtn+YnRqAZ1GrVhBGLRqDFoVvWMDWXagCK1aRZcIP0L99IT46Nzes8ySOgRBoEuEH8sPFGFzyMrRccFGnll2BIvdgUOSR7o3ZJbTMyaAa0YlExts5MVfjxIf4oNDlCiptZBb2UBqhB+Te8qfz20nKrloUBxfbcsl2FdHo8XBBxtOEOSjxS5KaFQCAUYtkiTRaHU4t2MmzE+PhDyJVW9xsOH+cRi0ajJK6pj88jpSw3359JqhSn/PBxtOoNOoWji3f7k1VzEudTFrSAL3T+nO1R9vZ1duNWO7hfParP4EGjumMJ6RkcGECRMwGAysWrWKhATP2kf/H+jM9dsb+DTBG/h48dIxqqqqSE5O5qqrruLll18+pW1sPFbOF1tzuGdydxJCfKi32Jnz4TaW3DKKUc+uprTWQoBRw693jGlTp8XFzV/son9CkHKxFkUJuyhhdYh8uzOf8/rH8vmWXC4YGIdKJbD6SAlpUQHoNSq+3pHn1iQb4a93G7m+fUJXRncNY1BS58Qbt2ZVsHh3AaIkMaFHJJEBBkJ9ddRb7KSG+5Fb2UBhtVm2jhAl2UZClIgKNCABH6w/wdCUEPbm1VDZYMEhya7cVwxPQqMSEARIiwpAQkKvUbM9u5IAg5YtWRXszKlyZhTcg8GLBsXx4cZs0qL88dVrkCQ5YyZJspN4kI+OpfuKlCBTlCRsDgm9RsW3O/O58YxUgn3kyaVtJyq5e3I3ZR8vrjhKRkkdx0rriQ32wWJzYHWI+OjUPHNeH77alktOZSPTekXTI9qflHD3Pp//fLqDc/rHMq13NAOeWEllgxW1SuC1S/px85e7Abk36pfbRmG2ixwoqGHjsXIuHZrA8PmrAdCoBBzOpvbpfaIVle7PNmdz7oA4bv5iF2szypR9poT5cv7AOJLDfJnWO5ql+4u46Qt3R/enz+3N9N7RBBg1ymvdnVvFuW9tAiA60MCnVw/hUFEth4pqmTu1h9v6n23O5pElnnWExnQL51hJHUE+On66dVS7DcwuDhw4wMSJEwkNDeW3334jOjq6Q+v9r9KZ63enenwWLFjAggULyM7OBiA9PZ158+YxdepUAEpKSrj//vtZsWIF1dXVjBkzhtdff52uXdsW7Pruu+945JFHOH78OKmpqTz11FOce+65yvN2u53HHnuML774guLiYqKjo7nyyit5+OGHUTk1JyRJ4vHHH+fdd9+lqqqKoUOH8uabb5Kent7abr148XKKBAcHc+edd/LMM89w3333ndKXbmG1iaX7i1m6v5htD07AqFMzpZe8HZNVvmCG+uo7FPQA5FU18sv+1j2PXOPsLqE5gAMF7k3Ufno19RZHC52ZHtH+nQ56QFb8La2zMP+83tz4+U525VZz4cA4jpbU8cW1Q3lj9TF+2FPYYr0ZfWOY1DOSNy+TL9qXHdyiOHfPGZ6IxebghdXHCDBqGJEaRv+EIPrEBVFYbWJ9aRmL9xSQV2nyeEy9YgK5bnQK4f56bvlyl1sjeYivjqRQX55aerjFemF+OhyixPbsSiobrIT46vjtUCn3nNldWeb7XQUUVMv7Pd6kj8Vfr8GoU9M9yp/FuwvYeKyc5bePabEPrVqllNqGJIWQHhPAhmPlboKGmaV1LFhznJn9YhmSFMKmY+VKuTLYR8uae8dx1Ufb2JVbTWZJHW/+fgyrXWRvfjVBPjqOlda77bO0zkJprZlGq509edUcb/Y8wJu/H+Psfu5K0aYmfV9FNWbOX7CJ0d3Cee2S/ie3XWvml/1FrDjYcoLOxbqMMnx1at6fM7jDQc/u3buZNGkScXFxrFy58k+ZsPxfplM9PnFxcTzzzDPs2LGDHTt2MH78eGbOnMnBgweRJIlzzjmHrKwslixZwu7du0lMTGTixIk0NLTeyLV582YuvvhiZs+ezd69e5k9ezYXXXQRW7ee1PJ49tlnefvtt3njjTc4fPgwzz33HM8//zyvv/66ssxzzz3HSy+9xBtvvMH27duJiopi0qRJ1NW1LW7lxYuXU+OOO+7AYDDwzDPPnNL6NSa5fDWzXwwBRi3+Bi03npGqPD+9dzTvXtHxcdycis6J1Pl7aKqtt3SsibmjqAR5/PtocZ1y0fxmZz778ms4Ud7A5qwKj+v9tLeQOrONb3fmk1VW72ZXccWIJPwNWmpMNn49WMKjPx6ksPpkkFPVaEPbirEryIatVY1WIvz1PH9BX7fn2hqTt9hELHaRz7fksnR/MZ9vyaWkzszjPx3kvXVZiKLUqimnyebAV6fhq225VNRbqW60KX1INofI/vwaGq12Zg9LpLjWzC/7ChmSHMKYbuFM7hmJKElKKUmjUmHUqZGQ2JxVQb3Fgc4pfhgdaCTQqKXS2XeTV2ni6x15vL32OGuOlnHrV7uVwMyFSoDfDpeyJ6+aDZnlip7OTWekMv+83rx12QBmD0+ksMrkNpLffLIuJdyP5y/oowQv205UMub53zlSVMfLF/drdYJQp1Hx7hWD6BnTsSrDli1bGDduHKmpqfz+++/eoOcU6FTGZ8aMGW6/P/XUUyxYsIAtW7ag1WrZsmULBw4cULIsb731FhEREXz11Vdce+21Hrf5yiuvMGnSJObOnQvA3LlzWbt2La+88gpfffUVIAdHM2fOZPr06QAkJSXx1VdfsWPHDkDO9rzyyis89NBDnHfeeQB88sknREZG8uWXX3L99dd35mV68eKlAwQGBnL33Xfz5JNPcu+99xIXF9ep9V0j4qO6hCnTMhkldazLKKPR6sDfoGnTgLIpmSV1SiC16D/DiAkyIjjLPCpBVtoVnI2kKkGW4ltxqIS538s9F6/P6k9yWOv7ig/umOFkcwRBoKDaxBUfbmuh8SIgoNe0PtVlsTl4bvlR5XW5uOKDbTx/YR+3fqdfD5ZQWG3mSHEtNoeEv0HDHRO7ohIEDhfVsuzAyYZjmyjx874iRTuALwAAQWlJREFUftpbyC7nlJULUWoZ/EztFUW4v56Vh0qY2S+WpfuLFPVrSZKbuGMCDcwamsCnVw8hq7yB+77dS0G1SWkMl3to5EzWOf1iUTmbqQGGPPUblw1NpHyrxW1EvikPT+/BuLRwvt6eT0yQgQsGxhHko+NgYS02h6ioVic4jUF7RAdQ0WBlZr8Y5p2VzpRX1nlU9p6QFsGLF/UlyFm6+3lfIesyyvDTaxnbLZyhKaHYHCK5lQ1c/M5WYoIMLLl5ZIvyYUq4Lx9eOVgRrcwoqeO6T3dgtomsyyxjW3alR98tjUrg7csHePR188S6deuYPn06/fr145dffvG2ZJwipzzV5XA4WLhwIQ0NDQwfPhyLRX5TDYaTnftqtRqdTseGDRta3c7mzZuZPNndpfjMM89k06ZNyu+jRo1i1apVZGTIqeq9e/eyYcMGpk2bBshd7cXFxW7b0ev1jB071m07zbFYLNTW1rr9ePHipePcdtttGI1GXnnllU6v6yoVNL3Mdov050BBDSabo8PCczaH6ObDFBNkJD7Eh7hgH2KDjEQHGokKNNBodeCn1xDmJ5fPfJwZh1vHd2FMt3BlVNvTT6BPx5pNm6MSoGuEH5N7RjIwMZiYJqaWqRG+aNQCBq2Kn28dxZBmpTSzXVSCnqZ6NAXVJpDcBQ0X7y7gvz8f4usd+VjsDkQJ9ufX0C3Sn0k9I922K4oi3SP98Ddo3HyiQL6JbH7eR3UN49z+sdgcEoFGrZsysYvIQIMyFdY3LpBlt4+hf/xJNetZQxKwiRKXDU3kkiEJXDQoXsmM/HDzSP4zNgW9B8sKFzaHiNkm8vLF/bhtQlfK6+WMjihJ2EVRKXW5gukFlw+kW6Q/DlFi6f6iVu1MDDq1EvQAnNUnhucu6MuZ6ZGoVXLQeMuXu4gMMNIj2p8p6VEtgh5Xf48r21VYbWLOh9uU966oxsyJVvZ/3ZgUxqdFenyuOStXrmTKlCkMHTqU5cuXe4OeP0CndXz279/P8OHDMZvN+Pn5sXjxYnr27InNZiMxMZG5c+fyzjvv4Ovry0svvURxcTFFRa3X3YuLi4mMdH/jIyMjKS4+eYdy//33U1NTQ1paGmq1GofDwVNPPcWsWbOUbbjWa76dnBx3PYqmzJ8/n8cff7yzp8CLFy9OAgICuP7661mwYAHz5s3r1JexS5vnlZUZ7Myu4tkLZME115RPRxSWzTYHN36+081AsrVyzRM/H+LOid3oHefuXP766mNM7xPd4UmaTiEIzB6eyBXDkwC4c9EeFu8uAMBml7hqZDK1Jpsywt8UR5Nma6NO3ULLqLV2EFGUA4UTFQ2IktTC2d7qkHh1lXwT+f1NI7jig23UO6fFmio0J4f5csIpohgf7INDFJGQ3MbdZw2JZ/55fZwK2RJfbsvl4R/cDU5BVrd+cJrnMevEUF++3JqrlKs8YXc2qEcHGugRHcCO7Eq6RPgRF+yD1S6hVsmZvaalKK1a4GBhLf3i5YZ3T4H07pwqvtqWi12UcDhExfqksNpEVYON3XlVPHZ2Ond/vYeCahNXj0qmzmxDq1ax4lAxIb46PrtmKHHOjGBNo405H25r0z6lKc2zgK3x008/ccEFFzBp0iS+/fZbtwSDl87T6YxP9+7d2bNnD1u2bOHGG29kzpw5HDp0CK1Wy3fffUdGRgYhISH4+PiwZs0apk6dilrdtrdI8whakiS3xxYtWsTnn3/Ol19+ya5du/jkk0944YUX+OSTTzq1nebMnTuXmpoa5Scvz3Oa1YsXL61z66230tjYyIcfftjusq4LpM0hKmJ2hTVmssrlhtInfz7Esv3yjUzzC7Ynluwp4PejZW6PtZYoUgmt6/0I7ZpinBoC0NCkb6j5S/pqay6v/JbByyszOF4mnwO1SkCnVrkFcM2DHsm5nMd9Oh8+r38sXSP8eHvtcbfnLbaTx3PXoj00NDE1FSWUspErU/TRxmxeX52JXZSwOYMMF2qVIIs27i9if0ENH25o3RPL7pBamHW6OFxU22bZz+5cz0+vYXxaBA1WB3vzqjFZHdidAkM6jcpNYVurVrEvv4b7v9uvBD0+Onm03oW/QQ42AwyyrMGu3Grigo08fW5vbhyXykWD4rlsaCJJYb7M7BuLQasmq6yBygYrgUYt394wXFGgNtscXPfZDjI9NEc3RxDgufP7cHmzkXdPfPPNN5x33nnMmDGD77//3hv0nAY6nfHR6XR06dIFgEGDBrF9+3ZeffVV3nnnHQYOHMiePXuoqanBarUSHh7O0KFDGTSode+aqKgot+wOQGlpqVv25t577+WBBx7gkksuAaB3797k5OQwf/585syZo+iIuCa+WttOc/R6PXp9xyZGvHjx4pnY2FguueQSXn31VW655RY0mta/Vsw2kTNfWYcguE/FuGwNogINxAYbOVZaT2Jo+301nhSdW8v4CILgpvT80cbsJs+1u6tTQgJ25lQCctN20wDr5/2F2BwiNofEq6sylcfVgoBWLSg3bYIg9yA98sMBRc8IUMo7VwxPpEd0AAKwLbtSHk+X4PMtufy4t5CMknrmndUTlSCf54qGk70m2c0awkVJUlzS312XpTy+PbsKf4MGk82BusnJ+nxLLp9vySUx1Idf7xjTpoO7TRSZ89E2JqdHMXtYIpe+t4VPrx6CRq1CQnILSJrjECXO6RfDZcMSMWjVVNRbmPnmRgCm9Y5SzkdTbSVdM4FCtUpgweUDeXfdcWVCziaKHC+tZ2z3cEZ3DWdYSoiSHfpg/QkenSHrNomixKyhsvVEX6dB7N2TT06zOUSJu77ew7YTla2+Bhcp4b48e34fBndgSvCzzz7jyiuvZNasWXz88cdt/m156Th/WLlZkiSlv8dFYGAg4eHhZGZmsmPHDmbOnNnq+sOHD2flypVujzV3lG1sbFTG1l2o1WpEZ6SfnJxMVFSU23asVitr1671OtN68fIXcOedd5Kdnc3ixYvbXK6q0UpuZSPldRY+v3YoP9w8kr7xQZwob6DGZOOM7hFklzegVgnkV3kex25KU+0dF61Jk6mbRTcZTVzJKxuslNaZKauzUF5voaLegtn2xye84oKNxAX7cOeiPby04ihbT5wsya04WOIx/2R1iDQ0CQp1ahVhfnq3Mo5DlJTS0Nhu4cwaksAlQxIYkhSCUadGlCSKa81klMjZh6tGJnHlyGTO6R/brs1H84ChKTUmG2YPbuhatUouN7Uxjt1ocdAzJoBgHy3LDxS7uddrVColkPOEKMGholq+35XPxmPljE+L+L/27ju+yWr/A/gne3SkTZt0DzqZZRSBlk0LtIAiKChe2V7cgsoFQZb4Y3m9FcSrICoyvAKCoiICAi2zQCllQymlk3TR3aTNPL8/0sSmTUdKSgo975d5YZMnz/N8k7b59jznfL8Iq71kqdIQ43kbRrM2n0jHzTzTOZtaHcG7uy8bkx5A3z/M10WIE6lFeH1nMl7ZfhETvzyLIZ/EI9zP2VjQcvGYLpA6mB9p0ekIPth3FQevNaxYXV+gxA675gxoUdLz9ddfY/r06Zg1axa2bdtGkx4rsuiVXLx4MWJjY+Hj44PKykrs2rULCQkJOHToEAD9kJxEIoGvry+uXbuGuXPn4tlnnzWZdDxt2jR4eXlhzZo1AIC5c+diyJAhWLduHcaPH49ff/0VR48eNZkQ/fTTT2PVqlXw9fVFt27dkJKSgri4OMyaNQuA/i+5efPmYfXq1QgODkZwcDBWr14NoVCIl1566aFfJIqimtanTx8MGzYMcXFxmDRpUqPbGZYZvz8qFEESfaXlf/T3xZWcMpy8U4Sne3piaoQfruSUNdoHqa6y6obtAhq71MVgmE6knhTujR+TcqDS6PDi1+cabL9qQnf8o3/zlyKawmMxUVGjxpKxXfB/f9wySeZ0hBgnWJsT6maPTyf1xL8P38aLX5/D4GBXTIvwBwNAV09HLH+6G/Ym5+JKbjnSi6og5LKReK9YPx+nXnaz+uAt2PHY6O4pwuAgibGrujnm5toEuNph26x+WPbrdVzNNW1X8UJfHwi4LOSUKIzjWc/28oSAy8K0CH8IuSxw2Uz8dbMALnZcfR80AP06iY1tI3jshn2znu7piWkRfmAzGTh+qwD3iuRYVlsEkMn4+302XOrisJioUmqQVSzHXzcLzM6zKanXXkKl0WHZrzcwONgVAwL01bRd7HmQ2PMgdeRBULvasLEpE1odwdJfr+OnOo1qGxMoscOP/xzQaAJV14YNGzBv3jy8/fbbWL9+fYM//KmHY1HiU1BQgKlTpyIvLw8ikQhhYWE4dOgQRo4cCQDIy8vDe++9h4KCAnh4eGDatGlYunSpyT6ys7NN3sTIyEjs2rULS5YswdKlSxEYGIjdu3ejf//+xm02btyIpUuX4o033kBhYSE8PT3x6quvYtmyZcZtFixYgOrqarzxxhvGAoZHjhyBg0PzvU4oinp477//Pp5++mkkJiYiIiLC7DZFVUq42nMxNcIPnx9Pw01ZhbGL96YT6RjTwwMzIv0x8/skXMgsQVSXple8FFepwGToL2MYRn8KK5QIcTP9uVdrdSYra4oqlZg1qBN+v5qHEk3D5AlAg8mwzc0ZNCfU3QFJmSVwsec1aHCp0RKM7eFhTCR6+TjhbmGVcaKx2J6H0d3F2J6YiYIKJQoq9JNuX+rvCzaTgaxiBY7fLsCdgiqMC/PAFy/1weaT6ZgXFYxjt0wL5m05lQGpAw8zB3aCxN58rR19jOZHfNgsBnzEQpNLbQbvjgyBu4iPErnKmFismtADdjw2dDoChVoLNpOBsT08cLTOeanrzcep30nd30UIO66+Kz2LxYSQy0JF7aXNum+NYT8cNgM5JdUY+u+ERuMzJ0Bih+2z+ln03up0BN+fzURyVqnZopk9vERYNaE7Xt95CffLqjEwyAVfTOljdkVcfWvXrsWiRYuwYMECrF271uLvOap5FiU+3377bZOPv/POO3jnnXea3CYhIaHBfc8//zyef/75Rp/j4OCA9evXN7lklsFgYMWKFVixYkWTx6coqm2MGTMGISEhiIuLw08//WR2m6JKJcK8nWor9DJQUHtpCdBXOd5wLA3vjQzBPwcHNDkaYsBkAPtej0RvX2dkPJBjX3IuXtmeBCcBFwT6y0E+zkJotASZxXK8uuMiuGwmyhRqDAmRoFRhPukBTC+jEUIw7bsLYDIYsOOx9BN1ib4lhqHNxNgwjwYjRCwmA0WVSrzxQ3KDxp4anc5kjs3lnDKTxw0TgQ0J2J2CKiz/7QaW/3YD/xodihA3B2N1ZMO8Jg6TqZ+HY+aSU2GlEp8cvo3494c1+ZoKuCwESe3BQG3NI8bf9XEMDVRNXyd94iG24yIi0AUCLhtzd13GU/7OkJVV438XsjFzYCcsHtMFsrIaOAs5EHBYYDMZ2J6YCSaDAaVGfxlscLAruCwmeBwmZGU12JWUDQGHhVHd3NDJ1Q4FFTVggGEySd0wgtLUJbq6+vg64d4DOcpqk7jsYgWW/nodL/T1RXcvxyYTjRq1FusO3cawUCn+cyTV5JKkPY8NlVaHsT08sGZiD/DYTAwJcUVnd0dMHeDXbB85QgiWL1+Ojz/+GCtWrMCyZcto0tNG6EVDiqKsgslk4t1338Wbb76JjIwMdOrUqeE2DAZySxXYnpiJudHBeGN4IOZsv4j3R4Vi/dE0fH4sDe6OfLzUv2XNFhlg4EGVCrKyaniI+PB0EqBGrUO+Wv8BPTxUgrnRIXC158KBx8GULeeMcz9S8ysxPFSCCb298cG+qyYfYoB+CbVBRbUG3TxFOHBV1ujcI7EdF0/5i+HmyAefw4RaS8DnsPDW8CAoNToESewBxt/Tm8O8RahR6+DtLEAPLxF6+TohObMUN/MqwGEx0KW2ku9Pr+lHzxQqLbJLFOjs7oAHlSr4ugjx3Yyn4CHiw7+20ON7o0LgIRJg3+uRxtebydTPb2Ix9ROmCytrwGIyGoxgsZn6SdWOfA5+fiMSXBbTOHcHAPLKq5FXXgM3Rx4GBUlw7l4xZOXVxqRLo9VhSj9fjO+pgay8BsFSB/iKhVg0pgtYDCC9qApjuruDwdBPcuewGHi2txd+uXQf0V3coNbq4O0sxKAgV/x8KRfX7hdDpdFBpdFhx7ksqDQ64+quunp4ifBsb68mV4XVZRhhNPjipT4IktrjVl4Funo6wlwVBUIIjtwswLazmXimpyciA11MzmVyX298OKYrlFotJPY84+u6ZmJYi86JEIIFCxbg008/xbp167BgwYIWPY9qHZr4UBRlNdOmTcOSJUuwYcMGsyO0Y3q44//+uImkzFJMi/DH9sQsiAQclCnUyCqWI0BihyM381FRo8arQwKa/YuXyQT+uV1fwf25Pt4okZsutBDb8dCrdhUOALw/KgRfJtyFWkswdYAfDlzNw9M9PSEScHC3sAorD9w0bptZrEB6URUCJfYQCTn4ILYzsorlDSrwGipB55ToE7qiSiXUWgJ/FzsMDnHFLyn3wWExQQiMzUg1OoJ9l3JNJhoTQqCtLSDYydUOc4boV4IZJgELuWxj2wNfF/199edBjQvzbPL1AvQjJHdXxTb62q747Qa+P5tp/JrLZmJGpB8eVKowONjV2H5hw1H9SjRDde1ShRpv/u8SYrq5g8tmIszbyVj4cei/45FVrMCwEFck3NF3u//42e64KaswKT4J6Gv+WOLvS12WzYMZ08MduaXViAxygSOfY1yWXpdCpcEfV/Nw7FYhhoZK8P3MfuCymVBqtFBqdOCxmRgX5olVE3rUTs62vBaUTqfD22+/jS+//BIbN27EW2+9ZfE+KMvQxIeiKKsRCoV4/fXXsX79eqxYsQJOTk6mj3PZmBbhj5N3ipCcVYKfLuZgQIALZm9LglKjg4DDgrsjH3uScnAxswQfP9sdHqLGi7wNCpLgRGoRZOU12Hep4QRTL2fT50Z1ccOmE+m4mluGf+29isHBriCEYEiIxFhAUCTg4NNJPSG24za4fPLVyy3vHQboP8TrNus0h8VkoLO7AyIDXRDuJ0aAxM6kKWdbMJf0EEJwIaMEv10xbZqq0uiQXijHsduF2DVngDH5mhtt2nyay2ZCLORiQIALVBodbsjKwWMz4SMWGldsKTV/Z3pKtbbJas0tUbf7vJcTHxXVduCxmeCymeCy9D297Lhs2PHYsOexYMdjQ2zHRVcPR3TxcDQ750arI7iUXYqfL93HgyolYrq54/MpvU0mfet0wDfT+mJgkKux51hraLVazJkzB1u3bsWWLVsabe1EWReDNLb2swOypK09RVHm5efnw8/PD6tWrcL8+fMbPF5UqcRrO5ORnFUKJ6G+J9KBq3nYMi0cEns+kjJLcOCqDJeyyyAScHB64XBjoTlzCCHILa1GiVwFHSG1E1/17RWCpA0XN+SUKHDiThE0Wh0Uan0hvL5+YlSrtbiVV4FOrnZYEGO+yrClfryQbewHZsBhMdDH1xkRgS7o38kFPX1EJku7bU2nI8gqUeB2XgVSCyqRXiRHgKsd+vo7o4eXyKTFQ11KjRajPjtpLMDIYTEhdeCBX1t1msnQr1SrUevAYjLh6aRvc1GqUMOOx4KQy4aAw4KAywSPrV8JxmIywGYyjIkMn8MCn8MCj80Ej80Ek8FAsVwFiQMPOSUKlFeroay9PKbUaKHWEmi0Oqh1+sKZgtrn6wiBSqOvoaTSalGl1KJMrkJeRQ0YALp7iTA0RNKmCaharcb06dOxZ88ebNu2Df/4xz/a7FgdgSWf3zTxqYMmPhRlHTNnzsSxY8eQnp4ODqdh0rLnYg42Hk/D1hlP4VTaA6w/moZTC4fDsTbB+ej3G9iTlIO1z4Xh6Z7NX75pr67mluH03Qew57Fhz9M3Xe3m6dhghRfVNEIIHlSpUKPWIre0GpeyS3HyThGu5pZDJODg3OIobD2TAW9nIZyFHON8Jq2udhK6Vt/TS6nWoUajRbVKCyaDATaLAQc+GyIBF2I7LvzEwmYnIVuDSqXCiy++iN9//x27du3Cc8891+bHfNJZ8vndfv7MoCjqifHuu+/i+++/x969e4099erq5eOEVc/2gEZH0MXDEWqtDrsuZCPcT4zuXo5IzioFm8Vs0GCzvruFlcgv1xcbVGp00Oj0f8WrtTpotDqoDH/x11ZI1uh00Or0q6AMq7H0o0T6kSJDk05Dl3JS+6/ha13t9lqdflsdIdASQFt7XI1WB62OQK0lWDKuCyICXCAScJBWUIVuXo5NXraj9PN1SuUqlChUyCiS40puOa7dL8MNWQXsuGx0crWDv6sQrvY8DAxyxe38SgwO1nc2nzmw4WT6h1FQUYNfUu7D1Z6H58O9rbbf6upqPP/88zh27Bj279+PsWPHWm3fVMvQxIeiKKsLCwvDyJEjERcXhxdffLHBnJIQNwdczi7D+YxixHb3gEKlxeqDtwHo54qotToQAizZfx2fTurZ6HHuFFRh2a/Xjd2625NNJ+5BVlaDhfuuGpekj+/liZ7eTpj8lA/seU/2r19CCFS1Cadao28AWqPWQq7UQq7SoESuQn5t5/J7D+QoV6jAYDDg5yJEgKs93EU89OvkjIl9vODjLDQ7l+ZeURUCJA0nJbdUlVKDm7IKlMiVGBYqBZfFRMKdQuw8l42E1ELoCODuyMfILm7GidoPQy6X45lnnkFiYiIOHDiA6Ojoh94nZbkn+yePoiibee+99xAbG4vTp09j8ODBDR5/PtwbM79PQlJGKd4eEWS8/16RHMVyJfr4OoMAuH6/HN29RA2eDwBjenhgSIgE353OwPdnMxtU5n3UnIQcfP5ib/DYTIT7OePXyzKTQog3ZRXwcRbiuS/P4p2oYIzp4f7Y1WqpUWtxt7AK2SUKXMwsRZDUHifuFOJKTrn+cpJGZ5xnY+DtLMBT/mKIBJzaKs0MuNjx4FV7v49Y0OQ8LnPKamswXcz8uxdaS+l0BDdkFZi3OwUFFUr4iIXYeS4b2SUKZJf8XVtpcLAr3hsZYpWkp6KiAmPGjMGVK1dw+PBhsz8T1KNBEx+KotrE6NGj0bVrV8TFxZn9Jc9kMrD+hV4Yt/E0yqvVxr/oDS0IDPnCtrOZmNLfF318nc0ex57HxjtRwZgzJAB7k3Ox5dQ9ZNVrvtlWgqT2WBTbGdVqLb45lYEbsnJsPpmOf/T3A5vFbND8tIuHI+aPDsXB63l483+XEN3FDa8PC0RvH6dHMrektcoVanwRnwYdAY7dKkBmsQJCrn5Ccqi7PS5nlzWog1TXuDBPfBBrnQnjBjoCXLtfDomD5Y2mr8vK8cwX+ian70QFI8DVDvN2XzY+3sfXCQtiOmNAgEuz+7op09f/aUpJSQliYmKQlpaGo0ePmnQmoB49mvhQFNUmGAwG3n33XcyZMwd3795FUFBQg22c7bj47z/6YNKmsw0ajp6793en6xK5Ct/OeKrJ4/E5LLw8wA9T+vni2K0CfH3yHi5mlVonmEbY8diI6uKGQ9fzcCuvAmotwU1ZBQw5DLNe5mOI0DAydfRWAY7eKoDEgYeRXd0wIlSKAYEuNrsMdju/AmfvFiPjgRyju7kjItAFVTUa7DiXiS2nMkx6ZClUWihUWjy4q2x0f1P6+eKVwZ3QqbbWjzWJ7bjYNqtfq1bEdfcUYe1EfVuN+NuFmBbhBwYD6ORqh4UxnTGqq1uLR+K8nAX6thqNJK6FhYUYOXIkZDIZ4uPj0atXL4vPl7IumvhQFNVmXn75ZSxevBhxcXH48ssvzW7Ty8cJK8d3b7Dsu7VYTAZG1RbRO3nnAdIKK3E2vbhB7y1ruFdYhcT0YpzPKIGy9tJOuJ8zhneWAkCDER9D1/f6nd+LKpXYnZSDpIwS6AiBxIGHiABXDAgQo6ePk9VWgVWr9Jep7hRUoqxa37KBVVsLR6sjKJYrwantBn8xqwTX7pdDwGHCnsfGiqe71sakr/qs1hJUq7U4cacIyY0kmEIuC4EPMQenOd7OwlY9j8lk4MV+viCEYNOJdBRVKvHNtL4YGiJp0Cy1OSJB45fBZDIZoqOjUVpaioSEBHTr1q1V50tZF018KIpqM3w+H/Pnz8fixYsxa9Ys9O3b1+x2U/r5Ir+8BlP6+YLD0rdX0NdwYYLJBNit6E49LFSKgUGuOHQ9H4ESe9wtrEJS5t8JijVUKjX4z5FUZBb/XaTwxad8cTm7DP3NXCa5U1CFyhoNvpjSB6/UVpw2CPdzxp5X9e0p7hZWYePxNOy5mIOiSiW6eDqiu6cjQt0dEOBqD1+xEB5OfGNhwPp0OoKcUgVu5VXiQZUSBPrJxoY6OEIuCzUaLQorlChVqFCl1ECh1EKh1kKu1KCyRo3KGg0qazSQqzRoSdGTfp3ESMkuBZ/NQmVto9VvT2fAQ8THK4MDWviKPjo6HQGTycDCmM7wd7FDFw/rljDJyspCVFQUVCoVTp48ieDg4OafRD0StI5PHbSOD0VZn1qtxoABA6BQKJCcnAyh0Pxf6fqCcjrYtcFlHq2O4MiNfJy7V4yr98uR+UButtN4a6x/oRd+OJ+FpEz9qEf66jHGyx6/X5Hh7R9TjNva89hY91wYXO25eOHrcyb7eW1oYIN5MEdu5GPOjmSzx2UygK6ejuji7gg3Rz4EXBY4tY2mmAwGJA48uDnyUVylQmaxHIUVNZCrtNibnIvn+nhjeGcJNFoCBz4b5+4Vw5HPgaa2ajEhwO38ygbtOeoL8xZBbMdFtUqLyX19MLyzFGI7Ll7acg5n04uN251eOLzVozPWYuhNll2swNazGZA48PDGsIaXX60hPT0dI0aMAIvFwvHjx+Hv798mx6H+Ruv4UBTVbnA4HOzYsQPh4eFYuHAhNm7caHY7bm2rgbZACIGzHReu9jyM6uKGihoNUnJKIVdqG3RNb4lwP2dEBupHdLp7iTCprw88nQTGVVyGxMfLWQBHPhsVNfoRkCqlBr9fkWFE7aWwum7n65unVtaoUVChhKysGr9e1rePkDrw0NXTEVIHHjRaArWOgM1k4E5BJY7cLECVUtPiS3nBUnvMjQrGvQdVUGp0GB4qxam0B8gpVSBQYo9TaQ9a/Do48Nm4lFWKGo0OT/k7I4btDgDo6y9GmUINF3suevk4tUnSc+JOEXZdyEaw1B7vjQptdLu/bhbg29P3wOew0MXDEUIOC++MCDbbrsIabt26haioKDg6OuLYsWPw8vJqk+NQrUdHfOqgIz4U1Xa++OILvP322/jzzz8RExPzSI9dWFGDfquPGb/2cxFiQm8v2HHZkKs0SCusQkW1GhcyWnYp7PVhgVhYr63FiTtFmP7dBQwLlaBMocbiMV3Qr5MYy369ju2JWcbtDC0YWAwGWCx9g1MGgwEBh4WhoRJ4OQlwM68Crnbc2qalQH55NW7IKlBY2fQIzKPGZTHg5SSExJGHLu4OmBcd0mYJRX0/X8rFe3uuwN9FiIR/DTd5bOOxNHSS2CHzgRx7LuaCw2Jg6gA/PBfubfGyeUtcuXIFI0eOhLu7O/766y+4uTVdgJOyHjriQ1FUu/Pmm2/iwIEDmDlzJq5duwZXV9dHdmxevcnBWcUKrD+aBi6LicHBrujl4wQBl4URnaVIzipFebUaFdVq3MqrhEqrw7BQCRLTi41J0c5zWThw1bSZZ7VK/1hCahEAQK7Sj/J8OLYLFsZ0BovJAIfFbHT1D6Afmdp4/C4uZ5fhflm12W3C/Zzx6pAAfPT7TZNt+ncSo6JGAzaT0ego1n8m9URPHxEIASQOPMjKavDBz1cxLswDm0/cg5YQGM7OsKqJyWCAw9K3d2AxGPqVavr/wGDo+3JptDqEuDs0mMzdlpxre4blldcYL2MZpBdV4bOjdzCyqxvWTOyByECXNq+XdPHiRYwaNQqdOnXCkSNH4OLS/FJ4yjboiE8ddMSHotqWTCZDWFgYhgwZgn379j2y4n1KjRahSw41u52hGaenkwBCLgsiARf3SxVwFHBQWKmEXKnBTxdzodKaHxXisBhw4HMQGeiCPr7OmNLPF0dvFUCp0emTB6a+eJ9+AjcTHCYDbJb+Po2WoEatBQHAYjBQm1/UJhl/f23HY8PFnosSuQqE6BMTFpNh7FTOZAAclv6yIZupT1QMyQq7dtJ4cwnY4yAluxQTvjwLAEheEg0Xex40Wh0uZZehsLIGvX2d4dXGXe4Nzpw5gzFjxqBbt244ePAgnJycHslxqb/RER+KotolT09PfP3113juuefw/fffY+bMmY/kuNzaD/rm5sHcq22fYODIZ+O5cG94OgmwKykHDjwWJvTyBJ/HAgMMgBDUaHQokSuh1eknUVerNUgvrEJBRQ1mDeqEAQEuyCyW4yl/scmxckoU8BG3fu5LR+/7FSCxx3N9vMFlM5GQWoTnwr2RU1qNMG8R+Bxx8zuwAkII9uzZg1mzZqFfv374/fffYW/fdsv3KeugIz510BEfino0Zs+ejT179uDy5csIDLSs3UBr/Tf+LiIDXXDm7gN8euSORc/t6uEIIZeFOwWV+HpaX5OKvldzy4xVgA0CXO1QolDh8rJR+DLhLsqr1VgU28Vkm+QsfbuHpurAUO0TIQRHjhzBhx9+iOTkZEyYMAE7d+5sdMUi1fboiA9FUe3a+vXrkZCQgKlTp+LkyZNgs9v+V1FRpRJXcspwr0je/MZ1cNlMfD6lNyZvTkRFjQYL912FoM6coep6xQgnhXtjQIALfrsiQ3GVEhcySlCmUKNEroK4duIvIQQHrsqQVlCFWYP8EeBqD39X61c3pqzvzJkzWLx4MU6ePInIyEgkJCRg6NChtj4tygJts3aUoiiqCQ4ODtixYwfOnz+PtWvXPpJj/nZFhhW/38TPKffNPu7nIjT2fXLk/52IBUnsESixA7t2TkxWsQK38yuNt/p9weQqDT45fBvX75dDR/RNPS/nlGHMhlPYm5wLQD8XZ150CPr4OmF4qNS4lJ1qv1JSUjB27FgMGjQI5eXlOHDgAE6fPk2TnscQHfGhKMomIiMj8eGHH2LFihUYNWoU+vXr16bH49WrESQScCBXaqCpnfdjSGC4bCb+McAPXyWkAwBu5lXg4LX8Rqsk13fwWj7EQi74HCYkDjz08XXGuXslyK+owWd/3YGsrBo9vEQAA/AWC8FgMCzuN7XnYg4yHsghEnDgLORAbMeDh4gPH7HQ6pfO1FodiqtUxgrOfA4TAg4LAi4LAg7rsesub6nU1FQsX74cu3fvRnBwMHbt2oVJkyaB2Ypq4lT7QBMfiqJsZunSpTh06BBefvllpKSkwM6u7S731G/86SMWILe0GmX1KjirNDpj0mOwLTHTwqXaBK8N089dqtuo9H5ZNeL+uoN+ncQYHOSK7t4ipGSXGvtmtdTe5FxcyCgx+1iAqx1GdnXD5Kd8WtwnS6PV4W5RFTKK5MgoliO7WIHsEv3tfll1oy0rGAxAwGHh5ILhcLW3vEt6e5adnY2PPvoI33//Pby8vLBlyxbMmDHjkVyWpdoWfQcpirIZDoeDnTt3onfv3pg/fz6++uqrNjtW/eXb1+83fnnJzZEHQmAsGHj9frlFH+wEgKa223z9VeN9/ZzR2d0BjgIO+vo5w4HPgc7CNSaG7u7m3Hsgx+aT97D55D0MDHLBK4MCMDREAma9EyGEICWnDD9fysXBa/lN7rMxhOi7tNfvQt8eqLW6Fo/S1VVQUIDVq1dj06ZNEIlEiIuLw6uvvgo+n98GZ0nZAk18KIqyqZCQEMTFxeG1117D2LFjMW7cuDY5jiXJRb9OLhjV1c3YZ0tVW4fH0uPVqLWov4L+xX6+eD7cGxqtDtful6NGrYOznWWXp4qb6aFlcOZuMc7cLYbUgYfhoVL09dfXtkkrrMKPF7JxO7/SouM2pj2WBLqQUQJ7Hhs9fZxatH1paSk+/fRTrF+/HhwOB8uWLcPcuXPp8vQnEE18KIqyuTlz5uDAgQOYPXs2rl27Bqm0YS+rR+n3KzL8fuXvysyh7g7Gyc0tQaBPfPgcFl4e4IsuHg4QctkQcFnwEglQrdKCxWQgzNsJTAagI0BFjRpypQZypQZlCrVJ/y19A1L96EVyVqnFDVYLK5XYfTEHuy/mWPS8lipVqFGmUMPPRdhu5vwMDGpZZXC5XI7PP/8cn3zyCZRKJebOnYt//etfEIsfTS0g6tGjiQ9FUTbHYDDw7bffokePHhg3bhz+/PNPq5f8f5hu7CM6S5FWUIUruS1raEpqe2xVq7Q4eC0fKw/cbPWxHweysmo8qFKiSqlBdy+RrU+nRZRKJb7++musWrUKJSUlmDNnDpYsWQJ3d3dbnxrVxmjiQ1FUuyCVSnHo0CGMHj0aQ4YMwZEjR6za2XrWwE74b/xdVCk1Fj2PyQAm9/XBd2cyLHqefsSHiTsFzV9O4rAY6O3jjACJHaSOfBy+no/U2ud18XDErbz2sdxd4sCDp5MAniI+PEQCBErt0NndAV09RBBwWc3voB3QaDTYsWMHVqxYgdzcXEyfPh3Lli2Dv7+/rU+NekRo4kNRVLvRu3dvnD59GtHR0Rg4cCCOHj2KoKAgq+z79WGBmDMkACnZpfjtigwHr+XhQVXzE3qn9POFj1hobIrZEgSATqdvnDm+lxd2JekvMb3Q1weLxnTW9+diMoy9sxgMBu4VVeGDfddwNr0Y+eU1xn3lligaOYr1OQs5CJDYw8dZAE8nATycBPBy4sNXLIS3sxB8zuOR3Jij0+mwb98+LF26FKmpqZg0aRIOHz6Mzp072/rUqEeMJj4URbUrISEhOHPmDEaOHIlBgwbh8OHD6Nmzp1X2zWIy0NdfjL7+Yix/uhuSMksQf7sQx28XIq2wqsH2w0MlWDquKwDAgd/yX5eEANraydTCOiMhbBYDTo0kUAESe+x5LQK9Vx4xaYJaaeEIVXM8RXyEuDsYR26kjnxIHHgIcLWDr7j9zNGxFkIIDh06hA8//BApKSmIjY3F//73P/Tp08fWp0bZiEWJz1dffYWvvvoKmZmZAIBu3bph2bJliI2NBaBfBrhw4UIcOXIEZWVlGDJkCDZu3Ijg4OAm92vIwtPT0xEYGIhVq1ZhwoQJxsf9/f2RlZXV4HlvvPEG/vvf/wIAZsyYgW3btpk83r9/f5w7d86SECmKagd8fHxw6tQpxMTEYNiwYfjjjz8QGRlp1WOwmAwMCHDBgAAXLBrTBffLqpGSXYo7BVV4UKXE4CBXjO7mblwG7sC3bOWVIeEx/DsuzMOYRJkzcO1xFFTUGAsqtgSDAYiFXAh5LH3ndyYD9nw2QqQOCJLaw03Eh7+LEO4iPthMJvgcpsXFEh9XKpUKJ06cwMqVK3H69GkMGjQIJ0+exODBg219apSNWfQT4O3tjbVr1xqHnrdt24bx48cjJSUFXbt2xbPPPgsOh4Nff/0Vjo6OiIuLQ3R0NG7evNloYbLExES88MIL+PjjjzFhwgT88ssvmDx5Mk6fPo3+/fsDAJKSkqDV/t0P5/r16xg5ciQmTZpksq+YmBhs3brV+DWX2/KhaYqi2heJRIL4+Hg8/fTTiI6Oxi+//ILRo0e32fG8nATwcmq84/nIrm747a2BqFHrUKPWokathVKjq71poVTr/1+l0UGt1WF8L/38JDseG34uQiSmF+Pfh1Oh1RFodDqEeTnBxZ6LzGIFrt8vR1GVskHS48Bjw8mOA6kDH15OAviKhfB1Eer/FetbbLSmVs2TSKfT4cqVKzh27BiOHTuGU6dOQS6Xo3fv3vjzzz8xevToJ240i2qdh+7OLhaL8e9//xuDBw9GaGgorl+/jm7dugEAtFotpFIp1q1bh1deecXs81944QVUVFTgzz//NN4XExMDZ2dn/Pjjj2afM2/ePBw4cABpaWnGb+QZM2agrKwM+/fvb3UstDs7RbU/1dXVmDx5Mg4fPoydO3di8uTJtj6lVvnjah5u5pVDoyVQaXXo6uEIb2chyqvVUGq0cHfkG+fQOAu58HDi06SmCYQQpKWl4dixYzh+/Dji4+NRXFwMgUCAwYMHY8SIEYiKikKfPn1oe4kO4JF0Z9dqtfjpp58gl8sREREBpVJfUKtudUsWiwUul4vTp083mvgkJibi3XffNblv9OjRWL9+vdntVSoVdu7ciffee69B9p6QkACpVAonJycMHToUq1atarIeiFKpNJ43oH/hKIpqXwQCAX7++WfMnDkTL774Im7duoV58+ZBJHo8lk0bjA3zwNgwD1ufxmNNJpMZR3SOHTuG3NxcsNls9OvXD2+88QaioqIwYMAA8HhPVvsMyrosTnyuXbuGiIgI1NTUwN7eHr/88gu6du0KtVoNPz8/LFq0CJs3b4adnR3i4uKQn5+PvLy8RveXn58PNzc3k/vc3NyQn59vdvv9+/ejrKwMM2bMMLk/NjYWkyZNgp+fHzIyMrB06VKMGDECycnJjf4QrFmzBh999JFlLwBFUY8ch8PB9u3b4evri9WrV+Ozzz7DW2+9hXnz5sHVtWWF6qjHT2lpKeLj442jOrdv3wYA9OzZE5MnT0ZUVBQGDx4MBwcHG58p9Tix+FKXSqVCdnY2ysrKsG/fPnzzzTc4ceIEunbtiuTkZMyePRtXrlwBi8VCdHS0cYjx4MGDZvfH5XKxbds2TJkyxXjfDz/8gNmzZ6OmpqbB9qNHjwaXy8Xvv//e5Hnm5eXBz88Pu3btwsSJE81uY27Ex8fHh17qoqh2TCaTIS4uDps2bQIhBHPmzMH8+fOtWvOHsg2FQoHTp08bR3QuXboEQggCAwMRFRWFqKgoDB8+HBKJxNanSrUzbXqpi8vlGic39+3bF0lJSdiwYQM2b96M8PBwXL58GeXl5VCpVJBIJOjfvz/69u3b6P7c3d0bjO4UFhY2GAUCgKysLBw9ehQ///xzs+fp4eEBPz8/pKWlNboNj8ejQ6IU9Zjx9PTEp59+ikWLFuHzzz/H559/ji+//BIzZszAggULEBgYaOtTpFpIrVbjwoULxkQnMTERarUa7u7uiIqKwptvvokRI0bAz8/P1qdKPUEeesYXIcRk1AQARCIRJBIJ0tLScPHiRYwfP77R50dEROCvv/4yue/IkSNml65u3boVUqkUY8eObfa8iouLkZOTAw8Pek2dop5ELi4u+Oijj5CVlYWVK1di//79CAkJwcsvv4wbN27Y+vQoMwwrr+Li4jB27FiIxWIMGjQIcXFxcHZ2xn/+8x/cuHEDMpkMO3fuxMyZM2nSQ1kfscCiRYvIyZMnSUZGBrl69SpZvHgxYTKZ5MiRI4QQQvbs2UPi4+NJeno62b9/P/Hz8yMTJ0402cfUqVPJBx98YPz6zJkzhMVikbVr15Jbt26RtWvXEjabTc6dO2fyPK1WS3x9fcnChQsbnFdlZSV5//33ydmzZ0lGRgaJj48nERERxMvLi1RUVLQ4vvLycgKAlJeXW/KyUBTVDigUCrJx40bi4+NDAJAJEyaQpKQkW59Wh6bT6UhaWhrZtGkTmTRpEnF1dSUACJ/PJ9HR0WTNmjXkwoULRK1W2/pUqcecJZ/fFiU+s2bNIn5+foTL5RKJREKioqKMSQ8hhGzYsIF4e3sTDodDfH19yZIlS4hSqTTZx9ChQ8n06dNN7vvpp59IaGgo4XA4pHPnzmTfvn0Njn348GECgKSmpjZ4TKFQkFGjRhGJRGI89vTp00l2drYl4dHEh6KeAEqlknz77bckODiYACCjRo0iCQkJRKfT2frUOgSZTEZ27NhBZsyYQXx9fQkAwmKxSEREBPnwww/J8ePHSXV1ta1Pk3rCWPL5/dB1fJ4ktI4PRT05tFot9u7di9WrV+Pq1asICQlBTEwMYmJiMHToUAiFQluf4hOhrKwMCQkJxnk6t27dAgD06NHDOCF5yJAh9Hcq1aYs+fymiU8dNPGhqCcPIQSHDx/G/v378eeffyI7Oxs8Hg9Dhw41JkKdO3emVX1bSKFQ4MyZMyYrr3Q6HQICAoxFA0eMGNFkDTWKsjaa+LQSTXwo6slGCEFqaioOHTqEQ4cOISEhAUqlEr6+vsYkKCoqiv7816FWq5GUlGSspXP27FmoVCq4ubkZE52oqCj4+/vb+lSpDowmPq1EEx+K6lgUCgVOnjxpTIRSU1PBZrMRERGBsLAwuLm5NbhJpdJGew8+DgghqKqqQkVFBcrLy423+l+Xl5fj3r17OHHiBKqqquDo6Ihhw4YZR3S6detGR8modoMmPq1EEx+K6tgyMzNx+PBhHD58GOnp6SgoKEBRURF0Op3JdnZ2dmaTIkNi5ObmBolEAgaDAa1WC41G0+S/1trG8K9cLm80qamoqGgQjwGDwYCDgwNEIhFEIhE8PDyMyU54eDjY7I7R2Z16/NDEp5Vo4kNRVH1arRbFxcUoKCho9FZYWGj8f41G0ybnwWKxwGazwWKxTP6//r8sFgt2dnZwdHQ0JjB1b03d7+DgQBt6Uo+lR9KklKIoqiNgsViQSqWQSqXo0aNHk9sSQlBaWoqCggI8ePAAAMwmJk0lLebuYzKZ9LISRVkJTXwoiqKshMFgQCwWQywW2/pUKIpqBB3TpCiKoiiqw6CJD0VRFEVRHQZNfCiKoiiK6jBo4kNRFEVRVIdBEx+KoiiKojoMmvhQFEVRFNVh0MSHoiiKoqgOgyY+FEVRFEV1GDTxoSiKoiiqw6CVm+swtC2rqKiw8ZlQFEVRFNVShs/tlrQfpYlPHZWVlQAAHx8fG58JRVEURVGWqqyshEgkanIb2p29Dp1OB5lMBgcHB6s2BKyoqICPjw9ycnI6XNf3jhw70LHj78ixAx07fhp7x4wdsF38hBBUVlbC09MTTGbTs3joiE8dTCYT3t7ebbZ/R0fHDvmDAHTs2IGOHX9Hjh3o2PHT2Dtm7IBt4m9upMeATm6mKIqiKKrDoIkPRVEURVEdBk18HgEej4fly5eDx+PZ+lQeuY4cO9Cx4+/IsQMdO34ae8eMHXg84qeTmymKoiiK6jDoiA9FURRFUR0GTXwoiqIoiuowaOJDURRFUVSHQRMfiqIoiqI6DJr4UBRFURTVYdDEpxUuXbqEkSNHwsnJCS4uLpgzZw6qqqrMbltcXAxvb28wGAyUlZU1u+/ExESMGDECdnZ2cHJywrBhw1BdXW18/M6dOxg/fjxcXV3h6OiIgQMHIj4+3lqhNctWsSckJIDBYJi9JSUlWTPEJtnyvQeAP/74A/3794dAIICrqysmTpxojbBaxJax+/v7N3jfP/jgA2uF1ixbv+8AoFQq0atXLzAYDFy+fPkhI7KMLeN/5pln4OvrCz6fDw8PD0ydOhUymcxaoTXLVrFnZmZi9uzZ6NSpEwQCAQIDA7F8+XKoVCprhtckW77vq1atQmRkJIRCIZycnKwUkR5NfCwkk8kQHR2NoKAgnD9/HocOHcKNGzcwY8YMs9vPnj0bYWFhLdp3YmIiYmJiMGrUKFy4cAFJSUl46623TPqOjB07FhqNBsePH0dycjJ69eqFcePGIT8/3xrhNcmWsUdGRiIvL8/k9sorr8Df3x99+/a1VohNsvV7v2/fPkydOhUzZ87ElStXcObMGbz00kvWCK1Zto4dAFauXGny/i9ZsuRhw2qR9hA7ACxYsACenp4PE0qr2Dr+4cOHY8+ePUhNTcW+ffuQnp6O559/3hqhNcuWsd++fRs6nQ6bN2/GjRs38Nlnn2HTpk1YvHixtcJrkq3fd5VKhUmTJuH111+3RjimCGWRzZs3E6lUSrRarfG+lJQUAoCkpaWZbPvll1+SoUOHkmPHjhEApLS0tMl99+/fnyxZsqTRx4uKiggAcvLkSeN9FRUVBAA5evRo6wKygC1jr0+lUhGpVEpWrlxpUQwPw5bxq9Vq4uXlRb755puHiqG1bP3e+/n5kc8++6y1p/9QbB07IYQcPHiQdO7cmdy4cYMAICkpKa0JpVXaQ/x1/frrr4TBYBCVSmXR81qjvcX+ySefkE6dOln0nNZqL7Fv3bqViEQiS0+/SXTEx0JKpRJcLtckMxUIBACA06dPG++7efMmVq5cie3btzfbKRYACgsLcf78eUilUkRGRsLNzQ1Dhw412aeLiwu6dOmC7du3Qy6XQ6PRYPPmzXBzc0N4eLgVozTPlrHX99tvv+HBgweN/vXRFmwZ/6VLl3D//n0wmUz07t0bHh4eiI2NxY0bN6wYYePaw3u/bt06uLi4oFevXli1atUjG/K3dewFBQX45z//iR07dkAoFFopqpazdfx1lZSU4IcffkBkZCQ4HM5DRNUy7Sl2ACgvL4dYLG5lNJZpb7FblVXTqA7g+vXrhM1mk08++YQolUpSUlJCJk6cSACQ1atXE0IIqampIWFhYWTHjh2EEELi4+ObzYITExMJACIWi8l3331HLl26RObNm0e4XC65c+eOcbvc3FwSHh5OGAwGYbFYxNPT85H99Wfr2OuKjY0lsbGxVo+xKbaM/8cffyQAiK+vL9m7dy+5ePEimTJlCnFxcSHFxcVPdOyEEBIXF0cSEhLIlStXyJYtW4irqyuZPXt2m8ZsYMvYdTodiYmJIR9//DEhhJCMjIxHPuJj6/eeEEIWLFhAhEIhAUAGDBhAHjx40Gbx1tUeYje4e/cucXR0JFu2bLF6nOa0l9jbYsSHJj61li9fTgA0eUtKSiKEEPLDDz8QNzc3wmKxCJfLJfPnzydubm5k3bp1hBBC3n33XfLCCy8Y992Sb4YzZ84QAGTRokUm9/fo0YN88MEHhBD9L8FnnnmGxMbGktOnT5Pk5GTy+uuvEy8vLyKTyZ7o2OvKyckhTCaT7N27t9Ux1/U4xP/DDz8QAGTz5s3Gx2tqaoirqyvZtGnTEx27OXv37iUAHuoD8HGIfcOGDSQyMpJoNBpCiHUTn8chfoOioiKSmppKjhw5QgYOHEjGjBlDdDpdh4idEELu379PgoKCrJLsP26x08SnDRUVFZFbt241eauurjZ5Tn5+PqmsrCRVVVWEyWSSPXv2EEII6dmzJ2EymYTFYhEWi0WYTCYBQFgsFlm2bJnZ49+7d48AMGbOBpMnTyYvvfQSIYSQo0ePEiaTScrLy022CQoKImvWrHmiY69r5cqVRCKRWO0a/+MQ//HjxwkAcurUKZNt+vXrRxYvXvxEx25Obm4uAUDOnTv3RMc+fvx4k/2yWCzjfqdNm9bq2B+X+M3JyckhAMjZs2c7ROz3798nISEhZOrUqSbzbTpC7IS0TeLDBgUAcHV1haurq0XPcXNzAwB899134PP5GDlyJAD96pu6y/KSkpIwa9YsnDp1CoGBgWb35e/vD09PT6Smpprcf+fOHcTGxgIAFAoFADS4jspkMqHT6Sw697oeh9gNCCHYunUrpk2bZrVr/I9D/OHh4eDxeEhNTcWgQYMAAGq1GpmZmfDz87Po3Ot6HGI3JyUlBQDg4eFh0bnX9TjE/vnnn+P//u//jI/JZDKMHj0au3fvRv/+/S069/oeh/jNIbV9tZVKpUXnXtfjEvv9+/cxfPhwhIeHY+vWrS2aQ9OcxyX2NmXVNKqD2LhxI0lOTiapqankiy++IAKBgGzYsKHR7c0N/+Xm5pLQ0FBy/vx5432fffYZcXR0JD/99BNJS0sjS5YsIXw+n9y9e5cQos/UXVxcyMSJE8nly5dJamoqmT9/PuFwOOTy5cttFm9dtord4OjRowQAuXnzptVjawlbxj937lzi5eVFDh8+TG7fvk1mz55NpFIpKSkpaZNY67NV7GfPniVxcXEkJSWF3Lt3j+zevZt4enqSZ555ps1irc/W3/cGtpjjQ4jt4j9//jzZuHEjSUlJIZmZmeT48eNk0KBBJDAwkNTU1LRZvHXZKnbD5a0RI0aQ3NxckpeXZ7w9Krb8vs/KyiIpKSnko48+Ivb29iQlJYWkpKSQysrKh46LJj6tMHXqVCIWiwmXyyVhYWFk+/btTW5v7pvB8AssPj7eZNs1a9YQb29vIhQKSURERINLG0lJSWTUqFFELBYTBwcHMmDAAHLw4EFrhdYsW8ZOCCFTpkwhkZGR1gilVWwZv0qlIu+//z6RSqXEwcGBREdHk+vXr1srtGbZKvbk5GTSv39/IhKJCJ/PJ6GhoWT58uVELpdbM7wm2fr7vv4+HnXiY6v4r169SoYPH07EYjHh8XjE39+fvPbaayQ3N9ea4TXJVrFv3bq10Tk4j4otv++nT59uNvb6+2kNBiG144YURVEURVFPOFrHh6IoiqKoDoMmPhRFURRFdRg08aEoiqIoqsOgiQ9FURRFUR0GTXwoiqIoiuowaOJDURRFUVSHQRMfiqIoiqI6DJr4UBRFURTVYdDEh6IoiqKoDoMmPhRFURRFdRg08aEoiqIoqsP4f8lE0Mp/DW36AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "armourdale_impervious.loc[armourdale_impervious['impervio_1'].isin([\n",
+    "                                                                    'Parking Lots',\n",
+    "                                                                    'Concrete Pads',\n",
+    "                                                                    'Parking Lots-Dirt'\n",
+    "                                                                    ])].plot(ax=ax)\n",
+    "armourdale.plot(ax=ax, fc='None')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
+       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
+       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
+       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
+       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
+       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
+       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
+       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
+       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
+       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
+       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY', 'WARD',\n",
+       "       'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sunroof.columns"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pypsa-illinois02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From ee44abb0c9439769076ba37bd38a3c6b1dcb601c Mon Sep 17 00:00:00 2001
From: Samuel Dotson <samgdotson@gmail.com>
Date: Wed, 27 Nov 2024 11:26:24 -0500
Subject: [PATCH 49/52] adds outage data

---
 Snakefile                                     |   7 +
 notebooks/15-community-solar-options.ipynb    | 692 ++++++++----------
 notebooks/16-outage-frequency.ipynb           | 584 +++++++++++++++
 .../gis_notebooks/rooftop_suitability.ipynb   | 680 +++++++++++++++++
 scripts/retrieve_outage_data.py               |  26 +
 5 files changed, 1604 insertions(+), 385 deletions(-)
 create mode 100644 notebooks/16-outage-frequency.ipynb
 create mode 100644 notebooks/gis_notebooks/rooftop_suitability.ipynb
 create mode 100644 scripts/retrieve_outage_data.py

diff --git a/Snakefile b/Snakefile
index 4e0bcac..127579c 100644
--- a/Snakefile
+++ b/Snakefile
@@ -37,6 +37,13 @@ rule retrieve_spatial_lut:
         spatial_lut = "data/spatial_data/spatial_lut.csv"
     script: "scripts/retrieve_lut.py"
 
+rule retrieve_outage_data:
+    input:
+        "scripts/retrieve_outage_data.py"
+    output: 
+        outages = "data/timeseries/outages.csv"
+    script: f"{input}"
+
 rule retrieve_census_data:
     output:
         census_data = "data/spatial_data/county_census_data.gpkg",
diff --git a/notebooks/15-community-solar-options.ipynb b/notebooks/15-community-solar-options.ipynb
index 2ab72cf..9c4129d 100644
--- a/notebooks/15-community-solar-options.ipynb
+++ b/notebooks/15-community-solar-options.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +50,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +93,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,7 @@
        "7.11031194057364"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -151,40 +151,12 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(12,8))\n",
     "sunroof.plot(ax=ax, fc='None', ec='k')\n",
-    "land_bank.plot(ax=ax, column='ACRE', legend=True)\n",
-    "# armourdale.plot(ax=ax, fc='None', ec='k')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['PARCEL', 'PARCEL_NBR', 'ACRE', 'STATE_ID', 'SID_COUNTY', 'SID_TWNSHP',\n",
-       "       'SID_QUAD', 'SID_SEC_NO', 'SID_SHEET', 'SID_QSEC', 'SID_BLOCK',\n",
-       "       'SID_PARCEL', 'SID_PRCL_S', 'SID_OWNER', 'OWNER_NAME', 'NUMB',\n",
-       "       'ADDR_EXT', 'DIR', 'ST_NAME', 'SUFX', 'MISC', 'CITY_left', 'STATE',\n",
-       "       'ZIP', 'DELQ_CODE', 'VACANT', 'LAND_USE', 'ORIONID', 'TotDue',\n",
-       "       'Shape_Leng', 'Shape_Area', 'index_right', 'CITY_right', 'WARD',\n",
-       "       'geometry'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "land_bank.columns"
+    "land_bank.plot(ax=ax, column='ACRE', legend=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,44 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
-       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
-       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
-       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'CITY_right',\n",
-       "       'WARD_left', 'geometry', 'index_right', 'region_name', 'state_name',\n",
-       "       'lat_max', 'lat_min', 'lng_max', 'lng_min', 'lat_avg', 'lng_avg',\n",
-       "       'yearly_sunlight_kwh_kw_threshold_avg', 'count_qualified',\n",
-       "       'percent_covered', 'percent_qualified', 'number_of_panels_n',\n",
-       "       'number_of_panels_s', 'number_of_panels_e', 'number_of_panels_w',\n",
-       "       'number_of_panels_f', 'number_of_panels_median',\n",
-       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
-       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
-       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
-       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
-       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
-       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY',\n",
-       "       'WARD_right'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "core_buildings.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -271,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -289,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -354,7 +289,7 @@
        "Industry                 6815.730465"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -376,7 +311,7 @@
        "unyt_quantity(15984.407, dtype=float32, units='ft**2')"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -516,7 +451,7 @@
        "[2 rows x 63 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -527,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -536,7 +471,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -557,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +509,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -622,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -673,14 +608,14 @@
        "    <tr>\n",
        "      <th>124</th>\n",
        "      <td>Church</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
        "      <td>923 S Bethany St</td>\n",
        "      <td>Kansas City</td>\n",
        "      <td>66105</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -696,15 +631,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>237</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -721,14 +656,14 @@
        "    <tr>\n",
        "      <th>361</th>\n",
        "      <td>Church</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
        "      <td>1101 Argentine Blvd.</td>\n",
        "      <td>Kansas City</td>\n",
        "      <td>66105</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -745,14 +680,14 @@
        "    <tr>\n",
        "      <th>576</th>\n",
        "      <td>Church</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
        "      <td>First Christian Church</td>\n",
        "      <td>0</td>\n",
        "      <td>1000 Argentine Blvd</td>\n",
        "      <td>Kansas City</td>\n",
        "      <td>66105</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -768,15 +703,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>604</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -792,15 +727,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>653</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -816,15 +751,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>664</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -840,15 +775,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>668</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -864,15 +799,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>717</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -889,14 +824,14 @@
        "    <tr>\n",
        "      <th>718</th>\n",
        "      <td>Church</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
        "      <td>933 Argentine Blvd</td>\n",
        "      <td>Kansas City</td>\n",
        "      <td>66105</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -912,15 +847,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>933</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>4</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -936,15 +871,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1577</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -960,15 +895,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1672</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -984,15 +919,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1709</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
        "      <td>Armourdale Community Ctr</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -1008,15 +943,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2182</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -1032,15 +967,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2263</th>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Public Attractions and Landmark Buildings</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>0</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
        "      <td>Kansas City</td>\n",
-       "      <td>None</td>\n",
-       "      <td>None</td>\n",
+       "      <td>-999</td>\n",
+       "      <td>-999</td>\n",
        "      <td>1</td>\n",
        "      <td>...</td>\n",
        "      <td>2.222399e+07</td>\n",
@@ -1061,58 +996,58 @@
       ],
       "text/plain": [
        "      THEME1 THEME2                                 FEATURECOD  \\\n",
-       "124   Church   None  Public Attractions and Landmark Buildings   \n",
-       "237     None   None  Public Attractions and Landmark Buildings   \n",
-       "361   Church   None  Public Attractions and Landmark Buildings   \n",
-       "576   Church   None  Public Attractions and Landmark Buildings   \n",
-       "604     None   None  Public Attractions and Landmark Buildings   \n",
-       "653     None   None  Public Attractions and Landmark Buildings   \n",
-       "664     None   None  Public Attractions and Landmark Buildings   \n",
-       "668     None   None  Public Attractions and Landmark Buildings   \n",
-       "717     None   None  Public Attractions and Landmark Buildings   \n",
-       "718   Church   None  Public Attractions and Landmark Buildings   \n",
-       "933     None   None  Public Attractions and Landmark Buildings   \n",
-       "1577    None   None  Public Attractions and Landmark Buildings   \n",
-       "1672    None   None  Public Attractions and Landmark Buildings   \n",
-       "1709    None   None  Public Attractions and Landmark Buildings   \n",
-       "2182    None   None  Public Attractions and Landmark Buildings   \n",
-       "2263    None   None  Public Attractions and Landmark Buildings   \n",
+       "124   Church   -999  Public Attractions and Landmark Buildings   \n",
+       "237     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "361   Church   -999  Public Attractions and Landmark Buildings   \n",
+       "576   Church   -999  Public Attractions and Landmark Buildings   \n",
+       "604     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "653     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "664     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "668     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "717     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "718   Church   -999  Public Attractions and Landmark Buildings   \n",
+       "933     -999   -999  Public Attractions and Landmark Buildings   \n",
+       "1577    -999   -999  Public Attractions and Landmark Buildings   \n",
+       "1672    -999   -999  Public Attractions and Landmark Buildings   \n",
+       "1709    -999   -999  Public Attractions and Landmark Buildings   \n",
+       "2182    -999   -999  Public Attractions and Landmark Buildings   \n",
+       "2263    -999   -999  Public Attractions and Landmark Buildings   \n",
        "\n",
        "                          NAME  AGENCY               ADDRESS    CITY_left  \\\n",
-       "124                       None       0      923 S Bethany St  Kansas City   \n",
-       "237                       None       0                  None  Kansas City   \n",
-       "361                       None       0  1101 Argentine Blvd.  Kansas City   \n",
+       "124                       -999       0      923 S Bethany St  Kansas City   \n",
+       "237                       -999       0                  -999  Kansas City   \n",
+       "361                       -999       0  1101 Argentine Blvd.  Kansas City   \n",
        "576     First Christian Church       0   1000 Argentine Blvd  Kansas City   \n",
-       "604                       None       0                  None  Kansas City   \n",
-       "653                       None       0                  None  Kansas City   \n",
-       "664                       None       0                  None  Kansas City   \n",
-       "668                       None       0                  None  Kansas City   \n",
-       "717                       None       0                  None  Kansas City   \n",
-       "718                       None       0    933 Argentine Blvd  Kansas City   \n",
-       "933                       None       0                  None  Kansas City   \n",
-       "1577                      None       0                  None  Kansas City   \n",
-       "1672                      None       0                  None  Kansas City   \n",
-       "1709  Armourdale Community Ctr       0                  None  Kansas City   \n",
-       "2182                      None       0                  None  Kansas City   \n",
-       "2263                      None       0                  None  Kansas City   \n",
+       "604                       -999       0                  -999  Kansas City   \n",
+       "653                       -999       0                  -999  Kansas City   \n",
+       "664                       -999       0                  -999  Kansas City   \n",
+       "668                       -999       0                  -999  Kansas City   \n",
+       "717                       -999       0                  -999  Kansas City   \n",
+       "718                       -999       0    933 Argentine Blvd  Kansas City   \n",
+       "933                       -999       0                  -999  Kansas City   \n",
+       "1577                      -999       0                  -999  Kansas City   \n",
+       "1672                      -999       0                  -999  Kansas City   \n",
+       "1709  Armourdale Community Ctr       0                  -999  Kansas City   \n",
+       "2182                      -999       0                  -999  Kansas City   \n",
+       "2263                      -999       0                  -999  Kansas City   \n",
        "\n",
        "        ZIP COMMENT  CHNG_TYPE  ...  yearly_sunlight_kwh_f  \\\n",
-       "124   66105    None          4  ...           2.222399e+07   \n",
-       "237    None    None          4  ...           2.222399e+07   \n",
-       "361   66105    None          4  ...           2.222399e+07   \n",
-       "576   66105    None          4  ...           2.222399e+07   \n",
-       "604    None    None          4  ...           2.222399e+07   \n",
-       "653    None    None          4  ...           2.222399e+07   \n",
-       "664    None    None          4  ...           2.222399e+07   \n",
-       "668    None    None          4  ...           2.222399e+07   \n",
-       "717    None    None          4  ...           2.222399e+07   \n",
-       "718   66105    None          4  ...           2.222399e+07   \n",
-       "933    None    None          4  ...           2.222399e+07   \n",
-       "1577   None    None          1  ...           2.222399e+07   \n",
-       "1672   None    None          1  ...           2.222399e+07   \n",
-       "1709   None    None          1  ...           2.222399e+07   \n",
-       "2182   None    None          1  ...           2.222399e+07   \n",
-       "2263   None    None          1  ...           2.222399e+07   \n",
+       "124   66105    -999          4  ...           2.222399e+07   \n",
+       "237    -999    -999          4  ...           2.222399e+07   \n",
+       "361   66105    -999          4  ...           2.222399e+07   \n",
+       "576   66105    -999          4  ...           2.222399e+07   \n",
+       "604    -999    -999          4  ...           2.222399e+07   \n",
+       "653    -999    -999          4  ...           2.222399e+07   \n",
+       "664    -999    -999          4  ...           2.222399e+07   \n",
+       "668    -999    -999          4  ...           2.222399e+07   \n",
+       "717    -999    -999          4  ...           2.222399e+07   \n",
+       "718   66105    -999          4  ...           2.222399e+07   \n",
+       "933    -999    -999          4  ...           2.222399e+07   \n",
+       "1577   -999    -999          1  ...           2.222399e+07   \n",
+       "1672   -999    -999          1  ...           2.222399e+07   \n",
+       "1709   -999    -999          1  ...           2.222399e+07   \n",
+       "2182   -999    -999          1  ...           2.222399e+07   \n",
+       "2263   -999    -999          1  ...           2.222399e+07   \n",
        "\n",
        "      yearly_sunlight_kwh_median  yearly_sunlight_kwh_total  \\\n",
        "124                  8223.137817               2.861974e+07   \n",
@@ -1189,7 +1124,7 @@
        "[16 rows x 63 columns]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1200,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1209,7 +1144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1218,7 +1153,7 @@
        "unyt_quantity(0.01895735, 'kW/ft**2')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1230,14 +1165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1246,7 +1174,7 @@
        "unyt_quantity(22833.75, 'kW')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1257,12 +1185,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -1279,12 +1207,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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oNb6+voX2YX/66ad59tlnufVWe6GMJk2acOLECd566y1GjRpFWFgYYM+iV61a1bFdbGysI+seFhaG2Wzm8uXLLln22NhYOnbsWKw2S8B+DdE0jYdaTuTjdW+UeBeM6rWq0GdEO6LqVy18ZZEvg8mAxVyZp24Q1zKjmwFzhgWA5z8fhapqGI16DCYDRpMeg9HgqIl/+sh5AEcXpHE9JzNyQn/QtDK5IDu05wzjb5vOG9NHFTtTVZhVs9cx9LF+RV7fYrHy+0/r7YPQIoPw8nZj2uQFgMYz744kvEZwibZPiGuJc9/ysjhWUaWmpqLTua6v1+sdZR1r1qxJWFgYS5cupUULe4Uts9nMqlWreOeddwBo1aoVRqORpUuXMmLECADOnTvH7t27effdd4vVdgnYryGKojDymcGOmRxL0pTfxhVpvR+/XMmF8wmEhvtz+5iuJd6Oyq5eq1rUaFitvJtR7kbe0o7EJPvg0CohPuXcGpHF5GbEkmEFICi04Axzeqp9kLm7pz05kByfiqePR4lkuAuzYcU+3nnmlzyfK4nCZ8UJ1sHe/S3ruBaLlZSkdA7sPAVARrrlqtsjhCh7gwYNYvLkyURGRhIdHc22bduYMmUK9957L2CPucaPH8+bb75J3bp1qVu3Lm+++Saenp7cfvvtAPj5+TFmzBiefPJJgoKCCAwM5KmnnqJJkyb06tWrWO2RgP0aYzW6YbOqUMxxd0vnb2XOt2vQNBhwSxuG3lnwrZqUpDSS4tOwWm14eJpIvJSCzqhnwZz/iL+UQr1G4RKw58HNwyQDUIH+/ZqVdxNEHozuRkeGvTA5A/ak+BR8/D2vqG9mcVmtKmmZx78cl4y3X/n2WdfpdOj0OlSbitVic3n9UjhZiMrpk08+4cUXX2Ts2LHExsYSHh7Ogw8+yEsvveRY55lnniEtLY2xY8dy+fJl2rVrx5IlSxw12AE+/PBDDAYDI0aMIC0tjZ49e/Ldd98VqwY7SMB+zbgYk8CcL1ewdO5mWnSqi4dX8SL25MR0Th61l9eLv5TCHzPWYLXYcPMwUaNeGN+++xc6ncJ7s+1VYX793ypmff4PAP1vacPCH/9lyP3dib+UAshJSojKyMPLjUZtahdp3ayA3eRuRFVVUhPT8Pb3sv/tl3aXGKcvGIvZSr3oajz//q0oikKg0x2bt+6ZxqYlO/lu53v4BhV/VtziMBh0mG0qNqvN5S6nqsqXoRAFUTUFVSubgU3FOY6Pjw9Tp05l6tSp+a6jKAqvvPIKr7zySr7ruLu788knn/DJJ58Uo6W5ScB+jUiKT+GP7/8FruwE4ZwQ0zSNb975i4w0+wn5qQ9uY+/mY+j02X25DIbsn7P6tGZVjwHQ5CQlRKXj7efJaz8/VqR1rVYbPv6e6HQ6bDaVCZ/dS2BmN5rSzrA7UxSFoCq+3NCnca7nzGlmUhJS0elLvz3+Qd6Y0y24eZhcvk9lgLUQoiRIwH6NUJwGRlxJsOycEdJyDhrLfOC8X70h+1aOLjN41+t1tGhXi20bj6JKil2Ia1r73o35ZZe9drFer6PXrfaqMG//8ECpZ5XrN43guQ9uRdM06kbnPybk5VnjSqRPe1F8//fTjp/jYhN59OWhKAqEVgsoYCshhA0dtjKaFqisjlMaJGC/Ruicb8Ha1GJvr+gUdDoFRaegKArhUSGY0y0oCo7slPOJz2DKDtiNJgMeXm64uxmIiApm28ajZXaSFEJULHqDnuL1zCy+kDA/Qvo2KdK6ZZntzxJUxZcBt7Yr8+MKIa5dErBfI9w8TNRrGoGiKC7BdFENvrU9g29t73h8z7jsaXSPHzhH10EtUJTsYv99bmpD+57RGI16gqv6M+a5wfZ1j8TStlM9PIvZh14IIYQQ15+K2oe9opGA/RoRWi2Aj357vFT2HVW/Ks9+fJfLMt8AL3wDvHKvW7sKUbWrlEo7hBBCCCGuRxKwCyGEEEKIcqGiQy2jvuVldZzSUHlbLoQQQgghxHVAMuxCCCGEEKJc2DQFWxn1LS+r45QGybALIYQQQghRgUnALoQQQgghRAUmXWKEqKTWX9zGZUsimqZRw6sajf3qlneThKhwjh6M4ej+c7i5G7mhT2MO7DzFqSPnad8zGm9fj/JunhDXPSnrWDQSsJeT2LPx/P79WgKCfWjcvjZ//bqJkFA/7n6oOzOn/s2mFXvpe1dn/pq3FU3TeP+bMbh7mABY/fcuLpxPxNPbjX7DW+fa9xuP/8iGFfsYfEcHbhzemoTLKVSNDOKvOZu4dCGR9t0a0rFno6t+Dd9MWUzi5VSGjerIxdOXCY0IRKfXcf70ZapUC6BazZCrPsbVOLjrNNv+PUTnvo1JvJRCWEQQASE+5dqmkjTvzFIOJZ8AoH/VrpUuYN9+4TWSLSfQUEHT7P/H/n9N0wj17EjDwEfLu5miDBw4ep45C7cBjomVURQYM6IjocG+udZPSUrjjg6vo2kan/05AVSNLav3o9pU+t3eAXfP7Hkg1v2zlx+nLadKuD839GnMij+2Mv+7tUxf9FSpBewnDsbw8fNzsFlsPDP1DsKjQrgYE8+sT5dhtVi5/fEbqeI0A+r8WRvZu/0keoOOzj0b0bF7w1JplxCi8pKAvZzM+WoVf/68AYBOg5qzZvk+AG6+qyP//LaZcycuUq/VWQ7tPQuA1WKDzHPLn79sYufmY4SG++cZsKuqhs2qcvzgeb5++082rdzPQy8NYe3S3Zw+fpGQML9CA/Y9m49y7kQcOp1Cj2G5jwGwauFOYs/G06x1FO8+9DW3PNobncHA7M+WMfyB7tz/wpArfXtKxO8z1rLij22YMyzMnLqYR167iYF3dSrXNpUknZLdo02thDPLxmfsJ8G8N9/nvY2RZdgacSVWbzjIqx/8iU6nQ69X0Ol09OsRzWP39ijWfmLjkli0ak+u5SMGtCI0OO9tMtItAGiqxpHdp5n+6jwAug5u4RKwZ83UnDUDtELumZuvxOq/d3HmxEXMGVYG3tqOoJDsC4v0NDN7Nx8DIC3VDEBKUjp//fgvAP3v6OgSsO/beYqVf+8GwM3dKAG7uK5omg5VK5se2loZHac0SMBeTpIT0wDw8DS5BFuapqHT2U8ozjNqF+fkkjUVt6ppKFknKzV7v0XZ1aKZ6/nnt82Y3Iz5Buw6vc6xbwBzuhV3b/tHKiPN7JgVtbxsX3cIgOMHYwCwWm3l1pbSkBV4AKiaWo4tuTKFfzYq30XI9cZiVTFbbED231ZGhrXY+yl+8Oz62ckKyiH7+8jxnM71eyr7e/DqPl9L529l05qDAHTo0dAlYNcbsoMCq8X+3hhN2adbi9n1PVJt2W3ZsOoA4164qqYJIa5BErCXE3dPe/eWWg3DHScUyDypZJ17nM4nOU9CBVEyT0hd+jVl0zJ71kpTNcfyouwrO+jPPxDUKa4N3bpyLx0HtgTgrx/+5YEXh2JyNxa53SUt+0Rtfw1aMd7DysA5w65VyuC24IDd3kVGVGRZWWtnWQFxXuKTUtm+/4z9u0Oxf4coipIZ9OeW355yJjMUp2M6B78A+szvgay//6x1r/b7wOSW/d1mznGRotc7fadnvkdGo96xzGp2fb02p/fR+XwgxPXAhoKtkPNBSR6rsirWN8O0adNo2rQpvr6++Pr60qFDBxYtWuR4/vz584wePZrw8HA8PT3p27cvhw4dKnS/c+fOpVGjRri5udGoUSPmzZvn8rzVauWFF16gZs2aeHh4UKtWLV577TVHIAaQnJzMo48+SvXq1fHw8KBhw4ZMmzatOC+vTI0a3wew3/50/iLXNI0HXhrGc5+Ppnmnui7LiyrrhNnvljbZQbqmFetWcFFOallZLZ1eT/XaoTS/oT6ePu6EhPvTfVgrlyxTedBnvq9Zr6E4Fz2Vga6yZ9gL/fq5tn5f1yJbHn9TzsFqTsdOX+LZqX/wzIfzeWbKfJ764HeefH8eaRmWPNfP7y6MTqcjOMyP4DA/9AYdNRuEM2bSIO5/YQheOfqlO7rEZJ4vsnZ5td8Hzt/bOQP24Kr+PDb5Zsa/M5KwiCAAfPw9GfvaTYx7eyTVa1dxWd8/0IuwagGEhPkRVOXaGWcjhCg5xcqwV69enbfffps6deoAMGPGDIYMGcK2bdto1KgRQ4cOxWg0Mn/+fHx9fZkyZQq9evVi7969eHl55bnP9evXM3LkSF5//XWGDRvGvHnzGDFiBGvXrqVdu3YAvPPOO0yfPp0ZM2YQHR3N5s2bueeee/Dz82PcuHEAPPHEE6xYsYIff/yRqKgolixZwtixYwkPD2fIkPLtS50XvU6Hp7cbNepUIc0pI6SpGm172PuXHz983rHc5rSOwagnslYIbbvUz3Pfjry3ptGxT2Oq1wyhfrMI/lm8y7G8MP1u60DLzvVdMle5jpN55guNCOR/a19yLB8xtleh+y8Ltz/Sk4vn4vHx82Tjkl1oauULagviHMxUxgy7UliGvRL2y7/e5BX0FpRhz+9zGhzgRc+O9XP9zj0zB9rn5O5pYsbq50hNTMfDxx29XsfND+bdbz6seiBtbqiHl7c7AAHBPkTWCXXponIlTG5OXVwsrgG7j58n/W/vmKPNbgy6u3Oe+3rsuYGsWbyTajWCqdUw/KraJURlo2plV72lMuftivWNNWjQIJfHkydPZtq0aWzYsAGj0ciGDRvYvXs30dHRAHz++edUqVKFn3/+mfvuuy/PfU6dOpXevXszadIkACZNmsSqVauYOnUqP//8M2AP6ocMGcKAAQMAiIqK4ueff2bz5s2O/axfv55Ro0bRrVs3AB544AG++OILNm/eXCEDdh9/T+ZuegWApX9ux2ZTUXSKy0nEZDJQPSoYnU5xOQnWb1yNFu1rc8vovL/8n5h8M4+/NhxFUejlNCh14jsjsGRYCQj2LrR9DVtG0bBlVIHrtO/RkHqNq+MbkPfFWHm78eY2AJw5dgEoWt/9yqSyDzpFKTjDLl1iKj6dTsHL00Tb5jV56O4u2FQNb8+8g2zI/45d/VphdGhRq1jHToxL5tba45j6zws0aFM73/U69WxEJ6dB9sPHdGX4mK7FOlZe7h1/I11ubILRzUDNumFXta/kxDT+/Gk9rTrXk4BdCJGnK04x2Gw25syZQ0pKCh06dCAjIwMAd3d3xzp6vR6TycTatWvzDdjXr1/PE0884bLsxhtvZOrUqY7HnTt3Zvr06Rw8eJB69eqxY8cO1q5dm2udP/74g3vvvZfw8HBWrlzJwYMH+eijj/J9DRkZGY52AyQmJhbnLSgxvQc2p1F0NWw2lYS4ZEcmKDwyiK9+fzzX+qMeLTiD7Z7PCXPNz2u45clBLhUUrsa9E/qWyH5KW2j1QL5b88I1V3PZuUtMhprBJXMCmqbZCyNqKl4GD7wMnuXYwoJJl5jKr2/3aPp2jy7y+vn9Rq+k23ZacjoA7l5uXDx7mbfu+xKjm5G3501wWe/yhUTMGVZ0eh0+fh4l9v3nH+RN68712LfpCKcPnqNR2+yLhtgzl3jn0e9B0xj1zECadrR3b/z1k78JDPOjxy3tXfb17pOz2LnxKPFxyYx4sHuJtE8IcW0pdsC+a9cuOnToQHp6Ot7e3sybN49GjRphsVioUaMGkyZN4osvvsDLy4spU6YQExPDuXPn8t1fTEwMoaGhLstCQ0OJiYlxPJ44cSIJCQk0aNAAvV6PzWZj8uTJ3HbbbY51Pv74Y+6//36qV6+OwWBAp9Px1Vdf0blz3llogLfeeotXX321uG9BqXh8yIekJqVTt0l13vj2AfRGfa6+mFcrODzwuuxmYDDqCa0eWN7NuCIPbn6JZGsKqqaioqFm1itXMwPzLOvjtrM+brvLtrdG9Gdk5IAybnHRFdolRjLsFd6ir5djtdgY9FDvq9pPYZ+FvKSnZiaJPN0wZ1jYs+FwnoPcP544mw1L7SUTH3trBP3vLNnSrlMe/RaA/218w7HMnGFl76ajACRcSnYs//HdBdRvGZUrYM8a72PLYxCvENc6tQzLOpbVcUpDsVtev359tm/fzoYNG3j44YcZNWoUe/fuxWg0MnfuXA4ePEhgYCCenp6sXLmSfv36odfrC9xnzoFFOcsBzp49mx9//JGZM2eydetWZsyYwfvvv8+MGTMc63z88cds2LCBP/74gy1btvDBBx8wduxYli1blu9xJ02aREJCguPfqVOnivt2lJisiis2m8aYls/y1QuzSY5PITEuqcSO8e1Ls0m4UPJ3ES7FxHP6UP4XZeLKpdrSSbWlk66aMasWrJoVm6YWqc+6rcJfnBUSpFXCgbTXm953d6H/fUWvud46OpINPz3J+h8n8Pqj9ovJ3h3q43UFWe+IelX5cf8UQqoH4uZuoknHejRun3vyMOfiBLoCBsReKUVRcpeSzGewrJu7kYzMuuzOsgbq2qzymRdC5K3YGXaTyeQYdNq6dWs2bdrERx99xBdffEGrVq3Yvn07CQkJmM1mQkJCaNeuHa1b513HGyAsLMwlmw4QGxvrknV/+umnefbZZ7n11lsBaNKkCSdOnOCtt95i1KhRpKWl8dxzzzFv3jxHP/emTZuyfft23n//fXr1yrsLiZubG25uJXN79GplnUg0VUVv0GO12FgzdyNJ8SmMeHJQIVsXzafrJxNY1b9E9uVs1+p9HNl5gnvfuLXE9329y+/EXxQVfiBqoX3YK3j7BQbjlfWqVBSFBjVDmXhvL2pVD8ZwBYG0wWggONw++VBQVX/e+/PpPNdr3K4O7p5uqKpGaETJ32nrelPbXH3zffw9uX18X3Q6hUin/u3DHu6FTx5jftw9TXh6u+FRQt11hKhMVBTUMiq3WFbHKQ1XXYdd0zSXfuAAfn5+ABw6dIjNmzfz+uuv57t9hw4dWLp0qUs/9iVLltCxY/YI+9TU1Fy1afV6vSNzYrFYsFgsBa5T0d3yYHfMGVb8g30wKRoh1QPx9HLDnJY7G3OlqkTmM2XgVeo6ogNdR3QolX1f78bUvBmrakVRdCgo6DP/b69frUOHgs5pmQ4dSub/Q9wrdjcgf1MjdOgB+2uxd4zI/r+fW4NybqEoTRFhAUSEBRS+4lW65eGepbr/O57OnVDxCfDirqf651p+64S8u6g99e7IEm+XEOLaUqyA/bnnnqNfv35ERESQlJTErFmzWLlyJYsXLwZgzpw5hISEEBkZya5duxg3bhxDhw6lT58+jn3cfffdVKtWjbfeeguAcePG0aVLF9555x2GDBnC/PnzWbZsGWvXrnVsM2jQICZPnkxkZCTR0dFs27aNKVOmcO+99wLg6+tL165defrpp/Hw8KBGjRqsWrWK77//nilTplz1m1QW8itJJq5vXULalHcTSk100LjyboIohE2zkGqJAezdsDRUNE2FzJ/d9UF4GIqXCIg5eZGLZy+jN+hp2Lp4lWGu1q5/D5CanE67G5vlei4tJZ2MNAsGox5vv4o7WFuIa41NU7CVUVnHsjpOaShWwH7+/Hnuuusuzp07h5+fH02bNmXx4sX07m0fcHTu3DkmTJjA+fPnqVq1KnfffTcvvviiyz5Onjzpkgnv2LEjs2bN4oUXXuDFF1+kdu3azJ4921GDHeCTTz7hxRdfZOzYscTGxhIeHs6DDz7ISy9l1/6eNWsWkyZN4o477uDSpUvUqFGDyZMn89BDD13RG1MZ7Vy7n4tnLtNleBuXW9Wxpy5ycv9ZWvduWo6tK13xF5PQ63V53m6+Hm3+Zw/Lf91IWGQwd08aXN7NEZVUquUcC0/elO/zjQMfJDow7wpg+fnt86X88dUKqkYF8+3mN6+2iRzeeZK5n/6Np48Hj31wp2N5wsUkLGYLweHZd5qmP/szx/ee4a+4/+Xaz8wPFvLrZ0tp0bUhb/6SuzqXEEKUp2IF7F9//XWBzz/++OM8/njBX3QrV67Mtezmm2/m5ptvzncbHx8fpk6d6lLGMaewsDC+/fbbAo99rZvz4UI2Ld1F2xub4e2f/avdueYAP0z+jRl7PijH1l2Z95/9hQ3L96HTKTz19gjadsvdTSI9zcyD3d/E3dPElyufxy2fyVauJ6cPx7Di1/+o16KGBOziiinFHGfw6RPfU71uGEPH9slnCxxzTVjMtqtvIHDpfAIrfv0P/xAfl4D9qxdnE3f2Mm/Oz7tve74q/GBtIa4tUiWmaK66D7uoOCIbhJOSmJZrucFoH8RaGaWnmklJstdbzq/kmc1qI/FyComXU2SYYiZDZlBkLaGgSIg85ajkc2DrMazWgj9z97w4jHtfGo7eUHD1sKLy9vWgdtPIXBO4uXmYHKUfs7z006NYzK6zkmapGhVCk451qRldvUTaJYQQJUkC9gpqzW8bAbhheLtC1sx2/+S8q7TcMKwNHQe3KpF2lTVV1fD286Bhs0j8g+wztK5ZdwgfbzeaN410rGfIrFt/NVVVriV97+xMn9s7YjCWTFAkrk+FTW6Vs1a+h7c7is71bzA93UJSQhqeXia8vN2Z+e4Ctq3ci5u7ibcXFDP7nYdG7erw2coXcy3vc+cNJF1OdlkWWsDA+/5330D/u2+46vYIIURpkIC9gmpyQ8MS25feoC+xbFZZOXngLEd3nkRVNaLqhPLaF6Mdz035dAkN6oY5AnYvHw/CIoMYPXFQnhOnXI/sgXrl+p2LiqjggP3wbjMfTp/GoxNu5PDB8zz346P458h0r12+j3df+Z077+vC3Q90I+FiErFnLuHl44nNpjpqkJe0ei1rArDs9y0kxafSc1grVv61k6P7zzH2hUGYTAYy0i3ExyXj6e3OgtkbOHfqEl36NCEo2BtvPw+qhJd+FRshrncqCmoZDQa9rss6iqtzOS6Zz95fhGrT6DWgKR272vto+4f4FrjdlElzSE1Op0u/ZnTp7zqY9OSxC1yISUDToHVHe838AztPcXjPGbr0a8qJ4xdp3Dwyr91WGFuX7eK7V36l6fBOKDqFh8f/AMC4sb3Q6xRsOcp1+gZ6O4L1RXM3s3fHKapFBnLrfV3LvO1CXCtyTmqXkyVD49TJOCaOnwnADd0b5ArYdZkZ999+3kir9rXJ8PfloqcPF21wYM8ZGjWNKHa7UpPTWfDDOkY+3AOz2UpyQhpuHkbc3Y2cORFH4uUU3D1N1GlUjVnTVnDm+EXiL6Uy++vVANz1aE+Cqviye8sxXrjvW3oOacHJE3Ec3HMG1aKycv5W+o9sxyOvDC1224QQojRIwF7OzGYra5btBaBhk2pF3m7jiv0kXk6hZv2qaJrGod2nsVlVgkJ9+XPOJubPtnep+fnvpwgM9mHGlMVsW3cYHz8PPpyyGJtNZdjIdtzzUMUsJxlaI4RWvZoQUiMIi9nG3I0HAVjyzx7SMyyoNtfe6h/MG+/4ecv6I6xdtofqUcEMHNEWb1+Psmy6KITNpvLH92ux2TTUzHEJtRtVo9UN9cq5ZSKnwrrEKDrXC+ecM37a17EH7KkpGWSkW9A7dZmx5dHf/ZuPlrB22V5sNpV+N7Xm1jFdcq1jzrDy3QeL+OGjv3n2ozuY/NhPANzxaC8O7T3Dfyv2U79pBFPnPILeYH8N8U6zRmddiGTNwv3P/G3UbWFPYmRkmFFtKonxKRw/GMPhPadRbRqNWkVRvWZIge+HEKL4tDKcOEmTDLu4Us63g4s1LXVmJQOdzj4t9rjhnwBw+6O9XPqQXr6UQmCwD5cu2E9WaalmNFUjPc2COSPvwVcVQYeBLekwsCVgn5xrbv/3APjtj60AuTLsDw/8kNizl+k2sDmXM0/Mp49f5PzZeAnYKxhN1fjyzT9dlrm5G/l91+RyapHIX8Ent5xFZHJeSAPoncr4qjbVMaszgDWP77z4y6mcPXUJgMT41DyPm/W9abOqJCemZ7dHyT5e1iB1Q2Z3QOfiL1lZf53Td2XWz1nrXYxJZOM/e/nug0UAjJt8swTsQohyIwF7OXOuSe+cnXp62IeO7OMr3z+Mj7/rRB5q5llF0enQ6+2zW2qahs2m4nwXO2vK7KwgPqiKr6M/e14ny4pIVTUiIwLRNDhz9jKqquWqGJOakkFqsj2DlxU01G0UTlCVgrsWibKn6BSCQv3Q6RR0egWdTsegOzsWvqEoc4Vn2F0D9LxmltbpFafnNUcADeRZUaYoSQznfe7bdiJ7fZvqeC7r+zNrf5pTxJ71feh88ZCVdc9az2az5Wh75fi+FKKyUbUy7MN+vUycJEpe1u1acC1buPe/I6iqRq3GeZcYi6obyu7Nxx1ZIb1Bh9Viw2axoThVBok5e5k6Dao6qqfYT5hZ2anKUfJPr9fx/Zf2yVmG3/4Zly6n5C7xmHUBk3nhAlCjdhX8A2UipYpGr9fx49rny7sZoggKq8OuKDkD9twZ9pxJicICcpfn8ynl6rzPVp3qseTXzfb92zTHc1kBtruXCQ9PE6GZA0i7D2iGp5dbrmNlBewRNUNYn7mvrJrxOp1S6HshhBClSQL2cpbvyUlRaNWtAROn3Zsruw7Qb2Q7olvXpEHm4FFHwG5TUUxOs5zGJDj2B4CmVboMu7MO7Wqz5J89eHu55/m8omS/jzpd5b2SFqJiKPhvKLI+/LX8Wcedkrz+5vwDPPH2cUenU2jToQ7nTl+iQeNq6A16vLzdcq8f6EXViED0el2+F9wGo54+N7dBp9fRoHkkv/z3MjqDDqPJwJJfN+PuaSI4zA+Ad394EICzJ+I4fOAcHXo0dATigSE+DLytPTq9Qt0mEYSE+RFZuwpgT2gMHX0DJw+fJ7RaIP1uLXqJXSFE0cnESUWjaJpM65YlMTERPz8/EhIS8PUtm64U6Wlmxtz8qb1bwC1tGHF3JwD+W7aboDA/ajcuWgWFbesOoSgKVcL9OXH8Ijs2H8Pbx4MRoztjMhnYtu4w6akZdOgVzd3DPybmbDy9+jflmZeGluKrKx0XLibh7mbExyc7aH/t4Rns3XaCMc/0p1qtKiQlphES6ket+mHl2FIhKjezLYl5x/IfmF7XbyQtQ54qwxaVPptNJTUpHb1Bj2ceFxRCVGblEecU1pZhS+/B6FU2M5RbUszM6/1thXj9xSUZ9nLm7mHip78m5FretlfjYu2nRce6jp/DawTTIbM8ZPbzdRw/Z/UhLdYg1wokJNgn17KXpo0qh5YIcW3T69xpFfIsCgqgy+w2oqCgQ0GHj6lGeTexxOn1ujzvagohSof0YS8aCdivQ+99djcA7h4yyZAQIn96xUgdv5vKuxlCCHHdk4D9OhQUkjtDLYQQQgghKiYJ2IUQQgghRLlQy3DipLI6TmmovMNlhRBCCCGEuA5Ihl0IIYQQQpQLGXRaNJJhF0IIIYQQogKTDLsQQgghhCgXkmEvGsmwCyGEEEIIUYFJhl0IUWGoqkpGugVNs09g4+YucwWIoktPM/PLtH9QFIU2PaM5vP8cVasH0spp4jghRMUiGfaikYC9ktE0jWfGfk9aqoWmrWrQZ1BzvvtmNTabis2mMnRYK5Yt24OmaaiqxkNjexFSQN31lX9uJzkhjTZd6xNaPbAMX0nhVFXlf+8sJDU5gz43tWLhzA1UqxmMV7APyxftxGpVGf1wD5q1qclX7y5Eb9DTol0tLsYkEFG7CkGhfuz67yi1GlalbuPqeR5j7/aTLJ23heAwP+54uAeLZm/kwrl4Wnepz5JfNxNYxYfAGsEc2n+OyKhgGjQM5+zpS9SpXxWb2cqh3acJiwji4M5TrF64gwG3d6D/re3K+J2q3PZsPcHRA+fw8fOkTqNw7hvyEQCde0fzwvu3Frp9UkIah/eewWZVCQ7zJapuGHs3HyPxcgo+/p4kxacSFhlEVP2qpf1SiiwtJYNzJ+MIDvPDN8CrvJtTKR3Ze5bPX5uPpqk8++HtVAkPwGK28vMnS1EUhWMnLrFuxT4aNI3gqzfmk5SQyvg3b6F15izQT4/4lMT4FIaP6cqNI9uX86sRQoiCScBeySQnpbNjywkAFB106FqftasPOJ5v174O/yzb43h8x12d8g3YNU1j6nNzyEiz8OS7I8slYE9PM7NtwxF0eh1RdUIJDfd3PHfpQhK/f/8vAA2bR7D8j200ahmF2aTn4N6zAMRfTsGcbmHBzA0AHN19mp0bj9BjSEssZitrFu2kTdcGnNp3BhR495dHCQkPcBxj0+oDLPp1M3qDjjse7sE37y8iOSGNQ7tPs3n1QXQ6hfrdGrB7+0mat45i+4Yj/Lf2IKMf6cnxHadYnbl/q9XG0f3nWLFgmwTsxbT6713M/3E9teqH8eCzAxzLL19MLtL2xw6c47kx3wAw4NZ2PPrSEKa/8huHdp6iy+CWrP5rO8PGdOWBF4aUSvuvxMGdp3j2ri+Y8M4Iet/UprybUymlpWSwd+txAMzpVgAUxZ490zSNjAwLABnpZuJiE0mKTyU9zezY/tTRWBLikkm8nFq2DRdCiCsgfdgrGZ3O6XZOZrcBZ6qmuTy2WtV896UoChazDYD01IySa2QxxF9K4dUnfublx39io9OFB0Ds2XjHzzqd/XUe3HXKZR3VphIXm5i9IPPtuXwhibTM12RONxNzKo6Yk3GoNtf3I+vtUjI3zHpeyTyeqmqY0y2O9a0W+/t18XwiZrM9SDiw46Tj96Kpru+/KJzBoAcyP6tK9udb04r2XuoztwewWe2/H4NR77JO1u+tojCY7O2zZH6GRPEpTt+Fqpr5d+v0+XH+m8z6nszr+9BqrVifDSGuN1ldYsrqX2UlAXul43yS0tDlCNhzBjm2Qk5GRmNW4FA+Jy3VKcDVG1xfS8KlFMfPWSdfRVEcAR6AzaaSnJjueJz1evwCvRxBvi3zGDq9Dn2OQA7sz2Wd/LPePucLI2tmEJ+akp2di7+ckv1eK4rjt1LUIFNky/q9F/ZZzY9zcJ4VkOUM2K9036XFYLTf3ExOSCM5Ma2cW1M5Kc65i8y/O+dlWcG7qmqOizrV6XNgcrP/Dqo43XETQoiKSgL2SkZv0OHj6wGAj59Hrgx7zoDRUkigkhXYlFcG0uaU8c51t8DpOZ3efvL18DQxxKnLSWStEGy27LZnDVK84/He9BtpXy/rJB4WEUhwmL/rMVTXE33rLvUAqNcku8+7p6cJAJNJ71jvxJFYnOJ1fDL7IcvJv/gcF40WG9UigwiPtHfNatmhdpG2Nzhd6GV9jm99tA+BVXzJuiCzFXCnqTyYMjPs376/iHeemFnOramcnDPsWX+Lbu5G3pjxIJO/f9DxfaKqGoFVfAgO88Noyu4FetcTfXls8s00kwGpQpQrDVBRyuRfZU6pSR/2Ssbd3Ujj5hGsX32QBo3CSU1IcV0hx6exsEDl4ZeGoGlQt3G1Em5p0Ti3L2fAHhzmR7eBzVBtGiHh/nQb2Bx3TxP1G4XjF+BJl17RVK8RzLH95wB7Br1+s0g0DfwCvHBrZGTwXZ2oGhlIi3a18fbzyHX8zr2j+eWr1Y5Av/ONTQirHkTDFlE0almDx18fzm+/bmL3jlP4+HliSbF3s7l8KYXn3riJ28f2JCjUF3dPE/c+1Q9Pb/fSequuWVnZT5vVRnCoL29OG016mpnQav5F2r5qZBBvfn0vBqOegGD7eI34Mxfp1LMhngFedBvcgvrNI0ur+VfEaDJgcjNgNBmkEs4VqtUgnC8XPYmiU6hSzX6hrDfoadWlPgAWFZq3q42Pnwc9BjTLtX3vm9uWaXuFEOJqKJrcw3dITEzEz8+PhIQEfH19y7s5+fry46Xs3n6SHn0as+2XNdz88gj0eh06vQ5/f09On76Eoijo9Aq1a4fi65s7UK0oUpLT2b31BG88OYsnXhma54k1L8ePxBJVuwpgD/TS0yxYrTb8illxQ9M0MtLsfdTdMzPpOU2ZvIDFC7bRuVsD0pMz2LL+MDqdwpzlE/H2qbjvbWWRlJBGako6RqOBwAIqGhXHy7d8SEi1AILDA7j1mYoz2FRc38xmK/8s20PvPo1duvZdjy6cucSXz80isIov4VEhrF+8g8j64Yx9/87ybto1rSLFOVlt6fHXQxi83MrkmNaUDJYPmF4hXn9xSYa9Enrg8d4A7Fi1l0uNqtOkWSTmdDN6ox69Xk9YVf8i72vW+wtw83Bj2CN9Sqm1BfPydqddl/qgKLn6sOf09/erWTFnAxOmjXEE62DPqnn5FHzym/v5UpIuJ9NlaBtqRWd3dzm09RgXz17ickw8A+7vlee2kVHBtGhdk5p1QrlxQDNH1w1RMnz8PPDJvPux69+DxJ6Oo2Hb2oTXrMLs9+YzeOyNeHgV785F1+FtCazqT/Nu0aXR5Kt2aO9ZPnvrT2w2FU3T0FSN6JY1GDtxQOEbi0rp/PkEXn1lHgf2n2PLlmM8/8IQl0Gy15v0lAzWzNtEnaYR7N94iAPbT5Keai58QyGuUxKwV2LNujaiWddGAJw+eI4qkcF4+xcvw7zou1V4+XqUW8CeJSDIG3ePvDPcWc4di2Xbij0kx6dQpXrxAuY/v1nBueMXqdGgmkvAPnXsV5jcjTRsm3c/1u9ens2gB3tz8x0dAHtGvqJVHLmWzP1sCRsWbWfCp6MJr1mFpT+s5sbR3YsdsPe4rVMptbBkpKVmsH/XaZdlJXV3QVQ8W/89yKJ/9nIgs/veiuX7CAjwYuwjva7boN0vyIfbnxlMQKgvdZvXYPf6QwTlGGMkrg8ycVLRSMB+jajVtAaLv1mBycNUrGCl0+BWuSpqlIfvF03Itezw9mPEnb1Mu/4tAWjfvwVB4QEEhvoX/wD5nBSf+uoh9AYdNRrmPbGSLUdwvmTGKi7HJkg3i1Li5mHCw9vNUf3oq50flHOLSkfO6k4gFYauVX/9vIHPXvmdwHA/goK8iYuzzy/w29zNREWFMGBg8/JtYDnxDfLm7heGOR43aCODf4UoiPRhd1KR+nZdicRLyZkVS7zLuyklwmK2YLXYip1dzUtG5oQpBpMh1+BWIcpaYnwqe7bb6/dnjTfx8/eibqPw8m6aKGH/rdzPyw98C0D9trXYGxPvqI5lNOr5/seHqFKl8p1vKqrjR2M5sPcsFosNi8XGjQOakZFhxWKx4h/ghcl0fecpK1Kck9WWLgvGlmkf9tWDPq8Qr7+4ru9P7jXGJ8ALS4YVi9nqUr7sSqiqitViw+RWOhUsVFUl9sxle7CiU1B0Cr4B3o7ayFaLlaO7TmGzqdRpVgO9Qc/J/WewWmxE1g/HrZDuMzkVd32bTSUjNQMPb3fHLetLMfH89/cO2t7YjECnW7c2m0pGmhnVpuLh5eYykU9xbV+9j9SkdAKq+NKwTXZZQ6vFyvaVewgODyQqOuKK93+1Zk/5i8S4JAC63dIeD39vtm04jGrTUHQK1WoEUa1GMGHVSr68pTnDwvsPfImqaoSEB9BvdDciG1bjxaHvMXbKKLYs3836P7dy+8TBRHeoV+LHLynPPjOLxKR0l2UtWtTg/ge6l1OLrszvf2wlLc2MzaZiUzX7/20qIcE+DB3csrybV2G07daAYaM7M++7tRz47yit+zdl446TeHqaSE018+P3/zLhqX7l3cwKY/3fO5n18RL7d3/j6ox///Zibb9p/RH+9+kyx+MfvlpFcubf24dfjCa6adl+f65ZvIs9W49jMduwWmxYLFb0eh1PvnULAKcPx/DK7Z8S3b4OT3w8ukzbJioXCdgroINbj/H5Uz8SWiMYk8nAgDHdadC2DnOmLuSvb1bSuGM9npp+X67t7uv0KmePXWDCR3fRe0R7AOZ9/y9z/reK+MxJiAJDfLjrsV7ceFNrl203LNrGxoXbefj9OzG5Gflv0TamP/UD3+2bWiqvMSPNwj1dJrsse+fnsTRtb78tmpaUzhO930S1qXy38118Arx4uMNLAEzf+AZRDYtfhvLTx7/h0Y/vdVm2fO5/fPXKb9hsNmo3juDNXx4H4OiOE7x550d8vultfv1oMb1v78SFM5f4cOzXfLD0eQLD/LFarJw+eI6U5AyeGvg+AJ8se446za68hOAXz//CsT2nade3Ga/OfNSxXNNg3e+b6Dy8XaEBu6ZppdYv9tSBs1w8F094rSrEXUzilw/+Zu/2k7nWm/TeSLr2bVrix798PgFFUTi9/wzt+7cgsmE1dq3dT3pqOqf2n2Xz0p30HdWFs0diSE1Mo06LmiXehqsRG5vI/v3nSMoRsJdnhvX3r1dy4kAMik5BUSC6TS16DG9T6Hb/bTrK+o1Hci1vUL8qQwe35NyxWH6b+hePfHRPibTzv6W7iDlxEb1eoXrdqjTrXL9Y22ekWzh64Bz1m1Tnf5Nm0qpXU1r3LvnPaF7ufbo/B3aeZu/W49QM86NDjxvp2q0hr7/2O917NCyTNlQWiZdTOLjD/p3icQVZ11btarF6eTgH9p4FoE2HOqxYshson1ltt284zMLZ/7ksc3M3OgJ2c5qF04diCJVCBqIQErBXQKlJaezffJT01AyO7ThBi+7RNGhbh9SkdC7FxJOaz8yIWdnwDKeR9t0HNsNkMvDpa/MBiItNJDWzlrizw9uOs/CbFTz07h0AtB/QivYDWpX0S3NwnuE0S9bMpAA+gd68PPNRXh75MapNxeRupNftnTAY9XhdYZnKvDp/WTKsXL6QCEByfKpjed2WNfl271QAfp+2hOZdG+Lt50mrXk0cXY6SLqcw4+VfGPFsdj9Mm3p1E/RUjQrGZrERkmMCJqPJwOOf5b5Iy+m5gW/R644b6HFb56tqR36e+uJ+x88Hdp0iKSGNalHBoGmoqr3aiUb+JTKvhsnNyHuLnwPsgbtXZmWZR6aOJig8kAffvZ2H3rsDRVFY8v0qzh6JqXAB+5xfNuYK1svSVy/OZv9/Rxj0QE+63mSfWGzLqv1sXrHPsU7txkXLQNasGZJnwJ71t121ZhVueXJQgfuwmK3oDTqXv/38LPxuFRsW7QCg58j2xQrYz526xOTxMzlz/CKTv7ybw9uOYzQayixgNxj1TJp6Owd2nKJjn2gSLibh6+vBe+/fVibHz4umaWxYvIO05AyiGrkOxi9P3r6ehNUIwmDQE1yMimdZatUJJapmiCNgv310Z8Y90x+DUe+YpK0s1WoQToeejRzHNxgNjjvJYL8z3ufOzkQ1vH67w8mg06KRgL0CatSuLj/un4JOp5B4KZmQavaZH+9+YRijXhwOQEpiGl9O/Il6rWox4L4eALhlBknpadkBu3+gNy071eX+Z/qz9PetHD8Yg5ZHsNyiezRGN2OuAahb1h3GarXRqHmkY4bVknDhfAK3PtEPq82GatOwWW1UqW4PUjVNY+6nS0hLyWDQAz3xCfDCaDLkeVehOB775N5cy9r0jOaduePRG/V4+uTfV17TNGo2juDN+U87lgVU8ePlX5/kwplLjHj8RnR6HYGhflfVxpd+eOSqtu9/X08i6xf/7sOU6Uv5Z81+NE3jxQkD6NC68FlG6zeJ4H9/jGfOlys4cTAGAP9gH8JrBBFVq0ohW1+dAKf3ufedN+R6PrxxFIYgfw7sPk39xhUjEAFo36EOBzPfK2eRZZRdiz0Zx7G9p1k3fzOoKl1v6UDn/s2oUa8qmqqiqhr97+hYpH2tWXvQ5bFOp6DX69Drs0+IVSKD893+7JEYPhr7Nc/PfBzfoMIr5Nz93FAG398Dd0836jQt2l2stJQMTp+4yBdv/cWRffYAburL85k672k8y6jPbJbgMD+Cw/yYMvZr9m08zCdrXsHds2zb4ExRFN6870ssGVbumji4wgTsnfo3o1P/os3HkZ9+Q1rSok1NDEY9wVV8QdVIiU+xz3ob6ldmlXkO7DrFtv+OcGDfGby83alVvyrh1QO44+EejnVCqgcy4ZPRZdIeUblJwF4BmdyNBGdmWJ37Sjt/yVgyLCz7aS3mdIsjYL9tfF8O7DiFVdGRGJ+Kr78nAFUjAhk+ujNWq4392wOoGhFI/KVknhjzDTabyqTJNxHdoV6e/X7fff5XEi6nMmXGfTS6iq4eOc2fuYFFcze7LFv25w5enHIbzdrW4vcv/uHi2csA3PHMIKwWGxtX7qde42qEXEHWJT+BoX54+Xmyf+cpDO55Z4Ubd6iHl6+n43Hsmcvs33qcVl0b4OXrQUi1QJr1asLpYxdyZcavxJY1B0hKSKNbEapHpKZkMO+Hf1FVjRbta9N5aPbsjTFnLmefsAqRnmEhKdme+bUUs2zltn8Pse3fQ47H7p4mIuuEElo9sFj7KUzs2XiWL9iO2WzBL9CLzesO4+Fp4rn3bgVg4+oDfDJ5AcGhvgQGebNu+T4G39quQgXsrVrVpFWrss/6pySlM3HM1+j0CpFdm3HrC0M4fvg8t/d4G0UBBQUUe5eY2xPSHHXx82O12Hj+ib4omdsoGkTUCS3WrK3htcN45+/ni7x+cQPK2LPxPP/gd5w6eoE7Hu5O3ehq/P79v5w6eoGpL8xl0pTbyqWkYseBLfn7+9X8t3gHXYa7zrZqsdiIT0xl9/6z/L54OzabysOjuxJdr3Syr/7BPva7HBWgUpizL99cQNz5BKwWFZvNZh8fYVW59eEeNG1XeDKhUZPqNGqS/Xn58PHv+PuHtQD8cW4apjKaXXjfjlOsXboHgLjYJE4evUCTVlEuAbsATVPQyijzXVbHKQ0SsFcAW1fv57cvljN60iA2/nuYmNOX6NijER2659+38eCeswx6ZjiRdUIdy1IybMyZuQGLxUarG+o7AnaAb6f8ze4tx+g5uAUdejZiy4YjnD11CYAjB2Jo2CSCjav2s3LhTvwCvXiolCdwyStbn5yUzq8z1tKsbS2+2TKZTUt3odPp8PTxID3NzBuP/8gz742ke2Yg+92HfzP369Xc+lB37njUPunRzv+OsuTXTaiqxsMvDsbHzzPXcXL6bcZaZny8DHcPE7eN7sjZExfpMqgFO/87hqZpjPv0Xi7FJrJlzQGCw/zZuHQX3779J9OWTsTkYeS2bm+TkWbG5G6k/y1tefSWz1BtKmOe6ku7rg2K9b78+vUqvn1vETf0a1qkgD09zcwPny8HwORmoHHLKADef2UeG9YcJCDQi//NebSAPdi5Ow0uPnLiAi0aVuOP79dis6j0u71DgRdJ1aKCMadbQKfg6+9Jkza1aNymVqHHLK4LMQnM+GgJAK261mfzv4dQFAXtXY2zJy8xf+YGLp5PJCDI2zGLpNV6dV2UKpqU5HR+//5fl2U+fp506h1NUBVfkuNT2L3uAEaTkUbt6+Lhbb9rpNfrCAr1ZePK/QC4e5hITzNz6UJSHkcpvHBYfFwy44d97LLs878mULN+1St7YaXAy8edC+cSAPjl6zVMnfkg7h5GZn2xkn+X7uHQnjPUK4eLufb9W/DFxslE5bgAWbHuAG9/spjUNNfJgyZNnsfsL+7HI5+EwtX4Ycc7Jb7PkvDfyn2cOXYx1/I60dWKFLDnVLVGCA1a10KnU/Itn2qzqZw/cRGjmwH/EN+rLtwA2eVbdToFvUGPXq/D6Jb3fhf+bxn985m8TwiQgL1CiDufwJZV+xkypht/ztrI5bhkzpyMo0W72i59gc8cu8Dcr1ahAN4Bnsz//l9ad6lHaLi9e0BSQpojO5ozS7ryrx3EnovH19+L/iPbkZyU3Q8+MbPv9polu1mxcAdh1QJKPWBv0qw66UObY7/WtVeJue3xPvgF2Cd+MrkZ6TQwu9JERoYVgOV/bOPM8Yvc+WgvyJzEyHkio3Mn4vhn/jYA7n26Hz5F6KGSlWVTVZX/lu9l/9bj1GxUjV++WAFAt4HN+eyVeezffpJBd3XC399+saFpGnq9juTMMQWe3m6cOHye44fOA/asZnGdPHQeNbPiBkBcTALxcUloqoZ/iA/BOSYW0TllCFVb9olo5d+7sVhsRZo5cP7i7Zw4fcnxWFM1khPT+fHDvwFo06NhvgF7UnwK6xbv5FJsomNZ4sUkht3bpdDjXg1zhsXe1swBtru2HmfrhiOOZVldu661Sa7SUjL48dN/ci33D/SiS7+mJMQlseibFVjNVqrVudcRsLt7mmjftYEjYA8I9iYiKoQ+w1qBpqFp2XXgjcbCTws6XR5ZqmJWCP7ymR+5/507Si3L7eXjziMvDOKD5+diMVt56+nZfDzrYeIvpdCtf9NyCdaz5AzWAb6dtS5XsA5wOSHV5e/8evDa/+4lPc3MgV2nsVptWM0qFosVnV7Hv8v20KlXwTMYfz/1b04fu0BGuhVzuoWIWiFMXfpcgdskXUrm3uYTAXj/70k0LoFKUwNGtKVdt4bEXUjMrBBj/3f8cCxRdVy7Dfa4I3fXvuuFioJKGfVhL6PjlAYJ2CuArC9jq83G5cxJNfZuO0l6usUlYI+LTWTRzxsAuPkhewk4q8XGC3d/AcAtj/Z2rGvLMRo+azCklpk9c65FnhUcZg0Wy/O0W8LV+nesPciCr1e5LBvxcM/8N8gMBjavOciRfefo2KsRf/9q71LjnEVVnAKJosYPWQPessrSBYf7U795Dcfzqqo5si0nDsYQ0C4ze6zZt1UUe9ZmzBN9OZQ50AnAdgXZ3aySkFkTNv36xXJ+z3yfRoztyT3Pug7ic56AR3V6wQaj3nFyKKxqzBffrybZaSBy3x6NMTiNc7BZ8n8dP328xN53v4ovqs3eB/pKBwUXxnl8hc3m+st1Dmg0DUeG3WKxlkpbyouiKI7XZl8AoeH+hNew94GvVjuMV399Ms9tVacB0Tq9jsatomjYLILJ435Cp1PQ6XTo9DrH90FBvHw9mDj1DhQl871XlGJ3B2vWtVGpVjQC6DWkBds2HGH5gu2cPnaR/1YfZNyrwwrfsBxMe/t2fl+8nenfr3ZZ/vEbtxapdnhWKV6L2Ypery+Vgd9lJbxGMPFxyXz0yvxcz3Xs1ajQgH3pb1u4GJPgeJySlHehBmdGk4HBD/bCYraU2IyrZ07Gcf+Iz3MtH357ex584kaXZeU5nkFUDhKwVwCOIDPHebIoc1o5r+Lc/SNnV4Csk2LW+nkF7Pm0rtA2XIlet7SlUWa3Cfv5XsG7gEDP+b1QFLCYbcRnXtx4emd/0YU61f/Oa3BtXrL662oaqFaV9BQzpw6fdzl21qh+c4bF8ZZkBUAGgw6LxYbVasNkyu5aUpTAJ6esKgYBmf3OXQJyW+7X43yBojodT29w/f0aCqgNnzNgUjXNpU9rQaXQHnppGA+9VDYBkJt79tdV9/5N2bPtRHafaafX0KN/U86ciAOu7KKpIguq4suCna/n+/znk2azav5mPL3ceGLq3TTtlJ0ldH4v9HodFrOV3VuOs37ZXpd9PPzC4ELb4eZuxN2k5/NJsx3LnvjwTlp0KXoXsHYDSr9Wu6IoPPriYNJSMnD3NNG2a/FKQZYlL0833Ey5+1abjPoCL2rWLt7FOxNmutxNuvm+rowp5bukpWXakzOIqF+NnnfcQGi1AM6fuYzRqMfNw4S7hxHvAooDZImoFYKPnycmNwMmd6O9klUhvPw8GfveHSXxEhzym0U8KKTwQdbXE6kSUzQSsFcAji9jxR58pibbs525Sh+6BK25P3QhYb4YMzOrOTPsPn4eXIxJcGQiA4K8admuFls3HnV8qZTlXdeoBuFENSj6QKqsV96+R0PadG3gEpA2bZvdX7pZ+9ro9DpUm4pWxBKLWUG+XqfQqE1Nzhy/wLF9ZxzPu7kb8c3squPh5Zbr4sc3wJO42CS8fT1cgqIrCdizAuWs11SjXhhte0aj0ylE1gvNtb7O5Y5C9ufDmCPgLihgb944gsPHYjl3Pjsj5eZuolWX+ugNenz8Cx8HUBacL4ay7ngEZp746jSsyt2P9EBBoVHzSMfFnNFUsQbTXS2LxcqR/THYMiu6aKpGtcggRwCQeCmZxDj7P+fPw6Ptn2PU23dy19ieqKqKTqeQFJ/O20/8jLefR+bfi700p05ftC+CjDQzFzIHhkN2NyWAy7EJrP9jM/3vK+Cu2VXQNA2L2Vqkid08vdx46WN7IFYeg0yLo0mDcB68y96dLKupoSEFDxo/e+Jirq5fFnPJ3Fm6dCGRjDQLNquKyc1AlVKYEA3g/MmLnD1yHg8fdzoObo23vzceXm5Ur+6Pmp6BxWzDnG4mPiGFEUXobvfmd/cXuk5ZCK3qz4QXB/PVx0sxGPQYjPZ/EUW4gBAiJ0UrShr3OlFeU/YmXU7h/JlLVK0RzI5Nx7CYbVSNCKRmvVCX/qR7txznzUe/B+C2x3uzZtEuouqFsW2VPUM2/p1bSUxKR7WpNGgaSUCwt2PbzWsOcuxgDBE1Q2ifOVGHxWJl2V87aNy8BhFRwcydsZbvP1tGWLUAvpg3DoDXn5zFuVOXeOnD20pl9sqislltnDp6gYAQH/wCvEhPNXP8UAy+/p6EVPV3GSD0U+Ysd4Pv6likQafpaWa+en8xgSE+3P5Qdy7GxOPmbmT/jlMoikLrLvU5e/wily4kEhTqh9Gk5+K5eKLqV8Xd043dW4+TnmqmYbNI0tPMHN57Fr1eR406VYpd0SYj3WKvO+9mKNKMqZqmYbHY0GVOb5/VveezdxeSlpqBwajn4Sf7FVq9Y9nqfSxfux9FUXj2sb74eBeexSprVouNCzEJmNwMePu6Y7WooIBXHm2Nj0smIbNSUkCQdx57q5zOn4vn7oEfuizT6XXMX/McJjcjezcd4czRWNKSM+g0oLnj1v6C6UvodVcXPLxK5veanJjG52//yanD57HZNDRNRWc0css9N9Ctb5NCt//npzV4+nrSYVDx53owZ1j45NX5JFxK4ZXP7ypSDffK4sCu0+zbftJ+MZY5t4Gbu5HBt7fPdxtVVdm4fJ+9zrfJgNFkIDjU96qrNP382TK+/2Cx47HeoOOJd0bSc1jJz88x8535zHj9Nxp3rMcHS7IrBz086EOOH3Atg/rwi0MYfFfRSo+K3MorzimoLe1+fxxDGZVZtaZksHHoxxXi9ReXBOxOKtIHWQghclr59y7eeu7XXMvnr30ed4+y67N84XwCd934Qa7lDzzZl+FFCKZOHTiLyd1IaI2QYh971+ZjTBz1FZqmcdtD3bn78d6Fb1RJzPpyJd99tNRlmcnNwB9bXy3ztvwyfTnfvrvQ8dg3wJPHJ99CpyJckBXX0d0nObLzJIFh/rTq0dixfM2inaSmZGA02ScbMpkMNM1RjEEUT0WKc7La0nbeuDIN2P8b9lGFeP3FJV1ihBCikmjaKgp3D1PmIFF7daXoZpF5V20pRUajnshaIej0OvQ6BV1mtaTfZ64vUsAeUd+1O5ymaZgzrCgKhXZzadK6Jnc/3osZHy3l5+krqN0ovNBBiJVGji47BoOeqhElO59BUfUd2Y6OfZpgMOrsg1i9TEW6Y3klajWOpFbj3PN83NCvbGaiFaIykIBdCCEqicBgH+avLfpkQ/lJS83gn4U7SUpM48L5RHvwr9gvAuo0qErvQuYA8A/05svfHnNZFnPmMkcP5J7F1dnnk2azb/MR9Ho9UxfbS+gt+20zK//YxpbVB9DpdYx6si8jHsp7YhlVVZk1bTn7d5yiU+9o/l26hw+enUP1WcHUqJN7jMeVUFWNBev20LVZbS7EJ1M3ovh3AYpr8aKdnI+J58iJC/g3CkNVNdTMLjGJRRyHkdWFRtM0FBSXcT5XwjfAyzF2R4jSJINOi0YCdiGEuM4kJabzydt/5flcvUbhhQbsAIt++Y+VC7ajoeHl7c7L00YVOs7l7LFYDu885VL96PSRWLasPgDYKx2lJOY9f4E5w8JrY79ny5qDANz5WC9OH7vAicOxfDPlb179/O5C21yYy0mpvPTVItbvOcH7bka8PUzMfPkuAnxKd+D1ooXb2b3r9BVtO/berziU40Jp0PBWPP5kv5JomhCigpCAXQghKok3n57F7q0n7KVQUXD3NPHyR3cQUbN4WWCjUU/TVlEkJaZx6aJ9Yi5Vs1edCS/iYMVPXprnqETj6e3GsQMx1KwfVuA2Hfs3J6JumFOlJY2wyECad6qLgr0MXnSbmnm32WRwyfjO/Hw5k78Zw9+/beaRF4cUqc0F2XrwNM9/+RcX4lMASMuwkJZh4Ye/N/P4zUWfCMxeD91eiSe/sn45ZXVpCgz04tbbO3DxYnL2QPLMeR7yq3CT1yi0opa0rQyOHYhhz+Zjjnky6jQKv6LZTkXFpWkKWhllvsvqOKVBAnZxVWxWG+np9nJuiqI4Agmjm8Gl1ru4tm1ee5BZX61yBH1ZAWCnno0YOaZrntscPRjDnG/XuCxTFGjSMop+N7cpi2aXi4T4VM6cjOPQ/nP4+nvSql1tvH3ci9QPPfFyKpcuJLksyzmrcVGkpmRg8jBxZt85+wKnqM9cxNr1ipK9WWpyBlMmzeGTHN1knH00/nuWz9nAPS8OZ+hD9inYTxw6z0cv/OZYx2DUc0M+2X1FUXjs1WEc3n2aU0cvEFm7Ck3a1qJ5hzpFam9Bfl+zize/X+Yy+RjA3X1bM3Zop0K3X7dkF28/9gM2q81RjrfvyHaMe3tkkY7fvHkNAgK8CAry4aab2xar7XkF8tdSLYmd/x1h+hsLHI+btauFTq+nceuo8muUEOVAAvbrhM2msurPHbh7mejoNEArq9+jalMx5JigI+bERU4ePEdAFV/qNquR127Zu/0kT9/zVa7lH3z/ANEt8t6mMoqNSWDae/aKCeOeH4TRaCAtNcNRMlBv0PP3nE2sWbSD0GoBPPb6TVd0nPNnL/Prd2vRVI323RrQunPRp8dWVRWL2T6zqU6nYHIzsm/PGQ4dOEezllFsXrqbU4fPc8tDPaiWmZE1Z1jsEzIpGqnJZtA0vP09izSzorP4Syns3noi1/I6DfOvtX/pQhIr/tqRa3nCpdQSDdgPHTjHmhX70ekVbujWEHeTnrjYJGrVD8Pb14MVi3dx+sRFLBYbt4/p4lJt5c+5m0lKTKPf0Jb4B3hx7PB5dm8/hY+fB916R7Pwj22kJKfTsk0tate196F+a9Kv2Kw2JrwyFM8clQ8SE9K4c/BHZKRbXJbXjw5n6v/uLbTfcc5AzNPbDcMV9FU+dzaeTesO5/nc4QPnirSPGSuftc/Iq9j7vxeWTbZkWMhINeeqGe7MarGxe9Mxeg9vnefzHl5uPPfxncyevoJHXhlaYkmBprXDMRr1ZDjVL39oSEfuG5R/OUVnCkqu2ucBTvXTnx/4NhNnPIJvUN4T5oy6x57BN2dYSU3JyJ5YOvP3bXIzusyt4KzvwGa0aV/LMQZBURTq1q9apHZXBjnnkNix8Si1l+2WgP0aopVhH3bJsIsSYc2c8EhRFAwmPX/8uJ5dm44x6I4ONGgWwe7Nx0lOSkO1qtiAXVtPYLXabxO26lCbPoNbAPbg/InxP6KqGtWrB/LspEHs3Xqc956ZjZePO8YP9Hz2/Bw0TaNt90b8+eO/APy8+TX8g7NPKOsXbefLF3+l04AWvPDdg3m2+RpK5LhQVZVhrV4B4KuFEzh35jLrVu4HYOQ9N7Dqzx3M+2EdAFUjAvl28ZPYbDa2rDlI75vyDjaKIuFyKgtmbQTss1q27lyPLf8e5OWHZtCxZyM2/nsIm1Wl+YAmbNh/muEDWjD2nm4AHN5zhnHDPwGgecc6vDXjAT58dyHHjsQSXj0Ay5HzXL6YRGCoL3dPsPdvfWbUVxzYeYqu/ZuyKjN4fvObMXh4GDl1JBarxUbLzvUKrekcVj2A7v2boujst/CVzMChYdOIfLcJDPGhWdtamdOG2+/O6HQKETULn1Rk24YjvD1xNr5+nkz/7TGXmvVnjl3g/Qk/AfDQK8M5fvoyP39v/4xH1ghm17+HWPTrZowmA9/9/STLF+3kv7X2ftHDb2/vErB/OXUJGekWatcLo22nuvyzaBe//LAON3cjLdvW5NMPFmOx2Og3KI4Jkwayf/dpVi7eBUC96GqMGN05V9tzBusAB/acxaaq6Ck4AH3987tRbRrnz8Xbs+RGPapNw2KxuszZUJjQqn7clFnb23GNnvnDDd0bFrp9RrqFWd+tdTzWNA2j0UBAkBcjR9+Q5za9b+9Ew7Z1aNg6e6KzoCq+PPb68MzD2z8D0a3y7hKTJapeGBOn3FZoGwtz8lQcJ0/E0blzPWqFB/HM7T14/bsljufnrNzB8K5NCfQtvP96q671mfnfq+gNOvR6HXqDzmVuiLtfuQWDm4GY47GERVXJdz+ff/Q3f83flmv5088Pok//ZnluM3h4a94b9yMHd57EYrZSp3F17irC5EKVRd8Rbel7Sxv2bT+FTmcfTBsoM4WK65AE7BXI9+//xZzP/wHg08VPM/vLlcTHJbNu2R4+mPkQX7z9J6ePXQSg94g2/D1/u2NbH193R8CuKAp79thn6kxJyeDUyTgSMwdypSSlc+xADOdPXwLA+WJz4/K93DiineOxo58p+Ufl4ZGBPPTsAMdUpJqmoWmU6yRLJcWcYc+Y7dp8nKCqfi7PKZndF3z8PLj5XnuAkpUJupqs39mTcY6fs27PH9sfg82qcvlSiiPYM5utmM1WTjvNNOkcIGS1JevXazHbHG12/nVmdcNwydxqsPCn9SyduxmAl7+8p9CAvXHLKBq3jCry6wT7AMMLZ+JdXnObLvV4qAhTqh87GEPCpRQSLqWQmpLhUm4uPdXM/m32bH9qcrrLa1MyLybAPhukalNdZkM1m7Ozv5qmOd7vi7GJmc/bPxMZ6RY0Nbs7yqYNR/jik2V065Ed7ObVj9ho1FM/Ohy9Tmcvzai3/79lu1roizABkM2mMrrv+6QkZWB1ms24SeuavPftfYVunyWiRjAPju9T5PVzslptLJizKddy/8D8A/ZmNzSg2Q0NXJb5+HvS/1b7hYOmafz180asFhtb/z1Ey051r7h9hVm6dDcfTv0bTdOYPm00NWoEM7hTNM3qhDNu6jzOXEwgLiGFl75exMfjhhfaXcnkZsQUkn85yvqta7P1n10smLaEl399skRfy8Kf1rF+yS7SUuwzZDsnXSq75Qu2k56awfFD50lOTMdithIY4sNtD3cv76aJEqRRdsm/ypxjlIC9AinsA+s8o5+iuJ7cnbNrzieXkyfjGD36S0belN3FwDmAadG5Hn/9YM8+xufoGxtRryq9RranTtPc9XGzGAx6qke51mPW6RRSUzIIKvjlVBp5TfOdFfR5+bgzYESOPqdXccfN+Xej2ux9iffvPAXYxws41ssMBp37LweHZV9U1GxQ1aWdoDkyqS4BrCNgz26DqqkoTp81tZQGsCUlpLkE6wCnj11k/fJ9dOhRcJbX+TNeUPsURXF53l6/3Om1aZpLV4Os7hpJiWk8+8gPjuX//XuI/sNaufYXdvoTvHghiV3bT9KjZyPHMpuauy+4h6eJT4oRWOekafa7MDldupiUx9oVx7HdJ6laKxR3TzcsZiu71+6nRY/GJMSnsuafvQy8qTVH9p7ls1d/d9SXnzJrLPWaVC/RdqSlmfnk06UszrwLAjD5zQVMnzYanU4hKiyQNx8cwJi3Z2G1qWzYc4LZy7dxW6+Whe5bVVXMGVaXAbyaquHt54GiKLTs2YRmXRsVuI+s7/HqEYF4eNq7UykK+BZQ/zyyTih3PNHXPtOpUU9gFb98161sPn/jD1KSclcN6ntza/wDr50ZjIUoCgnYK5Am7Wrz67R/8AvyJiwiyCU4UMBRCs1+29U1Krz5btfJSsLC/IiJSQCgY8e69OrTmOObj7Nt3WFHANOicz2CqvjRoXdjdHod1Wq5Vppo1b0RrboXfILZt+sUr46bmWt5uy71efWTO4v2wkvZiX2nmfHyL7z0y4RibWcw6LFabVgsuQN2XebvJsApm2Vyz86w7Vi9jwX/+4d7X72F8FpFrw9drUZ2d5Csrhm6PILqrDxBzuyxs2NHY4m7mOzYdsjoLhzec5o2Tl0esl6HczZYU7OD+5zP5cdmUzl2MMbeHs0eRJvcDNSsl3/VkKoRgQy/qyMJl+1t9PH3wsvXg+DQwmefc36tOS90q9UKISjUj7jzCUTUrsKZ2CSX7ZyDfU3VXPrIZmWtd207weH92X25dXlkv3U53m+9Xud0gYR9bEA+nrznK8d4A6NRT+vOdWnRrnaBXYgAbBYrffo1sd/JUlV8A71p16cJbk6TDX394mwS45IZ8nBvajXJ/2Lb2W8zN3ApLgmbVcXd3cioh/Oug57F3cPE5z9ldpPLfB+8vNwIDM47iNq/8TDxsYm0yJzFcs1vG9H7efP2i78RdyGJ9DQzN9/ZkeH33sBv36wBVePdp2bx6bzHS2xWy2PHLvDaa79zwukisUoVXx5/rLfLZyK6ZhiP3XQDH/6yio6No+jXrvAuQgDPjf6KHRuP5lr+x643HHe/9IaC+/lHZX4HT3h2IE2aF+1317hdbeKT0vn79628/uldRdqmojp/4gIb/tpK827R1GhUnaF3dcRmU0m4lGL/WzEZCKnqT1DotXNRIkRRScBegdSOrk7LLg2459mBePl60GtoS5IT0wirHkjdxtW5/5l+PH/ftwy8rT09h7akTsPwzP6Serx83F32FRzs4wjYQ8P8qFU3jK79mxEc5kfN+mEMv68bnfs1o0GLGrz0vzFX3GZbPhUldPqrSDOXsMgG1Zj04+PF2kan0zFz9SRGdHyDud+s4auFEwgI8sZkMlCvUThb1x7CP8gLHz8PxzaeXm5UiwomIMiHJT+uYc28TXQY0KJYAXtU3VA6dG+IolNo3s7e1zcrONXrdTRpFcXeHSfx8LEf93J8Kms3HqZxw3D8fDyYu+01AAxGAxs3HCb+sr1EnaZp3JLHZDRGN/tXgF+gvVxeQLA3AcE+3PxAN3oOb0VYRJDjuYJcOBfPozd/5rKsRp0qfDF/XL7bhFULwAMbv/1vGQBv/z6B5l2KFhw5f75yXlC4e5iY8P5tpKea8Qv0dqn8oegUolvWwGKxsWjOJlRVc+lKlHXHona9MJ54YRBffPg3qSlmWnfIo4xcVlWkzH77kTWDXS4k8hoMai/5Z+Pg7jMud0f27jjFpQvJhQbsFrONpT+vdzyu37wGDzzvWtLw6K5TxJ6Oo+dt2dVNDm09ijndQnTH+rn2uej3rfzw5QpSU8wABAR6FRqw6/U6ahdjYGO/Mdn7M2dY6XxHVwxGPZcv2S/Wvv50GQ2bRDDqiRvZ9u8hjh2I4czxi2xdd8hlkPzV2LfvrEuwDtC6dU0aN86dxb+9d0uqBHjTs1W9Is8im5xHJvjzP8YXubQjQP/BLeif2bWxqDauPsCMT5dx5uQlJoz6HxazFYvZypTvH8g16LmiS0lIY9/Gw0RFR1ADuPPRXuXdJFEGVBSUq7k1XcxjVVYSsFcgQWF+TP7pYcfje5/s6/J8WLVAbrrnBhq3iuLUgXMs/elf6jaJYOyrw3PtyyUDmRnQ3HhLG268xd41pn2PgjPnRdW8bS0+nfWwo9KMqmqoqoq3j0fhG5cRRVFcgrIroTfoiYgK5vy5eHQ6Hbc/1J3bH3LtR9m2e0PaZmavEy4m0WFASzoOKvxWujOTycDLH93hsqxdtwaEVPXD5GbEy8+Dg3vPMPymNqzccoRDx2J5bvI8pr4xkhrB3sSdi6deq8xA32kf+XW36ntTaxo1j6TH4BY0bhVFYIgPtTMru1Svlf/guJzS0yzo9DpHNx4oWma+as0QWmbexfH2L/qsilUjgujcuzGKgksf9Cwtb8gOTMOq+gMQUsWH6hGBRNUKoUvfJox6rBc+/p6MGNWZvkNbYjQZqB5p78gVWtWfvkNa4u5hwmjU0+4Ge7Ue56S60ahnyb8vuBw3KTGNiZNvQqdTaJBHd47jh2N5ZdxMAkN8HBU9sgbo+hfhwsjkZqBNj0aOi4RqefyOJv/+VK5lMccvkJKQmmfAfmj/WUewHlkzuEiTJl2p82cv89z93xF7Np4pPz3AqAe78+3ny1FtGu+8OJf//fIIEz+4jQ8m/kK/W9vRoWfJfE8B9OvXlC1bj7N+/WFq1AjiwIEYdJldpnIG5Yqi0LtN7veqIK9+MYqN/+y1X8hp4OZpJCTcN9/66QlxSSz56V9CqgXS7aa22GwqZ45dwGa1YbOqmQUFbOh0OhoWUHErPi6ZU5ljm/ZuP+lYnp5mrnQBe62mkTw745HyboYQFZIE7JVIeI0g7nvaXt3j1y+Ws3/bCdw88r5dPGBAM9q2s5f6qpejW0JKYhr/LdlBo7Z1CI0svCJHQbx9PajjW3GC85Lk7efBn7vecDxu3DySwCIO6PIL9sHg7cmCH9aRlmomLSWDtNQMBt/VierFnOSmx+AW9MCeddu1+RhDbutA7agQXpgwgK07T7Jw2S40TWPP+oOs/2MzE2c8is2mUq9+VZ56biCKorh0mXDWpV9Tx8/hkblHHaz9YwtWi42OA1pgcjeSFJ/CjNfnoaoavW7rSKO29sxzVN1QXpx6G5pmD3YO7TnDmiW7C3xda/7aztFDsdRobq8Ksn/HqVzjJaaO/560lAxum9CfqIbVHMtbd6pL6xyDEtct2s63k+dTp2kkEz+/x7G8RlQwN3RrQGRUMEf/O8TXT87gjd+fxj+zJGd4RCDhEXkPqu3Wp7HjZ6vFij4+kYfHdiOsVijGPLo3+Ph60KN/01zLs9SqF8b3i4rXNcuZt58nr83Iu2JTQW4Y3i7f55y7BEXWDGHE3YXXHS/IqYPnWPnrRu6cNCRXsOrubsJg0GExW3lj/M98PPthtm06xvZNxzh/LoE5P6zjzvu78nEB9dyvlKIoTHiiL3F3J+Pn58mOHSfo0qVB4RsWUVxMAp9M+sVl2VcrnsPbx5OYExf58c156PQ6hj96I1GNqnM5NpGvX5lLeK0qhNUIpkbDajzY9/1c+w0K9eXHf1/M97i1GlSlSrg/RoOePkNb4u3rgbePe6UL1sX1SyZOKhoJ2Cuw/xZvo23ffG6P5pO1ydK7T5Ncy1RVRVM1Lpy5xDtjvuT+N0Yw7JE+efbPLaqMdAuP3fIZNqcMu9Gk5/aHutOjFDN1ZUFRFJexAqPG9kTTNNb+vokGbWoTXK3gyim/z1jLjvWuta7bdmtY7IA9S8LFRAJ83YkK88bTw4S/nwcLl9kHzz3x4i+Me6AnE2c8yoTeb5CamMb0jZO5MZ9ScEX13thvyEg1M3Pf+wS6+5GRaubPb1YC0KB1TUfADtDB6a5Nu271uaOQSg7b/j3Iop83OB4361CHgXe5Botr/thCSmIaN97RySVgz0tqUjqnD5/HN3Mwms1q4+zR84zv8Tr3vHILA+/rxprf/iMjLXdZxbwc2nqUui1rcWDLUb595Veq1qzCwq+X8+jUUXToUp/YU3H8MuUvAkL9uONZe7cUi9nKNy/PQafXMea1W67qb6usOFc1utpKDWnJ6Tx541skXkqmas0QWt/YnKTEVCwZVhQN3npiJhkZVrx9PTh/5jJTX5rHM68N4+Hbp5NwOZV1K/czcnTnfGuOXy0vLze8MgPZkgzWARIvpVC1RjApiWlomoa3n6ejrn7SpWTW/L4Zm9VGt5vbE9WoOgajnmq1q6CqGrvWHaJOsxp06B2NXq9Hb9BhMOjQGfQu3e7yUrdhOK99cid6g46IqCv7brnWaJoGaixoZsDi+n/NAuT4v6E+irF4d1SEKGsSsFdQmqbx1bMz8w/Ys1d0ebhx4TZ2rdnP2r+2cflCIve8dBNDH+4NwLo/t/LGnZ/RpFN9vPw8mPneAgbd3xOT+5UHFYoCJ49eyLU89mz8Fe+zIlNVjddGfMhLs8fTeVjBMxJG1qlCeqoZDy8THp5uuHua8A8qerePnJbPWsfymWsJDg+g8/C2uQY9ZtHr9XlWtrkSOTOkPgFePPvVA+j0OuoVcJtep9NRSElxBt3V2V6VRVHYvGJfnuvUaBBOWkpGvneSXI6ZGXhmdctZ/vO/rFuwmeTLKVgy7EF6nRZRDMn8eyhMWnJmKdTENLav2kdKYhq9bu9E9Tr2O1YJcUn8+fUKIuuHZwfsGRbmfWav5X3f6yOKdJzyNuaxXtz3WG90eiXf7htF5eHtzl3PDWXdn1vxCPHno9d+Z+Pqg3h5uzFweCtOZX5XNG1fm51bTrB++T56DWnBO5/fzYq/d3PHmC6lFqyXttbdGvLNqueJj0sGTXMpr1i3RRTzz3/hsn71OmF8vWmyy7KXpo2+omNH1Sn6OJnKIiUxlQ/Hf49q03jg9VsIK+bdYO1C3uVF86J4jwcJ2MuNqikoZZT5LqsJmkqDol1LcxhfpcTERPz8/EhISMDXt/BKFaVJVVVW/bKe7rfmfXt62dxN/PbVSuo3i3SZ/nrF7HVsXrKTc6cukRCXxC3j+tH3bvskGluX72H6xJk07lSPx6eOKpF22mwqU16Yi06nQ6e3l8zzD/Sia7+mRNW99k4i5enM4RhiT150VNpITsng5Ok4UOz9mUNDfAjw9+L43tOoqkqtxkWrMlGQi2cvo2kagWH+JTarZF7MGZarHmtw4cwlDu44iW+AF0062LvL2Kw2Ei4m4enjgfsVdhFIjEvm8I4T+AR4UbdFlGN5cnwqO9fux8vXg2aZg2WtFiv//b0T1abSeciVT6BVmVw4cwmdXkdQmD9gr7Tz4O3TOXU8jpZta3J01ykS49Oo07Aq/t7ubFljn6SqQ5/GrFu+j8jaVZg277FS/XyJyichLomR9e016z9b8SK1mxQ8IDsnNaYxYC7ayl6PoPPJf4D8taQixTlZbWn8y9PoPcumC5ctNYPdI96rEK+/uCRgd1KRPsiVjTndgtVqs5cE1Cl4ersXvpEQ1whN0+zdPnSKo464febOksvm/P7pIuq3qUPDdqU3oVBxpadkcG/zZwiLCuHdhc9iyKwjvvD3rUx9808AuveJZvWfO9A0jV4Dm7N99QHiYhOJrFOFjjc2YfDtHQjIpxxkYaxWW66p60XldXzPKUKqB+Hl50lKYirj+ryFTq/j+W8epEb98GLtSz3fArSUoq3sdT86n6evoMWVT0WKc7LaEj27bAP2PSMrZ8AuXWKuAcd2n+Liucu06Z3/YDewBxVZAcTWf3bRsmfufu7Ofv5wEalJaTRuX5d2efSJd/b6g9+wedV+wF5P/t3ZjxbjFVQev09fxraVe7nvtVuIqFdwWbvZ7y/g8vkEQqoFcNP4wmfvLA3H957hoyd+YMC9Xek1sgNgnxzoyO5TqDYVv2Af1v3+H399+Q/f7p2Sa/uXb/qAKpHBPPJhydyRuVZsX7mHaRO+56EP7qZF92iS4lMZ2ex5l3XGv3crN45sT2pSGo+0f56aTSKxplt47Xd7YPD0fd9yMTYRNXOCndoNwnh5ym35HnPoo/1K9TVdCXcvN4Y/1pc5Hy7k3LELjr+JPgObseTP7ezdeZqVS/fQa3Bz/pm/jWV/bmfE6M7EHL/I468Px/sqBqyv336MT35cxddv3IGHe/6zjBbXv39tI+ZkHFWqB3JDMas8fblgPacvJGBTNe7o1YJGUfnPQyBy275iN616N8PLzxMvX0++2vA6AOsXbGHm5N94+IO78Q8papBlAooYsGtFG9ciRHmSgP0asPDblfwzax2/nZkGwLv3fM7979xBQI4Z7757eQ5r5/3H17veR8tjFsac/vx2FZfOJ6Da1EIDducJY67lezZHdp1k4987ue2pgYWuu2fDIVISUgkMK79JPjLSzOzbdIR2N2b//pLiUxjf710A+t99A31GtMXLN++ZFGs2jiDgGpmkZOVvm5g9dRGPf3AnDdvUuqp91W9Tm+d+fIyg8AAgewIrbz9PIuuGommaY1Itg1HPoAd74xfsQx2nLjXnzlzmQuZcCcBVjW8oTzc91pdet3VyBFJWq43p05YTXCMQn+NxJCWm8d/GozRrV4sdG49yPiaB53KULr0SNlXl2Jk43vtmGS8+3LfE7mYsm72BDX/vJKJeWLED9rW7jrHn+HkAerasU+ED9vQ0Mwt/20J080jqNKha7t2SnC9KbVYbm9YewpxhQbWp6D3c2LLuMHo3I01aRREUUkjFLsVYjHnoi9h1RohyJAF7BbX0h9X0vqtLkdd3npyjde+mjlk3U1MyOH0yDqNRT+MuDagSYS/d16q3a/WQw4fOExTsTUBAdtCQFYQ7T+2+b9MR4mISqNu8BqER2WUAG7etjYenG4pOIaJ20et3VzY6vQ6jyeBygZKfoWP7oGnQqmfjQtctLbrMKhXOE1w5dyFQbSr1W9emfus8JgYCRr9asQdOjm3zLG8ufK5IWbekyykc33eWlMS0qz6uh5c7NRpVd3n8yjf34x/sTf3mroNxTe4mhj/umh3/8NX5XM6chTZLQTOjXq0jB2JYudhe/vPOB7s5ZtEtCWfPxjuqKcXFJfPaq/PYves0AMOHtmT+TxtJuJyKZ7MIHnqmP4Nvy7/EZHHoMyvwLFqzl5aNIhjYrWT+zrIGLV/JeArnqkC2IsxDUN7MGVa++PBvAD778UHqNCj6ZFhXY9OqA1gtVmrWr0pYPiVVrVaVV8b9BMD4l4eycc8Flm/8HYDXP72zCAF7MT7jmgTs5UnKOhZNsb6Rpk2bxrRp0zh+/DgA0dHRvPTSS/TrZz8ZnT9/nokTJ7JkyRLi4+Pp0qULn3zyCXXrFtzncu7cubz44oscOXKE2rVrM3nyZIYNG+Z43mq18sorr/DTTz8RExND1apVGT16NC+88ILLF+S+ffuYOHEiq1atQlVVoqOj+eWXX4iMvPrBd2Vtx8o9RQ7YH/ngLh75IHtK6h63d3b8fPhADE898j0AU6aPYkCv7G4zCZdSOLb/LLWjqzHx6Z+5Z0xXBg6yV6VJTEglok09IhSNvqOy2/H9m/PZtmofT352D71v6wjApdhE/vhxHapNxWbTUAx60tPMJRoUlCer1cZfv27m35X7eHf6aJ74eHSRtmvZo/QC9Zhz8ezcdsJlWd0GVamZYyKd8JpVeHPuE1R1KiXp7uXGM5/fg16vo2pUCJ+//Sfu7kbuHX8jYJ+19OiBGOpGVyOwsJNiOfnxy5WcOBpLZJ9W+OVRG99qsXHm+AV8A7wcme6sEns2my3X+lvXH2bHpmOoqkanng1pkGOA24nD51k6dzOe3m5YUbgQk0DNemEMv8v+N2ByM9CuiDNyqqpKr4HN6NCtASj2bU0mAx4F9OG0Wm28+cJc0tLMdO7ekAFDWxXpWFlOHb/IL9+tJaxaAHcXUm6zODZsPMzktxZQNcyPhx/swZtv/MGlS9ndEBb8tYNeA5uxfOFOatevyuDb2hd55tDCOM8k+/63/9Cwdhi1I65uXgmA+1+7mdHPDaFmdO6Jrwqjd3ptahkH7Fv/O8q2/45isWRPvHTbvZ2pUsAdMr3Te5icnHum1tLy6tgZ2Kwqj74ylAG3tc9zHZNbdnhizrBgcjOQknmNm5FRhCpYSjHuWEmXGFEJFCtgr169Om+//TZ16tQBYMaMGQwZMoRt27bRqFEjhg4ditFoZP78+fj6+jJlyhR69erF3r178fLK+49n/fr1jBw5ktdff51hw4Yxb948RowYwdq1a2nXzp6Jeeedd5g+fTozZswgOjqazZs3c8899+Dn58e4cfaR3UeOHKFz586MGTOGV199FT8/P/bt24e7e+Uc/PjU1w+z+q8dfPryb/y88WXiLyXz9vNzARh+Rwc6dC24hnDWbcS0tAzHMovZxoypS2jWvjbe3m4s+GEdS+b8x/PTR5OebmHZ0t0cPHiONm1q0ahBOFs3HwfA6DTpjikzCDdnZH/BaZpG3PlEx+OVC7Yz4PYONL7KbgflKTUlg0W//IdOUTCj8c2n/xARlR0MrFu6hx8/Xkpo9QAefGEwNqsNN3cjwaF+pCSnk55mxs3NSEpiGkt//Q93DxOpqRa2rz/MhHdG5FuL3WZTsVlsoMDoXu+hqipvf3sf3n6ebFx9gH//2cvIMV1ISE7nvckLXLYd81D3XAG7l6+HYybR7z78m2MHY2jSuiY3j7FfhM34dBmL5myiWdvs39W29Uf48IW5vPjxnbh7u3Fk/zk69WiY58RKV+PSxSTWL3ct5+jhaSpS/f6dW46zY/MxTG4GLBYbphzZ0KP7zzHupk8YeHsHHnllKGAvSRlZvyp6g56dGw4TEh5A1czXlJFhZc6Mf1FtKkFVfHMF7GePX2Tu16sBiGoWwbGD52lzQz1HwH5g5ym+/3gptz7YnSZtahbY9r3bT/L0vV87HlcJ9ycg0Jtb7+tC7fp5d6GIu5DE2pX2MSKaRrED9qzeIt6+7pgy/54TLiXz6xcrcPMwcuf4vgVs7cqcYSX+YhL/bT3GlI/tJSwPH4llplNN/SwWi43LKel8OethIqOuPph2pndK1mSYrTw/dQHfTL4DT/erSxScPH2ZvTtOsXTCLKpWC6DPwOb0H16099u5Tf/uOUZcUgo2m4ZNVenarBY1q5bs35Cz3dtPMvv7dS7L+g1tkWfAvuLXjWz4eyfdnZI7e7afpHnrgj+7JcVg1NsvKiz2i+cDO07y2SvzsGRYyUi3MGRUZ4aM6oybu5GMdAsZGVaXOx6WogTs+upgPVDEFkmGvTxJhr1oihWwDxo0yOXx5MmTmTZtGhs2bMBoNLJhwwZ2795NdLQ90/T5559TpUoVfv75Z+6777489zl16lR69+7NpEmTAJg0aRKrVq1i6tSp/Pzzz4A9qB8yZAgDBtgH7kVFRfHzzz+zefNmx36ef/55+vfvz7vvvutYVqtW5QgYz525zJef/QPAkJta07xVFACXLiSSFJ/K0MbPMfz+buzcas+odu7hOl339k1HmfXtWu57vLfjlua93d8mLiaBAaOzv5AtZiuzvljB7s3H6NyrEUvm/AfAhQuJpKdb2LXzFLt2nsLH24NWrbK/uJ0zYk9+OhpN0/BwqgKjKAq1Goaj19tLOzZtV4daDYs3or+iSUlM46t3FwIw/IFuAMSei2fWt2u49Z4bSLycwrED5zCbLTz/wHecOX6Rpm1q8u539/HepDlsWLmfOg3Deejpfvz00RICgn2o0aga+7adYMmvm7j36f55Hve/Jbt4bfR0Og1sweWLSYD91vBnby1g/Yr9hIT5ERLmx4HVMQAEBftw08i26HQ6GjWuxjvjfsDTx4PH3rgZsFfviTkVx6v3/g//yBD2bj3hcucjOTENi8WG6jTwIKu7z8JfNnIhLoUTR2K5eD6Rhyfm3eYrFXP6Ep+8Pt9lmV+AJy071HHMQpqf/sNbsWPzMXvweCmFKjnGCWxebQ9ud2855lhm8nLn5JkEFs7eyK6NR6hSLZAXpo2mamQQHbo1wGQykJ5mxmrJHQzYbLm7FDl3M5o5bTlb/z1EfFwyn817vMC25+xrHXs2ntiz8Wxdf4QO3RvmuY1zN6asIKc4smr2p6dmBybpqWZ+/XIFbh4mBo+6Ad+AomUkj+w5zYShU+k4qAVVw/w4l9kPf8u247RuFYXbycucOxcPQLNmkTw9cSCBgSXfPz9nf+sTZy+xcPUebu5TyNwVTmw21fF71OsV9AY9f/+xnQ2r7YFe/KUUTG6GIgfszt+Vf63fx1/rsy9IwwJ9SjVgz6t+vfNn1NmOtQdYNW8Tjbtkf97MRQmCS4jRqCcjzUJifKrj2Icyu1GB/WIS7Fn2jHQLGelm14x7EeaZ0AVMQz3fCrSkgld0643iM+kKXoUQZeuK+7DbbDbmzJlDSkoKHTp0ICPDnsl1zmjr9XpMJhNr167NN2Bfv349TzzxhMuyG2+8kalTpzoed+7cmenTp3Pw4EHq1avHjh07WLt2rWMdVVX566+/eOaZZ7jxxhvZtm0bNWvWZNKkSQwdOjTf15CRkeFoN9hLDJWHpKR0R/asXoOq6HQKaWkWR+Bktdgc/SoBfvzfSo4cjGHCS/bJWvbtOs22/47yzafLqFLVzx44Z56grU5f2FmzbjrfBs06vjNFyR5EZ1+Q/WPWLJLOju4/x9EDMY7HB3adoXrtEPrc1KbI70FF43w7Oyu+slhsXMoMolU16ySvw5r5XmUFYkf2nwPg9PGLjhO4qmmOk+eJg9nvVa7jZu5Lr9OhKAqappGemkFaZqDlF+BFeEQgSZn9sP0DPLnldnv1l5OHz7Pyj234B3k7AvbTR2N55Eb7RWyVOvaLOee7I1ldyrQ8Xu/Jw7GomUFAWlppZKByZzpSkjI4cSS20IDduSuc899GlmOZn0fngOVCZhC5b8txUpPSObr3DDGn4hxZdl0eYzbyOoaja401O3B2bJtHW3IyGPV06N7AZe6CmvVCadOpXr7b6JyCU6u1+AF71ndJYkJ2//2sjGVGmpnLF5OKHLBnvVYDMHXKHTz59M+cPnMZgM1bjjN4YAtWLNtL335Nuf+BbqVWdtHH0416UVWIi08hLt7eDcdUzGMd3HGSyQ98Q9z5BB58ZRhDx3TLNWpeyeNzmp8HB3Vgz7EYUjNyd7GwFeGzcTUGDGtFx64NuP/WaY5l+X1W9Jnvk9np79qcR5tLS1b5z3Mn4wDX7i+d+zalRebfgpubkSTSMJkMjLjnBtJSMzCZDDRqXsRurqaOoKXZ+7MrJsBoH4yqmAATKEYUY1MUfcUeHHytk4mTiqbYAfuuXbvo0KED6enpeHt7M2/ePBo1aoTFYqFGjRpMmjSJL774Ai8vL6ZMmUJMTAznzp3Ld38xMTGEhrpOsBMaGkpMTHZQM3HiRBISEmjQoAF6vR6bzcbkyZO57TZ7CbTY2FiSk5N5++23eeONN3jnnXdYvHgxw4cPZ8WKFXTt2jXPY7/11lu8+uqrxX0LStU301cA0CC6GkP7Z/c3dx7k6OFpomr1AMfjlGT7RceWDUcAexBZxc3+heycLdyV2cVFb9Dj4eVGcFV7VtL5hFq7dhWGDG3lMro+vxk1s+QV4ChK5Z4ExTVgt7/+kFBfxmZmxrMGCOr1eiyZQWHW25R1vs+qxw32gDi7D3X+J+6s6j2KTkGnV7BZNc6ejHP8DlSn53O2c19mNtk5g+v8c1aA5pxJ0zm1z7FMcdp3KXbDjaobypQfHsxsp72tJjcjtTK7hSz/eS1Gk4Ebbsrdx1Wnz35dtjwqHulzzHoK2a/VJUOd+fr+mLXRMTtsXp/nWg2y7xg5MuxO+1aU/IP9nOo0DOf2B7qhZnaVyGpj7QIG/Dn317ZcSYY987X7O2W6swaq63SK46KlaPvKfm9DQnwZNrQ1n3y21PH8ylX7+HbG/QTmcXFfkmpFBDPjrbtYtv4AL35sr/nuWcxxMx8+OZPEeNfSf853m+56oBvRzYo+YU+LutWoFR5EUmoGZy4m4G4y4mbUo9fpSn0GV19/T7x9PRg4vBUGox6DQZfvwMzWPaPxC/KmXtMIQsP9OX82/qpKbBZXx17RpKVm0ChztuTwGsFM+uhOTG4GakdXI6SqPwDtuzfgwO4zVI0I5IbexR8TpAv4pCSbLUS5KnbAXr9+fbZv3058fDxz585l1KhRrFq1ikaNGjF37lzGjBlDYGAger2eXr16OQakFiTnLWLneuEAs2fP5scff2TmzJlER0ezfft2xo8fT3h4OKNGjXIEMUOGDHFk65s3b866deuYPn16vgH7pEmTmDBhguNxYmIiERHFm02tJOQ3d5W3t5HeQ5oT1bAawRFBwEYAAkN86NClfr7bK0p2Rs45w65lDrbT63X0uaUtfW5pC8DaNdn9/Ly93Qmp4ktyUprrDgvg7m5gSGZfXp8AL/R6HXUaVe4uMc5lL/MqF5cVmOkNOrSsACpzPU3L3FbJDn41TcPTy373yZrPbWqAZp3r89Hfz+Lt78nBB7/j3MlLqDYtO0DPDO7yCrQdHwOn5jpnZhu2iGTT6gOY07MzaU3b1GTej+tcgpTwGsGO16g47h7k2+Qr5unlVmCmrMvNeQ9Gg5wZ9tx/P8FhfkTUrkJouH/28XzsAYnRzYAuTYeq2hzv1foV+3DzMOHpreTqDw8QFhFIpz6NUW0q0R1qs3vrCZfPxY03tWb9P3vx8c+7PCbAwS1HeeWWqYx8ehDTpq3K9Xy3fk159p28q/I4d/+4ki4xWRdptZxmH866gFNVrVjl/LIulrK+d4YOacm+/Wf5b9NR+vVtSqcOdUs9WHfmPNCzuP3XO/Ztypo/t2E0GajXzB48tmpXm4BAL2rUqsJNd3QodntmTLInks5fTiLQxxNjGU7spNMpPP5s4XM+dOjXnA79mgMwafJNpKZkEFWn7Gamfuy1YS6Pffw96TKgWa71Hn1uUK5l4tqjaWVXDroyl50udsBuMpkcg05bt27Npk2b+Oijj/jiiy9o1aoV27dvJyEhAbPZTEhICO3ataN16/yn6A4LC3PJpoM9Y+6cdX/66ad59tlnufXWWwFo0qQJJ06c4K233mLUqFEEBwdjMBho1Mi1b3fDhg1Zu3Ztvsd2c3PDza1sZtcqSFCwN0HBPsRdTGLEHR1w9zASHOLD+j+2sGTGaqZvfIPg6oG88bG9dvHm9Ye5EJvo+IIdMaoT/Ye3YuuGI3z6zkJ8/T0ZdnsHkpPS8PD3ZPnaQwCOYMKQI9Pj5+dJs+aRKIpCncwTuk6nw8PThNlsLfRkvnzeZv6evdHx+OYHe1CzjMqDlRbfAC/cPIwYDHr88uh/27JTXYbfewONWkbx7cdL0el1jvepTsNw4mIPEBoeQGTdMKbOG4feqCekagAPvzQEN4/8J3nx8ffCp7n9eM3a1Saoih/+wd7Uja7GlnWHCcyseBIQ6I3RqCfc6U5LncbV8fB2o3ajak7782Tg3Z1p0DKKwKr+NG1bi5pOAxtDMvt+O1/0hVTNWqY6Yv/idAsoKVm3zfMS3SyCVh3q2N+DPMrC3TdxAPdNdA1c2veKZsjoG2h5Q31OH40l5tQlx8Dot74YXUhb9Lzwqb0S0+njF/Hy9iCsWvZ7Xy0qmJCqfkS3rJHfLnD3ciOqcXX88sl6FjTptNFk4MZBzTEa9PkOTC2Im7uRVh1q02dodl1xNw8j781+FKNb0cqUZgmtHkjDVlHUbmyvoqIoChPG9yU+IZWwcqjZ79xvvLgZ9tETB9K6W0P0Bh0NM8ftDL21ZMpOhgZUzApLOTVsUvZJKiFE8SlaQWeJIujZsycRERF89913uZ47dOgQDRo0YNGiRfTp0yfP7UeOHElSUhILFy50LOvXrx/+/v6OQadBQUG88cYbPPzww4513nrrLb799lsOHjwIQMeOHalduzY//PCDY51hw4bh4eHBzJkzi/RaynPK3qy+tjp99nTms6f8xcZF23n6y/upWrPw2uaqqmKzqWgajiyhOcPKkUMxWMw26jeq6ri6LErJxV++W4vVYuP2+/O+Q5FlylM/s/RX+wDW6DY1uXFEe3pnZu8rs0uxiWjYg15zhgVFUfDyLrzqUEpSOinJ6RiNhiuecj2n2HPxHD8cS2CwN3VKcECvqqqkJGdgMOgcZQVVVeXyxWR0OoWpr80nIT6V9l0bcOuYos8LIPKnaRq7txzn3JnL6HRK5j8d0S1qOC6gRNFZbfbvPYNB51KlRQiRW3nGOfm1pd5Pz6IvoKxtSbKlZnDwjrcrxOsvrmJl2J977jn69etHREQESUlJzJo1i5UrV7J48WIA5syZQ0hICJGRkezatYtx48YxdOhQl2D97rvvplq1arz11lsAjBs3ji5duvDOO+8wZMgQ5s+fz7Jly1wy44MGDWLy5MlERkYSHR3Ntm3bmDJlCvfee69jnaeffpqRI0fSpUsXunfvzuLFi1mwYAErV668mvenzOQcCAowcsIARk4o+pT2Op3OpasA2AfzNGxc/HrCACOcKswUZMg9XejYtwmaqtGkXR28/cquL2RpCqyS/cecVzeJ/Hj5uOPlU7LlRKtU9adKZr/OkqTT6fDJ0XdVp9MRlPnaX/34zhI/5vVOURSatK5JkzIqoXetM+h1GMp5hk4hxJWzd4kpq7KOZXKYUlGsgP38+fPcddddnDt3Dj8/P5o2bcrixYvp3bs3AOfOnWPChAmcP3+eqlWrcvfdd/Piiy+67OPkyZMuQWXHjh2ZNWsWL7zwAi+++CK1a9dm9uzZjhrsAJ988gkvvvgiY8eOJTY2lvDwcB588EFeeuklxzrDhg1j+vTpvPXWWzz++OPUr1+fuXPn0rlz0YJOceVqR1ejdnS1wlcUQgghhBDFdtVdYq4lFelWkRBCCCFESapIcU5WW+r8MAm9Z9lMcmlLTefwXW9ViNdfXFdch10IIYQQ4lpls6kkJKZhtdiY98dWjhyNxWqxYbWp+Pl68PSEfviWYTlMcX2TgF0IIYQQZcacbiEpIRUffy+XSZNyiktI4dyFRLw8TNSsVnqzxOYnISGVm279LN/nH3qguwTsJUCjVKf8yHWsykoCdiGEKAf/rdpPRpoFNA2jm4E60dUILuOyiDs2H+PksQv4+nvS9QomphGiMCsW7mDnpmMEVfHhzod7AjD782XM/HgJo54ZyP49Z/DwNDH2+cG55jH4Z+NBpny/gnZNajC4ZX0uXkzCZlUZNLQlPj6FB8rTP1jE5bgUQENVNTTNPnnYo/9n76zjpCj/OP6erevu4xru4Oju7i4JaQNFMBDFQMUGxUBsAf0hiIQICoh0d9fRDXdccl1bs78/Nm73dq/IA/f9evGCnXnmmWeG3ZnP832+YZbydc+qQ/hW8SamUZTV8dJScuhLpZJyFUqzY+duYRfsdsqNTqcjL6sAiVQwFaExkptdoE8pKerQ6fQPRu8S8k3bKRm1SoNGrUUQ9JVORVGHABQWqk0vG2OhsOIZgUD/f2QqriSVoCxUk5yQgU7UUSXC11QsB+Do0WukpeUgAG3b1WDH2pPk5ymp3SjCVG20PKz8dSeevq60693gjq79v8Y3U1dwKznb9Ll973q88eWw+zqGS+eSmP3VeoJDvVEWqtm99SyDR7ekdv2S88lXdq5fTObonouERPrSqE11CgpUFBSo8bZRTwFAWajmz3k7Uas0DBnbjrzcQvKyC4mIvnuFhDRqLXGHrhARE4inz90vKpWZnktBngqFg8yU4clI/I10Em9m4ObmiLZQQ15OAVVjg63alcRP01Zx+Uwi73w7Encv2/ewNE4ducraPw8SER1gEuzpqfrvfeK1NPZvPQvA6Je6Wgl2Y+G5/EI1K5Yd4NxZfdX0tu1rlEuw791+jqSEDItt5vUTANIS0nFytZ1SUCGXUrdOKHKZBJlMqv8jlyCTSmnUMJzQEOsaEHYqjk4n3McsMfe/psjdwi7YKxlxx65xeN8lBEFg9LgOFvuMedZFrV4UG3OpXzhzk5SkLHz83KhRO4STR65RkKdE4SinfpNIju65SEpiJhHRAVSvqy+ScfV8Ehq1Fm9/9woJ68HhLzBySj9GvNkP0L/sNv51mIXfbSIrvajEt1whY9Xxj+70dlgwf+Y60lNzGPtmL9w89A/2a+cSyc7Mx9vfneVztuLq4cTIST2sllk3rT7GtrUn8PRxZfJHA9n09xG++/BvfPzcmbv2FRZ/vxknFwd6j2iBwkFf2OjHaaspLFDRvEMsLTrVJCMth4J8Fc6uDqCDJ7t+jiAI9BjcBK8AD1Yu2kun3vXpP6olL4yYjSiKNG9bnZcqUK1v4beb+GPONmIbhJGUkEFGWi7tetVj29qTePm6Ehnhw9HdF3jq9V64G178EqlAenI2kTWCcHN34pWhPwCwMm4al88m8urwnwD434bXCDIrMrRs6X72778EQJOmUcybtZ5bKTk8N6WXTcGel1PAC90+IyTKn7qtYvj39z089VYf5k5biU+Ah4VgH991Bvk5hczf+165r708/LN0P//7egO9Bjfl6UndANiz8RSpiZlUifTlxqVUZHIp7XrWNYmLI7vOc/7EDR6f0MmirwtnbnIrNYcqYT6ERvjyvy/XsenvIwwf34Hew21XuPz55+0IEoGM9FzCI3wZPMi63sCLY+YSfy0NZxdHfl8zqcRrkRQrVnQ/wv91Oh3/bjxJw3rhuLs5ElNLn9M/J7uAPdvOsm/HOVq2r3HfBbtWK/Lr3O1s3hDHk8+0I7KaP3k5SgDqlVKMyharFu5hzaJ9hFb1x7fBCY6fuEFBgQpvbxcmTepOq5bRFu0P7jzPb99tBqBTnwa8PHoOAK5ujoRE+DLtxzEVOn9mWg47/jlK0rVbtOpZj1pNoshKz2XKqNm0612fboOb0KBVTIX6LItXhv1I4vV0atQL5aslEyz2/f7rLjatP0lM9SCS4+LJyynk9c+G0KF3/XL1vW7pAZSFama99SfvVvBeQFEBNPMKvXK5scpuUdVntUpjdeylG2kAJKZmUVVWFJS46Lc99OhVjzr1Sq6UDNgs96YrZhXvO952jRgAJycFX385vOhYnQ61WotapUF2HyvY2rEDdsFe6ThzIp5FP+9ArpAi02hZNnc7wWE+fL/qZXo1+sBUDdHVzZE/d74FwC/fbebogcu0al+DN6c9xrsTfyc/V4mruxOLN05m9aK97N18mv6jW5kE+ytDf6AgT8nTr/dk0NjSCyMZEQQBZ3dH8rILTNsK8pV8/+FKq7b3IvnQnz9vR6PW0mVgY2o30eewnv/5GvZuiKPH8BasXbIPgF4jWhFQrPpl4o1bHNp9gSBDZdCjey6gLFCjVKrRaEQWfrsJgFqNI033aPOqo+TlFOIb4EGLTjWZ/ekatv97gi79G/LEy10pzFcB+hdNdlY+qUlZJCdmkp9bSGpyFgDZWQVUBGPFSfOl1rycQgCyMvJJNBjVk+LT+d+sDQDUbhhO3MErdB/chK6PWVYVNheFOsPLUasVWfPPMZINYwT9/62xYFdJTn46UUfS9Vs4OCnIycwj6fot8nMK0WpEVIVqi7ZZt3LJzsgj9WYGfsFetju8DRJvpJOfq0Rpdr7VC/dwbO9FmrSvwcGd+kJqsfXDTIJ97vTVXD2fRFRsME07xJqO++DVJaQmZ9GsTQwfzhrBiQOXyUjL4cTBK/Qe3oKvp65AVahGEGDSJ4ORSiXs2n2epKQs1GoNzZtXsynYCwtU5OepsC0Xinjy1R4UFqgQBHBwVBBbv3TxcacoVRq+/mkTazaepFqkH0qVhpZNqiKXS8nJKmDv9nMApKVkl9HT3UciEViycA8An01bbdru6enMsn9KnvTYwijIJBKB7JxCCgr0v9P09DybolCuKBJehYUq0+8tL6cQJ5eKF3O5ei6RH6cuB/QW5FpNoky/5+3/HCOgitddF+xpSQaL9Y10q31Kpdr0t/HacirwXJI7yFAWqlHZuHflwVhZ20KwG1b6jKuBYD2BBUjPzgdAK4oWFbc3rDtBteiAMgX7tO9GoTWuOEr0zzhbNU/KQlmoZlDj9y2uwTfQg9+2T6lwX3ZsUImd2BMSEnjjjTdYu3YtBQUFxMTE8Msvv9CoUSN9dzodH3zwAXPmzCEjI4NmzZrx/fffU6tWLVMfSqWSyZMns3jxYgoKCujUqRM//PADISEVq5FjF+yVDONDSasRUau0FOarKDS8cASJgE6rI7ZuKNG1iqpdZmcaH2p6d4j8XL1lKje7gLzcQq5fTNb3bfagMj9PRRj11gDCY4tyrru5O/HTqokU5KvR6UTQ6V+Y92LVySgoza0yxnmB+fxAIrU+ucR4vYaHt7F51dhg07Jr8b6N59PpdKSn5nDIIAYTrqUhmp9QENAaHuRb1xwnMibAZn/lwTgWvegQLMYuakXTy0+rKXpxGIdvy5/SvOS8cb9KpeHrWest2wlFHZU02TLej2vnEgmrHgQUTSZsIWp1JF6/Vapgn/X+X2z99wSiVkTU6RC1Ojy9Xfhk7pNEVLN2STCKrzPHr5u2Gb/X5pYzrdl9N4p74wTLSGZ6LqC3Ltti+5rjFOTpf0uvfDoY0N8Do3ApbqkrwnjPrfcnXk5GkAgERvjToU/9Eo6/N6jVWo7F3QDg4pVUAJYmHKJtx+okX0ilVcdYTh+/QVCVuzfBKi+CIKBQyKxEoVJZcZEomgS7xHrZQrB+NhhX1MDyOyKRCDg6yq3al4W5sDQ+b8zPcS9WUmRyKWqVxuK5YMQohM1/E1pt+Z9LRnFtLlYrNDaDJdr8nHWaVUUURcKrB3HhfAppydmkp+YQEunHnD93c/RsAlpRJD0zz3Ss1MFSaIeF+5Z57iphdydQVSaXWl1/+gOY2Nq5v2RkZNCqVSs6dOjA2rVr8ff359KlS3h6eprafPbZZ8ycOZNff/2VmJgYPv74Y7p06cK5c+dwc9N7L7z88susXr2aJUuW4OPjw6uvvkrv3r05fPgwUmn5V2rsgr2SYXzYiwZfcCgSSlKJgKiFjr3q0WdoU+tjtKLVMl1+jpKEa7cALPZJJNbitzz0H9/FcrwyKaJWZNJj31hsl0glrDk3o0J9l4WLmyOZt3JNExLA7B5Znrs4RlFnnKAYxdaI5ztZCPz9W84QGROEo7PCdI8Srqah1WiLxKkgWE4QJAJqjflyb9F9FrUVezsbBbZO1JmuSTC7OG8/d+Ivp+rdcozHULLQNi/VbhQyUqmETp1rcfp0Aok3M03nME0WytKhZmNSFRPB5o2lcgl1m1crYb+etORsC2s5QMatXJuWUCjy29daWOaKfjNGzO+78f+++HfdtJpR7P/IeB/N76fxes2tgGKJExtjP9b7Lh2/htxBTmCEPymJmeTlKpFIBMKr+tvs626h1Yrs3XyaD9/sx/OvL6JQWXTPD164yewvRxEeev+zcJjj4FAk2IOCPWnXqSZPlHP1zxzjbzswxIuUcrQPMKsgbP5dbN6uBu/OGm7jiNKp3awqs7dM4YuJCwmO0ItKcyu+7h6YEmXG55uN5435O0UilSBqRZOBoTwEhXrj7OJAlYiyBbItjM9Dc5/55l1q09wQ5Nx9aHM+nvg75+MSqNs0issJtzh6Nt6iD62ow9lZgYurAzKpFKlUILoCcTZ3ilQqYcQLnZDKpMjlUuQKGZ6+ruh0Oovns51HixkzZhAaGsq8efNM2yIiIkz/1ul0zJo1i7fffpuBAwcCMH/+fAICAli0aBHjxo0jKyuLX375hd9++43OnTsDsHDhQkJDQ9m0aRPdunUr93jsgr0SsX1jHLu2nDZ9Lm4FcXRWoM4qsBIeRsGp1YpWy30qldqsnZmFXXZ7FnZbmAc/GoMlpVLJXX+Y1Wtele1rjnP9YjLNO9UEIDjCl+g6ofgGeeLu7YKo1VlYuIwYJysaG9crkUho1a02u9fHsfSnrfR8vJlesEsFJBKB1KQsvP3cTC87AXB2cWDouPZsWH4YgBbtY1m1aB9R1QMJr3oHFnajiDS7d+b++FVrBpOamElIZJHAM3ejcXCU4+Pvzi2D9UdhsBC6eTiZ+lYoZLz1dl8+m/GPSbBD0XfC1goFgJOLA4uPTQNg++qj3LySSmzjSMKiA3D3dOG7SQsIiQ6k/4SutOlVj+yMPJv9mFOrYTgeXi5k3spFkAgIEgEvb9cSszMYxxZsJi6LxmsmVszUstG6KRb7PZmEvqFtk7YxnDtxw6wLa8Fu8XUuQXcJpaxUFModWLP6OCtWHufi2SRyswuQy6VUrx2Ch5cz7375uO1Oy4lWK7L063XUbl6Nui31bhe52QV8+uYyDu2+wIjnOvD6S9348PN/TMcUFKj57Nt1fDdj+AMVHxNe7kpQsBdR1fxRKGQ2f8clcfNSMn/MWkurvg1p3K46Ht4uVKtVhaVrjlm0s3V1Lu5FvtFhUX68+dEAPHzdCLjNgEJBEAiLDuSbfydz9sAFNi3cQYdhrfnol6eRyiQEVLn7gYq/73iL3RvjyM22XvEyN+jI5VKUWtHmc7AkvizmE18e9q4/wZJvNuDs5sjEL4ZTu3EEbqWkP3R2cTD9tqQ2gum1osgH0wZXaAyHd55j7qdryM0qQKXSEFDFC6lUwifzn7ktV6eRL3Ypu5Gd2+M+Bp0al/+zsy1XSBwcHHBwsPxerFq1im7dujF48GC2b99OlSpVmDBhAs888wwAV65cISkpia5du1r0065dO/bs2cO4ceM4fPgwarXaok1wcDC1a9dmz549dsH+sHLyyDVOHLlm+mx64Ru+x/WbRrFz4ykr4SE1c/cQBIFa9cMQBAGZXIJcJqVe86poNSIBZkvddZpGkZdTSNBdWDIMiw7gn7OfIpFKKvTCXzpnG4t/3ALoJxMBVbzw9nNj2s9P2Wxfv2U1nF0diYotcgd69t0Bpn8Pn1jyF19azCXG1d0JH393k/Vn3Nt9GPNyN/5ZtNckcms1jCAozIenX+0OQKc+9dFqRYJCvXFxc+SJl7sx+iX9Qzz+qj44qmmb6tRuFEFwqDcSiVDuTAxGegxpSvNONXFwlCMIAqIo4ubhjIubI6FR/gwc04pn3+5LUnyRr2poVX90oo7QKD+iYoP5csl4rl1MQSqVEFbVn5c/GkizjrF4eltmpwgM9AQgPNwXQRAYNq4DqYmZtOte1+bYJBIJnr76Jb5+T7al35NtAZi94Q0AhsdMokaTKPpP6Mr4Dx8r1/VeOnOTXRtPWW1v0TGWqBhrC5rxZV6/WVEKtibtauAX6ElYTADpaXrh72GWiWPY851Y8NV6q5d0rXqhHN53iWBDXMOw8R3JvJVHVcP3q+/IlqQmZjFpetG1VMTCbovE+HSO7LtksU2t1hJ39Bo+d5hVSRRFPhj9Iwc3n8In0IPvNr6Fp58b1y8mmyZwv/+0lQ++Hcngfo1YtlI/2XRxVjDl5Z4P3FLYuVud2z42JT6ddQt2EBzlz5BJPWlt6Evn6kBGRp5pclXNRuYXZxcHJk8fROM2Mdy8mMino77jxa9G06BYcOrtUKNpNDWa6vtp3K7GHfdXEnKFjPa96tvc5+3jSmi4D2+93x+FRIJEKsHjNrK9VISczHzOH7+Ou5cLgSHeBJYx+Xll+iCunb3J2gU7EA2rlV7uTnw2qR9SicS0glARTuy/zLULyRZjAijIU96WYDfH6HpUWtpHO5Wb0NBQi8/vvfce77//vsW2y5cv8+OPP/LKK6/w1ltvceDAAV566SUcHBwYPXo0SUlJAAQEWD5XAgICuHZNr+WSkpJQKBR4eXlZtTEeX17sgr0S8ezLXenRvyHHD19FJpPSpXd9Rr1UNCvr2LMuMTWDqdXAMmuCuQUFYOa8py32fzpvrNW5pswacdfGrQ/kqfiDKz3V0h3i6vkkUhMzS2zffUgzug9pZnNfyo00nq7/BrO2vEvVetZZJeo0iuC513vi4KQX48+/24/n3+1n2u9nEK/jpxZte+dry3v0yvRBVv0arbQhEb6s2DsVqVSCQiFj3t8TS7yO0vD2d8fbhsif+MEAi8/uXi5M/HAAgiDQrmddHJ0UfDh4JucbhxPTKMrCOthtUBOb56oWHUDLVtE89XQ7nJwUdB3Q8LbGbBqTt4uFr255cHRS4ObhhE7UIep0pr9Lsq6GRfnRpE0MfmZuDL2GNTf9u4+N7C6tu9eltY1JSKMW1Ti87xJhkX6A/v/S/Dvx1OQeVse0aF6NqlH+CBKBiBJ8aN/5dAhqtcZm2s3Hn27D0KfaIJVKOHsyHmWhGrVai1ajxeE2/KWNnDxwmTnTVtGsTTQHN5+iMF/FjYtJHNt3kS9fX0rtplG4ujuRm13A52/9yVe/j+P8xWSysguY9s4AQu5iYPCDQKPW4ujigGOx9Hxt21Qv81i5QkbnfvoMR2mG76+qsCRXr9JZ+fNWFs/8l+j64Xy06IVS22am5ZB2MwPvAA+872H+fQcHGQ4OMqpF3z8XEmOsjVpd5NqWk5HHL+8vR6fT8ezHQ3AxWNx/enUBz305muM7z/LD64to+FYv3Jwd8HJ3pk50sM3+y0OrrrWRyCRoNSISqQSpTIqDowz1bfjiazVaLpy+yTvj55t+szXqhDDr9+due3x2itDp7k+WLOO5AG7cuIG7e9G7trh1HfRGkMaNGzN9+nQAGjRowKlTp/jxxx8ZPXq0qV1xY0d5vAtuxwPBLtgrEQoHOVWrB1HVENBXnBZmGS7M6dqnPg2aRuEfVPZDPzMtm+SraUTWDjVZkh8U3Qc3xS/IE7VKL1iqRPjetuVDFHVWmUrMqRYbTFikH8pCFYV5Shzv0MJSHIlEgvNd7rM0nF0c6F5MiL+77BWrdpkp2ayas4nmPRsQ0zDSYl/LltHUq12FK6cTuJCRS/Vi+ytKk/7NWbdkP4PrTwX07ji/73u31GMmT7OeBJVGj0FN6FHCBKSidO5Vj0bNq+Lp7YJWoy3XCtGThlWF0ggJL3nVypjODqDWXcgKI4oif87dzvyZ6xC1Is5ujgx6vgs9R7chKNyXq+eTkEolHNt9ge7DW7Bu1TFycwr55LWlvPvtCNzdnHF2VtzxOB40jTvX5u+bP95xPwGhPrw0azQ1m5Uee1ESqkI1WbdyyStHFpbtKw7w45tLeOKd/jz+Sq8y298uEonEpm97eVAWqPjr+w1otSJ9n+2EWzkt876BnjRsV8OUehigME/Jut92AjDm7f4mwR5sCC43TtLHdKzH9NcGUBxRFBlb5zUef6MfXUeX/TtUa0SWzNlutV2jERk+vmO5rsPIubgEXjGk+zRyxcx6b+fhw93d3UKw2yIoKIiaNWtabIuNjWX5cn0WqMBA/SQ4KSmJoKAi3ZaSkmKyugcGBqJSqcjIyLCwsqekpNCyZcsKjbni60x27il52QUs+mwViz5bVe5juvSuz/Cn29K5Z70y225btp+JHT8iNf7WnQzzrhAZE8igp9oy7LkOjHyhMx1616d5CZMSgP3rj/N4tZd557GZFtuzb+Vw8cgVnv5oKD5BJVsKNy3Zw+DIiUwZMLPENsUpzFNy80oK6WYpEO8lx3ee5aUOHzH9iR9JuJjEnNcX2myn0+kozFeWmT4zOz2X3z9dyfkjV2zuvxwXz+SenzF1yDcsnbmG94Z8xY3zibc1dpVSQ252gelPXgnZVyoLHl4uRFQLwNPblVnPzbFyNXsY2Lf5NPM+/9c0doVCxuMv9yDIYP2PiAlk/Lv9Adi07ADdDaso1WoE4+3p8kiI9buJm7crPZ9sT0TNiqVbK45gI0Xhg0JqiL0pztale1g+a02px2rUWn79cDm/TfuLrLSccp+zbstopv0+gak/F63uKpzk1G8XS4N2sSYLPEDf8XpXxmr1wxn+Wm+8DaudxREEgb4TulK1bvkmulKpBFez+ATQxzL5B9vuvzSCQ71NdR+MfPTD6BJa26koxsJJ9+tPeWnVqhXnzp2z2Hb+/HnCw/Wr+JGRkQQGBrJx40bTfpVKxfbt201ivFGjRsjlcos2iYmJxMXFVViw2y3slQx1oZr5Hy7H0cWB4a/3va0+Rke/xIz1bxMUZe2v6WB4QSsLbm/J90GiVWvJTM0mp1gwY/atHE7vPc+zn40s9XjzCqDF2fnXAb5/+Vee/2oMbQbq3W5uXknh1e6fkpGSTbV6YXy3rXRrcXE2LthOwsUknvhwaLmPyUrL4fyRKxTkFuIX6sPAl3vabJeelMmwsPH8cXMOnn4lWwkUjnICI/xwNgv4mj7yW9x9XHnh6ydN90Qqk3D+8GX2/XuMIa/0LrE/pSFvuMLRWuhFxATQa7jRPUUgOPL2sko8CF79efx9Pd+f/9vJumUHEEVDyXRRR0T1QD6ooAho0bkWrbrVZu/GU4ye1J3B49pbueJ0HdyE4/susnXVUY5vP8u7Xw2jRYfYB+6z/ijS9+n2dB3W0mZO8eJUqxvOoBe7UaNxVJlt74Qqod7Urhdqtd3D161MPwRHFwe+WD8FqVSK3x1W9fTwcePTv6xXAY1UbxhZ6iqfIAj0f14vmsvjTlCjXih/7J1qkW3tdr/znj6u9B3WHLlChlyhzxJzrzM72XnwTJo0iZYtWzJ9+nSGDBnCgQMHmDNnDnPm6FdbBEHg5ZdfZvr06URHRxMdHc306dNxdnZm+HB9hikPDw+efvppXn31VXx8fPD29mby5MnUqVPHlDWmvAi6e1Hh5iElOzsbDw8PsrKyylwquVdo1BqO7zyLo7MDtZrfXtBTTkYuzu7ONv2Ar5+7ybHtZ2g7oEmpQq+i/G/menKy8pFK9UFNIZF+9B3evOwDK0BhnpLM1GwcnBV4+Vfc51OlVKMsUCERBFw8LEtgXz93k0MbjtO4S13CaujzzKcnZ7H8u/UU5CrxreLF8MklC1mb5ytUIYo6HJ3L7yqTk5FH4pUUZHIpUXVKtiTpdDo0ai0yubTCL6FTe88jV8iIaRRFfk4hCZf0ucGrRPnrx+viUKIP+ZIvVjPv/T+Z8usE2g+yjCeY8eICtq08Yvr81JQ+DB7fqXgX5SIhMYNV64/rPwgCnm5ODBtoXaToYWXezPX88bPlcn1wmA+/rHu1wn3lZhdw9VwitZuULPzycwv5YNyvjHixC3WbV63wOe4X63ec5ou5mxBFkaG9G/PssNYPekh2Kgl/z9nCH9+spyBPiU+QBz/v+eBBD+mhpDLonOJjifhlKhJnx7IPuAuI+YVcffqjcl//P//8w5QpU7hw4QKRkZG88sorpiwxUFQ4afbs2RaFk2rXrm1qU1hYyGuvvcaiRYssCicVD3wtC7uFvZIhk8to1LF22Q1Lwc3LtcR9YdWDCat++4E8JbF1zXFSk4rcRrx8XMsU7Bq1lqsXksjPVSIIEBzmi09AyT8gRxcHAl38bnuMCgd5iUGRtu6Ld4AHz3w05PbPZ8MKXRZuXi64eZXtSy4IgqmgSUWp1aKoyqKzmyPRFShDr6/8KeDiYZ2izdvfnSpRfqaMHK7FJkUVYe2WUyz+66Dpc0iwV5mCPT0ps8Tl9LvJmcNXEEUdUpkEmUxKcKQfzq4Ve9lE1gikcetofYCtRIIgEQi6zTzoru5O1GocyezP/0WhkPHES12sJnHOro7MeAgC5E5dSCTfsPp36VrqAxtHXk4Bl07dRKvVotWIePm5ERUbXGlXJbRakcTETDRqLWqNFrVKi0ajJSDQg4DbDGgtzNNXMXZ2d6oU163RaMlI1Wc7Ki1e6V5xYMtpLp68gU7UoRVFqkT40emxuxNPY6fy0rt3b3r3LtlYJwgC77//vlWGGXMcHR359ttv+fbbb+9oLHbBXsk5sPYol05cZ9gb+uwVO5bv5/zhywx4oTs+DyCzw4Jpf3H9XCKxTaJ47MXupu3F84236V52irals7ey8NtNps9SmYTQKD9+XG27FPm7j33Jsa2niaobRmxbfdnfnqNaE2rIfrD8m7WE1ahCk6620xKac+bARfKzC2jUuQ6/fric5Ou36DysJY066SdLe/45wvkjV+g6ojXBVa1di0ri6qkbRNSq2KzZFh8+/TMZqdn62fv8cXgYUjLOeOIHBk3qSdV6EXd8js2Ld/PbRyt46bsnaVjOSeLodwYy8q3+NlfSn5nan2em9r/jcQH8vny/xeeS5MK0kd9x6fg1ajavxpGNx1l09Ye7cv7S+HDsz2Sm5Zo+z/jjReq2qFiQYo26ocx4dYnFNplcynNvVWwVx0hhgYr9289x8/otPLxdGDiq1W318yD5Yu5G/t1alOKz4AGIMiNXziXxxsifLLZNX/DsXUn1eC/IzS1kTLHxAlSrFsALL3WlTt2KP5N+eWcpq37cwIrkuVYrkg+CXmPa0G1YSxxdHG7bWHEn7N8Yx7+/7zF9btwh1i7Y7dxX7IK9knMrKZPrZxJMn+N2n2P9gh10HtnmgQj24zvPcmrvBXSijsdeLNr+wtS+FOar0YoiolakSTnSqRVHqxFRq0pOufXi10+Sl5VHcnw674+ZDUDjDjVNgn3Tot006lynXIL90PrjpMbfolHnOhzaFMfF49eIbVbVJNgPbjzJ2l+3U79dzQoJ9s+e+J4fDn5aapuF0//CL8SHbqVkOrh48gapNzMA0JjdkwtHr5BryCd8pzi5OuIf5oODU8VWAmylK7zbFPfUKymIb9Q7A1AVqnHxcGbEW9aZJe4FAaE+ODo7oNXora/GVKEVwbwqq5GKFAoy58aVVD56ZTE3r+sDyX+euZ76zarazGNfmcnJVaI0q3Cr0Yq3lfrsj23HyS1QIoo6NKKIKOqoHRlI27rl9xXXFfv/kcmlpkrAlRHz6srmXLyYzJnTCSbBnp9byLpFe1EWqKjeIJyGbUvODd92YFNCogNNcU8PGicXR7i36eNLpVaTKEO1WAGJREL4Q/b7qsw8iLSODyN2wV7J6fFkB3o82cH0ecLM0UyY+eCi0z/+cxKiqEMml5KTXcDXH64EnSFwzvB3q4418fIt2S0H9HltazWJ5Ok3e5kKPrl7OhMUZh3YZHxp+4V44xfijcQsNd6tmxnMeX0hz342kibd6hEaYzslZlpiBldOJSCRSqjRKJKR7ww0iabHJ/cmKzWbWi2KrGcyuRRnN8cKv6zeXz65zDbbl+2jWv2IUgW7o4tCn+KymEaY/s+b+mCxu0DLPo1o2acRAB8+OZuTe86DILD09Gf3RZSXhpVgL8HGbow3uJ/MWlVy4Fx58Q/y5Oe1r6LT6dBqRXSirsITJyMXTidw/VKK6bOHp/MDcRm4U4pPWI6fiUep0uBYwdz+v6zdT2qmZWD60Pb1KyTY05Kz6NCvoamYrbefO2E2ii7dTQ7tusCB7Wf1HwTBVIBLLpcxdnL3kg+kqJJzcYKCPPEzq+tQmK9i7kd/AzBgbPtSBXudNrHUaVNy1q7/Gh0HNqbjwMYPehh2/sPYBbudUjEWgDG+TJ3MfHXz83NtVqkMiSg7O8i5Uwm8+cw8q+1N21annlnw3KiaryKTy5h3fIZpm38Vb6YvewmAwBBv/Az555/6oKh0dWGBCrVKg0wuxcnZgaPbzzLzxfkAzFr/BtUbRiI1lLRv3beR1Th6PtOJxr0aERBRsUwA/mFlX/usbe+VmfZtzta3Tf++cTWV3Yv30aJDLP7F/Jx3bz2DXC6laeuY4l2Uikql4e+/DpsmWrlZ+eQackdXBn/V4laQ0oZ06Wwi/5u1AVd3R6Z8VnJGnkkTFnD5Ugo6UUefAY14ZkLZuZi3/nOc33/awseznyDQUCl40+pjrF6yH61W5Pm3ehNbN5Trl1JY+fteDmw/x2+bXy+zX5lcSpVy/E6Ko9PpUCk1FkWWOvaqz+mj14m/lkZYlD9Dn25b4Qq79xOtRstL3WZw7dxNQqsF8uO2dwBMv0eLtjZWIsqipNL2FWHht5u4ec0y9W39FtVo0q7iK4fl5XxcPKsW7bPa7uikKFOwy+VS5s1/FplcilwmNf3t4Ci3sL6bf2+U93hSJ4oi184nk5qYiVqlQaXUEFYtgKo1KxZDdfNGOudPJ6DVimg0on5lSyvSo38ji/SQdh5idJhin+7LuR5S7IK9knPhbCKXzunzYodH+RMQ6MH1SynUbhRR6sMqN7uAM8evo9WKhEX5ExxW/oC29NQc9m87i7OrA38t2odWo2XwE61pW6x0uC0N1bx9DarXKtvqKZXqA/Y0GksXGFt9Fq866OjiQIM2RZahoEh/crIKOGUIBmzctjorftvDgu83Exrpy9y/J1rkIS6e1rGwQIWoFTm+/xLTXvodQRDo2K8hG1Yc4q2vhnHlzE0A+o5ujadP6SsHr4+eQ4OW0Qx7rkOJbSrqD3ruZAI/fLoGFzdHatYJITMjHzd3J1JSs/nwtaVIpAKPDW/B2IldS+1n39YzXL+YgsJBRueBjZj90xbTvpHda1MlUj852frPMWZP/weNRsufB94rU8CfPnad+KupJMZnmLbJ5FJGjCv5HpTF3/MnoNPpDEululLT5KUkZnJ4zwUcHOUUFqhIuZnJPwZBPWB0K9MEsqBARX6eEtBPWEpi8pi53LiSQtf+jchIzSX+ShoLvt3E65/qJ4SH91zgXFw8AHk5hfxv6lKUCkfWLD2Am4cTq37fS1pKNl4+rgwYbe1LnpacTcL1W2C4PoDoWsHIFTIUpfjm5uUU8uUbf4AAU78bZfH/Mu6NnhYT68rMip82c/mU/v5pzX6XCrkMB4UMqVR/HVKJYBUbUxJqrZa4K0l6NxiNtVtdRQV79bqhJN1IR5DoUwF6eLvg7nV3/bh3rTzIpWNX6fZkB9y8XKjVMJzHn22PUVEYvxvlEaWCIBBWQuVdcxycFARH+OLgpMC7lAD/u8Gl0zd5acA3FtuGju9YYcF+9MAlvpn+j9X2jj3q3nfBHnfsGtevpKFWaWjQNMpUJdmOnfuBXbBXcvZuP8vvP+8AYMCwZlCoZuVvexjyTDuefKU7507cID9PiagVadgq2vQST7h+i6kTFgAw9tXuDHqiTbnPefb4Db557y88vF0IjPDlwumb7N5yxkqwe3i7sObw++TlKlGr1IhavUuMViMSfyWVkFIeZtVrh/DHzrdQqzT6F5Ph7VT8ATzxmydM1/TJi7+RdCOdx1/oxJz3ViAIAnO3vYVUJiX+SiofPP8bAIt2vmV60RtdOzoPbU67/o3RiiKOzg7s33qGBbM24OnrSnpSJlfPJdGuT320Gr17jkqptz6lp+Wy2BAY275PgzIFe2pSFlnpuaW2KQ8bVxxi4/JD6EQdLXro7/vnby+nU7fabFl5lFZda/HE5B4AiFodeQYhamTVgl2cP36D2k2j6D5Un35x+7/H2fbPcdw8nOjymOXSbudhLQk1TOo2/X2EbIOfvKgVkZaw3G5kxW+7rVZanJwVdyTYvT3L76xamK+f0CkL1aiUGo4fuGyyVFaLDTYJdvN5R2nZbBOup5GVkU/CtTS0Gn279NSiojFqg9iPiA4gMjqAZR9fIrih3p1Ko9ayctE+Eq6mERETYFOw79l6hh8+sRYgzq4O/LZ+Mi4lZJw5tu8iezefBmD5LzsYNLadaZ95BdVKj0rJoHEd8Az0wtXDmd++30xGei79BjbmhZFtEbU6XNwqlnXntw2H+e7v3SXur6il/sq5JJxdHdBqdYhaEZVSg9ddckUzokHCkjk7WDJnBy9/MZxuw1pQr+m9zckuk0v5ZefUe3oOI45OChydFabfJxT9dipCSZNQ7QModLZx9THWrTwKwKBRLXmmDCOJnfJR0YJGd3quh5WH6Cn/36IwX4lKqbESFkkGK+ahXed58pXufPb6H6Zgs1XHPjRFz5s/5MQKlqXOvKUXJ3nZhSYrjy0LpyAISGVSZk1dYRISRqQyCf+c+LjU8zi7OICLZY7yfxftJTsjHy8/N7oNaUrjzkWThPgrqVw+fZM9606SZLhmZaEaZ1cpErPldFHUUdOQqjA1OYtl83Yy+Mk2FsJz9cK9XD6bSGT1QFOgq7EHnU6HzrhuVoEIlYI8JdkZeZw9fp3TR69Rs4FlusQ/52wl7uAVZDIp7/w4ptS+UhIyOHngMgBNOtcq2mFQnVqNSEi4L1KpBK1WtKpkeGLfJXavO4lEKjEJdmPQnC2LtWh2nblmFUrVam2Zgt3ZxQFnVwfyc5WltrtXmLsX6XQ6i0mf+e/H3CJtHvS5Y/UR9m+MQxAEnnlvgGmykpSQgV+Ap9X5AgyuMZ7eLvj4uzPqncc4FadfhdFoRPJzC/X/VpccQG2L/FylVbCjOc3axxJa1Z8bl1KY9+U6YuqGUvceC7x7QdfR7XBwdjDVJ3iy10wSb6Rz9sQNsjLyyMtREhTqjU7UMXB0S7r2t3ZZK463e+nW7/Ja6o1cv5Ri8ZvSTwbvrguJ+TNaa2NV4GEntKo/87dNQVmoRq6QcTIuHt1tuNuFhPvStW8Dw6qsYfVFJi0x0PZe8b/vNnHiyDXT5/07z9sFu537il2wVzKO7bnAJy8tRJmvQlmoZlCxcsjGh7yoKapQaUSrFTF6KJoHDWq1t/8yEMshWG1ZQLSa27N+LP5+E2mJWdRoEE63IZZ5t42BdOYizHgec79VUSua/I3zc5WcOXHDesyG+6bViuQYA9RsvEwqElGu1Yrk5yo5dyKeuENXrQT7hbgE9m8+jUxR9ovG/P/P3LfXKLS1ZhVKtVrRytpktLiai0bBdKx15g1zoWh+fzVqLZilXD+y7TTHtp+hbqvqNO6sz6jTbUAj/IM8UBZq9BkUBKHEILjivPfGH1y+mGxyfenZtwEjnizfatBf366lw+OtrK7F/NwlCXbz7RdPxrNlxSEARr/eSz8J0oiIWh11m0Syf9tZi/6N33fjPa/dqjo3EvX5oTVqrenem/8G0hJucXLnWTo83ooufevjF+DOns2nDYHa+nP5B3ni7FpykS2ZXEqH3vVY8PVGdDq9y8GdCvbMjDw8PJ3vKG4hN7uAtUv36++ZQRgPf77kCn4evpauGFNnDkOjEfH2dePXbzeyadVRrpxPAuD8qZvlEux9W9Side1IvZgTBH0BN4mATKL/u6QMLxeOXSM7PRfvQA8ia4aYtr/2+VCS49NNheA8vFzwDby9fOYl4RvkSaue9ZBIJARUwGXxYeHqtTQmv74YpVKDSqVBrdYik0l4PrMzHdrH4u5uXcshPSmTzYt20ml4G1NNhdoNwqndIJx/ftvNpVPxFKo0jJzUHacKFKS7Gxzcc4GbN9JNnxOu3yLhxi2q3Gb9BDvFeIh9y+8XdsFeyVCrNGSnF2U4KP4iNQlNg8Ayt36aCwRzIS/cScYPs7LOtti6IQ7/KD96P9Ha4HOsAx2IOkhPy8G7gsvIDoZiQ6VluTBqLalMgpun3rJm7pcuijoL65j5vTBtM4ougwsMgI959VTReC6dqf+yggQtLGa2lmsroP4tglLN771g2X9x8WhEbpgUaNRFS9DGSYAtC7u5y4DRcuXm4WTlmnBq30X++HodgkQwCfbjBy6z8IctFu1kMikjxpcd1Hlg70U0Zt/bX+duZ9/uC3z781NlHpuelIlaqbYK4DUX7OaW9LYdYrlx7RaCRKClWZBucVHv5uHErZQcvP3ciKphnXXI/LtjJLyqv6mQknG1x3yy5O7rTn1DrnsnZwdysgrYaFYV1sgTE7uUes3e/u4EhnjTqE0MA564syqgxw9f5eO3/iQiyp9Pvh1R7klWcfJyCvnf52tNn6UySamCvThR1YvucbO21QkJ9yEiOhBBgDqNyy4iBvqJrK9HxXP+zftoBUe2nqbD4Ga8MXusaXv7XvUq3FdFiW0UyTtzx5bd8CFFIgikp1tm69FoRL7+dgO1a1WxKdjVSjWJl5JR21jNOLLzHHs3nATg0PazLDn80b0ZeAkMf6otWZn5bPr3ODKZlNr1w1CUw/hix87dwi7YKxlGS2d4TACePm44GqL6nV0U9OjfiD3rT7Jz3UnCovT+4TIzMWoebOXl42oK6qyo7UxhOKeTi8IkeEoywK39+wjHDl21ua/f0KYVF+yGnNbKApXVvj6jW5GbVUDVWlVo1CYGhUPR19fNrPKmMUXkky91MQTdWvvS12kaxd7NpwkM8Sa6W210Oh2tutamdpNIpDIpR/ZcIKCKFy5uTnyycBxAma4h5hMDsZRVjZJSFJpTu3EkI17sjCAIeHoXCRGJYFhhMbg5KRxkaLWi1SpHcIQfXn5u+AV5mra98H5/Hn+uA799s9FKsJuL1mYdaxJWLQCFg8xqoiYI+vtr6V5iPTmR2Mj4YQupVGIh2PVjKdehPD1tmP4fMilvztBXpHVyVlCnSQQf/DAKqVRKeNWi//uhI1owdEQLq34atq2Bo5MCnU6Hs6sjkz4YyImDV2jfsx5u7o48N6UXfmbW1eHj2jPsmXYW97xmg3DWHNcLiE0rj1JYoLLwRVc4yFGYTQirhPtQvU4I507Gm7a5eTghanWUNr/uNqgJnfo1LNfKV2mkpWTzxgu/IWp1HD98lbnfbGL8K93KPtAGxf+vy1pdW/DBnzTpUZ/YpkXFppRKNf/7fbd+tUGnI+lMPKIoonOW07xJ1dsa14NEp9PH8ggSodIFAl89dYPEK6nENIrCx+z5UJykG7eYO201Go0WUSuiUYs07RjLgKdKTkdrjrOzgpjoQBwc9MHUqak5XL+hd2X85vuNKOQyxoxqTe3aRSsbAeF+vPS97UmMuQuMtoLuZneDHetPsnNDUaxO3P7LKGRShj/b/r6P5VHD7sNePgRdadFX/zGys7Px8PAgKysLd/cHkxbt8pmbrF6wm25Dm1KjfjjJiZlcPJuITC6lWesYCvNVZGfm4+LmiIubI5fO3ESl0iCTSYmMCbTw31WpNKal4YoseedmF5CckKEvFiKVUFioxs3NicAQ60JNb764kKMHLpuWjo3nk0olfPHTGCKqViwt4rbVR8nPKcTdy4XWPcougGREqxW5fikFiSBQJcK3zOwBKpWG/ZtPE1rVn4i7VABDp9ORm12IRCqgUMisqvGlJGSQZ/APj4wtf6YElVJNTnYhUqkEnVZEpdLg4CTH07v0ANiyxpqSkq13F5BI8PBwum0Lq1ajRa3WmlY2RFEffGw+0SiJ3+ftJCMjTz+JEcDd3YmIKD/adHj08z/n5RaSn1uo/91IJLi4O97z4NHUpEz8Aj0pKFAx8an/cdUsf/uUjwfSoWv5Kt6aU1igYufak0hlRb/91t3rlPjMmdz5I/q/0J3W/YuqRObmKek15GurtvXrhPL1p8PKNY6E+HTycpX6+BODi5VOB1WjA0rMvmO+MmUurBd8vYE1Sw8Y3JVEnF0dmfBOX1p0qlni+fNyCni8+Uem3wBAWDV/Zv/7KreSs8nKyEWrEQmo4oW7V8m/jcuXktFqRDKz8skzxIUIgkCbdjVKzZZUXn56bSF/fbeed5dMpFW/kvOKXzmbyISeX1psa9gmhmnzn72t8+7YeY5/1x5HrpDioJCjUEjp368RMdHle/7eSs7S+8PLpcjkMrz87m4QcFl88sYfbF930mLbsGfaMeaF0leT8nMKeKbeZN5Z9DKxzR98pdzKoHOKjyV09ntInCoWaH67iAWF3Bj3QaW4/opit7BXMqJig5n4SVE+8YAgTwLMrCCOzvrIeyNVSxF+paWIKw1XdydcbSxX2uKTb0bc1bzd7fs0uK3jpFIJkRUQ3gqFjDYVmBCUB6M7RUn4V/GCKhWvTqtwkOPjV/FqmqUhCAIBAXfHJ1cqk5a5+lAS5fVXfxRxcXUsMSPM3Uan0/HnvJ3M/3YTH/4wmoYtqvHejME8P+ZnU6rLr6atpl7DCLx9XVky42+8Ajzp9kT7Uvv9YfYWVCqNaZImGiqLhtYMJqKENINfbLLOUlKSELVVEbYkvvlyHUcOXrHa/vvyF/EvIYVhSdbvi6dvkp1R5M6Rn6sk5WaGzbZGJBKJVaBxjiGAedncbaz8TV/WfvKMIXTq37DEfl57eRFZWdbVjNdvnYLtxLf6Qk/ZGfl4+7mVmclq9LuPMXRyH1xKeVaBfoW1Wu0q9BzeAgdHBTK5hFbFMoWVhkatZdb7f6FRa9EYJvXero6MfKp9hdIMA/zy1XrWrjhsivcQRZF+w5rz9KSKrwqd2HmGzQt30ue5LlRrUD6XKwC/QA/CovxMEwa5Qmqx8lYSTq6O/HhoBs4VzHxkx05x7ILdzh1RGYrs2LFjp3R2bTzFL1+tB+CDiQv5ZM6T1Kwfzuvv9+f915bi4CDj5Sm98TZUKG7QqQ6RtUPL7Hf1P8cotOFv3LljzRIFuy0cFDK++3w4giDw1fcbuXglpeyDilHis+g2FpE7929IQBUvUxl6J2cFzcpY9VE4ynnn21FkZ+YZVhuK4l6kJbgu2hzubUTf/TpLH6xbvU4IweE+yBUyXvlooM22zm5OOLuVbZAJDPXh21WTKjwWI4JEYNOqoxbbJBKBbgMaVViwK5Uai+xVoM9gVRpJCRms//sIGo1Y5NajEWnZNpp2Q1vgG1KxMYyd1I2xk7qZKnrrdLpyrUoKgoD7HayG/iewF04qF3bBbseOHTuPKEkJGfy9ZB9jxnekWbvq7N9+DmWBmmmvLua7pS/Qsl11xr/SjXqNIoiKDgDgxO7zHN15luqNy/YdLylWoSKWcdBbuusYsrS4mmXKGdCnZEt0cUoyHdzO+7ltj7q0reAKnFQqoVU32y5FEqmZ/3UZ+cMVhgJaGo3W4j6WZhwxBpmfOxnPuZPx+tgXLxeeus24hLuB0T3S/Bp6D212W7nm+wxtSvN21dmxIY51Kw4jV8joP9w6HsWc1KQsFhlqmJjzz7KDzP7zeTz99KsuapUGVYEKRxeHUlcKlUo10z9Zzc6d5wFo1Sqajz58rMLXYsfO7WIX7Hbs2LHzCHLs4BU+en0pudkF3LyRzuSPH2Pi8J9IvJGOKOpIS8nCy9eVAY83Mx2zftEevn19EVqNiH+INz1Glp6JpkRXljsIjTLvs7S89MV57e0+qNWGIHtBMARIg5fXg7duyuVS5AqZyc+/NJYsfwmAnJwClIVq0wJBaYuZRku+TCZFJpfi4Chj5Aud7srY74TZf080uJDor9/RSWGx/+alJI5sjqP3s6X7gYdG+hFqKMQnl8twdJLbjKkyp3hFayMffzsSf4Mry5FNJ7h2JoEfX/2N6f+8QeOu1tmBNBot69afpLBQbRLrcPupi+3YQqDkKfe9ONfDiV2w27Fjx84jhE6nz7PvqJCaKkvu33mef/48xNSvhjNp5Gxemz6Y6JpVrI718nc3ZSC6lZhZ5rkWznsWQRCQSCVIBL0ft0RSeh7+X95axGOTepssnMWZ8cEgBARDQHT5X67ZqdksnLkW0VCXQNTqGPRcR3xbxZR9cAlotSIFuYUIEgGXcriRfPfGYk7tvwSAXxUvPvz9eQBGv9yV0S9XrMiOm5sTbuU4J8BjT7Sm+8BGqFVafYEnnQ6FQsaJHadx83Ilsk5Yhc59twgtpdo1QE56Hqf3nCtTsBtp2KIaDVtUK7shEB0bxLxVE/XFlmRSfeEluQQXV0fTakVE7VBCa1Shcdd6+FbxNh07sfVU3l8xGS9/DzQakZkz11n1r61gMS47du4Uu2C3c8doNVqunE/S+/WJOkSdjpAIv1IDMO3cHQoKVBQUqNFqtUilUrzNMrMk3cwg+WYmvv7uVCnFZzQ3u4Brl1Lw8XMjMET/0irIU5KWnI1cISUwxJs9uy+g1YpIJAK164SQm1NIQb4KL28XTh2+irefG5ExgeTlKvHwdOb4/stkpOXQuX/DSpfW7lEmP0/JV28vx9fflS0/raX/8z1Z+oe+KNSCn7YSU7MKH/4wmmolBKs37VybZ94fiHeAB+36l5xBxIiHR+kVRm2OMacQXSliR3GbmXJCqgawd90JRFGHTCFl8PjOVC9WvKyi7FlzjOnP/kyDtjX4eMkLFgXNbJGakMHVs/qqtyNf63VH564IgiAwovF7pmqsgaE+zNs9FYlEYlWnoDJRvUlVXv/1+XvSt8JBTnCod6ltvANtW+n7Pd8NB8NqgMxGHQ/AKh2tnTvA7sNeLuyCvZKwec1xsjPy8PFzp20xP0iVUsOVszcRtSKxDSNMOX41Gi06nY6E6+kc2nUeNw9neg1uwv5Np4iqXYVNfx2hz4gW3ErL4fDei/j6e9C2S60SRmCJsQhS4vVbpCZm4eruSLVaITbbPtPrKxLNKsABNGwVzbS5T5Z6jszcAhb8c8CU17tX65pEBt+9qnF/LNjN5Ysp9OzfkLoNbb+4j+2/xMaVR6nVMJyeg5pY7Nu35QyXzt4kvFoAR3ae41aKXoAGRfox75uN9B7chIKsAvZujKNl19p06Fd+f9vbJSsjj+MHr+Di6kCjltHMnbOVlYYCPE2aRPLpjMdNbTf9c5zfZm+lc896nNl2Gq1GS9ehzdi07ABNOtRkwseDADh/KoG3xv1KrQbhfDn/GQB2bjjFzHeWU7tRBF8seIYvPl9DVpY+6OurWSP4dc42Th67Tq9e9djy12HcPZ3pOrAxv8/ZTv9hzdi+6ihZ6XkEhXrfcTXOu8Gq3/bg7OpA5wH6qplqtYZvPliJo5Oc59/ua2p3+OBlNvyrF309+zbAwUHGgv/tRCfqmPrRQFwNmR7OHLvO8f2XAegzvLlVgakHgU6n473n5hN3+CoA9RtXI+9mGi3b12DPtrPodDrWLD/Ie1+WniZxwLh760rx4rdlF8W6HWRyKU9O6QsCODop6D3mzjMQeRmyy1w+FU/KjXQCywikbdylDn7hfuh0OtJv5bFjzTHa9qp/x+MoiwNbTptWUwCTcK/dusY9P/fDgjGDdXkSJXQcVuQKJpVKGPt0O31hNEPqYplMiu99Titpx45dsFcSFny/meSbmdRvGmkl2DNSs3l5wDcAvPe/p3n/5cWmfb4B7jRqWY31fx0hLMqPE9tOE7f/EsNf7cmCr9ZTp0kkhw9cZtEvO4iKCaRtl1os+HItS3/YRL2W0Uz/7TlTX0uW7CPhZgbVqgagScli058HqduqOqt+202jNjF8/L+xZCRnIpVLcfcueljZqiRaHt/TnLxCFq49bPpcLzq4VMG+5n9bmff+n7Qd2JSXZo0BYOlPW1n0/SZiG4Tz6QLL/MDLF+0jIz0PF1cHm4JdqxXZtPoYm/85xrm4eCvBvnnVUXZtiKNOk0jSEzNJuJpGzQbhSBxkHN5zkbgj12jfrjq71p7A2dXRQrB/9OYyTh67jgB079uAJyeUXfWzOEnXb1GQr0SukBESpc9nf+FUAtMnL8HFzZEF6ydbuB4Ut/gYCxqplGoSr6Xp+7x2i6Trtziy42xRO0MQ3OVziaZtykKV6R7p+yr6/5RIJKZS73k5haiUGtKSsykwpAe8lZJNlqHC4emj129bsMcdusLJQ1eQy2UoHOXkZBfg6eVCSkIGEomAq7sThQUqgsN9uHo+GYWDDEcHKekpOfQc3oIqhuX49NQcZk9fjSBAozYxePm6sWfzaTauPIKbh5OFYL8Zn8GWjfriKPUbhuPl48rhA3phPv6pX2jeKprnX+7KyYNXmP/1BgA69K5XKQS7IAjUahRhEuwnL2Qw6t1BhFcN4MXRc2jaOpqxL1XMLeNe8fWk3zh3+DK1W0Qz+u0BfPTqEi6fTwIsk7q0716HF97qXe5+B42/88nGC83f4rt90wFw89SvWFVvGFGmWAe4eDqRDcsOmD57+brdF8EeUy+ML1e8hMyQctDZLHD3v0pORh4fP/sLpw9fQdTo041OWzSBhm2tJzH7/z3Kyh83MH31G1b7BEFgeBkBrnbuELuFvVzYBXslwcFQXVRZqLHeaWYQEIuJMq1GNIljjVqLWqlGrdKYrAiiVkSp1PdptLrcuJRiSktlzt69FzkZF0+rVtFc3RiHVCoxiXGjcMtOz8XBSWEh2L/98wUunUk0FWgSJALh1coumGSl6cuwfMgVMlw9XUxLlfpxaVEpNahV1im+jEvBJ49e59vP/mXgsOZUMVsi7d/0A1NqMK2NVGvG4dy8fgtHQyGmk8eus+Dn7YDe8mJsY7yXebmFrPrzEJfOJ5FpEK3GPNcV5bu3l3F4+1kCQr35dfe7+r7y9UI6L6cQtUprMVlSF7sGm1VqDf82XybXGtqZB2kZ3ViM98VCsEsFU5/mwYUaw3fEvPiPrftaXo4fuMzC7zbj6u6Ik5sTKTczadG+Ovs2nUYqk+Dj705KQgbtetdn+5rjAMTEBnD++A1CovxMgl2r1ZomJcbhGgPGrLKZmP/WdDrTxAQgKTGT9Fu5VuOsTLXnRr3QiTPHrnHiwBW0WpG/ft3N27OG8+1vz5pyvideSyM3M5/oevfWr/mnVxfQqn8T6rSxToeYeDWVy3Hx+AR5kZNdwHEb+dNBX5TpfvPUtKIViIAQb6b/8RKevuULXJXJpUUVgoWiys33Gk8fVyb1/Iyk67eQyiS8/v0TBITqjR+fjvken2Avnvlk+H0Zy+2SejOTSYO/Q6VUM+WbkTRoeWdFhpSFam5cTEZj9m4QS8jQI1PIynr92LHzwLEL9kqCUbC7ultb6qRmKcGKBzibl6XXakUkUikaTZGQW/vHAXwM/stGcZIcry8PXZoVXCIR0Ol0ZsJN/6ALj7V2i3F0UlCrBJeTUikmdMp6XnYd2YauI8u/zG0c+9VLKVy9lEK7zjUtBHvdJlGcPxVPTlaBTX9Eo6jViTrT4ERRNN0LidTMP9RwLTnZhcz7YUuxjm7vTWBLCIoWlRkF5GYWdvOsBT99uZa4o9cBcPVwJiDUm/ycQuo2r8aBTafoMqQZa3/dxrY/99PxiQ4AFOSrSE3Kwi/QAzeDb7JxomZVEdJwTeZX5uKit+o5OitM6dzKyjldGsaAQ61WZzP40OgHbe5jamx35ug1egwzWMXMbqNQbMJS/DdgLtDRFbVz93Diu7lP4WRDgFU0heG9RCqTMuXLYXwxZRk16obSY0hTAJNYP7ztDJ+O/x8OTgq+Xf8GXsUCP/dvPUP9FtVMz6PSyMnMZ6thomS8bQoHOXWbRhEU6k1ovSjOn0niRnwWUpm++qkxcPOJt/uT+3xXPPzc8PF354NvRlCQXyTOjf0FBHveye2oELOnLCY3Mx8nFwd8Qnz463870BkqpspkUnqOaEG1MnLTv/jxY7z4cflT/f3+8XIee6U3js62LeKiWBRAK1dIS/WhN5+MKguLcuPLFLIS+69MSGUSbiVnAVB4m0YOc3yDPJm//wPTe0wilZToDtOocx0adS5/USg7dh4EdsFeCRBFEbnBguvgqLDabx605+LmSLvudfSR71IJTs4ORaJKIyKTS9Cqi0T8tfPJBBnyKxsfVjUbRXLhZDw1GkSUOCZBEPSTgWIW9rtJ8dRvqRm5FKrUOCrujlWqeLBjcVE+7acx/PXbHmZ//q/N6zOKP/NhxtYOoXnHmvzw6Rq9YDZUk5U7yA1trcXb7VpuTOnczGSxaBasJ5FILMSq+fX9s+ygafXA0UlhstADpKdk03lQU/76bh3Htp2mzTD9JMiYXQMwBQzbEuwSiYBEEHB1c6RqTAC7/sVwjF7k34zPICjMB52ow/cOqqkaxYlO1BX92+z2GoWy+SqDIEhM11K8nX6/YPF3cbFtvvIgijrTnVcWqvlzyT7ad6qJp5cLMrnZJLry6HVAb239eI51/IhOp+PPHzaSm5lPbmY+637fw7CXuwP679X8mev5Y8422vepz+tfPF6mr296Wg4/TltttX3w2LY89Up39m0/x6Ed50zbazeJNAn2Go0t3aSata1e4eu8U07sPENOei6t+uld4XLSc7l5OYWCPCVth7Zg/dL9Fu2r1w8rU7BXlKFv9LPK/f3K2P9x4cxNVErL1dZ5f79EcEjJQZSvzBqFVqvF1d2ZELMVzslzx93VMd8r5A5FckSlsrz2j19eRHZmPqKoM0xgRNy9nPnoxzGl93mb1b7t3Gd0gv7P/TrXQ4r921wJuHQ2idPHbwB632GNWkPqjVv4h/kilUlxcnXgick9kMokRFUPYsqngy2O/3GGXjFptSIymaWFXavVWj20WveoR2CYj1WQpLnuECQCOo2OiOpBtO1Z1+RDfTdZsOagxedPft3E10u2I5NKeLxrQ8b2vzO/wWFPtiE/t5DEcwl4BnoSasMHtUWHGgSGeFnlBwao0zgSuVyGs5sjLTvVRFmgokqkH1cv6qswSqQSxk7pw1Ov9zS9dH183fjq5ydBpzNZ53xuMzhpwNh2tOlVHyeXorGZT+jkCqnFy16jLbJm120ciUatRRRFgorlKx76QhcA2g9qRmTtUHQORdY3qaEQjq8hT7G3rxs3EzKKCXYJCPoCN6071OTkgSucj0sgLSWbRi2q8vZnQ0wW3TvB+L3VoTMJaUdn/fULgmC6doVD0QQvMjYYJxcHImICLfpSGMWA4VlttKQXn2CZW9h1uqLzKpUaVq04TESkH3XqhdG2Rx1uXEnFwUFGUFjpmSgqC4Ig8MYPTzKxx2f0eaodjz2n9/fOz1Xy+WtL2Lf5NAB7NsRx/VIK4dUCSu1PrpDh7edGdkY+oP9/CqziRROD+C4+CbZYKbwHqJQa9m/RB1drtSIatWjIniSh2+CmVu23/bGXC0eu0KBjbZzdnJj0w9PkZheg0+lIS86m/9PtQNBPmGUKKbUalx6LkZ6azfK521EWqg054AVCovzpO7pVicfIbGTEib+WZiXWoWR3DiN179CF5EFj/jtWFVpWzz115BoZxdzRvO1Bn3b+Y9gFeyUgINiTie/2w8FBhn+QB2kJ6bzW5SO+2zcdTz93HJ0UDJ1QcjBVRDV/mraJwc3DiU7dalOjUQRte9Tl8I7zODgrcHPXW7U8vPQW0NpNo/D0cub03vNE1g6zCqYSBIHIGsGoVRra965P+97178l1n7tmXX483/CgVqps+PLboO+oVnTq38i0QmFOj34N0Kg19HL/gcGTeuIXYJ33OTDE25TKsDg9hzaj59CiojJXTyfww6sLadSpFgvWvoJOZxSCRT8jhYOMWnXvjhWuSYeaVtuCQ72Jqh5oOLecRo0icHKSI5NK8fAsSrE3/btRZfZftW44VeuGo1FradohFlEr4mr4rgSFePH5/LH4+LmTmprDW2/3xdXVEa1WJCjIw+TyEhrpx8c/jAZgzsz1KBzkd0WsA4RV9Segihce3i4m3/3w6EAee6otgkTAN8CdpPh06jWrikqpQSaT8tRrPawCQAOqeLHy5DSLbbH1Q6nfLIoxL3ax2F7cwl6zdgg/zRurF2ASAR9fvUjwDfBg4gcD7sp13k88fd2YvX2qaeIDsPyX7Sax7uPvzrs/jilTrAMEh/nw+/YpJe7vMbQZDVtFm1w6XO9xmldlgYrpLy202BYS5UdUbLBNwa4qUJF2M53Ni3fT59nOpCZnMabPLJt9O7s40KRLHWznydKTnpLDiv9ZVtZs0Cq6VMFui0496nL5QjKiqEMiFfSxRBKJhaB9FJErpAx7vhMKBzlVi9UI6P14MwryVab7IZFKcHap/G4+dsqH3rh1/871sGIX7JUAd09negxsZLGtcZe6FgFLN84n4uzmiE+Qdd7YHo81psdjRTmTG7TRW7gmfz4U0Jcnn/B6T6qEFmVg2bXqEL9+sJwx7w6kQcfaCIKAaOZv/O4c26nXEq+lcSs5C5lcauFSk5mWQ1pSlunH4OSiKNMq/+aYzuQVqriRlIHWEAQbHeaHXCbFv5zVCV3dnUwi0xYSqYThb/ajVsui4inHd5xh3gd/Mum7pwiPtS4eUxK5WXkc2hyHf6g3/kGe5T7ubhIZE8gPy14wfa5RI5gaNWzn1C4vMrkUN7nlPZTKpNRpHAlA0sWb+DrIqWOW7UUQBNOKjNFdZcDwFqz96zCb1xzn8v7zaLUiLXvWp26r23N3aNwmhl83vgbAgR3nyM9VElU9kLCq1t+r5p2sJzel4Rfoyac/P221vX7DCN6b9hgSiUBElD8uLg4EB3kgijocnRUmq74oiqZ0pA8b5mIdYOj4jhzdcwFRq2Pq96PxsTGxvR3a9Kh7V/opLzK5lIiYQCRSCTK5vlBO7xEt6FhCulUvf3feXfIysc30lmlJKf+XapUGjbr0eAxvPzd6DW9BQZ7SlBY3Iiaowtcx7pXuFT7mUUAikTB6ku1rH3EbWbbs2HnUsAv2SsrLP1mmKHy+1VRa9GrIlF8nVLiv1d+tpc3AZtRoUtW0LSQ6kNb9GnP51E0WfLEWgNAuZb9gV/+6k79+3oa7lwtLT0w3bd+66ihzPl5l+lynWRSfLS59rHWj9UKzRZ0Iq30atYaDG44jSCRUrROG1234Qu9edYg1v2zlxVljCIosEnm5mXmc2X+R/JyCinUoWPu0VyZG1ngFrUbL4otf39V+d/19EDdPF+oUz+lc7EYEBHuiLFBzLi6BjfN3UJCnJCjC77YFuzlN74OP87p5W9m5Yj/TVr9psf3F1u9x49xN3ls6kZZ99RPjLUv28PnTs/Gt4s3vd/l+328UChnv/fgEjs6KcgWbVlacXBz4cc0r5W7f7/nuuJitSvkGuPPXjrfIyy3UW7clxmqr+mxZ7mUUifL2d+eFDwfe9vgfNa6eSeDE7vMAdB3e8qEIfLXzgLCndSwXdsH+kOAT5IWHb8k+e1uW7Gb++8uYe/xzq6XT3X8fJDy2ioVgb923Ma37NmbOe3+attmyFs4cN4fgqoE8/npfq333kvycQt4ZOBOAdxe9SKu+ZVddLM7/3ltG/IUklPmWqeECI/zoPqZtqffTnDMHL/Fmn88RRZERk3sxeGKPCo/lXpMaf4usWzk4ON39l+LodwdZZWmpHhuMuw0Xh+4DGqIDUk5eoTBPiX8pQXKVjXrtaxFcLdBq+ys/jaUwX0mUWcBhi14NmX34kzL9ih8WPMwq5P5XMC9FD3oLr7OLg93V4i5x5sAlfnh9EQBCFU9S8woJDfKiSyt7MSc7dm4Hu2B/SJh34vNS9/uH+tK0RwOb6e++2fUhTm623Ua6DWtJ3ZYxaLUiuYJAXqGaYDN3j6i64TbdcIrnYGzeuRZB4UUuN+6edyYAnN0c+XLD2+hEkbAa5XdbMeet+RPIzykkMMLPYnvVuuFM+t7aHaIkPHzc6DhUHwBbvXEUjpXwhe4d6Mmi87Puifnf08/aRWLM2HY224Yb3FXeX3hvyo3fS4Ii/S1WYozUbG4dzOfi4YxLGRZXOw835gHJD6Pr04OmSrUAugxrCcCWgxfYf+oGrRpG2QW7HWvsWWLKhaCrTFU/HjDZ2dl4eHiQlZWFu/vd8eN81EhLzCQ7I0/v42vmO/3H7K2IWlG/2mTw3xz4dDub2Vfs3H/+/WULjTrXISDcr+zG/3HysvK5eSkZ7yAvfII82bApjiNHr5kCbUVRh7u7I5061CQi3Bcn+3f8kWTpzDXM+2A59drUYMY/rz/o4TzUvPrJCvYeu0LDmqF8996QBz2c/zSVSecYxxLyzYdInO5PtWixoJD4l96tFNdfUewW9v8Yf371D5kpWYz9ZMRtHe8b5ImvjYDL+TPXWbkH9Bre8p4J9tP7L5B4OYVOwyqWgeFhQKfToVZpQBBQVCCP8LxPVpGRko1WI9KmTwOad61D8rVU3h3wOTqdQJVqgbct2E/uPMOeVYcY93nZ2WcqO8/3nUXKzUzUag2vfjrEKjjymxfnsW3ZPsZ9NoKBL3bnxMl41m+Ms+pn+V+H+fHb0dSoXvHAQjuVHxd3Z/zDfPD0f7he6g+amzfS+ffPgzRsXpWGLaoB8PzItog6Hbl3oSCSHTv/VeyC/T9Gh8db3RO3iba96iFqjZkzAEGwLC5zl9n+5352LN//SAr2nWtP8smkRdRrXpVP5z9T7uN2/XOMm1dTAQiLCaR51zp4B3nxxq/P4xXgaRFgV1HqtIm1WWL+YSQvt5DcbH3Asa181+O/HMWgSb3wDda7gimVaqs2RuyuEo8uvcd2oPfYDg96GA8d70xYwM0b6RTmq0yCPSrUF28PZzKz803tMlJzyEzLQZBK+PWzNahVGqZ8N7rUrF92Hk0Enf7P/TrXw4pdsP/HsOmPfhd4Y+bwe9JvSUTUCql4lpcKcuLQVa5cSKLfsOb39DzFuXohCYCTBy5X6LioWlVw93ZBKpPgYyh8JFfIiKobXuIxOTmFFBSo8H+ErYg/z1yHRq3luTd6AVhMJDUa61R9nn7uFn77A/o1JDzMh7hTCew/WPR/0rplNCFV7s3vyU75KCxQsX+nPhNJszYxdhe8SoCxSmleMWu6TCpBqy1SS3/O2cqKn7cx4cOB7N98CoDCfKVdsNuxUwJ2wf4fpTBfRWJ8Ouh0RD6ES/q56bnsW3WI7Z3q0G7wvRHUVy4ksXvr2TIF+9F9l9iw4jAAr88YfMdWV5kp17cOURRNec7L4u05T/PHH/s5cvgq17IKAb17jSjquJWSTU5WAT5+bsRfTUOl1qCRCMz5eRs3EzP58P2BNG4UabPfT15bQlpKNs3bxxIa7ktWRh7tetZ9KMSRTqdjw99HKCxQMe71ngiCQKPWMUTVCEIml5VYNMucmrFVqBlbBY1Gy83ETH3KP0EgNNTbbmF/wGRnFTD9jWUA/L7+1YfiO/moY0wNqjYrfnc47joXrqVaVGOWK/TPOfPfkK0VLzv/AexpHcuFXbD/Rziy+zwBId54eumzt1y5kMxrI2fj7OrA8oPvP9jB3QbVm1dnzHQ3ohuVXi7cHKN4lUhKLnijVml466lfEDUiUXVDuXw+iU+m/Em1KF/SU3No2aUWy3/ZiX+wJxPe7QfAzeu32LrmOADOTjIuxiUQGOLNlK9vL07A3AK8Yfkh9mw4Rf0WVRn4VNtSj/vpx83s2XOBhPgMRFHk79/3Mvuzf4mMCUDU6rh6MZl+w5uzctE+AAa/0InLV/QuNOnpeSX2ez4ugcT4dELCfdm4/BA3LqfS+CGxZqYlZ5OdqV+GP3XkGrUbRTB+ar/b6ksmkxJmVnzMjh071jgY0gqbi+9Dcdc5ezkZH08X1u88g0arJd/gamae90JdzgrXduz8F7EL9keAU4evsmXlUbz8XBk6rgNb/j5CaDV/pgz9Hr9gT37e/jbzZ66nbc+6/DxtNXWbV+VJg3uA1kYe6T9/2cGlszd54/PH7/ellJu/ft7Gvo1xfLVqEqXV+czPLWTjkr00bBfLsz31ed2/X/UyUbFFRy34aj1XziXSvFMtOg9oSNzBKwAEVfUnJ6uAo/svc/3kDa6eT8I3wJ39W88QHm1Wut3shRN/OZXzJ+MpLLDM/V4RmrStzq71cVyIi2ffptMc3H4WlVJdpmDfsP4kWVl6NyFRqyM5IQOdTsetlBxkcr2VPtNMmJuP8caNWyX2K5Xpj83NLuDG5VRD/w9H/nHziZk9IZYdO/ceFzd9to9m7YqKnUml+mfIrcw8PvjuXwB6hOlXdgUgLDoAhYMcaTlXE+08YtjTOpYLu2B/BIi/nMq/S/cTEulHenIWa5fsp+fjzVEp1ahVGnZviOPSmZu07FIbAI1aayqxrtVYCq8/5m5n4XebUKs0vPBef1xcS061dOTwFVYaXEF0GASRTv+3l7cLr77eq1zj370+jmYdYysUpKrTgZunM1VrhZTabsnX61j23UbGTOlj2iaKlsJt94Y4rl9MJjDUG4m06IVhFHh6y7z+PqUmZQNw89otCvKUOBXLyW66r3cgaCNrBDFp2mNkZeTxr8Eabl4Wfd2iPfz7+x4CQ71566enTNsFsxz8ok5nup8ajRYHJ7nVuMz/veLvwzw+tDkuNnLMGwW7+X0rfg8rK+b35OEYsZ3bxT4hqxyERPhy8vBVi8qwMqmlEBcE8PLTF65zcnFk9oY37usY7dh5GLEL9kcBgybR6XQk3UgH9ILNuG/TX4fRakQO79IHZ2nUWmQGEVZcsCsLVaZlSWW+qlTBnpyUzW5Dn8UJNAQ9lsTF0wl8884KBAEuxCWw7PD7FRTsIlKZBHkZaQ9TEzIAUCuLBG/xF7tGrb9eUatDEAQkUgmiVjTdQ51OZzKiG9uqVRrUKg1OLg4WSXeMlqTi97WiRNbQW582LD+k789MXGek5nDh+HUKiwV1mVundGKRYFerNMgN/xbNgr7Mx1hQoCI9I8+2YJc+xILdzJiie0jGbKf82EMIKh9jnu/E8GfaWVTPNRfsUomAq4sDI55sT5NGUUTXCbXRi53/FHYf9nJhF+yPAObL/iZXBYOKlAgSU/VTo1DVaLQmS7LRemwMbAwOK/LRLSwsOZ2d/rwl7yvrN5Gfq+RCXHwZrUrpX1exlHrmBWCLi02JSZDq753UINiNAk+nKxJ7UrMXj6aYKK9RLxQHRzmOzgp6Pt6s/BdTjLEdppN6MxNBItCqVwMAtGbZTIwWb3OrOxT7HlgIdm2RYBf1qTd1Op3VKkBJFkoXNyfcPJyoVjOYA1vO6Ns+JOLXzcOJuk0iSU3KIjTKXjTKjp17jae3dZXrx3s35vFejZBIJBbVuJt1qnU/h2bHzkONXbA/Agjmgtwg2hSOcp6a0gcXN0fijt0AMKXL0mq0uLk70bFPfaRyqV58GXSoi1lKLWUZftjtOsTSuEmUycIvCAKGFOzlzmxiugYqZipzcXfC09etzHZPTOnLY+M74+nrysLZ2wFrsRka5U/85VQCDBlDxk/tC0DarVx9A52O9n3qc+VsIiFR/jRqHYNMLjWJ915Dm9JzSBOg4tdtC2WhGpUhIKvH0GZ07NfA4v+lVtOq+AR40LK7ZcGfUaNboVSqEQQBX183XB1keHi5IFdI6dq/IUf2XiK2Xii+/u7k5Slx9yxf+rTP540F9BOEzNQcJBKBgCqed3yd9wO5XMZnvzz9oIdh5x7h5Kygn2Fy/DAEQf9XKe4SY8eOBXYLe7kQdHbHPxOVqWRvRdiz6RTzvliHfxVPWnSIZevqo/Qd3Yp2verr9288xb9L9jH5syEs+2krXv7uDHqmvc2+CgtUfPbaUlzdnZj40UALi/Ld5Oa1NFYu2G1yJxn7Rk8UhuwC9wKdTkd+biGCIODgpLC4LpVSjSjqkCtkFtu1Biu7IBT5pt8PRjZ7n1vJWQDM3/0u/vco17dKpaGwUG0qduVU7L7YsWPHjp1Hi8qkc4xjCf3yIyROJbvf3k3EgkJuvDq1Ulx/RbELdjMq0xfZzn+XtKRMtBoRnU6HX5DnfZ0s2LFjx46dR5fKpHPsgr1i2F1i7NipZPgGej7oIdixY8eOHTv3B7tLTLmwr3/bsWPHjh07duzYsVOJsVvY7dixY8eOHTt27DwY7IWTyoXdwm7Hjh07duzYsWPHTiXGbmG3Y8eOHTt27PwnuX4phZ8+XYO3rxuTPxn0oIfzn0TQ6f/cr3M9rNgFux07duzYsWPnP0l+npIjey4SFOr9oIdix06pVMgl5scff6Ru3bq4u7vj7u5OixYtWLt2rWl/cnIyTzzxBMHBwTg7O9O9e3cuXLhQZr/Lly+nZs2aODg4ULNmTf766y+L/RqNhnfeeYfIyEicnJyIioriww8/NFWmLM64ceMQBIFZs2ZV5PLs2LFjx84jhEqlISs9j5zM/Ac9lP8s302cx7+/bHnQwygRY+XV4pWf7dxHdPf5z0NKhSzsISEhfPrpp1SrVg2A+fPn069fP44ePUrNmjXp378/crmclStX4u7uzsyZM+ncuTOnT5/GxcW6XDHA3r17GTp0KB999BEDBgzgr7/+YsiQIezatYtmzfQV7GbMmMFPP/3E/PnzqVWrFocOHeLJJ5/Ew8ODiRMnWvT3999/s3//foKDg2/nftixY6ecpGXlMfff/YAOnQ50hifh092bEehddhVaO3buNTv+Pc6XbyzDJ8CdhTveetDD+U/i7uOGo7PDgx5GiUgMxeJE8SFWcnb+E1RIsPfp08fi87Rp0/jxxx/Zt28fcrmcffv2ERcXR61atQD44Ycf8Pf3Z/HixYwdO9Zmn7NmzaJLly5MmTIFgClTprB9+3ZmzZrF4sWLAb2o79evH7169QIgIiKCxYsXc+jQIYu+EhISeOGFF1i/fr2p7cPI+TM30WpFJBKBmNhgBKHkqOaCPCWZ6Xk4Osnx8rWLpNsl41Yu504lIJNLadyi2oMezkNBToGSZTuOW21/rE1du2C3UykozFM96CH85xn9buX2C5dIDILdbmG3U8m5bR92rVbLsmXLyMvLo0WLFiiVSgAcHYuqVUmlUhQKBbt27SpRsO/du5dJkyZZbOvWrZuFO0vr1q356aefOH/+PDExMRw/fpxdu3ZZtBFFkVGjRvHaa6+ZJgwPKx9NWUZyUhaCAOv3vktKUhYrlx9C1OkIj/Cle+/6prbL/reDxT9upW2POrz26RAWfL0BUaujXa+6RNcOKfEc1y8mk3QjndUL93D1XCI//vsqcQcuA9CwTXVOHriERiMiCAJN29cAQK3WsnPLaTQaEa1WpFbdEMIi/ADYuiGO5KQsVEo1waHedO5e947uQUGekk9f+o3WPevR5bEmAKiUam5cSkUURcKjAxAkAscPXEEqkxAW5Y+PX+ki8fyJG6gK1XgHuBMc7gvAmPafkHIzkwHPtGP5Hwfx8HTmj42vmY75ZPIS1GotjVtF03NIU84evcr2VUfRiTqe+2DgHV3jw8Lx49eRy6XUrFnFYvvDmxzLzqNEWnI26Wk5qFUaqoT74uldtJorGgp5l2b0sPPfxugSo7Nb2O1Ucios2E+ePEmLFi0oLCzE1dWVv/76i5o1a6JWqwkPD2fKlCnMnj0bFxcXZs6cSVJSEomJiSX2l5SUREBAgMW2gIAAkpKSTJ/feOMNsrKyqFGjBlKpFK1Wy7Rp0xg2bJipzYwZM5DJZLz00kvlvhalUmmaaIC+TG5lwOhLJzUs1aWn5/LHor0AtGgdYyHYz5+MN/1b1OlYNnc7AOExAaUK9g1/HmT5z/q2Dk5yEq6k8sEzvwCw+NCHfPLyIvJyCpHJpaw+NR3QC+ZP3//b1MeLr/UwCfaVfx7ktGEszVpG37FgP3PkKge2nEYqk5gEe9KNdF7oNwuAnze+hrObE+9MWADAKx8OoGu/hqX2+clLC0m6fosBT7Xl2Xf6AqAzvNANf5k+G9m39SzKQjXePq4AXL+QzN+/6O/buPcHPNJCQKlUM3fuNlb8dZgqVbyYO+cpHB3lpv0lXXvxe2jHzr1k2a+7WLl4HwBvfDKIDj2Knj3G76JRlNl5cBTkKREkAgoHmcmqXRlwcXWgeYcauLo7Peih2LFTKhX+1VSvXp1jx46xb98+xo8fz5gxYzh9+jRyuZzly5dz/vx5vL29cXZ2Ztu2bfTo0QOpVFpqn8Vf/DqdzmLb0qVLWbhwIYsWLeLIkSPMnz+fL774gvnz5wNw+PBhvv76a3799dcKCahPPvkEDw8P05/Q0NAK3Il7h1GwG33rzK8pIz2PY4evmj4b/e5OHLjMv0sPmLaXZS2IO3i5QmPatO4ELz87z2KbRiOSnJjJ8L6zOHf6pmm7SqWpUN+2kMr0167VFC1T/vjRStO/daKOKxeSi9pLy/4qSwz30dxXsazvi1js/8L8PI+6z6NEIuH4iRsAJCRk8Mv/tpfrOLtet3M/kSuK3i9qldZiX2RMII7OCvtyUCVgxvO/MqDaq/QKnciJvWUno7hf+AV58v53o5g8vXK77jzKCBSldrznfx70xd4BFbawKxQKU9Bp48aNOXjwIF9//TWzZ8+mUaNGHDt2jKysLFQqFX5+fjRr1ozGjRuX2F9gYKCFNR0gJSXFwur+2muv8eabb/L4448DUKdOHa5du8Ynn3zCmDFj2LlzJykpKYSFhZmO0Wq1vPrqq8yaNYurV6/aPPeUKVN45ZVXTJ+zs7MrhWg3CkGjODS3Dp09ncD09//ij9V6NyKjMM+8lUfSjXSzPkr3x5vw/gCunNGvfEhklmL3Ulw83v7u5OUU4uWrtyzn5hRy7UqaRTutVkQilZCWmmOxXalUl+9CS0Eq07+ENeqiF3BhfpE/qlaroyCvaHWkPIJdMC59mitKg2A3bimuNT18XElLysLN0xkoEu5B4b6IWrFc531YkculvPF6L8ZPmI9WK7JixSHatK5O3br630jxuY6rkwONoqvg6lR5A8zsPHrI5EWC3fx5AVC3WVWee7vPI70S9rCgKix6Lygc5KW0tGPHji3uOA+7TqezcCsB8PDwAODChQscOnSIjz76qMTjW7RowcaNGy382Dds2EDLli1Nn/Pz862W0KRSqUmUjho1is6dO1vs79atG6NGjeLJJ58s8dwODg44OFQ+cVHcJcb8ZdPvscaEhPmYPrt5ONGodTSDn26LT4AHq3/bjSjqELWlmzlj6oQSU6docnLu+HUAeo1siX+IF+161WPhNxtp1a0OAD6+bsTWroJUJkUmlSCTSfEPcMfZWUGTFtVwcJAhV8hwcJBZjO92kRkEu1ZT9AJ+cnIP3hg5G9B/78zTcEll5RDshtuoM5vMFN1a2/crNNKXtKQsQgw+75GxwYx8pTuN2tVArnj0yxhUqxbAiBEtWLBgNzodfP7Fv2auMZYiKMzfk6/G93swA7XzyPFc989Jup6OVivy85Y3CKhiO0+2+e9QrbZe3es2qMk9G6Od8tOqZ33CYgL1LoYB7g96OHYqEzpB/+d+neshpUKK46233qJHjx6EhoaSk5PDkiVL2LZtG+vWrQNg2bJl+Pn5ERYWxsmTJ5k4cSL9+/ena9eupj5Gjx5NlSpV+OSTTwCYOHEibdu2ZcaMGfTr14+VK1eyadMmdu3aZTqmT58+TJs2jbCwMGrVqsXRo0eZOXMmTz31FAA+Pj74+FiKRLlcTmBgINWrV7+9O/MAKe536eLiQMPGkZw4do2uPesRUyPI1Patr4ZbHBsVG0yTttVp0blmhc4ZHhPID/9OxifQA3cvF3o+7kzLzrVw99IHcLXpEEubDrE2j502c5jN7XeCT6AHg8Z1MAWHAtSoH8av26YgkQh4+bpx5VKKaV95LN2zVujjG2RmL/jp88aiUWu5ciWVv5YdsvK/9vHTv1j8gvST0IjqQURUD+JRIz0lm80rDjFoXAcra+SI4S3ZvfsCly6lkJCQwc+/bOeF5zsjkwr4uutXHhAEPF3sPqB27h4qpQalwSq7ecVhhr/YxWY7hbmFXaPl8JZTqJRqXD1cqNMy+r6M1U7Z9Brd+kEPwY6dh5oKCfbk5GRGjRpFYmIiHh4e1K1bl3Xr1tGli/5BmpiYyCuvvEJycjJBQUGMHj2aqVOnWvRx/fp1C2t5y5YtWbJkCe+88w5Tp06latWqLF261JSDHeDbb79l6tSpTJgwgZSUFIKDgxk3bhzvvvvunVx7peWvTW+g0xVZyYOqeDHj6xEM6fNVmQF936548bbO6eikIDK2KHe9l6/bA00TqdNoUWjUpJxPIOlaKoHhfigc5ARU8TK1ia0byhufDEKrFakWW3befRcbQUXGCUFysu2A42de78GICR3xLiMDzcOMKIp88MwvnD9xA59ADzr2b2Sxv7hrzF9/HaJrl9rExASyYca4BzTqB8OJPee5ciqeZl3rEmg2mbRz9zGfhHcoJaB8wMiWdOnbAKkE5A5yRtWdQm5WPrWbV+Pz1ZPvx1Dt2LFzJ9zPgkYPcYyVoLOndDCRnZ2Nh4cHWVlZuLtXviW7ud9vpveAhgQFe5XaTq3SoNWIyORSC//O0rhw7CqJV1Kp3y4Wd2/XuzHcOyL5Whprft6MIJHQ46n2BIb7sXvlQfb9c5hX5z5318+Xl6skKSEDiVQgslpAme11Oh1ZqTk4OCtwcnUss31lJD+3kJ/eXU7V2iGEVw/irVGzcXFz5KcNr+MT4GHVfv6CXSxevI+xY9sxcEDj/2TmjR/fWsqqX7bx/m8TaNa1zoMeziNNTmY+1y4ks+LnbTz/4UCb30kjbw/4gkMbTxJZOxSlFvKzC6jRKIoPFj1/H0dsx07lpzLpHONYwj+ZhsTx/rxHxcJCrk15u1Jcf0WxC3YzHvQXOelaGldO3cA3yIvoBhEVOvbsoUtcPRVP9zHt+OPbDcybvpKI2GB+3PJ2ices/303pw5cQqfTkZeZx57VR/h601tUbxQFwK7VR9j9zxHCYoIY9uqDL0R16dhVLh67Srcn2lvtW/3jBro/3fG++ZWrVRp6ez3F8Df6MqaSFwaxRUpCBp6+rrzQ/TOSrt+iZuMoImpVYeW8nTTpEMsHv4y1co3RaLQkJ2dTpUrpE8bykpqUhW+A+0MVEGgU7E6ujkz4ZCidhzRHVajii7H62IoXvn4Cd59HdzWmsnIl7gZZt3JxcJIT29Re+KwykJ+n5NKZm/q6HRotarUWuUJGtdhgi1z5du4vD1rn2BpL+PT7LNjfejgF+6MfNfcQcXjTSb6dtIB2jzVjyjy9FTk1/hYjol7kq+3vU6tFjNUxB9YepWmPBpzac55/521DpdWxYak+Z3tZ6TTj9l9k01J9/uIGrfS+nuapCq+cimfrsv3UaRVTKQR71foRVK0fYXOf8ACsvb7BXji7PXx+27vXn+TTlxYy/r3+DHi2A9++vpTju8/TsntdQqL8Obj1DHEHLlOnWVWL42Qy6V0R61qtyN+/72XBd5uY+F4/Ovaqf8d93i96PdGWpl30lvWwmEAApHIZg17phYubEy4ezg9yeP9ZwmoEo1ZrQafP9y2RSnBwtGcieZDMnLqcXRtPWW1/cWpfeg1pZuMIO3bslIZdsFciQqID6Ty8FTUaR5m2efi6MWPdW4THVrF5zPnDl2naowGJV1Op0TgKnwBPmnWpg7aDiE9gyUvIALWaVkUQBARBoG6raPqM7UCImTtITINweoxpS0jVsl1EHjS9x9kOSLtXyBUyfr/w9X09552QeP0WgaHeCIJA7SZRuLg78u07y/EP9qTf2Hb8M38nGrWGyV8OIyM1x0qs3y2uXUrhq3dXcNZQZOvPX3fRoWe9h8bKHhYTRFiMZdCxVCohpmFUCUfYuR+sXLiXuZ/9a/rs6u7I4+M60GNIU5xdKl8msP8CxkxfxdGWkcHMjh07trG7xJhRmZaKHiaUhWoS49ORSiRIpRIkUgGJRIKLmyMubiUvc+l0OrJu5YIgIAj6HOduD9BCmXYzg+TraUTWCnkoLeclsfTHLSz8ZgMvfDiQboObArBjzTE+eXEhAL1GtKDv6FaERQfe03FotSJj+3xFYnyGadvL7/en+8CS6zTYsVMe/pq/izkz/rXaPmvpBKrX0Vd8vnwqnstxN/D0daNxp9ol9lWQryQ5IZP8/KJ0xQ4OciKiAx7pugt3G1EUURVq0IoiMpkUmUyCRCoxTc61WhFVgeqhjQF6WKlMOsc4lohp99cl5urbdpcYO7fBjNeWcP1iCgggICCRCHR9rDG9hzV/YGO6fC6JbetOoNWKaDUiPQc1ISzKr8T21y+l8OKQH6y2D32mHU++3NXGEXpmvbGUDX/sN32WKaT0GtGKZ6f2u+PS1Yc3nmDz7zt5/deioLMT+y6SfEOf1zkozId6xVK+bV22j1+m/sHXW6ea/PgfZm5cSmHel+vISstGo9Yyd/pqGretjk+AB2161mP76mPs2RBHx/6N7lisa7UiWq2IopQYAqlUwrjXe/HtxyvJzshHoxFJTcy6o/PaebTITM8DnQ5BIiARBASJ/o9LGaKuet1QHh/X3rRi6BvoQXStKoREFGXy2b36CL9/tpoajaNKFeyL527nj192WG1fuPF1fO9j/vDrl1MQBAGpVIJUJsXDyxlHJ8V9O396Wi5rVhwi/VYuolbUZy4TITo2iH5DmpZ5vEQi0VeZLYFLR68wtf9nLI2ffTeHbcfOI4tdsD9gEq6kcflsosW2xm2KfNVvJWVyKzETdx9XAsPuTxq5+Ktp/DGvKA9+wxbVShXsotZ2VdWyrFHuXi5E1ghGp9Oh0+lQOMio3zL6rrhH1GpV3crffdWvO9m97gQAHQc0shLsxiqmWo3t68nLzkcQhIfC+p6XU8DEQd9SkKeiRr1QEATycgr57t0VvPvTEwiCwMufDqF2kyjCou/M5Sn+WhpfvLOC2g3DGTupW6ltm7evQf1mUWxZc5ywKD9qN4y4o3PbebB8OGE+STfS0Yk6RFGHTtQxc9nzuNpIoVoenn/sW26lWFZOlkgl/Hui5OJ7ADUbhFOzQXipbRyc5Lh6OuNcyqpfaahUd17BuTycP5XA4rnbOLjzAhqzwnGvTXuMTr3r35cxANxKzWbh3O1W2/PzlOUS7Bq1lvTUHFRKteGPBlWhmrDoADy9XYlpXNUu1u3osad1LBd2wf6A6TaoCY1ax6BDBzp90GetxhGm/esX7mLBtL/p/VR7XvhyJGvmbSM/R0m7gY3xD9EXi7qVkk1BvgrQV0V193Qq07VErdZy/MhVQ1VUUf+3KBJTIxhJsaqh2mKCXKsViT9/E98q3ri4OxNeLYDv/nwerUZEFEVErb4KqX9Q6T70T0/pw9NT+vDvL1vISM6i59Md8SqWui0rLZtPRn3L5J/H41tCpUNbODo74Ohs6bsqlZmXMLcW5b2ebE+nx1vgWsK92/3XQQQBuoxuB0BiQgY3rqUhkUio2yAchUPl+Tm5uDnRsnNtNq88wtnjN6jXohrH91zg3PHr3ErOxjfQAzdPZwY83bZc/el0uhInUjKZlCsXkjl/+iYde9Yjqnrp1npHJwU97dUnHwniL6Vy43KKxbaSJrzlwcff3Vqw36X4hiETezBkYo8y23XsVY8qYT4UFuifqVKphOiaVfAtJa3k3eRWSjZ7t5612BYc5k1YVf/7cn4j3n5uPDaiBSlJWZw4cpWsjHyAMmuBGLlxKYUJ/azjfN75biStupS8wmHHjh3bVB6F8R+l1+OlR8sbXUOMonnxF2tIS8wkpkE4/iE+fPX+32xafdTqJfnhd6No2sY6q4yRgnwlb728yGr7a1P7El0sqK64YFcVqPjsie+5ePQq61WLcXRWlKtwUUmkJ2Vy62YGGhtlxZ1cHen5dCecb9NiZ077vg2oWqsKUpmEUBsvP0cXBxxLCVDrOqadxec9288xe9YGABb98zK+/g/WH27/ltMs/mELkz8bQkiUP89N7cuxfRe5lZzNuRM3ePz5jgx4oq2pem15USnVTGr3PpN/fo7I2qFW+wOreDF6QkfmzlzP1x+tZOb8Z+6Zr69GrSUrM89UgdbOg2XQM+3Iyco3uKIACKW6QZTFpI8GkpGWq7fW60S8y1m8LSMth6Qb6cgVUgKqeOPmWTTpTk1I583HZqHViGg0WqbOG0d1G2lzF368nE2/76L9kBY88cGQ276GOyUiOoABI1sQf+0WWkNKxNoNw4m+g2fs7eDj68azBpfGD19bysVzibRoV4Oocq7IKYpl6VE4yKy22bED2C3s5cQu2CsBF88mEnfsmiFfrf4BrdWK1G0UQd9nOtJtZCscnPRC0tnNCZc8JVKDFVyukN6WRcuWj3hkVX/iT1zFSdTSrG0MUqkUiVSCt69lISVHFwe+CX8GzQAAr4VJREFU3vURqsK7s0Q88u2BJe5TOCpoO+ju+PO36FqHFsCFUwn8+tV69m0/j7cDyBVyhk7ubWp3+exNrpxNIi+3EIlEQo1aQbzZ9wsialbhi7VvmtqZT2Tu1Of+TsjPVRJ38DIzXllMQZ6SmW8u4/PF43F1d2LSJ4P56PkFPDW5B71HtLCwkq/9ZQuJV5J56uNhpfavcJDTeWQbCs2C8OKvpnHtUjItO9ZEEAT6D2/O5n+Ocy4ugd9+3EKvQU3wKyNLUUW5dPYmM6euQCKVEF0zmLbd6lD/HmWzeRRJTczk2K7ziDodru5OtOpe16rNwa2nURaoEbUidVtG4+lTehG1rnd5pSQyJpDIku0MJbJ/y2m+fns5AE3a1eCVGUNMYxdFHTevpJralvTcajekBXXaxFZoJe9esOl/m3GRSPjou1EPdBzmvPv50AofExjqzbKD76FwkCFXyB6aTFB27FRW7IK9EnDi8FVmf7XeavtIoF7jSAufyzn7PrRoM2xsO2rVDyM/T798i06Hi5sjMTVLt8YUr1I5emw7BjzejPHN3iGjXSwffP9UiccKgoBMLkMmr5xfn7zsAsZ1/IR2fRvwzLsDrPZnpedyZPcFwqsFEOQELh6W1vt1yw6yeuFe0+fP/vckuZl5FOQUWrQTRTPBLhXIyson8WYmWq2on3xpRSRSgfr1S/etvRNO7L/El28uIz0lm26Dm7Dm972cOXqNv+btZNDYdjRqHcPMRePw9rcuUBRSPQgXz/Jl5RnwQneLz/u3n+WXmet4cWo/egxqglQmZeLUvrw8ei5Lft7BpTOJfPjdyLv6kj62/zKXDPEeF04lcGTPRX5a8dIdWXT/S1w9m8jM1xYDEBUbbCXY5370N6vm70Sj0vtNf7bsxTIFe2UgKz2PX79cZ/qs0WgtVnj8qnix/NJXpuBNqcz25Do0JpjQmPtrxbZFn+d78MOna5j+2lKL7c3aVS/Thz07PY/Nyw+ATodOB4071rQIKD+z7wKBkX54BXjeg5FbIpVKbjuWwc5/C0Gn/3O/zvWwUjkV13+Mkl4gt26k8VqXj/EO8mTKghdstvHxd6dDz3oVPqdMLqVl2+pIpAJSiYSYmsG4uDjg5OpIYZ6y7A4qMaJW5FZyFrnZBSXs1/9iBYlA3/GdrSYeA55obRLsTdvXILpBBD/t/RCKiU/zlQ2pVMLuPRf4fMYaizZeXi78ueKlO76m4hTmq/j16w3sWn+S3PQ8NGotF07GE1kjiCtnE1n49QY6D2iEp48rcpmEQxtP0nl4a3avOUqNhpH4BHlSp3VsmefRarRcOHKFGmbVI7UaLf8uO4Ao6pj75Voat47BL9CD6nVC6PN4U1Yt3s/B3Re4lZJzV7Nq9B/Zkh3rT3LOkMPdL9CDvLxCu2AvJxJp0fdXtOGHfGTnOZNYh5KDySsbru6ODH2uA3Om/wNA6+51LFxiJBIJzg9R6sDCQjU7NsRZbb98PqlMwZ6eksWc91eYPnv6ulkI9sUz/qb3uM407d7gro33XpGRnMXaeVsRtaJpFfbM4Sts/vOAqU1AqA+DJ3R+UEO0Y+e+YhfslYC2nWtRo3YIMpmE1BR9Cj6pVIKnpxN57aqXWIDiTlAoZEyd9pjpc2J8Bvt3nKXDiNZ4l+AfLIoiqfEZiDodzq6OeFRS65tRipTkpmIUKxKJQIMOtaz2B4Z4M2n6IL56608EQcDR2YGImiHW/ZhVhZUYctAXp7j//90iLTmLf5ceQK3SULtBGHEHr3D+ZDz9n2iNskDFCx8OxNPHldTETH7+ZA2nDl3mxL5LbFi0mxY96vHuggnlsn4nX0vlq3Fz+P7AdKQyKTqdDqlMysvvD+C1J38mP1fJdx+v5P1vRyEIAmOe70RWeh69Bje56ynwpFIJkz4cyAuDv0ej0dJnWHO7L3sFcPdyoX6raASJQKAhYN2cab89h1bUIZUISKQSXB4S66hUJqVF59p4eLuiUWup2fDerWjdD6QyCcFh1m45LdrXKPNYJ1dHGrStgUSijynwKhZX8+Ffr921cd5rCvIK2b3yEA7ODibBHn8phTULijKYObs50n1YC9wqGJdjx87DiF2wVwK8fFzxMojfqtWDymh99xjcehr5NqzpzdrXoOvjLay2qwo1PNF0KgADxnXk2Q8G3fMx3g5GIS1IbAtSnXF/CYJVEARcDW4yMkXJkyVzlxip7P4K9pBIP0ZM6MivszYQd+SaybJ+eMc5fvjnFVNZ9viLyRzYchqAaxf12TzOHLrMraRMfIO8yjxPcNVAfjj0KVm3cvl66gpqN45k8DPtqNM4kt5Dm/LP0gPs336O3ZtO0bpLbVxcHZky494F7EVUC+C9b0aQlZlPm672TBMVIbpOKJ/8PqHE/d63kQVl345zZKXn6YNEDZmumrerXmawaF5WPmk30wmPtZ4Inz+VgI+fG1KZFDd3R4vsTiURGOpNYKhe5C75eTvJv+0hKyNPXx9ArUUUdUyf82SFr+9B4Bfgwf9WT7qtYwNCvJm++PmyGz4EBEcF8P3ejy22tepZj9rNqpKblU/c/kv4h3jbxfqjgE7Q/7lf53pIsQt2OyYiqgVQv3lVwktIH2bu925uXa5s6AxCuiQDsnHsxf34zWnWvgZ/HHgXqbRksRBbJ4QBjzdD1IrIZBLc3Z2oZqiGaPzj5HTvsiI89mQbdqw7yeWzieTnqxj8TDseH9/JJNYBGrSpTtehzdiwdD/n4xLo/1xnhr/SE3dv26sj+49cYdve87w+oSuCIHBs/yVWL9lPYIAHB7ad5cjuCzRpV52ImECefLkb+7efIzM9j7zcQpv93QuatKl+385lp3QWzd3OubgEi21yuYzOfeqVuoKTeDmZnSv28+RHj1vtW7V4H5tWHQWgQ696BId4M+r5TuUf009bUCmtM06JovhAg8MfBMpCFYIgqVQpZyvCJyO/4cXvnsbVUy/KnV0dTe5N0XXDHuTQ7Ni57zycv+JHjPTUHHKy8tGJOoLCfCwEV1ns2BDHlfNJFtuqxQbTqGW1MqvizV31skVhDidnB9xLCUKUyiQMndhNnzmlUaTFvnWL9/LLtFVIpBIkUgGJRMLEGUNp2sna5eRe4+bpwsyVk0oMmGvUOppfN71eYuwA6JfZy8pl37RlNE3Nii81bhxJ48aRpRxxd5HJpUz6+DEmDv2BlMQsajWJwtnVOi3l2Lf7cf74dR5/oQtt+zSwKaRy85R89+s21mw6CUCd2CrUjvBnyjPz0Ol0DHumHcHhPty8dosv31zGV0sn4OLqqLda6iC0lMJalZFdW85wNi6e2DohtOpQti+/HdvY+i598d5fdOpdt1TBXq1BJNUa2P6tmK9UbV1znIBgzwoJ9v4jW5KckEFOVgFSmQSZTKp35xJ18Ijr9fhLKSz5fiNqpQaVUsOxPed54vVe9HuifPUWKhuDX+1TaqpdO48I9rSO5cIu2CsBC2atZ/2fhwD4ZvkLRNe2XiYuib1bz7D13xNW279dMr7MvL0+FcwbLpVJeWJKP5v7RFGHVhRRq7WG4kn6fz8IZHIpsaVU0HR0UuBY5dEIVKxWM5hxb/aiWs3gEis9unk68/2610q1Ls6cs4mNO86YPn/7y1YWfPME3QY2Yt3yQ/wxbycvvt2Xb95ZwcVTCSydvZURL3QmNPLhEuqg/64e2H2e9auO0b5bbULCfU0FvwCCQrxwvg8iITkxk7ij1/VuG1p90TGtRqRxy2oEh9z91IIqpZpLcfF4+7kTEGbtw3479BjQiOZtYkDvEIOzq+Md5+AvPpF+bEzrMo9JT8vh5vVb+hSjI1uWO3/7o0ZOZj6bVxyy2LbhjwP0HtWalOtpxO06S5dR91e8i6KIRq1FrdKiVmuRy6W4lLPabEmTOjt2/ovYBXslQDATUmW5mqyas4WcrDxGvNanxDahkb44O99fq0TPES3pOaLlfT2nHT19R1jHGxSnLFeAscNasevARQoMOapz85V89uMG3p/ci6P7LpGckMGK33bTd1RLVi7YzbG9F3l8fMd7ViDpXnL+dALrVx0DYNv6OLatt8zI8db0QbTrUvrK0DsvLCT++i19gKZEv6okk0mJrRfKC2/24sKRK7w38HNmrH+H0OrWE+c5szbw95L9NmMc3vl08D0R7GmJmbzS63NGvd6b4a/0vCt9bl5+kLjDV02fR77QmREGa/ibPabh5ObEwQ1xNOpShw+Wlc8vu1Pv+tSoE4LE4FbWtAwXqMICFTOm/Mnxg1cA8A1w5/Gn29J7SNPbu6iHmMiawfy4/nV97nMHOdfPJ5lWUR2dFfiHlj1Ru3EhicWz1gLQZWgLGrQtO9jVFtvWx/HVBytNFWONdO3XgFff72/6vGnhDtKTMhkyuS8A6SnZHNxxDlErUrVmFWLqlN+AZefhxJ7WsXzYBXsloCK+4RuX7CHlxi2TYJ84tR8TpvS2aOPgIEPhcH8qyqWmZPPphyv1lQ4lAhLD3+ERvox/qct9GYOd2+fqpRQW/LiFV9/rz3Oj2vLV3M2mffsOX+H42QQmf/wYrz/1C9cvp1K/WTWef7cfPYY2fSjFOkBgsBcjn2mHRqNFIpGg1YqsWXEIqUSCRCIgK8VVykhKUhaJN9Ktthvd2arWD2f20c9wdrOdaaVm3VASEzLYs+2s1b57FaisNkzGhk3qYbXv9+82sWbxPvqPaU2XgY2QyaQWqRFLonhg99bVR02C/dW5z5GTkUdknXAQBBIuJlGlWqCtbkxsWnWUjLQcMFROFQSBdSsO0Xd4c+Ql1H1QqzQmsQ6QlpxNbs79i6m4F8x8bTHHdp/XB/Ma/ny9ahJ+waUHijs6KYgwS1zgF+Rp+rdXgGe58q9n3cpl6/KDANRsUvW2BbuyUG0l1gG2rj1JbN0Qeg5sDOhXbs2/R/FXUpn11p8ADJvQ8Y4EuyiKTBv+NX3Hd6Veu/vvnmnHzt3ELtgrAebCR1eGYBc1WiRm7R2dFTzIDMPKQjUnjl232l6Q/3Dncv+vsOL3PezeegYvH1eef6MX2/ae52jcDQCeGd6aFo2iEASBgWNasWfTadr1qEOtElxvHhY8vV0Y9Wx7i21PVcBHGmDQ6JZkZeizo1w8m0hSQiY9BjSkXhP9Er5EIsHNq+S0p607xlKnYTg9BzQyFPQpClSuEnpvKm2qlBrkDrYrTubnFpKRlsu8L9cx78t1ODkrWHH0Qxu9WBJZPRCdqNNP1iUC1euGmvb5hfiQm5nP+t92kpWaTVCkP1WqBXI5Lp7V/9sGQPuBTajXusiCvmrxXs4XC2IF6Dm4SYmC3cFRjiAI6Mxyy/v4PdwuMcNf6oqoFS3cW+7VRK40MtPz2LvzPPUahlfYTSymZjBPvdQZuVyKXCFDLpfi4KjAP8iDwCpFE48aTauRk5Fn+mwu3o1uareLRCIhtlnMfSkUZecOsPuwlwu7YK8EjJrYhaHjOiBIBNw8Ss99/OYv41Ari0prZ6TmcONSskUbD29XwmNKt2TdLWwVYAFKTtFi54GTl1PIubh4FI5yTh27gUQqoXv/hkgkAm8+340nJs2noFDNybNFwmnMC50ZOb4jTvfZ1epuseTzVfw+/W9+OzcTT3/PO+6va987Lzzj4elMk5bVym54l1Ap1SUGtDsVE2OlBWSb4xPoSVJSlt4KrNMhc5Tzxy876DWkKS5ujkTWDmXeic94pv7r5GbkApCSkM663/S5tCNrVrEQ7G4eznh4u5gqdep0Ouo1rVpqakeFg5yJ7/ZFJ+oQdTrcPZypWa9o4rBjzTEObTuLRiPi4CRn4vTB5bq2+0V+TgG3kjLRakT8Q7xxdnMiMNSHPmPaUL9VjD6nukTAo4TMTnebWs2q8k/8twD8Pm8n772+lLmLniO8gvEqkdEBREYHlNkuKCoA82TG5u57WvHOJymDJvW64z7s2KkM2AV7JcDNwxnMUiDnZubx20d/Mv7LMVZtQ6MthfjRXef4/JVFFttadK3Nu7OfKvO8c3YeYPHBE2hFEVGnf9lpDS9erU7H5491p2P1qqWP3c2RQY83Q6fT6ZdvDS/ZQLOlWDuVh7ycQsYN/IacrAK6DW5C/LU0AI7su0R0bDDBgZ4m15j4xEwysvLx9nS5by5W9woXd2cCI/xY8tlKnvvC+ndVUS6evsm1C0n6YGuNPmDU2dUBbz936jaNKvP4xCspbF+2j8df73tb51cq1eTnqxBFnSlg1tFJjkcpmY0cnBR4llBoqn3v+ggCpCRkIooigiBw+shVapYSvA36nOn7zdx6Du48D4AuKxsJOga/0oec9FxqNK2GkyEdn5e/Oy0M1ZkDw31Nx2Zn5DF0TCsKC1SoCtW4uDvRoHX5Unh2H9CoxH0XTsazcbneUu3q4VRhwa4zTB5KSwN7JxzaeJLpT/wIwAdLJ9KsR30AqtcLo3q9+5+6UBAE0wTJKJ7Hj54D6A00kVH+/Ljg2Xt2fidnBWHV/JFIJab6JHYece6jD7vdwm7nrqLVaElPzChXW98gT5p31vvmGY3d0XVCSzmiiJxCFYlZOSXuV2nKzvLi5e3KuBfspaEfFlzcHGnSOoa1yw9xcv8lQiN8uXE1jYVzt9G6U02qhPnQv3t9dOjo1akOjg+5UAdIT8pk44JtePq6cfX0Tbo5juCDFa/SvGfD2+5z+7/H+fOXHVbbm7arXi7BnpmazeZFu25bsP+z+ig//LDZYlv37nV57fWSrYkhUf68PHOkzX1qpZqFszZYbJNKJWUKdm8/N6qE+4Cgj18JrxaAk7OC2i2r42iw5mekZHP55HWq1df3Vb1BBO/OH2/V1+mDl/ng6Z9Nn2s1iSq3YC8Nc+t8WW4l166kMu6Jn9Fh8B03PFNjagTx/c9lG0Fuh5hGUUye/QxSmYSq9SqXu5nRPUWjKbpv97oGR1RsMLP/ffWensOOnYcRu2CvhHj4uvP2opfL1bZu82p4+7kz4/lfTZagqJiylyEBpGVYjObvPUKMvy9RfvfGp9bO/UGr0bJ/xzladqwJwMjxHVm7/BBXzifTvEMs8dduoVJqWLV0P+Nf64lEIvDYHYjZe8m8aSvJSMtGJ+oY9+EgXMvIlQ/g4evGO0teJi0hg5z0XDoMaUFUnTsTRlXCfanfvKq+7oBEQCKV4O3rxvBy+sLHNq3G3GOf3fb5bVXxLct9wNHFgVpNba+YKRzlRNYIQmVwt5NIJISXw52hdcdYwiN89fdBEJBIBeo2jSLALMvNwk9X4hngRYs+jUvty9XTmdBqASgc5Tg4yomoUf6qz+fj4jl7/LpJZOtEHeHRATRsFU3r7nUIifJDKpUgV5TxyhMEm6JeV5Lr310gMNzXYqXhQaHT6fh+4jzGfTHadJ+Mno0SiQACSAThgQWbH9pxjoy0XNQqNSqlhsAQb5p3qvlAxmLHzoPALtgfAVSFKi6evGH6HFnOF52kDD/zozcSScvLtwv2h5iUxEymT17C2ZPxTP1qOK061cTH352u/Ruy4e8j7Nt6huadarJ/13nad6/zoIdbJjv/OULiVb0bzxNT+pZLsEtlUvxDffEPvXuiSCuBoOgAvUuMVl93oEArsmXtCYY+2eaunccWarWWxYv2Wm2/E8unk4sDN29moSwsio+Z/dlaajaOJKaUuhBbVx9j3bKDFtve+nqEhWBPS0gnJyMPjcq6+qg5tZtWZc7Wt25r/Id3XWDB15YrBN0HNaFhq2iq1Q6hWjlrW5T0SLyXgr2yIAgCHYe1tkhqMGxMa4Y/cW+/z+Vl3pfruHzmpulzi0417YL9UcEedFou7IL9AZKWmk1erlIfuKkr8pXU6XT4B3rg7u6EqlDF1VPxxDQqeZm9IF/F6Dd661MrClC1ZpVynb8swQ6lBJWakZddwJ+ztxgsWyKiqKNh2xo0aB1TrnH8l7h4KoGwagH3rVS4k4sDacnZAHzz4d/E1gvF29ft/+zdd3gUVRfA4d+29N4TkhB6C7333jsICNIEFUURPkApggUVFBREEMEGKlJUpAhSpTfpvddQEgLpfdt8f2yyyZK2mx647/Pk0Z2dnblZks2ZO+eew6uTu3L84DWinsSjTlCz8OdXqWzmz01xCqjkg42dtaHkXyHlFJvj+KEbHNmXuSRjTOPEQg/YZTJQq3U4OtkYS1HK5XIcHfJeL0qSMAnWzZXVRcLTud6LD3yU53GZK6uPsrxcwHh6OvHpvEHI5DJkqWk+yMD+Oem2Wb2p6Wd2WkWhI3sus3PTmdT3BVzdHXhtcpdsK/ek2b3xFId3XDCutfAu40bd5pXyFGg//ZmpzuUCUBCeNSJgL0bfL9nN7p0Xs3zu3fd60rFrLWIj4vl24s8s2Gf4o7f79yNcOnKdstXK0PO19jwOjeaHTzdy5dRd42tHvtudBu1yrzkb5OFK68rlDLc5ZYaybHKZDLlMbmgII5PhYZ/7DGZSYgprFu002WZrb21WwB4Xk0hykgaNRodWozV0xNPoCKronW1FC3NJkpRje/SilJKsYeXXO/jrh30MGtue4RM6F+r57t15QkCQB45Otkz8pB/TX1tBTFQiCz/cwIeLhuLoZMtb7/XixMFrvDKxi9mdB4vbR7+k5z7PHbmYd1e8VSzjcPNwwLeMq7G5T0xUAlqtHhvbws/5VyoVbNxkXhMiczk42TL5swHExSQChpSY4PpB+JfPuTLIuI/68sbM1Aotegm9To+1nWVdhI9cuktkXCI6SY9OZ+iYrNMbur/WKu9HcFDuFa9eGNWKfi+3RAbGqip5+d23tbOiUdOKfDl5NdERcYYFxTpDB9pX3utVLItAi9s/f55gz9aznD9512T7y293zDVgv3MtjEM7TBuTKZTyPAXsb3/cD61Gh1VqnxG7fFygCiWMmGE3iwjYi1FOM4RpP1OObg68OKW3cfudi/c4vvMcKcmGhhTXztzFxtaa2s0qGffJrblGmh41q9KjZt6aYmRk52DDy1N6GP5IptZjrlbfvJbSs/63mvMZOiWm+W79OALLe+V5TLfPh7Bn7WFGffJino+RHxGPYzmw7QJ9hjUjJjKBSYO+4cHtxwD8sWwPPYY0xc0r64odubl49DoVagZik8WsX2x0IovnbOHQ7st8vfI1KlTxoV6TivQa0oRNq47y3/6rbPvrJF37N6B5++o0L4BbypIkkZKkwcbCQM1Se7ed587NcMJDY3B0ssGjflUO77lMs7bVALhw8DIxT+Jo3sf8Dpdx0Yncv/2Ye7ceI0kSji52NOuQ+8Xu29Oz7zRcGlnbqGifh1KVSpUCpSr7kovm+HbzYc7fCcvyubE9m5kVsOd3DE87e+Q6jx9Gm2yLi07Ieud8mjXwS97/vWQtspQkCY1aQ2xEAkd2X+LqU7Xx6zYub9aESpXagXQb3MR4EeXkYpfrQubslKti/poGQXgWiYC9GL00ogU9etVj3e//cXDfVQA+XzAERycbvH0MdR6tba1onGEB4KiPBjLqo4HGx8271qZ5V0OJtDP7LjG162e071m7CL8LQ8A+8M28VYqpWM3XGLC7ezriH+SBUqXAyip/M5VBwQE0iKrNb7P/4qXp/fJ1LEsd3HGBBe+vJyEuGd9ANxq1qoJ/OU8e3H6MX1kPJswZkOdgPfpxLFO7f0a3l9vyxheZK348CY/l0O7LaLU6vvxgPQt/fRWVSsmo8Z04dfgGD+5GEBWRfWUgS4U/jOabWRvQ6fR8/N3LhXpH48CuSxz895LJtnbdahkD9kr1y6O3sLnMyYPX+HzSGuNjuULOC6Nb8fKkLvkfcClw7PB1tmw8hV5nKOeq1xnKu45/txu+Zcy78M+vnMolrjtwjish4cx8qQMuDjn3qMiLfb8f4cbZO4z+dLDJ9ve+GYFeLxnuoKQ2tcqYl1+QtJKcab3m8sbcoQRW9aNL7ZnZ7rvt7MeFMoas/DhtNRuXHwDg5U8G4VrGHX1q2qZPGRezFp827xSMk4sdm345hNJKQXKimnJVRFM9wZSsCMs6Fln5yEIgAvZiFBDoTkCgO75lXBj5SmuQyfD3dzO7acnT3LydaTuoaYmoOGCuHgMbs36lYQFdq87BjHmnm/G562fusvrLzaiTtUz78VXsnbJPz3nyIJKk+GQCqvgBhtzL4BZVOLf/UpGlxkSExbBkxu/U71CThNTW6N98/De1NpbjrVn9OH/sFi+ObUdNM8r+ZcfF04mh0/sSejs80/d1cP0xarepzkuvtebnJbu5eTWMNT8eYNjrbbGxtWLKZwNJSVYTnMcZrqclxCXzZt+FxMcavte9W87StkedAjl2VjL+EyqUcjr3rstLGTqWWttmn2d89/J9lColZSqaztY+fZdLr9OTlGBeQBEeGs3B3ZdBkqhZP4hK1fzMel2ay6fu8PDOY0OuderaFXdvZ+q3zv9dL3OFPYzmyP5rmbZfvfywyAL2TvWrEBzky7YTV4iITTR57lF0PI+ib9C8RhD9WhT8ouhWA5rQemDTTNur1Cm68op3Lz9AJoOUpJITyMpkMvpN6MH1S6EolHIqVfejXtvc7zxlJex+JAe3nTM+dvdyonnnkr/AXRBKGhGwlwDuHo64uNoT8TiOx49i0Gp1ePk4Y2Wt4uqpO2jUGsqU98Y1l1nZwKplmLI8c33jwrTrt4MEN6/C+eO3uHHunjF3tGG76tRrlXvg4Rvgyt8nPkCpVGQKqmMi4ji85QwASfEpOQbst8+HcPtCCAFV0utay+Vyhs7oD0Byopqw+5EEVvRi14ZTNGxVBVePgm1fvvW3Qxzeeo6Hd57QqW89dqw/RXhoNL8s/pcxU7rx/rcjcHbJf97lwIndTd4rSZI4f+wWf339DwqlnIEjW3B4zxWuX37I6h/307RNVSpW9aVSdcsCytzYO9rQvnc9Nv56GIBlczZTv3klnFztC/Q8aTx9nClbwQuZzHA3ZvwM82uYh1x+gLWtVaaAvVaj8sxZ8Yrxlr2tnTUVa5i3+Pb+3QiWfbENgNcmdrY4YN/xx39sW33UZFu9llWKNGDP2FUSwM7eGidnW8oGmX/Rv2LuFm5ffgiShJe/G29+8oJFYxjS1pCKc+52aKaAPY1USImnJWGNy88XvjB5vO3sxznOshcV70B35m+bmu/jaNWm/Tzyk75058YjYqISSExQk5yoxruMC9Wfw3UFwvNJBOzF6Pb1R6z+YT9arY7kJDUnj9w0Pvft2jcoX9mHT0cvI/xeBBO/HkGnIc2LcbRZ+2vhPzh7OHJy72X2bTxl3O7kam9WwC6XyzMFDWm8A9zp/nJrrKxVWNtmzo/W6XTcv/qQstUDaNilDg271Mm0T0hIBAEBbkwaupT4mCQ8vJ25dPou7XvVZfLnAzPtnx8D3+zI/k2nuHP5IQ07BOPq7kBURDw7N5zipTfaok9O4YtRK/j60Kxsv2dzyFJrRW/47QhhD6KIuBfBkZ0XmfbVSzTtbkiHmjyrD28NWYZGo2PBrI0s/m1MgQcnkiRx6bRhIZpSpaD30GaZWtwXpDF5TFORJIlmvRugUGQOFFw9HC2+cNu7/QJH9l4huG7+AoWW3ergX94rtbqTofqGV5miLaHapVcdGjWryNC+XwPQe0BDXh7T1qJjXD55m3NHbgAQlI884x6NqtGwsj8KuSEFRSlPX/xeM6hw85dvngtBr9PhV8EHeydD6s3EPgtITjSsFfIJcOf9H18p1DFkVJSpL4Wtfd/6NOsUjFajRaPW5XmB+6Y1/7Hsi63oMjRx6tqvfqkL2P/8aT8XT97h5YldCKyQ93VawvNHBOzFKCYqgX1PraBPU1LL/k7p8TkKpYKXpvamRpNKfL59Ojb2NuhkcspXK4OEoVpEzSYV832ugMq+jPsy686MAOpkDSe2n6Vs9aw7u+7YeYHPPtvM7NkD6D6oMYs+3EB46kKyk4euExuVUKCzwVY2KiZ8MYTJfb/izP4rvPZuVw7svMjY6T1wcLKlbtsaVKhdlsf3IvAum3P1jdz89cshfkztTFm9hmFm99uPN1K7aUWc3ewJqujNsNfbsnvrOSbM7FUgwbpOpzfJW5XJZPzvk/7cu/WY8lV98S+Xv++psKQkqfnlg7W8Nm94ls/PePl7Htx5YkwxGjKuIx37Zd/k59a1MPZuO8/ebeeN2/47cI3+w5qZPaYN645z7nSIsZQrAJLEwGwaGxUWpVKBu6cjy38fi0wmw9HJ8jxxn0B3YqMSkMlkVK4dyKo565H0el56r79FxxnYumjX3mQ075Vl3L54j0/+mkTDzoZx3LkaSlK8IU1Fq8m967OQNUNVl/yHGgHlPHDzcORxWIxxW1LqBVVpEBuVwNcfbuDiyTtER8QT+TiOYeM60KBl/rv5Cs8HEbAXo4AgD95+rwcKhZzzp+6ya/NZ/vd+L9y9nPD1N+SPfvTbm2jVOrwC0mfedvy8l7aDW5h07ft17mbOHTbkospkMuq2qsrg/3Ut0PHqdHpCbz9Gk6IhMTYJAEdXBwCadKpJk05Fm5doa29D///1yPK527cfs2nTaQAWLtzO99+NYvu6E1w7fx+5XManP4wqlNSN6g3LM+vX16ndvDJKlSJTTvf/lr6a73PodHrqN63ApjXOPA6LIfxxPPZONkRHxLP0441MWTAEgBeGN6Pv0KZY5dbd0QznTtxm4UcbmD7vRSpkaMxVropvia/eYGNnnW2wDvA4NIawe5HGxzcvPsgxYG/YvBKOzrbIwDgz7m7hIuKrl0I5sDdzHff2xZTbu2vrOSQJypbzpG2nYIte+795g3n8MIrQkAhs7KypGFwmX3eQAA5vO8uKzzYj6fVUq1+OiQuyv3DPr39/P0qddsFUbVwRVYaynN2HNic5UY1er8fRjAZdeSFJUuoaBim1wlbxdBEtDeo2rkCV4DJo1Fps7a2xs7PCy9e5uIdltuQkjUmJy2vn7/MkLLYYR1SCiLKOZhEBezFy93Ki+wsNAUO1izfe7YqdvbXJh3a56pk79C16eznthrQw2Rb9JJbQu08gdcYusHLupdAspVDI+eXiF7nvWEwiHsXw10/7sfJzZeVvh40txh89imXrtnNM+LgfSYlqbO2sCjXIbNC24LvvJSWk8POCbXQd1ISF7/3JjYsPaNiuGo/DYngSHkvtBkGc23+VvZvP0HtEC6rWCUShVFAQxe4un73HlNE/IUkS86b/yderX8fKOnMVnwc3wlg6+VdmrhmPlU3hlngsSBWD/XH3cUYGIIPyueT616xXlpr18rcoMT83PL58Zw3n/7tpuGuSesEgQ0aVOoG8m3qxZq6oiHjOnLrDbz8ZqoFUrOxDUqKaBk0q4OWTezB093oYX0/7ndC7EUQ9MVQfGjS2PSPf6W75N5bB1dN3uXfdUOrR3ccl1/0lSSIhPgWNVodOq0Oj0ePsYoudXe4pWj/P3kD4fcMFW0A1f+q0Mvz+Htl+nge3wgHwC/Jk5FTzSnmGhkTwJCzaWJve09eFMtncfXp4+zGvNP0AgJHTezNo/PNRnSgvtFodb0ztTnKShuRENcnJatw981ZtqzjYO9nQbVAjoiMSDL//MhneRbSwW3g2iIC9hLCyVmUZBGVlQ+TyTCW1xs0dwrgCHpNOqyP07hMiw2Ko1dzQBCk+OpH46ASs7axw9Ur/gx79JI513+811mV2crWn5/AW2R26wJ06eI1P3vqFpAQ19XrWQafT4+BgTXCwP0eP3uT334/Ro3sdbLPIhTeXTqtDoSzYes/mSknWsGfjKR7cfoJvoDuXT9/lyPYLNO4czH/7r3H2xB3qt65Kl/4NqFqn4HI6L50NYeorPxlbs9+58YhVy/Yy8u2Omfb1CvTA0dWe6MdxeAW4F9gYCsvl03fZtOoIx/dfNVb1AQoknSs3teqWRaXKsNBaZpit9zFjxtDVw5GgKr7GgDBtlraMBQtF09y68Yg5M/8yPr5xLYyvPtvMR/MGmRWwx0UncvVsiDGvWC6Xmf05lpOylX3pMKARMpmMgEq5Tz7ExSXTv9t8k23TP+xDWzNq6k9YOJyURDWSJBFUNX3BcVq1LqVKgVxh/hXWpp8PsiG1HCJA/1db88q00lu3/1FoNNcuPURK/Vnz9nOhanDmiaSn3brzmJVrjqDV6tFqDQ3x2rWpho21Cnc3e2oFZ53KmJW576/n4tkQwp6qjf/SK60YbuGai+Ji72DDuA/7FvcwSiRR1tE8ImAvZskJKfz0yXrjH92+Y9pz/+oD5gxbTKfhrXlzwYhMrzGn/m1eXD9/n6SEZGo1qcjfS3cQHRHPqq920G5AY2PA/sPM39n2y356vNKWt74cZnxtdEQ8fy7bbXzsE+hepAF7pWB/VFZKkhLUtGhcgccJydy4EU6lit5cv/6IqKgEHj6MokIFb4uPrdXo2PDDXratPsLX/0wulg57Lu4OdOzfkHU/7OOtj/px9ugNIh7FEnLxAZ6pgdXAMW2p3dC8hlXmqlTdD/8gD25dNcx21mlcnu4Ds25MpLJS8u7ysQV6/sL06GE0ezefzbS9y4CGhX7uLt1r06V73nK2R03J3+x1RtmtbTB3DY27jwuD/9cVdbLG+BorexsiwmMtThPKSC7puXP6FjNXvmXWxd+FsyGZtpmbd16vddZ3xCrXCSSwkg96nR4vC2qwV6ppGsxK+uzfTAdnO/q93t5wvrpBZp8jJwkJKSxftsfYNVqbesehSfOKdOpm+c/cuRN3+OLD9cbH1tYq7B1tqFbLn/fnZd+YLjY2id37TNO+jp+6A0DLZpUsCthDH0RlCtbBkGYiCM8LEbAXM41Gy98/7TM+btWrPnVaV2f+ng84vOF4kY5l65qjhFx/xBe/V6R8zbLEZNHZz9bRBntnW5P8eTBcRARU9Ean1aHV6PAwY3auIDm62DHqnW58Nf1Pflu0k8mLhjLpnTVs+ecsP/4wmseP4ywO1pMSUgi/H8mOtUf567s9APz6xT+M+bBoGzGlqdO0Iut+2MeK+Vt544M+zJu0hsjwWMZ+1JcWnWrmufpCTlQqJe98+gKTX/6BYWPb02tw4wLLs30cGo1Oo0MmM1QEyk1aYx+dXo9eL6HT6dHr9YamP0i4uFi2JsGrggej5/RDq9Wj0+nRag3He/g4Fhc3hxxfe3zXeaLCY9Fp9VSuG0SFmuYHHyVJhco+fPzliyaVamQyGeXNmNUGuHfrMSsX/2uyTalUkJykZuhbljVTS7uLI5PJiH4cy/XTd1AnmxeQXb70MNM2jTZ/C0WPbDtPQupanXIWlOysVj+It2e/YOz6XKVW9ne8nN0dePUjy8pg5ub6lVA2/nki03YPT0c6dcviBbl4uldBSoqGlBQNcTFJOb5Opco+vNBYuIi3XdeaVA0uw+NHsdjYqLCxVWFjZ0XNukVXL18oZKV45ruoiIC9mFnZqOjzaluQgaOLPWUqeGNjb4NfBW8q1897gx1LXD0bws9fbCXkxiO8UnPqajSvYszrzDgJN2b2i4yZnXlWJaCiN9/tyn/NXsh76knH/g3YuvYYV8+G8OhGOO/P7GNoh+1ki3MeFo0tn72JXeuOMWf1m/yz8hDJiWpO7r2MOlmDlRltuQtag9ZV6dCvPrv+Osnh7RcY9U43mrSvTkAhlwYrV9mHX7ZPxr6A7yxMe/EbHtwKx9pGxYbrua+NSE7W0LNL1vs5ONiwYctEs8/9TodZVBjcktUbMgc2ETGJVK+SfYAWERbNwokrefIwCoCXZ/a1OGD/YdkedqY2k5EyTGe/NaEzrdpUy/G1507c5tN3fjcG18igVcdg3phiXjQWeusRGxZt5Y0FI3F2saNx88rERSdyav8V9Do9MpmMm4lqqjUol+tFoI2tCjdPR+QKOXK5DLlCjpevs8XBOkB8dALLJv3M5J/exK+8Fy16N8DWwbwyoV5eTqhUCpNAMC+VXbZvOEV8bBJ6vUT9jjWRdHpkcplFpT9Dbj7m61mbjI8dnW3pPawZL41tb/F48mL6pNVZbtfm8QKmXCVvXnq1tclFnV+AOx7eOd9BsVIpkMtlqFQKVCoFCQkpODnaYm9vjZuFC/57vlD4d70EoaQTAXsxUyoVrP/GUJ6v35sd8fB1AQwVUBp3r5fja29ef8S/Oy4Y/+Ab/mP4/6DyXmbfco+NSuD0oesAJjPjrl5OLPp3Oo4uhVMhIaO0lKCz+y+zePJvTFz8MsFNK1l0DLlczqTPB/L1zHWUq+pL5TzOej6884T9W84SWNmHpPgU1i7excgpPYmNSmDQWx2LJVhP89r0XpzcfxV1ioaeQ5thY1c0izsLOljPC0UOM/t6vT7b57Iyb9f7bNl5PsvnNNqcj2XvaMt7P41BoZCjUCpwz8PdpISEZCIi4jNtTzZjRlmj1hETZXr3K2MOfm58y3vzxoKRJtvmjf+V47svGR87ONvxwY+vENw45zKTtRqV57d908w+d04cXR2IfJLAiGoT+fnyfBp1Nj99o2ff+rRqWw2ZXIZKaVhHo8yiY/SauRup0qA8ddtlXY1nzY/7Cb0XSaXqfty8GoY+deG6i5s9r79v3lge3n1i8jguJqlIUzdefq0NkmT426JUylEq5djZW1Opat4W2leo7EOFPBQxqFjBi91b3jE+LqqO00IpJKrEmEUE7MVMJpfRfpChNXalOkEWvfZ+SAR/PNUpMU3zlpXNDtitrFW4ps6SObunpwKorJRUzOF2bkEJvfOYxZNXUrNpZSLConl4K5zFk1ay5OAHFqdfBFTwYt6qvHd71en0vDfiO8LuRfL+spepVj+II9vP0f6FhrTr14Crp+9Q08ILiYLk6GLHN5snFniX1uJgnXpb29wLILlChru7g3EmV5E2qyuX5WkxcbXKPtSvHcjJp/KfnXObVba3plqDorn7lZUsu37mMw7q+0ob/DIsWvUN8sw1WC8MjTrXJqpu3tZhOJsxseBXwRtnj+xnhuUyw8/WolWvM7jDXKKeGC6qJAsaY7h7OdGotaG2tkwuo37zSlSwsAtufgwY0rTIzpWTp4Pz0h6sb/x2B7tWHqBu2xqM+iT73H1BKCwyyZJPomdcbGwszs7OxMTE4ORUNOWiwh9EMmXgYsDwgeYX5MEnK80LOPftvswn7/+V5XPNW1bmwzkDCmychWn+W8vZ8dshbOytWXb4IxaMW8FL7/aiZvPKJCWk8MuC7XQZ2IiyhVCqEuD2lYeUq5r+B3Xf5jN89vZKvPxcmLZoGO/0+4oaDcvjG+DGwX/O8P2B93F5BgLm3Gg1Wsa3/Ri5Qk6ZCt5M/en1HPe/cOQaU3rMA2DBzulUrlewC2ALw6+/H+WHXw+YbBs+qCmjhxbugukzp+9yO7VkIKTH2/UalCOwbM7VXlKSNURFxKc3XZIkbO2tcXXPOe9eyF3Ygyj0Oj1+ge48uBuBhGQM4n1ECT7uXQsl9O4TJL2exhbcASmptvy8n39//w9rWxVz/pyQ4773r4cSducxrl5OVKgdVCTjKwzFEefkNpaKU2ajsC6au7i6lGRufD69RHz/lhIz7MVMq9YRFhJhfKy0Mj93u069snz1raGKjCy1iHTaJIZjISxALCyjPujP4weRjPqgP96BHsz4+Q1unLmDRq1lXO+veHDnCVfPhvDF2rEFtuDx3p0n/LfvCjZyGd+8v46h/+vMS293BqBV99rsWneCE/uucOCfs8z69XVqNChPXHQC+zad4seP1zNpYfaNeJ4l9dsHo9PqcDbjAsXO0ZYaqXcfbEtACo05+navS+d2NVAp5cYUgpwWyxWUOnXLUiePC+asbVQieCwkGd/XMmXzV5pUq9ECoCyCn6ei8vePe9j0/R6s7azYeP+bAjmmOllNdHgsXoGWlyXNr/B7kVw+cQtb+9zXSvhX8sW/UsluEldaibKO5nl2PklKKVcvJ2Z8N8o4W2ZnxgdHGmcXO7NuA5d0Lp5OzFk/yfh4/59HSU5IoU6bGrTqUYfVi3cRdj+SyPC4fFef0ev1rFy6hz+WH0Sj0TH6TUMN39WLdtKsU03KVfVDJpPx1sf9GNNpHhuWH6Bd3/pY2ahw93FhxJQeLJ35Jx0HNaVWs+JLjcmr0PuRnDh4nZ4vNs51X6VKyaiPzL9LUz44gLmb381xH61Gi0wuL7DSpJIkodXoUCjlebqYc7C3xiH1d+5xWDQpiRpkchkyuQwPH+cC6RL7tNBbj4h5EkfVRvmv9x4RGsWFg1doPaBkpEHk1c2zd7jy3w26v2b5YtWnLXvnV8bMG5blc1t+O8yeDSeZsnAonn6mFz3fT13FnrWH6f5qe16anv962X99vZWEmCRenjUQgN++2MyTh1H0erUdf369DWtbKwaO78qDG2GUrVYGrVrDyo/XMfnHvKf0ZeXG+XtEPorBykZFnRZV8nWsgMq+1G1dDesCXDuzb90xFrz+A1tilxd52oyzhwNlKnhhY0aDrZzo9Xo0ah0atRYHJ9sCGp0gmBIBezGztbemedeCubWo0+qIfByHTqtHp9Oh0+rxCXDHyrp0/TN3eyW9msKLb7TjwD9nCbsXSWJ8MpC/gF0ulxP2INpYTWLHP+dp06seezedYuG03/nyz7dRKOR4+7sxYnJX4mOSTKqw9BjZip1r/2Px1DUs+Xc6SlXxNFLKi+uXHjBpxPeoU7RUqOZL9dqFvz7haV//71d2/HaIsZ8Poddr7fJ1rM/HLmfvekOVl6V736NsDpVdnpYQk8jVU7eRyeXUbV2N5CQ1cyet4cLx28Z9Fm8YT4Vcup7mhUatRZNSMIsQVVbKTCVWSyO/Cj64eOXvdztNdukKD+8+4fq5e1w8cZvE+JRMz7/62RBe/cyyTrE5CarujzpFa3x8aPMpbl24T/Nudfh37VHsnWzx8nNlxax1TFwyijYDmjDs/YIt8Qjw2xdbOLr9HL5Bnvx09KN8Havn6Lb0HG1+o6JTey7ywaCFBFX3Z9HerFftOrs7YO9sS0qSOtfAOTkxBRs7a66fukWleuXZsGgrfcZ1teh7yKjvmPZIHk5071E3y+dPH77O8i+306RdNYa82Z6rFx/w6dTfSUnRUrGqL59+PRSAGaN/4vSh6yhVCv6+ODvP43luiUWnZin9n/SlWHx0Ij99+AeSJOFXzosBE/JQJDeDRw+iGd3hc5Nt3+94B/9s2mKXBlbWKsZ/+gLvDP6WRTP/Yu6q1/M9C/Pup/1JTEjh6N4r3Lv9hOZtquLkas/VMyEc2HKGNr0M1Xn6jmqV6bUKpYK35w3m8cMoYyfENEv+t4IBk3ri6V8yu3yWr+JL2QpeXL/0kEUfb2LR6jeK/IJDo05NE7Ag9Ss78gz1oTUZAiNzhFwLZXq/BcjlMv6J+J7QkAiTYB1My5kWpMCqZSBDR828iI5JJCExBXt7a5r1Ti959+30tTy8HY6Hryvj5w/N71CLxIlD17ly/h51GpXH3Tf/qT4dhrbMcvu7L35DRFgMAJdP3mLzT3t587PCWzzYqGvWQWAamUyGk7sDPkGeqFI7XXuXLfjP6rTPKV0eylzml0wmQ5OSfoGq0+pZtnAHWq2eJi0rodPp2b3/BnWGt+fHJXt4c3KXbI+14LWl3L/xiC93f0D041gAylTOW4rKxYv3OXHsFnq9xMpfDxH6MJqyZT3o2du0MltsdCLXL9ynbCXDpI1/WXcCynly4vANYqMTjfulTYppNTp0On2hNTcUnm8iYC9GWq2W2xfuIZPJCiQ3W5FF+2xdLiXqCkpUeAzRj6IpVzNvebm3r4SyYv423vnixUy3FIMblmf4xC7Ualy+wG6ZvvFuN04fvUlKsoa/Vh1l9ITOyIGyFb1YOm0Nr306MNt/k8p1ylK5Tubvs/ebXfAo48bN62HY2lrhZ0F3xKKgUMh5+/3ejB+ylNvXwti46gj9R1i+uPLyyduGpkE6HY071LSozGXv19rTqFMtqtQLsvi8T6tSLwh1ihaVlRI7C29Dy2Qy7BxtjD9PDk62dOxXH71eQtJLqKyUOOfSPCklRUNMVCL2jjbY55LK9sdfx/lrw0n0ej06ncSkCV1omo8qLB/M3cTpC/cYOagpwwc1Q5kaIFw8dpOb5+8RYGbjo5Lg2IFrbFp9FKVSQXAB/FxkR5Xh4vTRvUj2bjhhDNhjYxJJSdZgZaXE+aka4T9+8y/qFC1vTOycr/PP2zQZvV7C2lbFz2c/QyYDL393ur3cJtvX/LnqCCeO3kTSS7wxsTNB5S3vuTDo7S50GNiEU3sv53nsiXFJvNV4Oj9dWmD2a6Ii4rkfFsuIz16iTLn0/PQNa48B4OJqh4ubPUcOXEOdosXDy4k3J3dhz9ZzRD6Ow93TkTZda/HwzmOiI+J5ceYLePq6suKLrVw5fZeGnevQsHMdLp++i5uXE94WrOu4eOEBv/x80Ph4/V8nUKkUmQL226kdntXJhgkBewcbvLwNd4JSMpRgzXgXW6PWoshD1arnmchhN48I2IuRi4cTC3bNsOg1YSER3LxwH71eT82mFXFxT18MaO9kS//RrVAoFak1ouU4u1nWoCI/8lpv6MDWc8ydvAatRsfSTzYxee6gTPsMLuCmI24eDrw0pg2/LdvLkFdbYyuTaNGnAcNrTyU+OpGg6mXoMizrmbrslKnoQ0qyhqlv/4aXjzNffjsCm2Ks2Z6VStXL0PPFJmxcdYRfl+ymZadgvFJr/5vrty+2cDL1j/9vZ2fjZuPM3rWHCajql2v1BKWDLSkKJdFxaixJNlnx/lo6jWiNX4X0QLTXqDb0GtXGorGnqdqgPH+FLDY+9vR1YeJnAy06xsF9V/ls1kYmTu3Ow+uPDM12mlWkXtPMuekJCSmEPYoxPk7JkBKzZfs5tu44j14vodfrmTGlJ/5+OQcf90MNTZtWrD3CrZAnfDK1DwC1mlXGO8A9U342wJyhX/PGgpG4eBZ9ZYS4mERioxPR6yX8gzxMLrzTZiMLe3Jh6qLh6PV6VFZKbpwNMZlxnv/RRo7su0LT1lX5cP5g4/YfFu1i68bTODnb0rxNFWrl44LC3il9vVFaZ98rZ0KIehKHT4AbT8Ji2P77MToPbETD1lUBCLn9hFPHbgGW1drPSCeTsXnDaQLMuNN67r+bnDp4Db8gD8JCIkhJ0tCmV10qBfsbg/W718NIjE8xlP2tkf2dorD7UXwzezMAA0e3olUXeBQWbXz+wb1Ihr/WhptXw9i68TSREXEA/LniIDevhFKzfhBtutbi2w/Xc2LfFYZP6sqLb3Yg5MYjzv93k9CQCH6av41DOy5SrU4gX67KuYpVRnJ55okfvT7zHzDr1M/v5GS1cVtacK5Wp9/Vs7JK/5zXqLXYiID9mTRnzhymT5/O+PHj+eqrrwDDOqqPPvqI7777jqioKBo3bsw333xDjRo1jK9LSUlh8uTJrF69mqSkJNq3b8+SJUvw9/e36PwiYC9lTu2/wqKpawH4/PdxuDRLD9gdnGx5ZWqPYhmXq5czrnnMQa1aJxBrGxVajY5/N5yiWcdgmnWskfsLcxAXnYidg3WWHVMvHrnG3JeX8NX+WbTuXJO9qw7w5Qd/ADBkcg9+mrWO2Cya2kDm5h8xT+JYt3gbI2b0Q6FUcOfWY2xsVFy/EsrypXt4Y0KnfH0fhWH4W+05sPMCkY/j+P2nA7z1Xs9s942NSiD0XiQ+/m7Gi7+M72lakJWUpCb0XhQuPq64p85AXTkbwv1bj/Eq44q7lyNajZ4Duy6x5sf9NGhWkemfDzC7IVO/Cd2xM6Py0Zwpv3Nkz2VmfDmYRi0rZ7tfYnwK+/45i6SXaNy2Gm5eht8jvU6PJBnSYczttiuTyfhjuaE0pJ29lTFgT4hJxNrOCqVKia+PC1Wr+HLlaihgqPefJvxxLBcuPTA+Nqd5UsZb7ikpWrau/Q91ipbeY9qbzDTeuxvBhNdXoNXqcXa2LZZgHWDDb0f4bekeALac+sjkvTWmbOgKN2CvkmHNxsMbj0zOlxbApdW4T0pIRmWl4ua1MOJik4iLTeL7RbtYtPyVXM/zy7wtaDU6XnijPU6u9rwzcDHdhzXHw8eZ4IbliQyP5ac5m5D0EnEJGo7vu0LPoc3Q6XQc2n6emo3Ta/xnvKGY1wmRKa/8REqyhrPHbzPm3W7otDrWLNuLXq8noLwnbbrXMe57+dRd1n67m2adgjm84wIAQVV9qRScHlh89MYvhIZEULmmPwvXjcv2vEpV+s+og6MN6hQt709aY9yW3QVaVOpnb9o6o7S89pQkNaEhERxJHVdEWAwxkQlIkmSSnmKOpwN2GxsVbdpVz7RfWsCeNsMOGQL2DGl4L0/uSstutbC1t861O7CQhVKQw378+HG+++47atWqZbJ97ty5zJ8/nxUrVlC5cmU++eQTOnbsyNWrV3F0NPxdmTBhAn///Tdr1qzB3d2dSZMm0aNHD06ePIlCYX56qAjYS5mMwWJ2JfTXrzvOti1n0OthzrxBeGTzR1qSJFKSNVjbqPKcanLy0HXUKVqq1QnEJY+z+Z6+Lrw+oxdfTvkdgK1r/8tzwK5O0fL3b4f5bdEuKgX7M+fnVzKltlSuX56Bk3vi5GaPQqmg55gOxETE07RHPULvhOPp7cSDG6FZHv/L0d/y1uLRxj8iN8+HsPaLzXj4utK4VwMmvLbcGAhsWX+SEa+2tqjyT1Gwd7Bh7LQe3L4axqBXMufpZ3Rs7xW+nPI74z7qR+N2VZEkiQFvdaTjoCYolHIcXQz/5kcO3uL4visMfrM9wycYUgd2rT/JllVHaderLoe2nyclWcOA1Jb1Vy48IDEhxeyA3SmX9JQ0928/Rp2i5c71sBwD9rjoBL6eaehhcHzfFY7+ewl3LyciHhtm+SrWKMOiv7IORg4fvEZSopq9/xo6gyrkMpRKBVqtjnPHbzP4NT1yuZz1i7bS6oUmBFYtQ6OG5Thw6JrxGCbB4lO/e+Z0bbXKUCpQrdHy9Yx1APgGuGVKDYiNSUp9TfEtkM643kOn05sG7IqCC9j/232J+JgkajYuz52L9/npkw0s3Tcz83hUCrQZZtgTEw2LUCOfxBP9JI4J3eZRsVYASqf0n7vaZs6ur/9+D8mJarq+1AwnV3vuXA3l87d/pUGbqgQvf43khBT+/fM4APXaBwOGz/W0nwMp40xvhp8NvRkR+80L97h25i6SXqLdC42xsbPKdEEkSbBy8S4AmnWoYRKwp61pyfgzGBEaxU+z/sK/ojedhjRPD2JzWTey4IP1xv/XanXI5DJCbj8x2ZaV9Dsuhudt7Q2z1UkJKSYXqnp9eq54dsfKTu8+9enduz6RUQks+/ZfatYKoHef+pn2Sw/YM6S/WGWeYXf3dsLdu3TV9BbMFx8fz0svvcT333/PJ598YtwuSRJfffUV7733Hv369QPg559/xtvbm1WrVjFmzBhiYmL48ccf+fXXX+nQwfD3b+XKlQQEBLBr1y46dzY/1U4E7MXs6J7L7Fh/Et9Ad16dnPtqd2d3ByrW9Ecmk2GTTSAYFRnPzRuGpizqHBYavT1oCdcvPmTN/mm45LHpysIPNhAeGk2z9tWY/uVgwh9EAYYc4Kxuy2enfZ96/LfnMgEVvBiSx/SXc0dusOi9PwisWoakRDXnjt1ix7oTdBnQyGQ/lZWS7qmVaK4cu46toy2vzTHcBk+IcSDyUQwXj1wnKT45Uz3xST++YXJxs2/dfwyY0I0uI1tjZa2iW+967N9zibjYJNRqLcuX7uHNSdkvpCouLTrUwN/PmZRENVbW2aftfD/HcEv75P7LLJq+FndvJxp3qklUeBxdBzfBJrW8m0yeOdhIu1Dy8HFGnvqHNe298y3jiqd3wVQFySgt4I7K5g5JGlmGi7i0C18Jw90TSZIyLSjOaN4nfxMfn56eIJPLaNUlmN2bz3Lh9F3j9zh0Rn/jPnKZDCcnG5ycbImNTXpqdtf0XFndmn+aVYZFuxqNDrlchl4vGRf1psm4rqWwZ7BzkjHQenocBRWwT35xCZdO3kGSJN77ZhgPrj7M9pj+5b3o+1o74x0zvc7wnms1OhLjk3l0L4KUJDXVuqXnNFcNNm+hcNqx0n7m0wLttJ8LmTxzEC6Tpe+fMS43mQk2I2A/tusCv3z2NwBNOtfCxs4KZerFkeHukenP9tMXh2mLwXVavfFnKjYinr++3kajjjXpNKS5cYY5JZc7QV37N+Sb2Yax6DR6lEo5r43vyC/f7SU5SYOHV9YBrlJpGoSnpZckJarxKuOKXCFHrzOsBUm7wNBZGLCn/cx5ejoy4/0+2e7XqX8DOvVvYLI4v0adQAYMb469Q8maiCnVSvgM+5tvvkn37t3p0KGDScB++/ZtwsLC6NQp/U66tbU1rVu35vDhw4wZM4aTJ0+i0WhM9vHz8yM4OJjDhw+LgL002bftPIf/vUxlM/8YNOtSi2ZdanH7amimxVEAEU/i2L/3ivGxlMMf/7RAbefG0wwYZVm+dpq0D/zjB64TERbN6FaGH+ZKtQL4evOknF5qQiaTMX3hS3ma6Y8Mj2XFF1tJSUzm/q1wYiLj8SvrzsO7Efw49x8at62GazaNf5w9nEhOTC/x5hPkyewNk6lUJyjLxZRPj++t+cNNyuq98mZ7hr/amvVr/2PVioNs/PM47ToHUy3Ysly1wqTX61m/ZCcrPvyTLiNa8eaXWdeshvQ/ymmBg1ar59SBa4Tdi6Ru8/Q69OkpBWTaVqachzFISD9O4VSsSPv3yS22kWWRw5qWCiNJUC+HGvs2tiqTgD1tZrRMWXd+2DQhy59hZ2c73p3YjdfH/Zw5YFc8PcNuRsCeYYa9Z6faLNt6iZQkTaZZz5wC5aKU8bavVptNwJ7PHPakhGTjxZdeo2PF7E34V/TOct+yVf145YN+xsdp/2SSXjKOR6/TM2psew7uMXye1jSz0ZVeMnwfaT/rDo62JMYl454aoGYMwtM+n2VymfGXR8oQRGf8yTAnJcZ0BlrKvC317oYx6H3qPbexsTLu5+HrApKEQ+pdtLSgNS3lI7efpzZdaxLxOBaVSkFw/SBkMhkvvNSUWvUM72PaBXvTVlXw8nE2/t4EVvAi9H4Uzqnn7f1yK9r0roe7t2Gf30/NQqGUo7JScuLQdRLjU3D1KJwuv1mVTK3XuAL18rFgXCgZYmNjTR5bW1tjbZ35ImzNmjWcOnWK48ePZ3ouLMywKNnb2/Rzxtvbm7t37xr3sbKywtXVNdM+aa83lwjYi9n+beeB7NNbsrPue8MshXcZV/R6icbtq1OnaUVSUrTcvxdp3C+n46bNXKxeuifPAXvah7ZcITOZtczLVaylwbparWXP5jOs+3Y3926GU7tpRayslcRFJ1KtQTke3o0gOUmT40WLb/nMf9BrNMk+WEtKSCHsXgRBVXw5vPUcLp6O1GiYnnNqa2eFrZ0VQ15uydFD17l39wkRT3Ke7S1qmhQt21bso3HXOgzLpUFMWmCb9m+j0+iM6QwZ/2BnPcNu2Obs5mAMGozHKcbgETKnoQDpETtSjpVvqgf7c/5sCAnxKdSo6Y+7pyMyZKkvz/lnOH02Of19UsgtD9itMwQSvt7OqKyUpCRpMs+wK0tKwJ5hHE8FiWUretGyUw3KV8lfZRsra5VxVpgsLshykvbza2dvjZ2DDd4B7tRpWQVHZ1tq1SuLSqUwewH5mA/6odXocHA2LDKd9OVgIh7FUDH1ot3Vy5mv/p6IXC7n9NEbuHs7UynYn4AKXpz/7xZOGVILW3WoTmA5T2Qy8AvIvepU694NqFSnrOGOTuqEzohxHdCodYYLw9SfT4Vxltr036JRu2p0G9KEGvXL0S5DxRQpRU311CB11LvdiHwUazLOrDg62zFyXMdM2ytXM11u3qRlZZpkSF8b/35vkhPVxu6jZcp5UibDgtmMOeKvvpu/UsjC8ysgIMDk8QcffMCHH35osu3evXuMHz+eHTt2YGOTffrm05/7T691y4o5+zxNBOzFTC6TocO8P9IZPbwbweXTd42P3bwcqdO0Im7uDrw1vhOLF+4ATAODp1nbpq2A1+TphwfSLwgUcnmh1a3Oilaj4+1BS7hz7RH1GpTl3s1wzh65Qcd+9dn55zGunw2hXa+63LlmqGjgZnk1tEwS45MZ0+ULJAkWbRjP4um/4+hqxzfbp2SaibGyUjL/2xE8eRxLYFDJqoNvbWvFl9un4+hmn+u/+dO38bVaXZZ5o86u9nj4OGOX4TZxxiA+7f3JeMu9MNg72hAVEZ/rwi8rayV1mlZEJpdRroovSqUCnwA3YqISAKhRP/vZ1Jkf92P3zouEhUYzZHhzAHb+edysi+4yfq4kJ2twyPA++Xg7Uy81yJIrZLmWiIT0PFqZDDRaHX1GtCAyPNbkrgeUnBn2arUDGDa2HQqFHGsb09+VFh1q0KJD/haZAyz4cxx7N5/h8cMogir78MoH/YxBc25GvNGOPi82wd3TEUdXe1Ycm2V87oulIywaR48RppMftZqYVg2yslZSJbUsbKVapkHDN3//z+RxnfrlqFO/nNnn9g50xzvQtA9El34NMu33/T8TkctlxhztNK4ejoyb1T/T/i+9k17MoGL1MlA9f30EcuKWzd1Q4dlVHGUd7927h5NTelpWVrPrJ0+eJDw8nPr109c36HQ69u/fz+LFi7l69SpgmEX39U3vCRAeHm6cdffx8UGtVhMVFWUyyx4eHk6zZs0sGrsI2IuZo4sdkY/jLO5W6Oxmj7u3U2oNd5lxNsLGRkWNDOkX5syw63V6NBpdntqwVw72x8nFjrHv9cTWzoqtIV9ZfIy8UKoUuLg5AI+4cT0cNy8nIsNjOX3kBmNn9adt7/rG26YF1RzIzsGGll1rsX75Af5eeYjXPujL3HG/sG7Zbl4cl7kajJ29NYH2JStYT+Nk5pqFX/ZNQyaXIQMmzh2EUqng7b4LAdBnCLrHf5q5Q2OzjsH4+LtRtpI3b37Yh6QENUFVfXHzcCy09t0vv92R6Ih4atTLOX3B0cWOOStyr/iRnXZPLYpu0602NRvkHlhNfzdzFac2LavSpmVVi87/ydTehtKtCjkymYyGdYKy3M/ZxZ51WyeiUMiNOdLFoWqtAKo+FZwWhjY96hj/P7Ci+TP21WoW/thKEktqlgvCs8jJyckkYM9K+/btOX/+vMm2l19+mapVqzJlyhTKly+Pj48PO3fupG5dQ6M0tVrNvn37+PxzQxPL+vXro1Kp2LlzJwMHGkoHh4aGcuHCBebOnWvRmEXAXsze/qA3URHxFi/A+2DZy9k/mbGyQA4VJ5q2rYZPGdd81YydtWR4nl+bX03aVuXM0ZvExiRRp2EQzm72vD37Bapm0dQo5MoDju84R/+3897G+v7tx9RuVolD28/z5/f7WLZtMnVbVmH1V9tp1bMufiVsJr0gZBVYz1s9FplMlutFZo36QdSoHwSAb4ZZv3KVC6+pT/P2mUuzFYUGzbNPoyoM1jksFM5ILpfhZOYssyAIQrEooYtOHR0dCQ4ONtlmb2+Pu7u7cfuECROYPXs2lSpVolKlSsyePRs7OzuGDBkCgLOzM6NHj2bSpEm4u7vj5ubG5MmTqVmzprFqjLlEwF7MmrStVuDHdHS0oVXbajg52eLv757tfi07B9Oyc3C2z5dkCfHJrPxmt/Gxq7czH347HBvbrNMJTv57ge+mraZyvXLUbGHZbCbA9t+PsfiDv3B0sWPMjN58Nn4lP36+mTc/HcAbHT9j5ZdbeXdR8V28FCU7M0sxCoIgCMKz7N133yUpKYmxY8caGyft2LHDWIMdYMGCBSiVSgYOHGhsnLRixQqLarADyCRLVzs+w2JjY3F2diYmJibXWyVC8dv653F+/2E/497vjZSYyJrPNzJvZ+aay2DI31025Tf6jO2EXxYLTXNz7fw9JvRbhCRJ9BvVivCH0Rzcdo45v45Bk6SmeoNy2BdSmocgCIIgFISSFOekjaXK+NkorItmIkiXkszVhdNLxPdvKTHDLpRanfvVp12POljbqEiKT2b8N6Oz3VehkDP2i+zLF548eA3fQHf8UlM3dFqdSXOXyjUD6Dq4Mf+sOsqGnw/y6YpXkSQJD28n/MsXwIpWQRAE4bkhSYa+CVqtPnUxtnlpbsLzq/hWIQlCNiRJIjlRnet+cnn6h5ytgw3+lf1yeUXWQm48YsaoH/nmw/VIkkTYnce81fojjvxz2mS/kRO74uRqh16nJyVJzYxvhotgXRAEoQioUzScOHyDo/uvcub4beN2SZJyrdAUdi+S04evZ/nctQv3WTRrIxdO3jF7LHevh3H9/D2TDqi5mTNqKYMr/489fxwFDFW2ejX4kH5NZrHi651mH+dZlFYlpqi+SisRsAslyuPQaD4cs4JP3vrF4tr02dHr9ez47SCX/ruOOlmdqSteYEVvOvarz6mD19m35Swunk4kJ6j5evzPxEam11B3dLHj+x3vMm/NWBq3K57FjYIgCKXNo/uRPLz7hKSElNx3zkZcbBLvjfuVD/63iqVfbAVgwZQ1dCs/iS8nr87xtYs/3MD0l39k1thfiHoSZ/LctnUn2LL2GO+P/YWvP9pg1limDv6Gt3vOJ/xhlNnjj49OJOpRDEmpTddKSslVofQQAbtQooSGRHBsz2VOHrjGnk2nc3+BGS4euc78N37km0krWTtvEz9+/BfvDF2GOiV9dmT0u91xcrHju9l/o9Xpmbx0NIFV/EhJMp3pd3K1J9iM8n2CUJKE3HnCjauhxT2MXD26H8mdq6EmDaDCH0YTei/CouOoUzTcuf6IO9cfkZyo5vTBazlWzCooOq2Oq2fvcePig0I/V35JkoRGozN2xzVnpjqvxvdewOjWn3J8z6U8HyOrADcuKhGAiEcxOb428rGhq+WRfy+REJds8lxaE7XKwf40zNDAKcexZNE8Ljf/W/wyy898RpsXGhvOK5cbm8s9PYkkCFkROexCsdLrJbZuOkWTFpVx93CkVuMKdB3UmK1r/2PZp5uo16IyLmbWDM8oPDwWDw9H5HIZNZtXoWXfhgRW8aPb6Da80ftr4mKSWP/zIQa91gYw1LUfPaUbC6b9yc/zt/Pmh3347O938tRM6lmn1+uRJEPJwJRkDVqNDlt7a5M/qJY6ee4uTyLjadu8Claqkv+xlJSQwtFdF9Dr9DRuH8ytyw+IfBxHkw41ci2TGvk4jgun7mBto6Jxa8srFmXlyJ7LREcl4u3rTL2mhkY9+7ae4+SBa1So5sfqVUeJjIhn65EZ+fp3KghRj+P48/u99Hm5JZ6+Ljy8H8nu7edRqhSc3HKWC8dv89EPowiq6seiD9Zz5cxdtFo9a4/OxMrMcpbXLz1k0vDvAOj7YmM2/Lif3iNbkOjrwK2QJ4x+sTmLV+xF0kvo9IZAtV2QHzvXn8TByZZlf0/I0/eWnKxhwoDF2NhZMf6T/iZ14Z+WEJ+MTCbDLptGWQ/vR7Ls650gSbRsV50OXWuZNYab18OIiohHo9Hh7uGYqbNomuvXwnhjzHJ8/Vzo1bs+F87fo36DcvTuUz/L/fMjLSjOz/VAxj4CaU3b5ArDcfW5BM4Zf240KabdgNMavDk42tDUzDunxo7FGvMDbQ+/zLXvFQo5er1OzLCX0LKOJY2YYX8OaTQ6dmw5y44tZ3kcHmv26/R6PVqNrsBmA56ExzLh1Z9Y+NkWvsuQwzd6SjfcvZ2IjUpk2aebLDqmRqPjt9VHGDbqe7btSG94MH3FGwyb3gd3bxeGjzc0OVq9dDePw9JnZjr2a0Bwg3JsWX2Uq2fvlapgPToinssn7/AkLDrTczqtjtMHrhJyPQy1WsuBHRe4d/txns8145Xl9Ah+j7P/3WLOW78yoM5Mrp0NyfPxdh+8wscLtvDJV/+Q8tQf0zTrVhxk4kvLWPv9vmyPExEWzZ3LD7l96T7Rj+O4fvo2f339DwfW/ZfnsWUnJjKeueNX8sXEVTwJi+bj15fz+du/EhuZkOtrt/91gtkT1zB74hqT2b4Htx8z8+XvmTHy+xxff+f6I/b8c5ZLZ9Lf85+X7GbhrI1s/uO4cdvR3ZfZueEUt6+FoU+NlCIex2U6XlZuXw1jwfQ/efTA/Fv+5po97lf++mEfkwcsNpzrZji/fL+PP1YeNgZPT8JiSE5Sc2L/VeJjk0lOVKPVmB/UZOzCGxZimJ2/ceEB90OjOXPxPpNm/cnVm4+4djucm3cfcyvkCYkJyUQ+jiPSzPcoK8rUmdfkRDXXL9zPcd8Zr//MmwMW89NX27N8PiE+hSP7r3LkwDXu3Xli9hh+XraX6RNW8cE7a/njtyPZ7pecmn/9KCyGPf9e5FAh3oVI66qdEJuU52NkNcOuslYhl8ty7RSuslbiG+hO+z71jE0G06RdCNy4/NDs7z+tGV9+A225MrVrtAWBv/D8sihg//bbb6lVq5axQ1TTpk3ZunWr8flHjx4xcuRI/Pz8sLOzo0uXLly/nvVCj4zWrVtH9erVsba2pnr16qxfv97kea1Wy4wZMyhXrhy2traUL1+eWbNmGX+5NBoNU6ZMoWbNmtjb2+Pn58fw4cN5+PChJd9eifHoXgSrF2zl8olbxMckZrnPpVN3OLLzAtfO37P4+OoUDV98sokvPtnE7ZvhZr9u2ujl9KjzPr8s/tfic2bFycWO+NR8vj07LnD6hGEhkb2jLW9+2BcHJ1vqNqtkXE0f+SgmU4rK0y5dfsgPy/ejVmvZt/+KcXta8H3jwn0eXHlI+Sq+pCRp+OnLrSb7vDWrL1bWSq6es/x9LU4n9l5mYv+F/LYw8x9/jUbH9CFL+Oe3wyQnqvl08hqmvvpTns91+1qY8f9DU4MhdTaBtjlu3n1MRJQh0E27Rfy0sPuRXDoTwt+rj2abB7ty3hbeaPsJY9vNZtuqQ1SqW45+b3ejZf/GeR5bduTy9I9OvU5vHNNNM1Ih4lOD9JRkDSkZFq0lJao5se8qJ/dfzTY1ISYqgZVLd/P51D9YuTS9D4EqLYDIEKimBRU3r4YSnXohsXu7ade+rCz9ZBMfvr6CHetOcCHD4r4Ck/a9pf5OHj1wDQCtVm/897dzsMHTx5lug9L/7SQLpsYMvXlT/z/130qnSz++OjVAsrezwsvDEV8vJ2OaQ36CJ6XS/LxkmVxG6L1IDv2bdZpIxvkCS773jJ2djx2+zsgXFpGUxSL+jL9rXt5OqWMunOnH4EYVAHh41/wLj6cpM1TtSntvp3w1lCkLh9F9aPMcX9uiUzDDJ3Ri8ucD8QlwM3nO2kaFvaMN7XrUMfm9zkn/V9syamoP3L3zVxbw029H8sXPrzJkTNt8Hae0E4tOzWNRwO7v789nn33GiRMnOHHiBO3ataN3795cvHgRSZLo06cPt27dYuPGjZw+fZqyZcvSoUMHEhKyn3U6cuQIgwYNYtiwYZw9e5Zhw4YxcOBA/vsvfVbs888/Z+nSpSxevJjLly8zd+5c5s2bx6JFiwBITEzk1KlTzJw5k1OnTvHXX39x7do1evXqlce3pXiFhUTwy9zNTOz5JX8u2ZXlPsvn/cOsN35m44qD+TpXxOM4HtyLJMqMmcG02S8pw2zGovf+YNpLS/J0bisrJePe6ZZ+rHn/oEn9Y+lkp+T7ne/Q6YWGyGQy5r3xE1P7zmd43WlcP3s322PWrhXAwBcaAnDsxG0OZqgMcPX0XcZ3/4INP+2jSZsqAOzdfNakOkDZit6s2DuVXsOa5el7Ki7XzhlmW7NqxJDWkVSdrOHQrosAxERmfSFoiX83nsr3MQB0GX6esgvY0zx5FItWm3UgVCFDe/nCbi+hyBCY6fV64wWhs7t9rq81CTwyBNgZZxCzu8V//84TDu40/BvGZ5itTBtPxvcmLXjMGIRl995l9DgshvCH0QBoNHm/EMtO2mxo2r+1QpkeUMtkhv/3r+CFrb01r03vgbuXISi6ctr8uzgZg912fevh6etCldqBVAzypG5wAL6px6xbI4ChfRvxx9LXjN1jtfm4g5gxbUOXyx0BRRafp6bfQ97u8KkyBOxJiWoe3o8yfq6anD91rHq9ZDxXbqkleTV2Vn9+P/spwyZ2yfMxVFYKflg3juUbxrPgp1eM21v1qEObnnVzfG3v4c1p0712ls+N+l9n1h2dyfBx5ned7PZSMwa83h6lUsHmZXmv8BJcP4jgekHGcsKCkBOLkkV79uxp8vjTTz/l22+/5ejRo6hUKo4ePcqFCxeoUaMGAEuWLMHLy4vVq1fzyiuvZHVIvvrqKzp27Mi0adMAmDZtGvv27eOrr75i9WrDyu8jR47Qu3dvunfvDkBQUBCrV6/mxIkTgKH1686dpr80ixYtolGjRoSEhBAYGGjJt1nsMs6QZJd+kjYTkN9bmAvmbAage596jJ/SPdv9zh2/zbljt1LPafgDE/U4jn9+OwzA5VN3qFYvyOLz121Qjnada7J7+3nu343gz98OM3hkS26dvYuDqz0ubob89b5vdGBi18+xd7YlMpcFRqNHtuLI0Zvcux/Jom92Ur9uWWxtrahcJ5Cq9YO4dPw221ceonnHGmg0Otw8HU1en3bO0iQxddY2489OGoVCjlKlQJ2iISHeMBNsyYzd0yoHl+HYvqucPnIDeWog8ODOE2qn5k5bKmOQIM8mSDEneOk+oiX2TrZUb1Aed1+XPI3FXEqVAr8gDxQKOQqlApW1Eq1GR0DF3JtyZTcLa+9oaBxiZa00jThNXpvxs0GfaXtWM+zWVsoMr9EhSRJ6vZRtLnvGc1iShmKuei0r4+3vhrOb4eImbRxarQ4rayUOzrZUrFHGMHYbFYPHtiM+NgnfsnkLavzLe2HvZIurhwNdu9VD10nH+j0X+WntYQ4ev8mVm4/o17WuMdB1dLbL8/cmk8kY/GZ7FHIZlTJcQGYl7UI6t4tUsCz329XNHt8yriiVCpRKeZafCWDI2Qawt7emRcsqRETEUyPY3/wTWeDpNJS8SElUYyUD76dmyIuTTqPl2slbxT2M0k/ksJslz6u7dDodf/zxBwkJCTRt2pSUFEMgYGOT3q1KoVBgZWXFwYMHsw3Yjxw5wv/+9z+TbZ07d+arr74yPm7RogVLly7l2rVrVK5cmbNnz3Lw4EGTfZ4WExODTCbDxcUlr99isQmo6M2MH19NDQo8s9zHOCtlxoxZQXByscPW3pqEuGTj7KU2w+xbfvLax7zdkf8OXSMhPoXflh+gTadger7RiX1/HMHO0RZPf3eqNSjPpMUjqdemOm7ezjkez8pKyf/e7sTEd9cQ/jiOn1ce4vVX2yKTyXjzkwGM6zqPxIRkug1oQL2WBbPor7ilBX6Ozll3W1VZK1Ena4xxYH4moMsEecC+qwSU86R6rQBO7LuSrwNmnA3P9pa0SX5A9jOSbfs1zPM4LOHs5sCPe98zPh73yQskJ6rNCvaUKgVKpQI7B2uTi0MHZ1sUSjk2dlbZBtMZZ/Yzpm4os5hht7Gzxt7Rhup1A7n3JI7YmCQ2/3WSmOsPuH8hhLnbphv33fLdLpr1aYirlzPDJ3SiRedgZk9YhU5X8Lm1L73dyeRx2gWCXicxef7gTIsCuw9uYvE53L2d6DW4CchkODrb8tp7PfH0c2Hb8j1EPIzEsXlVbG1UvDK4OX271AGgap1Aho3rQLXahgmeT4csZODknlSqV96ic6etkclN2mJ6X/9sAtCMcbwFv1+vT+jM6xM657qfn68rPy5/FSsrJX5lXOnQMdjsc1jq/vVQ9v95lCHT+ub5GEf+PsmKD//kl6sLCnBk+ePq7cLE78YU9zCE54TFAfv58+dp2rQpycnJODg4sH79eqpXr45Go6Fs2bJMmzaNZcuWYW9vz/z58wkLCyM0NPtyYmFhYXh7m85KeXt7ExaWnic7ZcoUYmJiqFq1KgqFAp1Ox6effsrgwYOzPGZycjJTp05lyJAhObaeTUlJMV5ogKFNbkng5OZA8251ctzHxs4KaxsVvnm4laZQKujay3ALMS1YqlEr59mgoEre+AW6cf3iQ+MMe9ptVr8gD1w8HHN6eY5c3R14+fV2LPt6B9161GbXLwe4fvoOAeU9qN40vcxWh0FNkSSJx/cjcfdzYeUn6+g/oTv2TpmDpLp1ytKxfQ12/nuRP9Ydp1OHYMqX86R89TJMnP8SdZpXxt0n58A/r8LDYvhh4Q7kchlyhRyFwlC+a9DIlvgV0uxQ/1fb0LpHXSqkzkw+zcpahTpZa0xrykuAHXrrEb7lvWndrTbWNlZ0G9QIT18Xmnaska8ZtIwpMbJsZhtrNypnDEpVViWvikzb3uZX1hg8pi2Ds8pZlSRem9INJClTp900Lm4OdH2hAaH3Io2BJUCXfg3w8nPFK8PP9OhJXRg9yZCC4ObvxrFD11Eo5SiU8kypD67ezsbzlQnywM3TkY+/f5lAM+4Y5JdCkf5vbmtvna8Z7jR+Ae6MnZ5+R9jd0/B3YOCk9G1D+xny43/5dD1KKyVD3ulJ/eaVjM8PmNQTn3L5a4ymTlaz+rONDJjUAztH04vpDr3qolFr6dAr63QOlUqJd+qdIgfHrC/E80OhlBNUzjAhdO30HZZMWUX5GgG8vSD7jtB55ehqT9VGebsDl0ZppRTlD59VYobdLBb/5atSpQpnzpwhOjqadevWMWLECPbt20f16tVZt24do0ePxs3NDYVCQYcOHejatWuux3z6drckSSbb1q5dy8qVK1m1ahU1atTgzJkzTJgwAT8/P0aMGGHyWo1Gw4svvoher2fJkpxzq+fMmcNHH31kwXdftHRaHTdO36ZKw8wfdF0GNaZmw/L0HdXK4uPa2Kj437QeFr+ubEVvhr7ZngYtDEG0UqmgRsNyNGpbPd8dP7v3rU+TFpW5fuImHw9bgoOLHe0HNcHTP/2CRJIkPnv1e87su8yyI7PQafXEPI7NMmAHGD6kKUf+u0F8fAoLvt7Owi9fQi6X0b5/wc3CProfyZKPN/HiG+2oVscQQMXFJrFvx4VM+3btU7/QAvaKwQFUDM7+omvZ7mlY26i4euEBKpWCStkE9llJTlRz4fgttiz+hw/+mEiVWgFUyXCBVzGft9GbNajAH3+fpFHdIGNe79Oad6hB8w6GVLt7Nx5x92ooTq721GqWHmDtX/cfl/+7zpi5Q/M1nuIS+SiGJRN/AaDDoCaZAjwAD28nxr/fJ9P2Nl1q0qZLzWyP3X9IE/oPMcxUq1M0mfKmm/U2/Z2wtbemQasqln4LeVK9ViA9U7QmqThFadOyf7Gxt2bIO4Zg/uzBqxzdcR6dVsfQydmnCZpDq9Hx2+z1dH+1faZ/z3pNK+JXxoXEuGQiwqJx93ExeT4wyINf17+dr/ObKzkxhSvHb5m96NJSzh5O1Guf/c+nOVr2a0SrQlhALgilhcUBu5WVFRUrGgLIBg0acPz4cRYuXMiyZcuoX78+Z86cISYmBrVajaenJ40bN6ZBgwbZHs/Hx8dkNh0gPDzcZNb9nXfeYerUqbz44osA1KxZk7t37zJnzhyTgF2j0TBw4EBu377N7t27c5xdB0O+/MSJE42PY2NjCQjIeaa5KOm0Ok5sP5NlwN6oTTVoU61IxzN59gtAaiqMQo5XGVe++KNg/qAoFHK8fJzx7F6X1+e8SLuBTXB6KpdcJpNRsVYg+/46ztJpa5j6/avG5zRqrXHmVZIktqw6yvIvttLjtdasWX+CCxcfcODgVVq3KtgUmK9m/MWZIzcIfxjNor/GoVQpsl24JVfkbRFZQXBOfS9rNyrPqj1Tsc2lVnhGEeExzBz9I2171aVP8HSWbJ6IX5AHYEjFSYpLxspWZXaN7IzOXrzPlE/WAXD8zB2zctUP/nOGX+ZuoUKwP4u3vWvcrrJWZhnklha29tbUa1sDhVJu0YLDiNAodFodXgEeZu2fl3+nwtSiTVVatDH9vdz26wFWfrYJv/JezP37nUI9v62jDcoMtf+vnwthw3eGCjx9x7TP9DlkrouHr6JOVvNn6DIcXLNejLzk3VUc23GeAW93ZvSHL+TpPAXBs4wbPV9pi18+J14KU2kqsytYRoZpBlhhn6u0yve9ZUmSTNJKwLAIFOD69eucOHGCjz/+ONvXN23alJ07d5rkse/YsYNmzdKrdCQmJma68lcoFCYLLtOC9evXr7Nnzx7c3XNPFbG2tsbaOv+LYQqLlY0VcZHxxT2MTE7uOEvj7gXfXAMMH8p9Xs9+tX6/sR3Zv/EEKhsVP321nUGjW3PxxG2+mbmOKQuH4unnwoJpf3D6kKE6zPH1J6lXpywtmleiRXPzutiZ4+alh1w8eYdajctx5sgN7lwL46/lBxj4Wht8/V35cP5g9DoJvV6PTi+h1+nxKZO5cUZhC7/3hOjwGCrXN5RVi49JJOZxHAlWCpzdHLB1sMnlCOkl8tI6z+ozpNPcOn+Pca0/YvLS0Vw5cpVLR6/zzX+zzf7jqtXpjCX2sltw+rS0ZiVPz8g6uTnkKzWroNy7+hBnD0ec3C0by4FNpwi5+QiAxPjkLP9tLv13nR9m/gHAF9umIpfL2b36ILER8Yz+NOsUQUtcPHyVW2fvUq15Vab0nIckQZX6QcxZPynfx7bE4c2nefIwCs8ylt2RunnmDh++8AWfb5+BXwWfLPf56o3v6fZKeyrXN+Sm/3rxC5PnFdlU8bGUylqFVqMz6+egkIsa5co3yJM3571UvIMQBCFHFgXs06dPp2vXrgQEBBAXF8eaNWvYu3cv27ZtA+CPP/7A09OTwMBAzp8/z/jx4+nTpw+dOqUvwhk+fDhlypRhzpw5AIwfP55WrVrx+eef07t3bzZu3MiuXbs4eDC9XGHPnj359NNPCQwMpEaNGpw+fZr58+czatQowFCn/YUXXuDUqVNs3rwZnU5nnLV3c3PDysr82cSS5vX5I4t7CJnUaF7VZEa7KMQ8icPZwxGFUsHnGyczbshSHuy/SXxcCvYqOeEPo/lq2u+89fELnD1yw/i6MmU9mDi1h3GGuSBIksTiD9dz5UwIzm72NGpblWN7rrDqm39p2bUmvgHuNC2gDpb55RXgYTLzun/DCb6etBKACV8Np8vQFrkf5Ok42mSRaHo5uLe+HpVams/8OQx7O2tqViuDXq83+3Z82sLKmxfvs2PtUToNMqR6+FX0yXYmsyhNaPUB/Sd0Z8i0Pha97q9vd/IkNBog20YwyYlqQm89MtnWqn8TtOqCKb/oUcYNuUKOnaMN9doZUpDKVCj8PPanTf52FFqNDpW1ZZ8xFeoE8euNxTnu06p/YzyzW+gJtOnbgJpNKqJQKfDKYb/cpF0Q5OSVjwbw4sTuhV7VSDCY/dYvHNt9GZ1Oz9qTH2FnxoSFIJQUFn0aPnr0iGHDhhEaGoqzszO1atVi27ZtdOzYEYDQ0FAmTpzIo0eP8PX1Zfjw4cycOdPkGCEhISZ/mJs1a8aaNWuYMWMGM2fOpEKFCqxdu5bGjdNz1RYtWsTMmTMZO3Ys4eHh+Pn5MWbMGN5//30A7t+/z6ZNho6YderUMTnfnj17aNOmjSXfppCNkCsPOLb1NP/88C+vfj6Upj0KZ5b9aYc3n+Lz0UtZcvhjylTwxs7Bhsatq/DXL4f558/jfPzNMA5vv8C9G+GcOXyNfqNasWX1UV6f0YuO/RsU+K1UmUzGuI/68nb/RcREJmClUmLnYE1ifAoP70TgG1Bya+raO9lStqofWrUWBxfzFve5eTrx3uJhhvdRBm5e6almTm4OdBraAr/yhqDO0rb3VSv6sGTOEIteM3JKD4ZN7oZOozOpe+3q5YyrV+EsJLbEF/++j3MeZvq/2PyOsepTxvc4o3pta7D6xkKTbd5ls64klRfeZT2Nx3tv+esFdlxL5TUNxRz1OtTK8XlXTydcPfPeECfySRznT91Fr5OQJAlnVzvqZ1PyNLCKb57Pk5O46EQunryNVq1Dq9FhZaOiev0gY2WajJa9u5KRHw3E2oI0OUst/PRvLpy5i1arZ3kR5eVnRavVG5uV5bdLqVCAxKJTs8ikwu4wUorExsbi7OxMTExMrvnvz6OEmEQe348gNiKOgKpliiw4SopP5tUG06jWqCJTl7/B5t+OcHD7eZ7EJBJ6Lwq/QDfGvtOVmSN/QKGUM/+Pt3DxdMTLL38pKFqNjo2/HKJ9n3pZ/qH7ecF21nybmuv6cgsCK3rTqX+DQlu4JRga+UgSIElIUmqVIwmsbJTifRdKhBOHb/Dem78aH9eqH8S8H14u0jHMGfcr+7ecNdk25auXaJNFRZpJ7Wfx0bpJOLgU3t2pGW+v5Phhw53Pf47OzLICUlH49M1fOLj1HACrj31QIlLoilpJinPSxlL9jdkorIvmbocuJZlL304vEd+/pUpefTQhz9QpGjRqQ2MUCUPTEasCTFuxd7bDvgBKrlniwY0wlCoFH6x+G5WtNaEhEfzw+Ra0Gh3dX2rK5nsneRgSyZmTd+k2uAkn9l9Fo9HlO1h/EhrNHz/sY9Ovh7l6LoTpCzNXHhk8th2Htp8nPjaJOk0r0ahNyUiDKQ0+H/8rB7eeRdJLSHqJDi804n9zX8zxNSsW7WTN9/uyfO6X7ZPxEmkFQjb27r+CVqtHp9Oj1enQ6SSaNamAh4XrDMxh72CNnb01iQmGtV3RUQlcOnuP6rUNBQ0+efUH4mOS6PpSM1pbUA7UEhVqlOHBnScoVQpUVkpUKgXOWUw6AJSp6FPoOfRWGVKbNJqsS5YWBU9fFwIqeBXb+YWsySTDV1Gdq7QSAfsz4JdvdrFz42niY5NISlQbt0+c1ZdOfYombWXjr4c4svsykt7QRVGlUjD7p9H5Pu7i8cvxLutJ7Y51+Prd1Yz9ZACDxrTlt8W72LXuBG261GLv9vPs3XqOr38bw+hpPfKdl7j+h72s+GwztVoaytod2HqeIz0v0jS1rGAaK2sV7y0ehpuHI45mppcIBnqdhFadvqAvu/bsGalTss/TliRD+sGZvRf5eOACvj70Mf6V/QpkrELpN3vuZmPfiDQBZV4slIC9Wq0AatUP4uj+qwCE3HrMymV7mL1kOAAX/rtJTEQ8DdoWXpWvbkObkWyrIiYmidatq3Lx4gPqZqgxn9HEZa8V2jjSZFzvpE7RYlOI6Tc5eW1GL16b0atYzi0I+SUC9mdAXEwSj8NiMm0vymSnB3eecPboTeNjKwsXi2VFrdZSrWM92vasQ1R4LMmJajb8sJcFWyZzYNs5Qm6EE/kwir5DmzJkTFscnfJX0u/utTB+W7id5IRk1CkaTu6+SOUGFbh2/j7ffLiBWo3LY/9U2cCyRdBYprjFRCZwfN8VYyMomUyGXCGjeafgPKehvPhWB7oOaYpcJkMml+HqmXvg9NrkrkiJyYYqMTJwcXekVe/6yGSGxjifDP6K6k0qseruEqxsSlbpQqF4KRTyTAF7YeYwDxrVkk696xp/V1yKeDH05UsP+XmFoXDDhvUncXS0YfCQpkU6howy3unVaApmgbTwDBE57GYRAfszoO/QZrTqFEx0ZAJREfEgM5Tiq1a7YGvKRz6O49H9SLRaHfYONpSvlj6D+XSHyuyqXJgrMSGF8YO/5d7tx9y8Gc5Hi4YxaFwn/liyi5CroUz49AUmvfgtD+8+4Z15g/IdrIfceMRbPb5Eq9FRrooPNnZWJCeqUUg6rKyVRITH8s+a/xjwapt8nac0Cr0XwZdTfs+0ffPlOXk+ZrmqeZv93rbyEMmpd5Eq1vRnyPj0Fuz9xnXDycMRG7uiLdWq0+qIi0rAJR8LFYXCldViaJ2+cAL2QzvOkxCbbHzcoFUVk0XEs359g2Xv/4l/IVbfUVmZpnw8fbHy5GEkKYlqylTMuvRlwY/HdIa9OOi0Or79bDNH9lxGq9Gh0RgW5L4wsiXD38q+lLAglBQiYH8G+AW64xdY+JVJ9v9zlmWf/g1Ag5aV+fjH9JSX0e90Y9SkrsjkMsOsaR4Ks8RFJ/L9Z5upUjuQ7oObUL6qL/duP+a/vVc4uvcywyZ349S+yyyYtIqFWyYzdcFg6resnGnWOy8CKnhRo2F5zh6+zu2rYXR6oRE6rZYR73Tn8pkQzh+7TddBz2eXPYVCjqunI3qd3pDyJBnqysuz6UpamBq0rY46RQMS+AS6m3RFrtG8aDpzPu3svktM7z6HzXG/mDTgKSzqFC0xkfHotHq0Wh06rR7/8p4WV+h5nmQZsOsKfqpNkiQ+GfuLyTaVlZJfDrxnXLju5eeCr6cdsWFRBX7+NE+vXVI/Vfbz9vkQoh/FFFnA3ufFxrTqWAMrKyXuZtxNKwyXz91j89pjxscOjjZUqRlQJH87BTOU4pnvoiICdsFsGRvVpNXCTlMQi1vv33nMrvWnOLj9Ao3aVuXVSV04tu8KSYlqvv1sC3U3VGT8vCH8s/IQWo2WVt1q5/ucaWQyGePnDOSNLvPQ6XTUaVGJtqkLwjz9XAv0XOa6cOI2+7eeQyZLbV2UGpi++EZbXAqx7N3TKgX7s+rQjBz3uX8jjMS4ZGzsrAisUni54zcu3CcsJML4eMQU0zULty/cQ6/X4+jmgJd/4f4hvnDsJrGR8Ti62rM16bci68R49WwI7w751mTbmmMf4uyWv7SLxCQ19x9GGWYetYYZyOpVfLEr4jsWheGHb19GLpOhUMhTv2RYF0LHV0mSqF6vLDGRCelVjMDk4tbKRkVwi6r4lCu4cpxPU6lMZ9j9A9xMLm4bdq5TaOfOSmA5TwIL8fs1R+UaZWjbvTZ7Uqvn9B7ajGFj2xfrmATBEiJgFzKJiojn1H+3iI9PJiE+mYS4ZJq0qoJCmT5LVRj5n9XqlKXXsGZs/OUQ33y4gQ++HcHQse35/outPHoQxe8/7mPYmx0Y99mgAj83gG+gOz/umUZyopoyxfzHBeDO1TD+Xnkk0/ZeQ5sWacBujiXvruLUnktUa1iBBTumGbe/3nAaLfs24qXpfQvkPE8HxU+v0xjX6kM0ai1dRrbmf4tHFcg5s/PDrA1cPX2H+m2qUfO3sYV6royUyixmi5/qyPnvqoPUbl0djxw6hT4Kj+Xi1YfG1IDbIU/4Y8MJk31++HoElYqhcVJB8yyi8n1yuZwvf38rx33sHG3pMrJNrsc6tucyN6+EotPo6DeqFXYO5l84ubjY0b6DYUbbykqBl5dzkV1QllRW1iqGjW1Pr8FNUCoVuHuLFDahdBEBu5DJvTtPmPvBepNtLm4O1K1XlqHjOqBQKvD2z1/ZxOyM+F9njvx7if92X2b/P+foPaQpOzacxNXdkTZFMMvt7l38jXfSBFbyptuLjUECKfV+oSRR5N35wkKjeXgvEr1kKMEoSRIOjrZUr+lv3KdaowpY21rhn+EWuyRJNOpSh8BqZYzbHtyN4Mi+K0iSoZqQpJcIquRN+UreZpVlnLJ4OBq11nDXQSbDxtZ0lnTZsU9BJiuQNKncpOUJ56d9fV54B7jx+vu9USoVKJUK5Ep5pp+Jq8dvUL5W2RwD9nOX7vPJvM05nuvpO2lC0Tm4/Tw7/zoJQOcBDS0K2D09nZj+XvFWQ0lOUrN2+UFSkjXGr+RkDeoUDZJe4uNFmUvlmn3sZA3D+n6NRq1Fo9ExdHQrBo/IvWtzUaWPCpYRZR3NIwJ2IRP7LALChPhkKtYoQ8UaZbJ4RcGxtbfm7Vl9mTH6J779eCN1m1Xk8x9fwdnV7rmbIarVqDy1Ghnamz8KecKTh1EkxiahK6A29OZ6fdh3xprSaWrXK8u8b4YbHw+b2jvT62QyGaM+Nr0bcudmON8v2JFpXytrJX8fnZlp+9Oq1Cmb4/NFlZML4F/Rm5RkTaEuHsyKja01Far7k5iQTGJ8ConxyURHxONjlx6cj10wMtfjqLKpRa1SKlCq5FiplIi+enDh5B0SElLQ6/QEVfLG1z/7i6CClLFWuFZT8BeFi8b9xLhFhXcXSqfVsyqbvglyucwkRSc3hzYeJ7BqGQKq+HH5wn2uXn5ITHSi8XlNEX8mpiRreHjnMcmJaspW9kGdokWv0+Pi4SAauAmFRgTsgpEkSZy//IC4DB+EaRLik7N4ReGo37IK7XvXM1ZxcMlnbu6zYPP3/3Jy13nsnGwZMbM/7kXYJCirxaV1GpTL07Ge/gMtV8hTZ+3zdLhi1XVYS5ISUtDrJU4fuo6ERMXqZXAq5BJ+926F885LS022Tf9qCD4BlgWSwdX8mD2zLyqVEqVSjouzHeXKejx3F8a5WfDhBh7cfQLAG1O707uIyiNmzEMvjIC9Tpsaue+UD9Y2KipW88XaRoWNjQrr1K+BI1vgbGHfiu+nraLvW10IqOLHoX1X+P2pVMGnq+AUtoTYJL75cAMXT9w22d6xfwMmfl44KZvPNFHW0SwiYH/O6XR6YuOSiI5LIjomif998Dt1agQw/4eXsbe3wd7RBnsHa2ztirbRxYTZL6BUiW50aUZ/PIjRH+f/D8HJvZd5cOcxOq0enVZHvVZVKV8957sm/Qc34efv9ppsGzSsWZ7OX7dxOX7a+DYKhRyVSoG7lxPRkQkkxOV+QXjm6E1mvrbcZFvNBuUKpEFXXiycsY5blx+abPvkp9HUb1GZmCdx6LQ6ZDIZrgWcZmVrnzk1IjE+JYs9sxYe8gSVjQoPL2eTxkEhd54QcucJtnZWeJWg1LDiplCkX8DoC6GyTHYCKnhRt1lFlCplofQVaNm/cKteKVUKvln1eoEc6+sDs7BJ/bnv2a8BzdtU48tPNhFyx3AhpVYXfsCeGJfE3rWH6fZKe9y8nKgU7M/FE7dxcLbF1s4aGzsr/Mt7Ffo4hOeXCNifU4lJaga89h1xCcmZZjdjE5KpUTuweAaWSgTroE7REB+TZExLkCQJRxc7rG3yfvG09ptdnD96w/jYztE214C9U/faVKnuZ2xyJJfL8nzb19bOmjKBpgGni5u9WXdRJL2UaaZRW8T54xllORGd+m/1UuUJaFI0lK1Whu9O5L1efVbcvZx4bVp37OxtsHOwxs7BhqDK5qcCndt/GXc/V1zbmQblo4cYZu1btq3G+5/2L9Axl2YZf9YLs9nS0xq1qUrFGmXQafX5rgBUUqWtZcmtJKlThgtLb18XvH1d6Na7HhFP4lCpFFSvVbA9R7Iik8lw8Ur/nRn1bjdemdrdJHVJyBuRw24eEbA/p2xtVCQkqTMF614ejtStUfgffkLuTu2/ykev/WSy7YPvRtGkY3Cej9muXwNcPRwNgbdChm/Z3BdgeXo54elV/BUVPHycaderLpJkCJbdPB3x8Ml6Jlir0RLzJA5338JZHH313D2CKvvg6++KTqenfDU/hr7dyfj82juL2b36ILVaFXz7eXtHG/qObJnn13cYavras6fu8O7bvxkfH9p/hV4d5tKzb31efVOUvZOnzrDLZLIizen/84f9bF5lSP1YuuV/lK1UdOszCtuS99fxz8pD6LR6/II8+HF/zmVjs9LvxaLti2HrYEOzXg2Mj1UFUMpYECwhfuKeUzKZjD+WvYqdrRVdXlpk3F6tkg/jRrUtxpEJhanL4KZ0GVx8Lcrzw8nVjn83nzXZVibIg95DM6fn3L30gLHNZrI94ZdMzxWE/VvO8O9f6SUQQ26EmwTs9k623Dp7l4q1gwrl/AUtY2divU4iKVFdbB0pS5pFa8Yil8uKPLdfXkypOEVCktClViAqzrtkQgkhctjNIgL255hn6m1GB3tr4lOrgMTEJgGQEJuIXifhWMgL6ITs1WtVhZVHP0gPFGTg4FT45QpLk4d3Iziw/QItO+f9rkNeBFX2oUw5TyS9hEIpz3L2c/ySV4p0THnl7GJPizZVMYaHqZ2KK1Qu/fXXC4IlHWS1Gh1//bSf6+fvY2tvhY2tFUFVfOiWepGcGJfEo5AIlFYKAir55ngseYbzpi3Af1ZUrRdEcqIahVKBs3vJ6ikhCCWVCNhLsQf3I/lxyW7u3HqMhCEXEAn0koSbuwMLl4006zguTrbEJ6SgkMuQJLh45BoT237EsBn9GDpD5LIWl5QkDbeuhJGSrDZWUnFwtsXFxQZJq6NCCZ+9DbvzmN/mbkImk1GlXjm6jzbcuYmOjOe72ZsN6QUS6PV6kGDUO13xzqFknspKSYtOwchkYOdgzdvvG0pJyrKoYuPi5cSgST0sKh1niY79G9Kxf8MCP665ls7+m8O7LiJDRlqkPXPRMCpWt7zLbFB5Tz6Y/UIBj/D59OhBJMvn/WOyrUGrKsaA/cKR67w/4Cusba3YGLY0q0MYWVursLW3eibLBLbr24B2fRvkvqMgCEYiYC/FYqITObD3SpbPWVIG7KtZA7GzscLezhq5XEZyQjKLD39CmWcoZ7I0Orr7EvOn/Zlp++uTO+JgZ1XiA/aYiDh2/nYIAOcMi8ZSEtXs2XQ60/4Dx7QlpzldO3trZiwYbNa53X1dGTVroEXjLU1iohJ4HBpjsk2rESksxc3e0ZYeLzUlNjqR5EQ1yYlqylVNv4hy8XCkRa/6KLJYVK/T6U1m80dO6sLISV2KZNyCUJzEolPziIC9FPP1c+W1tzqw6a8TyGWGmTa5XIYMGa7u5qeyeHuYLii0sbehUr281dkWCo67tzNlgjxITlSDzLDuwCfAjbrtahJYwbLyYYU105yTyvXKsSXieyRJMqmkYOtgTd+XWwCGqjOy1O/NzrFoO7jmx3+nbnPo2A00Gl1q11VwdLChXKAHtWv44+NVuGURq9YORK/VG1I/JcPdF0dny2pbAyQnqpnz7hr0eglXdwd0Oj1arZ4qwf70G9684Af+jHNxd+DNj/pl+3zleuWY8eubmbbHRsYzucscBk3qQftBhtl4nVbHD59sBAy/Hy2616F6HvsfCIJQ+omAvRRzdbNnwJAmDBjSpLiHIhSCsHuRJCekICEh6QwpT5PmDDC7Qc7O3w7y9fgVaDU6Xv7wBfq82Zm42CR0OgmdTm/88vd3Q6Es+NvuMpnMJFDfvfogKz74naWn5/HX6v8y7R9yN4KPFg/L8/k0ai07ft6Hu58rTbrXy/NxzLHn0FX+2XU+y+fefaszPTrWKtTz9x7aLMvFtrm5feEeB9b9R/shzSlTyRcJif/2Xc20n1ajEwF7EVIna3BwtWfea98TejucoVN7s/6HvWz4Mb1TaNSTOBGwZ5AQn8yKxf9ibaPilQmdcn+BUHKJRadmEQF7KfZup49p2a8JPV/vWNxDEQrBrSsPiQiPNdlmSR3ohh1rMW/rNJRWSjx8XTl75i7TJq/JtN8fG8bj6lb4C78ada1LpXrlC+346mQ1a+dtomLdoEIP2BVZ5M0D+Pk44+xYchcGJ8UncefSfZJSGy1ZWav48pfXUKoU+Pq7MfaFxTx5FINOVO4oUh5+rszbMoU1X26hZR9DbrckQYcXGhnWeGD4fyGdTqtn09pjuLjZi4BdeC6IgL0Um7tjZnEPQShEbXvWRafVG9KcUlOd7Bwyd7nMjouXEy5eTty7+hBk2Ve7KKpmMA4u9ji42KNO0dClX30Ma04N0x0jx3XMtXnS70v/5dalh2g1OnRaHbWbVaLPy62Mz9s72fHLtYWF+j2keefNzjwIi+bUuRAAalX35/1JPfB0dyjy1CNLVG9SmffXVjY+VijkVKsdwO1rjwgPjaZT33qkJGtw93QiOVGNTRF3OH6eKZQKXprSy/h4wBuiBn5Wdm0+y+Y/jpOcpAYgJiqRl3st5KXX2tChR+1iHp2QJ2KG3SwiYH+GLJ35J/Exieh1eiRJov/r7alYq3g7lgp5V6N+EDXqB+X7OJePXqdczQAUiqw78umKuMazlbWKCR/2tfh154/e5MS+9EXWx/deNgnYi1rGHjrVK/vi5eGY/c4lmFaj481BSzJtr1GvLFWC/YthREJB+P6r7UQ+iQfA2kbFhBm9cnlFyfLv6kMc2niC3mM7UTtDA7KoiDgun7tnfCxJEg/vRRIdGV8cwxSEIiMC9mfI/k0niUpNoXByc6D78Lx3QxSeHZ1GtAYMZUB79qmHQiE3+bItJbOojdpVxyfAHYXSMO6qdcsW63g6t6lOzWplkPQStWqU4sA2uzsCpXgmSoCj+69y/24EYKiwVJwB+5njt9Hr9MhTP3PkchmVqvliZa0iKjyGS//doHnP+iav0aRosvwZbNu1FjXrB2Fjo+LO9UdoNDqq1/KnTJBnEX03QkETVWLMIwL2Z0BMZDy/fbWdGs2rIOn1yORyOr7QkOAmFYt7aEIxOHPkBmu+3Y1Oq8fOwZqPvnsZgDL+boyf2JWkhBT2bDmLPnXRqUxfOj7Beg5vYfa+er0eSbKs6Y2lunWoWWjHLkoKuYzWXWoiT83LT+vq6ehiedUZQcjK7Hd/JyY60WTbz1sm4OPnyqL//cLD248zBexdRrahy8g2mY7l4eVEckIK/6w6wj9rjpKSpKF8NV+++XtiYX4LglDsRMD+DEhOVPP3r4dMtjXvKnL5nlexUQmcPXoTAKcsgq74uGQWfbjB+Ngv0J2GraoU1fCKxO9f/M2h9cdYdOTT4h5KiadQKpj2+bNbs/55tGTuPzwIicxxn63rTnD5XAhIMHFW9qUoC0KT1lU4tOcK8amdtAFjQ6hHIREkxCRm99Is3bhwn/XLDxgfpySLHgSlmshhN4sI2J8BHj7O2NhZkZyopu/o1rTrWx//8pbV6RaeHRlz1TMuKNVpdUwd9SPqFNM/bhp17hVBkhPV6HQ6QIa9ow1R4YamPa55rDeu1+v5bvZm7t0MT63TLueNmX3wK+ue7Wtu3HzEP9sMpRQVchmeno4MzKZyxovv9ubFd3vnaWyCUNoNGNEcL19nkpM0VK7uh5dP5t/Tcydus2fLWQD+91HfQl0sPfHDPjy89xNW1kqq1CjDqh/2G+9+DZ3aO8eqRFGPYrh45BoBVfwoW60MYPibF9zQUHHK2kaJp59roY1dEEoKEbA/AxRKBT/unY4MGbYO1tjYlo6cZCF3l0/d4eGdx0gSNOkQjINz7iUDHV3tqFDdD4VCjp1DejMimVyGykqJrZ013V9sbMhhV8qpFFwm12NOGvQNty4/xM3Lid8Oz+Tvb3egUWsZ/al5nUefJkmw8eeDJtuWyTcZ03eysmHTKbZsPWd87OmRfcAuCM8zT29nXhiWcx39jKVJ9XoJhaJwqxv1GNAQpUqBo7MtyGTY2Rv+TjXtXjfH1904c4ePB3/NkGl9GPF+fwCCG5Zn3uo3CnW8glDSiID9GeHm6ZT7Ttn4Z9URHt55TMvudahSW1SVKSkkSWLzrwfZvf4kAEt3TjErYK/VqDyLN4zPtH3/n0dx0SXTsFt9Dhy5TdNWVenYs45ZY0nLb5ZSS6O8OKW3SZUUS8nlMhq0qsKjB1HIZDLkChnNOtbI8TVXroYR4O+GVqsjNCyGqOgEXn/rZzp1CMbWVoVMJqNNq6rY2KjyPjBBeF6kzqjLZDL0en2hrvcAaNMlfc1HbQsaQNVuU521Id9gY29+SVuhdJFJErL8/EGx8FyllQjYn3OPH0bxzfvr0Ov0lK3sIwL2EkSvl7h16WH6hjx+0KhTNGzfeJroiCT0vp5s23mFM8ducfnsPbb8dYKpn/THp0zOt5TTbpdLqQtUrWzydxdHJpPx8Y+jLXrND0sNs+8JCSn8s+0cS5bt5uq1MK5eCzPu07B+ORGwC4IZIp/EAeDibo9KVXJDAStrFVae+fudTohPZt0P+1GnaNFotNRqVJ6a9cty/3oo1ZtUzv0AglAClNzfUqFI6PUS+iJqnCNYRqGQU7ayD3euhuYrv1Sr1bP48y2ZtkdGxBMZEU9ysibXY1jZqLC2NXwVN3t7a7p1qYVMLmP/gavcfxBlvJiRZdOBVBAEU37+bkRX8cHZNeeGZc8CjVrL6m93Gx/L5TL8/V04vPGECNhLArHo1CwiYBeEEmzK18OYumh4vo5hY6vik0VDsbFRcetaGCkpWqytlVjbqLCyVuHplXs61RdrxuZrDAXN3t6aF/o24IW+DYp7KIJQKr1Vyhop5YeVlelEgzpFS9nq/rwyZ0gxjUgQLCcC9uecu7czvUe2RK6Q07h9zjnEQtEriMoNcrmchs0qAVCzXlC+jyfArQv32L7yIE7uDrz0Tk+un7uHXpJQqRSUr577Il5BEIqOlbWS4AZBqKyUqKyVBFX2yXbf6MexfD32B9Rl/bh0/j6dutfm9bc7FuFonz+icZJ5RMD+nFOqFLz+geVt4gXheRZ65zEbl/2Lb5AnL73Tk3cHLiI5UY1XGVd+PvJBcQ9PEIQMlCoF8357Pcd9pvdfwKk9l3jjsxcZ9sELLFqyl7i4ZJIS1UU0SkHImQjYBUEoNkNqvEN8TCIDxnVm2JTSc4veO9CDriNa4ezuUNxDEQQhjxZ+som4mCQ0ap2xZ4VcIadccCAqlaGfxe6dF/D2dWbICPM7LQsWEjnsZhEBuyAIxUadokGdrEGryb15U0mikEFMaCTjv8rf+gJBEIrOpl8Ps3vjKdQpWt6ZN4jDe64QHZkAwJgxrajWoDwVaxkqpSmVhoBdLpeX6Co6wvND/BQKglAsHt19TLeXmuFT3ocKNUtfOVFdhouM1ac+BpmMQmwWKQhCPkWGx3L13D0AEuKSUVmlh0AtetfHy9fF+FhlZQjY6zcsx4AhTYp0nIKQFRGwC4JQLBLjkqhUqywt+zcu7qFYzCPAg8aDWjCiw1xGTuhE2x51stzvm/nbefIkDrlchlwuo0XrqrRuX71oBysIAgAq6/SQR63WYGWV8bHWdN/UGXaNtnTd/SuNxKJT84iAXRCEYlEuOJD7IVH8sWwPkl5i4BvtintIZju+/yqLPtoIQER4XLb7nTx2i3shEcbHfmVcRcAuCMXEKmPAnqI1zqIDaNSmgbkyNYf96e2CUFxEwC4IQrHZuvoopw9dB2DA620LpIxlUfjn92PG/9frs288pnvqOXkht38XBCF7tRpX4NWp3bGyVhFUyYeufesTE52IykqBs6udyb59XmhI89ZVcBMLywufWHRqFhGwC4JQbBTK9ABWr5dQKEpHwJ5xkaykz/4vgP6p5+SiE6sgFJsqtQKoUivA+LhPDrnpVar7UQW/ohiWIJhFBOyCIBQbuTxDwK7ToyglM9Cd+jfA1cMRhVKOfzmPbPfrmTqDJ0kSep1EtRqiqZIgCEJGIofdPCJgFwSh2Ax4vR0dX2iIXCEvVeki3QY2otvARrnuN/ClpkUwGkEQBOFZJwJ2QRCKTXDDcsU9BEEQBKE4iRx2s5SeKS1BEARBEARBeA6JGXZBEARBEASh2JTm3PKiImbYBUEQBEEQBKEEEwG7IAiCIAiCIJRgIiVGEARBEAQBOLb/KqeP3KRhq8rUa1qxuIfzfJAkw1dRnauUEgG7IAiCIAgl2spv/yUhNplBr7TGpRC7j146fZf1vxxiw6+HcXGzZ/X+aYV2LkGwhAjYBUEQBEEo0bb+eZyI8Di6DWxUqAE7GLoRS5KETqcvxPMIaUTjJPOIgF0QngPXLz/kt+/2IeklJCQkvUTN+kEMHNmiuIf2XNKotURHxOPp61LcQxGEUkEmMyy5k4oopaFT33p4+bkUybkEwRwiYBeE50B0ZAJH9l4x2WZnb11Mo3m+7d9+nkM7L3H22E2q1gpg2hcvYm2jKu5hCUKJFB0Rz5G9l0lOUgOg1xduwC4zTLDTb0QLgip5F+q5hFSicZJZRMAuCM8BuVyWaVspXntTql0+HcK+recAOLrnChq1VgTsgpCN0PuRLPxwg/GxXle4H1yB5b1o0rYqtvZWhXoeQbCUCNgF4TkQEOTBG+90RSYDmVyGTCbDL8CtuIf1XFIoFSaPtRpdMY1EEEo+Jxc7mrWrRuSTeBLiklGqCrcaddsetWnbo3ahnkMwJdMbvorqXKWVCNgF4Tng5etCnyFNinsYApgEHPWaVcTZzb4YRyMIJVuZsh68v3Aoh/4+ybHt5zi3+yKB5b2Ke1iCUOREwC4IglCEBoxuRe+XmqFUylFZK5HJMqcrCYJg6vqZu2z/9QAJsUn0eKVtcQ9HEIqcCNgFQRCKkL2DDfYZqtKd+vc85WoG4urlXHyDEoQSrt3AJiTFJlK9aaXiHopQ0MSiU7MUbjKYIAiCkCM7RxuUKkXuOwrCcyywih/3Lt9Hm6wp7qEIQrEQM+yCIAjFqGojMWMoCObo8nJbAqv6FfcwhAImGieZRwTsgiAIgiCUeK36Ny7uIQhCsREBuyAIgiAIglA8JKnoGoOU4gYkIoddEJ4R67/ZTujt8OIehiAIgiAIBUzMsAvCM+Lu5QfUblWtuIchCMUq7EEU4Q+jAQiq5I2Ti13xDkgQhByJHHbziIBdEJ4RUim+1ScIBeHN/osJuRmOJrV77IeLh9KkrbiIFQSh9LMoJebbb7+lVq1aODk54eTkRNOmTdm6davx+UePHjFy5Ej8/Pyws7OjS5cuXL9+Pdfjrlu3jurVq2NtbU316tVZv369yfNarZYZM2ZQrlw5bG1tKV++PLNmzUKvT+8xK0kSH374IX5+ftja2tKmTRsuXrxoybcnCKWeaMIjPM8SE1KMwTqU6nRVQRAEExYF7P7+/nz22WecOHGCEydO0K5dO3r37s3FixeRJIk+ffpw69YtNm7cyOnTpylbtiwdOnQgISEh22MeOXKEQYMGMWzYMM6ePcuwYcMYOHAg//33n3Gfzz//nKVLl7J48WIuX77M3LlzmTdvHosWLTLuM3fuXObPn8/ixYs5fvw4Pj4+dOzYkbi4uDy8LYJQ+nQb1RZPf7fiHoYgFJvWXWvRpX+DDFtExF6Ydv91nEVT1/Ll/1aKO3xC3klF/FVKyaR8/pa5ubkxb948WrZsSZUqVbhw4QI1atQAQKfT4eXlxeeff84rr7yS5esHDRpEbGysyUx9ly5dcHV1ZfXq1QD06NEDb29vfvzxR+M+/fv3x87Ojl9//RVJkvDz82PChAlMmTIFgJSUFLy9vfn8888ZM2aMWd9LbGwszs7OxMTE4OTklKf3QxAEQSg+er2e9b8cBqBp22r4lXUv5hE9u76esoatvxne6403vsTKRlXMIxJyU5LinLSxNO7xMUqVTZGcU6tJ5r/NM0vE92+pPFeJ0el0rFmzhoSEBJo2bUpKSgoANjbpb7pCocDKyoqDBw9me5wjR47QqVMnk22dO3fm8OHDxsctWrTg33//5dq1awCcPXuWgwcP0q1bNwBu375NWFiYyXGsra1p3bq1yXGelpKSQmxsrMmXIAiCUHrJ5XL6j2xB/5EtRLBeyKys0wN0jVpbjCMRSrO0RadF9VVaWbzo9Pz58zRt2pTk5GQcHBxYv3491atXR6PRULZsWaZNm8ayZcuwt7dn/vz5hIWFERoamu3xwsLC8Pb2Ntnm7e1NWFiY8fGUKVOIiYmhatWqKBQKdDodn376KYMHDzYeI+11Tx/n7t272Z57zpw5fPTRR5a+BYIgCILw3KvWoBzqFA1W1irkClElWhAKk8UBe5UqVThz5gzR0dGsW7eOESNGsG/fPqpXr866desYPXo0bm5uKBQKOnToQNeuXXM95tML5SRJMtm2du1aVq5cyapVq6hRowZnzpxhwoQJ+Pn5MWLECLOP87Rp06YxceJE4+PY2FgCAgJyHa8gCIIgPO9a96pH6171insYQmknGieZxeKA3crKiooVKwLQoEEDjh8/zsKFC1m2bBn169fnzJkzxMTEoFar8fT0pHHjxjRo0CDb4/n4+JjMpgOEh4ebzJa/8847TJ06lRdffBGAmjVrcvfuXebMmcOIESPw8fEBDDPtvr6+2R7nadbW1lhbW1v6FgiCIAiCIAhCkcn3PSxJkoz562mcnZ3x9PTk+vXrnDhxgt69e2f7+qZNm7Jz506TbTt27KBZs2bGx4mJicjlpkNVKBTGso7lypXDx8fH5DhqtZp9+/aZHEcQBEEQBEEoOUQOu3ksmmGfPn06Xbt2JSAggLi4ONasWcPevXvZtm0bAH/88Qeenp4EBgZy/vx5xo8fT58+fUwWgw4fPpwyZcowZ84cAMaPH0+rVq34/PPP6d27Nxs3bmTXrl0mC1V79uzJp59+SmBgIDVq1OD06dPMnz+fUaNGAYZUmAkTJjB79mwqVapEpUqVmD17NnZ2dgwZMiTfb5IgCIIgCIIgFBeLAvZHjx4xbNgwQkNDcXZ2platWmzbto2OHTsCEBoaysSJE3n06BG+vr4MHz6cmTNnmhwjJCTEZLa8WbNmrFmzhhkzZjBz5kwqVKjA2rVrady4sXGfRYsWMXPmTMaOHUt4eDh+fn6MGTOG999/37jPu+++S1JSEmPHjiUqKorGjRuzY8cOHB0d8/TGCIIgCIIgCIWsKOujl+IZ9nzXYX+WlKT6pIIgCIIgCAWpJMU5aWNp2mVWkdZhP7Lt/RLx/VtK1GESBEEQBEEQhBLM4ioxgiAIgiAIglAQinIxaGledCpm2AVBEARBEAShBBMz7IIgCIIgCELx0EuGr6I6VyklZtgFQRAEQRAEoQQTM+yCIAiCIAhC8RBlHc0iZtgFQRAEQRAEoQQTM+yCIAiCIAhCsZBRhFViiuY0hULMsAuCIAiCIAhCCSYCdkEQBEEQBEEowURKjCAIgiAIglA8JMnwVVTnKqXEDLsgCIIgCIIglGBihl0QSok4dQpHHt01TEYgIUkSekCSJDoHVkYlVxT3EAVBEATBIjKpCBedlt4JdjHDLgilRVhiHK/t/Ysx+/7i9X3reWP/Bt7cv4G3DmwkWast7uEJgiAIwjNjzpw5NGzYEEdHR7y8vOjTpw9Xr1412UeSJD788EP8/PywtbWlTZs2XLx40WSflJQUxo0bh4eHB/b29vTq1Yv79+9bPB4RsAtCKSGXZV+QSl+au0EIgiAIzy+piL/MtG/fPt58802OHj3Kzp070Wq1dOrUiYSEBOM+c+fOZf78+SxevJjjx4/j4+NDx44diYuLM+4zYcIE1q9fz5o1azh48CDx8fH06NEDnU5n0dskUmIEoZTIKWCXSvFCGkEQBEEoabZt22byePny5Xh5eXHy5ElatWqFJEl89dVXvPfee/Tr1w+An3/+GW9vb1atWsWYMWOIiYnhxx9/5Ndff6VDhw4ArFy5koCAAHbt2kXnzp3NHo+YYReEUiLHGXYRsAuCIAilkEySivQrr2JiYgBwc3MD4Pbt24SFhdGpUyfjPtbW1rRu3ZrDhw8DcPLkSTQajck+fn5+BAcHG/cxl5hhF4RSQgTsgiAIgpB/sbGxJo+tra2xtrbOdn9Jkpg4cSItWrQgODgYgLCwMAC8vb1N9vX29ubu3bvGfaysrHB1dc20T9rrzSVm2AWhlBABe8mx5qutDKgyiRcqTeSfXw4U93AEQRBKL30RfwEBAQE4Ozsbv+bMmZPjEN966y3OnTvH6tWrMz0ne+pvsyRJmbY9zZx9niZm2AWhlJAjFp2WFOpkDfHRiQBo1KJCjyAIQmly7949nJycjI9zml0fN24cmzZtYv/+/fj7+xu3+/j4AIZZdF9fX+P28PBw46y7j48ParWaqKgok1n28PBwmjVrZtGYxQy7IJQSCln2v65ihr1otRvQmI9Xv8Una8fRtEvt4h6OIAiCYAEnJyeTr6wCdkmSeOutt/jrr7/YvXs35cqVM3m+XLly+Pj4sHPnTuM2tVrNvn37jMF4/fr1UalUJvuEhoZy4cIFiwN2McMuCKVETrfPRMBetPwreONfwTv3HQVBEIQc5XcxqKXnMtebb77JqlWr2LhxI46Ojsacc2dnZ2xtbZHJZEyYMIHZs2dTqVIlKlWqxOzZs7Gzs2PIkCHGfUePHs2kSZNwd3fHzc2NyZMnU7NmTWPVGHOJgF0QSgmFTIaTlTVyZMhkMmSk5bVblgcnCIIgCELOvv32WwDatGljsn358uWMHDkSgHfffZekpCTGjh1LVFQUjRs3ZseOHTg6Ohr3X7BgAUqlkoEDB5KUlET79u1ZsWIFCoVl3cllkijgbBQbG4uzszMxMTEmuU2CIAiCIAilXUmKc9LG0qrF+yiVNkVyTq02mf0HZ5WI799SIoddEARBEARBEEowkRIjCIIgCIIgFA9JMnwV1blKKTHDLgiCIAiCIAglmJhhFwRBEARBEIqFTDJ8FdW5Sisxwy4IgiAIgiAIJZgI2AVBEARBEAShBBMpMYIgCIIgCELxEItOzSJm2AVBEARBEAShBBMz7IIgCIIgCEKxkOkNX0V1rtJKzLALgiAIgiAIQgkmZtgFQRAEQSgVztx+yIp/T5CQrCZRrSExWU1CiprEFA0fDe5E+9oVi3uIgqVEDrtZRMAuCIIgCEKpEBWfxJ7zN7N8Lj45pYhHIwhFRwTsgiAIgiCUCnZWqmyfS0zRFOFIhAIjpX4V1blKKZHDLgiCIAhCqWBnY5Xtc4kp6iIciSAULTHDLgiCIAhCqWBvnf0Me0KyCNhLI5kkISui3PKiOk9hEDPsgiAIgiCUCvbZzLCrFAp0pTgYE4TciBl2QRAEQRBKBTdHO36ZMAg7ayvsbaywt7bCzlqFSqko7qEJQqESAbsgCIIgCKWCSqGgdjm/4h6GUJBEWUeziJQYQRAEQRAEQSjBxAy7IAiCIAiCUDwkQF+E5yqlxAy7IAiCIAiCIJRgYoZdEARBEARBKBairKN5xAy7IAiCIAiCIJRgYoZdEARBEARBKB4SRVglpmhOUxjEDLsgCIIgCIIglGAiYBcEQRAEQRCEEkykxAiCIAiCIAjFQzROMouYYRcEQRAEQRCEEkzMsAuCIAiCIAjFQw/IivBcpZSYYRcEQRAEQRCEEkzMsAuCIAiCIAjFQjROMo+YYRcEQRAEQRCEEkzMsAuCIAiCIAjFQ1SJMYuYYRcEQRAEQRCEEkwE7IIgCIIgCIJQgomUmAyk1FslsbGxxTwSQRAEQRCEgpUW30glKTVEpMSYRQTsGcTFxQEQEBBQzCMRBEEQBEEoHHFxcTg7Oxf3MAQLiIA9Az8/P+7du4ejoyMyWe5V/GNjYwkICODevXs4OTkVwQhLF/H+5Ey8PzkT70/uxHuUM/H+5Ey8Pzl7Ft8fSZKIi4vDz8+vuIeSTsywm0UE7BnI5XL8/f0tfp2Tk9Mz88tcGMT7kzPx/uRMvD+5E+9RzsT7kzPx/uTsWXt/xMx66SQCdkEQBEEQBKF46IHckxoK7lyllKgSIwiCIAiCIAglmJhhzwdra2s++OADrK2ti3soJZJ4f3Im3p+cifcnd+I9ypl4f3Im3p+cifenaMgkCVkR5ZYX1XkKg0wqUbV9BEEQBEEQhGddbGwszs7OdKg8EaWiaC6KtLoUdl2bT0xMTKlblyBm2AVBEARBEITiIarEmEXksAuCIAiCIAhCCSYCdkEQBEEQBEEowZ6bgP3UqVN07NgRFxcX3N3dee2114iPj89y34iICPz9/ZHJZERHR+d67CNHjtCuXTvs7e1xcXGhTZs2JCUlZdovJSWFOnXqIJPJOHPmjMlzMpks09fSpUtN9jl//jytW7fG1taWMmXKMGvWrAJrL1yS35+zZ88yePBgAgICsLW1pVq1aixcuNDktXfu3MnyPdy2bZtF70N2SvL7AxASEkLPnj2xt7fHw8ODt99+G7VabbLPs/rz06tXLwIDA7GxscHX15dhw4bx8OFD4/MrVqzI8mdDJpMRHh4OPNs/P7m9P/B8f/7k9v6UhM8fKNnvETy/n0F37txh9OjRlCtXDltbWypUqMAHH3xg8r2XhM+gEk0vFe1XKfVcBOwPHz6kQ4cOVKxYkf/++49t27Zx8eJFRo4cmeX+o0ePplatWmYd+8iRI3Tp0oVOnTpx7Ngxjh8/zltvvYVcnvmtfffdd3PsLrZ8+XJCQ0ONXyNGjDA+FxsbS8eOHfHz8+P48eMsWrSIL774gvnz55s1zpyU9Pfn5MmTeHp6snLlSi5evMh7773HtGnTWLx4caZ9d+3aZfIetmvXzqxx5qSkvz86nY7u3buTkJDAwYMHWbNmDevWrWPSpEnGfZ7ln5+2bdvy+++/c/XqVdatW8fNmzd54YUXjM8PGjTI5GciNDSUzp0707p1a7y8vEzO9yz+/OT2/qR5Xj9/cnt/ivvzB0r+e/Q8fwZduXIFvV7PsmXLuHjxIgsWLGDp0qVMnz7deIzi/gwSnhHSc2DZsmWSl5eXpNPpjNtOnz4tAdL169dN9l2yZInUunVr6d9//5UAKSoqKsdjN27cWJoxY0auY/jnn3+kqlWrShcvXpQA6fTp0ybPA9L69euzff2SJUskZ2dnKTk52bhtzpw5/2/v/mKbrPc4jn+2nnVdxalzbnMhMsAjIS44HWqGGrINM4wJXPkHdKgoYSFTYzgeA0FcpkjCFNR5YdQMHBDjQBO8QLMQuIC4LApLwE0RnKAbqzOZDkl0g/R7LrR1hRaqZ3v6tH2/kueiz5/fr/3mty8/vv09T624uNiCweAl+7+YZIjP+VasWGGVlZXh1999911c1/0Tbo/P7t27LTMz0/r7+8P73n//fcvOzrbh4eHw+0rl8TPWrl27LCMjw0ZHR6MeHxwctKysLGttbQ3vS/XxM1a0+KR7/hnrUuPHzNn8Y+b+GJGDIm3YsMGmTp0a87jTOcithoeHTZLNm/a0zf/3fx3Z5k172iSFx2UySYsK+8jIiLxeb0TFICcnR5J04MCB8L6enh41NjaqtbU1aoXzfIODg+rs7FRBQYHmzJmjwsJCzZ07N6JNSfrxxx+1bNkybd26VX6/P2Z79fX1ys/P16233qq33npLweBfP8nV0dGhuXPnRjwPtqamRqdOndKJEycu+V4vJlniM9bw8LDy8vIu2L9gwQIVFBTojjvu0M6dO+Nq61LcHp+Ojg6VlpZGVN9ramo0MjKigwcPhs9J1fEz1tDQkLZv3645c+YoKysr6jmtra3y+/1Rq8ypOH7Gulh80jX/jBXP+JGczT+S+2NEDooUa3yEOJ2DkBrSYsJeVVWlQCCgpqYmjY6O6ueffw5/XTUwMCDpjz/4RYsWqampSdddd11c7fb29kqSGhoatGzZMn366ae65ZZbVF1drWPHjkmSzEyPPvqo6urqNHv27Jhtvfjii9qxY4f27NmjBx98UCtXrtTLL78cPh4IBFRYWBhxTeh1IBCIMxLRJUN8xuro6FBbW5uWL18e3jdp0iRt3LhRO3fu1O7du1VdXa0HHnhA27ZtizsOsbg9PtHGxlVXXSWv1xseG6k6fkKee+45XXbZZbr66qv1/fffa9euXTHbbWlp0eLFi8P/oEupO35CLhWfdM0/IX9n/DidfyT3x4gc9Jdvv/1Wzc3Nqquri9mu0znI/eyvRztO9CbWsCdEQ0NDzBs5QtsXX3yhG2+8Ue+9955effVV+f1+FRUVadq0aSosLJTH45EkrVq1SjNnztTDDz8cd/+hCtTy5cv12GOP6eabb9amTZs0Y8YMtbS0SJKam5t1+vRprVq16qJtrVmzRhUVFSorK9PKlSvV2NiopqamiHMyMjIiXtufN+ucvz8V4xPS3d2thQsXau3atbr77rvD+/Pz8/XMM8/otttu0+zZs9XY2KgVK1Zow4YNMdtKpfhEGwNmFrE/FcdPyLPPPquuri61t7fL4/FoyZIlUW9m6+joUE9Pjx5//PGI/ak6fuKNT7rmn3jjEzKe+SfVYpTuOUj6Yy39/Pnzdd999+mJJ56I2uZ45iCkmYQsxBknP/30k3311VcX3X777beIawKBgP3666925swZy8zMtLa2NjMzu+mmmywzM9M8Ho95PB7LzMw0SebxeGzt2rVR++/t7TVJtnXr1oj9999/vy1evNjMzBYuXBjRrsfjCbe7ZMmSmJ/twIEDJskCgYCZmdXW1tqCBQsizjl06JBJst7e3rSIT3d3txUUFNjq1atjxm2sbdu2mc/ni3k8VeLz/PPP26xZsyLaGBoaMkm2d+9eM0vd8RPNDz/8YJLss88+u+DY0qVLraysLOa1Y6XC+InmYvEJSZf883fiM975xyx1YkQOMuvv77cbbrjBamtrI9bSn288c1CyC69hn/qkzZ/+H0e2eVOfTNo17En9S6f5+fnKz8//W9eEvoJraWmRz+cLV0k+/PDDiMdYff7551q6dKn279+v6dOnR22rpKRExcXFOnr0aMT+b775Rvfcc48k6Y033tBLL70UPnbq1CnV1NTogw8+0O233x7zfXZ1dcnn8+nKK6+UJFVUVGj16tUaHR2V1+uVJLW3t6u4uFglJSVR20il+HR3d6uqqkqPPPKI1q1bF9dn6erq0rXXXhvzeKrEp6KiQuvWrdPAwED487a3tys7O1vl5eXhc1Jx/ERjf1btRkZGIvafOXNGbW1tWr9+fVyfJRXGTzSx4jNWuuSfaKLFZyLyj5Q6MUr3HNTf36/KykqVl5dr8+bNMdfHj3cOQppJ7P8XnNPc3GwHDx60o0eP2ptvvmk5OTn2+uuvxzx/3759F9xB3tfXZzNmzLDOzs7wvk2bNllubq7t2LHDjh07ZmvWrDGfz2fHjx+P2m60O8E//vhje/vtt+3IkSN2/Phxe+eddyw3N9eeeuqp8Dm//PKLFRYW2qJFi+zIkSP20UcfWW5urr3yyiv/PChjuDk+X375pV1zzTX20EMP2cDAQHgbHBwMn7Nlyxbbvn279fT02Ndff21NTU2WlZVlGzdu/OdBGcPN8Tl37pyVlpZadXW1HTp0yPbs2WOTJ0+2+vr68DmpOn46OzutubnZurq67MSJE7Z371678847bfr06RFPozAze/fdd83n89nQ0NAF7ydVx0888Unn/BNPfNyQf9weo3TOQf39/Xb99ddbVVWV9fX1RYyR8yUyB7kRFfa/J20m7LW1tZaXl2der9dmzZoV8TilaKL9MYcmS/v27Ys4d/369TZ58mTz+/1WUVFh+/fvj9lutAnXJ598YmVlZTZp0iTz+/1WWlpqr732mp09ezbi2sOHD9tdd91l2dnZVlRUZA0NDf/347BC3ByfF154IXSnSMQ2ZcqU8DlbtmyxmTNnmt/vt8svv9zKy8sv+Brz/+Hm+JiZnTx50u69917LycmxvLw8q6+vv2DCmorj5/Dhw1ZZWWl5eXmWnZ1tJSUlVldXZ319fRf0WVFREfNr/lQdP/HEJ53zTzzxcUP+MXN3jMzSNwdt3rw56viIVg9NZA5yo/CEfUq9zZ+60pFt3pT6pJ2wZ5iN08+MAQAAAHE4ffq0rrjiCs2bUq9/ZWZf+oJxcC44oj0n39Tw8LByc3Md6XO8JPUadgAAACQxC/6xOdVXkkrqxzoCAAAAqY4KOwAAABIj/KNGDvWVpKiwAwAAAC5GhR0AAACJEQw9WMepvpITFXYAAADAxaiwAwAAIDFYwx4XKuwAAACAizFhBwAAAFyMJTEAAABIDJODS2Kc6WYiUGEHAAAAXIwKOwAAABKDm07jQoUdAAAAcDEq7AAAAEiMYFBS0MG+khMVdgAAAMDFqLADAAAgMVjDHhcq7AAAAICLMWEHAAAAXIwlMQAAAEgMlsTEhQo7AAAA4GJU2AEAAJAYQZPkUOU7SIUdAAAAwASgwg4AAICEMAvKzJkfNHKqn4lAhR0AAABwMSrsAAAASAwz59aW85QYAAAAABOBCTsAAADgYiyJAQAAQGKYg491ZEkMAAAAgIlAhR0AAACJEQxKGQ49bpHHOgIAAACYCFTYAQAAkBisYY8LFXYAAADAxaiwAwAAICEsGJQ5tIbdWMMOAAAAYCJQYQcAAEBisIY9LlTYAQAAABdjwg4AAAC4GEtiAAAAkBhBkzJYEnMpVNgBAAAAF6PCDgAAgMQwk+TQ4xapsAMAAACYCFTYAQAAkBAWNJlDa9iNCjsAAACAiUCFHQAAAIlhQTm3ht2hfiYAFXYAAADAxZiwAwAAAC7GkhgAAAAkBDedxocKOwAAAOBiVNgBAACQGNx0Ghcm7AAAAEiIczorObRS5ZzOOtPRBGDCDgAAAEd5vV4VFRXpQGC3o/0WFRXJ6/U62ud4yLBkXoEPAACApPT7779rdHTU0T69Xq98Pp+jfY4HJuwAAACAi/GUGAAAAMDFmLADAAAALsaEHQAAAHAxJuwAAACAizFhBwAAAFyMCTsAAADgYkzYAQAAABf7H3hGjPL6REc4AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x600 with 2 Axes>"
       ]
@@ -1348,77 +1276,6 @@
     "plt.tight_layout()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
-       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
-       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
-       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry',\n",
-       "       'region_name', 'state_name', 'lat_max', 'lat_min', 'lng_max', 'lng_min',\n",
-       "       'lat_avg', 'lng_avg', 'yearly_sunlight_kwh_kw_threshold_avg',\n",
-       "       'count_qualified', 'percent_covered', 'percent_qualified',\n",
-       "       'number_of_panels_n', 'number_of_panels_s', 'number_of_panels_e',\n",
-       "       'number_of_panels_w', 'number_of_panels_f', 'number_of_panels_median',\n",
-       "       'number_of_panels_total', 'kw_median', 'kw_total',\n",
-       "       'yearly_sunlight_kwh_n', 'yearly_sunlight_kwh_s',\n",
-       "       'yearly_sunlight_kwh_e', 'yearly_sunlight_kwh_w',\n",
-       "       'yearly_sunlight_kwh_f', 'yearly_sunlight_kwh_median',\n",
-       "       'yearly_sunlight_kwh_total', 'install_size_kw_buckets_json',\n",
-       "       'carbon_offset_metric_tons', 'existing_installs_count', 'CITY_right',\n",
-       "       'WARD', 'building_area', 'area_fraction'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "core_buildings.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NC    135\n",
-       "Name: THEME3, dtype: int64"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "core_buildings.loc[core_buildings['FEATURECOD'].isin(['Commercial and Retail'])].loc[:, 'THEME3'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1428,62 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0        1.294453e+06\n",
-       "1        3.424272e+06\n",
-       "2        7.356163e+06\n",
-       "3        2.914559e+04\n",
-       "4        2.778234e+06\n",
-       "             ...     \n",
-       "67781    6.116406e+03\n",
-       "67782    6.036308e+03\n",
-       "67783    1.027241e+06\n",
-       "67784    1.085022e+04\n",
-       "67785    8.179494e+04\n",
-       "Name: Shape_Area, Length: 67786, dtype: float64"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "parcels['Shape_Area']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS', 'CITY',\n",
-       "       'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y', 'NUMSTORY',\n",
-       "       'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY', 'DATE_MOD',\n",
-       "       'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'geometry'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "buildings.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1493,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1502,9 +1304,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "'index_right' cannot be a column name in the frames being joined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[31], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m non_vacant_df \u001b[38;5;241m=\u001b[39m \u001b[43mnon_vacant_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43marmourdale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpredicate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwithin\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mindex_right\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\geopandas\\geodataframe.py:2391\u001b[0m, in \u001b[0;36mGeoDataFrame.sjoin\u001b[1;34m(self, df, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2307\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msjoin\u001b[39m(\u001b[38;5;28mself\u001b[39m, df, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   2308\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Spatial join of two GeoDataFrames.\u001b[39;00m\n\u001b[0;32m   2309\u001b[0m \n\u001b[0;32m   2310\u001b[0m \u001b[38;5;124;03m    See the User Guide page :doc:`../../user_guide/mergingdata` for details.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2389\u001b[0m \u001b[38;5;124;03m    sjoin : equivalent top-level function\u001b[39;00m\n\u001b[0;32m   2390\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2391\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgeopandas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\geopandas\\tools\\sjoin.py:120\u001b[0m, in \u001b[0;36msjoin\u001b[1;34m(left_df, right_df, how, predicate, lsuffix, rsuffix, distance, on_attribute, **kwargs)\u001b[0m\n\u001b[0;32m    114\u001b[0m _basic_checks(left_df, right_df, how, lsuffix, rsuffix, on_attribute\u001b[38;5;241m=\u001b[39mon_attribute),\n\u001b[0;32m    116\u001b[0m indices \u001b[38;5;241m=\u001b[39m _geom_predicate_query(\n\u001b[0;32m    117\u001b[0m     left_df, right_df, predicate, distance, on_attribute\u001b[38;5;241m=\u001b[39mon_attribute\n\u001b[0;32m    118\u001b[0m )\n\u001b[1;32m--> 120\u001b[0m joined, _ \u001b[38;5;241m=\u001b[39m \u001b[43m_frame_join\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    121\u001b[0m \u001b[43m    \u001b[49m\u001b[43mleft_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    122\u001b[0m \u001b[43m    \u001b[49m\u001b[43mright_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    123\u001b[0m \u001b[43m    \u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    124\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    125\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    126\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlsuffix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    127\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrsuffix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    128\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpredicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    129\u001b[0m \u001b[43m    \u001b[49m\u001b[43mon_attribute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mon_attribute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    130\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    132\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m joined\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\geopandas\\tools\\sjoin.py:469\u001b[0m, in \u001b[0;36m_frame_join\u001b[1;34m(left_df, right_df, indices, distances, how, lsuffix, rsuffix, predicate, on_attribute)\u001b[0m\n\u001b[0;32m    467\u001b[0m right_nlevels \u001b[38;5;241m=\u001b[39m right_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnlevels\n\u001b[0;32m    468\u001b[0m right_index_original \u001b[38;5;241m=\u001b[39m right_df\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mnames\n\u001b[1;32m--> 469\u001b[0m right_df, right_column_names \u001b[38;5;241m=\u001b[39m \u001b[43m_reset_index_with_suffix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mright_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrsuffix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mleft_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    471\u001b[0m \u001b[38;5;66;03m# if conflicting names in left and right, add suffix\u001b[39;00m\n\u001b[0;32m    472\u001b[0m left_column_names, right_column_names \u001b[38;5;241m=\u001b[39m _process_column_names_with_suffix(\n\u001b[0;32m    473\u001b[0m     left_column_names,\n\u001b[0;32m    474\u001b[0m     right_column_names,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    477\u001b[0m     right_df,\n\u001b[0;32m    478\u001b[0m )\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\geopandas\\tools\\sjoin.py:288\u001b[0m, in \u001b[0;36m_reset_index_with_suffix\u001b[1;34m(df, suffix, other)\u001b[0m\n\u001b[0;32m    286\u001b[0m         \u001b[38;5;66;03m# check new label will not be in other dataframe\u001b[39;00m\n\u001b[0;32m    287\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_label \u001b[38;5;129;01min\u001b[39;00m df\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;129;01mor\u001b[39;00m new_label \u001b[38;5;129;01min\u001b[39;00m other\u001b[38;5;241m.\u001b[39mcolumns:\n\u001b[1;32m--> 288\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    289\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m cannot be a column name in the frames being\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    290\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m joined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(new_label)\n\u001b[0;32m    291\u001b[0m             )\n\u001b[0;32m    292\u001b[0m         column_names[i] \u001b[38;5;241m=\u001b[39m new_label\n\u001b[0;32m    293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df_reset, pd\u001b[38;5;241m.\u001b[39mIndex(column_names)\n",
+      "\u001b[1;31mValueError\u001b[0m: 'index_right' cannot be a column name in the frames being joined"
+     ]
+    }
+   ],
    "source": [
     "non_vacant_df = non_vacant_df.sjoin(armourdale, predicate='within').drop(columns=['index_right'])"
    ]
@@ -2639,7 +2457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2648,7 +2466,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "impervious = impervious.drop(columns=['index_right'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2657,12 +2484,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "impervio_1\n",
        "Pervious                    2641\n",
        "Buildings                   2337\n",
        "Sidewalks                   1072\n",
@@ -2679,10 +2507,10 @@
        "Bridges                        8\n",
        "Roads-Dirt                     6\n",
        "Athletic Facilities            5\n",
-       "Name: impervio_1, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2691,6 +2519,55 @@
     "armourdale_impervious['impervio_1'].value_counts()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['impervious', 'impervio_1', 'SHAPE__Len', 'SHAPE__Are', 'CITY', 'WARD',\n",
+       "       'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "impervious.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unyt import acre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import unyt as u"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u.unyt_array"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -2699,18 +2576,57 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: >"
+       "unyt_array([0.06690873, 0.01670632, 0.00296433, ..., 0.00825656, 0.06897805,\n",
+       "       0.01146388], 'acre')"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(u.unyt_array(armourdale_impervious['SHAPE__Are'].values)*foot**2).to(acre).to_value()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_impervious = armourdale_impervious.assign(acres=(u.unyt_array(armourdale_impervious['SHAPE__Are'].values)*foot**2).to(acre).to_value())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_impervious = armourdale_impervious.assign(css_potential = armourdale_impervious['acres'] / 4.45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Community Solar Potential for Individual Parking Lots'}>"
+      ]
+     },
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 1200x800 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -2718,12 +2634,18 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots()\n",
+    "fig, ax = plt.subplots(figsize=(12,8))\n",
     "armourdale_impervious.loc[armourdale_impervious['impervio_1'].isin([\n",
     "                                                                    'Parking Lots',\n",
-    "                                                                    'Concrete Pads',\n",
+    "                                                                    # 'Concrete Pads',\n",
     "                                                                    'Parking Lots-Dirt'\n",
-    "                                                                    ])].plot(ax=ax)\n",
+    "                                                                    ]) & (armourdale_impervious.acres > 3*4.45)].plot(ax=ax, \n",
+    "                                                                             column='css_potential',\n",
+    "                                                                             legend=True,\n",
+    "                                                                            #  ec='red',\n",
+    "                                                                             linewidth=.2,\n",
+    "                                                                             legend_kwds=dict(label='MW Solar'))\n",
+    "ax.set_title('Community Solar Potential for Individual Parking Lots')\n",
     "armourdale.plot(ax=ax, fc='None')"
    ]
   },
diff --git a/notebooks/16-outage-frequency.ipynb b/notebooks/16-outage-frequency.ipynb
new file mode 100644
index 0000000..3dbae44
--- /dev/null
+++ b/notebooks/16-outage-frequency.ipynb
@@ -0,0 +1,584 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Esimating Outage Frequency and Duration for Wyandotte County\n",
+    "\n",
+    "This notebook uses data compiled with the EAGLE-I tool to estimate the outage frequency and duration of grid outages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sb\n",
+    "import requests\n",
+    "import json\n",
+    "import os\n",
+    "from glob import glob\n",
+    "import geopandas as gpd\n",
+    "from us import states\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 0: Gather data\n",
+    "\n",
+    "This step retrieves data from the EAGLE-I dataset and filters it down to the county level. Here, we examine Wyandotte County, Kansas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outage_files = {2018:\"https://figshare.com/ndownloader/files/42547879\",\n",
+    "                2019:\"https://figshare.com/ndownloader/files/42547885\",\n",
+    "                2020:\"https://figshare.com/ndownloader/files/42547894\",\n",
+    "                2021:\"https://figshare.com/ndownloader/files/42547891\",\n",
+    "                2022:\"https://figshare.com/ndownloader/files/42547897\",\n",
+    "                2023:\"https://figshare.com/ndownloader/files/44574907\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fips_code</th>\n",
+       "      <th>county</th>\n",
+       "      <th>state</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>run_start_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1003</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-01-01 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1011</td>\n",
+       "      <td>Bullock</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-01-01 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1015</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-01-01 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1021</td>\n",
+       "      <td>Chilton</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-01-01 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1029</td>\n",
+       "      <td>Cleburne</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>142</td>\n",
+       "      <td>2023-01-01 00:00:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26101046</th>\n",
+       "      <td>55095</td>\n",
+       "      <td>Polk</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2023-12-31 23:45:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26101047</th>\n",
+       "      <td>55105</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-12-31 23:45:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26101048</th>\n",
+       "      <td>55109</td>\n",
+       "      <td>St. Croix</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2023-12-31 23:45:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26101049</th>\n",
+       "      <td>55129</td>\n",
+       "      <td>Washburn</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2023-12-31 23:45:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26101050</th>\n",
+       "      <td>56039</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-12-31 23:45:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>26101051 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          fips_code     county      state  sum       run_start_time\n",
+       "0              1003    Baldwin    Alabama    1  2023-01-01 00:00:00\n",
+       "1              1011    Bullock    Alabama    9  2023-01-01 00:00:00\n",
+       "2              1015    Calhoun    Alabama    4  2023-01-01 00:00:00\n",
+       "3              1021    Chilton    Alabama    4  2023-01-01 00:00:00\n",
+       "4              1029   Cleburne    Alabama  142  2023-01-01 00:00:00\n",
+       "...             ...        ...        ...  ...                  ...\n",
+       "26101046      55095       Polk  Wisconsin    0  2023-12-31 23:45:00\n",
+       "26101047      55105       Rock  Wisconsin    1  2023-12-31 23:45:00\n",
+       "26101048      55109  St. Croix  Wisconsin    0  2023-12-31 23:45:00\n",
+       "26101049      55129   Washburn  Wisconsin    0  2023-12-31 23:45:00\n",
+       "26101050      56039      Teton    Wyoming    2  2023-12-31 23:45:00\n",
+       "\n",
+       "[26101051 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv(outage_files[2023])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/6 [04:22<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "File \u001b[1;32m<timed exec>:3\u001b[0m\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m   1014\u001b[0m     dialect,\n\u001b[0;32m   1015\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m   1023\u001b[0m )\n\u001b[0;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\common.py:728\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    725\u001b[0m     codecs\u001b[38;5;241m.\u001b[39mlookup_error(errors)\n\u001b[0;32m    727\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[1;32m--> 728\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    729\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    732\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    733\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    734\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    736\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n\u001b[0;32m    737\u001b[0m handles: \u001b[38;5;28mlist\u001b[39m[BaseBuffer]\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\common.py:389\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    386\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m content_encoding \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    387\u001b[0m             \u001b[38;5;66;03m# Override compression based on Content-Encoding header\u001b[39;00m\n\u001b[0;32m    388\u001b[0m             compression \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmethod\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m--> 389\u001b[0m         reader \u001b[38;5;241m=\u001b[39m BytesIO(\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    390\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m IOArgs(\n\u001b[0;32m    391\u001b[0m         filepath_or_buffer\u001b[38;5;241m=\u001b[39mreader,\n\u001b[0;32m    392\u001b[0m         encoding\u001b[38;5;241m=\u001b[39mencoding,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    395\u001b[0m         mode\u001b[38;5;241m=\u001b[39mfsspec_mode,\n\u001b[0;32m    396\u001b[0m     )\n\u001b[0;32m    398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_fsspec_url(filepath_or_buffer):\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\http\\client.py:489\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    488\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 489\u001b[0m         s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_safe_read\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlength\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    490\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m IncompleteRead:\n\u001b[0;32m    491\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\http\\client.py:638\u001b[0m, in \u001b[0;36mHTTPResponse._safe_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    631\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_safe_read\u001b[39m(\u001b[38;5;28mself\u001b[39m, amt):\n\u001b[0;32m    632\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Read the number of bytes requested.\u001b[39;00m\n\u001b[0;32m    633\u001b[0m \n\u001b[0;32m    634\u001b[0m \u001b[38;5;124;03m    This function should be used when <amt> bytes \"should\" be present for\u001b[39;00m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;124;03m    reading. If the bytes are truly not available (due to EOF), then the\u001b[39;00m\n\u001b[0;32m    636\u001b[0m \u001b[38;5;124;03m    IncompleteRead exception can be used to detect the problem.\u001b[39;00m\n\u001b[0;32m    637\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 638\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(amt)\n\u001b[0;32m    639\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m<\u001b[39m amt:\n\u001b[0;32m    640\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(data, amt\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(data))\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\socket.py:718\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 718\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    719\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m    720\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\ssl.py:1314\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1310\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m   1311\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1312\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[0;32m   1313\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[1;32m-> 1314\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1315\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
+      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\ssl.py:1166\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m   1164\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1165\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1166\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1167\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1168\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "frames = []\n",
+    "for year, url in tqdm(outage_files.items())\n",
+    "    df = pd.read_csv(url, parse_dates=True, index_col='run_start_time')\n",
+    "    frames.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Up'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"UP\".capitalize()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wyandotte_outage = outage_2023.loc[(outage_2023.county == 'Wyandotte')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wyandotte_outage.loc[:,'run_start_time'] = pd.to_datetime(wyandotte_outage['run_start_time'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:7588: FutureWarning: Dtype inference on a pandas object (Series, Index, ExtensionArray) is deprecated. The Index constructor will keep the original dtype in the future. Call `infer_objects` on the result to get the old behavior.\n",
+      "  return Index(sequences[0], name=names)\n"
+     ]
+    }
+   ],
+   "source": [
+    "wyandotte_outage.set_index('run_start_time', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot data for a specific outage of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'wyandotte_outage' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mwyandotte_outage\u001b[49m\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2023-07-12\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2023-07-19\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msum\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mplot()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'wyandotte_outage' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "wyandotte_outage.loc['2023-07-12':'2023-07-19','sum'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['fips_code', 'county', 'state', 'sum', 'run_start_time'], dtype='object')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wyandotte_outage.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\SDotson\\AppData\\Local\\Temp\\ipykernel_5724\\2300140524.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  wyandotte_outage['run_start_time'] = pd.to_datetime(wyandotte_outage['run_start_time'])\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wyandotte_outage = wyandotte_outage.set_index('run_start_time').resample('15min').ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fips_code</th>\n",
+       "      <th>county</th>\n",
+       "      <th>state</th>\n",
+       "      <th>sum</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>run_start_time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 08:00:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 08:15:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 08:30:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 08:45:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 09:00:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31 21:15:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31 21:30:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31 21:45:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31 22:00:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-12-31 22:15:00</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>Wyandotte</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>35002 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     fips_code     county   state  sum\n",
+       "run_start_time                                        \n",
+       "2023-01-01 08:00:00      20209  Wyandotte  Kansas    0\n",
+       "2023-01-01 08:15:00      20209  Wyandotte  Kansas    0\n",
+       "2023-01-01 08:30:00      20209  Wyandotte  Kansas    0\n",
+       "2023-01-01 08:45:00      20209  Wyandotte  Kansas    0\n",
+       "2023-01-01 09:00:00      20209  Wyandotte  Kansas    0\n",
+       "...                        ...        ...     ...  ...\n",
+       "2023-12-31 21:15:00      20209  Wyandotte  Kansas    8\n",
+       "2023-12-31 21:30:00      20209  Wyandotte  Kansas    8\n",
+       "2023-12-31 21:45:00      20209  Wyandotte  Kansas    8\n",
+       "2023-12-31 22:00:00      20209  Wyandotte  Kansas    8\n",
+       "2023-12-31 22:15:00      20209  Wyandotte  Kansas    1\n",
+       "\n",
+       "[35002 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wyandotte_outage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='run_start_time'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wyandotte_outage['sum'].plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pypsa-illinois02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/gis_notebooks/rooftop_suitability.ipynb b/notebooks/gis_notebooks/rooftop_suitability.ipynb
new file mode 100644
index 0000000..8587d0f
--- /dev/null
+++ b/notebooks/gis_notebooks/rooftop_suitability.ipynb
@@ -0,0 +1,680 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "from us import states\n",
+    "from zipfile import ZipFile\n",
+    "import os\n",
+    "import sys\n",
+    "from glob import glob\n",
+    "import json\n",
+    "from io import BytesIO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = requests.get(\"https://data.nrel.gov/system/files/121/small_suitability.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = ZipFile(BytesIO(r.content))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z.extractall(\"../../data/spatial_data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roof_gdf = gpd.read_file(\"../../data/spatial_data/small_suitability/opv_national_small_suitability.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fipsstco</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state_fips</th>\n",
+       "      <th>zip_name</th>\n",
+       "      <th>county</th>\n",
+       "      <th>state</th>\n",
+       "      <th>state_zip</th>\n",
+       "      <th>zip_int</th>\n",
+       "      <th>locale</th>\n",
+       "      <th>nbld</th>\n",
+       "      <th>pct_suitab</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>41035</td>\n",
+       "      <td>97737</td>\n",
+       "      <td>41</td>\n",
+       "      <td>GILCHRIST</td>\n",
+       "      <td>KLAMATH</td>\n",
+       "      <td>OR</td>\n",
+       "      <td>OR_97737</td>\n",
+       "      <td>97737</td>\n",
+       "      <td>City Small</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.827528</td>\n",
+       "      <td>POLYGON ((-121.55762 43.42257, -121.55732 43.4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>41013</td>\n",
+       "      <td>97752</td>\n",
+       "      <td>41</td>\n",
+       "      <td>POST</td>\n",
+       "      <td>CROOK</td>\n",
+       "      <td>OR</td>\n",
+       "      <td>OR_97752</td>\n",
+       "      <td>97752</td>\n",
+       "      <td>Town Fringe</td>\n",
+       "      <td>700</td>\n",
+       "      <td>0.799901</td>\n",
+       "      <td>POLYGON ((-120.25104 44.4377, -120.24783 44.43...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>41013</td>\n",
+       "      <td>97753</td>\n",
+       "      <td>41</td>\n",
+       "      <td>POWELL BUTTE</td>\n",
+       "      <td>CROOK</td>\n",
+       "      <td>OR</td>\n",
+       "      <td>OR_97753</td>\n",
+       "      <td>97753</td>\n",
+       "      <td>Town Fringe</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>0.814984</td>\n",
+       "      <td>POLYGON ((-121.10827 44.33769, -121.10822 44.3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>41017</td>\n",
+       "      <td>97756</td>\n",
+       "      <td>41</td>\n",
+       "      <td>REDMOND</td>\n",
+       "      <td>DESCHUTES</td>\n",
+       "      <td>OR</td>\n",
+       "      <td>OR_97756</td>\n",
+       "      <td>97756</td>\n",
+       "      <td>Town Fringe</td>\n",
+       "      <td>7648</td>\n",
+       "      <td>0.809712</td>\n",
+       "      <td>POLYGON ((-121.10817 44.34245, -121.10818 44.3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>41017</td>\n",
+       "      <td>97759</td>\n",
+       "      <td>41</td>\n",
+       "      <td>SISTERS</td>\n",
+       "      <td>DESCHUTES</td>\n",
+       "      <td>OR</td>\n",
+       "      <td>OR_97759</td>\n",
+       "      <td>97759</td>\n",
+       "      <td>Rural Fringe</td>\n",
+       "      <td>1822</td>\n",
+       "      <td>0.726288</td>\n",
+       "      <td>MULTIPOLYGON (((-121.77008 44.03068, -121.7682...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  fipsstco    zip state_fips      zip_name     county state state_zip  \\\n",
+       "0    41035  97737         41     GILCHRIST    KLAMATH    OR  OR_97737   \n",
+       "1    41013  97752         41          POST      CROOK    OR  OR_97752   \n",
+       "2    41013  97753         41  POWELL BUTTE      CROOK    OR  OR_97753   \n",
+       "3    41017  97756         41       REDMOND  DESCHUTES    OR  OR_97756   \n",
+       "4    41017  97759         41       SISTERS  DESCHUTES    OR  OR_97759   \n",
+       "\n",
+       "   zip_int        locale  nbld  pct_suitab  \\\n",
+       "0    97737    City Small    30    0.827528   \n",
+       "1    97752   Town Fringe   700    0.799901   \n",
+       "2    97753   Town Fringe  1271    0.814984   \n",
+       "3    97756   Town Fringe  7648    0.809712   \n",
+       "4    97759  Rural Fringe  1822    0.726288   \n",
+       "\n",
+       "                                            geometry  \n",
+       "0  POLYGON ((-121.55762 43.42257, -121.55732 43.4...  \n",
+       "1  POLYGON ((-120.25104 44.4377, -120.24783 44.43...  \n",
+       "2  POLYGON ((-121.10827 44.33769, -121.10822 44.3...  \n",
+       "3  POLYGON ((-121.10817 44.34245, -121.10818 44.3...  \n",
+       "4  MULTIPOLYGON (((-121.77008 44.03068, -121.7682...  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roof_gdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale = gpd.read_file(\"../../data/spatial_data/armourdale_shape.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fipsstco</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>state_fips</th>\n",
+       "      <th>zip_name</th>\n",
+       "      <th>county</th>\n",
+       "      <th>state</th>\n",
+       "      <th>state_zip</th>\n",
+       "      <th>zip_int</th>\n",
+       "      <th>locale</th>\n",
+       "      <th>nbld</th>\n",
+       "      <th>pct_suitab</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8870</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66102</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66102</td>\n",
+       "      <td>66102</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>6553</td>\n",
+       "      <td>0.667659</td>\n",
+       "      <td>MULTIPOLYGON (((-94.64517 39.09151, -94.6452 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8871</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66104</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66104</td>\n",
+       "      <td>66104</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>7206</td>\n",
+       "      <td>0.655492</td>\n",
+       "      <td>POLYGON ((-94.73651 39.16928, -94.73573 39.169...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8872</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66105</td>\n",
+       "      <td>66105</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>1430</td>\n",
+       "      <td>0.692308</td>\n",
+       "      <td>POLYGON ((-94.60721 39.0897, -94.60722 39.0874...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8873</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66106</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66106</td>\n",
+       "      <td>66106</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>5688</td>\n",
+       "      <td>0.666029</td>\n",
+       "      <td>MULTIPOLYGON (((-94.79281 39.04355, -94.7931 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8874</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66109</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66109</td>\n",
+       "      <td>66109</td>\n",
+       "      <td>Rural Fringe</td>\n",
+       "      <td>6361</td>\n",
+       "      <td>0.780883</td>\n",
+       "      <td>POLYGON ((-94.90053 39.18718, -94.90073 39.187...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8875</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66111</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66111</td>\n",
+       "      <td>66111</td>\n",
+       "      <td>Suburb Large</td>\n",
+       "      <td>2983</td>\n",
+       "      <td>0.764832</td>\n",
+       "      <td>POLYGON ((-94.7951 39.04352, -94.79694 39.0435...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8876</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66112</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66112</td>\n",
+       "      <td>66112</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>2459</td>\n",
+       "      <td>0.666267</td>\n",
+       "      <td>POLYGON ((-94.74978 39.12858, -94.7463 39.1290...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9231</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66118</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66118</td>\n",
+       "      <td>66118</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>97</td>\n",
+       "      <td>0.628866</td>\n",
+       "      <td>POLYGON ((-94.61523 39.11228, -94.6152 39.1122...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9446</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66101</td>\n",
+       "      <td>66101</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>4286</td>\n",
+       "      <td>0.747084</td>\n",
+       "      <td>POLYGON ((-94.61512 39.1137, -94.61512 39.1131...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9447</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66103</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66103</td>\n",
+       "      <td>66103</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>4353</td>\n",
+       "      <td>0.658397</td>\n",
+       "      <td>POLYGON ((-94.60732 39.0821, -94.60733 39.0819...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9448</th>\n",
+       "      <td>20209</td>\n",
+       "      <td>66115</td>\n",
+       "      <td>20</td>\n",
+       "      <td>KANSAS CITY</td>\n",
+       "      <td>WYANDOTTE</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>KS_66115</td>\n",
+       "      <td>66115</td>\n",
+       "      <td>City Midsize</td>\n",
+       "      <td>253</td>\n",
+       "      <td>0.596838</td>\n",
+       "      <td>POLYGON ((-94.63554 39.15407, -94.63149 39.154...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     fipsstco    zip state_fips     zip_name     county state state_zip  \\\n",
+       "8870    20209  66102         20  KANSAS CITY  WYANDOTTE    KS  KS_66102   \n",
+       "8871    20209  66104         20  KANSAS CITY  WYANDOTTE    KS  KS_66104   \n",
+       "8872    20209  66105         20  KANSAS CITY  WYANDOTTE    KS  KS_66105   \n",
+       "8873    20209  66106         20  KANSAS CITY  WYANDOTTE    KS  KS_66106   \n",
+       "8874    20209  66109         20  KANSAS CITY  WYANDOTTE    KS  KS_66109   \n",
+       "8875    20209  66111         20  KANSAS CITY  WYANDOTTE    KS  KS_66111   \n",
+       "8876    20209  66112         20  KANSAS CITY  WYANDOTTE    KS  KS_66112   \n",
+       "9231    20209  66118         20  KANSAS CITY  WYANDOTTE    KS  KS_66118   \n",
+       "9446    20209  66101         20  KANSAS CITY  WYANDOTTE    KS  KS_66101   \n",
+       "9447    20209  66103         20  KANSAS CITY  WYANDOTTE    KS  KS_66103   \n",
+       "9448    20209  66115         20  KANSAS CITY  WYANDOTTE    KS  KS_66115   \n",
+       "\n",
+       "      zip_int        locale  nbld  pct_suitab  \\\n",
+       "8870    66102  City Midsize  6553    0.667659   \n",
+       "8871    66104  City Midsize  7206    0.655492   \n",
+       "8872    66105  City Midsize  1430    0.692308   \n",
+       "8873    66106  City Midsize  5688    0.666029   \n",
+       "8874    66109  Rural Fringe  6361    0.780883   \n",
+       "8875    66111  Suburb Large  2983    0.764832   \n",
+       "8876    66112  City Midsize  2459    0.666267   \n",
+       "9231    66118  City Midsize    97    0.628866   \n",
+       "9446    66101  City Midsize  4286    0.747084   \n",
+       "9447    66103  City Midsize  4353    0.658397   \n",
+       "9448    66115  City Midsize   253    0.596838   \n",
+       "\n",
+       "                                               geometry  \n",
+       "8870  MULTIPOLYGON (((-94.64517 39.09151, -94.6452 3...  \n",
+       "8871  POLYGON ((-94.73651 39.16928, -94.73573 39.169...  \n",
+       "8872  POLYGON ((-94.60721 39.0897, -94.60722 39.0874...  \n",
+       "8873  MULTIPOLYGON (((-94.79281 39.04355, -94.7931 3...  \n",
+       "8874  POLYGON ((-94.90053 39.18718, -94.90073 39.187...  \n",
+       "8875  POLYGON ((-94.7951 39.04352, -94.79694 39.0435...  \n",
+       "8876  POLYGON ((-94.74978 39.12858, -94.7463 39.1290...  \n",
+       "9231  POLYGON ((-94.61523 39.11228, -94.6152 39.1122...  \n",
+       "9446  POLYGON ((-94.61512 39.1137, -94.61512 39.1131...  \n",
+       "9447  POLYGON ((-94.60732 39.0821, -94.60733 39.0819...  \n",
+       "9448  POLYGON ((-94.63554 39.15407, -94.63149 39.154...  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roof_gdf.loc[roof_gdf['county']=='wyandotte'.upper()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "roof_gdf.loc[roof_gdf['county']=='wyandotte'.upper()].plot(ax=ax, column='zip', categorical=True, legend=True, )\n",
+    "                                                        #    legend_kwds=dict(label='Percent of suitable rooftops'))\n",
+    "armourdale.plot(ax=ax, fc='None', ec='k')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "armourdale_buildings_res = gpd.read_file(\"../../data/spatial_data/armourdale/building_footprints.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['THEME1', 'THEME2', 'FEATURECOD', 'NAME', 'AGENCY', 'ADDRESS',\n",
+       "       'CITY_left', 'ZIP', 'COMMENT', 'CHNG_TYPE', 'SOURCE', 'X', 'Y',\n",
+       "       'NUMSTORY', 'BLDGHEIGHT', 'THEME3', 'LAT', 'LONG', 'MOD_BY', 'ADDED_BY',\n",
+       "       'DATE_MOD', 'DATE_ADDED', 'Shape__Are', 'Shape__Len', 'index_right',\n",
+       "       'CITY_right', 'WARD', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale_buildings_res.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1584, 28)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "armourdale_buildings_res.loc[((armourdale_buildings_res['Shape__Are'] < 5000)&\n",
+    "                              (armourdale_buildings_res['FEATURECOD'].isin(['Building General'])))].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1509.2140000000002"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2180*0.6923"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "TX    1840\n",
+       "CA    1771\n",
+       "NY    1711\n",
+       "PA    1695\n",
+       "IL    1336\n",
+       "OH    1145\n",
+       "MO     991\n",
+       "FL     979\n",
+       "MI     970\n",
+       "IA     961\n",
+       "VA     898\n",
+       "MN     859\n",
+       "NC     801\n",
+       "WI     759\n",
+       "KY     751\n",
+       "IN     745\n",
+       "GA     712\n",
+       "KS     693\n",
+       "WV     674\n",
+       "TN     624\n",
+       "AL     623\n",
+       "OK     614\n",
+       "WA     586\n",
+       "NJ     583\n",
+       "NE     578\n",
+       "AR     557\n",
+       "MA     526\n",
+       "CO     524\n",
+       "LA     503\n",
+       "MD     465\n",
+       "OR     420\n",
+       "ME     419\n",
+       "SC     410\n",
+       "MS     409\n",
+       "AZ     395\n",
+       "ND     382\n",
+       "NM     362\n",
+       "MT     356\n",
+       "SD     350\n",
+       "CT     284\n",
+       "UT     281\n",
+       "ID     275\n",
+       "VT     257\n",
+       "NH     246\n",
+       "NV     181\n",
+       "WY     174\n",
+       "DC      84\n",
+       "RI      79\n",
+       "DE      66\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roof_gdf['state'].value_counts()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pypsa-illinois02",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/scripts/retrieve_outage_data.py b/scripts/retrieve_outage_data.py
new file mode 100644
index 0000000..8d263cc
--- /dev/null
+++ b/scripts/retrieve_outage_data.py
@@ -0,0 +1,26 @@
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+outage_files = {2018:"https://figshare.com/ndownloader/files/42547879",
+                2019:"https://figshare.com/ndownloader/files/42547885",
+                2020:"https://figshare.com/ndownloader/files/42547894",
+                2021:"https://figshare.com/ndownloader/files/42547891",
+                2022:"https://figshare.com/ndownloader/files/42547897",
+                2023:"https://figshare.com/ndownloader/files/44574907"}
+
+if __name__ == "__main__":
+
+    frames = []
+    pbar = tqdm(outage_files.items())
+    for year, url in pbar:
+        pbar.set_description(desc=f"{year}")
+        df = pd.read_csv(url, parse_dates=True, index_col='run_start_time')
+        frames.append(df)
+
+    outages = pd.concat(frames, axis=0)
+
+
+    outages = outages.loc[outages['county'] == snakemake.config['county'].capitalize()]
+
+    outages.to_csv(snakemake.output.outages)
\ No newline at end of file

From 38a3a54e9effa616a40c6ac0dfe3d7f66f029530 Mon Sep 17 00:00:00 2001
From: Samuel Dotson <samgdotson@gmail.com>
Date: Wed, 27 Nov 2024 11:55:25 -0500
Subject: [PATCH 50/52] adds oe417 files

---
 notebooks/16-outage-frequency.ipynb | 535 ++++++++++++++++++++++------
 1 file changed, 424 insertions(+), 111 deletions(-)

diff --git a/notebooks/16-outage-frequency.ipynb b/notebooks/16-outage-frequency.ipynb
index 3dbae44..818a6f1 100644
--- a/notebooks/16-outage-frequency.ipynb
+++ b/notebooks/16-outage-frequency.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,9 @@
     "from glob import glob\n",
     "import geopandas as gpd\n",
     "from us import states\n",
-    "from tqdm import tqdm"
+    "from tqdm import tqdm\n",
+    "import xlrd\n",
+    "from os import devnull"
    ]
   },
   {
@@ -53,7 +55,125 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oe417_files = {2018:\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=78\",\n",
+    "                2019:\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=79\",\n",
+    "                2020:\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=80\",\n",
+    "                2021:\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=81\",\n",
+    "                2022:\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=82\",\n",
+    "                2023:\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=84\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 17%|█▋        | 1/6 [00:02<00:13,  2.77s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING *** file size (86212) not 512 + multiple of sector size (512)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 33%|███▎      | 2/6 [00:05<00:11,  2.79s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING *** file size (96452) not 512 + multiple of sector size (512)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 50%|█████     | 3/6 [00:08<00:08,  2.88s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING *** file size (120004) not 512 + multiple of sector size (512)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 67%|██████▋   | 4/6 [00:11<00:06,  3.01s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING *** file size (133828) not 512 + multiple of sector size (512)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 83%|████████▎ | 5/6 [00:14<00:03,  3.02s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING *** file size (134340) not 512 + multiple of sector size (512)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6/6 [00:17<00:00,  2.97s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING *** file size (119492) not 512 + multiple of sector size (512)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "frames = []\n",
+    "for year, url in tqdm(oe417_files.items()):\n",
+    "    df = pd.read_excel(url, skiprows=1)\n",
+    "    frames.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -77,53 +197,95 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>fips_code</th>\n",
-       "      <th>county</th>\n",
-       "      <th>state</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>run_start_time</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Date Event Began</th>\n",
+       "      <th>Time Event Began</th>\n",
+       "      <th>Date of Restoration</th>\n",
+       "      <th>Time of Restoration</th>\n",
+       "      <th>Area Affected</th>\n",
+       "      <th>NERC Region</th>\n",
+       "      <th>Alert Criteria</th>\n",
+       "      <th>Event Type</th>\n",
+       "      <th>Demand Loss (MW)</th>\n",
+       "      <th>Number of Customers Affected</th>\n",
+       "      <th>Event Month</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1003</td>\n",
-       "      <td>Baldwin</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2023-01-01 00:00:00</td>\n",
+       "      <td>January</td>\n",
+       "      <td>01/01/2018</td>\n",
+       "      <td>18:21:00</td>\n",
+       "      <td>01/02/2018</td>\n",
+       "      <td>18:11:00</td>\n",
+       "      <td>Tennessee:</td>\n",
+       "      <td>SERC</td>\n",
+       "      <td>Public appeal to reduce the use of electricity...</td>\n",
+       "      <td>Severe Weather</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1011</td>\n",
-       "      <td>Bullock</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2023-01-01 00:00:00</td>\n",
+       "      <td>January</td>\n",
+       "      <td>01/01/2018</td>\n",
+       "      <td>17:43:00</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Texas:</td>\n",
+       "      <td>TRE</td>\n",
+       "      <td>Public appeal to reduce the use of electricity...</td>\n",
+       "      <td>Severe Weather</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1015</td>\n",
-       "      <td>Calhoun</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023-01-01 00:00:00</td>\n",
+       "      <td>January</td>\n",
+       "      <td>01/01/2018</td>\n",
+       "      <td>21:37:00</td>\n",
+       "      <td>01/02/2018</td>\n",
+       "      <td>10:30:00</td>\n",
+       "      <td>Tennessee:</td>\n",
+       "      <td>SERC</td>\n",
+       "      <td>Public appeal to reduce the use of electricity...</td>\n",
+       "      <td>System Operations</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1021</td>\n",
-       "      <td>Chilton</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2023-01-01 00:00:00</td>\n",
+       "      <td>January</td>\n",
+       "      <td>01/02/2018</td>\n",
+       "      <td>10:00:00</td>\n",
+       "      <td>02/12/2018</td>\n",
+       "      <td>08:00:00</td>\n",
+       "      <td>New York: Niagara County;</td>\n",
+       "      <td>NPCC</td>\n",
+       "      <td>Fuel supply emergencies that could impact elec...</td>\n",
+       "      <td>Fuel Supply Deficiency</td>\n",
+       "      <td>675</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1029</td>\n",
-       "      <td>Cleburne</td>\n",
-       "      <td>Alabama</td>\n",
-       "      <td>142</td>\n",
-       "      <td>2023-01-01 00:00:00</td>\n",
+       "      <td>January</td>\n",
+       "      <td>01/02/2018</td>\n",
+       "      <td>07:30:00</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>South Carolina:</td>\n",
+       "      <td>SERC</td>\n",
+       "      <td>Public appeal to reduce the use of electricity...</td>\n",
+       "      <td>Severe Weather</td>\n",
+       "      <td>0</td>\n",
+       "      <td>717000</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -132,139 +294,290 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26101046</th>\n",
-       "      <td>55095</td>\n",
-       "      <td>Polk</td>\n",
-       "      <td>Wisconsin</td>\n",
+       "      <th>343</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-12-25 00:00:00</td>\n",
+       "      <td>08:35:00</td>\n",
+       "      <td>2023-12-25 00:00:00</td>\n",
+       "      <td>15:00:00</td>\n",
+       "      <td>Consolidated Edison of New York, Inc.</td>\n",
+       "      <td>NPCC</td>\n",
+       "      <td>Unplanned evacuation from its Bulk Electric Sy...</td>\n",
+       "      <td>- Other</td>\n",
        "      <td>0</td>\n",
-       "      <td>2023-12-31 23:45:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>December</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26101047</th>\n",
-       "      <td>55105</td>\n",
-       "      <td>Rock</td>\n",
-       "      <td>Wisconsin</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2023-12-31 23:45:00</td>\n",
+       "      <th>344</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-12-27 00:00:00</td>\n",
+       "      <td>14:44:00</td>\n",
+       "      <td>2023-12-28 00:00:00</td>\n",
+       "      <td>04:14:00</td>\n",
+       "      <td>Otter Tail Power Co</td>\n",
+       "      <td>MRO</td>\n",
+       "      <td>Electrical System Separation (Islanding) where...</td>\n",
+       "      <td>- Weather or natural disaster</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8000</td>\n",
+       "      <td>December</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26101048</th>\n",
-       "      <td>55109</td>\n",
-       "      <td>St. Croix</td>\n",
-       "      <td>Wisconsin</td>\n",
+       "      <th>345</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-12-27 00:00:00</td>\n",
+       "      <td>11:11:00</td>\n",
+       "      <td>2023-12-27 00:00:00</td>\n",
+       "      <td>11:12:00</td>\n",
+       "      <td>Pacificorp</td>\n",
+       "      <td>WECC</td>\n",
+       "      <td>Physical attack that could potentially impact ...</td>\n",
+       "      <td>- Suspicious activity - Transmission equipment...</td>\n",
        "      <td>0</td>\n",
-       "      <td>2023-12-31 23:45:00</td>\n",
+       "      <td>0</td>\n",
+       "      <td>December</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26101049</th>\n",
-       "      <td>55129</td>\n",
-       "      <td>Washburn</td>\n",
-       "      <td>Wisconsin</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2023-12-31 23:45:00</td>\n",
+       "      <th>346</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-12-30 00:00:00</td>\n",
+       "      <td>21:00:00</td>\n",
+       "      <td>2023-12-30 00:00:00</td>\n",
+       "      <td>22:15:00</td>\n",
+       "      <td>American Electric Power (Regulated Generation)</td>\n",
+       "      <td>RF</td>\n",
+       "      <td>Physical threat to its Facility excluding weat...</td>\n",
+       "      <td>- Suspicious activity</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>December</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26101050</th>\n",
-       "      <td>56039</td>\n",
-       "      <td>Teton</td>\n",
-       "      <td>Wyoming</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2023-12-31 23:45:00</td>\n",
+       "      <th>347</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2023-12-30 00:00:00</td>\n",
+       "      <td>05:00:00</td>\n",
+       "      <td>2023-12-30 00:00:00</td>\n",
+       "      <td>06:48:00</td>\n",
+       "      <td>Lower Colorado River Authority</td>\n",
+       "      <td>Texas RE</td>\n",
+       "      <td>Physical threat to its Facility excluding weat...</td>\n",
+       "      <td>- Suspicious activity</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>December</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>26101051 rows × 5 columns</p>\n",
+       "<p>2006 rows × 12 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "          fips_code     county      state  sum       run_start_time\n",
-       "0              1003    Baldwin    Alabama    1  2023-01-01 00:00:00\n",
-       "1              1011    Bullock    Alabama    9  2023-01-01 00:00:00\n",
-       "2              1015    Calhoun    Alabama    4  2023-01-01 00:00:00\n",
-       "3              1021    Chilton    Alabama    4  2023-01-01 00:00:00\n",
-       "4              1029   Cleburne    Alabama  142  2023-01-01 00:00:00\n",
-       "...             ...        ...        ...  ...                  ...\n",
-       "26101046      55095       Polk  Wisconsin    0  2023-12-31 23:45:00\n",
-       "26101047      55105       Rock  Wisconsin    1  2023-12-31 23:45:00\n",
-       "26101048      55109  St. Croix  Wisconsin    0  2023-12-31 23:45:00\n",
-       "26101049      55129   Washburn  Wisconsin    0  2023-12-31 23:45:00\n",
-       "26101050      56039      Teton    Wyoming    2  2023-12-31 23:45:00\n",
+       "        Month     Date Event Began Time Event Began  Date of Restoration  \\\n",
+       "0    January            01/01/2018         18:21:00           01/02/2018   \n",
+       "1     January           01/01/2018         17:43:00              Unknown   \n",
+       "2     January           01/01/2018         21:37:00           01/02/2018   \n",
+       "3     January           01/02/2018         10:00:00           02/12/2018   \n",
+       "4     January           01/02/2018         07:30:00              Unknown   \n",
+       "..        ...                  ...              ...                  ...   \n",
+       "343       NaN  2023-12-25 00:00:00         08:35:00  2023-12-25 00:00:00   \n",
+       "344       NaN  2023-12-27 00:00:00         14:44:00  2023-12-28 00:00:00   \n",
+       "345       NaN  2023-12-27 00:00:00         11:11:00  2023-12-27 00:00:00   \n",
+       "346       NaN  2023-12-30 00:00:00         21:00:00  2023-12-30 00:00:00   \n",
+       "347       NaN  2023-12-30 00:00:00         05:00:00  2023-12-30 00:00:00   \n",
+       "\n",
+       "    Time of Restoration                                   Area Affected  \\\n",
+       "0              18:11:00                                      Tennessee:   \n",
+       "1               Unknown                                          Texas:   \n",
+       "2              10:30:00                                      Tennessee:   \n",
+       "3              08:00:00                       New York: Niagara County;   \n",
+       "4               Unknown                                 South Carolina:   \n",
+       "..                  ...                                             ...   \n",
+       "343            15:00:00           Consolidated Edison of New York, Inc.   \n",
+       "344            04:14:00                             Otter Tail Power Co   \n",
+       "345            11:12:00                                      Pacificorp   \n",
+       "346            22:15:00  American Electric Power (Regulated Generation)   \n",
+       "347            06:48:00                  Lower Colorado River Authority   \n",
        "\n",
-       "[26101051 rows x 5 columns]"
+       "    NERC Region                                     Alert Criteria  \\\n",
+       "0          SERC  Public appeal to reduce the use of electricity...   \n",
+       "1           TRE  Public appeal to reduce the use of electricity...   \n",
+       "2          SERC  Public appeal to reduce the use of electricity...   \n",
+       "3          NPCC  Fuel supply emergencies that could impact elec...   \n",
+       "4          SERC  Public appeal to reduce the use of electricity...   \n",
+       "..          ...                                                ...   \n",
+       "343        NPCC  Unplanned evacuation from its Bulk Electric Sy...   \n",
+       "344         MRO  Electrical System Separation (Islanding) where...   \n",
+       "345        WECC  Physical attack that could potentially impact ...   \n",
+       "346          RF  Physical threat to its Facility excluding weat...   \n",
+       "347    Texas RE  Physical threat to its Facility excluding weat...   \n",
+       "\n",
+       "                                            Event Type Demand Loss (MW)  \\\n",
+       "0                                       Severe Weather          Unknown   \n",
+       "1                                       Severe Weather          Unknown   \n",
+       "2                                    System Operations          Unknown   \n",
+       "3                               Fuel Supply Deficiency              675   \n",
+       "4                                       Severe Weather                0   \n",
+       "..                                                 ...              ...   \n",
+       "343                                            - Other                0   \n",
+       "344                      - Weather or natural disaster               14   \n",
+       "345  - Suspicious activity - Transmission equipment...                0   \n",
+       "346                              - Suspicious activity          Unknown   \n",
+       "347                              - Suspicious activity                0   \n",
+       "\n",
+       "    Number of Customers Affected Event Month  \n",
+       "0                        Unknown         NaN  \n",
+       "1                        Unknown         NaN  \n",
+       "2                        Unknown         NaN  \n",
+       "3                        Unknown         NaN  \n",
+       "4                         717000         NaN  \n",
+       "..                           ...         ...  \n",
+       "343                            0    December  \n",
+       "344                         8000    December  \n",
+       "345                            0    December  \n",
+       "346                      Unknown    December  \n",
+       "347                            0    December  \n",
+       "\n",
+       "[2006 rows x 12 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pd.read_csv(outage_files[2023])"
+    "pd.concat(frames)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/6 [04:22<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "File \u001b[1;32m<timed exec>:3\u001b[0m\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m   1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m   1014\u001b[0m     dialect,\n\u001b[0;32m   1015\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1022\u001b[0m     dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m   1023\u001b[0m )\n\u001b[0;32m   1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m   1617\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m   1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   1878\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m   1879\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1881\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1882\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1883\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1884\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1885\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1886\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1887\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1888\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1889\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1890\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1891\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\common.py:728\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    725\u001b[0m     codecs\u001b[38;5;241m.\u001b[39mlookup_error(errors)\n\u001b[0;32m    727\u001b[0m \u001b[38;5;66;03m# open URLs\u001b[39;00m\n\u001b[1;32m--> 728\u001b[0m ioargs \u001b[38;5;241m=\u001b[39m \u001b[43m_get_filepath_or_buffer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    729\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_or_buf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    730\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    731\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompression\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    732\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    733\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    734\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    736\u001b[0m handle \u001b[38;5;241m=\u001b[39m ioargs\u001b[38;5;241m.\u001b[39mfilepath_or_buffer\n\u001b[0;32m    737\u001b[0m handles: \u001b[38;5;28mlist\u001b[39m[BaseBuffer]\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\site-packages\\pandas\\io\\common.py:389\u001b[0m, in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m    386\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m content_encoding \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    387\u001b[0m             \u001b[38;5;66;03m# Override compression based on Content-Encoding header\u001b[39;00m\n\u001b[0;32m    388\u001b[0m             compression \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmethod\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgzip\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m--> 389\u001b[0m         reader \u001b[38;5;241m=\u001b[39m BytesIO(\u001b[43mreq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    390\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m IOArgs(\n\u001b[0;32m    391\u001b[0m         filepath_or_buffer\u001b[38;5;241m=\u001b[39mreader,\n\u001b[0;32m    392\u001b[0m         encoding\u001b[38;5;241m=\u001b[39mencoding,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    395\u001b[0m         mode\u001b[38;5;241m=\u001b[39mfsspec_mode,\n\u001b[0;32m    396\u001b[0m     )\n\u001b[0;32m    398\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_fsspec_url(filepath_or_buffer):\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\http\\client.py:489\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    487\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    488\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 489\u001b[0m         s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_safe_read\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlength\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    490\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m IncompleteRead:\n\u001b[0;32m    491\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\http\\client.py:638\u001b[0m, in \u001b[0;36mHTTPResponse._safe_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    631\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_safe_read\u001b[39m(\u001b[38;5;28mself\u001b[39m, amt):\n\u001b[0;32m    632\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Read the number of bytes requested.\u001b[39;00m\n\u001b[0;32m    633\u001b[0m \n\u001b[0;32m    634\u001b[0m \u001b[38;5;124;03m    This function should be used when <amt> bytes \"should\" be present for\u001b[39;00m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;124;03m    reading. If the bytes are truly not available (due to EOF), then the\u001b[39;00m\n\u001b[0;32m    636\u001b[0m \u001b[38;5;124;03m    IncompleteRead exception can be used to detect the problem.\u001b[39;00m\n\u001b[0;32m    637\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 638\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(amt)\n\u001b[0;32m    639\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data) \u001b[38;5;241m<\u001b[39m amt:\n\u001b[0;32m    640\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m IncompleteRead(data, amt\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(data))\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\socket.py:718\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    716\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m    717\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 718\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    719\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[0;32m    720\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\ssl.py:1314\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1310\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m   1311\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1312\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[0;32m   1313\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[1;32m-> 1314\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1315\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1316\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
-      "File \u001b[1;32mc:\\Users\\SDotson\\AppData\\Local\\miniforge3\\envs\\pypsa-illinois02\\Lib\\ssl.py:1166\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m   1164\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1165\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1166\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1167\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1168\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+      "WARNING *** file size (119492) not 512 + multiple of sector size (512)\n"
      ]
     }
    ],
    "source": [
-    "%%time\n",
-    "frames = []\n",
-    "for year, url in tqdm(outage_files.items())\n",
-    "    df = pd.read_csv(url, parse_dates=True, index_col='run_start_time')\n",
-    "    frames.append(df)"
+    "df = pd.read_excel(\"https://www.oe.netl.doe.gov/download.aspx?type=OE417XLS&ID=84\", skiprows=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Event Month</th>\n",
+       "      <th>Date Event Began</th>\n",
+       "      <th>Time Event Began</th>\n",
+       "      <th>Date of Restoration</th>\n",
+       "      <th>Time of Restoration</th>\n",
+       "      <th>Area Affected</th>\n",
+       "      <th>NERC Region</th>\n",
+       "      <th>Alert Criteria</th>\n",
+       "      <th>Event Type</th>\n",
+       "      <th>Demand Loss (MW)</th>\n",
+       "      <th>Number of Customers Affected</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>July</td>\n",
+       "      <td>2023-07-14 00:00:00</td>\n",
+       "      <td>15:00:00</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Missouri: Kansas:</td>\n",
+       "      <td>SERC</td>\n",
+       "      <td>Loss of electric service to more than 50,000 c...</td>\n",
+       "      <td>- Weather or natural disaster</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>163156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>July</td>\n",
+       "      <td>2023-07-30 00:00:00</td>\n",
+       "      <td>20:30:00</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>Missouri: Kansas:</td>\n",
+       "      <td>SERC,MRO</td>\n",
+       "      <td>Loss of electric service to more than 50,000 c...</td>\n",
+       "      <td>- Weather or natural disaster</td>\n",
+       "      <td>Unknown</td>\n",
+       "      <td>72173</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "'Up'"
+       "    Event Month     Date Event Began Time Event Began Date of Restoration  \\\n",
+       "176        July  2023-07-14 00:00:00         15:00:00             Unknown   \n",
+       "198        July  2023-07-30 00:00:00         20:30:00             Unknown   \n",
+       "\n",
+       "    Time of Restoration      Area Affected NERC Region  \\\n",
+       "176             Unknown  Missouri: Kansas:        SERC   \n",
+       "198             Unknown  Missouri: Kansas:    SERC,MRO   \n",
+       "\n",
+       "                                        Alert Criteria  \\\n",
+       "176  Loss of electric service to more than 50,000 c...   \n",
+       "198  Loss of electric service to more than 50,000 c...   \n",
+       "\n",
+       "                        Event Type Demand Loss (MW)  \\\n",
+       "176  - Weather or natural disaster          Unknown   \n",
+       "198  - Weather or natural disaster          Unknown   \n",
+       "\n",
+       "    Number of Customers Affected  \n",
+       "176                       163156  \n",
+       "198                        72173  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "\"UP\".capitalize()"
+    "df.loc[df['Area Affected'].str.contains(\"Kansas\")].dropna(how='any', axis=0)"
    ]
   },
   {

From 8f1b5b4d17659be4e2e023a0b89b24b4cd515f89 Mon Sep 17 00:00:00 2001
From: Samuel Dotson <samgdotson@gmail.com>
Date: Tue, 3 Dec 2024 16:41:59 -0500
Subject: [PATCH 51/52] adds rule to retrieve renewables

---
 .env.template                   |  8 ++++++++
 Snakefile                       | 13 +++++++++++--
 config.yml                      |  1 +
 scripts/retrieve_outage_data.py |  4 ++--
 scripts/retrieve_renewables.py  |  6 ++++--
 5 files changed, 26 insertions(+), 6 deletions(-)

diff --git a/.env.template b/.env.template
index 8163d37..bd36fb1 100644
--- a/.env.template
+++ b/.env.template
@@ -1,6 +1,14 @@
 # Register at https://www.eia.gov/opendata/register.php for an API key
 EIA_API_KEY=
 
+# Request an API key at https://developer.nrel.gov/signup/
+NREL_API_KEY=
+NAME=
+REASON=
+AFFIL=
+EMAIL=
+MAILING_LIST=
+
 # Request access at https://api.census.gov/data/key_signup.html
 CENSUS_API_KEY=
 
diff --git a/Snakefile b/Snakefile
index 127579c..c430dbc 100644
--- a/Snakefile
+++ b/Snakefile
@@ -7,6 +7,7 @@ from dotenv import load_dotenv
 state = config['state']
 state_abbr = states.lookup(state).abbr
 
+county_name = config['county']
 community_name = config['community_name']
 
 env_file = Path("./.env").resolve()
@@ -41,14 +42,15 @@ rule retrieve_outage_data:
     input:
         "scripts/retrieve_outage_data.py"
     output: 
-        outages = "data/timeseries/outages.csv"
+        outages = "data/timeseries/outages.csv",
+        county_outages = f"data/timeseries/{county_name.lower()}_outages.csv"
     script: f"{input}"
 
 rule retrieve_census_data:
     output:
         census_data = "data/spatial_data/county_census_data.gpkg",
         state_blockgroups = f"data/spatial_data/{state.lower()}_blockgroups.gpkg",
-        county_blockgroups = f"data/spatial_data/{config['county'].lower()}_blockgroups.gpkg"
+        county_blockgroups = f"data/spatial_data/{county_name.lower()}_blockgroups.gpkg"
     script: "scripts/retrieve_census_data.py"
 
 rule retrieve_project_sunroof:
@@ -111,6 +113,13 @@ rule retrieve_nrel_costs:
         costs = "data/technology_costs.csv"
     script: "scripts/retrieve_nrel_costs.py"
 
+rule retrieve_renewable_profiles:
+    input:
+        supply_regions = "data/spatial_data/supply_regions.shp"
+    output:
+        solar = "data/time_series/solar.csv"
+    script: "scripts/retrieve_renewables.py"
+
 rule calculate_historical_expenses:
     input:
         lead_community = f"data/spatial_data/{community_name.lower()}_lead.csv" 
diff --git a/config.yml b/config.yml
index 68cf9a9..ba63d80 100644
--- a/config.yml
+++ b/config.yml
@@ -8,6 +8,7 @@ census_year: 2020
 census_level: 'tract'
 usrdb_start_date: "2024-07-23"  # today?
 usrdb_future_date: "2099-01-01"  # some date in the future, replaces NaT values
+solar_years: [2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020]
 
 # price data
 retail_price_elec: 0.1129  # from google, $/kWh
diff --git a/scripts/retrieve_outage_data.py b/scripts/retrieve_outage_data.py
index 8d263cc..cdee585 100644
--- a/scripts/retrieve_outage_data.py
+++ b/scripts/retrieve_outage_data.py
@@ -19,8 +19,8 @@
         frames.append(df)
 
     outages = pd.concat(frames, axis=0)
-
+    outages.to_csv(snakemake.output.outages)
 
     outages = outages.loc[outages['county'] == snakemake.config['county'].capitalize()]
 
-    outages.to_csv(snakemake.output.outages)
\ No newline at end of file
+    outages.to_csv(snakemake.output.county_outages)
\ No newline at end of file
diff --git a/scripts/retrieve_renewables.py b/scripts/retrieve_renewables.py
index 1486031..f073197 100644
--- a/scripts/retrieve_renewables.py
+++ b/scripts/retrieve_renewables.py
@@ -39,7 +39,7 @@ def handle_datetime(dataframe):
     return frame
     
 
-def retrieve_solar_timeseries(region):
+def retrieve_solar_timeseries(region, save_years=True):
     """
     Retrieves data from NREL's national solar radiation database (NSRDB).
 
@@ -73,7 +73,9 @@ def retrieve_solar_timeseries(region):
     full_df = pd.concat(all_frames, axis=0)
     full_df = handle_datetime(full_df)
     
-    
+    return full_df
+
+
 def process_solar_timeseries(df, normalize=True):
     """
     Converts solar radiation timeseries to a

From 7accf35c9f39d06ef1e44b9fa39a6328f6e84843 Mon Sep 17 00:00:00 2001
From: Samuel Dotson <samgdotson@gmail.com>
Date: Thu, 12 Dec 2024 10:04:41 -0600
Subject: [PATCH 52/52] adds rule to retrieve 'supply regions'

---
 Snakefile                          |   10 +-
 config.yml                         |   22 +-
 dag.png                            |  Bin 88507 -> 88768 bytes
 environment.yml                    |    1 +
 functions/nrel_data_api.py         |    2 +-
 notebooks/07-nrel-atb.ipynb        | 7368 ++++++++++++++++++++++++----
 notebooks/13-primary-school.ipynb  |   50 +-
 scripts/retrieve_renewables.py     |   16 +-
 scripts/retrieve_supply_regions.py |   16 +
 9 files changed, 6447 insertions(+), 1038 deletions(-)
 create mode 100644 scripts/retrieve_supply_regions.py

diff --git a/Snakefile b/Snakefile
index c430dbc..2f25bff 100644
--- a/Snakefile
+++ b/Snakefile
@@ -33,6 +33,14 @@ rule targets:
         costs = "data/technology_costs.csv",
         dag = "dag.png"
 
+rule retrieve_supply_regions:
+    input:
+        script = "scripts/retrieve_supply_regions.py",
+        community = f"data/spatial_data/{community_name.lower()}_shape.gpkg"
+    output: 
+        supply_regions = "data/spatial_data/supply_regions.shp"
+    script: "scripts/retrieve_supply_regions.py"
+
 rule retrieve_spatial_lut:
     output: 
         spatial_lut = "data/spatial_data/spatial_lut.csv"
@@ -117,7 +125,7 @@ rule retrieve_renewable_profiles:
     input:
         supply_regions = "data/spatial_data/supply_regions.shp"
     output:
-        solar = "data/time_series/solar.csv"
+        solar = "data/timeseries/solar.csv"
     script: "scripts/retrieve_renewables.py"
 
 rule calculate_historical_expenses:
diff --git a/config.yml b/config.yml
index ba63d80..f6c1ce0 100644
--- a/config.yml
+++ b/config.yml
@@ -9,21 +9,31 @@ census_level: 'tract'
 usrdb_start_date: "2024-07-23"  # today?
 usrdb_future_date: "2099-01-01"  # some date in the future, replaces NaT values
 solar_years: [2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020]
+# solar_years: [2018]
 
 # price data
-retail_price_elec: 0.1129  # from google, $/kWh
+# retail_price_elec: 0.1129  # from google, $/kWh
+retail_price_elec: 0.1534  # from google, $/kWh
 # https://www.kansasgasservice.com//media/KGS/Tariffs/20-RSS.pdf
 retail_price_gas: 2.3485 # $/Mcf, 0.0080126123 $/kWh
 
 # ATB cost options
+atb_year: 2024  # the ATB publication year // DO NOT CHANGE
 atb_params:
-    atb_year: 2023  # the ATB publication year // DO NOT CHANGE
-    case: 'Market'  # 'R&D'
+    core_metric_case: 'Market'  # 'R&D'
     scenario: 'Moderate'  # 'Conservative', 'Advanced'
-    scale: 'Residential'  # 'Utility', 'Commercial'
+    # scale: 'Residential'  # 'Utility', 'Commercial'
     maturity: 'Y'  # 'N'
-    crp: 30  # '20'
-    cost_year: 2025  # Any year 2020-2050
+    crpyears: 30  # '20' // the loan duration
+    core_metric_variable: 2025  # Any year 2020-2050 // cost year
+
+technology_options:  # From ATB
+  - UtilityPV  # for community solar
+  - ResPV  # for rooftop solar
+  - CommPV  # for school/community center rooftop solar
+  - Commercial Battery Storage
+  - Utility-Scale Battery Storage
+  - Residential Battery Storage
 
 # model options
 topology: "sectoral"  # or building type // NOT IMPLEMENTED
diff --git a/dag.png b/dag.png
index 427db4a134085a73461df4219ee25f272c3e2542..be515434eb07ecca2b4f2a105e00e0a0288072ea 100644
GIT binary patch
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diff --git a/environment.yml b/environment.yml
index 443d604..7959352 100644
--- a/environment.yml
+++ b/environment.yml
@@ -45,3 +45,4 @@ dependencies:
       - nrelpy
       - us
       - census
+      - rasterio
\ No newline at end of file
diff --git a/functions/nrel_data_api.py b/functions/nrel_data_api.py
index 9f6467e..f1aab75 100644
--- a/functions/nrel_data_api.py
+++ b/functions/nrel_data_api.py
@@ -21,7 +21,7 @@
               'year':2019,
               'leap_day':'false',
               'selector':'POINT',
-              'utc':'true',
+              'utc':'false',
               'interval':'60',
               'attr_list':['ghi']}
 
diff --git a/notebooks/07-nrel-atb.ipynb b/notebooks/07-nrel-atb.ipynb
index c94ef65..e5f6811 100644
--- a/notebooks/07-nrel-atb.ipynb
+++ b/notebooks/07-nrel-atb.ipynb
@@ -2,85 +2,52 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from nrelpy.atb import ATBe"
+    "from nrelpy.atb import ATBe, as_dataframe"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "atbe = ATBe(year=2023)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Utility', 'Commercial', 'Residential']"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "atbe.get_index_values('scale')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import yaml\n",
-    "\n",
-    "with open(\"../config.yml\", 'r') as file:\n",
-    "    config = yaml.safe_load(file)"
+    "df = as_dataframe(2024, 'electricity', True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'atb_year': 2023,\n",
-       " 'case': 'Market',\n",
-       " 'scenario': 'Moderate',\n",
-       " 'scale': 'Residential',\n",
-       " 'maturity': 'Y',\n",
-       " 'crp': 30,\n",
-       " 'cost_year': 2025}"
+       "Index(['atb_year', 'core_metric_key', 'core_metric_parameter',\n",
+       "       'core_metric_case', 'tax_credit_case', 'crpyears', 'technology',\n",
+       "       'technology_alias', 'techdetail', 'techdetail2', 'resourcedetail',\n",
+       "       'display_name', 'default', 'scale', 'maturity', 'scenario',\n",
+       "       'core_metric_variable', 'units', 'value'],\n",
+       "      dtype='object')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "config['atb_params']"
+    "df.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -104,284 +71,414 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>date_time</th>\n",
-       "      <th>temp_db</th>\n",
-       "      <th>rel_humidity</th>\n",
-       "      <th>wind_speed</th>\n",
-       "      <th>wind_direction</th>\n",
-       "      <th>ghi</th>\n",
-       "      <th>dni</th>\n",
-       "      <th>dhi</th>\n",
+       "      <th>atb_year</th>\n",
+       "      <th>core_metric_key</th>\n",
+       "      <th>core_metric_parameter</th>\n",
+       "      <th>core_metric_case</th>\n",
+       "      <th>tax_credit_case</th>\n",
+       "      <th>crpyears</th>\n",
+       "      <th>technology</th>\n",
+       "      <th>technology_alias</th>\n",
+       "      <th>techdetail</th>\n",
+       "      <th>techdetail2</th>\n",
+       "      <th>resourcedetail</th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>default</th>\n",
+       "      <th>scale</th>\n",
+       "      <th>maturity</th>\n",
+       "      <th>scenario</th>\n",
+       "      <th>core_metric_variable</th>\n",
+       "      <th>units</th>\n",
+       "      <th>value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2018-01-01 00:00:00</th>\n",
-       "      <td>2005-01-01 01:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>61</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>80</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>100</td>\n",
+       "      <td>Fixed O&amp;M</td>\n",
+       "      <td>Market</td>\n",
+       "      <td>ITC</td>\n",
+       "      <td>20</td>\n",
+       "      <td>OffShoreWind</td>\n",
+       "      <td>Offshore Wind</td>\n",
+       "      <td>Class3</td>\n",
+       "      <td>Fixed-Bottom</td>\n",
+       "      <td>Class3</td>\n",
+       "      <td>Offshore Wind - Class 3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Utility</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>$/kW-yr</td>\n",
+       "      <td>90.527200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2018-01-01 01:00:00</th>\n",
-       "      <td>2005-01-01 02:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>57</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>90</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>1</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>10000</td>\n",
+       "      <td>Fixed O&amp;M</td>\n",
+       "      <td>Market</td>\n",
+       "      <td>ITC</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Utility-Scale PV-Plus-Battery</td>\n",
+       "      <td>Utility-Scale PV-Plus-Battery</td>\n",
+       "      <td>Class7</td>\n",
+       "      <td>Utility-Scale PV-Plus Battery</td>\n",
+       "      <td>Class7</td>\n",
+       "      <td>PV+Storage - Class 7</td>\n",
        "      <td>0</td>\n",
+       "      <td>Utility</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Conservative</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>$/kW-yr</td>\n",
+       "      <td>74.700765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2018-01-01 02:00:00</th>\n",
-       "      <td>2005-01-01 03:00:00</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>57</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>90</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>2</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>100000</td>\n",
+       "      <td>CFC</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>NPD2</td>\n",
+       "      <td>NPD 2</td>\n",
+       "      <td>Hydropower - Lake</td>\n",
+       "      <td>Hydropower - NPD 2</td>\n",
        "      <td>0</td>\n",
+       "      <td>Utility</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>367.203957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2018-01-01 03:00:00</th>\n",
-       "      <td>2005-01-01 04:00:00</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>56</td>\n",
-       "      <td>6.2</td>\n",
-       "      <td>80</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>3</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>100001</td>\n",
+       "      <td>CFC</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>NPD2</td>\n",
+       "      <td>NPD 2</td>\n",
+       "      <td>Hydropower - Lake</td>\n",
+       "      <td>Hydropower - NPD 2</td>\n",
        "      <td>0</td>\n",
+       "      <td>Utility</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Conservative</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>385.064448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2018-01-01 04:00:00</th>\n",
-       "      <td>2005-01-01 05:00:00</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>56</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>90</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>4</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>100002</td>\n",
+       "      <td>CFC</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>NPD3</td>\n",
+       "      <td>NPD 3</td>\n",
+       "      <td>Hydropower - Lake</td>\n",
+       "      <td>Hydropower - NPD 3</td>\n",
        "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                               date_time  temp_db  rel_humidity  wind_speed  \\\n",
-       "2018-01-01 00:00:00  2005-01-01 01:00:00      8.0            61         5.7   \n",
-       "2018-01-01 01:00:00  2005-01-01 02:00:00      8.0            57         5.1   \n",
-       "2018-01-01 02:00:00  2005-01-01 03:00:00      8.0            57         5.1   \n",
-       "2018-01-01 03:00:00  2005-01-01 04:00:00      7.0            56         6.2   \n",
-       "2018-01-01 04:00:00  2005-01-01 05:00:00      7.0            56         5.1   \n",
-       "\n",
-       "                     wind_direction  ghi  dni  dhi  \n",
-       "2018-01-01 00:00:00              80    0    0    0  \n",
-       "2018-01-01 01:00:00              90    0    0    0  \n",
-       "2018-01-01 02:00:00              90    0    0    0  \n",
-       "2018-01-01 03:00:00              80    0    0    0  \n",
-       "2018-01-01 04:00:00              90    0    0    0  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weather = pd.read_csv('../data/timeseries/weather_year.csv', index_col=0)\n",
-    "weather.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Resource Class</th>\n",
-       "      <th>GHI Bin (kilowatt-hours/square meters/day [kWh/m2/day])</th>\n",
-       "      <th>Mean Direct Current (DC) Capacity Factor</th>\n",
-       "      <th>Population</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>&gt;5.75</td>\n",
-       "      <td>19.6%</td>\n",
-       "      <td>12554678.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.5–5.75</td>\n",
-       "      <td>19.3%</td>\n",
-       "      <td>21403290.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.25–5.5</td>\n",
-       "      <td>18.0%</td>\n",
-       "      <td>13476871.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5–5.25</td>\n",
-       "      <td>17.0%</td>\n",
-       "      <td>30603630.0</td>\n",
+       "      <td>Utility</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Advanced</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>357.403254</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.75–5</td>\n",
-       "      <td>16.1%</td>\n",
-       "      <td>45176116.0</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>4.5–4.75</td>\n",
-       "      <td>15.9%</td>\n",
-       "      <td>39880837.0</td>\n",
+       "      <th>572227</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>99811</td>\n",
+       "      <td>FCR</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>*</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>*</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Advanced</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.076453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>4.25–4.5</td>\n",
-       "      <td>15.2%</td>\n",
-       "      <td>31742606.0</td>\n",
+       "      <th>572228</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>99812</td>\n",
+       "      <td>CRF</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>*</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>*</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.072517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>4–4.25</td>\n",
-       "      <td>14.5%</td>\n",
-       "      <td>80155804.0</td>\n",
+       "      <th>572229</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>99813</td>\n",
+       "      <td>FCR</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>*</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>*</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.076453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>3.75–4</td>\n",
-       "      <td>13.9%</td>\n",
-       "      <td>40755023.0</td>\n",
+       "      <th>572230</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>99814</td>\n",
+       "      <td>CRF</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>*</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>*</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Conservative</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.072517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10.0</td>\n",
-       "      <td>&lt;3.75</td>\n",
-       "      <td>12.7%</td>\n",
-       "      <td>10255830.0</td>\n",
+       "      <th>572231</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>99815</td>\n",
+       "      <td>FCR</td>\n",
+       "      <td>R&amp;D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>Hydropower</td>\n",
+       "      <td>*</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>*</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Conservative</td>\n",
+       "      <td>2026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.076453</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>572232 rows × 19 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Resource Class GHI Bin (kilowatt-hours/square meters/day [kWh/m2/day])  \\\n",
-       "0             1.0                                              >5.75        \n",
-       "1             2.0                                           5.5–5.75        \n",
-       "2             3.0                                           5.25–5.5        \n",
-       "3             4.0                                             5–5.25        \n",
-       "4             5.0                                             4.75–5        \n",
-       "5             6.0                                           4.5–4.75        \n",
-       "6             7.0                                           4.25–4.5        \n",
-       "7             8.0                                             4–4.25        \n",
-       "8             9.0                                             3.75–4        \n",
-       "9            10.0                                              <3.75        \n",
-       "\n",
-       "  Mean Direct Current (DC) Capacity Factor  Population  \n",
-       "0                                    19.6%  12554678.0  \n",
-       "1                                    19.3%  21403290.0  \n",
-       "2                                    18.0%  13476871.0  \n",
-       "3                                    17.0%  30603630.0  \n",
-       "4                                    16.1%  45176116.0  \n",
-       "5                                    15.9%  39880837.0  \n",
-       "6                                    15.2%  31742606.0  \n",
-       "7                                    14.5%  80155804.0  \n",
-       "8                                    13.9%  40755023.0  \n",
-       "9                                    12.7%  10255830.0  "
+       "        atb_year  core_metric_key core_metric_parameter core_metric_case  \\\n",
+       "0           2024              100             Fixed O&M           Market   \n",
+       "1           2024            10000             Fixed O&M           Market   \n",
+       "2           2024           100000                   CFC              R&D   \n",
+       "3           2024           100001                   CFC              R&D   \n",
+       "4           2024           100002                   CFC              R&D   \n",
+       "...          ...              ...                   ...              ...   \n",
+       "572227      2024            99811                   FCR              R&D   \n",
+       "572228      2024            99812                   CRF              R&D   \n",
+       "572229      2024            99813                   FCR              R&D   \n",
+       "572230      2024            99814                   CRF              R&D   \n",
+       "572231      2024            99815                   FCR              R&D   \n",
+       "\n",
+       "       tax_credit_case  crpyears                     technology  \\\n",
+       "0                  ITC        20                   OffShoreWind   \n",
+       "1                  ITC        20  Utility-Scale PV-Plus-Battery   \n",
+       "2                  NaN        20                     Hydropower   \n",
+       "3                  NaN        20                     Hydropower   \n",
+       "4                  NaN        20                     Hydropower   \n",
+       "...                ...       ...                            ...   \n",
+       "572227             NaN        20                     Hydropower   \n",
+       "572228             NaN        20                     Hydropower   \n",
+       "572229             NaN        20                     Hydropower   \n",
+       "572230             NaN        20                     Hydropower   \n",
+       "572231             NaN        20                     Hydropower   \n",
+       "\n",
+       "                     technology_alias techdetail  \\\n",
+       "0                       Offshore Wind     Class3   \n",
+       "1       Utility-Scale PV-Plus-Battery     Class7   \n",
+       "2                          Hydropower       NPD2   \n",
+       "3                          Hydropower       NPD2   \n",
+       "4                          Hydropower       NPD3   \n",
+       "...                               ...        ...   \n",
+       "572227                     Hydropower          *   \n",
+       "572228                     Hydropower          *   \n",
+       "572229                     Hydropower          *   \n",
+       "572230                     Hydropower          *   \n",
+       "572231                     Hydropower          *   \n",
+       "\n",
+       "                          techdetail2     resourcedetail  \\\n",
+       "0                        Fixed-Bottom             Class3   \n",
+       "1       Utility-Scale PV-Plus Battery             Class7   \n",
+       "2                               NPD 2  Hydropower - Lake   \n",
+       "3                               NPD 2  Hydropower - Lake   \n",
+       "4                               NPD 3  Hydropower - Lake   \n",
+       "...                               ...                ...   \n",
+       "572227                            NaN                NaN   \n",
+       "572228                            NaN                NaN   \n",
+       "572229                            NaN                NaN   \n",
+       "572230                            NaN                NaN   \n",
+       "572231                            NaN                NaN   \n",
+       "\n",
+       "                   display_name  default    scale maturity      scenario  \\\n",
+       "0       Offshore Wind - Class 3        1  Utility        Y      Moderate   \n",
+       "1          PV+Storage - Class 7        0  Utility        Y  Conservative   \n",
+       "2            Hydropower - NPD 2        0  Utility        Y      Moderate   \n",
+       "3            Hydropower - NPD 2        0  Utility        Y  Conservative   \n",
+       "4            Hydropower - NPD 3        0  Utility        Y      Advanced   \n",
+       "...                         ...      ...      ...      ...           ...   \n",
+       "572227                        *        0      NaN      NaN      Advanced   \n",
+       "572228                        *        0      NaN      NaN      Moderate   \n",
+       "572229                        *        0      NaN      NaN      Moderate   \n",
+       "572230                        *        0      NaN      NaN  Conservative   \n",
+       "572231                        *        0      NaN      NaN  Conservative   \n",
+       "\n",
+       "        core_metric_variable    units       value  \n",
+       "0                       2022  $/kW-yr   90.527200  \n",
+       "1                       2022  $/kW-yr   74.700765  \n",
+       "2                       2026      NaN  367.203957  \n",
+       "3                       2026      NaN  385.064448  \n",
+       "4                       2026      NaN  357.403254  \n",
+       "...                      ...      ...         ...  \n",
+       "572227                  2026      NaN    0.076453  \n",
+       "572228                  2026      NaN    0.072517  \n",
+       "572229                  2026      NaN    0.076453  \n",
+       "572230                  2026      NaN    0.072517  \n",
+       "572231                  2026      NaN    0.076453  \n",
+       "\n",
+       "[572232 rows x 19 columns]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "resource_class = pd.read_html(\"https://atb.nrel.gov/electricity/2024/residential_pv\", header=0)[0][:-1]\n",
-    "resource_class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resource_class = resource_class.set_index(resource_class['Resource Class'].astype(int)).drop(columns=['Resource Class'])"
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading NREL ATB electricity from 2024\n",
+      "Download Successful.\n"
+     ]
+    }
+   ],
    "source": [
-    "resource_class.columns = ['ghi_bin', 'avg_cf', 'population']"
+    "atbe = ATBe(year=2024)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sdotson\\AppData\\Local\\Temp\\ipykernel_15808\\1129787922.py:1: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
-      "  resource_class.loc[:, 'avg_cf'] = resource_class['avg_cf'].apply(lambda x: float(x.strip('%'))/100)\n"
-     ]
+     "data": {
+      "text/plain": [
+       "['Utility', 'Commercial', 'Residential']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "resource_class.loc[:, 'avg_cf'] = resource_class['avg_cf'].apply(lambda x: float(x.strip('%'))/100)"
+    "atbe.get_index_values('scale')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -405,153 +502,5051 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>ghi_bin</th>\n",
-       "      <th>avg_cf</th>\n",
-       "      <th>population</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Resource Class</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>&gt;5.75</td>\n",
-       "      <td>0.196</td>\n",
-       "      <td>12554678.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.5–5.75</td>\n",
-       "      <td>0.193</td>\n",
-       "      <td>21403290.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.25–5.5</td>\n",
-       "      <td>0.180</td>\n",
-       "      <td>13476871.0</td>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>display_name</th>\n",
+       "      <th>Biopower - Dedicated</th>\n",
+       "      <th>CSP - Class 2</th>\n",
+       "      <th>CSP - Class 3</th>\n",
+       "      <th>CSP - Class 8</th>\n",
+       "      <th>Coal integrated retrofit 90%-CCS</th>\n",
+       "      <th>Coal integrated retrofit 95%-CCS</th>\n",
+       "      <th>Coal-95%-CCS</th>\n",
+       "      <th>Coal-99%-CCS</th>\n",
+       "      <th>Coal-IGCC</th>\n",
+       "      <th>Coal-IGCC-90%-CCS</th>\n",
+       "      <th>Coal-new</th>\n",
+       "      <th>Commercial Battery Storage 1Hr</th>\n",
+       "      <th>Commercial Battery Storage 2Hr</th>\n",
+       "      <th>Commercial Battery Storage 4Hr</th>\n",
+       "      <th>Commercial Battery Storage 6Hr</th>\n",
+       "      <th>Commercial Battery Storage 8Hr</th>\n",
+       "      <th>Commercial DW - Class 1</th>\n",
+       "      <th>Commercial DW - Class 10</th>\n",
+       "      <th>Commercial DW - Class 2</th>\n",
+       "      <th>Commercial DW - Class 3</th>\n",
+       "      <th>Commercial DW - Class 4</th>\n",
+       "      <th>Commercial DW - Class 5</th>\n",
+       "      <th>Commercial DW - Class 6</th>\n",
+       "      <th>Commercial DW - Class 7</th>\n",
+       "      <th>Commercial DW - Class 8</th>\n",
+       "      <th>Commercial DW - Class 9</th>\n",
+       "      <th>Commercial PV - Class 1</th>\n",
+       "      <th>Commercial PV - Class 10</th>\n",
+       "      <th>Commercial PV - Class 2</th>\n",
+       "      <th>Commercial PV - Class 3</th>\n",
+       "      <th>Commercial PV - Class 4</th>\n",
+       "      <th>Commercial PV - Class 5</th>\n",
+       "      <th>Commercial PV - Class 6</th>\n",
+       "      <th>Commercial PV - Class 7</th>\n",
+       "      <th>Commercial PV - Class 8</th>\n",
+       "      <th>Commercial PV - Class 9</th>\n",
+       "      <th>Geothermal - Deep EGS / Binary</th>\n",
+       "      <th>Geothermal - Deep EGS / Flash</th>\n",
+       "      <th>Geothermal - Hydro / Binary</th>\n",
+       "      <th>Geothermal - Hydro / Flash</th>\n",
+       "      <th>Geothermal - NF EGS / Binary</th>\n",
+       "      <th>Geothermal - NF EGS / Flash</th>\n",
+       "      <th>Hydropower - NPD 1</th>\n",
+       "      <th>Hydropower - NPD 2</th>\n",
+       "      <th>Hydropower - NPD 3</th>\n",
+       "      <th>Hydropower - NPD 4</th>\n",
+       "      <th>Hydropower - NPD 5</th>\n",
+       "      <th>Hydropower - NPD 6</th>\n",
+       "      <th>Hydropower - NPD 7</th>\n",
+       "      <th>Hydropower - NPD 8</th>\n",
+       "      <th>Hydropower - NSD 1</th>\n",
+       "      <th>Hydropower - NSD 2</th>\n",
+       "      <th>Hydropower - NSD 3</th>\n",
+       "      <th>Hydropower - NSD 4</th>\n",
+       "      <th>Land-Based Wind - Class 1 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 10 - Technology 4</th>\n",
+       "      <th>Land-Based Wind - Class 2 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 3 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 4 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 5 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 6 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 7 - Technology 1</th>\n",
+       "      <th>Land-Based Wind - Class 8 - Technology 2</th>\n",
+       "      <th>Land-Based Wind - Class 9 - Technology 3</th>\n",
+       "      <th>Large DW - Class 1</th>\n",
+       "      <th>Large DW - Class 10</th>\n",
+       "      <th>Large DW - Class 2</th>\n",
+       "      <th>Large DW - Class 3</th>\n",
+       "      <th>Large DW - Class 4</th>\n",
+       "      <th>Large DW - Class 5</th>\n",
+       "      <th>Large DW - Class 6</th>\n",
+       "      <th>Large DW - Class 7</th>\n",
+       "      <th>Large DW - Class 8</th>\n",
+       "      <th>Large DW - Class 9</th>\n",
+       "      <th>Midsize DW - Class 1</th>\n",
+       "      <th>Midsize DW - Class 10</th>\n",
+       "      <th>Midsize DW - Class 2</th>\n",
+       "      <th>Midsize DW - Class 3</th>\n",
+       "      <th>Midsize DW - Class 4</th>\n",
+       "      <th>Midsize DW - Class 5</th>\n",
+       "      <th>Midsize DW - Class 6</th>\n",
+       "      <th>Midsize DW - Class 7</th>\n",
+       "      <th>Midsize DW - Class 8</th>\n",
+       "      <th>Midsize DW - Class 9</th>\n",
+       "      <th>NG 1-on-1 Combined Cycle (H-Frame)</th>\n",
+       "      <th>NG 1-on-1 Combined Cycle (H-Frame) 95% CCS</th>\n",
+       "      <th>NG 1-on-1 Combined Cycle (H-Frame) 97% CCS</th>\n",
+       "      <th>NG 2-on-1 Combined Cycle (F-Frame)</th>\n",
+       "      <th>NG 2-on-1 Combined Cycle (F-Frame) 95% CCS</th>\n",
+       "      <th>NG 2-on-1 Combined Cycle (F-Frame) 97% CCS</th>\n",
+       "      <th>NG 2-on-1 Combined Cycle (H-Frame)</th>\n",
+       "      <th>NG 2-on-1 Combined Cycle (H-Frame) 95% CCS</th>\n",
+       "      <th>NG 2-on-1 Combined Cycle (H-Frame) 97% CCS</th>\n",
+       "      <th>NG Combined Cycle F-Class integrated retrofit 90%-CCS</th>\n",
+       "      <th>NG Combined Cycle F-Class integrated retrofit 95%-CCS</th>\n",
+       "      <th>NG Combined Cycle H-Class integrated retrofit 90%-CCS</th>\n",
+       "      <th>NG Combined Cycle H-Class integrated retrofit 95%-CCS</th>\n",
+       "      <th>NG Combustion Turbine (F-Frame)</th>\n",
+       "      <th>NG Fuel Cell</th>\n",
+       "      <th>NG Fuel Cell 98% CCS</th>\n",
+       "      <th>Nuclear - Large</th>\n",
+       "      <th>Nuclear - Small</th>\n",
+       "      <th>Offshore Wind - Class 1</th>\n",
+       "      <th>Offshore Wind - Class 10</th>\n",
+       "      <th>Offshore Wind - Class 11</th>\n",
+       "      <th>Offshore Wind - Class 12</th>\n",
+       "      <th>Offshore Wind - Class 13</th>\n",
+       "      <th>Offshore Wind - Class 14</th>\n",
+       "      <th>Offshore Wind - Class 2</th>\n",
+       "      <th>Offshore Wind - Class 3</th>\n",
+       "      <th>Offshore Wind - Class 4</th>\n",
+       "      <th>Offshore Wind - Class 5</th>\n",
+       "      <th>Offshore Wind - Class 6</th>\n",
+       "      <th>Offshore Wind - Class 7</th>\n",
+       "      <th>Offshore Wind - Class 8</th>\n",
+       "      <th>Offshore Wind - Class 9</th>\n",
+       "      <th>PV+Storage - Class 1</th>\n",
+       "      <th>PV+Storage - Class 10</th>\n",
+       "      <th>PV+Storage - Class 2</th>\n",
+       "      <th>PV+Storage - Class 3</th>\n",
+       "      <th>PV+Storage - Class 4</th>\n",
+       "      <th>PV+Storage - Class 5</th>\n",
+       "      <th>PV+Storage - Class 6</th>\n",
+       "      <th>PV+Storage - Class 7</th>\n",
+       "      <th>PV+Storage - Class 8</th>\n",
+       "      <th>PV+Storage - Class 9</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 1</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 10</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 11</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 12</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 13</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 14</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 15</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 2</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 3</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 4</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 5</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 6</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 7</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 8</th>\n",
+       "      <th>Pumped Storage Hydropower - National Class 9</th>\n",
+       "      <th>Pumped Storage Hydropower One New Reservoir - National Class 1</th>\n",
+       "      <th>Pumped Storage Hydropower One New Reservoir - National Class 3</th>\n",
+       "      <th>Pumped Storage Hydropower One New Reservoir - National Class 4</th>\n",
+       "      <th>Pumped Storage Hydropower One New Reservoir - National Class 5</th>\n",
+       "      <th>Residential Battery Storage - 5 kW - 12.5 kWh</th>\n",
+       "      <th>Residential Battery Storage - 5 kW - 20 kWh</th>\n",
+       "      <th>Residential DW - Class 1</th>\n",
+       "      <th>Residential DW - Class 10</th>\n",
+       "      <th>Residential DW - Class 2</th>\n",
+       "      <th>Residential DW - Class 3</th>\n",
+       "      <th>Residential DW - Class 4</th>\n",
+       "      <th>Residential DW - Class 5</th>\n",
+       "      <th>Residential DW - Class 6</th>\n",
+       "      <th>Residential DW - Class 7</th>\n",
+       "      <th>Residential DW - Class 8</th>\n",
+       "      <th>Residential DW - Class 9</th>\n",
+       "      <th>Residential PV - Class 1</th>\n",
+       "      <th>Residential PV - Class 10</th>\n",
+       "      <th>Residential PV - Class 2</th>\n",
+       "      <th>Residential PV - Class 3</th>\n",
+       "      <th>Residential PV - Class 4</th>\n",
+       "      <th>Residential PV - Class 5</th>\n",
+       "      <th>Residential PV - Class 6</th>\n",
+       "      <th>Residential PV - Class 7</th>\n",
+       "      <th>Residential PV - Class 8</th>\n",
+       "      <th>Residential PV - Class 9</th>\n",
+       "      <th>Utility PV - Class 1</th>\n",
+       "      <th>Utility PV - Class 10</th>\n",
+       "      <th>Utility PV - Class 2</th>\n",
+       "      <th>Utility PV - Class 3</th>\n",
+       "      <th>Utility PV - Class 4</th>\n",
+       "      <th>Utility PV - Class 5</th>\n",
+       "      <th>Utility PV - Class 6</th>\n",
+       "      <th>Utility PV - Class 7</th>\n",
+       "      <th>Utility PV - Class 8</th>\n",
+       "      <th>Utility PV - Class 9</th>\n",
+       "      <th>Utility-Scale Battery Storage - 10Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 2Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 4Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 6Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 8Hr</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5–5.25</td>\n",
-       "      <td>0.170</td>\n",
-       "      <td>30603630.0</td>\n",
+       "      <th>core_metric_case</th>\n",
+       "      <th>crpyears</th>\n",
+       "      <th>maturity</th>\n",
+       "      <th>scale</th>\n",
+       "      <th>scenario</th>\n",
+       "      <th>technology</th>\n",
+       "      <th>core_metric_parameter</th>\n",
+       "      <th>core_metric_variable</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Market</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">20</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">N</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Utility</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Advanced</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Coal_FE</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">CAPEX</th>\n",
+       "      <th>2022</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6687.029422</td>\n",
+       "      <td>6838.869776</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10021.092720</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6593.009480</td>\n",
+       "      <td>6739.290179</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9965.496171</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2024</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6498.989537</td>\n",
+       "      <td>6639.710581</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9910.023169</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2025</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6405.093143</td>\n",
+       "      <td>6540.130984</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9854.426620</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2026</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6311.073200</td>\n",
+       "      <td>6440.551386</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9798.953618</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>4.75–5</td>\n",
-       "      <td>0.161</td>\n",
-       "      <td>45176116.0</td>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.5–4.75</td>\n",
-       "      <td>0.159</td>\n",
-       "      <td>39880837.0</td>\n",
+       "      <th rowspan=\"5\" valign=\"top\">R&amp;D</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">100</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Y</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Utility</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Moderate</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Pumped Storage Hydropower</th>\n",
+       "      <th rowspan=\"5\" valign=\"top\">Variable O&amp;M</th>\n",
+       "      <th>2046</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>4.25–4.5</td>\n",
-       "      <td>0.152</td>\n",
-       "      <td>31742606.0</td>\n",
+       "      <th>2047</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>4–4.25</td>\n",
-       "      <td>0.145</td>\n",
-       "      <td>80155804.0</td>\n",
+       "      <th>2048</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>3.75–4</td>\n",
-       "      <td>0.139</td>\n",
-       "      <td>40755023.0</td>\n",
+       "      <th>2049</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>&lt;3.75</td>\n",
-       "      <td>0.127</td>\n",
-       "      <td>10255830.0</td>\n",
+       "      <th>2050</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>79680 rows × 182 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                 ghi_bin  avg_cf  population\n",
-       "Resource Class                              \n",
-       "1                  >5.75   0.196  12554678.0\n",
-       "2               5.5–5.75   0.193  21403290.0\n",
-       "3               5.25–5.5   0.180  13476871.0\n",
-       "4                 5–5.25   0.170  30603630.0\n",
-       "5                 4.75–5   0.161  45176116.0\n",
-       "6               4.5–4.75   0.159  39880837.0\n",
-       "7               4.25–4.5   0.152  31742606.0\n",
-       "8                 4–4.25   0.145  80155804.0\n",
-       "9                 3.75–4   0.139  40755023.0\n",
-       "10                 <3.75   0.127  10255830.0"
+       "display_name                                                                                                              Biopower - Dedicated  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              CSP - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                            NaN   \n",
+       "                                                                                                    2023                            NaN   \n",
+       "                                                                                                    2024                            NaN   \n",
+       "                                                                                                    2025                            NaN   \n",
+       "                                                                                                    2026                            NaN   \n",
+       "...                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                            NaN   \n",
+       "                                                                                                    2047                            NaN   \n",
+       "                                                                                                    2048                            NaN   \n",
+       "                                                                                                    2049                            NaN   \n",
+       "                                                                                                    2050                            NaN   \n",
+       "\n",
+       "display_name                                                                                                              CSP - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                            NaN   \n",
+       "                                                                                                    2023                            NaN   \n",
+       "                                                                                                    2024                            NaN   \n",
+       "                                                                                                    2025                            NaN   \n",
+       "                                                                                                    2026                            NaN   \n",
+       "...                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                            NaN   \n",
+       "                                                                                                    2047                            NaN   \n",
+       "                                                                                                    2048                            NaN   \n",
+       "                                                                                                    2049                            NaN   \n",
+       "                                                                                                    2050                            NaN   \n",
+       "\n",
+       "display_name                                                                                                              CSP - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                            NaN   \n",
+       "                                                                                                    2023                            NaN   \n",
+       "                                                                                                    2024                            NaN   \n",
+       "                                                                                                    2025                            NaN   \n",
+       "                                                                                                    2026                            NaN   \n",
+       "...                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                            NaN   \n",
+       "                                                                                                    2047                            NaN   \n",
+       "                                                                                                    2048                            NaN   \n",
+       "                                                                                                    2049                            NaN   \n",
+       "                                                                                                    2050                            NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal integrated retrofit 90%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                     \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                               NaN   \n",
+       "                                                                                                    2023                                               NaN   \n",
+       "                                                                                                    2024                                               NaN   \n",
+       "                                                                                                    2025                                               NaN   \n",
+       "                                                                                                    2026                                               NaN   \n",
+       "...                                                                                                                                                    ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                               NaN   \n",
+       "                                                                                                    2047                                               NaN   \n",
+       "                                                                                                    2048                                               NaN   \n",
+       "                                                                                                    2049                                               NaN   \n",
+       "                                                                                                    2050                                               NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal integrated retrofit 95%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                     \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                               NaN   \n",
+       "                                                                                                    2023                                               NaN   \n",
+       "                                                                                                    2024                                               NaN   \n",
+       "                                                                                                    2025                                               NaN   \n",
+       "                                                                                                    2026                                               NaN   \n",
+       "...                                                                                                                                                    ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                               NaN   \n",
+       "                                                                                                    2047                                               NaN   \n",
+       "                                                                                                    2048                                               NaN   \n",
+       "                                                                                                    2049                                               NaN   \n",
+       "                                                                                                    2050                                               NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal-95%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                   6687.029422   \n",
+       "                                                                                                    2023                   6593.009480   \n",
+       "                                                                                                    2024                   6498.989537   \n",
+       "                                                                                                    2025                   6405.093143   \n",
+       "                                                                                                    2026                   6311.073200   \n",
+       "...                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                           NaN   \n",
+       "                                                                                                    2047                           NaN   \n",
+       "                                                                                                    2048                           NaN   \n",
+       "                                                                                                    2049                           NaN   \n",
+       "                                                                                                    2050                           NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal-99%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                   6838.869776   \n",
+       "                                                                                                    2023                   6739.290179   \n",
+       "                                                                                                    2024                   6639.710581   \n",
+       "                                                                                                    2025                   6540.130984   \n",
+       "                                                                                                    2026                   6440.551386   \n",
+       "...                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                           NaN   \n",
+       "                                                                                                    2047                           NaN   \n",
+       "                                                                                                    2048                           NaN   \n",
+       "                                                                                                    2049                           NaN   \n",
+       "                                                                                                    2050                           NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal-IGCC  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable              \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                        NaN   \n",
+       "                                                                                                    2023                        NaN   \n",
+       "                                                                                                    2024                        NaN   \n",
+       "                                                                                                    2025                        NaN   \n",
+       "                                                                                                    2026                        NaN   \n",
+       "...                                                                                                                             ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                        NaN   \n",
+       "                                                                                                    2047                        NaN   \n",
+       "                                                                                                    2048                        NaN   \n",
+       "                                                                                                    2049                        NaN   \n",
+       "                                                                                                    2050                        NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal-IGCC-90%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                      \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                       10021.092720   \n",
+       "                                                                                                    2023                        9965.496171   \n",
+       "                                                                                                    2024                        9910.023169   \n",
+       "                                                                                                    2025                        9854.426620   \n",
+       "                                                                                                    2026                        9798.953618   \n",
+       "...                                                                                                                                     ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                NaN   \n",
+       "                                                                                                    2047                                NaN   \n",
+       "                                                                                                    2048                                NaN   \n",
+       "                                                                                                    2049                                NaN   \n",
+       "                                                                                                    2050                                NaN   \n",
+       "\n",
+       "display_name                                                                                                              Coal-new  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                       NaN   \n",
+       "                                                                                                    2023                       NaN   \n",
+       "                                                                                                    2024                       NaN   \n",
+       "                                                                                                    2025                       NaN   \n",
+       "                                                                                                    2026                       NaN   \n",
+       "...                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                       NaN   \n",
+       "                                                                                                    2047                       NaN   \n",
+       "                                                                                                    2048                       NaN   \n",
+       "                                                                                                    2049                       NaN   \n",
+       "                                                                                                    2050                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial Battery Storage 1Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                             NaN   \n",
+       "                                                                                                    2023                                             NaN   \n",
+       "                                                                                                    2024                                             NaN   \n",
+       "                                                                                                    2025                                             NaN   \n",
+       "                                                                                                    2026                                             NaN   \n",
+       "...                                                                                                                                                  ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                             NaN   \n",
+       "                                                                                                    2047                                             NaN   \n",
+       "                                                                                                    2048                                             NaN   \n",
+       "                                                                                                    2049                                             NaN   \n",
+       "                                                                                                    2050                                             NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial Battery Storage 2Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                             NaN   \n",
+       "                                                                                                    2023                                             NaN   \n",
+       "                                                                                                    2024                                             NaN   \n",
+       "                                                                                                    2025                                             NaN   \n",
+       "                                                                                                    2026                                             NaN   \n",
+       "...                                                                                                                                                  ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                             NaN   \n",
+       "                                                                                                    2047                                             NaN   \n",
+       "                                                                                                    2048                                             NaN   \n",
+       "                                                                                                    2049                                             NaN   \n",
+       "                                                                                                    2050                                             NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial Battery Storage 4Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                             NaN   \n",
+       "                                                                                                    2023                                             NaN   \n",
+       "                                                                                                    2024                                             NaN   \n",
+       "                                                                                                    2025                                             NaN   \n",
+       "                                                                                                    2026                                             NaN   \n",
+       "...                                                                                                                                                  ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                             NaN   \n",
+       "                                                                                                    2047                                             NaN   \n",
+       "                                                                                                    2048                                             NaN   \n",
+       "                                                                                                    2049                                             NaN   \n",
+       "                                                                                                    2050                                             NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial Battery Storage 6Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                             NaN   \n",
+       "                                                                                                    2023                                             NaN   \n",
+       "                                                                                                    2024                                             NaN   \n",
+       "                                                                                                    2025                                             NaN   \n",
+       "                                                                                                    2026                                             NaN   \n",
+       "...                                                                                                                                                  ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                             NaN   \n",
+       "                                                                                                    2047                                             NaN   \n",
+       "                                                                                                    2048                                             NaN   \n",
+       "                                                                                                    2049                                             NaN   \n",
+       "                                                                                                    2050                                             NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial Battery Storage 8Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                             NaN   \n",
+       "                                                                                                    2023                                             NaN   \n",
+       "                                                                                                    2024                                             NaN   \n",
+       "                                                                                                    2025                                             NaN   \n",
+       "                                                                                                    2026                                             NaN   \n",
+       "...                                                                                                                                                  ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                             NaN   \n",
+       "                                                                                                    2047                                             NaN   \n",
+       "                                                                                                    2048                                             NaN   \n",
+       "                                                                                                    2049                                             NaN   \n",
+       "                                                                                                    2050                                             NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial DW - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Commercial PV - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Geothermal - Deep EGS / Binary  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                             NaN   \n",
+       "                                                                                                    2023                                             NaN   \n",
+       "                                                                                                    2024                                             NaN   \n",
+       "                                                                                                    2025                                             NaN   \n",
+       "                                                                                                    2026                                             NaN   \n",
+       "...                                                                                                                                                  ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                             NaN   \n",
+       "                                                                                                    2047                                             NaN   \n",
+       "                                                                                                    2048                                             NaN   \n",
+       "                                                                                                    2049                                             NaN   \n",
+       "                                                                                                    2050                                             NaN   \n",
+       "\n",
+       "display_name                                                                                                              Geothermal - Deep EGS / Flash  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                            NaN   \n",
+       "                                                                                                    2023                                            NaN   \n",
+       "                                                                                                    2024                                            NaN   \n",
+       "                                                                                                    2025                                            NaN   \n",
+       "                                                                                                    2026                                            NaN   \n",
+       "...                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                            NaN   \n",
+       "                                                                                                    2047                                            NaN   \n",
+       "                                                                                                    2048                                            NaN   \n",
+       "                                                                                                    2049                                            NaN   \n",
+       "                                                                                                    2050                                            NaN   \n",
+       "\n",
+       "display_name                                                                                                              Geothermal - Hydro / Binary  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                          NaN   \n",
+       "                                                                                                    2023                                          NaN   \n",
+       "                                                                                                    2024                                          NaN   \n",
+       "                                                                                                    2025                                          NaN   \n",
+       "                                                                                                    2026                                          NaN   \n",
+       "...                                                                                                                                               ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                          NaN   \n",
+       "                                                                                                    2047                                          NaN   \n",
+       "                                                                                                    2048                                          NaN   \n",
+       "                                                                                                    2049                                          NaN   \n",
+       "                                                                                                    2050                                          NaN   \n",
+       "\n",
+       "display_name                                                                                                              Geothermal - Hydro / Flash  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                         NaN   \n",
+       "                                                                                                    2023                                         NaN   \n",
+       "                                                                                                    2024                                         NaN   \n",
+       "                                                                                                    2025                                         NaN   \n",
+       "                                                                                                    2026                                         NaN   \n",
+       "...                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                         NaN   \n",
+       "                                                                                                    2047                                         NaN   \n",
+       "                                                                                                    2048                                         NaN   \n",
+       "                                                                                                    2049                                         NaN   \n",
+       "                                                                                                    2050                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              Geothermal - NF EGS / Binary  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                           NaN   \n",
+       "                                                                                                    2023                                           NaN   \n",
+       "                                                                                                    2024                                           NaN   \n",
+       "                                                                                                    2025                                           NaN   \n",
+       "                                                                                                    2026                                           NaN   \n",
+       "...                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                           NaN   \n",
+       "                                                                                                    2047                                           NaN   \n",
+       "                                                                                                    2048                                           NaN   \n",
+       "                                                                                                    2049                                           NaN   \n",
+       "                                                                                                    2050                                           NaN   \n",
+       "\n",
+       "display_name                                                                                                              Geothermal - NF EGS / Flash  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                          NaN   \n",
+       "                                                                                                    2023                                          NaN   \n",
+       "                                                                                                    2024                                          NaN   \n",
+       "                                                                                                    2025                                          NaN   \n",
+       "                                                                                                    2026                                          NaN   \n",
+       "...                                                                                                                                               ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                          NaN   \n",
+       "                                                                                                    2047                                          NaN   \n",
+       "                                                                                                    2048                                          NaN   \n",
+       "                                                                                                    2049                                          NaN   \n",
+       "                                                                                                    2050                                          NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NPD 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NSD 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NSD 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NSD 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Hydropower - NSD 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 1 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 10 - Technology 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                              \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                        NaN   \n",
+       "                                                                                                    2023                                                        NaN   \n",
+       "                                                                                                    2024                                                        NaN   \n",
+       "                                                                                                    2025                                                        NaN   \n",
+       "                                                                                                    2026                                                        NaN   \n",
+       "...                                                                                                                                                             ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                        NaN   \n",
+       "                                                                                                    2047                                                        NaN   \n",
+       "                                                                                                    2048                                                        NaN   \n",
+       "                                                                                                    2049                                                        NaN   \n",
+       "                                                                                                    2050                                                        NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 2 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 3 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 4 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 5 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 6 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 7 - Technology 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 8 - Technology 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Land-Based Wind - Class 9 - Technology 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                       NaN   \n",
+       "                                                                                                    2023                                                       NaN   \n",
+       "                                                                                                    2024                                                       NaN   \n",
+       "                                                                                                    2025                                                       NaN   \n",
+       "                                                                                                    2026                                                       NaN   \n",
+       "...                                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                       NaN   \n",
+       "                                                                                                    2047                                                       NaN   \n",
+       "                                                                                                    2048                                                       NaN   \n",
+       "                                                                                                    2049                                                       NaN   \n",
+       "                                                                                                    2050                                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                        \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                  NaN   \n",
+       "                                                                                                    2023                                  NaN   \n",
+       "                                                                                                    2024                                  NaN   \n",
+       "                                                                                                    2025                                  NaN   \n",
+       "                                                                                                    2026                                  NaN   \n",
+       "...                                                                                                                                       ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                  NaN   \n",
+       "                                                                                                    2047                                  NaN   \n",
+       "                                                                                                    2048                                  NaN   \n",
+       "                                                                                                    2049                                  NaN   \n",
+       "                                                                                                    2050                                  NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Large DW - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                 NaN   \n",
+       "                                                                                                    2023                                 NaN   \n",
+       "                                                                                                    2024                                 NaN   \n",
+       "                                                                                                    2025                                 NaN   \n",
+       "                                                                                                    2026                                 NaN   \n",
+       "...                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                 NaN   \n",
+       "                                                                                                    2047                                 NaN   \n",
+       "                                                                                                    2048                                 NaN   \n",
+       "                                                                                                    2049                                 NaN   \n",
+       "                                                                                                    2050                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                    NaN   \n",
+       "                                                                                                    2023                                    NaN   \n",
+       "                                                                                                    2024                                    NaN   \n",
+       "                                                                                                    2025                                    NaN   \n",
+       "                                                                                                    2026                                    NaN   \n",
+       "...                                                                                                                                         ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                    NaN   \n",
+       "                                                                                                    2047                                    NaN   \n",
+       "                                                                                                    2048                                    NaN   \n",
+       "                                                                                                    2049                                    NaN   \n",
+       "                                                                                                    2050                                    NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Midsize DW - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 1-on-1 Combined Cycle (H-Frame)  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                 NaN   \n",
+       "                                                                                                    2023                                                 NaN   \n",
+       "                                                                                                    2024                                                 NaN   \n",
+       "                                                                                                    2025                                                 NaN   \n",
+       "                                                                                                    2026                                                 NaN   \n",
+       "...                                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                 NaN   \n",
+       "                                                                                                    2047                                                 NaN   \n",
+       "                                                                                                    2048                                                 NaN   \n",
+       "                                                                                                    2049                                                 NaN   \n",
+       "                                                                                                    2050                                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 1-on-1 Combined Cycle (H-Frame) 95% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                         NaN   \n",
+       "                                                                                                    2023                                                         NaN   \n",
+       "                                                                                                    2024                                                         NaN   \n",
+       "                                                                                                    2025                                                         NaN   \n",
+       "                                                                                                    2026                                                         NaN   \n",
+       "...                                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                         NaN   \n",
+       "                                                                                                    2047                                                         NaN   \n",
+       "                                                                                                    2048                                                         NaN   \n",
+       "                                                                                                    2049                                                         NaN   \n",
+       "                                                                                                    2050                                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 1-on-1 Combined Cycle (H-Frame) 97% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                         NaN   \n",
+       "                                                                                                    2023                                                         NaN   \n",
+       "                                                                                                    2024                                                         NaN   \n",
+       "                                                                                                    2025                                                         NaN   \n",
+       "                                                                                                    2026                                                         NaN   \n",
+       "...                                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                         NaN   \n",
+       "                                                                                                    2047                                                         NaN   \n",
+       "                                                                                                    2048                                                         NaN   \n",
+       "                                                                                                    2049                                                         NaN   \n",
+       "                                                                                                    2050                                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 2-on-1 Combined Cycle (F-Frame)  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                 NaN   \n",
+       "                                                                                                    2023                                                 NaN   \n",
+       "                                                                                                    2024                                                 NaN   \n",
+       "                                                                                                    2025                                                 NaN   \n",
+       "                                                                                                    2026                                                 NaN   \n",
+       "...                                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                 NaN   \n",
+       "                                                                                                    2047                                                 NaN   \n",
+       "                                                                                                    2048                                                 NaN   \n",
+       "                                                                                                    2049                                                 NaN   \n",
+       "                                                                                                    2050                                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 2-on-1 Combined Cycle (F-Frame) 95% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                         NaN   \n",
+       "                                                                                                    2023                                                         NaN   \n",
+       "                                                                                                    2024                                                         NaN   \n",
+       "                                                                                                    2025                                                         NaN   \n",
+       "                                                                                                    2026                                                         NaN   \n",
+       "...                                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                         NaN   \n",
+       "                                                                                                    2047                                                         NaN   \n",
+       "                                                                                                    2048                                                         NaN   \n",
+       "                                                                                                    2049                                                         NaN   \n",
+       "                                                                                                    2050                                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 2-on-1 Combined Cycle (F-Frame) 97% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                         NaN   \n",
+       "                                                                                                    2023                                                         NaN   \n",
+       "                                                                                                    2024                                                         NaN   \n",
+       "                                                                                                    2025                                                         NaN   \n",
+       "                                                                                                    2026                                                         NaN   \n",
+       "...                                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                         NaN   \n",
+       "                                                                                                    2047                                                         NaN   \n",
+       "                                                                                                    2048                                                         NaN   \n",
+       "                                                                                                    2049                                                         NaN   \n",
+       "                                                                                                    2050                                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 2-on-1 Combined Cycle (H-Frame)  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                 NaN   \n",
+       "                                                                                                    2023                                                 NaN   \n",
+       "                                                                                                    2024                                                 NaN   \n",
+       "                                                                                                    2025                                                 NaN   \n",
+       "                                                                                                    2026                                                 NaN   \n",
+       "...                                                                                                                                                      ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                 NaN   \n",
+       "                                                                                                    2047                                                 NaN   \n",
+       "                                                                                                    2048                                                 NaN   \n",
+       "                                                                                                    2049                                                 NaN   \n",
+       "                                                                                                    2050                                                 NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 2-on-1 Combined Cycle (H-Frame) 95% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                         NaN   \n",
+       "                                                                                                    2023                                                         NaN   \n",
+       "                                                                                                    2024                                                         NaN   \n",
+       "                                                                                                    2025                                                         NaN   \n",
+       "                                                                                                    2026                                                         NaN   \n",
+       "...                                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                         NaN   \n",
+       "                                                                                                    2047                                                         NaN   \n",
+       "                                                                                                    2048                                                         NaN   \n",
+       "                                                                                                    2049                                                         NaN   \n",
+       "                                                                                                    2050                                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG 2-on-1 Combined Cycle (H-Frame) 97% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                               \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                         NaN   \n",
+       "                                                                                                    2023                                                         NaN   \n",
+       "                                                                                                    2024                                                         NaN   \n",
+       "                                                                                                    2025                                                         NaN   \n",
+       "                                                                                                    2026                                                         NaN   \n",
+       "...                                                                                                                                                              ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                         NaN   \n",
+       "                                                                                                    2047                                                         NaN   \n",
+       "                                                                                                    2048                                                         NaN   \n",
+       "                                                                                                    2049                                                         NaN   \n",
+       "                                                                                                    2050                                                         NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG Combined Cycle F-Class integrated retrofit 90%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN       \n",
+       "                                                                                                    2023                                                                NaN       \n",
+       "                                                                                                    2024                                                                NaN       \n",
+       "                                                                                                    2025                                                                NaN       \n",
+       "                                                                                                    2026                                                                NaN       \n",
+       "...                                                                                                                                                                     ...       \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                                NaN       \n",
+       "                                                                                                    2047                                                                NaN       \n",
+       "                                                                                                    2048                                                                NaN       \n",
+       "                                                                                                    2049                                                                NaN       \n",
+       "                                                                                                    2050                                                                NaN       \n",
+       "\n",
+       "display_name                                                                                                              NG Combined Cycle F-Class integrated retrofit 95%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN       \n",
+       "                                                                                                    2023                                                                NaN       \n",
+       "                                                                                                    2024                                                                NaN       \n",
+       "                                                                                                    2025                                                                NaN       \n",
+       "                                                                                                    2026                                                                NaN       \n",
+       "...                                                                                                                                                                     ...       \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                                NaN       \n",
+       "                                                                                                    2047                                                                NaN       \n",
+       "                                                                                                    2048                                                                NaN       \n",
+       "                                                                                                    2049                                                                NaN       \n",
+       "                                                                                                    2050                                                                NaN       \n",
+       "\n",
+       "display_name                                                                                                              NG Combined Cycle H-Class integrated retrofit 90%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN       \n",
+       "                                                                                                    2023                                                                NaN       \n",
+       "                                                                                                    2024                                                                NaN       \n",
+       "                                                                                                    2025                                                                NaN       \n",
+       "                                                                                                    2026                                                                NaN       \n",
+       "...                                                                                                                                                                     ...       \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                                NaN       \n",
+       "                                                                                                    2047                                                                NaN       \n",
+       "                                                                                                    2048                                                                NaN       \n",
+       "                                                                                                    2049                                                                NaN       \n",
+       "                                                                                                    2050                                                                NaN       \n",
+       "\n",
+       "display_name                                                                                                              NG Combined Cycle H-Class integrated retrofit 95%-CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN       \n",
+       "                                                                                                    2023                                                                NaN       \n",
+       "                                                                                                    2024                                                                NaN       \n",
+       "                                                                                                    2025                                                                NaN       \n",
+       "                                                                                                    2026                                                                NaN       \n",
+       "...                                                                                                                                                                     ...       \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                                NaN       \n",
+       "                                                                                                    2047                                                                NaN       \n",
+       "                                                                                                    2048                                                                NaN       \n",
+       "                                                                                                    2049                                                                NaN       \n",
+       "                                                                                                    2050                                                                NaN       \n",
+       "\n",
+       "display_name                                                                                                              NG Combustion Turbine (F-Frame)  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                    \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                              NaN   \n",
+       "                                                                                                    2023                                              NaN   \n",
+       "                                                                                                    2024                                              NaN   \n",
+       "                                                                                                    2025                                              NaN   \n",
+       "                                                                                                    2026                                              NaN   \n",
+       "...                                                                                                                                                   ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                              NaN   \n",
+       "                                                                                                    2047                                              NaN   \n",
+       "                                                                                                    2048                                              NaN   \n",
+       "                                                                                                    2049                                              NaN   \n",
+       "                                                                                                    2050                                              NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG Fuel Cell  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                           NaN   \n",
+       "                                                                                                    2023                           NaN   \n",
+       "                                                                                                    2024                           NaN   \n",
+       "                                                                                                    2025                           NaN   \n",
+       "                                                                                                    2026                           NaN   \n",
+       "...                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                           NaN   \n",
+       "                                                                                                    2047                           NaN   \n",
+       "                                                                                                    2048                           NaN   \n",
+       "                                                                                                    2049                           NaN   \n",
+       "                                                                                                    2050                           NaN   \n",
+       "\n",
+       "display_name                                                                                                              NG Fuel Cell 98% CCS  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Nuclear - Large  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                    \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                              NaN   \n",
+       "                                                                                                    2023                              NaN   \n",
+       "                                                                                                    2024                              NaN   \n",
+       "                                                                                                    2025                              NaN   \n",
+       "                                                                                                    2026                              NaN   \n",
+       "...                                                                                                                                   ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                              NaN   \n",
+       "                                                                                                    2047                              NaN   \n",
+       "                                                                                                    2048                              NaN   \n",
+       "                                                                                                    2049                              NaN   \n",
+       "                                                                                                    2050                              NaN   \n",
+       "\n",
+       "display_name                                                                                                              Nuclear - Small  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                    \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                              NaN   \n",
+       "                                                                                                    2023                              NaN   \n",
+       "                                                                                                    2024                              NaN   \n",
+       "                                                                                                    2025                              NaN   \n",
+       "                                                                                                    2026                              NaN   \n",
+       "...                                                                                                                                   ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                              NaN   \n",
+       "                                                                                                    2047                              NaN   \n",
+       "                                                                                                    2048                              NaN   \n",
+       "                                                                                                    2049                              NaN   \n",
+       "                                                                                                    2050                              NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 11  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 12  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 13  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 14  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              Offshore Wind - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                            \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                      NaN   \n",
+       "                                                                                                    2023                                      NaN   \n",
+       "                                                                                                    2024                                      NaN   \n",
+       "                                                                                                    2025                                      NaN   \n",
+       "                                                                                                    2026                                      NaN   \n",
+       "...                                                                                                                                           ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                      NaN   \n",
+       "                                                                                                    2047                                      NaN   \n",
+       "                                                                                                    2048                                      NaN   \n",
+       "                                                                                                    2049                                      NaN   \n",
+       "                                                                                                    2050                                      NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                    NaN   \n",
+       "                                                                                                    2023                                    NaN   \n",
+       "                                                                                                    2024                                    NaN   \n",
+       "                                                                                                    2025                                    NaN   \n",
+       "                                                                                                    2026                                    NaN   \n",
+       "...                                                                                                                                         ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                    NaN   \n",
+       "                                                                                                    2047                                    NaN   \n",
+       "                                                                                                    2048                                    NaN   \n",
+       "                                                                                                    2049                                    NaN   \n",
+       "                                                                                                    2050                                    NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              PV+Storage - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                           0.58   \n",
+       "                                                                                                    2047                                                           0.58   \n",
+       "                                                                                                    2048                                                           0.58   \n",
+       "                                                                                                    2049                                                           0.58   \n",
+       "                                                                                                    2050                                                           0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 11  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                           0.58   \n",
+       "                                                                                                    2047                                                           0.58   \n",
+       "                                                                                                    2048                                                           0.58   \n",
+       "                                                                                                    2049                                                           0.58   \n",
+       "                                                                                                    2050                                                           0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 12  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                           0.58   \n",
+       "                                                                                                    2047                                                           0.58   \n",
+       "                                                                                                    2048                                                           0.58   \n",
+       "                                                                                                    2049                                                           0.58   \n",
+       "                                                                                                    2050                                                           0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 13  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                           0.58   \n",
+       "                                                                                                    2047                                                           0.58   \n",
+       "                                                                                                    2048                                                           0.58   \n",
+       "                                                                                                    2049                                                           0.58   \n",
+       "                                                                                                    2050                                                           0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 14  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                           0.58   \n",
+       "                                                                                                    2047                                                           0.58   \n",
+       "                                                                                                    2048                                                           0.58   \n",
+       "                                                                                                    2049                                                           0.58   \n",
+       "                                                                                                    2050                                                           0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 15  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                           0.58   \n",
+       "                                                                                                    2047                                                           0.58   \n",
+       "                                                                                                    2048                                                           0.58   \n",
+       "                                                                                                    2049                                                           0.58   \n",
+       "                                                                                                    2050                                                           0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower - National Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                 \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                           NaN   \n",
+       "                                                                                                    2023                                                           NaN   \n",
+       "                                                                                                    2024                                                           NaN   \n",
+       "                                                                                                    2025                                                           NaN   \n",
+       "                                                                                                    2026                                                           NaN   \n",
+       "...                                                                                                                                                                ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          0.58   \n",
+       "                                                                                                    2047                                                          0.58   \n",
+       "                                                                                                    2048                                                          0.58   \n",
+       "                                                                                                    2049                                                          0.58   \n",
+       "                                                                                                    2050                                                          0.58   \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower One New Reservoir - National Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN                \n",
+       "                                                                                                    2023                                                                NaN                \n",
+       "                                                                                                    2024                                                                NaN                \n",
+       "                                                                                                    2025                                                                NaN                \n",
+       "                                                                                                    2026                                                                NaN                \n",
+       "...                                                                                                                                                                     ...                \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                               0.58                \n",
+       "                                                                                                    2047                                                               0.58                \n",
+       "                                                                                                    2048                                                               0.58                \n",
+       "                                                                                                    2049                                                               0.58                \n",
+       "                                                                                                    2050                                                               0.58                \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower One New Reservoir - National Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN                \n",
+       "                                                                                                    2023                                                                NaN                \n",
+       "                                                                                                    2024                                                                NaN                \n",
+       "                                                                                                    2025                                                                NaN                \n",
+       "                                                                                                    2026                                                                NaN                \n",
+       "...                                                                                                                                                                     ...                \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                               0.58                \n",
+       "                                                                                                    2047                                                               0.58                \n",
+       "                                                                                                    2048                                                               0.58                \n",
+       "                                                                                                    2049                                                               0.58                \n",
+       "                                                                                                    2050                                                               0.58                \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower One New Reservoir - National Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN                \n",
+       "                                                                                                    2023                                                                NaN                \n",
+       "                                                                                                    2024                                                                NaN                \n",
+       "                                                                                                    2025                                                                NaN                \n",
+       "                                                                                                    2026                                                                NaN                \n",
+       "...                                                                                                                                                                     ...                \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                               0.58                \n",
+       "                                                                                                    2047                                                               0.58                \n",
+       "                                                                                                    2048                                                               0.58                \n",
+       "                                                                                                    2049                                                               0.58                \n",
+       "                                                                                                    2050                                                               0.58                \n",
+       "\n",
+       "display_name                                                                                                              Pumped Storage Hydropower One New Reservoir - National Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                                   \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                                NaN                \n",
+       "                                                                                                    2023                                                                NaN                \n",
+       "                                                                                                    2024                                                                NaN                \n",
+       "                                                                                                    2025                                                                NaN                \n",
+       "                                                                                                    2026                                                                NaN                \n",
+       "...                                                                                                                                                                     ...                \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                               0.58                \n",
+       "                                                                                                    2047                                                               0.58                \n",
+       "                                                                                                    2048                                                               0.58                \n",
+       "                                                                                                    2049                                                               0.58                \n",
+       "                                                                                                    2050                                                               0.58                \n",
+       "\n",
+       "display_name                                                                                                              Residential Battery Storage - 5 kW - 12.5 kWh  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                  \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                            NaN   \n",
+       "                                                                                                    2023                                                            NaN   \n",
+       "                                                                                                    2024                                                            NaN   \n",
+       "                                                                                                    2025                                                            NaN   \n",
+       "                                                                                                    2026                                                            NaN   \n",
+       "...                                                                                                                                                                 ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                            NaN   \n",
+       "                                                                                                    2047                                                            NaN   \n",
+       "                                                                                                    2048                                                            NaN   \n",
+       "                                                                                                    2049                                                            NaN   \n",
+       "                                                                                                    2050                                                            NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential Battery Storage - 5 kW - 20 kWh  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                                \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                          NaN   \n",
+       "                                                                                                    2023                                                          NaN   \n",
+       "                                                                                                    2024                                                          NaN   \n",
+       "                                                                                                    2025                                                          NaN   \n",
+       "                                                                                                    2026                                                          NaN   \n",
+       "...                                                                                                                                                               ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                          NaN   \n",
+       "                                                                                                    2047                                                          NaN   \n",
+       "                                                                                                    2048                                                          NaN   \n",
+       "                                                                                                    2049                                                          NaN   \n",
+       "                                                                                                    2050                                                          NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                              \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                        NaN   \n",
+       "                                                                                                    2023                                        NaN   \n",
+       "                                                                                                    2024                                        NaN   \n",
+       "                                                                                                    2025                                        NaN   \n",
+       "                                                                                                    2026                                        NaN   \n",
+       "...                                                                                                                                             ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                        NaN   \n",
+       "                                                                                                    2047                                        NaN   \n",
+       "                                                                                                    2048                                        NaN   \n",
+       "                                                                                                    2049                                        NaN   \n",
+       "                                                                                                    2050                                        NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential DW - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                              \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                        NaN   \n",
+       "                                                                                                    2023                                        NaN   \n",
+       "                                                                                                    2024                                        NaN   \n",
+       "                                                                                                    2025                                        NaN   \n",
+       "                                                                                                    2026                                        NaN   \n",
+       "...                                                                                                                                             ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                        NaN   \n",
+       "                                                                                                    2047                                        NaN   \n",
+       "                                                                                                    2048                                        NaN   \n",
+       "                                                                                                    2049                                        NaN   \n",
+       "                                                                                                    2050                                        NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Residential PV - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                             \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                       NaN   \n",
+       "                                                                                                    2023                                       NaN   \n",
+       "                                                                                                    2024                                       NaN   \n",
+       "                                                                                                    2025                                       NaN   \n",
+       "                                                                                                    2026                                       NaN   \n",
+       "...                                                                                                                                            ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                       NaN   \n",
+       "                                                                                                    2047                                       NaN   \n",
+       "                                                                                                    2048                                       NaN   \n",
+       "                                                                                                    2049                                       NaN   \n",
+       "                                                                                                    2050                                       NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 1  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 10  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                          \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                    NaN   \n",
+       "                                                                                                    2023                                    NaN   \n",
+       "                                                                                                    2024                                    NaN   \n",
+       "                                                                                                    2025                                    NaN   \n",
+       "                                                                                                    2026                                    NaN   \n",
+       "...                                                                                                                                         ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                    NaN   \n",
+       "                                                                                                    2047                                    NaN   \n",
+       "                                                                                                    2048                                    NaN   \n",
+       "                                                                                                    2049                                    NaN   \n",
+       "                                                                                                    2050                                    NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 2  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 3  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 4  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 5  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 6  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 7  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 8  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility PV - Class 9  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                   NaN   \n",
+       "                                                                                                    2023                                   NaN   \n",
+       "                                                                                                    2024                                   NaN   \n",
+       "                                                                                                    2025                                   NaN   \n",
+       "                                                                                                    2026                                   NaN   \n",
+       "...                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                   NaN   \n",
+       "                                                                                                    2047                                   NaN   \n",
+       "                                                                                                    2048                                   NaN   \n",
+       "                                                                                                    2049                                   NaN   \n",
+       "                                                                                                    2050                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility-Scale Battery Storage - 10Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                         \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                   NaN   \n",
+       "                                                                                                    2023                                                   NaN   \n",
+       "                                                                                                    2024                                                   NaN   \n",
+       "                                                                                                    2025                                                   NaN   \n",
+       "                                                                                                    2026                                                   NaN   \n",
+       "...                                                                                                                                                        ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                   NaN   \n",
+       "                                                                                                    2047                                                   NaN   \n",
+       "                                                                                                    2048                                                   NaN   \n",
+       "                                                                                                    2049                                                   NaN   \n",
+       "                                                                                                    2050                                                   NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility-Scale Battery Storage - 2Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                        \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                  NaN   \n",
+       "                                                                                                    2023                                                  NaN   \n",
+       "                                                                                                    2024                                                  NaN   \n",
+       "                                                                                                    2025                                                  NaN   \n",
+       "                                                                                                    2026                                                  NaN   \n",
+       "...                                                                                                                                                       ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                  NaN   \n",
+       "                                                                                                    2047                                                  NaN   \n",
+       "                                                                                                    2048                                                  NaN   \n",
+       "                                                                                                    2049                                                  NaN   \n",
+       "                                                                                                    2050                                                  NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility-Scale Battery Storage - 4Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                        \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                  NaN   \n",
+       "                                                                                                    2023                                                  NaN   \n",
+       "                                                                                                    2024                                                  NaN   \n",
+       "                                                                                                    2025                                                  NaN   \n",
+       "                                                                                                    2026                                                  NaN   \n",
+       "...                                                                                                                                                       ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                  NaN   \n",
+       "                                                                                                    2047                                                  NaN   \n",
+       "                                                                                                    2048                                                  NaN   \n",
+       "                                                                                                    2049                                                  NaN   \n",
+       "                                                                                                    2050                                                  NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility-Scale Battery Storage - 6Hr  \\\n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                        \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                  NaN   \n",
+       "                                                                                                    2023                                                  NaN   \n",
+       "                                                                                                    2024                                                  NaN   \n",
+       "                                                                                                    2025                                                  NaN   \n",
+       "                                                                                                    2026                                                  NaN   \n",
+       "...                                                                                                                                                       ...   \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                  NaN   \n",
+       "                                                                                                    2047                                                  NaN   \n",
+       "                                                                                                    2048                                                  NaN   \n",
+       "                                                                                                    2049                                                  NaN   \n",
+       "                                                                                                    2050                                                  NaN   \n",
+       "\n",
+       "display_name                                                                                                              Utility-Scale Battery Storage - 8Hr  \n",
+       "core_metric_case crpyears maturity scale   scenario technology                core_metric_parameter core_metric_variable                                       \n",
+       "Market           20       N        Utility Advanced Coal_FE                   CAPEX                 2022                                                  NaN  \n",
+       "                                                                                                    2023                                                  NaN  \n",
+       "                                                                                                    2024                                                  NaN  \n",
+       "                                                                                                    2025                                                  NaN  \n",
+       "                                                                                                    2026                                                  NaN  \n",
+       "...                                                                                                                                                       ...  \n",
+       "R&D              100      Y        Utility Moderate Pumped Storage Hydropower Variable O&M          2046                                                  NaN  \n",
+       "                                                                                                    2047                                                  NaN  \n",
+       "                                                                                                    2048                                                  NaN  \n",
+       "                                                                                                    2049                                                  NaN  \n",
+       "                                                                                                    2050                                                  NaN  \n",
+       "\n",
+       "[79680 rows x 182 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "resource_class"
+    "atbe.data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "avg_cf = weather.ghi.mean()/weather.ghi.max() # W/m^2"
+    "import yaml\n",
+    "\n",
+    "with open(\"../config.yml\", 'r') as file:\n",
+    "    config = yaml.safe_load(file)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4"
+       "{'core_metric_case': 'Market',\n",
+       " 'scenario': 'Moderate',\n",
+       " 'maturity': 'Y',\n",
+       " 'crpyears': 30,\n",
+       " 'core_metric_variable': 2025}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(resource_class['avg_cf'] - avg_cf).abs().sort_values().index[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "selection = atbe(\n",
-    "    core_metric_case='Market',\n",
-    "     crpyears=30,\n",
-    "     maturity='Y',\n",
-    "     scale='Commercial',\n",
-    "     scenario='Moderate',\n",
-    "     core_metric_variable=2025,)"
+    "config['atb_params']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -575,22 +5570,13 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th></th>\n",
        "      <th>display_name</th>\n",
        "      <th>Commercial Battery Storage 1Hr</th>\n",
        "      <th>Commercial Battery Storage 2Hr</th>\n",
        "      <th>Commercial Battery Storage 4Hr</th>\n",
        "      <th>Commercial Battery Storage 6Hr</th>\n",
        "      <th>Commercial Battery Storage 8Hr</th>\n",
-       "      <th>Commercial DW - Class 1</th>\n",
-       "      <th>Commercial DW - Class 10</th>\n",
-       "      <th>Commercial DW - Class 2</th>\n",
-       "      <th>Commercial DW - Class 3</th>\n",
-       "      <th>Commercial DW - Class 4</th>\n",
-       "      <th>Commercial DW - Class 5</th>\n",
-       "      <th>Commercial DW - Class 6</th>\n",
-       "      <th>Commercial DW - Class 7</th>\n",
-       "      <th>Commercial DW - Class 8</th>\n",
-       "      <th>Commercial DW - Class 9</th>\n",
        "      <th>Commercial PV - Class 1</th>\n",
        "      <th>Commercial PV - Class 10</th>\n",
        "      <th>Commercial PV - Class 2</th>\n",
@@ -601,8 +5587,36 @@
        "      <th>Commercial PV - Class 7</th>\n",
        "      <th>Commercial PV - Class 8</th>\n",
        "      <th>Commercial PV - Class 9</th>\n",
+       "      <th>Residential Battery Storage - 5 kW - 12.5 kWh</th>\n",
+       "      <th>Residential Battery Storage - 5 kW - 20 kWh</th>\n",
+       "      <th>Residential PV - Class 1</th>\n",
+       "      <th>Residential PV - Class 10</th>\n",
+       "      <th>Residential PV - Class 2</th>\n",
+       "      <th>Residential PV - Class 3</th>\n",
+       "      <th>Residential PV - Class 4</th>\n",
+       "      <th>Residential PV - Class 5</th>\n",
+       "      <th>Residential PV - Class 6</th>\n",
+       "      <th>Residential PV - Class 7</th>\n",
+       "      <th>Residential PV - Class 8</th>\n",
+       "      <th>Residential PV - Class 9</th>\n",
+       "      <th>Utility PV - Class 1</th>\n",
+       "      <th>Utility PV - Class 10</th>\n",
+       "      <th>Utility PV - Class 2</th>\n",
+       "      <th>Utility PV - Class 3</th>\n",
+       "      <th>Utility PV - Class 4</th>\n",
+       "      <th>Utility PV - Class 5</th>\n",
+       "      <th>Utility PV - Class 6</th>\n",
+       "      <th>Utility PV - Class 7</th>\n",
+       "      <th>Utility PV - Class 8</th>\n",
+       "      <th>Utility PV - Class 9</th>\n",
+       "      <th>Utility-Scale Battery Storage - 10Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 2Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 4Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 6Hr</th>\n",
+       "      <th>Utility-Scale Battery Storage - 8Hr</th>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>scale</th>\n",
        "      <th>technology</th>\n",
        "      <th>core_metric_parameter</th>\n",
        "      <th></th>\n",
@@ -630,11 +5644,121 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Utility</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">UtilityPV</th>\n",
+       "      <th>CAPEX</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>1491.640744</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>21.001077</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
        "    <tr>\n",
-       "      <th rowspan=\"7\" valign=\"top\">CommPV</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Residential</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">ResPV</th>\n",
        "      <th>CAPEX</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -651,19 +5775,18 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "      <td>1731.273000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CFC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
+       "      <td>2518.968052</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -679,16 +5802,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
-       "      <td>61.870938</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fixed O&amp;M</th>\n",
@@ -707,19 +5820,18 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "      <td>17.591546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>OCC</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
+       "      <td>28.701450</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -735,24 +5847,28 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
-       "      <td>1669.402062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CF</th>\n",
+       "      <th rowspan=\"4\" valign=\"top\">Commercial</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">CommPV</th>\n",
+       "      <th>CAPEX</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>1744.249795</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -763,19 +5879,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.191976</td>\n",
-       "      <td>0.123419</td>\n",
-       "      <td>0.185015</td>\n",
-       "      <td>0.175138</td>\n",
-       "      <td>0.165884</td>\n",
-       "      <td>0.158300</td>\n",
-       "      <td>0.156201</td>\n",
-       "      <td>0.148828</td>\n",
-       "      <td>0.141347</td>\n",
-       "      <td>0.135677</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FCR</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -791,19 +5894,36 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
-       "      <td>0.044491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>LCOE</th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>17.963336</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -819,25 +5939,32 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>56.262649</td>\n",
-       "      <td>87.515822</td>\n",
-       "      <td>58.379664</td>\n",
-       "      <td>61.671863</td>\n",
-       "      <td>65.112419</td>\n",
-       "      <td>68.231939</td>\n",
-       "      <td>69.148898</td>\n",
-       "      <td>72.574405</td>\n",
-       "      <td>76.415676</td>\n",
-       "      <td>79.608753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">Commercial Battery Storage</th>\n",
-       "      <th>Fixed O&amp;M</th>\n",
-       "      <td>32.989528</td>\n",
-       "      <td>38.207464</td>\n",
-       "      <td>48.643337</td>\n",
-       "      <td>59.079209</td>\n",
-       "      <td>69.515082</td>\n",
+       "      <th>CAPEX</th>\n",
+       "      <td>1248.675000</td>\n",
+       "      <td>1482.539952</td>\n",
+       "      <td>1950.269857</td>\n",
+       "      <td>2417.999761</td>\n",
+       "      <td>2885.729665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -860,12 +5987,29 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>OCC</th>\n",
-       "      <td>1319.581113</td>\n",
-       "      <td>1528.298565</td>\n",
-       "      <td>1945.733470</td>\n",
-       "      <td>2363.168375</td>\n",
-       "      <td>2780.603279</td>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>30.088934</td>\n",
+       "      <td>35.724305</td>\n",
+       "      <td>46.995048</td>\n",
+       "      <td>58.265790</td>\n",
+       "      <td>69.536533</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -888,23 +6032,14 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th rowspan=\"7\" valign=\"top\">DistributedWind</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Utility</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Utility-Scale Battery Storage</th>\n",
        "      <th>CAPEX</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4474.789672</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
-       "      <td>4422.927131</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -915,24 +6050,11 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CFC</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>160.703184</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
-       "      <td>158.840644</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -943,6 +6065,18 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3638.921285</td>\n",
+       "      <td>1068.896874</td>\n",
+       "      <td>1711.402977</td>\n",
+       "      <td>2353.909080</td>\n",
+       "      <td>2996.415182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Fixed O&amp;M</th>\n",
@@ -951,16 +6085,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
-       "      <td>35.912100</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -971,24 +6095,11 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>OCC</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4314.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
-       "      <td>4264.086488</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -999,24 +6110,28 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>85.265982</td>\n",
+       "      <td>23.280383</td>\n",
+       "      <td>38.776782</td>\n",
+       "      <td>54.273182</td>\n",
+       "      <td>69.769582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CF</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Residential</th>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Residential Battery Storage</th>\n",
+       "      <th>CAPEX</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.505564</td>\n",
-       "      <td>0.169145</td>\n",
-       "      <td>0.467740</td>\n",
-       "      <td>0.454523</td>\n",
-       "      <td>0.440773</td>\n",
-       "      <td>0.423729</td>\n",
-       "      <td>0.398052</td>\n",
-       "      <td>0.361518</td>\n",
-       "      <td>0.316618</td>\n",
-       "      <td>0.265176</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1027,24 +6142,23 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FCR</th>\n",
+       "      <td>3410.447071</td>\n",
+       "      <td>4144.345381</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
-       "      <td>0.045528</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1057,22 +6171,39 @@
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>LCOE</th>\n",
+       "      <th>Fixed O&amp;M</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>82.180485</td>\n",
+       "      <td>99.865005</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>53.576918</td>\n",
-       "      <td>160.138743</td>\n",
-       "      <td>57.909558</td>\n",
-       "      <td>59.593436</td>\n",
-       "      <td>62.064058</td>\n",
-       "      <td>63.924318</td>\n",
-       "      <td>68.047776</td>\n",
-       "      <td>74.924589</td>\n",
-       "      <td>85.549875</td>\n",
-       "      <td>102.145890</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -1089,489 +6220,686 @@
        "</div>"
       ],
       "text/plain": [
-       "display_name                                      Commercial Battery Storage 1Hr  \\\n",
-       "technology                 core_metric_parameter                                   \n",
-       "CommPV                     CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                   32.989528   \n",
-       "                           OCC                                       1319.581113   \n",
-       "DistributedWind            CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "\n",
-       "display_name                                      Commercial Battery Storage 2Hr  \\\n",
-       "technology                 core_metric_parameter                                   \n",
-       "CommPV                     CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                   38.207464   \n",
-       "                           OCC                                       1528.298565   \n",
-       "DistributedWind            CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "\n",
-       "display_name                                      Commercial Battery Storage 4Hr  \\\n",
-       "technology                 core_metric_parameter                                   \n",
-       "CommPV                     CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                   48.643337   \n",
-       "                           OCC                                       1945.733470   \n",
-       "DistributedWind            CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "\n",
-       "display_name                                      Commercial Battery Storage 6Hr  \\\n",
-       "technology                 core_metric_parameter                                   \n",
-       "CommPV                     CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                   59.079209   \n",
-       "                           OCC                                       2363.168375   \n",
-       "DistributedWind            CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "\n",
-       "display_name                                      Commercial Battery Storage 8Hr  \\\n",
-       "technology                 core_metric_parameter                                   \n",
-       "CommPV                     CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                   69.515082   \n",
-       "                           OCC                                       2780.603279   \n",
-       "DistributedWind            CAPEX                                             NaN   \n",
-       "                           CFC                                               NaN   \n",
-       "                           Fixed O&M                                         NaN   \n",
-       "                           OCC                                               NaN   \n",
-       "                           CF                                                NaN   \n",
-       "                           FCR                                               NaN   \n",
-       "                           LCOE                                              NaN   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 1  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.505564   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 53.576918   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 10  \\\n",
-       "technology                 core_metric_parameter                             \n",
-       "CommPV                     CAPEX                                       NaN   \n",
-       "                           CFC                                         NaN   \n",
-       "                           Fixed O&M                                   NaN   \n",
-       "                           OCC                                         NaN   \n",
-       "                           CF                                          NaN   \n",
-       "                           FCR                                         NaN   \n",
-       "                           LCOE                                        NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                   NaN   \n",
-       "                           OCC                                         NaN   \n",
-       "DistributedWind            CAPEX                               4422.927131   \n",
-       "                           CFC                                  158.840644   \n",
-       "                           Fixed O&M                             35.912100   \n",
-       "                           OCC                                 4264.086488   \n",
-       "                           CF                                     0.169145   \n",
-       "                           FCR                                    0.045528   \n",
-       "                           LCOE                                 160.138743   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 2  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.467740   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 57.909558   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 3  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.454523   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 59.593436   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 4  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4474.789672   \n",
-       "                           CFC                                 160.703184   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4314.086488   \n",
-       "                           CF                                    0.440773   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 62.064058   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 5  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.423729   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 63.924318   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 6  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.398052   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 68.047776   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 7  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.361518   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 74.924589   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 8  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.316618   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                 85.549875   \n",
-       "\n",
-       "display_name                                      Commercial DW - Class 9  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                              4422.927131   \n",
-       "                           CFC                                 158.840644   \n",
-       "                           Fixed O&M                            35.912100   \n",
-       "                           OCC                                4264.086488   \n",
-       "                           CF                                    0.265176   \n",
-       "                           FCR                                   0.045528   \n",
-       "                           LCOE                                102.145890   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 1  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.191976   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 56.262649   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 10  \\\n",
-       "technology                 core_metric_parameter                             \n",
-       "CommPV                     CAPEX                               1731.273000   \n",
-       "                           CFC                                   61.870938   \n",
-       "                           Fixed O&M                             17.591546   \n",
-       "                           OCC                                 1669.402062   \n",
-       "                           CF                                     0.123419   \n",
-       "                           FCR                                    0.044491   \n",
-       "                           LCOE                                  87.515822   \n",
-       "Commercial Battery Storage Fixed O&M                                   NaN   \n",
-       "                           OCC                                         NaN   \n",
-       "DistributedWind            CAPEX                                       NaN   \n",
-       "                           CFC                                         NaN   \n",
-       "                           Fixed O&M                                   NaN   \n",
-       "                           OCC                                         NaN   \n",
-       "                           CF                                          NaN   \n",
-       "                           FCR                                         NaN   \n",
-       "                           LCOE                                        NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 2  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.185015   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 58.379664   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 3  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.175138   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 61.671863   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 4  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.165884   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 65.112419   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 5  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.158300   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 68.231939   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 6  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.156201   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 69.148898   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 7  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.148828   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 72.574405   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 8  \\\n",
-       "technology                 core_metric_parameter                            \n",
-       "CommPV                     CAPEX                              1731.273000   \n",
-       "                           CFC                                  61.870938   \n",
-       "                           Fixed O&M                            17.591546   \n",
-       "                           OCC                                1669.402062   \n",
-       "                           CF                                    0.141347   \n",
-       "                           FCR                                   0.044491   \n",
-       "                           LCOE                                 76.415676   \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "DistributedWind            CAPEX                                      NaN   \n",
-       "                           CFC                                        NaN   \n",
-       "                           Fixed O&M                                  NaN   \n",
-       "                           OCC                                        NaN   \n",
-       "                           CF                                         NaN   \n",
-       "                           FCR                                        NaN   \n",
-       "                           LCOE                                       NaN   \n",
-       "\n",
-       "display_name                                      Commercial PV - Class 9  \n",
-       "technology                 core_metric_parameter                           \n",
-       "CommPV                     CAPEX                              1731.273000  \n",
-       "                           CFC                                  61.870938  \n",
-       "                           Fixed O&M                            17.591546  \n",
-       "                           OCC                                1669.402062  \n",
-       "                           CF                                    0.135677  \n",
-       "                           FCR                                   0.044491  \n",
-       "                           LCOE                                 79.608753  \n",
-       "Commercial Battery Storage Fixed O&M                                  NaN  \n",
-       "                           OCC                                        NaN  \n",
-       "DistributedWind            CAPEX                                      NaN  \n",
-       "                           CFC                                        NaN  \n",
-       "                           Fixed O&M                                  NaN  \n",
-       "                           OCC                                        NaN  \n",
-       "                           CF                                         NaN  \n",
-       "                           FCR                                        NaN  \n",
-       "                           LCOE                                       NaN  "
+       "display_name                                                     Commercial Battery Storage 1Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                   \n",
+       "Utility     UtilityPV                     CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential ResPV                         CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Commercial  CommPV                        CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                     1248.675000   \n",
+       "                                          Fixed O&M                                   30.088934   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "\n",
+       "display_name                                                     Commercial Battery Storage 2Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                   \n",
+       "Utility     UtilityPV                     CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential ResPV                         CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Commercial  CommPV                        CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                     1482.539952   \n",
+       "                                          Fixed O&M                                   35.724305   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "\n",
+       "display_name                                                     Commercial Battery Storage 4Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                   \n",
+       "Utility     UtilityPV                     CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential ResPV                         CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Commercial  CommPV                        CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                     1950.269857   \n",
+       "                                          Fixed O&M                                   46.995048   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "\n",
+       "display_name                                                     Commercial Battery Storage 6Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                   \n",
+       "Utility     UtilityPV                     CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential ResPV                         CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Commercial  CommPV                        CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                     2417.999761   \n",
+       "                                          Fixed O&M                                   58.265790   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "\n",
+       "display_name                                                     Commercial Battery Storage 8Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                   \n",
+       "Utility     UtilityPV                     CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential ResPV                         CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Commercial  CommPV                        CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                     2885.729665   \n",
+       "                                          Fixed O&M                                   69.536533   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                             NaN   \n",
+       "                                          Fixed O&M                                         NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 1  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 10  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Commercial  CommPV                        CAPEX                               1744.249795   \n",
+       "                                          Fixed O&M                             17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 2  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 3  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 4  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 5  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 6  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 7  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 8  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Commercial PV - Class 9  \\\n",
+       "scale       technology                    core_metric_parameter                            \n",
+       "Utility     UtilityPV                     CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential ResPV                         CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Commercial  CommPV                        CAPEX                              1744.249795   \n",
+       "                                          Fixed O&M                            17.963336   \n",
+       "            Commercial Battery Storage    CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                      NaN   \n",
+       "                                          Fixed O&M                                  NaN   \n",
+       "\n",
+       "display_name                                                     Residential Battery Storage - 5 kW - 12.5 kWh  \\\n",
+       "scale       technology                    core_metric_parameter                                                  \n",
+       "Utility     UtilityPV                     CAPEX                                                            NaN   \n",
+       "                                          Fixed O&M                                                        NaN   \n",
+       "Residential ResPV                         CAPEX                                                            NaN   \n",
+       "                                          Fixed O&M                                                        NaN   \n",
+       "Commercial  CommPV                        CAPEX                                                            NaN   \n",
+       "                                          Fixed O&M                                                        NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                                            NaN   \n",
+       "                                          Fixed O&M                                                        NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                                            NaN   \n",
+       "                                          Fixed O&M                                                        NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                                    3410.447071   \n",
+       "                                          Fixed O&M                                                  82.180485   \n",
+       "\n",
+       "display_name                                                     Residential Battery Storage - 5 kW - 20 kWh  \\\n",
+       "scale       technology                    core_metric_parameter                                                \n",
+       "Utility     UtilityPV                     CAPEX                                                          NaN   \n",
+       "                                          Fixed O&M                                                      NaN   \n",
+       "Residential ResPV                         CAPEX                                                          NaN   \n",
+       "                                          Fixed O&M                                                      NaN   \n",
+       "Commercial  CommPV                        CAPEX                                                          NaN   \n",
+       "                                          Fixed O&M                                                      NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                                          NaN   \n",
+       "                                          Fixed O&M                                                      NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                                          NaN   \n",
+       "                                          Fixed O&M                                                      NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                                  4144.345381   \n",
+       "                                          Fixed O&M                                                99.865005   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 1  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 10  \\\n",
+       "scale       technology                    core_metric_parameter                              \n",
+       "Utility     UtilityPV                     CAPEX                                        NaN   \n",
+       "                                          Fixed O&M                                    NaN   \n",
+       "Residential ResPV                         CAPEX                                2518.968052   \n",
+       "                                          Fixed O&M                              28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                        NaN   \n",
+       "                                          Fixed O&M                                    NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                        NaN   \n",
+       "                                          Fixed O&M                                    NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                        NaN   \n",
+       "                                          Fixed O&M                                    NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                        NaN   \n",
+       "                                          Fixed O&M                                    NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 2  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 3  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 4  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 5  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 6  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 7  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 8  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Residential PV - Class 9  \\\n",
+       "scale       technology                    core_metric_parameter                             \n",
+       "Utility     UtilityPV                     CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential ResPV                         CAPEX                               2518.968052   \n",
+       "                                          Fixed O&M                             28.701450   \n",
+       "Commercial  CommPV                        CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                       NaN   \n",
+       "                                          Fixed O&M                                   NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 1  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 10  \\\n",
+       "scale       technology                    core_metric_parameter                          \n",
+       "Utility     UtilityPV                     CAPEX                            1491.640744   \n",
+       "                                          Fixed O&M                          21.001077   \n",
+       "Residential ResPV                         CAPEX                                    NaN   \n",
+       "                                          Fixed O&M                                NaN   \n",
+       "Commercial  CommPV                        CAPEX                                    NaN   \n",
+       "                                          Fixed O&M                                NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                    NaN   \n",
+       "                                          Fixed O&M                                NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                    NaN   \n",
+       "                                          Fixed O&M                                NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                    NaN   \n",
+       "                                          Fixed O&M                                NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 2  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 3  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 4  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 5  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 6  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 7  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 8  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility PV - Class 9  \\\n",
+       "scale       technology                    core_metric_parameter                         \n",
+       "Utility     UtilityPV                     CAPEX                           1491.640744   \n",
+       "                                          Fixed O&M                         21.001077   \n",
+       "Residential ResPV                         CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "Residential Residential Battery Storage   CAPEX                                   NaN   \n",
+       "                                          Fixed O&M                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility-Scale Battery Storage - 10Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                         \n",
+       "Utility     UtilityPV                     CAPEX                                                   NaN   \n",
+       "                                          Fixed O&M                                               NaN   \n",
+       "Residential ResPV                         CAPEX                                                   NaN   \n",
+       "                                          Fixed O&M                                               NaN   \n",
+       "Commercial  CommPV                        CAPEX                                                   NaN   \n",
+       "                                          Fixed O&M                                               NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                                   NaN   \n",
+       "                                          Fixed O&M                                               NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                           3638.921285   \n",
+       "                                          Fixed O&M                                         85.265982   \n",
+       "Residential Residential Battery Storage   CAPEX                                                   NaN   \n",
+       "                                          Fixed O&M                                               NaN   \n",
+       "\n",
+       "display_name                                                     Utility-Scale Battery Storage - 2Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                        \n",
+       "Utility     UtilityPV                     CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Residential ResPV                         CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Commercial  CommPV                        CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                          1068.896874   \n",
+       "                                          Fixed O&M                                        23.280383   \n",
+       "Residential Residential Battery Storage   CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "\n",
+       "display_name                                                     Utility-Scale Battery Storage - 4Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                        \n",
+       "Utility     UtilityPV                     CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Residential ResPV                         CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Commercial  CommPV                        CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                          1711.402977   \n",
+       "                                          Fixed O&M                                        38.776782   \n",
+       "Residential Residential Battery Storage   CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "\n",
+       "display_name                                                     Utility-Scale Battery Storage - 6Hr  \\\n",
+       "scale       technology                    core_metric_parameter                                        \n",
+       "Utility     UtilityPV                     CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Residential ResPV                         CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Commercial  CommPV                        CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "            Commercial Battery Storage    CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                          2353.909080   \n",
+       "                                          Fixed O&M                                        54.273182   \n",
+       "Residential Residential Battery Storage   CAPEX                                                  NaN   \n",
+       "                                          Fixed O&M                                              NaN   \n",
+       "\n",
+       "display_name                                                     Utility-Scale Battery Storage - 8Hr  \n",
+       "scale       technology                    core_metric_parameter                                       \n",
+       "Utility     UtilityPV                     CAPEX                                                  NaN  \n",
+       "                                          Fixed O&M                                              NaN  \n",
+       "Residential ResPV                         CAPEX                                                  NaN  \n",
+       "                                          Fixed O&M                                              NaN  \n",
+       "Commercial  CommPV                        CAPEX                                                  NaN  \n",
+       "                                          Fixed O&M                                              NaN  \n",
+       "            Commercial Battery Storage    CAPEX                                                  NaN  \n",
+       "                                          Fixed O&M                                              NaN  \n",
+       "Utility     Utility-Scale Battery Storage CAPEX                                          2996.415182  \n",
+       "                                          Fixed O&M                                        69.769582  \n",
+       "Residential Residential Battery Storage   CAPEX                                                  NaN  \n",
+       "                                          Fixed O&M                                              NaN  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "atbe(**config['atb_params']).loc[(slice(None), config['technology_options'], ['CAPEX','Fixed O&M'])].dropna(how='all', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selection = atbe(\n",
+    "    core_metric_case='Market',\n",
+    "     crpyears=30,\n",
+    "     maturity='Y',\n",
+    "     scale='Residential',\n",
+    "     scenario='Moderate',\n",
+    "     core_metric_variable=2025,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['DistributedWind', 'ResPV', 'Residential Battery Storage'], dtype='object', name='technology')"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "selection"
+    "selection.index.get_level_values('technology').unique()"
    ]
   },
   {
@@ -2802,7 +8130,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "kansas-city",
+   "display_name": "pypsa-illinois02",
    "language": "python",
    "name": "python3"
   },
@@ -2816,7 +8144,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.10"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/13-primary-school.ipynb b/notebooks/13-primary-school.ipynb
index 70338a5..84f55b9 100644
--- a/notebooks/13-primary-school.ipynb
+++ b/notebooks/13-primary-school.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -44,7 +44,7 @@
        "<Axes: xlabel='timestamp'>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -63,6 +63,48 @@
     "df.loc['2018-09-21','out.electricity.total.energy_consumption'].resample('h').mean().plot()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "floor_area_represented\n",
+       "3.675263e+06    35040\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['floor_area_represented'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6284.266553333334"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[:,'out.electricity.total.energy_consumption'].resample('h').mean().sum()/1e3"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -155,7 +197,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "kansas-city",
+   "display_name": "pypsa-illinois02",
    "language": "python",
    "name": "python3"
   },
diff --git a/scripts/retrieve_renewables.py b/scripts/retrieve_renewables.py
index f073197..61a1b1b 100644
--- a/scripts/retrieve_renewables.py
+++ b/scripts/retrieve_renewables.py
@@ -9,7 +9,8 @@
 
 from nrel_data_api import parameters, make_csv_url
 
-model_years = np.array(snakemake.config['model_years']).astype('int')
+# model_years = np.array(snakemake.config['model_years']).astype('int')
+model_years = np.array([2018]).astype('int')
 
 def handle_datetime(dataframe):
     """
@@ -63,15 +64,18 @@ def retrieve_solar_timeseries(region, save_years=True):
             parameters['lat'] = j
             URL = make_csv_url(parameters=parameters, 
                             kind='solar')
-            df = pd.read_csv(URL, skiprows=2)[:8760]
-            df.rename(columns={'GHI':f"{n}"}, inplace=True)
+            df = pd.read_csv(URL, skiprows=2)
+            df['date'] = pd.to_datetime(df[['Year','Month','Day','Hour','Minute']])
+            df = df.drop(columns=['Year','Month','Day','Hour','Minute']).set_index('date')
             frames.append(df)
     
-            solar_df = pd.concat(frames, axis=1)
-    
+        solar_df = pd.concat(frames, axis=1)
+        if save_years:
+            solar_df.to_csv(f"data/timeseries/solar_{year}.csv")    
         all_frames.append(solar_df)
+
     full_df = pd.concat(all_frames, axis=0)
-    full_df = handle_datetime(full_df)
+    # full_df = handle_datetime(full_df)
     
     return full_df
 
diff --git a/scripts/retrieve_supply_regions.py b/scripts/retrieve_supply_regions.py
new file mode 100644
index 0000000..a872c9f
--- /dev/null
+++ b/scripts/retrieve_supply_regions.py
@@ -0,0 +1,16 @@
+import geopandas as gpd
+
+
+if __name__ == "__main__":
+    gdf = gpd.read_file(snakemake.input.community)
+        
+    centroids = (gdf.to_crs(epsg=snakemake.config['projected_crs'])\
+                 .centroid.to_crs(epsg=snakemake.config['geographic_crs']))
+    gdf['x'] = centroids.x
+    gdf['y'] = centroids.y
+
+    gdf['name'] = [snakemake.config['community_name']]
+
+    gdf.to_file("data/spatial_data/supply_regions.shp")
+    
+    
\ No newline at end of file