regression_analysis.ipynb

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    "# Import modules\n",
    "import sys\n",
    "sys.path.append('/mnt/e/')\n",
    "\n",
    "import os\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import GlacierClass as gc\n",
    "import metrics as met\n",
    "# import plotlib as gpl\n",
    "import climate as clm\n",
    "import climplotlib as cpl\n",
    "# import utilities as utils\n",
    "import plotutils as plu\n",
    "import matplotlib.pyplot as plt\n",
    "from shapely.ops import Point\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np\n",
    "# import pwlf\n",
    "# import matplotlib.ticker as mticker\n",
    "# import rasterio as rio\n",
    "# import matplotlib.colors as colors\n",
    "# import matplotlib.cm as cm\n",
    "import xarray as xr\n",
    "import statsmodels.api as sm\n",
    "from scipy import optimize"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/home/taryn/.local/lib/python3.8/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.8.1-CAPI-1.13.3). Conversions between both will be slow.\n",
      "  warnings.warn(\n"
     ]
    }
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   "execution_count": 2,
   "source": [
    "# User Parameters\n",
    "\n",
    "# -- Glacier geodatabase\n",
    "# ---- relative path for file geodatabase container glacier information\n",
    "fgdb = '/mnt/e/northwest-decadal/gis-data/nwgreenland_decadal.gdb'\n",
    "# ---- file geodatabase layer names\n",
    "termini_layer = 'annual_termini'\n",
    "points_layer = 'glacier_points'\n",
    "boxes_layer = 'glacier_boxes'\n",
    "\n",
    "# -- Climate directories and variables\n",
    "ecco_sst_dir = '/mnt/e/data/ECCO-V5alpha/'\n",
    "icesdk_file = '/mnt/e/data/ices_dk_NWGreenland_ocntemp/1118187b.csv'\n",
    "hadley_sst_dir = '/mnt/e/data/hadley-oi/SST/'\n",
    "ecco_sic_dir = '/mnt/e/data/ECCO_v5alpha/SIarea/'\n",
    "hadley_sic_dir = '/mnt/e/data/hadley-oi/ICE/'\n",
    "noaa_sic_dir = '/mnt/e/data/NSIDC_NOAA_G02202V3_monthly_sic/'\n",
    "noaa_var = 'goddard_merged_seaice_conc_monthly'\n",
    "mar_dir = '/mnt/e/data/MARv3.11/ERA_1979-2020-6km/'\n",
    "fglacier_points = '/mnt/e/northwest-decadal/northwest_decadal_2021/glacier_points.csv'\n",
    "\n",
    "# -- Other data files\n",
    "oceanmask_file = '/mnt/e/data/GimpOceanMask_90m/GimpOceanMask_90m.shp'\n",
    "bedmachine_file = '/mnt/e/data/BedMachineGreenland_v3/BedMachineGreenland_Bed250m.tif'\n",
    "measures_file = '/mnt/e/data/MEaSUREs_Greenland_20yr_velocity_v1/greenland_vel_mosaic_v_v1_500m.tif'\n",
    "\n",
    "# -- Relative path to save figure output\n",
    "outpath = '/mnt/e/northwest-decadal/test/'\n",
    "\n",
    "# -- Spatial bounds\n",
    "ocean_bounds = [82, 68, -40, -76]\n",
    "ice_bounds = [79, 68, -47, -74]\n",
    "ocean_depth = 250\n",
    "topo_range = [0, 1000]\n",
    "\n",
    "# -- Temporal bounds\n",
    "times = ['1972-09-01', '2020-08-31']\n",
    "\n",
    "# -- Thresholds\n",
    "sic_minimum = 15\n",
    "ice_minimum = 90\n",
    "\n",
    "# -- Discrete sampling points\n",
    "ocean_points = [(69.00, -53.00),#(69.25, -52.25)\n",
    "                (70.85, -54.25),\n",
    "                (73.25, -58.00),\n",
    "                (74.50, -60.00),\n",
    "                (75.50, -62.00),#(75.70, -61.75)\n",
    "                (75.75, -65.00),\n",
    "                (76.25, -71.00),\n",
    "                (77.00, -72.00)]#(77.00, -72.50)]\n",
    "ocean_point_names = ['Disko Bay',\n",
    "                     'Uummannaq Fjord',\n",
    "                     'Upernavik Icefjord',\n",
    "                     'South Melville Bay',\n",
    "                     'Central Melville Bay',\n",
    "                     'North Melville Bay',\n",
    "                     'Wolstenholme Bay',\n",
    "                     'Inglefield Fjord']\n",
    "glacier_points = pd.read_csv(fglacier_points)\n",
    "glacier_coords = [(glacier_points.iloc[g].Latitude, glacier_points.iloc[g].Longitude) for g in glacier_points.index]\n",
    "\n",
    "# -- Geographic information (for plotting)\n",
    "projection = ccrs.NorthPolarStereo(central_longitude=-45, true_scale_latitude=70)\n",
    "\n",
    "# -- Select figure formatting style and set default figure properties\n",
    "style = 'pub-cryo'\n",
    "figformat = 'pdf'\n",
    "figwidth = plu.figureWidth(style)\n",
    "plu.globalDesignProperties(style)"
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   "execution_count": 3,
   "source": [
    "# GLACIER DATA ANALYSIS\n",
    "\n",
    "# -- Load glacier data\n",
    "termini = gpd.read_file(fgdb, layer=termini_layer, driver='FileGDB')\n",
    "\n",
    "points = gpd.read_file(fgdb, layer=points_layer, driver='FileGDB')\n",
    "points.sort_values(by='Glacier_ID', inplace=True)\n",
    "points.set_index('Glacier_ID', drop=False, inplace=True)\n",
    "\n",
    "refboxes = gpd.read_file(fgdb, layer=boxes_layer, driver='FileGDB')\n",
    "refboxes.sort_values(by='Glacier_ID', inplace=True)\n",
    "refboxes.set_index('Glacier_ID', drop=False, inplace=True)\n",
    "\n",
    "# -- Get scope of glaciers and time\n",
    "GIDS = points.Glacier_ID.values\n",
    "\n",
    "termini['Year'] = pd.to_datetime(termini['Year'], format='%Y')\n",
    "YEAR_START = termini.Year.min().year\n",
    "YEAR_END = termini.Year.max().year\n",
    "YEARS = range(YEAR_START, YEAR_END)\n",
    "\n",
    "DATE_START = termini.Source_Date.min()\n",
    "DATE_END = termini.Source_Date.max()\n",
    "\n",
    "# -- Initialize dictionary of Glacier objects to store glacier information\n",
    "all_glaciers = {id: gc.Glacier(id) for id in GIDS}\n",
    "for id in all_glaciers:\n",
    "    all_glaciers[id].refbox = refboxes.loc[id].geometry\n",
    "    all_glaciers[id].officialname = points.loc[id].Official_Name\n",
    "    all_glaciers[id].greenlandicname = points.loc[id].Greenlandic_Name\n",
    "    all_glaciers[id].alternativename = points.loc[id].Alternative_Name\n",
    "\n",
    "# -- Construct an observation time series for each glacier\n",
    "for id in GIDS:\n",
    "\n",
    "    # get reference line and all observations for glacier ID\n",
    "    glacier = termini.query('Glacier_ID == @id')\n",
    "    # refbox = all_glaciers[id].refbox\n",
    "\n",
    "    # loop through all observations and process data\n",
    "    for n in range(len(glacier)):\n",
    "        observation = glacier.iloc[n]\n",
    "\n",
    "        # create a Terminus Observation for a row in geodataframe\n",
    "        obs = gc.TerminusObservation(gid=int(observation.Glacier_ID),\n",
    "                                     qflag=observation.Quality_Flag,\n",
    "                                     termination='',\n",
    "                                     imageid=observation.Image_ID,\n",
    "                                     sensor=observation.Sensor,\n",
    "                                     date=pd.to_datetime(\n",
    "                                         observation.Source_Date),\n",
    "                                     terminus=observation.geometry,\n",
    "                                     referencebox=all_glaciers[id].refbox)\n",
    "\n",
    "        # add glacier observation to time series\n",
    "        all_glaciers[id].addObservation(obs)\n",
    "    \n",
    "    # update all attribute series for glacier\n",
    "    all_glaciers[id].updateObservedValues()\n",
    "    all_glaciers[id].updateDerivedValues(YEARS)\n",
    "\n",
    "first_full_year = pd.to_datetime(str(met.firstFullYear(all_glaciers))+'-09')"
   ],
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   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Load MAR data and resample, subset\n",
    "\n",
    "mar_data = clm.loadData(mar_dir, dtype='mar')\n",
    "mar = mar_data.resample(TIME='AS', keep_attrs=True).mean()\n",
    "mar = clm.subsetGeographic(mar, ice_bounds, dtype='mar')\n",
    "mar = clm.subsetIce(mar, threshold=ice_minimum)\n",
    "mar = clm.subsetTopography(mar, topo_range, dtype='mar')\n",
    "\n",
    "mar.SMB.load()\n",
    "mar.SF.load()\n",
    "mar.RU.load()"
   ],
   "outputs": [
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     "output_type": "execute_result",
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       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;RU&#x27; (TIME: 42, SECTOR1_1: 1, Y20_465: 196, X12_251: 66)&gt;\n",
       "array([[[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "...\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * TIME       (TIME) datetime64[ns] 1979-01-01 1980-01-01 ... 2020-01-01\n",
       "  * X12_251    (X12_251) float32 -738.0 -732.0 -726.0 ... -360.0 -354.0 -348.0\n",
       "  * Y20_465    (Y20_465) float32 -246.0 -240.0 -234.0 ... 1.056e+03 1.062e+03\n",
       "  * SECTOR1_1  (SECTOR1_1) float32 1.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'RU'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>TIME</span>: 42</li><li><span class='xr-has-index'>SECTOR1_1</span>: 1</li><li><span class='xr-has-index'>Y20_465</span>: 196</li><li><span class='xr-has-index'>X12_251</span>: 66</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-90f3d755-047d-4d07-a172-0f62c9bad678' class='xr-array-in' type='checkbox' checked><label for='section-90f3d755-047d-4d07-a172-0f62c9bad678' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "...\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]],\n",
       "\n",
       "\n",
       "       [[[nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         ...,\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan],\n",
       "         [nan, nan, nan, ..., nan, nan, nan]]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-9502b831-216f-405d-bf74-74faf95ee314' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9502b831-216f-405d-bf74-74faf95ee314' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1979-01-01 ... 2020-01-01</div><input id='attrs-40014411-b5b5-44d5-9275-a324a3ea005b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40014411-b5b5-44d5-9275-a324a3ea005b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b80d2f97-997d-45ec-b9aa-6987c1d36e44' class='xr-var-data-in' type='checkbox'><label for='data-b80d2f97-997d-45ec-b9aa-6987c1d36e44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1979-01-01T00:00:00.000000000&#x27;, &#x27;1980-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1981-01-01T00:00:00.000000000&#x27;, &#x27;1982-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1983-01-01T00:00:00.000000000&#x27;, &#x27;1984-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1985-01-01T00:00:00.000000000&#x27;, &#x27;1986-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1987-01-01T00:00:00.000000000&#x27;, &#x27;1988-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1989-01-01T00:00:00.000000000&#x27;, &#x27;1990-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1992-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1993-01-01T00:00:00.000000000&#x27;, &#x27;1994-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1995-01-01T00:00:00.000000000&#x27;, &#x27;1996-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1997-01-01T00:00:00.000000000&#x27;, &#x27;1998-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2001-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2003-01-01T00:00:00.000000000&#x27;, &#x27;2004-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2005-01-01T00:00:00.000000000&#x27;, &#x27;2006-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-01-01T00:00:00.000000000&#x27;, &#x27;2008-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2009-01-01T00:00:00.000000000&#x27;, &#x27;2010-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2011-01-01T00:00:00.000000000&#x27;, &#x27;2012-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2014-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2016-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>X12_251</span></div><div class='xr-var-dims'>(X12_251)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-738.0 -732.0 ... -354.0 -348.0</div><input id='attrs-fd288946-3a30-4013-889d-f70facd0183c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd288946-3a30-4013-889d-f70facd0183c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d70abd16-088e-4d35-99c9-3c826e81fef4' class='xr-var-data-in' type='checkbox'><label for='data-d70abd16-088e-4d35-99c9-3c826e81fef4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>km</dd><dt><span>long_name :</span></dt><dd>x</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-738.00006, -732.00006, -726.00006, -720.00006, -714.00006, -708.00006,\n",
       "       -702.00006, -696.00006, -690.00006, -684.00006, -678.00006, -672.00006,\n",
       "       -666.00006, -660.00006, -654.00006, -648.00006, -642.00006, -636.00006,\n",
       "       -630.00006, -624.00006, -618.00006, -612.00006, -606.00006, -600.00006,\n",
       "       -594.00006, -588.00006, -582.00006, -576.00006, -570.00006, -564.00006,\n",
       "       -558.00006, -552.00006, -546.00006, -540.00006, -534.00006, -528.00006,\n",
       "       -522.00006, -516.00006, -510.00003, -504.00003, -498.00003, -492.00003,\n",
       "       -486.00003, -480.00003, -474.00003, -468.00003, -462.00003, -456.00003,\n",
       "       -450.00003, -444.00003, -438.00003, -432.00003, -426.00003, -420.00003,\n",
       "       -414.00003, -408.00003, -402.00003, -396.00003, -390.00003, -384.00003,\n",
       "       -378.00003, -372.00003, -366.00003, -360.00003, -354.00003, -348.00003],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Y20_465</span></div><div class='xr-var-dims'>(Y20_465)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-246.0 -240.0 ... 1.062e+03</div><input id='attrs-e555fb11-1fde-422a-960b-7f0411aeb0f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e555fb11-1fde-422a-960b-7f0411aeb0f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb3ee667-79b5-47d3-a103-8650694e896d' class='xr-var-data-in' type='checkbox'><label for='data-eb3ee667-79b5-47d3-a103-8650694e896d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>km</dd><dt><span>long_name :</span></dt><dd>y</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-2.460000e+02, -2.400000e+02, -2.340000e+02, -2.280000e+02,\n",
       "       -2.220000e+02, -2.160000e+02, -2.100000e+02, -2.040000e+02,\n",
       "       -1.980000e+02, -1.920000e+02, -1.860000e+02, -1.800000e+02,\n",
       "       -1.740000e+02, -1.680000e+02, -1.620000e+02, -1.560000e+02,\n",
       "       -1.500000e+02, -1.440000e+02, -1.380000e+02, -1.320000e+02,\n",
       "       -1.260000e+02, -1.200000e+02, -1.140000e+02, -1.080000e+02,\n",
       "       -1.020000e+02, -9.600002e+01, -9.000002e+01, -8.400002e+01,\n",
       "       -7.800002e+01, -7.200002e+01, -6.600002e+01, -6.000002e+01,\n",
       "       -5.400002e+01, -4.800002e+01, -4.200002e+01, -3.600002e+01,\n",
       "       -3.000002e+01, -2.400002e+01, -1.800002e+01, -1.200001e+01,\n",
       "       -6.000014e+00, -1.344145e-05,  5.999987e+00,  1.199999e+01,\n",
       "        1.799999e+01,  2.399999e+01,  2.999999e+01,  3.599999e+01,\n",
       "        4.199999e+01,  4.799999e+01,  5.399999e+01,  5.999999e+01,\n",
       "        6.599999e+01,  7.199999e+01,  7.799999e+01,  8.399999e+01,\n",
       "        8.999999e+01,  9.599999e+01,  1.260000e+02,  1.320000e+02,\n",
       "        1.380000e+02,  1.440000e+02,  1.740000e+02,  1.800000e+02,\n",
       "        1.920000e+02,  2.700000e+02,  2.760000e+02,  2.820000e+02,\n",
       "        2.880000e+02,  2.940000e+02,  3.000000e+02,  3.060000e+02,\n",
       "        3.120000e+02,  3.180000e+02,  3.240000e+02,  3.300000e+02,\n",
       "        3.360000e+02,  3.420000e+02,  3.480000e+02,  3.540000e+02,\n",
       "        3.600000e+02,  3.660000e+02,  3.720000e+02,  3.780000e+02,\n",
       "        3.840000e+02,  3.900000e+02,  3.960000e+02,  4.020000e+02,\n",
       "        4.080000e+02,  4.140000e+02,  4.200000e+02,  4.260000e+02,\n",
       "        4.320000e+02,  4.380000e+02,  4.440000e+02,  4.500000e+02,\n",
       "        4.560000e+02,  4.620000e+02,  4.680000e+02,  4.740000e+02,\n",
       "        4.800000e+02,  4.860000e+02,  4.920000e+02,  4.980000e+02,\n",
       "        5.040000e+02,  5.100000e+02,  5.160001e+02,  5.220001e+02,\n",
       "        5.280001e+02,  5.340001e+02,  5.400001e+02,  5.460001e+02,\n",
       "        5.520001e+02,  5.580001e+02,  5.640001e+02,  5.700001e+02,\n",
       "        5.760001e+02,  5.820001e+02,  5.880001e+02,  5.940001e+02,\n",
       "        6.000001e+02,  6.060001e+02,  6.120001e+02,  6.180001e+02,\n",
       "        6.240001e+02,  6.300001e+02,  6.360001e+02,  6.420001e+02,\n",
       "        6.480001e+02,  6.540001e+02,  6.600001e+02,  6.660001e+02,\n",
       "        6.720001e+02,  6.780001e+02,  6.840001e+02,  6.900001e+02,\n",
       "        6.960001e+02,  7.020001e+02,  7.080001e+02,  7.140001e+02,\n",
       "        7.200001e+02,  7.260001e+02,  7.320001e+02,  7.380001e+02,\n",
       "        7.440001e+02,  7.500001e+02,  7.560001e+02,  7.620001e+02,\n",
       "        7.680001e+02,  7.740001e+02,  7.800001e+02,  7.860001e+02,\n",
       "        7.920001e+02,  7.980001e+02,  8.040001e+02,  8.100001e+02,\n",
       "        8.160001e+02,  8.220001e+02,  8.280001e+02,  8.340001e+02,\n",
       "        8.400001e+02,  8.520001e+02,  8.580001e+02,  8.640001e+02,\n",
       "        8.700001e+02,  8.760001e+02,  8.820001e+02,  8.880001e+02,\n",
       "        8.940001e+02,  9.000001e+02,  9.060001e+02,  9.120001e+02,\n",
       "        9.180001e+02,  9.240001e+02,  9.300001e+02,  9.360001e+02,\n",
       "        9.420001e+02,  9.480001e+02,  9.540001e+02,  9.600001e+02,\n",
       "        9.660001e+02,  9.720001e+02,  9.780001e+02,  9.840001e+02,\n",
       "        9.960001e+02,  1.002000e+03,  1.008000e+03,  1.014000e+03,\n",
       "        1.020000e+03,  1.026000e+03,  1.032000e+03,  1.038000e+03,\n",
       "        1.044000e+03,  1.050000e+03,  1.056000e+03,  1.062000e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>SECTOR1_1</span></div><div class='xr-var-dims'>(SECTOR1_1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0</div><input id='attrs-d1636630-c75c-4672-af00-7699ad4609f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1636630-c75c-4672-af00-7699ad4609f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-638a2da9-172a-44df-8805-77bce62c4e8b' class='xr-var-data-in' type='checkbox'><label for='data-638a2da9-172a-44df-8805-77bce62c4e8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>level</dd><dt><span>long_name :</span></dt><dd>sector</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([1.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f261147-d0a7-4e83-9dff-10c1c7e84296' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5f261147-d0a7-4e83-9dff-10c1c7e84296' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
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     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
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  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Load Ocean Temperature Data\n",
    "\n",
    "# -- ECCO\n",
    "ecco_sst = clm.loadData(ecco_sst_dir, dtype='ecco5')\n",
    "ecco_sst = clm.subsetGeographic(ecco_sst, ocean_bounds, dtype='ecco5')\n",
    "ecco_sst = clm.subsetTime(ecco_sst, times, dtype='ecco5')\n",
    "ecco_sst_monthly = ecco_sst.resample(time='MS', keep_attrs=True).mean()\n",
    "ecco_sst_monthly = clm.eccoTemperature(ecco_sst_monthly, dtype='ecco5')\n",
    "ecco_sst = ecco_sst.resample(time='AS', keep_attrs=True).mean()\n",
    "ecco_sst = clm.eccoTemperature(ecco_sst, dtype='ecco5')\n",
    "ecco_sst.temperature.load()\n",
    "ecco_sst_monthly.temperature.load()\n",
    "\n",
    "# -- ICES-DK\n",
    "icesdk_data = clm.loadData(icesdk_file, dtype='icesdk')\n",
    "icesdk_data = clm.subsetGeographic(icesdk_data, ocean_bounds, dtype='icesdk')\n",
    "icesdk_data = clm.subsetTime(icesdk_data, times, dtype='icesdk')\n",
    "\n",
    "# -- Hadley-OI\n",
    "hadley_sst = clm.loadData(hadley_sst_dir, dtype='hadley')\n",
    "hadley_sst = clm.subsetGeographic(hadley_sst, ocean_bounds, dtype='hadley')\n",
    "hadley_sst = clm.subsetTime(hadley_sst, times, dtype='hadley')\n",
    "hadley_sst_monthly = hadley_sst.resample(time='MS', keep_attrs=True).mean()\n",
    "hadley_sst = hadley_sst.resample(time='AS', keep_attrs=True).mean()\n",
    "hadley_sst.SST.load()\n",
    "hadley_sst_monthly.SST.load()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
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       "  * time     (time) datetime64[ns] 1972-09-01 1972-10-01 ... 2020-08-01\n",
       "  * lon      (lon) float32 284.5 285.5 286.5 287.5 ... 316.5 317.5 318.5 319.5\n",
       "  * lat      (lat) float32 68.5 69.5 70.5 71.5 72.5 ... 77.5 78.5 79.5 80.5 81.5</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'SST'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 576</li><li><span class='xr-has-index'>lat</span>: 14</li><li><span class='xr-has-index'>lon</span>: 36</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-902f9576-53aa-4575-b6c5-91e09d61014a' class='xr-array-in' type='checkbox' checked><label for='section-902f9576-53aa-4575-b6c5-91e09d61014a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.378 0.289 0.289 0.289 0.289 1.92 ... 1.015 1.015 1.015 1.477 1.939</span></div><div class='xr-array-data'><pre>array([[[ 1.378     ,  0.28899992,  0.28899992, ...,  1.785     ,\n",
       "          1.7163334 ,  1.6476667 ],\n",
       "        [ 0.77900004,  0.77900004,  0.77900004, ...,  1.5916667 ,\n",
       "          1.4907779 ,  1.3898889 ],\n",
       "        [ 0.77900004,  1.8086666 ,  1.8086666 , ...,  1.5630952 ,\n",
       "          1.4574444 ,  1.3517936 ],\n",
       "        ...,\n",
       "        [-0.74899995, -0.74899995, -0.74899995, ..., -0.16093515,\n",
       "         -0.17765506, -0.19437495],\n",
       "        [-0.74899995, -0.74899995, -0.74899995, ..., -0.9739629 ,\n",
       "         -0.9826481 , -0.99133325],\n",
       "        [-1.4596666 , -1.3745832 , -1.2895    , ..., -1.8       ,\n",
       "         -1.8       , -1.8       ]],\n",
       "\n",
       "       [[-0.65666664, -1.0426667 , -1.0426667 , ...,  0.93969226,\n",
       "          0.9176409 ,  0.89558965],\n",
       "        [-1.3933332 , -1.3933332 , -1.3933332 , ...,  0.7428889 ,\n",
       "          0.68803704,  0.6331852 ],\n",
       "        [-1.3933332 ,  1.052     ,  1.052     , ...,  1.119619  ,\n",
       "          1.1275556 ,  1.1354921 ],\n",
       "...\n",
       "        [ 0.24741936,  0.24741936,  0.19608288, ...,  0.35404477,\n",
       "          0.31153145,  0.26901817],\n",
       "        [ 0.24741936,  0.24741936,  0.19608288, ...,  3.211732  ,\n",
       "          3.3799427 ,  3.5481534 ],\n",
       "        [ 1.4486173 ,  1.0322119 ,  0.6158064 , ...,  0.7234562 ,\n",
       "          1.0694009 ,  1.4153455 ]],\n",
       "\n",
       "       [[ 1.5884794 ,  1.5499997 ,  1.4947462 , ...,  5.2901206 ,\n",
       "          4.9642096 ,  4.6382985 ],\n",
       "        [ 1.5701383 ,  1.5701383 ,  1.5701383 , ...,  5.1599336 ,\n",
       "          4.757165  ,  4.354396  ],\n",
       "        [ 1.904055  ,  1.904055  ,  2.2934098 , ...,  5.6520605 ,\n",
       "          5.3069887 ,  4.9619174 ],\n",
       "        ...,\n",
       "        [ 0.7943777 ,  0.7943777 ,  0.836728  , ...,  0.87591404,\n",
       "          0.839063  ,  0.802212  ],\n",
       "        [ 0.7943777 ,  0.7943777 ,  0.836728  , ...,  4.062555  ,\n",
       "          4.30679   ,  4.551025  ],\n",
       "        [ 2.082986  ,  1.8782581 ,  1.67353   , ...,  1.0146543 ,\n",
       "          1.4765964 ,  1.9385384 ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-910bf18f-cbb0-48a6-a8ba-21c2765d6cbf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-910bf18f-cbb0-48a6-a8ba-21c2765d6cbf' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1972-09-01 ... 2020-08-01</div><input id='attrs-65777651-0f8f-4400-a467-b2562ee98876' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65777651-0f8f-4400-a467-b2562ee98876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ced62f77-50c4-4c7a-9039-5361687e4400' class='xr-var-data-in' type='checkbox'><label for='data-ced62f77-50c4-4c7a-9039-5361687e4400' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1972-09-01T00:00:00.000000000&#x27;, &#x27;1972-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1972-11-01T00:00:00.000000000&#x27;, ..., &#x27;2020-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-07-01T00:00:00.000000000&#x27;, &#x27;2020-08-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>284.5 285.5 286.5 ... 318.5 319.5</div><input id='attrs-26971d87-c19c-45a5-8170-bba577bab3a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26971d87-c19c-45a5-8170-bba577bab3a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b469db6c-0ea2-4b36-acd8-4294a050ab71' class='xr-var-data-in' type='checkbox'><label for='data-b469db6c-0ea2-4b36-acd8-4294a050ab71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([284.5, 285.5, 286.5, 287.5, 288.5, 289.5, 290.5, 291.5, 292.5, 293.5,\n",
       "       294.5, 295.5, 296.5, 297.5, 298.5, 299.5, 300.5, 301.5, 302.5, 303.5,\n",
       "       304.5, 305.5, 306.5, 307.5, 308.5, 309.5, 310.5, 311.5, 312.5, 313.5,\n",
       "       314.5, 315.5, 316.5, 317.5, 318.5, 319.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>68.5 69.5 70.5 ... 79.5 80.5 81.5</div><input id='attrs-fa3c74bb-ef0a-40d3-afd3-5f39ecbe062e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa3c74bb-ef0a-40d3-afd3-5f39ecbe062e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7753a486-2b90-4ee9-8fa9-c4e5c5820405' class='xr-var-data-in' type='checkbox'><label for='data-7753a486-2b90-4ee9-8fa9-c4e5c5820405' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5, 78.5, 79.5,\n",
       "       80.5, 81.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97d42732-d371-4f7e-a4f2-46754e9ad213' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-97d42732-d371-4f7e-a4f2-46754e9ad213' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'SST' (time: 576, lat: 14, lon: 36)>\n",
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       "          1.7163334 ,  1.6476667 ],\n",
       "        [ 0.77900004,  0.77900004,  0.77900004, ...,  1.5916667 ,\n",
       "          1.4907779 ,  1.3898889 ],\n",
       "        [ 0.77900004,  1.8086666 ,  1.8086666 , ...,  1.5630952 ,\n",
       "          1.4574444 ,  1.3517936 ],\n",
       "        ...,\n",
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       "          1.1275556 ,  1.1354921 ],\n",
       "...\n",
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       "          0.31153145,  0.26901817],\n",
       "        [ 0.24741936,  0.24741936,  0.19608288, ...,  3.211732  ,\n",
       "          3.3799427 ,  3.5481534 ],\n",
       "        [ 1.4486173 ,  1.0322119 ,  0.6158064 , ...,  0.7234562 ,\n",
       "          1.0694009 ,  1.4153455 ]],\n",
       "\n",
       "       [[ 1.5884794 ,  1.5499997 ,  1.4947462 , ...,  5.2901206 ,\n",
       "          4.9642096 ,  4.6382985 ],\n",
       "        [ 1.5701383 ,  1.5701383 ,  1.5701383 , ...,  5.1599336 ,\n",
       "          4.757165  ,  4.354396  ],\n",
       "        [ 1.904055  ,  1.904055  ,  2.2934098 , ...,  5.6520605 ,\n",
       "          5.3069887 ,  4.9619174 ],\n",
       "        ...,\n",
       "        [ 0.7943777 ,  0.7943777 ,  0.836728  , ...,  0.87591404,\n",
       "          0.839063  ,  0.802212  ],\n",
       "        [ 0.7943777 ,  0.7943777 ,  0.836728  , ...,  4.062555  ,\n",
       "          4.30679   ,  4.551025  ],\n",
       "        [ 2.082986  ,  1.8782581 ,  1.67353   , ...,  1.0146543 ,\n",
       "          1.4765964 ,  1.9385384 ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1972-09-01 1972-10-01 ... 2020-08-01\n",
       "  * lon      (lon) float32 284.5 285.5 286.5 287.5 ... 316.5 317.5 318.5 319.5\n",
       "  * lat      (lat) float32 68.5 69.5 70.5 71.5 72.5 ... 77.5 78.5 79.5 80.5 81.5"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Load Sea Ice Concentration Data\n",
    "\n",
    "# -- Hadley-OI\n",
    "hadley_sic = clm.loadData(hadley_sic_dir, dtype='hadley')\n",
    "hadley_sic = clm.subsetGeographic(hadley_sic, ocean_bounds, dtype='hadley')\n",
    "hadley_sic = clm.subsetTime(hadley_sic, times, dtype='hadley')\n",
    "hadley_sic.SEAICE.load()\n",
    "\n",
    "# -- NOAA\n",
    "noaa_sic = clm.loadData(noaa_sic_dir, dtype='noaa')\n",
    "noaa_sic = clm.subsetGeographic(noaa_sic, ocean_bounds, 'noaa')\n",
    "noaa_sic = clm.subsetTime(noaa_sic, times, dtype='noaa')\n",
    "noaa_sic[noaa_var].load()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;goddard_merged_seaice_conc_monthly&#x27; (time: 493, ygrid: 66, xgrid: 58)&gt;\n",
       "array([[[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2019-12-01\n",
       "  * ygrid      (ygrid) float32 -7.625e+05 -7.875e+05 ... -2.362e+06 -2.388e+06\n",
       "  * xgrid      (xgrid) float32 -1.238e+06 -1.212e+06 ... 1.625e+05 1.875e+05\n",
       "    latitude   (ygrid, xgrid) float64 76.64 76.83 77.03 ... 68.18 68.17 68.15\n",
       "    longitude  (ygrid, xgrid) float64 -103.4 -102.8 -102.3 ... -41.11 -40.51\n",
       "Attributes:\n",
       "    valid_range:    [  0 100]\n",
       "    long_name:      Goddard Edited Climate Data Record of Passive Microwave M...\n",
       "    standard_name:  sea_ice_area_fraction\n",
       "    units:          1\n",
       "    flag_values:    [-5 -4 -3 -2 -1]\n",
       "    flag_meanings:  pole_hole lakes coastal land_mask missing_data\n",
       "    datum:          +ellps=urn:ogc:def:crs:EPSG::4326\n",
       "    grid_mapping:   projection\n",
       "    reference:      http://nsidc.org/data/nsidc-0051.html http://nsidc.org/da...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'goddard_merged_seaice_conc_monthly'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 493</li><li><span class='xr-has-index'>ygrid</span>: 66</li><li><span class='xr-has-index'>xgrid</span>: 58</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b12298d2-0d07-472c-a2c6-00f6e7269650' class='xr-array-in' type='checkbox' checked><label for='section-b12298d2-0d07-472c-a2c6-00f6e7269650' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... 2.54 2.54 2.54 2.54 2.54 2.54 2.54</span></div><div class='xr-array-data'><pre>array([[[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-200a6a06-dc61-4e66-9b42-85e2f5414cd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-200a6a06-dc61-4e66-9b42-85e2f5414cd7' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1978-11-01 ... 2019-12-01</div><input id='attrs-de15edb2-b370-4e4c-9968-812a73d497fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de15edb2-b370-4e4c-9968-812a73d497fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-895a68b9-3cfe-4642-9e5f-a012226e5a76' class='xr-var-data-in' type='checkbox'><label for='data-895a68b9-3cfe-4642-9e5f-a012226e5a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>ANSI date</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1978-11-01T00:00:00.000000000&#x27;, &#x27;1978-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1979-01-01T00:00:00.000000000&#x27;, ..., &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ygrid</span></div><div class='xr-var-dims'>(ygrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.625e+05 ... -2.388e+06</div><input id='attrs-89a00539-fc91-47c1-8999-3e06e7db71bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89a00539-fc91-47c1-8999-3e06e7db71bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b182177d-3088-4fec-a860-c888e158ec1c' class='xr-var-data-in' type='checkbox'><label for='data-b182177d-3088-4fec-a860-c888e158ec1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_range :</span></dt><dd>[-5350000.  5850000.]</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>long_name :</span></dt><dd>projection_grid_y_centers</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ -762500.,  -787500.,  -812500.,  -837500.,  -862500.,  -887500.,\n",
       "        -912500.,  -937500.,  -962500.,  -987500., -1012500., -1037500.,\n",
       "       -1062500., -1087500., -1112500., -1137500., -1162500., -1187500.,\n",
       "       -1212500., -1237500., -1262500., -1287500., -1312500., -1337500.,\n",
       "       -1362500., -1387500., -1412500., -1437500., -1462500., -1487500.,\n",
       "       -1512500., -1537500., -1562500., -1587500., -1612500., -1637500.,\n",
       "       -1662500., -1687500., -1712500., -1737500., -1762500., -1787500.,\n",
       "       -1812500., -1837500., -1862500., -1887500., -1912500., -1937500.,\n",
       "       -1962500., -1987500., -2012500., -2037500., -2062500., -2087500.,\n",
       "       -2112500., -2137500., -2162500., -2187500., -2212500., -2237500.,\n",
       "       -2262500., -2287500., -2312500., -2337500., -2362500., -2387500.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xgrid</span></div><div class='xr-var-dims'>(xgrid)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.238e+06 -1.212e+06 ... 1.875e+05</div><input id='attrs-7a7a8bfa-e9ab-492f-9289-a81267381f96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a7a8bfa-e9ab-492f-9289-a81267381f96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b0b82e3-9ddc-44fc-968f-3c94160ee95a' class='xr-var-data-in' type='checkbox'><label for='data-7b0b82e3-9ddc-44fc-968f-3c94160ee95a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>valid_range :</span></dt><dd>[-3850000.  3750000.]</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>long_name :</span></dt><dd>projection_grid_x_centers</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-1237500., -1212500., -1187500., -1162500., -1137500., -1112500.,\n",
       "       -1087500., -1062500., -1037500., -1012500.,  -987500.,  -962500.,\n",
       "        -937500.,  -912500.,  -887500.,  -862500.,  -837500.,  -812500.,\n",
       "        -787500.,  -762500.,  -737500.,  -712500.,  -687500.,  -662500.,\n",
       "        -637500.,  -612500.,  -587500.,  -562500.,  -537500.,  -512500.,\n",
       "        -487500.,  -462500.,  -437500.,  -412500.,  -387500.,  -362500.,\n",
       "        -337500.,  -312500.,  -287500.,  -262500.,  -237500.,  -212500.,\n",
       "        -187500.,  -162500.,  -137500.,  -112500.,   -87500.,   -62500.,\n",
       "         -37500.,   -12500.,    12500.,    37500.,    62500.,    87500.,\n",
       "         112500.,   137500.,   162500.,   187500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(ygrid, xgrid)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>76.64 76.83 77.03 ... 68.17 68.15</div><input id='attrs-9d2a0238-3328-463f-b73f-a190fdcf66f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d2a0238-3328-463f-b73f-a190fdcf66f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b88fec7b-293a-4be6-8c88-9207ca006314' class='xr-var-data-in' type='checkbox'><label for='data-b88fec7b-293a-4be6-8c88-9207ca006314' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_range :</span></dt><dd>[ 0. 90.]</dd></dl></div><div class='xr-var-data'><pre>array([[76.64054774, 76.83395914, 77.0263152 , ..., 82.85666207,\n",
       "        82.81228952, 82.76086874],\n",
       "       [76.51966791, 76.71125843, 76.90175135, ..., 82.63030787,\n",
       "        82.58730391, 82.53744916],\n",
       "       [76.39605508, 76.5858172 , 76.7744408 , ..., 82.40379613,\n",
       "        82.36208116, 82.31370307],\n",
       "       ...,\n",
       "       [65.93102992, 66.03368899, 66.13473026, ..., 68.62637798,\n",
       "        68.6120885 , 68.59543059],\n",
       "       [65.7354472 , 65.83717942, 65.93730114, ..., 68.40369392,\n",
       "        68.38956507, 68.37309415],\n",
       "       [65.53955703, 65.64037518, 65.73959022, ..., 68.18116072,\n",
       "        68.16718925, 68.15090156]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(ygrid, xgrid)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-103.4 -102.8 ... -41.11 -40.51</div><input id='attrs-4925a85c-dbef-4fca-abf4-2c5a79b4ee66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4925a85c-dbef-4fca-abf4-2c5a79b4ee66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d9b47eb-01ea-4944-a875-0b7768761f23' class='xr-var-data-in' type='checkbox'><label for='data-9d9b47eb-01ea-4944-a875-0b7768761f23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd></dl></div><div class='xr-var-data'><pre>array([[-103.36021844, -102.83560949, -102.29526642, ...,  -34.77783137,\n",
       "         -32.9694039 ,  -31.18497466],\n",
       "       [-102.52880771, -101.99689931, -101.44933799, ...,  -35.09581679,\n",
       "         -33.34070735,  -31.60750225],\n",
       "       [-101.71247231, -101.17383824, -100.61965528, ...,  -35.39479584,\n",
       "         -33.69006753,  -32.00538321],\n",
       "       ...,\n",
       "       [ -72.89727103,  -72.41651812,  -71.9315437 , ...,  -41.63353934,\n",
       "         -41.02326902,  -40.4139011 ],\n",
       "       [ -72.64597536,  -72.16814237,  -71.68618372, ...,  -41.66908262,\n",
       "         -41.06521794,  -40.46222749],\n",
       "       [ -72.39881863,  -71.9238927 ,  -71.44493441, ...,  -41.70388403,\n",
       "         -41.10629244,  -40.50954808]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75437e0f-eb3b-4ee6-9dee-5a54da193eea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75437e0f-eb3b-4ee6-9dee-5a54da193eea' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>valid_range :</span></dt><dd>[  0 100]</dd><dt><span>long_name :</span></dt><dd>Goddard Edited Climate Data Record of Passive Microwave Monthly Northern Hemisphere Sea Ice Concentration, Goddard Edited</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[-5 -4 -3 -2 -1]</dd><dt><span>flag_meanings :</span></dt><dd>pole_hole lakes coastal land_mask missing_data</dd><dt><span>datum :</span></dt><dd>+ellps=urn:ogc:def:crs:EPSG::4326</dd><dt><span>grid_mapping :</span></dt><dd>projection</dd><dt><span>reference :</span></dt><dd>http://nsidc.org/data/nsidc-0051.html http://nsidc.org/data/nsidc-0079.html</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'goddard_merged_seaice_conc_monthly' (time: 493, ygrid: 66, xgrid: 58)>\n",
       "array([[[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]],\n",
       "\n",
       "       [[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54],\n",
       "        [ nan,  nan,  nan, ..., 2.54, 2.54, 2.54]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 1978-11-01 1978-12-01 ... 2019-12-01\n",
       "  * ygrid      (ygrid) float32 -7.625e+05 -7.875e+05 ... -2.362e+06 -2.388e+06\n",
       "  * xgrid      (xgrid) float32 -1.238e+06 -1.212e+06 ... 1.625e+05 1.875e+05\n",
       "    latitude   (ygrid, xgrid) float64 76.64 76.83 77.03 ... 68.18 68.17 68.15\n",
       "    longitude  (ygrid, xgrid) float64 -103.4 -102.8 -102.3 ... -41.11 -40.51\n",
       "Attributes:\n",
       "    valid_range:    [  0 100]\n",
       "    long_name:      Goddard Edited Climate Data Record of Passive Microwave M...\n",
       "    standard_name:  sea_ice_area_fraction\n",
       "    units:          1\n",
       "    flag_values:    [-5 -4 -3 -2 -1]\n",
       "    flag_meanings:  pole_hole lakes coastal land_mask missing_data\n",
       "    datum:          +ellps=urn:ogc:def:crs:EPSG::4326\n",
       "    grid_mapping:   projection\n",
       "    reference:      http://nsidc.org/data/nsidc-0051.html http://nsidc.org/da..."
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Comparison of ocean data with Wood et al (2021)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "wood_data = pd.read_csv('./wood2021_subset.csv')\n",
    "wood_data = wood_data.iloc[0:6]\n",
    "wood_data.set_index('Unnamed: 0', inplace=True)\n",
    "wood_data.iloc[2:] = wood_data.iloc[2:].astype('float')\n",
    "\n",
    "cw1 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'CW1']\n",
    "cw2 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'CW2']\n",
    "nw1 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'NW1']\n",
    "nw2 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'NW2']\n",
    "nw3 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'NW3']\n",
    "nw4 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'NW4']\n",
    "n1 = wood_data.loc[:, wood_data.loc['Ocean Model Sample Area'] == 'N1']\n",
    "wood_pieces = [cw2, cw1, nw4, nw3, nw2, nw1, n1, n1]\n",
    "\n",
    "cw1_dw = cw1.loc[:, cw1.loc['Categorization'] == 'DW']\n",
    "cw2_dw = cw2.loc[:, cw2.loc['Categorization'] == 'DW']\n",
    "nw1_dw = nw1.loc[:, nw1.loc['Categorization'] == 'DW']\n",
    "nw2_dw = nw2.loc[:, nw2.loc['Categorization'] == 'DW']\n",
    "nw3_dw = nw3.loc[:, nw3.loc['Categorization'] == 'DW']\n",
    "nw4_dw = nw4.loc[:, nw4.loc['Categorization'] == 'DW']\n",
    "n1_dw = n1.loc[:, n1.loc['Categorization'] == 'DW']\n",
    "wood_pieces_dw = [cw2_dw, cw1_dw, nw4_dw, nw3_dw, nw2_dw, nw1_dw, n1_dw, n1_dw]\n",
    "\n",
    "def plot_decadal_temperature_wood(ax, region):\n",
    "    total_mean = region.loc['1992-2017'].mean()\n",
    "    ax.hlines(region.loc['1992-1997'].mean() - total_mean, xmin=1992, xmax=1998, linewidth=5, alpha=0.7, color='dodgerblue', label='Wood regional average')\n",
    "    ax.hlines(region.loc['1998-2007'].mean() - total_mean, xmin=1998, xmax=2008, linewidth=5, alpha=0.7, color='dodgerblue')\n",
    "    ax.hlines(region.loc['2008-2017'].mean() - total_mean, xmin=2008, xmax=2018, linewidth=5, alpha=0.7, color='dodgerblue')\n",
    "\n",
    "def plot_individual_glaciers_wood(ax, region):\n",
    "    glacier_means = region.loc['1992-2017'].values\n",
    "    ax.hlines(region.loc['1992-1997'].values - glacier_means, xmin=1992, xmax=1998, linewidth=1, alpha=0.2, color='dodgerblue', label='Wood individual glaciers')\n",
    "    ax.hlines(region.loc['1998-2007'].values - glacier_means, xmin=1998, xmax=2008, linewidth=1, alpha=0.2, color='dodgerblue')\n",
    "    ax.hlines(region.loc['2008-2017'].values - glacier_means, xmin=2008, xmax=2018, linewidth=1, alpha=0.2, color='dodgerblue')\n",
    "\n",
    "def plot_decadal_temperature_ecco(ax, coordinate):\n",
    "    ecco_deepwater = clm.selectPointData(data=ecco_sst, dtype='ecco5', coordinate=coordinate)\n",
    "    ecco_depth = ecco_deepwater.sel(\n",
    "        DEPTH_T=slice(ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values*0.4, ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values)\n",
    "        ).temperature.mean(dim='DEPTH_T')\n",
    "    total_mean = ecco_depth.sel(time=slice('1992-01-01', '2017-12-31')).mean().values\n",
    "    ax.hlines(ecco_depth.sel(time=slice('1992-01-01', '1997-12-31')).mean().values - total_mean, xmin=1992, xmax=1998, linewidth=5, alpha=0.7, color='darkorange', label='ECCO deepest 60%')\n",
    "    ax.hlines(ecco_depth.sel(time=slice('1998-01-01', '2007-12-31')).mean().values - total_mean, xmin=1998, xmax=2008, linewidth=5, alpha=0.7, color='darkorange')\n",
    "    ax.hlines(ecco_depth.sel(time=slice('2008-01-01', '2017-12-31')).mean().values - total_mean, xmin=2008, xmax=2018, linewidth=5, alpha=0.7, color='darkorange')\n",
    "\n",
    "\n",
    "# fig, axs = plt.subplots(nrows=4, ncols=2, sharex=True, sharey=True, figsize=(6, 10))\n",
    "# axs = axs.flatten()\n",
    "# wood_pieces = [cw2, cw1, nw4, nw3, nw2, nw1, n1, n1]\n",
    "# for coordinate in ocean_points:\n",
    "#     nax = axs[ocean_points.index(coordinate)]\n",
    "#     plot_decadal_temperature_wood(nax, wood_pieces[ocean_points.index(coordinate)])\n",
    "#     plot_individual_glaciers_wood(nax, wood_pieces[ocean_points.index(coordinate)])\n",
    "#     plot_decadal_temperature_ecco(nax, coordinate)\n",
    "#     nax.set_ylabel('Temperature anomaly ($^oC$)')\n",
    "#     nax.set_xlabel('Year')\n",
    "#     nax.set_title('Ocean temperatures at {}'.format(ocean_point_names[ocean_points.index(coordinate)]))\n",
    "#     nax.legend(loc='lower right', bbox_to_anchor=(0.99, 0.01))\n",
    "\n",
    "# plt.savefig('{}ocean_temperature_comparison.png'.format(outpath), bbox_inches='tight', dpi=300)\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "source": [
    "# derive a correction for each region to match Wood's numbers\n",
    "\n",
    "def piecewise_linear(x, y0, k1, k2):\n",
    "    '''Piecewise linear function that breaks at x0=2003'''\n",
    "    x0 = 2003.\n",
    "    return np.piecewise(x, [x < x0], [lambda x: k1*x + y0 - k1*x0, lambda x: k2*x + y0 - k2*x0])\n",
    "\n",
    "\n",
    "def regional_correction(coordinate, wood_data, ecco_data):\n",
    "    # -- Get data for curve fit\n",
    "    years = np.array([1995., 2003., 2013.])\n",
    "    wood_total_mean = wood_data.loc['1992-2017'].mean()\n",
    "    wood = np.array([\n",
    "        wood_data.loc['1992-1997'].mean() - wood_total_mean, \n",
    "        wood_data.loc['1998-2007'].mean() - wood_total_mean, \n",
    "        wood_data.loc['2008-2017'].mean() - wood_total_mean])\n",
    "    mine_total_mean = ecco_data.sel(time=slice('1992-01-01', '2017-12-31')).mean().values\n",
    "    mine = np.array([\n",
    "        float(ecco_data.sel(time=slice('1992-01-01', '1997-12-31')).mean().values - mine_total_mean), \n",
    "        float(ecco_data.sel(time=slice('1998-01-01', '2007-12-31')).mean().values - mine_total_mean), \n",
    "        float(ecco_data.sel(time=slice('2008-01-01', '2017-12-31')).mean().values - mine_total_mean)])\n",
    "    diff = mine - wood\n",
    "    # -- fit curve to data (difference between my data and wood's)\n",
    "    p, e = optimize.curve_fit(piecewise_linear, years, diff)\n",
    "\n",
    "    return p\n",
    "\n",
    "\n",
    "def plot_decadal_temperature_ecco_bias_corrected(ax, coordinate, ecco_data, wood_data):\n",
    "# -- get ECCO data at point for bottom 60% of water column\n",
    "    ecco_deepwater = clm.selectPointData(data=ecco_data, dtype='ecco5', coordinate=coordinate)\n",
    "    ecco_depth = ecco_deepwater.sel(\n",
    "        DEPTH_T=slice(\n",
    "            ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values*0.4, \n",
    "            ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values)\n",
    "        ).temperature.mean(dim='DEPTH_T')\n",
    "    # -- apply regional correction (piecewise linear fit)\n",
    "    p = regional_correction(coordinate, wood_data, ecco_depth)  \n",
    "    # -- use regional correction to calculate corrected ECCO temperature anomaly\n",
    "    years_all = pd.to_datetime(ecco_depth.time).year.astype('float')\n",
    "    correction = piecewise_linear(years_all, *p)\n",
    "    ecco_depth_bias_corrected = ecco_depth - correction\n",
    "    total_mean = ecco_depth_bias_corrected.sel(time=slice('1992-01-01', '2017-12-31')).mean().values\n",
    "    ecco_depth_bias_corrected_anomaly = ecco_depth_bias_corrected - total_mean\n",
    "    # -- plot ECCO decadal corrected temperature anomalies\n",
    "    ax.plot(pd.to_datetime(ecco_depth.time).year, ecco_depth - ecco_depth.mean(), '--r', label='original anomaly')\n",
    "    ax.plot(pd.to_datetime(ecco_depth_bias_corrected_anomaly.time).year, ecco_depth_bias_corrected_anomaly, '--k', label='corrected anomaly')\n",
    "    ax.hlines(ecco_depth_bias_corrected_anomaly.sel(\n",
    "        time=slice('1992-01-01', '1997-12-31')).mean().values, \n",
    "        xmin=1992., xmax=1998., linewidth=5, alpha=0.7, color='darkorange', label='ECCO deepest 60%')\n",
    "    ax.hlines(ecco_depth_bias_corrected_anomaly.sel(\n",
    "        time=slice('1998-01-01', '2007-12-31')).mean().values, \n",
    "        xmin=1998., xmax=2008., linewidth=5, alpha=0.7, color='darkorange')\n",
    "    ax.hlines(ecco_depth_bias_corrected_anomaly.sel(\n",
    "        time=slice('2008-01-01', '2017-12-31')).mean().values, \n",
    "        xmin=2008., xmax=2018., linewidth=5, alpha=0.7, color='darkorange')\n",
    "\n",
    "\n",
    "# idx = 0\n",
    "# coordinate = ocean_points[idx]\n",
    "# wood_piece = wood_pieces_dw[idx]\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "# plot_decadal_temperature_wood(ax, wood_piece)\n",
    "# plot_decadal_temperature_ecco_bias_corrected(ax, coordinate, ecco_sst, wood_piece)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=4, ncols=2, sharex=True, sharey=True, figsize=(7, 10))\n",
    "axs = axs.flatten()\n",
    "# wood_pieces = [cw2, cw1, nw4, nw3, nw2, nw1, n1, n1]\n",
    "wood_pieces_dw = [cw2_dw, cw1_dw, nw4_dw, nw3_dw, nw2_dw, nw1_dw, n1_dw, n1_dw]\n",
    "for coordinate in ocean_points:\n",
    "    nax = axs[ocean_points.index(coordinate)]\n",
    "    wood_piece = wood_pieces_dw[ocean_points.index(coordinate)]\n",
    "    plot_decadal_temperature_wood(nax, wood_piece)\n",
    "    plot_individual_glaciers_wood(nax, wood_piece)\n",
    "    plot_decadal_temperature_ecco_bias_corrected(nax, coordinate, ecco_sst, wood_piece)\n",
    "    # nax.set_ylabel('Temperature anomaly ($^oC$)')\n",
    "    # nax.set_xlabel('Year')\n",
    "    nax.set_title('{}'.format(ocean_point_names[ocean_points.index(coordinate)]))\n",
    "    if ocean_points.index(coordinate) == 7:\n",
    "        nax.legend(loc='lower left', bbox_to_anchor=(1.05, 0.01))\n",
    "\n",
    "cpl.subplotGridLabels(\n",
    "    fig, \n",
    "    title='Comparison of corrected ocean deepwater temperatures', \n",
    "    ylabel='Temperature anomaly ($^oC$)', \n",
    "    xlabel='Year')\n",
    "\n",
    "plt.savefig('{}suppfig5_ocean_temperature_corrected.png'.format(outpath), bbox_inches='tight', dpi=300)\n",
    "plt.savefig('{}suppfig5_ocean_temperature_corrected.pdf'.format(outpath), bbox_inches='tight', dpi=300)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Create data table for multiple linear regressions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "vars_list = ['ECCO_depth', 'ECCO_SST', 'Hadley_SST', 'Mean_SST', 'Mean_Temp', 'Hadley_SISL', 'NOAA_SISL', 'Mean_SISL', 'Hadley_ASIC', 'NOAA_ASIC', 'Mean_ASIC', 'SMB', 'Snowfall', 'Runoff']\n",
    "X = pd.DataFrame(columns=vars_list)\n",
    "Y = []\n",
    "\n",
    "wood_data = wood_pieces_dw\n",
    "\n",
    "for coordinate in ocean_points:\n",
    "    idx = ocean_points.index(coordinate)\n",
    "    # Get mean glacier length trend for ocean zone\n",
    "    zone_gids = glacier_points[glacier_points.Ocean == idx+1].GID\n",
    "    zone_glaciers = met.filterGlaciers(all_glaciers, zone_gids.values, idtype='keep')\n",
    "    zone_lengths = pd.DataFrame(index=zone_gids.values, columns=zone_glaciers[list(zone_glaciers.keys())[0]].interplengths.index, data=[zone_glaciers[x].interplengths.values.flatten() for x in zone_glaciers])\n",
    "    zone_annual_length_change = zone_lengths.mean().diff()\n",
    "    zone_annual_length_change_filter = zone_annual_length_change[zone_annual_length_change.index >= 1992]\n",
    "    zone_annual_length_change_filter = zone_annual_length_change_filter[zone_annual_length_change_filter.index <= 2017]\n",
    "    Y.extend(zone_annual_length_change_filter.values)\n",
    "\n",
    "    # Get MAR data for glacier zones\n",
    "    zone_glacier_coords = [glacier_coords[x] for x in zone_gids.index]\n",
    "    # -- SMB\n",
    "    zone_anom_SMB = clm.bulkAnomalyMAR(mar, zone_glacier_coords, 'SMB')\n",
    "    zone_mean_anom_SMB = zone_anom_SMB.mean()\n",
    "    zone_mean_anom_SMB = zone_mean_anom_SMB[zone_mean_anom_SMB.index >= pd.to_datetime('1992-01-01')]\n",
    "    zone_mean_anom_SMB = zone_mean_anom_SMB[zone_mean_anom_SMB.index <= pd.to_datetime('2017-01-01')]\n",
    "    # -- Snowfall\n",
    "    zone_anom_SF = clm.bulkAnomalyMAR(mar, zone_glacier_coords, 'SF')\n",
    "    zone_mean_anom_SF = zone_anom_SF.mean()\n",
    "    zone_mean_anom_SF = zone_mean_anom_SF[zone_mean_anom_SF.index >= pd.to_datetime('1992-01-01')]\n",
    "    zone_mean_anom_SF = zone_mean_anom_SF[zone_mean_anom_SF.index <= pd.to_datetime('2017-01-01')]\n",
    "    # -- Runoff\n",
    "    zone_anom_RU = clm.bulkAnomalyMAR(mar, zone_glacier_coords, 'RU')\n",
    "    zone_mean_anom_RU = zone_anom_RU.mean()\n",
    "    zone_mean_anom_RU = zone_mean_anom_RU[zone_mean_anom_RU.index >= pd.to_datetime('1992-01-01')]\n",
    "    zone_mean_anom_RU = zone_mean_anom_RU[zone_mean_anom_RU.index <= pd.to_datetime('2017-01-01')]\n",
    "\n",
    "    # Get climate data for ocean zone\n",
    "    # -- ECCO deepwater (bottom 60%) mean temperature with Wood biases applied and regional corrections to match Wood\n",
    "    wood_piece = wood_data[idx]\n",
    "    ecco_deepwater = clm.selectPointData(data=ecco_sst, dtype='ecco5', coordinate=coordinate)\n",
    "    ecco_depth = ecco_deepwater.sel(\n",
    "        DEPTH_T=slice(\n",
    "            ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values*0.4, \n",
    "            ecco_deepwater.temperature.dropna(dim='DEPTH_T').DEPTH_T.max().values)\n",
    "        ).temperature.mean(dim='DEPTH_T')\n",
    "    # -- apply regional correction (piecewise linear fit)\n",
    "    p = regional_correction(coordinate, wood_piece, ecco_depth)\n",
    "    years = pd.to_datetime(ecco_depth.time).year.astype('float')\n",
    "    correction = piecewise_linear(years, *p)\n",
    "    ecco_depth_bias_corrected = ecco_depth - correction\n",
    "    total_mean = ecco_depth_bias_corrected.sel(time=slice('1992-01-01', '2017-12-31')).mean().values\n",
    "    ecco_depth_bias_corrected_anomaly = ecco_depth_bias_corrected - total_mean\n",
    "    \n",
    "    # -- ECCO SST\n",
    "    ecco_surface = clm.selectPointData(ecco_sst, dtype='ecco5', depth=0, coordinate=coordinate).temperature\n",
    "    # -- Hadley SST\n",
    "    hadley_surface = clm.selectPointData(hadley_sst, dtype='hadley', coordinate=coordinate).SST\n",
    "    hadley_surface = hadley_surface[hadley_surface.time >= pd.to_datetime('1992-01-01')]\n",
    "    hadley_surface = hadley_surface[hadley_surface.time <= pd.to_datetime('2017-01-01')]\n",
    "    # -- Mean SST\n",
    "    mean_surface = (ecco_surface + hadley_surface)/2\n",
    "    # -- Mean ocean temperature\n",
    "    mean_temp = (ecco_depth + mean_surface)/2\n",
    "    # -- Hadley sea ice season length\n",
    "    hadley_sisl = clm.seaIceSeasonLength(hadley_sic, 'SEAICE', dtype='hadley', minimum=sic_minimum, coordinate=coordinate)\n",
    "    hadley_sisl = hadley_sisl[hadley_sisl.time >= pd.to_datetime('1991-01-01')]\n",
    "    hadley_sisl = hadley_sisl[hadley_sisl.time <= pd.to_datetime('2017-01-01')]\n",
    "    # -- NOAA sea ice season length\n",
    "    noaa_sisl = clm.seaIceSeasonLength(noaa_sic, noaa_var, dtype='noaa', minimum=sic_minimum, coordinate=coordinate)\n",
    "    noaa_sisl = noaa_sisl[noaa_sisl.time >= pd.to_datetime('1991-01-01')]\n",
    "    noaa_sisl = noaa_sisl[noaa_sisl.time <= pd.to_datetime('2017-01-01')]\n",
    "    # -- Mean sea ice season length\n",
    "    mean_sisl = (hadley_sisl + noaa_sisl)/2\n",
    "    # -- Hadley mean annual sea ice concentration\n",
    "    hadley_asic = clm.selectPointData(hadley_sic, dtype='hadley', coordinate=coordinate)\n",
    "    hadley_asic = hadley_asic.resample(time='AS').mean().SEAICE    \n",
    "    hadley_asic = hadley_asic[hadley_asic.time >= pd.to_datetime('1992-01-01')]\n",
    "    hadley_asic = hadley_asic[hadley_asic.time <= pd.to_datetime('2017-01-01')]\n",
    "    # -- NOAA mean annual sea ice concentration\n",
    "    noaa_asic = clm.selectPointData(noaa_sic, dtype='noaa', coordinate=coordinate)\n",
    "    noaa_asic = noaa_asic.resample(time='AS').mean()[noaa_var]    \n",
    "    noaa_asic = noaa_asic[noaa_asic.time >= pd.to_datetime('1992-01-01')]\n",
    "    noaa_asic = noaa_asic[noaa_asic.time <= pd.to_datetime('2017-01-01')]\n",
    "    noaa_asic = noaa_asic * 100\n",
    "    # -- Mean mean annual sea ice concentration\n",
    "    mean_asic = (hadley_asic + noaa_asic)/2\n",
    "\n",
    "    climate_data = pd.DataFrame(data=[ecco_depth_bias_corrected_anomaly.values, ecco_surface.values, hadley_surface.values, mean_surface.values, mean_temp.values, hadley_sisl.values, noaa_sisl.values, mean_sisl.values, hadley_asic.values, noaa_asic.values, mean_asic.values, zone_mean_anom_SMB.values, zone_mean_anom_SF.values, zone_mean_anom_RU.values]).transpose().rename(columns={0: 'ECCO_depth', 1: 'ECCO_SST', 2: 'Hadley_SST', 3: 'Mean_SST', 4: 'Mean_Temp', 5: 'Hadley_SISL', 6: 'NOAA_SISL', 7: 'Mean_SISL', 8: 'Hadley_ASIC', 9: 'NOAA_ASIC', 10: 'Mean_ASIC', 11: 'SMB', 12: 'Snowfall', 13: 'Runoff'})\n",
    "    \n",
    "    X = X.append(climate_data)\n",
    "\n",
    "X\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>ECCO_depth</th>\n",
       "      <th>ECCO_SST</th>\n",
       "      <th>Hadley_SST</th>\n",
       "      <th>Mean_SST</th>\n",
       "      <th>Mean_Temp</th>\n",
       "      <th>Hadley_SISL</th>\n",
       "      <th>NOAA_SISL</th>\n",
       "      <th>Mean_SISL</th>\n",
       "      <th>Hadley_ASIC</th>\n",
       "      <th>NOAA_ASIC</th>\n",
       "      <th>Mean_ASIC</th>\n",
       "      <th>SMB</th>\n",
       "      <th>Snowfall</th>\n",
       "      <th>Runoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.305149</td>\n",
       "      <td>-0.710124</td>\n",
       "      <td>0.230972</td>\n",
       "      <td>-0.239576</td>\n",
       "      <td>1.358917</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>39.666668</td>\n",
       "      <td>43.583332</td>\n",
       "      <td>41.625000</td>\n",
       "      <td>1.405736</td>\n",
       "      <td>-0.020657</td>\n",
       "      <td>-1.470766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.641381</td>\n",
       "      <td>0.422287</td>\n",
       "      <td>0.297607</td>\n",
       "      <td>0.359947</td>\n",
       "      <td>1.488934</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>39.166668</td>\n",
       "      <td>44.916664</td>\n",
       "      <td>42.041664</td>\n",
       "      <td>-0.150615</td>\n",
       "      <td>-0.059560</td>\n",
       "      <td>0.109222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.928120</td>\n",
       "      <td>0.279228</td>\n",
       "      <td>0.365452</td>\n",
       "      <td>0.322340</td>\n",
       "      <td>1.325132</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>42.999996</td>\n",
       "      <td>39.750000</td>\n",
       "      <td>0.110094</td>\n",
       "      <td>-0.023836</td>\n",
       "      <td>-0.123166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.072366</td>\n",
       "      <td>1.030626</td>\n",
       "      <td>0.666801</td>\n",
       "      <td>0.848713</td>\n",
       "      <td>1.514567</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.583334</td>\n",
       "      <td>38.250004</td>\n",
       "      <td>34.416668</td>\n",
       "      <td>-0.567572</td>\n",
       "      <td>-0.048316</td>\n",
       "      <td>0.497577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.153645</td>\n",
       "      <td>0.596595</td>\n",
       "      <td>0.884159</td>\n",
       "      <td>0.740377</td>\n",
       "      <td>1.418131</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.250000</td>\n",
       "      <td>47.083328</td>\n",
       "      <td>38.166664</td>\n",
       "      <td>0.534904</td>\n",
       "      <td>0.102300</td>\n",
       "      <td>-0.488453</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.288684</td>\n",
       "      <td>-0.728937</td>\n",
       "      <td>-0.302112</td>\n",
       "      <td>-0.515525</td>\n",
       "      <td>-0.470662</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>45.583332</td>\n",
       "      <td>52.333336</td>\n",
       "      <td>48.958336</td>\n",
       "      <td>0.287518</td>\n",
       "      <td>0.104359</td>\n",
       "      <td>-0.199719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.289229</td>\n",
       "      <td>-0.995672</td>\n",
       "      <td>-0.143426</td>\n",
       "      <td>-0.569549</td>\n",
       "      <td>-0.497799</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>42.250000</td>\n",
       "      <td>46.083336</td>\n",
       "      <td>44.166668</td>\n",
       "      <td>-0.249601</td>\n",
       "      <td>-0.068821</td>\n",
       "      <td>0.219337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.288273</td>\n",
       "      <td>-1.068604</td>\n",
       "      <td>-0.432342</td>\n",
       "      <td>-0.750473</td>\n",
       "      <td>-0.589136</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>46.916668</td>\n",
       "      <td>51.333332</td>\n",
       "      <td>49.125000</td>\n",
       "      <td>-0.669531</td>\n",
       "      <td>-0.096446</td>\n",
       "      <td>0.550667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.276878</td>\n",
       "      <td>-0.698580</td>\n",
       "      <td>-0.154162</td>\n",
       "      <td>-0.426371</td>\n",
       "      <td>-0.433180</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>42.083332</td>\n",
       "      <td>41.416664</td>\n",
       "      <td>-0.093802</td>\n",
       "      <td>0.071879</td>\n",
       "      <td>0.351038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.274675</td>\n",
       "      <td>-0.782388</td>\n",
       "      <td>-0.206906</td>\n",
       "      <td>-0.494647</td>\n",
       "      <td>-0.468816</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>44.416668</td>\n",
       "      <td>53.333328</td>\n",
       "      <td>48.875000</td>\n",
       "      <td>0.250889</td>\n",
       "      <td>-0.029920</td>\n",
       "      <td>-0.185769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ECCO_depth  ECCO_SST  Hadley_SST  Mean_SST  Mean_Temp  Hadley_SISL  \\\n",
       "0    -0.305149 -0.710124    0.230972 -0.239576   1.358917          6.0   \n",
       "1    -0.641381  0.422287    0.297607  0.359947   1.488934          5.0   \n",
       "2    -0.928120  0.279228    0.365452  0.322340   1.325132          6.0   \n",
       "3    -1.072366  1.030626    0.666801  0.848713   1.514567          5.0   \n",
       "4    -1.153645  0.596595    0.884159  0.740377   1.418131          5.0   \n",
       "..         ...       ...         ...       ...        ...          ...   \n",
       "21    0.288684 -0.728937   -0.302112 -0.515525  -0.470662         10.0   \n",
       "22    0.289229 -0.995672   -0.143426 -0.569549  -0.497799         11.0   \n",
       "23    0.288273 -1.068604   -0.432342 -0.750473  -0.589136         10.0   \n",
       "24    0.276878 -0.698580   -0.154162 -0.426371  -0.433180         11.0   \n",
       "25    0.274675 -0.782388   -0.206906 -0.494647  -0.468816         11.0   \n",
       "\n",
       "    NOAA_SISL  Mean_SISL  Hadley_ASIC  NOAA_ASIC  Mean_ASIC       SMB  \\\n",
       "0         7.0        6.5    39.666668  43.583332  41.625000  1.405736   \n",
       "1         7.0        6.0    39.166668  44.916664  42.041664 -0.150615   \n",
       "2         6.0        6.0    36.500000  42.999996  39.750000  0.110094   \n",
       "3         7.0        6.0    30.583334  38.250004  34.416668 -0.567572   \n",
       "4         9.0        7.0    29.250000  47.083328  38.166664  0.534904   \n",
       "..        ...        ...          ...        ...        ...       ...   \n",
       "21        8.0        9.0    45.583332  52.333336  48.958336  0.287518   \n",
       "22        8.0        9.5    42.250000  46.083336  44.166668 -0.249601   \n",
       "23        8.0        9.0    46.916668  51.333332  49.125000 -0.669531   \n",
       "24        7.0        9.0    40.750000  42.083332  41.416664 -0.093802   \n",
       "25        8.0        9.5    44.416668  53.333328  48.875000  0.250889   \n",
       "\n",
       "    Snowfall    Runoff  \n",
       "0  -0.020657 -1.470766  \n",
       "1  -0.059560  0.109222  \n",
       "2  -0.023836 -0.123166  \n",
       "3  -0.048316  0.497577  \n",
       "4   0.102300 -0.488453  \n",
       "..       ...       ...  \n",
       "21  0.104359 -0.199719  \n",
       "22 -0.068821  0.219337  \n",
       "23 -0.096446  0.550667  \n",
       "24  0.071879  0.351038  \n",
       "25 -0.029920 -0.185769  \n",
       "\n",
       "[208 rows x 14 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Covariance matrix"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "x = X.to_numpy()\n",
    "m_covariance = np.cov(x, rowvar=False)\n",
    "m_covariance = pd.DataFrame(data=m_covariance, columns=vars_list, index=vars_list)\n",
    "m_covariance"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ECCO_depth</th>\n",
       "      <th>ECCO_SST</th>\n",
       "      <th>Hadley_SST</th>\n",
       "      <th>Mean_SST</th>\n",
       "      <th>Mean_Temp</th>\n",
       "      <th>Hadley_SISL</th>\n",
       "      <th>NOAA_SISL</th>\n",
       "      <th>Mean_SISL</th>\n",
       "      <th>Hadley_ASIC</th>\n",
       "      <th>NOAA_ASIC</th>\n",
       "      <th>Mean_ASIC</th>\n",
       "      <th>SMB</th>\n",
       "      <th>Snowfall</th>\n",
       "      <th>Runoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ECCO_depth</th>\n",
       "      <td>0.258750</td>\n",
       "      <td>0.114764</td>\n",
       "      <td>0.146563</td>\n",
       "      <td>0.130664</td>\n",
       "      <td>0.069675</td>\n",
       "      <td>-0.204189</td>\n",
       "      <td>-0.439717</td>\n",
       "      <td>-0.321953</td>\n",
       "      <td>-2.635558</td>\n",
       "      <td>-3.641359</td>\n",
       "      <td>-3.138458</td>\n",
       "      <td>-0.114544</td>\n",
       "      <td>-0.005185</td>\n",
       "      <td>0.117815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECCO_SST</th>\n",
       "      <td>0.114764</td>\n",
       "      <td>0.668551</td>\n",
       "      <td>0.610379</td>\n",
       "      <td>0.639465</td>\n",
       "      <td>0.621298</td>\n",
       "      <td>-0.712885</td>\n",
       "      <td>-0.919928</td>\n",
       "      <td>-0.816407</td>\n",
       "      <td>-9.057739</td>\n",
       "      <td>-10.923773</td>\n",
       "      <td>-9.990756</td>\n",
       "      <td>-0.120589</td>\n",
       "      <td>-0.003096</td>\n",
       "      <td>0.123820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hadley_SST</th>\n",
       "      <td>0.146563</td>\n",
       "      <td>0.610379</td>\n",
       "      <td>0.811631</td>\n",
       "      <td>0.711005</td>\n",
       "      <td>0.715849</td>\n",
       "      <td>-0.860127</td>\n",
       "      <td>-0.965574</td>\n",
       "      <td>-0.912851</td>\n",
       "      <td>-10.897635</td>\n",
       "      <td>-12.068516</td>\n",
       "      <td>-11.483075</td>\n",
       "      <td>-0.097652</td>\n",
       "      <td>-0.003777</td>\n",
       "      <td>0.102647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_SST</th>\n",
       "      <td>0.130664</td>\n",
       "      <td>0.639465</td>\n",
       "      <td>0.711005</td>\n",
       "      <td>0.675235</td>\n",
       "      <td>0.668573</td>\n",
       "      <td>-0.786506</td>\n",
       "      <td>-0.942751</td>\n",
       "      <td>-0.864629</td>\n",
       "      <td>-9.977687</td>\n",
       "      <td>-11.496145</td>\n",
       "      <td>-10.736916</td>\n",
       "      <td>-0.109121</td>\n",
       "      <td>-0.003436</td>\n",
       "      <td>0.113234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_Temp</th>\n",
       "      <td>0.069675</td>\n",
       "      <td>0.621298</td>\n",
       "      <td>0.715849</td>\n",
       "      <td>0.668573</td>\n",
       "      <td>0.786114</td>\n",
       "      <td>-0.860745</td>\n",
       "      <td>-0.935177</td>\n",
       "      <td>-0.897961</td>\n",
       "      <td>-8.634496</td>\n",
       "      <td>-10.249098</td>\n",
       "      <td>-9.441797</td>\n",
       "      <td>-0.059800</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>0.064106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hadley_SISL</th>\n",
       "      <td>-0.204189</td>\n",
       "      <td>-0.712885</td>\n",
       "      <td>-0.860127</td>\n",
       "      <td>-0.786506</td>\n",
       "      <td>-0.860745</td>\n",
       "      <td>2.995703</td>\n",
       "      <td>1.633315</td>\n",
       "      <td>2.314509</td>\n",
       "      <td>14.798341</td>\n",
       "      <td>13.489304</td>\n",
       "      <td>14.143822</td>\n",
       "      <td>0.131583</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>-0.142705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOAA_SISL</th>\n",
       "      <td>-0.439717</td>\n",
       "      <td>-0.919928</td>\n",
       "      <td>-0.965574</td>\n",
       "      <td>-0.942751</td>\n",
       "      <td>-0.935177</td>\n",
       "      <td>1.633315</td>\n",
       "      <td>3.066146</td>\n",
       "      <td>2.349731</td>\n",
       "      <td>15.501959</td>\n",
       "      <td>21.181607</td>\n",
       "      <td>18.341783</td>\n",
       "      <td>0.286361</td>\n",
       "      <td>0.019005</td>\n",
       "      <td>-0.285406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_SISL</th>\n",
       "      <td>-0.321953</td>\n",
       "      <td>-0.816407</td>\n",
       "      <td>-0.912851</td>\n",
       "      <td>-0.864629</td>\n",
       "      <td>-0.897961</td>\n",
       "      <td>2.314509</td>\n",
       "      <td>2.349731</td>\n",
       "      <td>2.332120</td>\n",
       "      <td>15.150150</td>\n",
       "      <td>17.335455</td>\n",
       "      <td>16.242803</td>\n",
       "      <td>0.208972</td>\n",
       "      <td>0.009012</td>\n",
       "      <td>-0.214055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hadley_ASIC</th>\n",
       "      <td>-2.635558</td>\n",
       "      <td>-9.057739</td>\n",
       "      <td>-10.897635</td>\n",
       "      <td>-9.977687</td>\n",
       "      <td>-8.634496</td>\n",
       "      <td>14.798341</td>\n",
       "      <td>15.501959</td>\n",
       "      <td>15.150150</td>\n",
       "      <td>199.615064</td>\n",
       "      <td>203.941013</td>\n",
       "      <td>201.778038</td>\n",
       "      <td>2.098591</td>\n",
       "      <td>0.122450</td>\n",
       "      <td>-2.180270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOAA_ASIC</th>\n",
       "      <td>-3.641359</td>\n",
       "      <td>-10.923773</td>\n",
       "      <td>-12.068516</td>\n",
       "      <td>-11.496145</td>\n",
       "      <td>-10.249098</td>\n",
       "      <td>13.489304</td>\n",
       "      <td>21.181607</td>\n",
       "      <td>17.335455</td>\n",
       "      <td>203.941013</td>\n",
       "      <td>245.590935</td>\n",
       "      <td>224.765974</td>\n",
       "      <td>3.077876</td>\n",
       "      <td>0.230596</td>\n",
       "      <td>-3.095412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean_ASIC</th>\n",
       "      <td>-3.138458</td>\n",
       "      <td>-9.990756</td>\n",
       "      <td>-11.483075</td>\n",
       "      <td>-10.736916</td>\n",
       "      <td>-9.441797</td>\n",
       "      <td>14.143822</td>\n",
       "      <td>18.341783</td>\n",
       "      <td>16.242803</td>\n",
       "      <td>201.778038</td>\n",
       "      <td>224.765974</td>\n",
       "      <td>213.272005</td>\n",
       "      <td>2.588234</td>\n",
       "      <td>0.176523</td>\n",
       "      <td>-2.637841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMB</th>\n",
       "      <td>-0.114544</td>\n",
       "      <td>-0.120589</td>\n",
       "      <td>-0.097652</td>\n",
       "      <td>-0.109121</td>\n",
       "      <td>-0.059800</td>\n",
       "      <td>0.131583</td>\n",
       "      <td>0.286361</td>\n",
       "      <td>0.208972</td>\n",
       "      <td>2.098591</td>\n",
       "      <td>3.077876</td>\n",
       "      <td>2.588234</td>\n",
       "      <td>0.235129</td>\n",
       "      <td>0.015073</td>\n",
       "      <td>-0.228409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snowfall</th>\n",
       "      <td>-0.005185</td>\n",
       "      <td>-0.003096</td>\n",
       "      <td>-0.003777</td>\n",
       "      <td>-0.003436</td>\n",
       "      <td>0.000517</td>\n",
       "      <td>-0.000981</td>\n",
       "      <td>0.019005</td>\n",
       "      <td>0.009012</td>\n",
       "      <td>0.122450</td>\n",
       "      <td>0.230596</td>\n",
       "      <td>0.176523</td>\n",
       "      <td>0.015073</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>-0.009185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runoff</th>\n",
       "      <td>0.117815</td>\n",
       "      <td>0.123820</td>\n",
       "      <td>0.102647</td>\n",
       "      <td>0.113234</td>\n",
       "      <td>0.064106</td>\n",
       "      <td>-0.142705</td>\n",
       "      <td>-0.285406</td>\n",
       "      <td>-0.214055</td>\n",
       "      <td>-2.180270</td>\n",
       "      <td>-3.095412</td>\n",
       "      <td>-2.637841</td>\n",
       "      <td>-0.228409</td>\n",
       "      <td>-0.009185</td>\n",
       "      <td>0.233455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ECCO_depth   ECCO_SST  Hadley_SST   Mean_SST  Mean_Temp  \\\n",
       "ECCO_depth     0.258750   0.114764    0.146563   0.130664   0.069675   \n",
       "ECCO_SST       0.114764   0.668551    0.610379   0.639465   0.621298   \n",
       "Hadley_SST     0.146563   0.610379    0.811631   0.711005   0.715849   \n",
       "Mean_SST       0.130664   0.639465    0.711005   0.675235   0.668573   \n",
       "Mean_Temp      0.069675   0.621298    0.715849   0.668573   0.786114   \n",
       "Hadley_SISL   -0.204189  -0.712885   -0.860127  -0.786506  -0.860745   \n",
       "NOAA_SISL     -0.439717  -0.919928   -0.965574  -0.942751  -0.935177   \n",
       "Mean_SISL     -0.321953  -0.816407   -0.912851  -0.864629  -0.897961   \n",
       "Hadley_ASIC   -2.635558  -9.057739  -10.897635  -9.977687  -8.634496   \n",
       "NOAA_ASIC     -3.641359 -10.923773  -12.068516 -11.496145 -10.249098   \n",
       "Mean_ASIC     -3.138458  -9.990756  -11.483075 -10.736916  -9.441797   \n",
       "SMB           -0.114544  -0.120589   -0.097652  -0.109121  -0.059800   \n",
       "Snowfall      -0.005185  -0.003096   -0.003777  -0.003436   0.000517   \n",
       "Runoff         0.117815   0.123820    0.102647   0.113234   0.064106   \n",
       "\n",
       "             Hadley_SISL  NOAA_SISL  Mean_SISL  Hadley_ASIC   NOAA_ASIC  \\\n",
       "ECCO_depth     -0.204189  -0.439717  -0.321953    -2.635558   -3.641359   \n",
       "ECCO_SST       -0.712885  -0.919928  -0.816407    -9.057739  -10.923773   \n",
       "Hadley_SST     -0.860127  -0.965574  -0.912851   -10.897635  -12.068516   \n",
       "Mean_SST       -0.786506  -0.942751  -0.864629    -9.977687  -11.496145   \n",
       "Mean_Temp      -0.860745  -0.935177  -0.897961    -8.634496  -10.249098   \n",
       "Hadley_SISL     2.995703   1.633315   2.314509    14.798341   13.489304   \n",
       "NOAA_SISL       1.633315   3.066146   2.349731    15.501959   21.181607   \n",
       "Mean_SISL       2.314509   2.349731   2.332120    15.150150   17.335455   \n",
       "Hadley_ASIC    14.798341  15.501959  15.150150   199.615064  203.941013   \n",
       "NOAA_ASIC      13.489304  21.181607  17.335455   203.941013  245.590935   \n",
       "Mean_ASIC      14.143822  18.341783  16.242803   201.778038  224.765974   \n",
       "SMB             0.131583   0.286361   0.208972     2.098591    3.077876   \n",
       "Snowfall       -0.000981   0.019005   0.009012     0.122450    0.230596   \n",
       "Runoff         -0.142705  -0.285406  -0.214055    -2.180270   -3.095412   \n",
       "\n",
       "              Mean_ASIC       SMB  Snowfall    Runoff  \n",
       "ECCO_depth    -3.138458 -0.114544 -0.005185  0.117815  \n",
       "ECCO_SST      -9.990756 -0.120589 -0.003096  0.123820  \n",
       "Hadley_SST   -11.483075 -0.097652 -0.003777  0.102647  \n",
       "Mean_SST     -10.736916 -0.109121 -0.003436  0.113234  \n",
       "Mean_Temp     -9.441797 -0.059800  0.000517  0.064106  \n",
       "Hadley_SISL   14.143822  0.131583 -0.000981 -0.142705  \n",
       "NOAA_SISL     18.341783  0.286361  0.019005 -0.285406  \n",
       "Mean_SISL     16.242803  0.208972  0.009012 -0.214055  \n",
       "Hadley_ASIC  201.778038  2.098591  0.122450 -2.180270  \n",
       "NOAA_ASIC    224.765974  3.077876  0.230596 -3.095412  \n",
       "Mean_ASIC    213.272005  2.588234  0.176523 -2.637841  \n",
       "SMB            2.588234  0.235129  0.015073 -0.228409  \n",
       "Snowfall       0.176523  0.015073  0.005970 -0.009185  \n",
       "Runoff        -2.637841 -0.228409 -0.009185  0.233455  "
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Ocean Temperatures"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "fig, axs = plt.subplots(\n",
    "    nrows=4, ncols=2,\n",
    "    sharex=True, sharey=True,\n",
    "    figsize=(figwidth*2, figwidth*2.5))\n",
    "axs = axs.flatten()\n",
    "labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']\n",
    "\n",
    "for coordinate in ocean_points:\n",
    "    nax = axs[ocean_points.index(coordinate)]\n",
    "    ecco_profile = clm.selectPointData(data=ecco_sst, dtype='ecco5', coordinate=coordinate)\n",
    "    ecco_depth_profile_first = ecco_profile.sel(time='1992-01-01')\n",
    "    ecco_depth_profile_last = ecco_profile.sel(time='2017-01-01')\n",
    "    nax.plot(ecco_depth_profile_first.temperature, -ecco_depth_profile_first.DEPTH_T, color='silver', label='1992')\n",
    "    nax.plot(ecco_depth_profile_last.temperature, -ecco_depth_profile_last.DEPTH_T, color='dodgerblue', label='2017')\n",
    "    nax.set_title('Point {}: {}'.format(\n",
    "        ocean_points.index(coordinate)+1,\n",
    "        ocean_point_names[ocean_points.index(coordinate)]))\n",
    "    nax.set_xticks([-2, -1, 0, 1, 2, 3])\n",
    "    label = labels[ocean_points.index(coordinate)]\n",
    "    nax.annotate(text=label, xy=(-0.15, 1.05), xycoords='axes fraction')\n",
    "\n",
    "handles = nax.get_legend_handles_labels()\n",
    "labels = handles[1]\n",
    "fig.legend(handles=handles[0], labels=labels, ncol=2, loc='upper center', bbox_to_anchor=(0.5, 0.04))\n",
    "cpl.subplotGridLabels(\n",
    "    fig,\n",
    "    title='Ocean temperature profile',\n",
    "    ylabel='Depth (m)',\n",
    "    xlabel='Temperature ($^oC$)')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('{}ocean_temperature_profiles.png'.format(outpath), bbox_inches='tight', dpi=300)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 453.6x567 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# SST by month"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "fig, axs = plt.subplots(\n",
    "    nrows=4, ncols=2,\n",
    "    sharex=True, sharey=True,\n",
    "    figsize=(figwidth*2, figwidth*2.5))\n",
    "axs = axs.flatten()\n",
    "labels = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']\n",
    "\n",
    "for coordinate in ocean_points:\n",
    "    nax = axs[ocean_points.index(coordinate)]\n",
    "    ecco_sst_point = clm.selectPointData(data=ecco_sst_monthly, dtype='ecco5', depth=0, coordinate=coordinate)\n",
    "    hadley_sst_point = clm.selectPointData(data=hadley_sst_monthly, dtype='hadley', coordinate=coordinate)\n",
    "    nax.scatter(pd.to_datetime(ecco_sst_point.time.values).month, ecco_sst_point.temperature.values, color='dodgerblue', alpha=0.3)\n",
    "    nax.scatter(pd.to_datetime(hadley_sst_point.time.values).month, hadley_sst_point.SST.values, color='darkorange', alpha=0.3)\n",
    "    nax.set_title('Point {}: {}'.format(\n",
    "        ocean_points.index(coordinate)+1,\n",
    "        ocean_point_names[ocean_points.index(coordinate)]))\n",
    "    nax.set_xticks([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
    "    nax.set_xticklabels(['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D'])\n",
    "    label = labels[ocean_points.index(coordinate)]\n",
    "    nax.annotate(text=label, xy=(-0.15, 1.05), xycoords='axes fraction')\n",
    "\n",
    "cpl.subplotGridLabels(\n",
    "    fig,\n",
    "    title='SST monthly distribution',\n",
    "    ylabel='SST ($^oC$)',\n",
    "    xlabel='Month')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('{}monthly_sst.png'.format(outpath), bbox_inches='tight', dpi=300)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 453.6x567 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Linear Regressions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "def linreg(x, y):\n",
    "    results = sm.OLS(y, sm.add_constant(x)).fit()\n",
    "    print(results.summary())\n",
    "    return results\n",
    "\n",
    "def plotLinReg(ax, x, y, dataname):\n",
    "    # compute linear regression\n",
    "    results = linreg(x, y)\n",
    "    # plot raw data\n",
    "    ax.scatter(x, y, label=dataname+' data')\n",
    "    # plot regression line\n",
    "    x_pred = np.linspace([ax.get_xbound()[0]], [ax.get_xbound()[1]], 10)\n",
    "    y_pred = results.params[0] + results.params[1]*x_pred\n",
    "    ax.plot(x_pred, y_pred, '--', label=dataname+f' linear regression\\n($slope={results.params[1]:.3f}, R^2={results.rsquared:.3f}, p={results.pvalues[1]:.2E}, se={results.bse[1]:.3f}$)')\n",
    "    ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Single linear regressions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Between certain variables"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# SST vs 250m temperature\n",
    "x = X['ECCO_depth']\n",
    "y = X['ECCO_SST']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotLinReg(ax, x, y, dataname='ECCO')\n",
    "ax.set_xlabel('ECCO deepwater temperature ($^oC$)')\n",
    "ax.set_ylabel('ECCO SST ($^oC$)')\n",
    "ax.set_title('SST vs deepwater temperature')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               ECCO_SST   R-squared:                       0.076\n",
      "Model:                            OLS   Adj. R-squared:                  0.072\n",
      "Method:                 Least Squares   F-statistic:                     16.98\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           5.48e-05\n",
      "Time:                        16:31:25   Log-Likelihood:                -244.53\n",
      "No. Observations:                 208   AIC:                             493.1\n",
      "Df Residuals:                     206   BIC:                             499.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.4398      0.055     -8.051      0.000      -0.548      -0.332\n",
      "ECCO_depth     0.4435      0.108      4.120      0.000       0.231       0.656\n",
      "==============================================================================\n",
      "Omnibus:                       52.508   Durbin-Watson:                   0.307\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               85.367\n",
      "Skew:                           1.396   Prob(JB):                     2.90e-19\n",
      "Kurtosis:                       4.434   Cond. No.                         1.97\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# SST vs sea ice concentration\n",
    "y = X['ECCO_SST']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = X['Hadley_ASIC']\n",
    "plotLinReg(ax, x, y, dataname='Hadley')\n",
    "\n",
    "x = X['NOAA_ASIC']\n",
    "plotLinReg(ax, x, y, dataname='NOAA')\n",
    "\n",
    "x = X['Mean_ASIC']\n",
    "plotLinReg(ax, x, y, dataname='Mean')\n",
    "\n",
    "ax.set_xlabel('Mean annual sea ice concentration (%)')\n",
    "ax.set_ylabel('ECCO SST ($^oC$)')\n",
    "ax.set_title('SST vs mean annual SIC')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               ECCO_SST   R-squared:                       0.615\n",
      "Model:                            OLS   Adj. R-squared:                  0.613\n",
      "Method:                 Least Squares   F-statistic:                     328.7\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           1.51e-44\n",
      "Time:                        16:31:26   Log-Likelihood:                -153.56\n",
      "No. Observations:                 208   AIC:                             311.1\n",
      "Df Residuals:                     206   BIC:                             317.8\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const           1.7492      0.126     13.907      0.000       1.501       1.997\n",
      "Hadley_ASIC    -0.0454      0.003    -18.131      0.000      -0.050      -0.040\n",
      "==============================================================================\n",
      "Omnibus:                        7.393   Durbin-Watson:                   0.652\n",
      "Prob(Omnibus):                  0.025   Jarque-Bera (JB):                7.241\n",
      "Skew:                           0.393   Prob(JB):                       0.0268\n",
      "Kurtosis:                       3.467   Cond. No.                         179.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               ECCO_SST   R-squared:                       0.727\n",
      "Model:                            OLS   Adj. R-squared:                  0.725\n",
      "Method:                 Least Squares   F-statistic:                     548.0\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           5.96e-60\n",
      "Time:                        16:31:26   Log-Likelihood:                -117.83\n",
      "No. Observations:                 208   AIC:                             239.7\n",
      "Df Residuals:                     206   BIC:                             246.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.9595      0.107     18.362      0.000       1.749       2.170\n",
      "NOAA_ASIC     -0.0445      0.002    -23.408      0.000      -0.048      -0.041\n",
      "==============================================================================\n",
      "Omnibus:                        0.214   Durbin-Watson:                   0.807\n",
      "Prob(Omnibus):                  0.899   Jarque-Bera (JB):                0.367\n",
      "Skew:                           0.018   Prob(JB):                        0.833\n",
      "Kurtosis:                       2.798   Cond. No.                         202.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               ECCO_SST   R-squared:                       0.700\n",
      "Model:                            OLS   Adj. R-squared:                  0.699\n",
      "Method:                 Least Squares   F-statistic:                     480.8\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           9.06e-56\n",
      "Time:                        16:31:26   Log-Likelihood:                -127.53\n",
      "No. Observations:                 208   AIC:                             259.1\n",
      "Df Residuals:                     206   BIC:                             265.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          1.9536      0.114     17.211      0.000       1.730       2.177\n",
      "Mean_ASIC     -0.0468      0.002    -21.927      0.000      -0.051      -0.043\n",
      "==============================================================================\n",
      "Omnibus:                        0.465   Durbin-Watson:                   0.738\n",
      "Prob(Omnibus):                  0.793   Jarque-Bera (JB):                0.419\n",
      "Skew:                           0.109   Prob(JB):                        0.811\n",
      "Kurtosis:                       2.975   Cond. No.                         194.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Against retreat"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "# Retreat vs 250m temperature\n",
    "x = X['ECCO_depth']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotLinReg(ax, x, Y, dataname='ECCO 250m')\n",
    "ax.set_xlabel('ECCO deepwater temperature ($^oC$)')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs deepwater temperature')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.078\n",
      "Model:                            OLS   Adj. R-squared:                  0.074\n",
      "Method:                 Least Squares   F-statistic:                     17.54\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           4.16e-05\n",
      "Time:                        16:31:26   Log-Likelihood:                 60.700\n",
      "No. Observations:                 208   AIC:                            -117.4\n",
      "Df Residuals:                     206   BIC:                            -110.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1056      0.013     -8.383      0.000      -0.130      -0.081\n",
      "ECCO_depth    -0.1039      0.025     -4.189      0.000      -0.153      -0.055\n",
      "==============================================================================\n",
      "Omnibus:                       20.401   Durbin-Watson:                   2.294\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               49.527\n",
      "Skew:                          -0.400   Prob(JB):                     1.76e-11\n",
      "Kurtosis:                       5.253   Cond. No.                         1.97\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "# Retreat vs SST\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = X['ECCO_SST']\n",
    "plotLinReg(ax, x, Y, dataname='ECCO SST')\n",
    "\n",
    "x = X['Hadley_SST']\n",
    "plotLinReg(ax, x, Y, dataname='Hadley SST')\n",
    "\n",
    "x = X['Mean_SST']\n",
    "plotLinReg(ax, x, Y, dataname='Mean SST')\n",
    "\n",
    "handles = ax.get_legend_handles_labels()[0]\n",
    "handles[0].set_color('dodgerblue')\n",
    "handles[1].set_color('darkorange')\n",
    "handles[2].set_color('gray')\n",
    "handles[3].set_color('dodgerblue')\n",
    "handles[4].set_color('darkorange')\n",
    "handles[5].set_color('gray')\n",
    "\n",
    "ax.set_xlabel('Sea surface temperature ($\\degC$)')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs SST')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.060\n",
      "Model:                            OLS   Adj. R-squared:                  0.055\n",
      "Method:                 Least Squares   F-statistic:                     13.08\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           0.000375\n",
      "Time:                        16:31:27   Log-Likelihood:                 58.603\n",
      "No. Observations:                 208   AIC:                            -113.2\n",
      "Df Residuals:                     206   BIC:                            -106.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1304      0.014     -9.021      0.000      -0.159      -0.102\n",
      "ECCO_SST      -0.0564      0.016     -3.617      0.000      -0.087      -0.026\n",
      "==============================================================================\n",
      "Omnibus:                       14.117   Durbin-Watson:                   2.194\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               28.312\n",
      "Skew:                           0.297   Prob(JB):                     7.11e-07\n",
      "Kurtosis:                       4.707   Cond. No.                         1.69\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.024\n",
      "Model:                            OLS   Adj. R-squared:                  0.019\n",
      "Method:                 Least Squares   F-statistic:                     4.987\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):             0.0266\n",
      "Time:                        16:31:27   Log-Likelihood:                 54.688\n",
      "No. Observations:                 208   AIC:                            -105.4\n",
      "Df Residuals:                     206   BIC:                            -98.70\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0963      0.014     -7.076      0.000      -0.123      -0.069\n",
      "Hadley_SST    -0.0322      0.014     -2.233      0.027      -0.061      -0.004\n",
      "==============================================================================\n",
      "Omnibus:                       11.070   Durbin-Watson:                   2.153\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               25.037\n",
      "Skew:                           0.071   Prob(JB):                     3.66e-06\n",
      "Kurtosis:                       4.694   Cond. No.                         1.38\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.042\n",
      "Model:                            OLS   Adj. R-squared:                  0.038\n",
      "Method:                 Least Squares   F-statistic:                     9.115\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):            0.00286\n",
      "Time:                        16:31:27   Log-Likelihood:                 56.703\n",
      "No. Observations:                 208   AIC:                            -109.4\n",
      "Df Residuals:                     206   BIC:                            -102.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1092      0.013     -8.467      0.000      -0.135      -0.084\n",
      "Mean_SST      -0.0473      0.016     -3.019      0.003      -0.078      -0.016\n",
      "==============================================================================\n",
      "Omnibus:                       11.807   Durbin-Watson:                   2.168\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               24.486\n",
      "Skew:                           0.192   Prob(JB):                     4.82e-06\n",
      "Kurtosis:                       4.636   Cond. No.                         1.24\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "# Retreat vs mean temperature\n",
    "x = X['Mean_Temp']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotLinReg(ax, x, Y, dataname='Mean Ocean Temperature')\n",
    "ax.set_xlabel('Average ocean temperature between SST and 250m ($\\degC$)')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs mean ocean temperature')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.015\n",
      "Model:                            OLS   Adj. R-squared:                  0.011\n",
      "Method:                 Least Squares   F-statistic:                     3.235\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):             0.0736\n",
      "Time:                        16:31:28   Log-Likelihood:                 53.820\n",
      "No. Observations:                 208   AIC:                            -103.6\n",
      "Df Residuals:                     206   BIC:                            -96.97\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0915      0.015     -6.027      0.000      -0.121      -0.062\n",
      "Mean_Temp     -0.0265      0.015     -1.799      0.074      -0.055       0.003\n",
      "==============================================================================\n",
      "Omnibus:                       11.267   Durbin-Watson:                   2.159\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               25.775\n",
      "Skew:                           0.075   Prob(JB):                     2.53e-06\n",
      "Kurtosis:                       4.718   Cond. No.                         1.77\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "# Retreat vs sea ice season length\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = X['Hadley_SISL']\n",
    "plotLinReg(ax, x, Y, dataname='Hadley SISL')\n",
    "\n",
    "x = X['NOAA_SISL']\n",
    "plotLinReg(ax, x, Y, dataname='NOAA SISL')\n",
    "\n",
    "x = X['Mean_SISL']\n",
    "plotLinReg(ax, x, Y, dataname='Mean SISL')\n",
    "\n",
    "handles = ax.get_legend_handles_labels()[0]\n",
    "handles[0].set_color('darkorange')\n",
    "handles[1].set_color('darkorchid')\n",
    "handles[2].set_color('gray')\n",
    "handles[3].set_color('darkorange')\n",
    "handles[4].set_color('darkorchid')\n",
    "handles[5].set_color('gray')\n",
    "\n",
    "ax.set_xlabel('Sea ice season length (months)')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs sea ice season length')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.005\n",
      "Model:                            OLS   Adj. R-squared:                  0.001\n",
      "Method:                 Least Squares   F-statistic:                     1.136\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):              0.288\n",
      "Time:                        16:31:28   Log-Likelihood:                 52.772\n",
      "No. Observations:                 208   AIC:                            -101.5\n",
      "Df Residuals:                     206   BIC:                            -94.87\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          -0.1728      0.064     -2.681      0.008      -0.300      -0.046\n",
      "Hadley_SISL     0.0081      0.008      1.066      0.288      -0.007       0.023\n",
      "==============================================================================\n",
      "Omnibus:                       14.122   Durbin-Watson:                   2.170\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               39.411\n",
      "Skew:                          -0.055   Prob(JB):                     2.77e-09\n",
      "Kurtosis:                       5.130   Cond. No.                         42.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.032\n",
      "Model:                            OLS   Adj. R-squared:                  0.028\n",
      "Method:                 Least Squares   F-statistic:                     6.911\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):            0.00921\n",
      "Time:                        16:31:28   Log-Likelihood:                 55.632\n",
      "No. Observations:                 208   AIC:                            -107.3\n",
      "Df Residuals:                     206   BIC:                            -100.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2665      0.063     -4.260      0.000      -0.390      -0.143\n",
      "NOAA_SISL      0.0194      0.007      2.629      0.009       0.005       0.034\n",
      "==============================================================================\n",
      "Omnibus:                       10.510   Durbin-Watson:                   2.227\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               23.206\n",
      "Skew:                           0.042   Prob(JB):                     9.14e-06\n",
      "Kurtosis:                       4.634   Cond. No.                         41.6\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.021\n",
      "Model:                            OLS   Adj. R-squared:                  0.016\n",
      "Method:                 Least Squares   F-statistic:                     4.440\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):             0.0363\n",
      "Time:                        16:31:28   Log-Likelihood:                 54.418\n",
      "No. Observations:                 208   AIC:                            -104.8\n",
      "Df Residuals:                     206   BIC:                            -98.16\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2547      0.072     -3.539      0.000      -0.397      -0.113\n",
      "Mean_SISL      0.0180      0.009      2.107      0.036       0.001       0.035\n",
      "==============================================================================\n",
      "Omnibus:                       12.671   Durbin-Watson:                   2.209\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               32.575\n",
      "Skew:                           0.021   Prob(JB):                     8.44e-08\n",
      "Kurtosis:                       4.938   Cond. No.                         47.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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GYZiGcWk2oJ9diwIoAMC6azMMwzAMwzBtgkuzgWcfRSAMwzBMx5eYmPiiQCCYTyl1butYGIbpsCil9NuQkJAv69vYYPJKCPkO2trW+o44tZWCYxiGYR4jfD7//7p06VIlkUjyG5slkWEYpiEqlYp/9erVaQAMS14B/GCckBiGYZjHGN/MzKy6rYNgGKbjEggEajQynGuDySul9CQAEEK6A7hCKaVE+zW6xeO8MgzDMAzDMExzcJmkYA2llALaBggAVhk3JIZh6toybzV+3LkLW+atbutQGKbdSUlJEUVGRvrVXufh4dGN6/6RkZF+KSkpovqOY6jff//dvGfPnv7h4eHS4OBg/0OHDlnUjqesrIw3ZMgQ77CwMGloaKh06tSp7gDw/vvvu65fv962oeOOHj3a8/fffzcHgBdeeMGrJTF2JB9//LFzXFycaVvHwbQvXEYbENb53cQYgTAM07BscRnAo9olwzDt1kcffeR+4MCBmz4+PkqVSoWSkhJ+7e3r16+36969e9WKFStuAUBubi6//iM17ODBg+mtFW9tKpUKAsHDaYFarQafb3CYjR6Tq3//+9/3mr0z89jiUvN6gxCyjBDyHCFkKYAbxg6KYZgH2dx3BDREu+QgJiYGS5YsQUxMDLcTxM4CVgm0S4Z5jPz6668WvXv39gsNDZU+99xzXSorKwkAfP75547dunULiIqK8i4uLn4oM0tLSxM+88wzPn369PF75plnfO7evSvYt2+f5eTJk/WzTiIyMtI3NTVVVHs/iUSiOXz4sGVRURFPIBDAzs5OXXu7mZmZ5vLly6bXr18XAYCTk9MD27nQ1+KuXbvWbuDAgV0GDRrUxdfXt+uRI0fMASAuLs40MjLSr0+fPn7Dhg3zLi8vJwDQr18/3/DwcGn37t0DYmNjzQBtje/o0aM9BwwY4LNlyxbbuud5++233SIjI/0KCgr4w4YN846IiPDr06eP35UrV0wA4LvvvrORSqWBgwcP7tKvXz9ffU2zq6tr91deecXj+eef96nvXpaVlfGefvpp3169eknDw8Olly5dMjl06JBF9+7dA3r37u03ZswYT+DBGucPPvjAJSQkxD8oKMh/z549Vvr4X3jhBa8BAwb4+Pv7B164cEFs6P1kOh4uyet0ANkARgG4A4BNDs8wj5g4vhdcDkVBHN+LU3mZTAZKKWQyGafyMXG3saTsE8TE3W5JmA06PWsnjo8vxOlZO7nFw5JvxkBXrlyRhIeHS/Uv/fr+/ftXnD9/PlUmk6X4+flVf//997Z37twR7Nq1y/7ChQvXdu7cmZGTkyOqe7zZs2d3+vTTT3P+/vvv1ClTpuRHR0c7jx49ujQuLs68qqqKXL9+XSQQCODn56eovd+OHTsyzp07Zx4cHBxYX3I7ffr0gj59+lSMHz/e29PTs9vGjRsbbCrA1R9//HFz/fr1GWvWrHEEgJkzZ3ps37494++//06NiIgoX7t2rT0AHD58+GZcXFzK9u3b0xcuXOim39/ExIQeO3Ysbdq0aYW1j6tWq8mLL75YfP78+dRPP/3U+cUXXyw6d+5c6urVq7M+/PBDN5VKhWXLlrn9/fff1w8ePHjr3r17Ndeal5cnXLRo0b3jx4+n1Xcvk5KSxFZWVqr4+PiUuLi4lK5du8r3799vvXjx4jvnz59P3bt3b0btWM6ePWt67tw5i8TExOvHjh27MX/+/E5qtTbvt7e3Vx07dixt9uzZ9zZs2GDf0vvJtH9cxnlVAtjwCGJhGKYhtM6yCaGhoZDJZAgNDeVUXqYMAwWBTBmGqOZF2Khr56WglI9r56XoxyWeWsl3VBSHiC5tBKhauxz4TYvjZTqebt26VZ49ezZV/7u+dlImk4kXLlzoplAoePn5+QJLS8uClJQUE6lUWiUUCmFra6vx9vZ+aHSE69evmy5YsMAd0D429/T0lPP5fAwbNqx4586d1snJyaaTJ0/Or7ufn5+fYvfu3bcB4Ouvv7abP3++64EDBzL02wUCAaKjo3Ojo6NzMzMzBREREQGTJk0qau519+jRoxIAvL29FUVFRQIASEtLM504caIXAMjlctK/f//S8vJy8tZbb3VKS0sT8/l85Obm1jQJjIiIKK/v2Dwejw4YMKACAJKTk03Pnj1rsXnzZkfdddCcnByBnZ2d0sbGRgMAXbt2rdTv6+joqPT19VU0dC8jIyMre/bsWTly5EgvW1tb1YoVK+4uXLjw3pIlS1y2bdtm9+yzz5bNmTOn5v5evXpVHBYWVs7j8WBvb6+2s7NT3rt3TwAAoaGhFQDg5eWliI2NtWzuvWQ6juY3RGEY5pEJfMkByT/lIXC0A6fyUVFR3JI+ndCwXrpkN6y5ITYqoNtxXLvyDAK6nQAQ3nQ8BibfMWYfQHZPjFDnaqMk36eXZyL5pxLcG52JfvM9jHAGxliWLl3qsnjx4rsDBw6smD59ujullEilUnlqaqpYqVSivLycd+vWrYceNfv5+VV98sknOX379q0CgOrqagIAs2bNyp84caJnSUmJ4Msvv8ypu9+FCxfEPXr0qAYANzc3pUajeWCw25SUFJGHh4fS1NSUOjg4qIVCIcevpPWrPZYupZQAgK+vb9V///vfW507d1bqY9+3b58Vn8+HTCZLkclk4n/9618++v34fH69MRBCwONpH9D6+/tXR0ZGlr/66qvF+mMKBAJaUFAgLCkp4UkkEk1ycrKkvmPWdy+rq6vJokWLcnk8HubOneuyceNGu3fffTd/x44dmRqNBl5eXt1ee+21mprgwMDA6q1btzpoNBoUFhbyCwoKhM7Ozqp67kFLbifTQTSZvBJCxJRSNmYfw7ShfvM94DxGAR8f4yROhia7huoX9An6dVcDhA9gXqvHI8s1AwWFLNfMODXHB/IAjXbJkteOZezYsUXTpk3z9Pb2rra0tFRbWlqq3dzcVGPHji0MCQkJ8Pb2rnZzc1PU3W/t2rVZU6dO7VxZWckDgFdffTV/5syZhZ6enkqxWKzp1q1bmYmJyUOZ0qpVqxyTkpIkpqamGh6Ph02bNj3QFicxMdF07Nix3kKhkCqVSjJ79uwciURCAWD16tUuP/zwgz0AjBo1qvDDDz98qGaXi/Xr19+eOHGip0qlIgAwb968e88880z5ihUrXCIjI/169+5db01rY5YtW5bz+uuve6xfv96RUorBgweXLFmyJHf+/Pl3e/fu7d+pUye5nZ2d0sTERFN33/ruZVBQUPV7773Xic/nQ6PR4Icffkj//PPPnY4dO2ZJKcVTTz1VamtrW3Osvn37VoWHh5f36NHDX6PRkH//+99Zze1ExnR8pLFvKbpxXWMppc89upAMFxYWRhMSEto6jCalpaXBx8en6YLtFIu/bXXo+GNngV7aCBI0zSiP9WNiYmpqao2RhGtrXrU13x05ea37GSKEyCilrVrdnpSUlBEcHNyspKujGDRoUJeVK1dmBwUFyds6lrYml8uJiYkJlcvlRNcRLNXDw0PV1nExHV9SUpJ9cHCwZ33bGq151U1M8DchJJxSGmeU6BiGefwN/AY3PecYLfk2es2xkWu+mY5BLpeTQYMGdenUqZOCJa5a69ats9u7d69deXk5b/z48QUscWUeBS5tXscCeJkQooa2uwillLZoEGeGYdqZ2Fnazk4duGb02oE8BIzq2DWjTPtmYmJCT548mdbWcbQnH3zwQf4HH3zwWNe0M+1Pk0NlUUp9KaVelFIf3c8scWWYx03t3vpGYOjQXYa6diAPVK1rm8owDMM81ppMXgkhdoSQFYSQLYQQASHk5UcRGMMwj86W7GgsLl2MLdnRRjl+aGgoCCGcRw8wVMAoBxC+dskwDMM83rg0G9gG4DsAH1JKVYSQNwDsMmpUDMM8UtkWGu30sxYPdRRuFVFRUZBKpUZr89pvvgdrLtBOfX4o2XXL6XQX/e/7pkVcA4CxG88F6Ne92c8r59PhgXd7fv5nUGGFQggAvo7mlX++3//aO7sSO/96KcdeX+bRX8Hj58iRI+anTp0yv3nzpsnChQvvdevWjbXfZToULjNsmVJKDwLQN8ImjRXmghDyOiHkLCHkDCGkZ51tcwkh53Xbvia1B3BjGMYoRJVCgOqWHBg6A9bp5Zk4NqEEp5dntiRMpgP6dHjg3YzlUTL9K9zLtjLcy7ay9jp9Upr46fOX9Ov+fL//NQD4+uWet2uXaczRo0fN5syZ49rQ9sWLFztu2LChxTNaGWrt2rV2PXr08O/Zs6f/6dOnJVzL1LdOo9Hg1Vdf9QgJCfHv1q1bQGMzdL322mudAgICAkNDQ6WhoaHSvLw8PgAMGTKkfNmyZfdCQkIqs7KyuP3RG3hNDW1vaL1YLO6pnx1t9erVRpsl68yZM6Y9e/b0DwsLk/bp08cvOTn5gRnQGrq/jd13/X3Wx79o0SInQ2Jq6J7069fP18bGJnju3Lkuje1vLK35Htd3/3JycgQjRozwak5sXGpeqwkh4QBACAkGYPD4cLURQmwAvAugDwA3ADuBBybd+R+l9Ctd2X0ABgA42pJzMkyHFzsLXYzYoUohUQJEt+TA0BmwzqUcQ+WwTJSkeKAfXm9htI8em6SgY1i+fLnztm3bGpzj+OLFi5KFCxfee5Qx5eXl8b/99lvHxMTE6xkZGcKJEyd6yWSylKbKHDlyJK2+/WQymTglJUV88eLF60VFRbygoKDAutO66l29elXy888/p0mlUsW4ceM679+/32rGjBmFALBt2zZrjUaDoUOHGvx/elPX1ND2xvZzdHRUxMXFpTR81tbRqVMn5dGjR1NtbGw0e/futfr444/dfv7553T99obub2P3/erVq5L//ve/N5tTg93YPdm+fXtGTEyMZXZ29kPTFxtba7/HDd0/CwsLzblz50wjIiKqDImPS/I6HcAKAI4AFgBo6eTh4QBOUUoVANIJIRaEEBNKqRwAKKU3apWV458a3wcQQqYCmAoArq6uSEtr/x1A5XJ5h4izIcaM3/5SNKwy9qDEczzyg6KbLB8XF1czZmV4eNMzNqVsqcSdWCVSBl6C9M16Kz7atS6XNoJQNeiljbjpOafVj+9aCty1JHAtBaf32MfHp+b+cylf2TkTIBSVnTM75N9A8k8lgAZI/ikPzmMeGs++w+jo/wY1pqioiFdYWChwc3NTaTQaTJ48udO1a9ckarUaM2bMuD9lypSitLQ00+Dg4OpNmzbZrFu3zgkARowYUbxs2bJ7ANCtW7cAd3d3eX5+vlAoFNITJ07cOHDggOXatWudqquref379y9ds2aNQU0X/vrrL7PevXuXi8Vi6u/vrygvL+dXVVURU1NT2liZhvbz8PBQCoVCKpfLSUlJCd/Kykrd0LlzcnJEUqlUUVZWxrt+/brp9OnT8wBg69atNhs2bHAcPnx4UVxcnGl4eLhBiUNT19TQ9sb2y8/PF/bq1UtqY2Oj+vrrr7OkUmm9f2j1vUf1TRbRkNpDeYnFYo1AIKB1ttd7fxu775mZmSYBAQHNanrR2D3p0qVLk7UJDX3Wd+/ebWXMz62h73FD92/YsGElu3fvtmn15JVSmg1ggiEHbYIdgNrzOBcDsAXwwDR7hJD+AFwA/NVAXJsAbAK0kxR0hMHbO/Qg8zBy/Af3AFQN64w9sB71Q5PFd+3aBUop0tLS8PLLTfchPH5UBmiAu0eViPqiA74HQdNqBvk3xnvgs/ozw8obGENYL910r2GhHfJv4N7ofyYp6MhjvXb0f4Mac+nSJbG7u7sCAC5evCjOysoy0dfkqdVqVFZWEqFQSC9duiTesmWLw99//52i+w82cMGCBbkmJiY0MzPTZOfOnek9evSoHjlypFdsbKzZmjVrnE6cOHHD1NSURkRE+OXm5vKdnJzUAFBeXk4GDBjw0Ag8w4YNK46Ojs4FgLy8PIGNjU1NomNpaam+f/++QD91a2Nl6lvXqVMnpbe3t9zb27tbVVUVb+3atfXWNN+4cUNUVlbGDwsLk6akpJguWrQo+9lnn60EgMmTJxdNnjy5qO4+XK6HyzU1tL2x/W7evHnZxcVF9dNPP1m+/vrrnufOnUutG4dSqUR971FUVFQ519j1SktLeYsWLXLbsmVLRu31Dg4O6vrub0Prb9y4IaqoqOBHRERIAUAikahPnDjB+Rsil89HY+r7rCclJZk09Lltq/e4oc+tbtpfO673S6/B5JUQsoJS+hEh5E9ox3etQSkdZOiJaikEYF3rdyvdutrnDgKwHMAIyiYqfjIETftnnFEOQkNDa8YN5SJglAOSf8rruL3RjTzIv7EZu8OWsbFJCjqWoKCg6rCwsIqhQ4d6h4aGVixdujQ3Pj7e1N/fv2rv3r3W06dPz9NPySoSiahYLKYymcz06aefLu3Ro0c1APD5fHr8+HGL/Px8Yf/+/f0AoLCwUKDRaGr6YZibm9OmHnXb29ur4uLiauYxLSsr4zs6OqqaKmNhYaEpLi5+aL+ff/7ZMicnR3j79u3LBQUF/L59+/qPHj26pHZNLgDEx8ebjhkzpuD777/P2rBhg+2ZM2fMm7pvXK6HyzU1tL2x/VxcXFQAMHr06NI5c+bU+4eWmJj40HtkYaHtZco1dkA72cTIkSO9P/jgg3uhoaHVtbc1dH9/++03i/rWx8fHm0ZFRRXu27fvoS8RKpUKoaGh/nXXDxo0qGTFihU5XO5lU+r7rO/cudO2oc9tW73HDd1XrtdZV2M1ryt1yynNPXgDzgNYSggRQluzWq5vMgAAhBAfAN8DGE0pZQMfPykGfmNQW05DZ1RiyQfDPN6CgoKq9W0DKysreV999VUOAPj6+nadPXt2fnx8vCQkJKQyJydHKJfLCQDs27fP0svLq1ogECA+Pl6irytJTk4WZWVlmbi6uioXLFhwd8qUKUXAP1Oh6s/JpRarf//+FdHR0W5yuZxkZmYKzczM1HUTzfrKDB48uGz58uUudfejlMLa2lotEAhgbW2tUSqVRKVSEQD0xo0bIl9fXwUAJCUlmXbt2rUK0Na0Ll261E2j0YDHa7ifNtdauaauqaHtDa0vKSnhmZmZaQQCAc6fP29qY2OjArS1mvrrAVDve9S3b99KQ2JXq9UYNWqU14gRI4onTZpUXLd8Q/e3ofVJSUmmISEhlfXdT4FAgKSkpOsN3nAO97I+te9LfZ91hUJBGvrcttV73ND9S05OFgcGBlbXjacpDSavlFL9RfgBiNVNFUsADATQYIP4plBKiwgh6wGchLZG9z1CSAiA5ymlKwCsgbZmdrtuoIEVlFJuXZoZhmmWLfNWI1tcBvdqC7z5Zeu3qTU6A2cIM3TGL9Zhq/2zsbHR2NjYqLKysgSzZs3qlJWVZaJUKsm4cePy7e3t1RcuXJC88cYbBZ6enopx48Z5f/fddw729vaqHTt2ZADAhQsXJPb29srIyEhfjUZDNm/enCGRSOiECRO8Nm3a5CgQCOisWbNyJ06cWFNbxKUWy8HBQT1lypT7ffr0kRJCsGbNmpohN1544QWvgwcPptdXpqH9Ro4cWbpr1y7b0NBQqVwu57311lv3LSwsNHK5nERFRfmkpqYmA8CVK1ckc+bMyQUAsVhMu3TpUn3y5EmJvulAfbjWyjUUW2PX09h+iYmJ4pkzZ3qamZmpCSH49ttvb9e9nobeIz6fb1DsO3bssDl+/LhVXl6ecM+ePXYBAQGV27dvz9LH3tD9bWj9lStXJDdu3BD//PPPtgAwevTownnz5nGeLaWxz8f48eM7JyQkmCsUCpKYmCiJjY29Wfe+vPLKK53rftbfeeed/IY+t231Hjd0/w4fPmz19ttv3+d6v/RIU0/lCSFHKaXP1fo9llI60NATGVNYWBhNSEho6zCa1JHbm2n/89a2+euI/3kbe3pSo4udVdPm1RijDSxetATgUUBDsGixYe1fuTLm53/LewuRbS2Ce7ECb/5naZPllyxZAkopCCH47LOmr/e7cBmoGiB84K0440y08CjUfQ8IITJKaVhrniMpKSkjODi4TZ6axcbGmh06dMjK0M4pABAREeH3xx9/pOkfQ3c0f/zxh1l8fLzZJ598YnAi0B7Vdz0d/T1qDY/T+5yTkyOYOnVqp19//TW9vu1JSUn2wcHBnvVt4zTOa53fO15XbabFrh3IAzQdd/rNhPgEUEqREN/+v+TUSzfagLGmb3WvtgA0RLvsgLKtRdpJFqy5jShj6IxfAaMcAB6bwau9GzhwYEVzElfgn3amrR3TozJo0KCKxyGh0avvejr6e9QaHqf32cXFRdVQ4toULkNl/UUI+QHAcQDPADjVnBMxHVuH7/DU0dUabcAYOmRTgVrcqy1qmj1wwdpMM3VduXLlWlvHwDSOvUeMHpehsuYTQoYC6AZgF6X0N+OHxbQ3Rv/P28A2i4Zyr7I0KLlpb80MTifMQ/JPbyBQ4YB+7arRDkcGTrJg6P2X2oyG5kAepOzLFcMwzGOPS7MBALgM4DSAEkJIpBHjYZ5UlzYCRnws/uaXczBx0sucaxhrzyDVHnT0ZhuGNnsw9P5fO5AHqu7A94dhGIbhrMnklRCyCMBeAD9BO3zWImMHxTyBgqZpe8MY6bE4Ymehy0F/bQ0vB/b29g8sm3J6eSa+C5fh9PLMpgs3Q4dvcxk0DdSA97c5bVIJvwPfH4ZhGIYzLm1eh1FKexNCTgB4FkDT0x8xjKEMHOfVYLVr/jicJz8//4FlU2rX/BljNIYO3+bSwEkWmtMmtSOOgsEwDMMYjkuzgXLdkgAQAZAaLxyGMRJW89e2DKz5jomJwZIlSxATw22IZ2PXfDMMwzDtB5fk9RAhxBTAFgAp0E4uwDAdy8BvcPOF65xrd6OiovDZZ59xrv3rN98Db8WFGq327/TyTBybUNJhk7OYuNv4vOwTxMRxm9+k3bV5NTD5ZhiGYYynyeSVUrqaUlpFKd1BKfWklH7wKAJjmI5ky7zVWLxoCbbMW22U43f0DlsyZRgoeJApuY2H3+5qvo08zi7DMAzDXYPJKyHkO0LIpvpejzJA5gkROwtYJTBazVZMTAx27drF+TG0obLFZdpB8sVlRjl+R++wFRrWS5uMhvXiVL691Xwb2uyEebRSUlJEhJDQnTt3WuvXeXh4dNP/vG7dOruePXv69+rVSzp06FDvrKysmv4elZWVxMLCIuQ///mPXd3jTpgwoXN4eHi9TeXUajVefvnlzj179vQPDQ2VvvDCC14AsHbtWru5c+e6AMCOHTusg4OD/Xv16iUNCQnxT0xMFNeNrb5riYyM9NMf63//+5+lwTekA8rMzBS89dZb7m0dB9MxNNZhi3XMYh6d2kNlGaHjVkJ8AkC0S2OM2yrJ8EClZyYkGcZJnjp6h62oqChIpdIOOz2yoR3OmEfPy8ureuXKlc4TJ04s5vH+qZc5cuSI+Y4dO+z/+uuvFHNzc7p3716rcePGeZ09e/YGAOzZs8d61KhRBfv377d97733CvT7VVdXk6tXr5ra29urbty4IfL19VXUPt9PP/1kqVKpkJiYeB0AcnNz+XVj+uyzz9wvXLiQbGNjo6mqqiJKpZIYck3vvvtuQdOlDKdSqSAQ1P/ff2PbWnLcpnh4eKi+++677GbtzDxxGqx5pZSebOj1KANknhDGHirLyCICBsD1tyhEBAzgVN7QDklGZ+Sab4YxNmdnZ2X37t0rf/zxR+va67dt22Y3f/78HHNzcwoA48aNK6msrOSnp6cLAWDPnj22n3766T2RSKTJyMgQ6vfbt2+f1dChQ4snTZpUsHXrVtu657OwsNDcunVLnJiYKNZoNHByclLXLcPj8eiRI0csKisriampKbW0tDRoatP333/fdf369baAtrZ2xowZbr169ZIOHz7cW19m1qxZbvqa3d27d1sBwLfffmvbu3dvv5CQEP9x48Z11mi0p3V1de3+yiuveDz//PMPfAtbu3at3dChQ70HDhzYZenSpU7ff/+9TWhoqDQ0NFT64YcfugBAUVERr3///j6RkZF+U6ZMcdfXSL///vuuo0eP9hwwYIDPli1bbJctW+YYGhoqDQkJ8V+1apU9AGzatMmme/fuAb179/abNWuWm0ajwYgRI7xCQ0OlvXv39vvtt9/Ma9c4X7p0ySQ8PFzaq1cvaVRUlHd5eTkBABcXl+4vv/xy5+DgYP+pU6eyWtonGJdxXjMIIXJCSKZueYsQkkgI4fa/NPNY2DJvNX7cuctobTox8BvgfZXRhsuSpHsAGqJdGkFJ98vIGR6Dku6XOZU3tEOS0TtsGXmSCIZ5FBYvXpyzcuVKZ32yBgB3794VeXl5PVBr6u7ursjIyBDm5uby5XI58fHxUU6cOLGwdpK6Z88e2ylTphSOHz+++M8//7Sqe66hQ4eWv/LKK/kzZszw6NSpU/clS5Y41i2zZ8+eW3v27LH18fHpNmTIEO/6ame5UqvVZNKkSYXx8fEpRUVF/Pj4ePH+/fstdT+nnDp1KjU6OtpNo9Hg5ZdfLj5//nzqxYsXr5eXl/N///13cwDIy8sTLlq06N7x48fT6h6/oqKC/8cff9ycNWtW/n/+8x+ns2fPpspkspSkpCRJXFyc6X/+8x+Hvn37lp89ezY1NDS0sva+JiYm9NixY2m9evWq/PPPPy3j4+NTEhISru/cudP+3r17/D179tj9+OOPt86fP5+6du3aO/fv3+ffuXPHJD4+PuX8+fOpgwYNKq99vPfff9998eLFd+Lj41MCAgKq1qxZ4wAAhYWFwuXLl9+5cOHC9T///NOqsLCQ60RLzGOGyxv/GwBfSqkHAF8AsQBeBPClEeNi2hljt+k0tgh5ClxjBiNCnmKU4xuajBraIenctWPIGRaDc9eOtSTMhnXwmm+GAYAuXboog4KCKn/44Qdr/ToXFxdFenq6qHa5O3fuiDw9PZXbt2+3zcnJET311FO+mzdvdvjll19sAKCgoICfkJBg/sYbb3QeNGiQz507d0Tnzp0zrXu+2bNnF5w/fz718uXLV3ft2mWfnJz8wHnCw8Orfvnll/S7d+9e7tatW9Xy5cudmnttfD6fRkZGVgHa5Pv+/fuCpKQk07///tsiPDxc+txzz/kqFAqSm5srOHLkiHmfPn38evXqJb148aJZRkaGCAAcHR2VdZs/6PXq1aucx+MhOTnZ5O7du6KnnnrKLzw8XJqVlWVy8+ZN0Y0bN0x69+5dAQBPPfVURe19IyIiygHg4sWLpjdu3DDt06ePNDIyUlpeXs6/deuW6Kuvvsr+4osvnF944QWv3bt3Wzs7O6tff/31vH/9619eEyZM6Fy7xhsA0tPTxQMHDiwHgH79+pVfv35drItf4eHhoeLxeHBxcVHm5+c3r40C0+FxeeODKKWZAEApzSSEdNUtK5rakXl8uFdbIFtcBvdqi7YOpVn6TSlD30t9QYyUnIWGhkImk3FORg0dhL/SMxPgUe3SGIw9SYSRbZm3uubzyXUKYObxFB0dnfOvf/2ri/73V199teDzzz93HTRoUJm5uTndv3+/pVgs1nh5eSl/+uknm7Nnz6bY29urAeDFF1/0kslk4r/++st89uzZOR9//HEeAPzyyy8W27dvt4uIiKhpk5mRkSG0tLRU29raaqytrTVmZmZqSukDbVovXLgg7tGjRzWgbdaQlZX1QHLbEpRS0q1bt+r+/fuXbt26NQvQttMVi8X0008/df/jjz9udO7cWRkVFeVNKQWgTYAbOh6fr60Ulkqlcg8PD/mZM2dShEIh1Go1KKW4fv26OC4uzmzkyJFlZ86cMauzLwWA7t27V3Xt2rXyyJEjN3k8HuRyOTExMaFlZWW83bt3366qqiLe3t7dXnrppZIZM2YUvPvuuwXr16+3Xb58udOHH354X388Ly+v6tjYWPOhQ4eWnz592lwqlVYDACEPNhnWXxfz5OGSvP5NCDkMIB5AGIDzhBABgCtGjYxpV978cg7S0tI6bocVI3e4MTQZNZSxvzysnfEtihzvw+a+I97dMN0o5zBETExMzZcBLvfV6E8GYmehy6WN2prpDpzkPwm6dOmiDAkJqTxx4oQlAERFRZWnpaUVPP3001I+n09tbW1Ve/bsSU9NTRWp1WqiT1wBYNCgQSVbt261S0hIMNu8efPtWuvLZ8+e7aFWq7P1SV56errwgw8+6EIIoWq1mgwZMqS4a9eu8qNHj5rr95s7d65bXl6eUCQSUTMzM/WuXbsyAO3je337TgCIjY29IZFIDM7Exo0bV3LmzBmz8PBwKSGEuri4KH/++ef08ePHFwwcONCvS5cuVYYe09nZWf3OO+/kRkRESPl8PhUIBHT37t3p7733Xt7IkSO7REREWPr4+FQLhcKH2u/26tWresCAAaXh4eFSPp9PxWKxJjY2Nm3mzJnu165dM1WpVOTVV1/Nu3v3rmDMmDHefD4fSqWS/Oc//3ngW/mKFSvuTJ06tfOnn34Ke3t75f79+9MNvQ7m8Ua4fHMhhPSAtslAGqU00ehRGSgsLIwmJCS0dRhN6tDJH1j8bSp2FuiljdqaYw7Jk6HJ3+JFSwAeBTQEixZ/1hoRP8SQ+79kyRJQSkEIwWefNR2P0WteVwm0bYIJX9s2u4Oq+x4QQmSUUm6D73KUlJSUERwczG1eZaZDUSqVEAqF2LBhg+25c+fMd+zY0TFnTWE6hKSkJPvg4GDP+rZxai9CKb0A4EJrBsUwj9Lp5ZlI/qkE90ZnchoL1NDk7/Ssnbh2XoqA3ino982k1gj5wXjibkOm/AShcTJEDWy6fO02uFziN/ZQX4YytBmG0ZsKBE3758sDwzyB1Go1+vTpIyWEgBCCXbt2sdpQps2wxs7ME6H2DFVckldDk79r56WglI9r56Xo1xoB141HGQYKApkyDFwaJxia/EXIr+NazL8Q4PO/lgXaAENrRo3dDMNgbJxX5gnH5/Mhk8mM0+OVYQzU2Axbw3VL74bKMExHYegMVQZPT9o7BYSoEdDbOP+2S5QigOqWHBg8Q9WUMrz1cl/0m2KcNqPZpqXaNqmmpUY5PsMwDPPkaKzm9UMAhwBsBtCqY7oSQl4HMBUABfBO7Xa0hBAxgC0APABkAniTUlrdmudnnjyGzlBlaM1fv3/9jX5dJuuGmmr9ZgMVAiVAdEsjOJ0wD9cOvIEAhQP6cWiW8MRhHbYYhmHajcaS1yuEkBQAnQghqQD0Y1RQSqlfI/s1ihBiA+BdAH0AuAHYCTzwpPV1ANcppRMJIZ/pfv+2uedjmEfCyNPbGtu5a8dQOTQTxdc80A+vt3U47c+ljSAd+P1tU0cWuOLv9S41v0/+7RoAYOvQgJp1fWbmYMgXd/GVdxAqC7RjfjpIKzEr7hr2v9EZV36yrynDtNiRI0fMT506ZX7z5k2ThQsX3uvWrZu8rWNiGEM0Nj3s25RSKYDNlFI/Sqmv7tXsxFUnHMApSqmCUpoOwIIQYlJre39oa3wB4Ffd7wzTvrWzQf4NnX7W2OPIhvUKAyEEYb1atWP7oxM0DbQdvb8dypAv7iK6RFbz6hxZic6RlQ+s0yelc29dqlk3K06b5I75/vYDZRpx9OhRszlz5rg2tH3x4sWOGzZseGiqV2Nbu3atXY8ePfx79uzpf/r0aQnXMv369fO1sbEJnjt3rkvtsnPmzHHt0aOHf3h4uPT8+fMPTZ6g99prr3UKCAgI1E/1mpeXxweAIUOGlC9btuxeSEhIZVZWlrCh/VtyTfVtLyws5Onj7t69e8Avv/xiAQBnzpwx7dmzp39YWJi0T58+fnUne2htjd2/xmKp7/3Q3+Pw8HBpeHi4dNGiRQZPRGHIvXpUmvuZrW+9TCYT6+9PSEiIv7W1dQgA5OTkCEaMGOHVnPia7LBFKX1X1/7VH0AKpfTX5pyoFjsARbV+LwZgCyCnnu36bQ8hhEyFtukBXF1dkZb20Gx37Y5cLu8QcdbH/lI0umTsQbHneOQHRbf68ePi4mqG8QkPD2/146dsqcSdWCVSBl6C9M16/w5bxnOO9gUARniPbe5ao8ilBDY5Vpw+Q7U7nEml0ibL1x5tgMvxDX2/pFIpPD09YWJiYpS/AftL0bDK2IMSI30+4TkHcpeZMDExMcr7+6h05H+DuFi+fLnztm3bbje0/eLFi5KFCxfee5Qx5eXl8b/99lvHxMTE6xkZGcKJEyd61e341FCZ7du3Z8TExFhmZ2fXJFBnz541lclkZhcuXLielpYmnDhxotf58+dT6zv31atXJT///HOaVCpVjBs3rvP+/futZsyYUQgA27Zts9ZoNBg6dGh5ffu25Joa2m5lZaWJi4u7LhQKkZycLBo7dmyXkSNHXuvUqZPy6NGjqTY2Npq9e/daffzxx24///yzUUYzaOr+NRZLfe/H1atXJf/9739vNrf22tB71fI70PyYuJRpaH1cXFwKAGzevNnm2LFjFgDg4uKisrCw0Jw7d840IiLCoDGJm0xeCSHfABADOAvgRULIMErpDENOUkchAOtav1vp1tW3ve62GpTSTQA2AdpxXjtCL+AOPc7owT0AVcM6Yw+sR/3Q6offtWsXKKVIS0vDyy+/3OrHP35UBmiAu0eViPqi9d+DtdPWoci5EDb3bPHuxrebLG/oUFxiWSRcwAOg4fQZMjExQXV1NUxMTDiVjyA/4VrMYAR0O8GpvKHv15bP9yJblQJ3gRRvfjquyfKGOr3QHdfSziDA53/oN8o4f2Ot/ferUVIUpVeh/J4CGhWF1wCbh8qo1WqUlZWhtLQU7u7uyM/Px4ULF2rW9evXD35+3B+Gdeh/g5pQVFTEKywsFLi5uak0Gg0mT57c6dq1axK1Wo0ZM2bcnzJlSlFaWpppcHBw9aZNm2zWrVvnBAAjRowoXrZs2T0A6NatW4C7u7s8Pz9fKBQK6YkTJ24cOHDAcu3atU7V1dW8/v37l65Zs8agpgt//fWXWe/evcvFYjH19/dXlJeX86uqqoipqSltqkyXLl0eauSenJwsDgkJqQAAHx8fZXZ2tknd4+nl5OSIpFKpoqysjHf9+nXT6dOn5wHA1q1bbTZs2OA4fPjwori4ONPw8HCDEoemrqmx7fpJHoqLi/mBgYGVAODh4VEzeLJYLNYIBIIGB6Cv7z0yMTHhPMFDU/evsVjqez8yMzNNAgICmt3swtB7VVdDn/Xdu3dbNfdz25LPbFP77tq1y27evHk1XyCHDRtWsnv3bptWT14BdKeUPq37eQsh5C9DTlCP8wCWEkKEAFwAlFNKa7/xJwEMA3BRtzzZwvMxrcHI41waOrSToapC42tmkAJa/xxFLgUA0S05MHQorrz+f0FlWQ5BqTmAXk2Wr66ufmDZ9A6FDy6bYOj7la1K0Y42oOI2GoOhyf21m6NBKQ/Xbo42ylBlhnbYUlVrUHFfgfJ7CriGWSDvWiWuH8hH+T0FynMV6D3bDdY+Avy56AasHMyh8MxBlgAoLS2FUCjE4MGD8fvvvyMuLg7m5uawtLTEhAkTwOPxYG5uDldXV1haWsLR0dEYV9shXbp0Sezu7q4AgIsXL4qzsrJM9LU9arUalZWVRCgU0kuXLom3bNni8Pfff6fo/oMNXLBgQa6JiQnNzMw02blzZ3qPHj2qR44c6RUbG2u2Zs0apxMnTtwwNTWlERERfrm5uXwnJyc1AJSXl5MBAwY89O1h2LBhxdHR0bkAkJeXJ7CxsamZxcvS0lJ9//59QefOnWsSIS5l9Hr06FG1YcMGx+rqanLhwgVxbm6uKC8vj1876QKAGzduiMrKyvhhYWHSlJQU00WLFmU/++yzlQAwefLkosmTJxfVPTaX6+ESb2Pb09PThWPGjPFOT08Xf/PNNxm1z1NaWspbtGiR25YtWx5Yr6dUKlHfexQVFVXONXau96+pWADtPa6oqOBHRERIAUAikahPnDhh0KON5t4rvfo+60lJSSYNfW6N/ZltbN979+7xb926JX7++edravsDAwOrt27damfIPQO4Ja98QkhnSultQognx30aRCktIoSshzYppQDeI4SEAHieUroCwDYA3xNCTgHIBjC5JedjWkkHn161yPE+wKPaZTtgaPKnsiwHiG7JAZ/Ph1qtrpmvvCnnTPxRGfU7ijP8OSV/hr5fZmoBKogSZmpu/3wYmtxbe0lQdLMa1l5GaBICPNBhS/PsOmiUFAIxD+nHilB2R5ukCs146DXTDX8ty0Dqr4UwcVMBbmXwUZijMLcYdl284BkpwZ+XjuDHv8pAThFETo9E//49cPRoJpRKwNHREXZ22n/HBwwYgOeffx483j9dEyQSCezt7Y1zjY+RoKCg6rCwsIqhQ4d6h4aGVixdujQ3Pj7e1N/fv2rv3r3W06dPz9NPxyoSiahYLKYymcz06aefLu3Ro0c1APD5fHr8+HGL/Px8Yf/+/f0AoLCwUKDRaPSdl2Fubk71SUND7O3tVXFxcTV/iGVlZXxHR0eVoWX0QkNDq1966aXCp556ys/T01PepUuXKldX14fKxsfHm44ZM6bg+++/z9qwYYPtmTNnzOs7Xm1crodLvI1t9/LyUspkspSUlBTRgAEDpBMmTLgMAHK5nIwcOdL7gw8+uBcaGlrvt+7ExMSH3iMLCwuNIbFzuX9cYgG09zgqKqpw37599TZVUalUCA0N9a+7ftCgQSUrVqzIae69qq2+z/rOnTttG/rcGvsz29i+27Ztsx0xYkRR7X/TmovL/yTvA9hPCLGCtg3qey09KaX0ewDf11l9UbetCsCElp6DYWpzr7aoGSTfKMevsjTo+MZO1tVq9QPLplR6ZQJEt+TA0JrRCoECIES75MDQ5L44o/qBZUsoK9UovSNHxT0lyu8p4PG0FWin93F8ix8qVD4o+fU0vEdJ4PKMCDfOFcNZ7Ik74qvIV2Ti9OoKVNAKzD81Hzdv3URi4k0UFFfB0sES0u4OsLS0hEPQeFhaWmrbz+o899xzD8UhFDarH80TKygoqFrfFrGyspL31Vdf5QCAr69v19mzZ+fHx8dLQkJCKnNycoRyuZwAwL59+yy9vLyqBQIB4uPjJfrp0pOTk0VZWVkmrq6uygULFtydMmVKEaBNamo/ouZSi9W/f/+K6OhoN7lcTjIzM4VmZmbquo/4uZSpbf78+Xnz58/Pi4+PFy9dutRFIND+V37jxg2Rr6+vAgCSkpJMu3btWgVoa1qXLl3qptFo0FjiwLX2sql4G9pe+/GxtbW12szMTA1o/50aNWqU14gRI4onTZpUrD9O7esBUO971Ldv30pDYm/s/jUWS32SkpJMQ0JC6n2cDwACgQBJSUnXGzuGofeq7j2p77OuUChIQ59bY39mG9t33759dps3b86ofZzk5GRxYGCgwf9wc+mwdR5cnlMyTEvEztIOQ2SkcTSLhFUAodqlEUhtRkNzIA9SjpMgGJuDgwPy8vLg4GCceAyffrYzKjtnQXLbOOPsBoxywLUDeU1OQqGSax/na1QUNl6muHG4ADmJ5SjPlaO8sBJPr3TGtdg7yI3hQ+xMcd/2OkxKVaiUe8F+jBOiBkdg16GtyDYRoTTFEu7PuaNPn064e5cPlSoQlpaWMDc3h0AggFQqrbeznLHekyedjY2NxsbGRpWVlSWYNWtWp6ysLBOlUknGjRuXb29vr75w4YLkjTfeKPD09FSMGzfO+7vvvnOwt7dX7dixIwMALly4ILG3t1dGRkb6ajQasnnz5gyJREInTJjgtWnTJkeBQEBnzZqVO3HixBL9ObnUYjk4OKinTJlyXz+16po1a2q+Ib7wwgteBw8eTG+ozPjx4zsnJCSYKxQKkpiYKImNjb0JAH379vVVq9XExsZGtXnz5kxAm6BERUX5pKamJgPAlStXJHPmzMkFALFYTLt06VJ98uRJib7pQH241l42FG9T15OQkGA6Z86cTnw+n6pUKrJixYosANixY4fN8ePHrfLy8oR79uyxCwgIqNy0aVN27etp6D3SP13iGntD908fe32xbN++Pau+98PExITeuHFD/PPPP9sCwOjRowvnzZuXxyWGpu5lffeq7nsMAK+88krnup/1d955J7+hz62xP7MNrU9OThYpFArSs2fPBxLVw4cPW7399tsGPxIl+m8xHVlYWBhNSEho6zCa1NE7Sxg1/lUC7TiphA+8X+/TshZZHL1YO1IxBRZFL2r1428Kk2kbwRBgakLTtYWG1lwaGr/hx48GCAEoxaLo6FY//unx83AtbRQCfA6g354vW/34a2d8iyIHbZvmaV9NwZ24Mm370nsKuPYxh013Hn6efB3Ku0Jo/HIhklbBKpCgKLsCfVwG41bVRVzOSoCllSUsLS0xbNgwiMViXLt2DRYWFrC0tERRURECAwObjKU9q/s3TAiRUUpbdfyypKSkjODg4PzWPCZXsbGxZocOHbIytFMVAERERPj98ccfafrH0B3NH3/8YRYfH2/2ySeftI+2US1U3/V09PeopR639zgnJ0cwderUTr/++mu9I0skJSXZBwcHe9a3rUXtVxmm1QRN+6fm1Qh4Sh40Qg14ypa3talPSdfLNUNNcekQZmjNpaEMPX6YMB4yVRhChdy+BN6+fRuUUty+3eCoRC1SX/yKcnVNQiovVcF3mB1Sj9zH9QOF4N30gVWOE0qkqfjttyNQJdrC2d4VZ+T7oTheBcl5Cfxf64qhwwcjQRYPuVwOCwsLhIRYwcvLAf6a5zCc9/xDcYSF/ZPXVVRUGOVamdYzcODAioEDBzbrjSorK+N35KRo0KBBFYMGDXpsPqT1XU9Hf49a6nF7j11cXFQNJa5N4Zy8EkJsKaXcuiIzjKEGfmPUmYs0Qg1AdEsjMHSQf0PbdBKFAFSkAlFw+5M19PhR4sOIwmFOZQEgLy/vgWVTrt0coxsNYEy9HcLUCg0qcpUQmfMhMufDITsI1fkaCIVCHPq/c3B5RoQre3MhSe+M8s7pKLS8BdXlKigVSox9ZTIu//cyVLalIEoBnDvbw+c5H9jb26N7yVswNzd/oONafePSNtWB4PTyTCT/VIJ7ozPRbz63pg9Mx3LlypVHMoYm03zsPWL0uIzzOgzAvwEICSHBALZQSl8zemQM04EY2iHM0DadVKQCiG5phOMbypA2tZRSBIx2QvL+PLj1tsbFH3JQmFOMziMkuHczH6kbFFAWE5T1vAqJlwZKfhXKxYDQ0hLlLvdgbuUKdbYVfF90QGRkVxQWukChCIOlpSVMTU1BCIHqaBnAA6hQjT59+tSc28rKqlWu99qBPECjXdabvFINoCjXNr0QWQAaFcBjD7YYhmGMgcu/rp8AiABwmFKqIoR0MnJMDNPqbHKdao3z2jRD21y++XyattnDUx10+tDgmQY125g5c2bNzzWP83MV4Es0MOmkxsUdd1F13QQ51emossqDk981VA/WgJcdhptZ9khTxyPthBUsLC3x1PI+8PB1w/UUSU370j179iA//zYcHBzw+pRXHzi3re3Dk+7Z3Hfk/v5SCqgqte2r+SIg75I28VSWAybWgGsf4Mb/gMJr2vXqagSMmo2ys/9D76f+B+xRa9c/tw6wcAe2BgKqKkBoBvR4B3jq34CiDBA/POkAwzAM03JcklcFpbSKEKLv2UUaLc0w7ZCprBfEuv5gXBjaZnTLrzbItv4M7r8q8ObAFgZbD/NMF5R3ugfzLGdO5Q1NvleecEOF4FOYnRDiw1rxq5UaVNxXwtxZiHtpxbh7uhKl9ypwNesiSqxuw9rVDDjZGY7EC7e6HoFKUAUrGys4WLriqSnP455cDgWxxpl991BhU4kUk2y8t2AMgGceiiEoKKjm5/x8bX+fvLz7gKoa4AkBjRIoSv0n0bTwAOz8gStbEWx2FMTkPtQSVwDTgaSNwM2D2nKKMmD070CuDDg0FlBWAHwTYOC3QOArwJHXAKG5tsbU/Wlt8lp5X7ufyAIQOqNf6HJQ4UYQr6FA+DxteRsfbcI6IxcQSgBSq+kBS1wZhmGMhkvyek43qYAzIWQ1gNNGjolhWl3AKAck/9T0UEp6Bs8gZS3SziBlLWq6cDNo1KIHlk0xJPnOu1YBdZ4dxBoelEIlzp07h/SzedD84YlCs3SU+aeCSuQQ8IXoaTIINp5WKCm9DY1IgYLiKkzfMAZOTk6Qy30g4gOEL9ImfmVZcFNUAspynLW9Dz4xgY9HLBCv1CagdoGA/zjgzKfA/SRtoskTAGP+QG/h33hGdBwiKIB1y4HxpwChBRDzsi6hNAe6vqpNXktuQWCbAwVEKLbRTdbn3Aswd9OWFZkDJlZAp2eBqdnahJNX61vMq0kP35TgOjXQqwQg0AAZR4BRhx7cJmpy7HeGYRimFXEZ5/VjQshgALcAXKWU/mb8sBimdfWb7wHnMQr4+BhnnFH3Mh6yLTRwL+M2moGhNaPN6RCW+PdFdPXqgct/pIPvqER5eRly/yBArhlSJSfBs1NALa6GqMgaVtkOKPe6DWJWjuLCfLj4CdArNAEafgFU8kpY+PSH0PMZ4Pc3gaoCeHlm4FaJCfK6fwany4uB/XtgoigHQIG3i4Gs48Dpj3U1muZwUnjjtsgJLjQPqMjVJaBmupvXH3Dq9U+SCQDd38TaCyHo1rMPhg0f+c+FvX7l4Yvt+zlkP3z7YLMBp57aV10Ck4fXcWEbAFpwBcQ2oHn7MwzDMK2GS4ctDwDXdC8QQpwB5NLHYYBY5onxxUfLoTCTQ1RhggUr5rf68d90j9aOU2vFB7CwyfKGD/LfCZWeWbDLsgYKU6CuLEPR/WIonANx52wKBKnlyCguxl1NCSwCBMi/Xw2n0xEoSS5AjHQ37G0JbDpL4UBl8LaPh5uJEvbWCti8dgRmV9eh0Go+FBBBTk3g9dQJQCUHEn7WJpSm5oBIl/R5DgZ4Itj3MIe9xAlw6A5U9QH6fq5NVPXJoc9I7UvnVnI0KAgOaYai5zPRD15c54fbWQwZMQZDRoxp8r7omVlWoUi3NIrCa9r2UoWsszPDMExb49JsYD8ATwApAKQAsgBYEkIWUkr3GjE25gmyZd7qmt76b345p9WPrzCTA0S3NIb6xqmtvA/IS7WP0AkBHEOAe/FA3mWM9rqD3OybUHSdpn1knrimpn2mqvtMFFtEoHrrq6gsMcUVlRtcnUTIzpFCoNHghxczkdnzKqhFGezdb0JUQhGYx4etTSXMzZXw7NELFg6dYR91AgKxBSAKA6y8tW00h/QG+CK4C80BgVgbV58F2LXdpqbm8l3Lztr4B216+DqlYx9eZ2rX5O2xN1Uhr0oAe1NuoyUYWjOdLS7TNtsQl3E6vsGCpoFe2ghipHGIGYZhGO64JK+pAJ6jlJYRQiwAfAtgOoATAFjyyrQKQ5MPQ5MbQbkEKvNKCMolD2+kGqAq/5+OQEIzwLoLkHkMKMvSrqcaoOc7QPoRIPW//5TtvxIAQdXF36CptoHJxR0QmNoBfZcAPw0B5CXaR+QOQaBDtkOVcxHIjIdA5QRi5g1TM0vs/d8V2CePQ468Ghn2yaDJSTAzvQnbtPfgZO6KYodryMm/A5VrDjT37fDKvv4Q2w6AQNRw77P169fXDGU1c+bofzZYuNVbvsy0AiBUu+TA0PufVykACNEuOWhuzbQkw0iDoQz8Bjc953ToGfIeZykpKSJ/f//u69atS581a1YhAIwdO7bzmTNnLO/cuXO5tc/3+++/my9YsMBdIBBQuVxOli1bdmf48OFlHh4e3TIzM6+UlZXxXnrpJc/8/HwhpRShoaEVmzZtyn7//fddfXx8qmfOnFnvmOmjR4/2nDp1av7gwYPL9dNwtnbs7dHHH3/s/OKLL5aEh4cb6dEJ87jh8j+JFEC57ucKAH66RLbUeGExTxr3YgWyrUVwL1Y0XlCjBuTFSEuMhT2R43riCW1yk3YQUJRok0pTO20N4dXtwJ0zgKIMr2juIDZmOdxc44Hv/f/phT5oM+A5CNjWtaZ9JnxGAX0Xa9ttlmZq1+trFyUOgEvEPx2BzJwBgSl+TJmKYrd8kNTOGDLoWZSfPw+laBvM1Y44d/t3VGQVQXHhC5hUWcH66HQoumVCY1cK/64COLp6wsHBG16OQjxr+xScfKzrJKZBNdPDKhwLYO7cdKctQycRUFmWA0S35MDw5NIDlZ2zIbnNLbk0tMOciUkpKnVL5skUGBhYeeDAAZtZs2YVVlVVkTt37oj4fL5Rmrd99NFH7gcOHLjp4+OjVKlUKCkpeeCb5Pr16+26d+9etWLFilsAkJuby3Gck38YK3FVqVQQCB7+r1+tVj8wmUdrHJOrf//73/eavTPzROLyadsNQEYIuQygK4BdhBABgKNGjYzp+DQqbYJYM7SRu3asqtux/ySPtlKg0zN4s/cxIOdvwMMH+DMfeP5bIPFr4NK32nLKcmD8GaC6CPh5ON6yFKBcAdx1e0V7rpS92kfgQnPAvpt2ncQJcA4DhOaouFoFDeHDKmIAMHLQPz3WRRbanucz60ny+n4OAFAqlSgrK0NpRgaK84VwN3sZN5LTcDU9CdR8P0pLysAr9IH4VihKgq7gbKwMrlJ7VF+1gsDOFoHuPeDQxRJderuApxFAsIQHQurpTNSKDJlEoDns7e2Rl5cHe3t7TuVtbe+iUrfkwtAOc0UuJQCPapfME8nKykotFArpnTt3BH/++af54MGDSzZv3uwIAAUFBfxJkyZ1LioqElBKsXnz5tvdunWTz5gxwy0xMdGsrKyM/8Ybb+R9+OGH+YcOHbJYtmyZi42NjSotLU08f/78nDfeeKOo9rkkEonm8OHDlpMmTSqysbHR2NnZqWtvNzMz0xw/ftz0+vXrIn9/f4WTk9MD27nQ1+KuXbvW7uDBg9Y8Hg/p6enir7/++vaQIUPK4+LiTGfPnt1Jo9HA1tZWtW/fvnRzc3Par18/X4VCwauqquKtXr06c+DAgRXvv/++6+3bt0VFRUWCcePGFU6bNq2w9nleeOGFovj4eLPDhw/frO8+fffddzYrV6508fT0lFdUVPDmz59/b/jw4WWurq7dBwwYUHLnzh3Rd999d3vKlCmdq6ureWKxWLNr164MCwsLTVRUVJeqqioeIQSbN2/OyMzMFC1YsMBdIpGoO3XqpNi/f39G7RrnDz74wOXo0aNWGo0GH3/8cc748eNL3n//fde0tDST8vJy/t27d0W7d+++1aNHj+rmflaYjo/LaAOrCCE7AXgByKCU3tdtWmrUyJhHS6PWjn+pLNcmm9be2kQx53xNoilSuQPUGzi14J+E0q6rdtzL43OAzFjtvqpKYPo94Mr3wF9z/0kSn98E2PoDl7/7J3kU6wacD54GBE7SrjfVJUS+owCPAdoaTqGFtic6jw/MKoQEgARAzZD0UT8+fE1eQ2p+LPwlE5TmQQ0rwE474gClFNXV1SgtLYWpqSnEAgmO/3ES5VWlqNZUghaI4V4UiktVx1EhLICztx3U+UKkJfLBd1XCxtYDQc97wMrWEt9l/QCNz01oTOR4631dm92a3Mv9odAefKw/86HtdRk6PWznzp2Rn5+Pzp07cypvKP04rPplU+5YKQHwdMvWJ8nwQKVnJiQZbOrWJ9no0aOLtm/fbnP69GmLb7/9NlOfvH766afOL774YtHUqVOLzp07Z/rhhx+6HTly5NaXX36ZY2lpqamqqiIBAQFd33nnnQIAKC0t5Z86dSo1OztbOHz4cJ+6yeuOHTsyFi1a5LJy5UoXd3d3+bZt2277+fnVPDaaPn16QX5+vmD8+PHexcXFggULFtytnTA2xx9//HHzzz//NPu///s/pyFDhpTPnDnTY/fu3em+vr6Kzz//3HHt2rX2H3/8cd7hw4dvWlpaahITE8UzZ870GDhwYCoAmJiY0GPHjqXVPa5arSYvvvhi8bp16+7MnDnTre59OnTo0K1ly5a5XbhwIVkikdCuXbsG6vfNy8sTLlq06J6vr69i+PDh3p9++mnOc889V/HDDz9YR0dHO7/66quFVlZWqr/++itddy6sWrXKcfHixXdGjRpVqlY/mNOfPXvW9Ny5cxaJiYnXCwsL+WFhYQEvvfRSCQDY29urDh48mP7tt9/abtiwwX7Tpk3ZLbmfTMfGZbQBewAvAbDT/kpAKV1i9MiYhlH6T6KpqgKsvICSDKAg+Z/k0ztK207zwtp/aj49h2jH1fz5RaA0XbveyhN46Shw/D0gecc/iea4k0DpbSBpQ806nrW5diB2sZ12gHiRhbYjEAAETwe6vaFLNHXjXgZN1b7q+tevD6/rWs+MwxZuDbbRbIpGo0FlZSXMzc1x7949xMvioPavwplb1bD4ZRAK75Xg9L2DEAgEsLS2BE55QJzpiuruRXDxt0GvqK4oTeLB3MMcPk6jYe4igrWnuMHzOfPykC3m0OxBx9DH+oZOD5uQkFCz5PRYP72zQcmfoY/1JXIeKkSARMFtKDFDRQQMwLUD3MfxZR5PEyZMKH766aelVlZWKg8Pj5o/luTkZNOzZ89a6JNZgUBAAWDlypUOMTEx1jweDwUFBYK7d+8KAKBr166VAoEAnp6eytLS0oeepfv5+Sl27959GwC+/vpru/nz57seOHAgQ79dIBAgOjo6Nzo6OjczM1MQERERMGnSpKK6x+GqR48elQDg7e2tKCoqEgBAWlqa6cSJE70AQC6Xk/79+5eWl5eTt956q1NaWpqYz+cjNzdXqD9GREREvW2CeDweHTBgQEVD9yknJ0dgZ2entLGx0ejvjX5fR0dHpa+vrwIArl+/brpgwQJ3QJukenp6yiMjIyt79uxZOXLkSC9bW1vVihUr7i5cuPDekiVLXLZt22b37LPPls2ZM6fmG/DVq1fFYWFh5TweD/b29mo7OzvlvXv3BAAQGhpaAQBeXl6K2NhYy+beS+bxwKUa5yCAX6Ed55V5lBRlwIGofx6xew0FBqwF9vbXzhYksgDMXIBXLwB3zwLJO/9JHt2fBkwsdWVctett/bXH7bNQOxi8yAIQ6f4NeG6d9lWbmTMwKqbm1+o03Zf28LkPx2orNcINqB+lFJRS3L59W/s4v7QUEqEFPKx8ceTEIdwtyoRcVQVzoTW8U5/HPXk6VGbl4FebwsXdGTnnKmHpaI0RPpPh9ZQdLDuZoHqCCmIbwYOP8/0MCIovBqDRLZtm7Mf6hlLYFQCEapccGPpYv0IEgOiWRtBvvgf6zWe1rk86c3NzOnz48KKuXbs+8EjZ39+/OjIysvzVV18tBoDq6mqSl5fH37Vrl/3169evKhQK4uPj002j0QAACGl8IskLFy6I9Y+t3dzclBqN5oEdUlJSRB4eHkpTU1Pq4OCgFgqFLWp7WzseSikBAF9f36r//ve/tzp37qzUX9O+ffus+Hw+ZDJZikwmE//rX/+q6WHYUPtfQgh4PO2Xyvruk0AgoAUFBcKSkhKeRCLRJCcnS+o7pp+fX9Unn3yS07dv3yr9vtXV1WTRokW5PB4Pc+fOddm4caPdu+++m79jx45MjUYDLy+vbq+99lpNjXRgYGD11q1bHTQaDQoLC/kFBQVCZ2dnVT33oCW3k3kMcEleCymlXxg9EuZhAgnQb6k2Ga3daWjcSW37ztoCXta+6ur98cPrnMNaP9ZWUl1djeLiYpSWlqK0tBTBwcG4m3UPx48eR4W8HGXlZfAX94ZVSSckKn6HlY0VOgc74tYuOW4VCmHm6oM+XiHoN8sHxTflkJerYO7kBbPLH4Bc/VY71NHAbx46r6mt8IHfDR6qyUKjHS3BQsPpOrk0FWiJsLAwg2pGDe2w1d4Ye6g1puNYsmRJbt11y5Yty3n99dc91q9f70gpxeDBg0uio6NzfX19q8LCwvx9fX2rra2tuT3WALBq1SrHpKQkiampqYbH42HTpk23a29PTEw0HTt2rLdQKKRKpZLMnj07RyKRUABYvXq1yw8//GAPAKNGjSr88MMPubW9qWP9+vW3J06c6KlSqQgAzJs3794zzzxTvmLFCpfIyEi/3r17G/zHXN99WrJkSe78+fPv9u7d279Tp05yOzs7pYmJyUP/0K1duzZr6tSpnSsrK3kA8Oqrr+YHBQVVv/fee534fD40Gg1++OGH9M8//9zp2LFjlpRSPPXUU6W2trY1x+rbt29VeHh4eY8ePfw1Gg3597//ndXcTmTM44009Q2GELIJwBUAMgAUACilZ40fGndhYWFU/5i0PUtLS+vQQ+20JH61Wo3y8nKUlpZCLBbDwcEBf/31F+7fv4+ysjIQlQD9PKIQd+00ckoz4ORlC1JhAvmBzlApVOB1qkTvyd7wCHZA6t5SmDuLYO4sgo2XKcxdmq7OOz1+Pq6l/QsBPv9Dvz3Lmyy/ZMkSUEpBCMFnn33WZPll7/8fVJblEJSa45NVHzRZ3tDkWD/aACiwKHpRk+UNteSTpaBCNYiSj8+WNT3JgqHxfzH3KygkVRBVmmLBV/XU3LfQ4kVLAB4FNASLFjf9fjVHR//7BR6+BkKIjFLaqt9mk5KSMoKDg5uVkDHtk1wuJyYmJlQul5Pu3bsHxMbGptZulsEwxpCUlGQfHBzsWd82LjWvQgA9dC9Am8C2q+SVaVtyuRxlZWWwt7fH/fv3kZKSUlNz2rtnJEiFCX449B1MxRJY21rB7G4nSDI6466qBKZWFnj2gzBk/yFHxrFieDn1QHef3ggY7QCNkkL1kgamNgIQ3j81zb1mWhgc4zkTKSqjfkdxhhT9OJQ3tE2nyqJCW3NpwW2cVEOHmjI2KtBo29QKuNUcGxq/wrRaO9SXqXE6CLMOWwxjPOvWrbPbu3evXXl5OW/8+PEFLHFl2hqX0QYmP4pAmPaHUgq1Wg2BQIDU1FSkpaUhMzMTpqamiIiIQMyvh3HpchIoKCQm5og0/xfu3ctBTsU9+D7lAicXdxyfdQ+WduYIcx4H6XAHeD9vg4yTxTAZzoe5sxckDkLwhTx41tOvCyaAyLx1HhlVemYBPKpdcmBom05RlVhbs1jFrc2rocmxsbmXqJFtxYd7CbcRfQyN310gRbYqBe4C47SNZh22GMZ4Pvjgg/wPPviA1aYz7QaX0QZ8AMwH0AnaB5eglA5q7gkJIbYAdgCwAnARwLu0VtsFQog7gB8B8HSv9yil7b9NQAdUWVmJ/Pz8mlrS7t27Qy6X4+DBgygtLUV5eTnC/CPhaxmCU5fioaEa+IV74N5xFXYsTEK1yhqdHQZj/N4Q5CZVIPvvUvh290KIkxROQWYQmfMR9szDHR88+1s/8mt1r7aoaRNpDIbWLBqaHBvbm2sWG1Te0Pjf/HScUR+7sw5bDMMwTw4uzQa2AlgGYBGAJQAGtPCccwHspZTuJIR8D2AwgCO1tpcBeIlSep8QEghgI4CnWnjOJwalFHK5HKWlpSgrK4NQKISHhwfOnDmDzMxMlJaWQqFQ4K1XZyDhTBKupFyGnbM1xAIJTsVkQH6fgJZ5oc9Qd4S97YXfZt7ELesiBDo/DY1dGfr2D0SZrxz8abwHHue79LSAS0/jJIat4c0v5xg1eTJ2cswwDMMwjBaX5FVJKT1CCJlPKf2NEPJ2C8/ZH8CXup9/1f1ek7xSSmtPkSMHwNrW6Gg0GlRUVKCsrAyOjo4oLS3FhQsXahLVXr16wc/PD2vWrIGZxBxW1paw47kjv0qI+zkAX+GKETP7I+NQFXaPuAxzZ0t4OT+LvmM8IDTjIUui6wjl5AszJyH4Ah5GbPrnMW+abqgsC1eTtroFTyz3KkvjJsexs4BLG4EGRmOoy9AOW6eXZyL5pxLcG53JakifMF/Ff+W6M3mni/73bUO2XQOA14+8HqBfNylwUs7cXnPvPr3n6aAieZEQALytvCt/efGXax+d/KjzkYwj9voyj/4KHj9HjhwxP3XqlPnNmzdNFi5ceK9bt27yto6JYQzBZbSBWABDoH2UnwhgAqU0pNknJCQFgD+llBJCngUwnlI6rZ5yfGjHmF1NKY2tZ/tUAFMBwNXVNfTkyZPNDemRkcvlMDGpP/FTq9VQq9UQiUTIyspCWVkZKisrQQhBaGgoLl26hKtXr0IkEkEsNkVo536oLlQh+34mbNzNYO1mhsyNJlDdF0JVQeH6rAjSKaa4fVAOVSWF2I5A7MCDXYgQVEMf6ADVGvF3BMaM/8edu2p6u0+cVM+QZa3AmPF3OegPQtWghI+bL1xvsvyuXbtqRmN4+eWmr/fYhBJAA4AHDNht1QoRP3od/fMPPHwNvr6+j9VoA0ePHjU7dOiQ1erVq+tNchcvXuzo6OiomjFjRotmuzLU2rVr7bZu3epACMHatWsz+/XrV8mlTL9+/XyvXr0qeeutt+5/9dVXOYC2EuP111/3uHTpkkSlUpF33nknt6HZu1577bVOcXFxFhKJRA0AR44cSXNwcKhp2L58+XKH4ODgqqFDhxo8rFZT11Tf9jNnzpi+8847nXk8HhUIBPT777/PCAwMVABAfddqDIWFhbznnnvOTygU0qqqKt7SpUuzR44cWVa7zKlTpyTz5893U6lUpEePHpXffvttdmP76e+zhYWFGgCGDh1avHjx4oeGbGtM3ftFCKEN3atHpTnvcWPr58yZ43rixAlLoVBIv/7668zevXtX5eTkCKZOndrp119/Ta97/paONjAO2hEGZgF4BUCT/1PpEs8z9WyKAVAEbXvXYt2yoX9ENgL4rb7EFQAopZsAbAK0Q2W11yFs9P/BV1RUQCaTwdzcHKWlpQgMDIS5uTl27NhR8yi/d+/eeLrPs7h+8SbUVTx09ukMRbYQmdv4ILl+8M71xsiNgSjLkePvNdkwdxbBz8kFvj1t4RBohvuOFTB3EsHU9p/H+T7vt961dPShgh5VswFjncOo9z9oGnBpI0jQNE7nqN1hi0v5e6MzkfxTHgJHO8DHp2PWvHb0zz/weFxDY5YvX+68bdu22w1tv3jxomThwoX3HmVMeXl5/G+//dYxMTHxekZGhnDixIleMpkshUuZ7du3Z8TExFhmZ2fXjAcok8nEKSkp4osXL14vKiriBQUFBTaUvF69elXy888/p0mlUsW4ceM679+/30qfuG/bts1ao9GgOYlrU9fU0PZOnTopjx49mmpjY6PZu3ev1ccff+z2888/pwNAfddqDFZWVpq4uLjrQqEQycnJorFjx3YZOXLkNf326upqMm/ePLeYmJib+lnFmtrv6tWrkv/+9783m1uDXd/9+t///nezoXv1KDT3PW5o/dmzZ01lMpnZhQsXrqelpQknTpzodf78+VQXFxeVhYWF5ty5c6YRERFVXOPjMtpAga7tqR20Na/2HPZRA+hT3zZCiDmAYQB26ZYH6imzEkAOpXRd3W3tjb6zU1lZGQgh8Pf3x/nz53H9+vWabR9++CFycnJwLfkaHB2dYGFugcy/SoEiJTrlhcPFzw49xnrg59euY9fnl2Hu5A47qQQRk7yQc6EMZaYK+DvZw9xZBImDEOYuIvxrR8BDsTh2NWuDO8AAMPrA+FveX4psCw3cy3h4c1Xrj8N6OmEerh14AwEKB/Qb2HQ8hnbY6jffA85jFB02cWXav6KiIl5hYaHAzc1NpdFoMHny5E7Xrl2TqNVqzJgx4/6UKVOK0tLSTIODg6s3bdpks27dOicAGDFiRPGyZcvuAUC3bt0C3N3d5fn5+UKhUEhPnDhx48CBA5Zr1651qq6u5vXv3790zZo1BjVd+Ouvv8x69+5dLhaLqb+/v6K8vJxfVVVFTE1NaVNlunTpoqx7PA8PD6VQKKRyuZyUlJTwraysGhwiJCcnRySVShVlZWW869evm06fPj0PALZu3WqzYcMGx+HDhxfFxcWZhoeHc04auFxTQ9trD7ElFos1+ml6AaC+a61Pfe+RiYkJ5ym3+Hw+9BMfFBcX8wMDAx+oTTx69KiZmZmZZvTo0d6VlZW86Ojou0OGDClvbL/MzEyTgICAZje9qO9+OTg4qPX3s+69qqu+z7uZmZnGmJ/bhrY3tD45OVkcEhJSAQA+Pj7K7OxsE/3xhg0bVrJ7926bVk1eCSG7oE1cM3WrKIC/DLkJdXwFYAchZAaASwD+0J1nDbQdwzoDeA/AGULICQB5lNKXWnC+ZlEqlTVJqZOTE1QqFU6dOoWysjKUlZWha9euiIiIwJ49e0AIgYWFBRxtnWGr7ARy1wpu8m4YOsYDFekEh9+6hfJ7CvBKwtDjyy5w622BU//OhLkT4BXQCQ5dJSCEYMQmKQTiB+d+d+lhAZceDQTJcBc7C10MaNNpbMaewcvQcViT9+cBVLtkbVKZjujSpUtid3d3BQBcvHhRnJWVZRIXF5cCaJtlVVZWEqFQSC9duiTesmWLw99//52i+w82cMGCBbkmJiY0MzPTZOfOnek9evSoHjlypFdsbKzZmjVrnE6cOHHD1NSURkRE+OXm5vKdnJzUAFBeXk4GDBjw0ETSw4YNK46Ojs4FgLy8PIGNjU1Ngmlpaam+f/++QD+tK9cyeg4ODmpvb2+5t7d3t6qqKt7atWvrrWm+ceOGqKysjB8WFiZNSUkxXbRoUfazzz5bCQCTJ08umjx5clHdfbhcD5d4m9peWlrKW7RokduWLVsy6ou9IUqlEvW9R1FRUeVcYweA9PR04ZgxY7zT09PF33zzzQMxZGdni65duya5dOnS1ZKSEv6AAQP8bt68eZXH49W7340bN0QVFRX8iIgIKQBIJBL1iRMn0gy5rsbuF5d7VffznpSUZDJ16tTOxvzcNrS9ofU9evSo2rBhg2N1dTW5cOGCODc3V5SXl8f38PBQ6aYFtjPknnFpNuBKKX3GkIM2hlJaAOCh/00ppbN1P+ZBOzHCI5GSkoK7d++irKwMVVVVGDduHM6fP48///wTlpaWsLCwwLBhw2BpaQlbW1t4enrCwsICd46ocOxwOlzu9YeJpQCD/q8L/v5PNk7vyNTN/uQIa0tbmEs16PuRKcychbhbdBueftYAgAGfez0US93EtV1pZ8mfwS5tBKFqbackI8S/5fO9NeOYvvnpuCbL62eES0hI4JRciiqFUJgpIKrk9qdhb2+PvLw82Ns3+aBEi9ZZtjLWYYt5lIKCgqrDwsIqhg4d6h0aGlqxdOnS3Pj4eFN/f/+qvXv3Wk+fPj1PP12rSCSiYrGYymQy06effrq0R48e1QDA5/Pp8ePHLfLz84X9+/f3A4DCwkKBRqOp6TBgbm5O9QlDQ+zt7VVxcXE1A1aXlZXxHR0dVYaW0fv5558tc3JyhLdv375cUFDA79u3r//o0aNLatfkAkB8fLzpmDFjCr7//vusDRs22J45c8a8qfvG5Xq4xNvYdrlcTkaOHOn9wQcf3AsNDTVo1pLExMSH3iMLC+03eq6xA4CXl5dSJpOlpKSkiAYMGCCdMGHC5dqxh4aGltva2mp0L1VOTo7Azc1NVd9+8fHxplFRUYX79u176EuESqVCaGiof931gwYNKlmxYkVNu96G7hfXe1X3815dXc0z9ue2oe0Nre/cubPypZdeKnzqqaf8PD095V26dKlydXVtdod8LsnrOUJId0rp5aaLdjylpaUAADc3N1haWoJSirCwMISHh4OQBzs19enzT0uIMps82DrzYOYkhIWbtgNEn/fcHzq+yIwPiZ024SAlhneSajeMnPwZXdA0UF2bTmPIVqVoa0ZVnP7tNJjCTKEdR9aMW3v9vLy8B5ZNKel2udYMVa0/ccK1A3mARrtkyStjDEFBQdX69pKVlZU8facfX1/frrNnz86Pj4+XhISEVObk5AjlcjkBgH379ll6eXlVCwQCxMfHS/QdmJOTk0VZWVkmrq6uygULFtydMmVKEfDPNKn6c3Kpwerfv39FdHS0m1wuJ5mZmUIzMzN13USTSxk9Simsra3VAoEA1tbWGqVSSVQqFQFAb9y4IfL19VUAQFJSkmnXrl2rAG1N69KlS900Gg14vIYrSbjWXjYVb0Pb1Wo1Ro0a5TVixIjiSZMmFTcYiE7t6wFQ73vUt2/fSkNir/3o29raWm1mZvZAswt97EqlEuXl5byCggKhk5OTqqH9kpKSTENCQh7qgAcAAoEASUlJTfaAre9+iUQi+sILL3jXd6/q3pe6n/eIiIgyY39uG9re2H7z58/Pmz9/fl58fLx46dKlLgKBNgVNTk4WBwYGGvRFpsHklRByA9p6GAJgPCFEqfuZUkofuuiOqlevXg+t07draUzgmCdsJh8jJ3/GdjphHpJ/egOBHNt0Gkokt4BCXAqRnNtQVg4ODsjLy4ODQ/v4HFV6ZupmIMtsunAzBIxyQPJPbAYsxnhsbGw0NjY2qqysLMGsWbM6ZWVlmSiVSjJu3Lh8e3t79YULFyRvvPFGgaenp2LcuHHe3333nYO9vb1qx44dGQBw4cIFib29vTIyMtJXo9GQzZs3Z0gkEjphwgSvTZs2OQoEAjpr1qzciRMn1gznyKUGy8HBQT1lypT7ffr0kRJCsGbNmpo/shdeeMHr4MGD6Q2VGT9+fOeEhARzhUJBEhMTJbGxsTdHjhxZumvXLtvQ0FCpXC7nvfXWW/ctLCw0crmcREVF+aSmpiYDwJUrVyRz5szJBQCxWEy7dOlSffLkSYm+6UB9uNZeNhRvU9ezY8cOm+PHj1vl5eUJ9+zZYxcQEFC5ffv2rPquNSYm5lbt62noPdL/f8019oSEBNM5c+Z04vP5VKVSkRUrVmTVjt3e3l49ffr03D59+kiVSiVZsmRJtkAgwLlz5+rd78qVK5IbN26If/75Z1sAGD16dOG8efO41Ro0cj8buld132cAeOWVVzrX/ry/9tprRY/ic1vf9sb269u3r69arSY2NjaqzZs316w/fPiw1dtvv33fkHvW5FBZHUFYWBjVP4Ztzzp6T9+OHP93veJBNTwQngZvxT/8haUuQ9ukLl60pGaorEWLP2uy/JIlS2pGovjss6bLL45erPvqCCyKXtR0+cX/zJi1aFHT5f/vjY0o75QL8ywnfPB9019QmjfOq3a0gY5a89qRP/96da+BEPJYDZUVGxtrdujQIStDO6cAQEREhN8ff/yRpn8M3dH88ccfZvHx8WaffPKJQUlAe1Xf9XT096g1PG7vc3OHymqykSUh5COie35OtD5qcbQM84gFdPkJhKgQ0OUnTuVrd3jiQpLhAWiI7rF700JDQ2vG8G0PLJJC4XIoChZJ3OJJSEgApRRcvzRe+ylX22zgJ4OGPuRsy7zVWLxoCbbMW22U4zMdw8CBAyuak7gC2rZ5HTkpGjRoUMXjktAA9V9PR3+PWsPj9j67uLio6ktcm8Klh9AQqque1S2HGHoSRlvzdGxCCU4v5/ZYNiYmBkuWLEFMTAy3E8TOAlYJtEsjMDT+9qbflDJMebkv+k0pa7owDE8uIwIGwPW3KEQEtHT25Pq5FykBDdEuOyBDvzwY+vnPFpdp2xyLub2/BoudhS4H/Y3298W0vStXrlxruhTTlth7xOhxSV4lhBAxAOiWbDDRZqjdYYULQ2v+cGkjoO9QZQSGxt/uDPxGO3MUx85mUVFR+OyzzziPZdrPOQpvjQtDP2du5Q2tuayodn5g2ZSwsDAQQhAWxvWJMKmzbJy+rS7XNruGfnkw9PPvXqzQJvfFxpmA5vRmC2zedQanNxtpel6GYRiGMy7J6xoAcYSQnQD+BrDKqBE9pgJGOQA8cO6wYvBj5aBpAOFrl0ZgaPxPmtNHhuG73X/j9JFhRjl+kUsBwKPapRFUh51DzvAYVIed41Q+Pz//gWVrM/Tz7xyoBOGp4RxonJrp5BtjQKkAyTfGGOX4DMMwDHdcZtjaSwj5E0AXAGmU0ocGNmaaZugMQ4bOYISB3xh1CCs2Q1LjtEkND8k3xqBfWwcDw8eRLXIu1CbHztymfK89PSwXW361Qbb1Z3D/VYE3jTCDlyzXDBQUslyzhweRbhWG1UwzDMMwxsNpVHxKaSGlNJ4lrk+u9tbm1eA2we2MoY/dBaVmANUtjcC9jKd97F7GbaIMQ5tVZNsItW1SbYwz/4ixO8AFvqR98hD4EnvywDAM09a4TFLAMO1ukHlDpz81tkDfn3At7V8I8PkfgKaH4po5c6ZBx1dZVAJEt+QgLCzMoJrRN1ctNCie9sbgJxUGYk8eGIZh2g+WvDKctLdB5g19bG1s/aaUod+lvkZrc+xebYFscRncq7l1GDJ2Mmco9ypLg+JnGIZhmIY0mbwSQvwATAFgB12DL0rpG0aOi2ln2lvNU3tLzozd5rije/PLOY/FIP8MwzBM2+PSwG0PgAsAvgewRfdiGOYRMnQcU0PbBJ9enonvwmXtpk0zwxgiJSVFFBkZ+cC05R4eHt247h8ZGemXkpIiqu84hvr999/Ne/bs6R8eHi4NDg72P3TokEXteMrKynhDhgzxDgsLk4aGhkqnTp3qDgDvv/++6/r16225nGPu3Lkua9eutWto+9mzZ01/++0385ZcB8O0Z1yaDWRRSncbPRKGYRpkk2OFIpcS2ORYcSpvaJvgcynHUDk0E8UpHuiH11sYbT1iZ6HLpY3aZhWshpp5jH300UfuBw4cuOnj46NUqVQoKSnh196+fv16u+7du1etWLHiFgDk5uby6z9S8yUkJEiys7NFQ4cOLW/tYzNMe9BgzSsh5GVCyMsAygghqwghE2utYxjmETK9EAmXQ1EwvRDJqbyhve8rPTMBHtUujeHSRhAjTqLBMA359ddfLXr37u0XGhoqfe6557pUVlYSAPj8888du3XrFhAVFeVdXFz8UAKZlpYmfOaZZ3z69Onj98wzz/jcvXtXsG/fPsvJkyd30peJjIz0TU1NFdXeTyKRaA4fPmxZVFTEEwgEsLOzU9febmZmprl8+bLp9evXRQDg5OT0wPaG/Pbbb+YBAQGBAwYM8ElISKgZdmTUqFGe4eHh0sDAwIAff/zRCgDWrVvn9OOPP9qHh4dL09PThTNmzHDr3bu3X2BgYMDKlSvtDbl/DNMeNdZswFf3ugGgBICP7nfWaI1h6jJwel5DH+tbe0keWDbF0KGswsJCdTNyGakDXNA0UCNOosF0XHP2XuzkvSAmdM7ei52aLt2wK1euSMLDw6X6l359//79K86fP58qk8lS/Pz8qr///nvbO3fuCHbt2mV/4cKFazt37szIyckR1T3e7NmzO3366ac5f//9d+qUKVPyo6OjnUePHl0aFxdnXlVVRa5fvy4SCATw8/N7YFq3HTt2ZJw7d848ODg4sL7kdvr06QV9+vSpGD9+vLenp2e3jRs3cmoq8NFHH3U6ePBgWmxsbJpcLq8ZcHjbtm2ZcXFxKceOHUuNjo52B4C33347d+LEiflxcXEpXl5eyi+//DJHdw+ur1u3zrn2/gzTETXYbIBSuhgACCFDKaW/6dcTQp59FIExTIdSe3peDo/FDX2sX5xR/cCytRm9A9zAb3DTcw7rsMU85JeLdxw1VLtcPS4kq7nH6datW+XZs2dT9b/r25jKZDLxwoUL3RQKBS8/P19gaWlZkJKSYiKVSquEQiFsbW013t7eD/1hXb9+3XTBggXuAKBWq+Hp6Snn8/kYNmxY8c6dO62Tk5NNJ0+e/NAUc35+fordu3ffBoCvv/7abv78+a4HDhzI0G8XCASIjo7OjY6Ozs3MzBREREQETJo0qckx1MvLy/m+vr4KAAgNDa3QxzV37lzX+Ph4Mz6fj7t37z6UhAPAypUrHWJiYqx5PB4KCgoEd+/eFXh5eRlnOjqGeQS4dNj6qM7vhg1QyTBPAgOn5zX0sT6bnpd5XI0McbvPI9qlMY6/dOlSl8WLF9+Nj49PGTx4cAmllEilUnlqaqpYqVSiqKiId+vWLXHd/fz8/KpWr16dGRcXlyKTyVJ+/PHH2wAwa9as/O3bt9vHxsZavfzyy8V197tw4ULNsdzc3JQajeaBWs6UlBRRVVUVAQAHBwe1UCikXK7DzMxMffPmTSEAJCYmmgHA33//bXr16lVTmUyW8ssvv9zk8XgUAEQiEVWpVAQA8vLy+Lt27bI/f/58ysmTJ1MtLCzUGo2G491jmPapwZpXQsgkAK8CCCGE/KFbLQRQ9SgCY5iO5HTCPFw78AYCFA7ox2H6U0P1C/sSfUUbQYKmAWAdnpjHx+pxIVktqXFtytixY4umTZvm6e3tXW1paam2tLRUu7m5qcaOHVsYEhIS4O3tXe3m5qaou9/atWuzpk6d2rmyspIHAK+++mr+zJkzCz09PZVisVjTrVu3MhMTk4cSz1WrVjkmJSVJTE1NNTweD5s2bbpde3tiYqLp2LFjvYVCIVUqlWT27Nk5EomEAsDq1atdfvjhB3sAGDVqVOGHH35YU7P75ZdfZg8fPtzXyclJYW5urgaAoKAguUqlIr169ZJ269at0sLCQg0AAwYMKN+4caPjkCFDxBs3bszy9fWtCgsL8/f19a22trZWteb9ZZi2QCit/0sfIcQKgDWA9wGs0q1WALhHG9qpjYSFhVH9XO7tWUcf55LF37DvwmWgam3l61txTdemLlmyBJRSEELw2WefNX2CVQLUnOD9jvl/D/v8tL2610AIkVFKw1rzHElJSRnBwcEPPU5/nAwaNKjLypUrs4OCguRtHQvDPK6SkpLsg4ODPevb1mCzAUppCaX0NoD/A0B1LyEAJ0IIa+zNMLUEjHIA4XN/rG9oswHW4Ylh2p5cLif9+/f3cXR0VLLElWHaDpdxXvcD8ASQAkAKIAuAJSFkIaV0r6EnJITYAtgBwArARQDv1leTqyuXptv+g6HnYZhHqd98D/Sbz332MYM7SLEOTwzT5kxMTOjJkyfT2joOhnnScemwlQqgC6X0KQBdAFwH0BPA3Gaecy6AvbrjmQEY3EC5BQDONvMcDMMwDMMwzGOIS82rFIB+lo4KAH6U0jJCSGkzz9kfwJe6n3/V/X6kdgFCiAcAFwANNmQlhEwFMBUAXF1dkZbW/r8My+XyDhFnQ1j8bYvF37Y6evzA43ENDMMwXJLX3QBkhJDLALoC2EUIEQA42sxz2gIo1v1crPu9rkUAlgEY19BBKKWbAGwCtB22OsLj1I7e4YPF34ZiZ4Fe0o02wGEc2ZiYGMhkMoSGhnJqnnB6eSauHchDwCgHg5o/GKJD3390/PiBx+MaGIZhmmw2QCldBe2j/W8ADKOUrqKUqiilSxvahxDCJ4T8Xc/rUwBF0LZ3hW5ZWGff7trT0mvNvSiGedROL8/Ed+EynF7ObXpVQ2fYQtJ67fSqSes5Fa89CQIX1w7kgaq1S4bpaFJSUkSEkNCdO3da69fpJykAgHXr1tn17NnTv1evXtKhQ4d6Z2Vl1VTcVFZWEgsLi5D//Oc/dnWPO2HChM61Z+uqTa1W4+WXX+7cs2dP/9DQUOkLL7zgBQBr1661mzt3rgsA7Nixwzo4ONi/V69e0pCQEP/ExERx3dia0lTZpUuXOnI9FsM8LppMXgkh9gBegjaBnU4IaXJcH0qpmlLap57X5wBOAhimKzpM93ttoQCkhJAjAF4B8BEhJNyAa2KYRy75v9rkL/m/3JI/Q5PLmOphWFL+GWKqhzVdGM2YBKFXMghRI6BXMqfyDNPeeHl5Va9cudK57gD8R44cMd+xY4f9X3/9lRIfH5/y+uuvF4wbN85Lv33Pnj3Wo0aNKti/f/8DTwGrq6vJ1atXTc3NzdU3btx4aOaqn376yVKlUiExMfG6TCZL+e677x765vrZZ5+5nzhxIjU+Pj7l3LlzKT4+Pg+NJ9tSmzZtYskr88Th0mHrILQ1pLcA3NS9WuIrABMJIacAKAH8AQCEkDWEEAdK6TZK6VOU0iEAfgCwglIa18JzMoxxkTrLJhiaXMpUvUDBg0zVi1P5qKgofPbZZ5xHNOjXZTLemtAb/bpM5lSeYdobZ2dnZffu3St//PFH69rrt23bZjd//vwcc3NzCgDjxo0rqays5KenpwsBYM+ePbaffvrpPZFIpMnIyBDq99u3b5/V0KFDiydNmlSwdevWh5q3WVhYaG7duiVOTEwUazQaODk5qeuW4fF49MiRIxaVlZXE1NSUWlpaNjm1lVqtxsiRI7169eolffPNNzvp1//6668WvXv39gsNDZU+99xzXSorK8m3335re//+fVF4eLh03rx5zjKZTNynTx+/Xr16SSMiIvzu3r3LpWkgw3Q4XJLXQkrpF5TSH/WvlpyQUlpAKY3SJaizKKUa3frZlNK8OmWj2TBZTEcQOEY7zmvgGG7jvBqaXEqUIoDqlsZg4PS2DNNqDkzthMU2oTgwtVPThRu3ePHinLq1r3fv3hV5eXk9UOPp7u6uyMjIEObm5vLlcjnx8fFRTpw4sbB2krpnzx7bKVOmFI4fP774zz//tEIdQ4cOLX/llVfyZ8yY4dGpU6fuS5YseagGdM+ePbf27Nlj6+Pj023IkCHeubm5/Kau4ccff7Q2MzPTxMfHp4wbN65QrVYTAOjfv3/F+fPnU2UyWYqfn1/1999/bzt9+vRCR0dHRVxcXMqXX355TyqVys+cOZMaHx+f8uKLLxatWrWKzSfNPJa4JK93CSHvEkL6EkIiCSGRRo+KYR5zhrZ5reCrAKJbGsPAb7Qzd3HoDMYwreryfx1BNdplC3Xp0kUZFBRU+cMPP1jr17m4uCjS09Mf+NZ3584dkaenp3L79u22OTk5oqeeesp38+bNDr/88osNABQUFPATEhLM33jjjc6DBg3yuXPnjujcuXOmdc83e/bsgvPnz6devnz56q5du+yTk5MfOE94eHjVL7/8kn737t3L3bp1q1q+fLlTU9eQkpIi7tWrVwUAPPvssxX69TKZTNy3b1/fXr16SY8cOWKdlZUlrLtvenq66Pnnn/fp1auXdOvWrQ7Z2dlG+rbLMG2LS/IqBNADwBQAb+mWDMPUYmiHJ0PbvLoXKwAN0S4Z5nHS/aX7IDztshVER0fnrFy50ln/+6uvvlrw1VdfOZeXlxMA2L9/v6VYLNZ4eXkpf/rpJ5uzZ8+mnDp16sbZs2dTPT095TKZTLxjxw6b2bNn55w6derGqVOnbmzYsCFj+/btD3ToysjIEBYWFvIAwNraWmNmZqamlD7QcOjChQti/c/Ozs7Kuu1x6+Pn51edkJAgAYCTJ0+a6dcvXbrUZfHixXfj4+NTBg8eXKI/F5/Pp2q1tsXCqlWrHMeNG1eoa9ub185mcmeYVtNkexhKKWsExzBNCBjlUDPUFBehoaE1Q1lx8eaIIu1QWU+xx/rMY2bUpiyM2pTVWofr0qWLMiQkpPLEiROWABAVFVWelpZW8PTTT0v5fD61tbVV7dmzJz01NVWkVquJvb19TVvVQYMGlWzdutUuISHBbPPmzbdrrS+fPXu2h1qtzubztU/+09PThR988EEXQghVq9VkyJAhxV27dpUfPXrUXL/f3Llz3fLy8oQikYiamZmpd+3alQEAeXl5wsjISD99udjY2BsSiYQCwMSJE4v3799v06tXL2loaGgFn8+nADB27NiiadOmeXp7e1dbWlqqLS0t1QAwYsSIomeffdZ30KBBJaNGjSqeM2eOx969e21dXFzYN13msUWa+mZGCAkDsAaABbQjASyjlM4zfmjchYWF0YSEBuczaDc6+hiLLP62xeJvWx09fuDhayCEyCilYa15jqSkpIzg4OD81jwmwzBPnqSkJPvg4GDP+rZxaTawCsAoaDtuqQBw6+7MMAzDMAzDMK2MS/KqpJTeB8AazzAMwzAMwzBtikvyeosQMheAFSHkPQApRo6JYRiGYRiGYerFJXmdAaAEwDkACgDvGDUihmEYhmEYhmkAl9EGVAA2PoJYGIZhGIZhGKZRDda8EkJuEEJS67xuEEJSH2WAzBMidhawSqBdPgEMnaSgozu9PBPHJpTg9PKHpn9nGIZhGIM0mLxSSn0ppX51Xr6UUr+G9mGYZru0EaBq7fIJYOgkBR3dtQN5gIb7JA4MY4iUlBQRIST0m2++qZnedezYsZ3d3Ny6G+N8v//+u3nPnj39w8PDpcHBwf6HDh2yAAAPD49uAFBWVsYbMmSId1hYmDQ0NFQ6depUdwB4//33XdevX2/b2LH15s6d67J27Vq7hrafPXvW9LfffjNvaDvDPM64tHllGOMLmgYQvnb5BAgNDQUhhPMkBR1dwCgHgAfOkzgwjKECAwMrDxw4YAMAVVVV5M6dOyL9AP+t7aOPPnLft2/fzbi4uBSZTHY9IiKisvb29evX23Xv3r0qISEhRSaTpXz++ec5rR1DQkKC5Pjx4xatfVyG6QhY8sq0DwO/Ad5XaZdPgKioKHz22WeIiopq61AeiX7zPTBgtxX6zfdo61CYx5SVlZVaKBTSO3fuCPbu3Ws1ePDgEv22goIC/rBhw7wjIiL8+vTp43flyhUTAJgxY4Zb7969/QIDAwNWrlxpDwCHDh2yiIiI8Bs2bJi3n59f4Pfff29T91wSiURz+PBhy6KiIp5AIICdnZ269nYzMzPN5cuXTa9fvy4CACcnJ3XdY9Tnt99+Mw8ICAgcMGCAT0JCQs3UsKNGjfIMDw+XBgYGBvz4449WALBu3TqnH3/80T48PFyanp4urO9aGOZxxZJXhmEYps0sOLWgU/CO4NAFpxZ0aumxRo8eXbR9+3abffv22b7++uuF+vWffvqp84svvlh07ty51NWrV2d9+OGHbgDw5Zdf5pw/fz5VJpNdX7dunbNcLicAUFpayj948OCtP/7448aqVauc655nx44dGefOnTMPDg4OjIyM9E1NTRXV3j59+vSCPn36VIwfP97b09Oz28aNGzk1Ffjoo486HTx4MC02NjZNHwsAbNu2LTMuLi7l2LFjqdHR0e4A8Pbbb+dOnDgxPy4uLsXLy0vZ0LUwzOOowdEGCCGfoIGJCSil/zZaRAzDMMwT43D6YUcN1eBw+mHHL576Iqslx5owYULx008/LbWyslJ5eHio9OuTk5NNz549a7F582ZHABAIBBQAVq5c6RATE2PN4/FQUFAguHv3rgAAunbtWikQCODp6aksLS3l1z2Pn5+fYvfu3bcB4Ouvv7abP3++64EDBzL02wUCAaKjo3Ojo6NzMzMzBREREQGTJk0qair+8vJyvq+vrwIAQkNDKwBArVZj7ty5rvHx8WZ8Ph93794V1bdvfdfi5eWlNOD2MUyH0dhQWdmPLAqGYRjmiTTMa9j9w+mHHYd5Dbvf0mOZm5vT4cOHF3Xt2rW69np/f//qyMjI8ldffbUYAKqrq0leXh5/165d9tevX7+qUCiIj49PN41GAwAgpPFKywsXLoh79OhRDQBubm5KjUbzwA4pKSkiDw8PpampKXVwcFALhUJObW/NzMzUN2/eFHbp0kWZmJho5uPjI//7779Nr169aiqTyVJycnIEfn5+3QBAJBJRlUpFAKCxa2GYx1GDySuldPujDIR5wsXO0o40EDTNKO1eY2JiIJPJEBoaapR2pqeXZ+LagTwEjHJg7ToZxgBfPPVFVktrXGtbsmRJbt11y5Yty3n99dc91q9f70gpxeDBg0uio6NzfX19q8LCwvx9fX2rra2tVfUdrz6rVq1yTEpKkpiammp4PB42bdp0u/b2xMRE07Fjx3oLhUKqVCrJ7NmzcyQSCQWA1atXu/zwww/2ADBq1KjCDz/8MF+/35dffpk9fPhwXycnJ4W5ubkaAIKCguQqlYr06tVL2q1bt0oLCws1AAwYMKB848aNjkOGDBFv3Lgxq7nXwjAdEaG08S+EhJDhAL4A4AFAA6CIUur9CGLjLCwsjCYkJLR1GE1KS0uDj49PW4fRbEaNf5VAO1QW4Ws7brWyxYuXQNsKhmDRos9a/fgrJm9DpUcmJJke+Gjr661+fMTOAr20EcRIyf2jwD7/ba/uNRBCZJTSsNY8R1JSUkZwcHB+0yUZhmEalpSUZB8cHOxZ3zYuHbaiAfQHkAjAB8DBVouMYfSMPFSWJMMD0BDt0ggqPTMBHtUujeHSRpAnaBxchmEYhmkIl+S1lFJaCIBPKS0AEGzkmJgnkZGHyoqQDoDL4ShESAcY5fhhYdpxW8PCjDRua9A00CdoHFyGYRiGaUhjHbb0MgkhpgDOEUJ+A8BpvLqGEEJsAewAYAXgIoB3aZ22C4QQbwD/AWAGIJtS+mpLzskw/eZ7wHmMAj4+xql5jYqKMu6YrQO/wU3POR3+sTXzRNBoNBrC4/GMMkEAwzCPP11a2OC/IU3WvFJKX6eUVgGYD2ABgBEtjGkugL2U0qegTU4H11NmHYCplNIBLHFlGIbpUK7k5eVZ1e2BzzAMw1VlZaWYEPJQ50u9JmteCSF1k8cgaGtOm6s/gC91P/+q+/1IrfN1BiAB8B9CiBOAtZTSn1pwPoZhGOYRUalUU+7du7f53r173cAmwmEYpnnUarV6YUMbuYw28In+RwChAKoppROaGw0hJAWAP6WUEkKeBTCeUjqt1vYIaJPZQABlAM4CeIpSWlTnOFMBTAUAV1fX0JMnTzY3pEdGLpfDxMSkrcNoNhZ/22Lxt62OHj/w8DX4+vq2+mgDDMMwxtZkzSuldFnt3wkh+5vahxDCB3Cmnk0xAIqgbe9arFsW1ilTCOAypfSO7lgXAfgCiKsT1yYAmwDtUFkdoS1gRx9qh8Xftlj8baujxw88HtfAMAzDpdmAa61f3QFIm9qH0v9v786jpCrvNI5/HxBwI44oJoMc48KA4NEk054ZN1xyDDKQxSOeTDKOmugo5mBwmTMGM05izIaGCS5xAi4ZotHjEhHRNuowKooSl3ZBRUXUkcG4IEiECbI0v/njvq2Vorr7dndV3y76+ZxzT926/d57n6rqxp9vvfe+0Qwc1MrxdgTGATemx9llTZYC20saCKwj64F9AzMzMzPr9fLcbeCG9BjASmBSF895CXCdpG8Bi4D7ACRdCvw4IlZIOg/4HdAPuDoiWh20a2ZmZma9R55hA0dV84TpXrFb3FMoIs4uWZ8HzKvmec3MzMys/uUZNnAocBawC9lFW0REbe70bmZmZmbWhjzDBn4JTARqNO+lmZmZmVk+eYrXVyJiYc2TmJmZmZm1I0/x2kfSbKCJNFVXRPykpqms17n2O9NZvu0ahn44kFMvPqfoOGZmZtZD5Sle59Q6hNnybddAn8gezczMzFqR524Dv+6OINa7Df1w4Ec9r2ZmZmatyXO3gcnA2cAmsrsNREQMr3Eu62U8VMDMzMzyyDNs4CRgZESsr3UYMzMzM7O29MnRZnHNU5iZmZmZ5ZCn53UU8KqkF9PziIgxNcxkZmZmZlZRnuJ1Qs1TmJmZmZnl0O6wgYh4A2gGhgC7p8XMzMzMrNvludvA94ExwF7A/wBrgGNqG8vMzMzMbEt5LtgaFxGHAkuAQ4H3ahvJzMzMzKyyPMXr2vQooD8wonZxzMzMzMxal6d4vUvSdsC1wMvA/NpGst5owdRlXP03TSyYuqzoKAA0NjZy0UUX0djYWHSUzLxJ7DN3X5g3qegkZmZmhcpzwdb0iFgXEddFxJ4R8c/dEcx6lxdnryCas8eeoKmpiYigqamp6CiZRTNRNMOimUUnMTMzK1Senlezmht53GDUN3vsCRoaGpBEQ0ND0VEyB0wk1BcOmFh0EjMzs0Lluc+rWc0dNmUPDpuyR9ExPjJ+/HjGjx9fdIyPHX0lr+55DsOGDSs6iZmZWaHc82pmZmZmdaPd4lXSBElPSFoi6RVJS7ojmJmZmZlZuTzDBr4HjI2It6pxQkmDgOuAnYBngMkREWVtzgeOAwK4KSJ+Xo1zm5mZmVl9yzNs4KVqFa7JecDNETEa2IGy2bokDQROAQ4CDgHOkLRDFc9vZmZmZnUqT8/rLpKeIOslDYCIOL0L5zwCuDit35me31Py83XAH4DtyCZGWAds7ML5zMzMzGwrkad4/WGVzzkIWJ3WV6fnH4mITZLuJpsQoQ/wo4jYUH4QSacDpwMMGTKEpUuXVjlm9a1fv74ucrbG+Yvl/MWq9/ywdbwGM7N2i9eImC+pL7AbWU9ou1L7Ryr8qBF4n2y86+r0uKps3+HABGBvsuJ1vqQ5EfFmWa6rgKsADjzwwKiHWwgtXbq0rm91VM/5F0xdxuLb/sioCQN71C258mpsbKSpqYmGhoaedQuvvOZNIhbNRAdMhKOvLDpNp9Tz73+LreE1mJnludvAPwGLgReAR4G57e0TEc0RcVCF5Ydk08uOS03HseV0swLWRMT6iFgHrAd2zP+SzLb04uwVsLnnzODVUT1uxq+O8gxhZmZWJXku2DoD2B9YBIwAnu3iOS8BTpD0MNlY1vsAJF0qaXBEvAw8Lun3kh4DHknbzDpt5HGDoU/PmcGro3rcjF8d5RnCzMysSvKMef1jRGyQ1AfYAOzblRNGxEpgi+89I+LskvXzu3IOs3KHTdmDTx2/gWHD6m/IAGQzfo0YMaJ+v/L1DGFmZlYleXpen5C0HdlwgeeA5bWNZGZmZmZWWZ4Ltqak1WmSromI1bWNZGZmZmZWWZ4LtvaWdKuke4G1ks7thlxmZmZmZlvIM2zgKrJ7vfaLiE1UGK9qZmZmZtYd8hSvERGLap7EzMzMzKwdeYrXVZK+CgyQ9BXgnRpnMjMzMzOrKE/xehrwObKZsQ4mu++rmZmZmVm3U0QUnaHLJK0A3ig6Rw67Au8VHaILnL9Yzl+ses8PW76GT0dEfc7cYWa9VqvFq6TXgE3lm8nGwA6vdbCtkaQnI+LAonN0lvMXy/mLVe/5Yet4DWZmbd3n9QFgKHA3cFNEeKyrmZmZmRWq1TGvEXEq8CXgf4HLJM2VNLbbkpmZmZmZlWnzgq2I2AA8BDwMDAJGdUeordhVRQfoIucvlvMXq97zw9bxGsysl2trzOsJwPHARuBWYG5ErO/GbGZmZmZmf6at4nUz8BTZLbIAPmoYEWNqH83MzMzM7M+1dcHWXt2WwszMzMwsh7Yu2HqjtaU7A24NJH1O0iOSHpJ0v6S9i87UUZKGS9oo6bCis3SGpAZJ90l6QNIlRefpCGV+IWmhpCckfb3oTHlIulfSCkkXpOeSdIWkhyXdJWlQ0RnbUiH/SZIeT3/HN0kaUHTGtpTnL9n+TUkbi8plZtZVeWbYsq57CxgbEYcD04AfFJynM/4NmF90iM6Q1B+YCkyIiKMi4ryiM3XQfsB+EXEw8HngRwXnyetU4F9Knh8DbB8Ro4FbgJ7+OZTnXwAcnP6OlwH/WEiq/MrzI2lbYAJZfjOzuuTitRtExNsRsSY9Xc+Wkz/0aJL+FngbWF50lk46GFgL3Jh6vkcXHaiD/gBskNQPGAisKjhPLhFR/vtyBHBXWr8zPe+xyvNHxGsR0Zye9vi/4wrvP8BkYAYl1zCYmdUbF6/dSNIOZL1mPys6Swf9K1nPZb0aAnwGOAE4EbhakoqN1CHvA68AS4BnqJ+e13K78PEFoKuBnYuL0nmS9gXGAjcXnaUjJO0MHB4Rd7Xb2MysB3Px2k1Sr9nNwMURsbjoPHlJGg88GREri87SBauARyPig4h4k2xu93qaz/0LwO7AMGBf4Cc9fbxlK1YBf5HWd+LjQrZuSBoK/Br4WkR8WHSeDjofqKvx3mZmlbh47QaS+gC/AeZExJyC43TUZ4EjJd1DVkRNk/TpYiN12GPAcEnbSBoI7AbUUzEu4P30lfUaoD/Qt9hInTIfGJfWx1FnY6gl7QrcBpwREa8WnacThgPfTX/LfymprnqOzcxatHqfV6seSccDs4An06bnIuLbxSXqHEmzgGsiYkHRWTpK0onARKAfMDUibi84Um6S+gLXkvW8DgCuj4jLi03VPklXA4eQZX4eOA64AjgA+AA4qSf36FfIvxw4FliamlwfEdcWk6595fkj4tiSny2NiGFFZTMz6woXr2ZmZmZWNzxswMzMzMzqhotXMzMzM6sbLl7NzMzMrG64eDUzMzOzuuHi1czMzMzqhotX6zJJfSTNlPSIpIcl3dDF441Nt7aqe5IeTDe2r9bxJpesf0PSBe207yvpDknbVCtDybH3lPTlkuezJB2WY79Rki6tdh4zM+sdqv4fNOuVjgG2iYhDASQN6srBIuKeqqTaOk0GOnKP12OB+RGxqQZZ9gS+DMztyE4RsVjSPpIGR8SKGuQyM7OtmHterRr+D/grSSMlKSJWAUjaSdItkv5b0v2ShqXtF0t6QNJTkk4vP1hpj6Kko1KP7oOSpqdtR0ian7bNkKSy/Y+U9Hg6x3+mbftLmpdy3CJpu7T93nScxyUdnLadI+mxtP9Zadtpadtjkk4pyTlH0mxJz0sa3dabJOmnKfdCSV9M2y6UdIOkuZKekbRv2v73kp6VdFvKeKSkc4HdU95T02EPbOf8XwXuLnlfWl7/i5K+JulWSc+19HRLGp6OP1/SzSXv07LUu/57SdPSsc8Fxqf2DWnbyZIaU7vdJG0v6Xcln9fw1G4e8JW23i8zM7OKIsKLly4vwCnAA8DrwNlp21SyOeABPgP8Nq3vkB4HAEuAfmXH+gZwAdm0qC8Bn0zb+6ZtTwM7pW3TgS+W7X85MCat90mPDwF7pPWzgDPLsowE7k/rTwIDW/YHBgPPkk3L2j+tD04556R2h7S8vrIsDwJDgbHAjLRt+3QMARcCl6bt/wBMS69zCTCQbEawF4AjU5ulZe9Te+d/nqxXHOBI4In0mg4C3kpZBgFPpzZzgMPT+veAyWn9Q+BTJZ/JJ9Lxrik516ySz/67wJnAXwM3lrRp+Tz+Dris6N9bL168ePFSf4uHDVhVRMSvgF9J+gTwkKS5wP7AEZLOSM1avrr+lqRjgWZgt7S8WeGwg4GVEfFOOkezpMFkX1ffkTpcdwReLtvvZ8B3JJ0M3E82tep+wHVpn22BealX8TJJI1KW3dP+ZwOXS+oHzAA2kk3puwFA0nPAXqltU3pcBuzSxlvU8l48mJ4PKGlfeowvALsC70TEmnS+p9s4bt7zt1gUEZslLQeWRMSfgD+19LACw4FH0/qjZFO6ArwZEW+nPMuBnXPk2YfsfzSaJP0GWAl8H1idI6eZmVlFLl6tyyQNAdZGxAfAGmAtWQ/dC8DCiLg9tesvaWfgm2Tz2/cjKzxV8cCwAhjUMjZSUh/gPeA1st7Wtem4/cr2WxkRZ6bhBEsk3UrWA/n1iHirJQswHmiOiNGSRvHx2M2nImKBsgut7iDrJTwg7QNZIfo6MAoonV+5tddBei/ui4iWYQj9I2JDKqbLj/Ee8ElJO5L1eH625Oeby47b3vlfAIaR9ZaWt680N/QSsl7ch9Ljy620FbCBLf8NKc8zAPh5REQaCnIicAVZkfx8hfObmZm1ycWrVcNQYLqkzWS/U3dGxKuSfgzMkPRtskKmEfh3YDGwAHiRrDeuolTwTALmSlpP9tX2OWns59xUnG4GzgEWlex6rqQxZF+P/1dEfJCOM6uk0P0psBA4X9I84JGS/a+XtCtZD+2VEfGupP9ImQF+kYrp3G9QRNwt6ZDU8xrAcrJCrlLbZkkXpvO9DrxLVigCLJR0O3BzzlPfSlakv9Rew2QKMDO9t++2ljF5DthH0m+BH7TSZhRZL/Ymss/j5LT9aODUVvYxMzNrlSIqdb6YWZEk9YuIjanYbiIbw/t2J47TF7gdOC5qc8eBDku93BNbeqHNzMw6wsWrWQ8k6TTgBLILo26MiGnt7GJmZtYruHg1MzMzs7rh+7yamZmZWd1w8WpmZmZmdcPFq5mZmZnVDRevZmZmZlY3XLyamZmZWd34f1TeZi5npllGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "# Retreat vs mean annual sea ice concentration\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = X['Hadley_ASIC']\n",
    "plotLinReg(ax, x, Y, dataname='Hadley ASIC')\n",
    "\n",
    "x = X['NOAA_ASIC']\n",
    "plotLinReg(ax, x, Y, dataname='NOAA ASIC')\n",
    "\n",
    "x = X['Mean_ASIC']\n",
    "plotLinReg(ax, x, Y, dataname='Mean ASIC')\n",
    "\n",
    "handles = ax.get_legend_handles_labels()[0]\n",
    "handles[0].set_color('darkorange')\n",
    "handles[1].set_color('darkorchid')\n",
    "handles[2].set_color('gray')\n",
    "handles[3].set_color('darkorange')\n",
    "handles[4].set_color('darkorchid')\n",
    "handles[5].set_color('gray')\n",
    "\n",
    "ax.set_xlabel('Mean annual sea ice concentration (%)')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs mean annual SIC')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.032\n",
      "Model:                            OLS   Adj. R-squared:                  0.027\n",
      "Method:                 Least Squares   F-statistic:                     6.831\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):            0.00962\n",
      "Time:                        16:31:29   Log-Likelihood:                 55.593\n",
      "No. Observations:                 208   AIC:                            -107.2\n",
      "Df Residuals:                     206   BIC:                            -100.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          -0.2210      0.046     -4.803      0.000      -0.312      -0.130\n",
      "Hadley_ASIC     0.0024      0.001      2.614      0.010       0.001       0.004\n",
      "==============================================================================\n",
      "Omnibus:                       12.057   Durbin-Watson:                   2.142\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               26.867\n",
      "Skew:                           0.156   Prob(JB):                     1.47e-06\n",
      "Kurtosis:                       4.733   Cond. No.                         179.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.058\n",
      "Model:                            OLS   Adj. R-squared:                  0.053\n",
      "Method:                 Least Squares   F-statistic:                     12.57\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           0.000486\n",
      "Time:                        16:31:29   Log-Likelihood:                 58.359\n",
      "No. Observations:                 208   AIC:                            -112.7\n",
      "Df Residuals:                     206   BIC:                            -106.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2613      0.046     -5.712      0.000      -0.352      -0.171\n",
      "NOAA_ASIC      0.0029      0.001      3.545      0.000       0.001       0.004\n",
      "==============================================================================\n",
      "Omnibus:                       10.848   Durbin-Watson:                   2.126\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               21.364\n",
      "Skew:                           0.181   Prob(JB):                     2.30e-05\n",
      "Kurtosis:                       4.528   Cond. No.                         202.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.046\n",
      "Model:                            OLS   Adj. R-squared:                  0.042\n",
      "Method:                 Least Squares   F-statistic:                     10.02\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):            0.00179\n",
      "Time:                        16:31:29   Log-Likelihood:                 57.137\n",
      "No. Observations:                 208   AIC:                            -110.3\n",
      "Df Residuals:                     206   BIC:                            -103.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2477      0.047     -5.303      0.000      -0.340      -0.156\n",
      "Mean_ASIC      0.0028      0.001      3.165      0.002       0.001       0.005\n",
      "==============================================================================\n",
      "Omnibus:                       11.537   Durbin-Watson:                   2.133\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               23.817\n",
      "Skew:                           0.183   Prob(JB):                     6.73e-06\n",
      "Kurtosis:                       4.617   Cond. No.                         194.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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aMBgMBoPBYDAYnYk5FbbWUUopoA5AALC2c01iMBj3TOIiYC1P/W4mCQkJiI+PR0JCgtltTq7Jw5cxUpxck9ceKxmMDiEzM9NiyJAhwbrbvL29w81tP2TIkODMzEwLQ+dpDzk5OXwulys+cOCArXbbuXPnBDExMaKYmBhR//79QzZt2uQIADExMaKcnBw+AJw9e1Y4fPjwoJiYGNGAAQNCVq5c6ap/bm2/9u3bZ7dhwwane7W1u/DEE0/4PWgbGF0Hc8QrX++zZWcYwmAwOpC0L9pc5lcqlYJSCqlUanabjB9KQJXqdwaDoWbbtm1Os2fPvv3NN980ict33nnHIz4+vjApKSnz/PnzVyZOnHhHt01ZWRn32Wef9du4cWNeUlJSplQqvdKvX786Y9eYOnXqnUWLFpV3tO1KpdLovsbGxnaf917aAsD+/fuv39MJGD0Kc8RrFiHkXULIo4SQ1QCyOtsoBuO+0EbvZFs9kw/UKxkxHyBtK/MrFotBCIFYLDa7TejkXiBc9TuD0RU5cOCA7aBBg4LFYrHo0UcfDZDJZAQA/vOf//QODw8PlUgk/pWVlVz9dtnZ2fxHHnkkcPDgwcGPPPJIYFFREW/v3r12c+bM0VadxJAhQ4KuXr1qod/2119/dfjkk08KsrOzBXV1dQQArKysVMePH7e5desWl8PhwNXVtZlK/O677+zHjh1b1bdvXzkAcDgcTJky5Y7+ubWsX7/e+c0333QH1N7bF154wWvo0KFBsbGxwdprvvvuu73FYrEoMjIyZO3atS6mxsPb2zv85Zdf9hwyZEhwdXV1kzY4ePCgbVxcXNC4ceP8//GPf3gmJCTYREdHi2JiYkQzZ870VqlUaGxsxIQJE/yio6NFL730kqfWO7x+/XrncePG+Y8ePTpg9erVrlu2bHEUi8UisVgs+uc//+muPX+/fv1CBw0aFDx16lRfAJg3b16fyMjIkEGDBgV/+eWXjlr7ACA/P583fPjwoOjoaNGIESMCi4qKeNr9Cxcu9IyOjhY9/vjj/q08FoxujjnidQGAAgCTARQCYEXvGT2DNnonzfZMakRxxve3zPNK6ohocwSy9pikpKQW+5oEc8oS4PVGYPSG5ttNCGmJRILly5dDIpGYtleHuKXeeDFJjLil3ma3YTA6g0uXLllpp+VjYmKaiumMGDGi9uzZs1elUmlmcHBw/ZYtW5wKCwt5O3fudDl//nzGjh07couLi1sI0Ndee83rnXfeKf7rr7+uzp07t3TlypVuU6ZMuZOUlGRTV1dHrly5YsHj8RAcHNyg2y45OVkQEBBQb2NjQ8ePH1+5Z88eewD43//+l19aWsqLjY0NiYiICDl9+rRQt11+fr6Fl5dXs3O1hZEjR1afOnUqy9fXV/7TTz/ZnTt3TvD777/bJScnZ6akpFzZsWOHy82bN7mGxgMAlEolmTRpUuXZs2ev2traqnTPffv2bf5PP/10/dNPPy385z//6X3o0KHspKSkTKFQSPfs2WP/7bffOtja2iqTk5MzJ02aVKlUKpvSTNTW1nIPHz6cs2jRotJPPvnE9fTp01elUmlmamqqVVJSknDfvn0Oq1atKjx79uzVPXv25ALA0aNH7ZOTk6+cPXv26gsvvFCha8uKFSvcp02bVp6cnJz51FNPVSxfvtxNa/+zzz5bnpycnFlRUcFNTk4WtHcsGV0fc/K8KgBsvA+2MBj3l4j5auFqpndSLBZDKpW27pnUiOLQgO+Rce2p1r2SOiJaWuPaJJCNiUitiM7Ozm6xT3caX1dQGtvOYPQUwsPDZadPn76q/az11EmlUsGyZcs8GxoaOKWlpTw7O7uyzMxMS5FIVMfn8+Hk5KTy9/dvkR3hypUrwrfeeqsPoJ5K9/X1lXO5XIwfP75yx44dDunp6cI5c+aU6rfbunWrc0ZGhnDYsGFBcrmc4+TkpJg9e3alm5ubcvPmzQUAChISEmxeffVV7+Tk5ExtOy8vr4ZLly4J9c9nLrGxsbXa85SWlvLq6uo4WVlZwsGDB4sAoKamhnvt2jULuVxO9McDADgcDh01alStoXNHRETILC0taVFREa+wsNBi3LhxgQAgk8k4IpGovqqqihsdHS0DgJEjRzY7R3R0dA2Hw0F6erplUVGRxbBhw4IB4M6dO9ycnByLZcuW3YyPj3fftm2b88iRI6sXL15cGh8fXzBt2jRfDoeDt95662ZUVFTT/cnOzha8/vrrtwHgkUceqfnuu+8cAYDL5dIhQ4bUAUCfPn0abt++bU4RJkY3hd1cxsPL6A1NnklzkEgk5nklNaI4bm414kabMQXvFAqUXQKcQiEWtS6QxWIxUlJSQClFQkJCM5tCJ/dCxg8lLQSzg68AFTn1cPBlzgjGw8Xq1avdV61aVTR69OjaBQsW9KGUEpFIJL969apAoVCgpqaGc+3atRa/GMHBwXVvv/128dChQ+sAoL6+ngDAokWLSmfNmuVbVVXFe//994t126hUKvz55592aWlpGRyOemJzyJAhwSUlJdyioiJe//795QDg5eWlUKmaOTfx1FNPVa1du9bt8uXLpdrQgR9//NHuySefNBo6oItuTl1KKfr161fXt29f2aFDh3I4HA7kcjmxtLSkjz76aID+eGjba23Wh8vlUgBwc3Nr9PLykv/+++9Z9vb2KgCQy+Vk165d9r///rsdAPzxxx/Wem0BACKRSO7t7S0/depUJp/Ph1KpBKUUdXV1nK+//jpPpVLBz88v/Lnnnqt44okn7sycObPqt99+s3n77bc9f/vttxzt+QIDA+uPHz9uEx4eLj9+/LhNYGCg3JDN2n4xeiatildCiIBSynL2MRjm0kZRjPKMpnfJ7NYFskQiaRbCoHt83FJvg57Vytz6Zu8MxsPCtGnTKubPn+/r7+9fb2dnp7Szs1N6eno2Tps2rTwyMjLU39+/3tPTs8V0/fr16/PnzZvnI5PJOADw3HPPlb700kvlvr6+CoFAoAoPD6+2tLRsVsjnt99+swkICKjXFYFxcXHV27dvdywpKeE988wzjkKhUKVSqfDhhx8W6LZ1dnZWbt++/fqCBQu85XI5R6FQkEmTJpWbK171iY6Orh81atSdmJgYEZfLpQKBQJWYmJhtaDzMPSeHw8EHH3yQP3bs2EBKKTgcDtatW5f/zDPPVO7bt88pOjpaNGDAgFo+n9+iwJGbm5vylVdeuRUbGyvicrmUx+PRXbt2Xf/00097HT161I5SimHDht2xsbFRab2zcrmc/Pvf/272BWHlypXFM2fO9Nu2bZuLUChU7d69my3kegghmixYhneqv8olUkofvX8mtZ2oqCiakpLyoM0AAGRnZyMwMPBBm9Fh9KT+dNm+JC66G75gpuhNSEho8tCa4w0+uSavySPbVcMGuuz9aSfdoT+EECmlNKojz5mamprbv3//FtPpPYkxY8YEfPTRRwUREREGvX4PI1rP7uHDh63fe+8992PHjrWMa2Iw2kBqaqpL//79fQ3tM+l51RQm+IsQEkMpbbk6hMFg3Dtt9dRC7X0ViURmiyNjHlkGg2E+crmcjBkzJsDLy6uBCdfmPPHEE/7l5eW8hoYG8vnnn9940PYwejbmxLxOAzCTEKIEQKHWtPecxJnBYPRQ2uFJNpe2epzNpTt4phkPHktLS/rHH38wj6IBdONSGYzOptVUWZTSIEqpH6U0UPMzE64MBsM47SiQYC7tKaRgDqzYAoPBYHQfWhWvhBBnQsiHhJCvCCE8QsjM+2EYg9GjaENBBHOLIWR+JeuapVnbUSDBXNpTSMEcWLEFBoPB6D6YEzawDcCXAP5JKW0khLwAYGenWsVg9DR0vZGtTKUbyySgT2GiAlB1wdyt7YjhNRez05W1ERYT3Hn852C6x1cnr7trP++dH5sBANO+OBOq3fb3OL/idx4PKxr4n98jymsb+AAQ1NtG9vvrIzJe2XnO50BasYv2mPvfg57HoUOHbE6cOGGTk5NjuWzZspvh4eEsfpfRrTCnwpaQUrofgLYw8T3nTiOEzCaEnCaEnCKEDNTb9yYh5Kxm3/+IbvI6BqO70gZvpNi1FgQqiF01ub6NeG0bH7uM4scTwJmgDsEz5rHVra71QEvWMh5K3nk8rCh3jUSqfcX4Ocli/Jxkutu0ovTcO39L0277/fURGQDwv5kDb+geY4ojR45YL1682MPY/lWrVvXeuHGjU8f1zjzWr1/vPGDAgJCBAweGnDx50srcYwxtO3XqlHDgwIEhUVFRosGDBwenp6e3qA6m5fnnn/cKDQ0N05ZkLSkp4QLAY489VvPuu+/ejIyMlOXn5/M7o0+G9peXl3MGDBgQEhMTI+rXr1/ozz//bNvWPnUEixcv9tDacfbs2RaFIU6cOGE1dOjQoEGDBgUvWLCgT2s2asdZW91txYoVrm21SX+8jI3V/aQ999jU9ri4uCBHR8f+2rLGAFBcXMybMGGCX1ttM8fzWk8IiQEAQkh/ADVtvYguhBBHAP8AMBiAJ4AdAOJ0DvmRUvqB5ti9AEYBOHIv12S0gU5cbHM/6ayFPe2mDd5ISe1/IbFRArVcnFyzCBn7nkdooA3i8N9m5yjl5wKUopCqi/QY89jqx3OySluMnsqaNWvctm3bZnSl+4ULF6yWLVt2837aVFJSwv388897nzt37kpubi5/1qxZflKpNLO1Yw4dOpRtqJ2Xl5fiyJEjVx0dHVV79uyx//e//+35008/Gcx1evnyZauffvopWyQSNUyfPt1n37599gsXLiwHgG3btjmoVCqMGzeuzf/TW+uTsf329vaqpKSkK3w+H+np6RbTpk0LmDhxYkZb+nSvnD59WiiVSq3Pnz9/JTs7mz9r1iy/s2fPNlVmq6+vJ0uWLPFMSEjIcXR0bKokYcrGy5cvW3333Xc57fVgGxqvpKSkTENjde8j0H6bzLnHptpt3749NyEhwa6goKBJ+Lu7uzfa2tqqzpw5I4yNja0z1z5zxOsCAB8C6A3gLQCtB+2ZJgbACUppA4DrhBBbQoglpVQOAJTSLJ1j5bjr8W0GIWQegHkA4OHhYbBU5oNALpd3GVvaQ0DaFyBUCZr2BXJ8F5vsj0vaStjn7kaV79MojVjZ6rmTkpKacl/GxMSYPDbzKxkKExXwHM2H6O8GHRUm0RVyIpG6zHl3uTcuvk83jWv6eyUA5SIjewrCpxWiVMd+Pz8/XL9+HYGBgU3jqvuuxeNRPgoTFfB4VO1g0f7c1caiu9wfc+lp/enqVFRUcMrLy3menp6NKpUKc+bM8crIyLBSKpVYuHDh7blz51ZkZ2cL+/fvX79p0ybHTz/91BUAJkyYUPnuu+/eBIDw8PDQPn36yEtLS/l8Pp8eP34864cffrBbv369a319PWfEiBF31q1b16bQhT///NN60KBBNQKBgIaEhDTU1NRw6+rqiFAopKaOMdbO29u76X+iQCBQ8Xg8o8nai4uLLUQiUUN1dTXnypUrwgULFpQAwNatWx03btzY+/HHH69ISkoSxsTEmC0azOmTqf3ailuVlZXcsLAwGQC0pU+G7pF+sQhTpKenCyIjI2sBIDAwUFFQUGCpa/uRI0esra2tVVOmTPGXyWSclStXFj322GM1pmzMy8uzDA0NbXfohaHxamhoaLJJd6wMYeh5t7a2VnXmc2tsv6l2AQEBCkPXGj9+fNWuXbscO1S8UkoLAMwwu8et4wygQudzJQAnAM2qaBBCRgBwB/CnEbs2AdgEqIsUdJVk4N0hMblJNKVNScT8ZmLIIPt3A1QJh9zdcJj8Taun3rlzJyilyM7OxsyZptf9HTsiBVRA0REFJO+1fTzF4rtlVrX2d5t7E/gNgG/gACDsqjaFkxscJqu36fLss8/ebWakb4HvAXhPZ8N7Bg974HSb+2MmPa0/XZ20tDRBnz59GgDgwoULgvz8fMukpKRMAFAqlZDJZITP59O0tDTBV1991euvv/7K1PyDDXvrrbduWVpa0ry8PMsdO3ZcHzBgQP3EiRP9EhMTrdetW+d6/PjxLKFQSGNjY4Nv3brFdXV1VQJATU0NGTVqVIsMPOPHj69cuXLlLQAoKSnhOTo6NlWxsrOzU96+fZvn4+PT9I/c2DGm2t25c4ezYsUKz6+++irX0HhkZWVZVFdXc6OiokSZmZnCFStWFIwcOVIGAHPmzKmYM2dOhX4bc/pjTp9M7b9+/Tp/6tSp/tevXxds2LChme2t9UmhUMDQPZJIJDXm2j5gwIC6jRs39q6vryfnz58X3Lp1y6KkpISrFacFBQUWGRkZVmlpaZerqqq4o0aNCs7JybmsrZqmb2NWVpZFbW0tNzY2VgQAVlZWyuPHj7fpW6ux8VKpVDA2VrroP++pqamW8+bN8+nM59bYfnOed33CwsLqt27d6mzmcAEwIV4JIR9SSv9FCPkd6vyuTVBKx7TlInqUA83+B9trtuleOwLAGgATqKkSYIyOpy2LbTRC19xV5bqCsjVCJ/dqyrvZHjprYc/9hi0kYjDaTkRERH1UVFTtuHHj/MVice3q1atvJScnC0NCQur27NnjsGDBghIrKysKABYWFlQgEFCpVCocPnz4nQEDBtQDAJfLpceOHbMtLS3ljxgxIhgAysvLeSqVqmkdho2NDdUKBmO4uLg0JiUlcbWfq6urub17925s7RhbW1tVZWWlwXZyuZxMnDjR/4033rgpFosN1nxOTk4WTp06tWzLli35GzdudDp16pRNa+NmTn/M6ZOp/X5+fgqpVJqZmZlpMWrUKNGMGTMumtunc+fOtbhHtra2qrbYLhaL65966qnyYcOGBfv6+soDAgLqPDw8mtkuFotrnJycVJpXY3FxMc/T07PRkI3JyclCiURSvnfv3hbhKo2NjRCLxSH628eMGVP14YcfNjnsjI2XUCikhsZKH/3nvb6+ntPZz62x/eY87x2BKc/rR5r3uR18zbMAVhNC+FB7Vmu0IQMAQAgJBLAFwBRKaY8uMdjtaeOq8rYISibaGAxGW4iIiKjXxtLJZDLOBx98UAwAQUFBfV977bXS5ORkq8jISFlxcTFfLpcTANi7d6+dn59fPY/HQ3JyspXWV5Kenm6Rn59v6eHhoXjrrbeK5s6dWwHcLYGqvaY5HqwRI0bUrly50lMul5O8vDy+tbW1UjdkwNgxY8eOrV6zZo27fjulUonJkyf7TZgwofLZZ5+t1D1PVlaWRVBQUAMApKamCvv27VsHqD2tq1ev9lSpVNB6EA1hrveytT4Z26877ezg4KC0trZWAmrPuKE+6fYHgMF7NHToUFlbbAeApUuXlixdurQkOTlZsHr1ance764U0tquUChQU1PDKSsr47u6ujYaszE1NVUYGRlpcEqfx+MhNTX1itEBNzFeuvt1x8rQuOg/77GxsdWd/dwa22/O865Penq6ICwszOAXFmMYFa+UUu3NDgaQqCkVSwCMBtDu0m+U0gpCyGcA/oDao/sqISQSwN8opR8CWAe1Z3a7JtHAh5RS0wkvGQyGcVpbhJe4CAlJNyBVREEcFW3yC4a5lahYJSzG/cbR0VHl6OjYmJ+fz1u0aJFXfn6+pUKhINOnTy91cXFRnj9/3uqFF14o8/X1bZg+fbr/l19+2cvFxaXx66+/zgWA8+fPW7m4uCiGDBkSpFKpyObNm3OtrKzojBkz/DZt2tSbx+PRRYsW3Zo1a1aV9prmeLB69eqlnDt37u3BgweLCCFYt25dU6qPJ554wm///v3XDR1jrN3XX3/teOzYMfuSkhL+7t27nUNDQ2Xbt2/Pl8vlRCKRBF69ejUdAC5dumS1ePHiWwAgEAhoQEBA/R9//GGlDR0whLneS2O2meoPAKSkpAgXL17sxeVyaWNjI/nwww/zjfVp06ZNBbr9MXaPtDG05toOAEOHDg1SKpXE0dGxcfPmzc1sd3FxUS5YsODW4MGDRQqFgsTHxxfweDxs3brV4LhfunTJKisrS/DTTz85AcCUKVPKlyxZ0qZqJ4bGy9hY6d9nAHjmmWd8dJ/3559/vuJ+PLeG9ptq9/TTT/ukpKTYNDQ0kHPnzlklJibmAMAvv/xi//LLL99uy5iR1mblCSFHKKWP6nxOpJSObstFOpuoqCiakpLyoM0AcP/j3Ez9MzcpIMzMKtCT4vZ6Ul8AM/qjvcdU84WdcIHX1bM3zZ6NzImIr34bFBwQQjDKYrbRZ+rLGCmoUn2qF5OMh3/Ex8eDUgpCCJYvX94x/WnD9bsC3eF5I4RIKaVRHXnO1NTU3P79+z+QWbPExETrgwcP2rd1cQoAxMbGBh8+fDhbOw3d3Th8+LB1cnKy9dtvv90mEdBVMdSf7n6POoKedp+Li4t58+bN8zpw4ECL7BKpqaku/fv39zXUzqw8r3qf2770m9FpmCprabKUZieW8LzvmFG9KiEhATt37jRatapH5j9tEq6kRY7ZZs9GxHyI+VIQUIjFYpPPlLmVqFglLMaDYPTo0bXtEa7A3TjTjrbpfjFmzJjaniJoAMP96e73qCPoaffZ3d290ZBwbQ1zUmX9SQj5BsAxAI8AONHWizA6D1MLm0wukGrjYqsujRnVq1qrWqUr2HrMdLTuPdYbl2bPxmgJJKMB7aicvJhn9JkyNxaZVcJidDcuXbp033JoMtoHu0cMLeakylpKCBkHIBzATkrpr51vFsNcTP0zNykg7rWEZ1cqZhAx/27MZkKCwT63lungXrMb6NMlYjNN3GNTz0a7BKKZz4O5sbBdYvwYDAaD0SUxJ2wAAC4COAmgihAypBPtYXQXulLYwegNkDbGgIIYDpGAWqzNnDnTpGB7MUncYULJ1NR7j8TM58FkKIsOD934MRgMBsNsWhWvhJAVAPYA+B7q9FkrOtsoRjcgYn6LOMoHSasxlomLELA/pFlcbEJCAuLj45vFwXZU7OtDF5tp5vNgbizsQzd+DAaDwTAbc7INnKWUDiKEHAcwEsA3lNJZ98M4c3mYsw10Nj2mP2t5aFqmrllxb2hFPFvN/uBg/bn/9LRsAwwGo+dwr9kGajTvBIAFAFEH2cVg3D8i5oPqeQYNeQEfeo+fGZkbzMGQV7s99MgsEAwGg8G4J8wRrwcJIUIAXwHIhLq4AIPRvRi9ATlPXGm2mEgikWD58uXN4mA7OvbVGOaIstYEoEvayg4Rms3ooFhmc2NbW4PFvjIYDAZDn1bFK6X0Y0ppHaX0a0qpL6X0jfthGKObYsBz15mxpd0Vc0RZawLQPnd3xy+a66BY5o7K8/rQe8IZDAaD0QKj4pUQ8iUhZJOh1/00kNHNMOC5MyTC7ptHrZVpcFPezc4U2OaIstYEYJXv0x2/aG70BnVM8D2mPzPk1W4P98sTzujeZGZmWhBCxDt27HDQbvP29g7X/vzpp586Dxw4MCQ6Olo0btw4//z8/KY0kTKZjNja2kZ+8sknzvrnnTFjhk9MTIzJUDlDxyxevNijf//+IVFRUaJBgwYFA8DBgwdtp0+f7gMACoUCb7zxhntUVJRILBaLRo4cGZienm6he47XX3/d47PPPnMC1OU42zQg3Zj169c7//jjj3YP2g5G18ZUntdv7psVjJ6DgeIHhnKsdnReVaPoimnfxS3ykeoXL9DNQ1r0g1uLwgUdlX/UnFyqrSX6L41YCYfJ7NeUwQAAPz+/+o8++sht1qxZlRzOXb/MoUOHbL7++muXP//8M9PGxobu2bPHfvr06X6nT5/OAoDdu3c7TJ48uWzfvn1Or776apm2XX19Pbl8+bLQxcWlMSsryyIoKKhB/5qGjjl37pzg7NmzNqmpqVcA4NatW1z9ditWrHCrqanhJiUlZXI4HFy9etWitrbWqDNp//79ba5AZA6NjY3g8QzLAKVSCS63hemt0t52Wv7xj3+UtX4U42HH6C8LpfQPY6/7aSDj/nLP3sbRG5Ag+hnxp12bvJntii3toIVDLabB9TzD+t5NXTFryDvKYjAZjK6Jm5ubol+/frJvv/3WQXf7tm3bnJcuXVpsY2NDAWD69OlVMpmMe/36dT4A7N692+mdd965aWFhocrNzeVr2+3du9d+3Lhxlc8++2zZ1q1bnQxd09AxVlZWqpKSEv6JEyeslEolXF1dlfrt9uzZ4/zf//63UCuyg4ODGwYMGFBvrG9aL/LBgwdtY2Njg8ePH+8fHBwctmXLFkcAyM7O5j/yyCOBgwcPDn7kkUcCi4qKeAAwefJk35iYGFFYWFjot99+aw+oPZvjxo3zHz16dMDq1atdda8TExMjevHFF/vExcUFXbp0yXL69Ok+gwYNChaLxaJjx45ZaW0ICQkJGzVqVOC4ceP8169f76y18eWXX/YcMmRIcFlZGXf8+PH+sbGxwYMHDw6+dOmSpUqlwoQJE/zEYrFo0KBBwb/++qtNSkqKIDIyMmTQoEHBw4cPDwKae5z/+9//ukRERIRERESErFu3zllr/+jRowPGjBkTEBQU1PfQoUM2xsaN0XMxJ89rLiFETgjJ07xfI4ScI4SMuh8GMjoPQ0LVkDgze+W4RnBKU5LvfbFORxVB0J8G1xOzEokEy4fcgiRzIpC4qJmY1RXY2jHgTMg2ON3/sMfwMhhdgVWrVhV/9NFHbiqVqmlbUVGRhZ+fXzOvaZ8+fRpyc3P5t27d4srlchIYGKiYNWtWua5I3b17t9PcuXPLn3766crff//d3tD1DB0TEhLSsGLFisK3337bs0+fPv1eeuklT/129fX1HK2Ybit37tzh7t+//9rhw4ez1q5d6wYAr732mtc777xT/Ndff12dO3du6cqVK90AYNu2bXlJSUmZR48evbpy5co+2nPU1tZyDx8+nLNy5cpb+uePioqSnTx5Muu3336zCwgIkJ89e/bqTz/9lPPGG294A8CSJUv6HDx4MCsxMTH7zp07TS5WpVJJJk2aVHn27Nmr77zzjtukSZMqzpw5c/Xjjz/O/+c//+l5+/ZtbmFhoWVycnLm2bNnr44ZM6bm4MGD9s8880zp2bNnrx47dixL146ioiLepk2beiclJWUmJSVlbty40VUrygHg8OHDOZ999lnuunXrerdnHBndm1bLwwL4FcB7lNI8Qog3gGUAVkNdtCC6M41jdC66QlXrATU0na8/tW4UjeAU81MgbYy5t8U6BsIPOgRDJVN1hLLk9Q0G+6gdg0JkYnnS9Bb7DY1lR5dMBZqHLbhNbb27DMbDREBAgCIiIkL2zTffOGi3ubu7N1y/ft1C17NZWFho4evrq9i+fbtTcXGxxbBhw4KUSiWRyWScVatW3SorK+OmpKTYvPDCCz7a48+cOSOMjY2t057D1DHPPPNM5TPPPFNZV1dHxowZE3jgwAFbQkiTnQKBQFVTU0PaI2D79u0r4/F48PX1VWjF45UrV4RvvfVWH0A9be/r6ytXKpV48803PZKTk625XC6KioqaYmqjo6NrdEMrdBk5cmQNAFy8eFGYkpJik5iYaA8A1dXVXEAtfAMDAxUAIBaLa7XtOBwOHTVqVC0ApKenC0+fPm27efPm3gDA4/Gom5ubcvbs2SVPPvmkn5WVlWr16tVFixYtKn377bfdn3jiCb9+/frVvfvuuze157ty5YplSEhInUAgoAAQEhJSl5mZaQEAAwYMkAGAv79/Q0VFhTk6htHDMCdVVgSlNA8ANO99Ne+1ppsxujqGpsUNTeebvXJc49WUxPjc+2KdDlo4ZBZmrLBvbQwMLsDq4JKpAAtb6HDMDE8xZ/ahs73vcrkchYWFSE1NxY0bNwAAf/31V6dcqzuzcuXK4o8++shN+/m5554r++CDD9xqamoIAOzbt89OIBCo/Pz8FN9//73j6dOnM0+cOJF1+vTpq76+vnKpVCr4+uuvHV977bXiEydOZJ04cSJr48aNudu3b2+2oMvYMbdu3eIWFhbyAEAoFFIHB4dGXU8wAEyfPr3sjTfe8NRuz87O5p8/f15gTv90RbCW4ODguo8//jgvKSkpUyqVZn777bc3/vrrL+Hly5eFUqk08+eff87hcDhNQtlUTCqXy6UA0Ldv37qnn366VOv5vHjxYjoAWFlZKXNycvgAcP78eWtdu7SCOCQkpP7111+/qW179OjRLLlcThYuXFj2888/Xx82bFj1mjVrXAUCAd20aVPB/v37rx87dswuKSlJqD2fSCSSZ2RkCOvr60l9fT25cuWKUCQSNeiPAaW05YAwejzmfGP5ixDyC4BkAFEAzhJCeAAudapljE7HnEVDQOsLh5ow5NXsDphhd2tjYHAszfQei8VipKRIwa2yxqpV8Yhyq4Wk9r8GPbbNPeMt1o+0xEzvrz5t8Qbr0lEL2u4bul8wTIyPObMPBr3v7UAmk6GwsBClpaUoKyuDWCyGlZUVNmzYAGdnZ7i4uKBv374AgLCwsHZfp6cSEBCgiIyMlB0/ftwOACQSSU12dnbZ8OHDRVwulzo5OTXu3r37+tWrVy2USiVxcXFpikkdM2ZM1datW51TUlKsN2/efENne81rr73mrVQqC7TCb8+ePU6GjnnllVdKZs+e7atSqUApJREREbUTJkyo/uWXX2y1x65atermkiVLPKKjo0WEENja2io3btzY7m8969evz583b56PTCbjAMBzzz1X+uyzz1Y2NjaS6OhoUXh4uMzW1rZF7K0pFi9eXDpnzhwvbbaEyMhI2RdffFGwZs2aAolEEuTm5tZgaWmpsrCwaOE9fvfdd4tnz57t/dlnn/WmlGLs2LFVc+bMKZ86dao/l8uFQqEgn3zySd7mzZudvvnmG2dCCHr37q2IiIio3717NwDA09Ozce7cuSXR0dEhADBv3rzbHh4eje0dI0bPotXysABACBkAIAhANqX0XKdb1UZYedjOoyf1pyv35csYKYrGJQAcCgIVltvENytla4js7GwE5n5sVJwmJCRAmpIEMS8FEuFvzc7Vmsg0VDrX3H60t7zuA7k/HRjaoT+mpvqjXeV9Lec6rl7Oxp36CpSVleGFF15AXl4e/vrrryahKhKJYGenzhxkyOt2L7DysIy2IpfLiaWlJVWpVBg+fHjQ//3f/xUOHz5c9qDtYvQ8TJWHNStWhFJ6HsD5jjSK0T0xJnoM/nNvp9evW2Ggj4bGojWxGDq5FyozvSHzzYPYrR6oNTN/qwnPoVQqBQUH0sYoSCJ8m+1rzUtoKL2ZOdy3FGgdhZmzBebMPuh731UqFcrLywEAdtYOOLT/MIpLCnGnrhKcekt4pY1CsSIHHNc6DH8pFL169QKfz0dQUBCCgoLurV8MRifxww8/2K1du9atrq6OM2jQoBomXBkPAhbozGgTxkSPwWlVM6dkuzUG+mhoLFoTi3FLvRGH2Tpb3jfv+iZCE+4K0BhgdHPh1UJk6olwXbHWmtdRX5h3i3CBDqS+vr5pir+3oxsUt7n4/sgu1MirYGtnA8urXrA47wcaykGfiH4Y+mIIZNcJuJM5cPARgW/V/pyYDMb9ZsaMGVUzZsyoetB2MB5ujIpXQsjjlNKDhBB/Sum1+2kUo+tizLNm0FPXWRkDuhIdXZTBiLfakIDM/EqGY0deQOjkJYgb3VIwmvIWthCZWhGeurHF9Q2J8daKOWjpdjGwJrhz5w5u3ryJsrIy3Cq6jX6usci5koukwt/h7OwMexsnnPmuDL2d3BDoNwQuEZaInRqOOwVy2LhagMO/O+Vv1/8BdoTBYDC6OaY8r/8EcBDAZgAdmtOVEDIbwDwAFMArunG0hBABgK8AeAPIA/B3SqnR5M2M+4sxz5pBodRdF3DpYVKAjd6ABPl4SE9LIZYnNI2D/ljERb2POAutyNWMiSGhquPJTZCPbxKIhgRkYaICUAHp36kzD9yTONSKcKps4Uk2JMZ17Rk1ebZRYd5Ri5juFyqVChwOBxkZGSgqKkJZWRlqy+oxQDAGabkpKJUXImSYNyyq7XHx8G04effCxKC5CPibE4TOPOD5u3Gp2dnZAAC7PpYPsksMBoPR4zAlXi8RQjIBeBFCrgLQug0opTS4vRckhDgC+AeAwQA8AewAEKdzyGwAVyilswghyzWfP2/v9Rg9mDZ4KQGtp1LaZi9gawLMrDy4hkIoDG3T8eRKT989ryEB6Tmaj8LDiiYb45Z6tz8GV/tFQ7e9BkNiXNeeOInxUIGuGAOrUqlQWVkJS0tLWFhY4JeEX1FWWobyyjI4Ug94lwxCVs1l8CxVGPIkB1V5AtQXnEdM1GA4+AnQu581OFyCZlEeDOMcessDf33m3vR5zq8ZAICt40Kbtg1+qRiPvVeED/wjICtTV7nqJZJhUVIG9r3gg0vfuzQdw7hnDh06ZHPixAmbnJwcy2XLlt0MDw+XP2ibGIy2YKo87MuUUhGAzZTSYEppkObVbuGqIQbACUppA6X0OgBbQoiua2IE1B5fADig+cxgtMRIHlVjeVMLExXtypFqMIerDmblwTWUS9bQNp38trrnlVj+guU28ZBY/tJ0qOjvVgh76q5tCQkJiD/lgoS6sc3GRHc8tLlI078zki/WzPy6hkr+GqLVMsCdSF1dHQoKCnDhwgXI5XJcy7iB9Ws/xXv/9x62b/saP71zBnsnpSN3ZyP6FBHMC8tHTHkW/HxPY/pr4/HCgI0IL1iLoWH7MfjJDIQ86QK3SBu1cGWYz2PvFWFllbTp5TNEBp8hsmbbtKL0zWtpTdsWJalF7tQtN5odY4IjR45YL1682MPY/lWrVvXeuHGjwVKvncn69eudBwwYEDJw4MCQkydPWpl7jKFt5eXlnAEDBoTExMSI+vXrF/rzzz/bGjofADz//PNeoaGhYWKxWCQWi0UlJSVcAHjsscdq3n333ZuRkZGy/Px8vrH299InY/sFAsHAmJgYUUxMjOjjjz92AdRfKJ977jnvyMjIkPDw8NAvvvii0+9RWlqaJY/HG/jbb781Ky9rypYTJ05YDR06NGjQoEHBCxYsaKpYph1nbb9WrFjRrOxua5gaS2N23g/ae4+NbY+LiwtydHTs/+abb7oDQHFxMW/ChAl+7bGt1QVblNJ/EEIeBxACIJNSeqA9F9LBGUCFzudKAE4Aig3s1+5rASFkHtShB/Dw8GiaonvQyOXyLmNLRyCXy1H5wzOwz92NKt+nURqx0uixSUlJTemBYmJiTJ438ysZChMV8BzNh+jvBv+Wt4qL79N37dIZ88DAwCY7dO+F20gubh5TwuNRfqv3yCVtZdO53aauhNtUewANBtuJRCJUVVVBKpWiqqrKcN99F6tfADLfStP0fT5Ef1dvg5HzikQiAADdPxGEKkHTvkCO5jxyuRxuU9Fk29Gdd7MLDPV1bBoT3fFI/7gEUEE9j0Jg1ljcL9r7u1NTU4OqqircuXMH9XX1CPXuj7TzF3Ht9hXY2wsglPOQ8q8KUIUQDp7+eDx0Czw5J1FEwtE4bw5IYChcrn4CObcPnMe7ocHeCxWcIlQM+bL5hdpoW0/7W9AdWLNmjdu2bdtuGNt/4cIFq2XLlt00tr8zKCkp4X7++ee9z507dyU3N5c/a9YsP6lUmtnaMYcOHco21M7e3l6VlJR0hc/nIz093WLatGkBEydOzDB07cuXL1v99NNP2SKRqGH69Ok++/bts1+4cGE5AGzbts1BpVJh3LhxNR3dJ1P7e/fu3ZCUlNSs/1KpVJCZmSm4cOHClYqKCk5ERETY/Pnzy9tqV1tYvny5R0xMTIu+G7Olvr6eLFmyxDMhISHH0dGxWdWJy5cvW3333Xc57fFgtzaWxuzsbNp7j0212759e25CQoJdQUGBBQC4u7s32traqvSr15lDq+KVELIBgADAaQCTCCHjKaUL2zQKzSkH4KDz2V6zzdB+/X1NUEo3AdgEqPO8dpX8nV05l2h7yM7OhkPuboAq4ZC7Gw6Tv2l5kGaqeWf1MlAQZGdnY+bMmSbPe+yIFFVhF1FskQdktjcR/v8hbuk3cEDzByow92Pg5hdA7/lAoI4d87LBHZGp9shmqr2Z2inykylLmk+j79fps519qym/vv32WwBAVlYW7O3tTU7TFx2pAlRAUaIckl4DzUoldjJnKzLOihA6KBNxgYEGQwF0sws4SFY1jYnu83gyL68pRjZsStdaRGXqd4dSCkop0tLSmlb2W3HtEO4QDen5ZMjrSuDnfQfKSw64sPEW7ERhGMG9g0FO7wHufmh8MRPCie+DKKqBqsWAw6fws3IFtHlT+26/r/1hdDwVFRWc8vJynqenZ6NKpcKcOXO8MjIyrJRKJRYuXHh77ty5FdnZ2cL+/fvXb9q0yfHTTz91BYAJEyZUasuShoeHh/bp00deWlrK5/P59Pjx41k//PCD3fr1613r6+s5I0aMuLNu3bo2hS78+eef1oMGDaoRCAQ0JCSkoaamhltXV0eEQiE1dYypdtpCCZWVldywsDCjqaqKi4stRCJRQ3V1NefKlSvCBQsWlADA1q1bHTdu3Nj78ccfr0hKShLGxMS0STi01idT+0tLS/nR0dEiR0fHxv/973/5IpGowdvbW8Hn86lcLidVVVVce3t7owUVDN0jS0vLNpXZPXr0qLWrq6tCW01MF2O2HDlyxNra2lo1ZcoUf5lMxlm5cmXRY489VgMAeXl5lqGhoe0KvTA1Vqbs1GLsWd+1a5d9Zz63xvabahcQEKDQv8748eOrdu3a5djh4hVAP0rpcM3PXxFC/mzLBQxwFsBqQggfgDuAGkqp7k3/A8B4ABc073/c4/UY90prWQM00/difgqkjTFm5QYNndwLR+ryAE4rsaIGaHURUGu5T7XxqTZ3j8v44QX1Ob+/hTgLf8ApFCjPQIL1G5CeEkDMGwv7zTbI+HfzmFmtiGx2jZQkUHCQkpyColVuCJ3cC2cyj0I2/gaK6oE+E0Kg/DkQVAWcTHoDSCLq80anI5OXiQIHC/ThifD3d6bf7XNyGChVv8fp90MzdubmIu2qi6hUKhXKyspgZ2eHmpoanDhxAmWlpSgtuQ2RsA9cG6NxrjQVjihCP69TyP5jFK5aKhAuHgZnzl4E+lZC1c8RnH80At6hAA0ByBwAQFNRd0tbwMborDKjG5OWlibo06dPAwBcuHBBkJ+fb6n18CmVSshkMsLn82laWprgq6++6vXXX39lav7Bhr311lu3LC0taV5enuWOHTuuDxgwoH7ixIl+iYmJ1uvWrXM9fvx4llAopLGxscG3bt3iurq6KgGgpqaGjBo1qkUo3fjx4ytXrlx5CwBKSkp4jo6OTWLMzs5Oefv2bZ6Pj0/TP3Jjxxhrd/36df7UqVP9r1+/LtiwYUOuofHIysqyqK6u5kZFRYkyMzOFK1asKBg5cqQMAObMmVMxZ86cCv025vTHnD6Z2p+Tk3PR3d298fvvv7ebPXu275kzZ6726tVL6e/vL/f39w+vq6vjrF+/3qD3XKFQwNA9kkgkNebaDgCrV69237lz5/VFixZ56R9vzJaCggKLjIwMq7S0tMtVVVXcUaNGBefk5FzOycmxqK2t5cbGxooAdfnc48ePmz3lYmqsTNmpxdCznpqaamnsue3se2zO865LWFhY/datW50N7TOFOeKVSwjxoZTeIIT4mtnGKJTSCkLIZ1CLUgrgVUJIJIC/UUo/BLANwBZCyAkABQDm3Mv1GB1Aa1kDNOJWEuMDyWjzqjHFLfVGVYLOIqTERUDqZwAI0H+h4WpRmmNDJ/dr8mLqLzxKSEiAtHoZxPwUSGJ8Wly32cIny7uiPLRBs7DI/3uAKnHykAQZ17ahSJIAgELaGAX37PFqAakj+rQiUkuvXr3gU5EAaWMUhLk+TSJRNj6vacljIc2EOycQVMlBRvYUgBBQFZBxVoQiyXWAQ1HQqDOrlrgIof62yMiZgtDJrkDiIoh5NyBVREEsjmoxPvreXs6EbBTSzKZ9bV1E1dHprurq6lBaWoqqqiqEh4fj4sWL+CPxd1RWV8OaTxB2yw11jYNQVFWPfr2O4MmAi0g58wp4A+9g7PCxcBGegl3wo+j7vD8gdNZ4T18HoBfE38HVqBjdh4iIiPqoqKjacePG+YvF4trVq1ffSk5OFoaEhNTt2bPHYcGCBSVWVlYUACwsLKhAIKBSqVQ4fPjwOwMGDKgHAC6XS48dO2ZbWlrKHzFiRDAAlJeX81QqVdODZWNjQ/WnwPVxcXFpTEpKakrmW11dze3du3dja8fY2tqqKisrDbbz8/NTSKXSzMzMTItRo0aJZsyYcVH/usnJycKpU6eWbdmyJX/jxo1Op06dajVm0pz+mNMnU/vd3d0bAWDKlCl3Fi9e7A0AP/30k11xcTH/xo0bF8vKyrhDhw4NmTJlSpWudxoAzp071+Ie2draqtpi++7du+0HDhxY6+bmZtC7a8wWFxeXRrFYXOPk5KTSvBqLi4t5ycnJQolEUr53794WgruxsRFisThEf/uYMWOqPvzww2JTY9WanVoMPes7duxwMvbcdvY9Nud57wjMEaKvA9hHCLGHOgb11Xu9KKV0C4AtepsvaPbVAZhxr9dg3EfamRKrmadw7UTNVtqqx3T5ckmTiNKWI9UKSnVVKQJpY4xBId3cOylpuk7caE2qqcQtQBoXGTlTQFWAVa436nyvQ8xLgX1gPTKuTWsm+nTTWAFAaWkpXvLOg6TsF5zEGmRwdapn+aj/tomjxLC30AjI6Eyg7LJamMZkglPZoPa81tsi4f8eVwtU/g2gH0VxqCU8+sUAaV9AlvgN3KoCIbssBCwXQZrioo53NVAYoaAxs5mHu6rfRRQ3SOHhWAus/W+r2RrS95UAtG0puSilKC8vR1lZGUpv34SF8g4GRoRjz4EkXM/NQC9BLSxqCLL/HY2yBg9YknA8P2E77FxDcIPbGw6DPDHKzw82bpPAsbDA6EW6Z5/S6vUZDx8RERH12lg6mUzG+eCDD4oBICgoqO9rr71WmpycbBUZGSkrLi7my+VyAgB79+618/Pzq+fxeEhOTrbS/h6np6db5OfnW3p4eCjeeuutorlz51YAd0ujaq9pjhdrxIgRtStXrvSUy+UkLy+Pb21trdQXZYaOGTt2bPWaNWvc9dvpTt06ODgora2tm8RNVlaWRVBQUAMApKamCvv27VsHqD2tq1ev9tSmgjOGuV651vpkbH9VVRXH2tpaxePxcPbsWaGjo2MjoP574eDgoOTxeHBwcFApFArS2NhIsrKy+Nr+ADB4j4YOHSpri+3nz58Xnjx50nbYsGE2mZmZwuzsbIGfn9+14ODgBlO2aPukUChQU1PDKSsr47u6ujampqYKIyMjDYZu8Hg8pKamXjE64CbGypSduvfZ0LPe0NBAjD23nX2PzXnedUlPTxeEhYW1OR2qOQu2zgKIbuuJGQyT6Kd0iph/1/NqslpU85AErReRMyEb8fEJcHFxQWlpqenQBRNlaxPk4yGtcYVD7BUIToXBtzYaTw1NBtJ+Ax7LAw4RZHyvFk9xS72bxHCT4HOtBcozgP4voSokHMXSBHj0EyMWo+56LyXeSECCWkDykiDxTwAAZCQ/hdDJI/D3pd7AWh5W3VkGEIKUhmgQororTofMR8W3gQAIKnLqgbQvIOaNhbQxSl1NS29s+vBETZ5XQOeLwE0BJDYtwytahCTo/NnRDzWglEIul+PKxXMoLcxCWcltBNrWwS9oGnYeOQxhbS76CK6i6toIpF7qDQvnQIg9nDD2n1Uor/BD/Th3OIpcIHDkISfHH/aBgYgw+eAwGIZxdHRUOTo6Nubn5/MWLVrklZ+fb6lQKMj06dNLXVxclOfPn7d64YUXynx9fRumT5/u/+WXX/ZycXFp/Prrr3MB4Pz581YuLi6KIUOGBKlUKrJ58+ZcKysrOmPGDL9Nmzb15vF4dNGiRbdmzZrVVF3KHC9Wr169lHPnzr09ePBgESEE69aty9Pue+KJJ/z2799/3dAxxtqlpKQIFy9e7MXlcmljYyP58MMP8wG1QJFIJIFXr15NB4BLly5ZLV68+BYACAQCGhAQUP/HH39YaUMHDGGuV86Ybab6AwDnzp0TvPTSS77W1tZKQgg+//zzGwAwceLEOzt37nQSi8UiuVzOefHFF29bWFhQ3f4Yu0fa+F9zbX///fdvArgJAFOmTPGdN29eaXBwcIPWdkO22NraqmxtbbFgwYJbgwcPFikUChIfH1/A4/Fw6dIlq6ysLMFPP/3kpDln+ZIlS8xOaWNsrIzZqX+fn3nmGR/9Z/2VV14pNfbcdvY9NvW8P/300z4pKSk2DQ0N5Ny5c1aJiYk5v/zyi/3LL79829zx0kJ0pzy7K1FRUTQlJeVBmwGg5y3S6LT+rOWp400JV52aqS0YEJ/x8fGglIIQguXLDYcuZGdnIzMzE9KUJIh5KZAIf2txbe15oCJwPygB4QIvJonVU+f7boJSAoADwlHhxRmxLQWwTr/ia5Y3synhiyWQ3hRA7FaPlJvaDAsUK2xXY9O3fwEgAAHmrdkCpG7Eh2lfQeabD6tcb1gPkqGkpAS9evXCSy+9hO+mXUZFTj0s7LhQ1CgQGvA94uZWG/eA64xZU/ED11pIav/bfJuOwNV6Xv98LxdXDl+FtaIRFQIL2D1yDkrrOyitBUZzrgNB8Ui+/BvceZnw86rHme1Pw97eAQ5+NvCOViFkeiCqbzZC4MAzWQqV/e7cfwghUkppVEeeMzU1Nbd///6lHXlOc0lMTLQ+ePCgfVsXpwBAbGxs8OHDh7O109DdjcOHD1snJydbv/32220WAl0RQ/3p7veoI+hJ97m4uJg3b948rwMHDlw3tD81NdWlf//+vob23VP8KoPRbu6ldGxTKdPP1JWorN8ApWoxKLzujZNr8oxObavDCtTppCQRvi2EsFgsRkpKCsChqIq4iFiRurhc+ne3AXABUBCOCpwhPyO++m2Ik6SQjFaf++SaPGR8f6ZJSIrlzb3F0psC9bVvCnQsImoB/a1m0RfV9A8U9pfCYX8pAiDAzQC1d7a0VK0JntrbF9nZ2Tg2swpUxUHGtacQN1rP26zbN82YJSTdgLRRNzb2fZxckwdp3RbYcO6g4NwB/G1IBCR/fxQnrqmwYc0SVDRYwOaROgRdtkJlySO4c9ENIyYJYGfnjZSvn4RjLUGU/5PwHe4ApyAhwmZRcHjN401tPYyLVgajoxg9enTt6NGja9vTVhtn2tE23S/GjBlTO2bMmHb1vStiqD/d/R51BD3pPru7uzcaE66tYbZ4JYQ4UUo7Ne8a4yHiXkrHNokxFUCVd8WgisA+rR8yLhtfRd8UfuBaf/ccmjhbrfdRS52ferbjyxjdbAIULyZHI35Vgjq2VhEFbQRtxg8lzYSkBOoYWyQuAtZOhNhmBqQ1/nARKlFSp447i4qKwsk1msVcFAh7qldT/8L6HUXGpZEIHZQJT9datdfWtR4JCQnQzjT0mRAC1YFAOPgK8GWMFA6+AlTm1qvDEyx0si5EzAdSP0eKIgqOKEXNhR04VXIMvVGA20cl4MZwIOcIIeQ0Qnq6DHm/UBTf4UNw+zG8uTUARak2+D3tAmR9CuBo74iQibPAt+LCx0Byg9Mf5XfoAi8G435w6dIlg7lSGV0Hdo8YWszJ8zoewP8B4BNC+gP4ilL6fKdbxmAYQ6+UqditHtJb1vDkiaAyUQkL0FmwpZ3eB5qqXGnLsQJoqmyVsark7mFEidBBmQCiIY6K1nhV7864Gl3Fr/F6SsguSFY0Ij4+HoA6nEAikeDLVVKAqs1Qiz3NIjK6FHH91PYllD0GQAyUXUbKTeumUxfSTCxPmq4W2KpGNN7MhodLIapOAVULFuLaxVMo5XihLLkaQy2DEBHSF+npl2ABIey5TriVOhzVNVZwTgyAveUtzPjjSWQfKodCpkSInxAOfgJYOPDgOxIoPnESlFLUkVzwrSYYHeOumoqLwWAwGD0DczyvbwOIBfALpbSREGI03xiD0REYSs3UIhWUzpS4ZPT7Td5PvGPmRXTDFjQeYLHcQLqpi1pbemtsUS+IMpRTNW6pt2Gxphciob/4zKDo1S15GzEf0lO9QAFIFWI45LnA0+8Y7EklvFwsgcy9cJ7oDij/h3I444bKCRM4FNkWC3GR1x99fKwQ5OSD4x/NQkOZDYJdHNF/4QAET3DGDZ9KWPf7LxxurQVv4GwATyLwMae7Y37g7ngYWzSnRXvfdL2/DAaDwWB0NOaI1wZKaR0hRLuyiyVPZHQqhjx3LVbAGytEYCKTgEkSF0GS+QXg+kZT6IBEcjcl15nMoziyKg9RbncXOZlKL6VFLeheAGfCcBSezoRYngCJ5S+Q2HyBhII3sGqVFFa53vD1jUbG97dAUtdj6KwiwG88cP0XwG88ZFW38ZRdCqwbilHgvwgZHD7yGn1RS4S4Wk5RvLE3Cq5WQ+D1DP62pB8qz3Fwdn0ZHLMJhDWDcW2PHPUDU3AnrBpuiio85fseMEG9UM1nuAMw/D8A/tNiSPTHXCKRaKqSTVTnyNUbX+19q8yth8eKmzgqTUBVQuvV07Si1+NRPgLfM/+WMRgMBuPhxHjCt7uc0RQVcCOEfAzgZCfbxHjICZ3cC0Rv+l8sFjdN5QNQi0fNdH8zdEWtKfSP03xOKRaCUoqUlLtxrhk/lEDmnQeAIuWmEPHVbyMhqXk+6iahl5KsDklIXNTUVptrVSsEkfo5QJWQl2ZhOP84wgP34rowCQNi34dTxK84dTEPt2q5OCq/iNXSGKy71A+nBDNxpc8WCKufg2NDOGjmQKjAhfxCP2SdsIXglisgdYfX933R3/ZDzPljAJ78OhR3CuSoCruICvebUNrVotCR3zRmJ9fk4csYqTrm1gAtxryV8dW9b7rCtzW0Y1SYaLAAC4PBYDAYzTAnz+u/CSFjAVwDcJlS+mvnm8V4mDE0/d5imt7Ygi/9LAa6nljfxQaP01bl8ixvhFWlL2S+eeDVWCM+Ph5isRgOvr6ozPWGzO8GQKDOGKCzUAvQCQXgJWsyIXwOjPgA/ScrILy2Dna8cxBaVqLEfRJK7BehIP0kwLVAekMYHEg56vzy8FtdNOzKwhAQoMClspnI3lcLZ8tR4DRYYG5KFE59kAe5pRJ9o4NAvO/gYhGFqP/XsHeZjozkMIT6fw8epw64/Dkw9lMAOmV4NQvC+tTbAqOXqxd91UlhFeqNjB/6GQx3MFhuNmI+Tm7WVPtqaJ7VQfe+Naue1grasAmPR/mtHstgMBgMRqt5Xgkh+v/VGgDcol0oQSzL89o5nFyTh/TvSxA2xfCqcWNT5U20dwq/I9HJu5r9xBWD90Y/tysAFD+hLgtLCIHwujdk3nmwutEHYcE/QNoYgyH9/TBa7IOr3yajMv06GkLfRNyEY0DiS1Aq6tFg0QvCp3+DNKcCBX/tRFmNCiqBC+a+9g7OnLmE61l56BPoimvnC3HnsB0syp3AVVlgzEcB8IyxQ+rXN3EztQaFSdXgSHJQxLnSYpzpWh6I7qIzp1B1gQQzQhp0+/yocHabFlZpq5ppc+B2FD3pdwfoHv3paXleGQxGz+Fe87zuA+ALIBOACEA+ADtCyDJK6Z6OMpLR9cj4oQRQGV813iIOVR9jcakdjSmRrOeJNSTkxGIxpClJ8KxUQkWUCJ3qBp+wUJw/fx7c60HoH7gLjpwKOIRWIMhVAMnsg8Bvf4fs+DWU3OwNynPC5Z+Kkd8HKCPvolJWCXFDMoYe34ky1eOwcJyFUU/64OYRFbaPzQJVWcLRrx+inwzAQBHFzm9SAcoBJSp1DCqAm57nIb0phXilGFLplWbjrO1DP8dX8KTif00pw1Ce0azowt0FVL5wz3WDvUUvaN3FTZ7iGDHiJG3LCGA0qwKDwWAwGPcBc8TrVQCPUkqrCSG2AD4HsADAcQBMvPZgQif3Qvr3xkVKa6vP21SIoC1eWv1jdRPwn45vEqZqkecKT/ItVP8OhMejMkgFyer8rClnkX0uEUEcG9heBMb0csNg338Aoi/VQvB6Hayv/AOXs8LBcbVDlo0zOEoVSi+IIR37O6qGBIEnCIbSAVDyCNwib8OuPAiBbi4Y0NAXu348iN31zrDubYEgiTN8fT3h8ngDbt95FxeqAJmVEh9+agFPIgKoPwAVHO2u4csYjjpmtOHuFwOxTo5X4O6XhrRyB1hE/axZRKUeD91MDdpY0oqcOgCk2ZcQgyEBOpjyqhvNqgDDmSIYjM4mMzPTIiQkpN+nn356fdGiReUAMG3aNJ9Tp07ZFRYWXuyMa+bk5PCDg4Mjfvrpp6sTJkyoBtTlTxcsWOADqMu1Llq06Na8efMqYmJiRLt27boWEBCgOHv2rPBf//pXn/r6eo5CoSATJ06s0K0pDwDe3t7heXl5l/bt22d369YtnrZPPR1t6dEHbQej62POgi0RgBrNz7UAgiml1QDudJpVjC5B3FJvjNplb1SESCQSLF++3LgIGr1B7Qk0x+tq7kIrQ8dqFm9JFVF3FwlRigvSv0AphQX9BRGibfCvWol5ws9BoMJg/l94XrAVobxv4OqUitxLXiiaegXnQj7FYd/N+Nl3J9B/IWqDs3HOKhYAgZ1KhgtJg8GvsIfTb8MwbdA8NKoUcE6KQV0+B5mn85FXfAM80oi6egcABLW3FWioUQKJi2DzrRUu3AEo4aCkjgdKKQqUV1D8+K+o6ncJldX+TVkW1F8I1CEL9scIltvEQ1LzAZC4qNmXBalUCozegATRz4g/7YozmUebzhE6uRcIUcLRPhuAClQJo4uz9DF7wVXiIoML1DJ+MLu0NxISEhAfH4+kpCSz2+jS2sIzxsNBWFiY7IcffnAEgLq6OlJYWGjB5XI7Lbxt27ZtTrNnz779zTffOGm3vfPOOx7x8fGFSUlJmefPn78yceLEZv8ny8rKuM8++6zfxo0b85KSkjKlUumVfv361Rm7xtSpU+90hnBVKpVG9zU2trFcdwe1BQAmXBnmYo543QVASgjZDiAJwC5CCA/AkU61jHF/0RMh5qIVHgkJCW1q10JwGMseYAjtseFzgYosIPAJYHY6xGIxnhLsxatO24ENjnjB7SgIIRDxlRAIqtDgGgYn/0FYZv0u8iyj8YlsMXbXL8B++ST0GRuJtIwcXC/lwcreBb143iAcQGQvhsfp0Rjv74NBiiw4WpSAKAEfx3Nw7WsDccwAlD76JypiUlAdegWXq84A/V9CWNCPUNd61Qg5jeAW81JAoEIvYSMIIeqFVBwKmV8eOE9ca1qtL5FI4L5fAvu0fkjPmnq372lfwP5iP1jl+gCUwMXFBfHx8UhJSQGlFDKfXBCOqsnz+eKa7Xjq8WdANL/p5opKg5kGgJbPid4XCd2MA+Y+G1qhnHU1u10itD2CmdHzsLe3V/L5fFpYWMjbs2eP/dixY6u0+8rKyrjjx4/3j42NDR48eHDwpUuXLAFg4cKFnoMGDQoOCwsL/eijj1wA4ODBg7axsbHB48eP9w8ODg7bsmWLo6Hr/frrrw6ffPJJQXZ2tqCuro4AgJWVler48eM2t27d4nI4HLi6ujZTid9995392LFjq/r27SsHAA6HgylTphh1BK1fv975zTffdAeAmJgY0QsvvOA1dOjQoNjY2GDtNd99993eYrFYFBkZGbJ27VoXADhw4IDtoEGDgsVisejRRx8NkMlkBFB7dF9++WXPIUOGBFdXVzf9/z948KBtXFxc0Lhx4/z/8Y9/eCYkJNhER0eLYmJiRDNnzvRWqVRobGzEhAkT/KKjo0UvvfSSp7e3d7jWxnHjxvmPHj06YPXq1a5btmxxFIvFIrFYLPrnP//prj1/v379QgcNGhQ8depUXwCYN29en8jIyJBBgwYFf/nll45a+wAgPz+fN3z48KDo6GjRiBEjAouKinja/QsXLvSMjo4WPf744/5mPhqMHog52QbWEkJ2APADkEspva3ZtbpTLWN0Os2meC0MT70DMDml32rcqxFa5HI1lD2AUoAQoPIaUPwXUHVN/fOQlUDYs8DeEUDe74C9HzDwVUgenwAa3AgZvzduKOzg7hOMl6qr8fvvv6PUphSVlZVQ+L6DAZN+hMeBRIiEdvAOdUfRIRVunZGh8lc3WNpzMfTncBQlV6PMsw7ecfZw8BPAxvVdEM7/4WndsejFb5p+151mx2gJ4kYD0I5vdLqmDC0gEfwCCX4B+r8EjN6Azz77DCUlatFVoLoCq751AEbpjRbByYtrkHHpEYSGH0dGegnslf1gf6kfbk5IwN21kxS9OLfx4ozYu7GvmnF1+OsyKnLq4eArMOv+GA0r0I9j1gsN0Q0piI/fZtazoQ0/EV73bldlLhaDy9AyZcqUiu3btzuePHnS9vPPP8/bvHlzbwB455133CZNmlQxb968ijNnzgj/+c9/eh46dOja+++/X2xnZ6eqq6sjoaGhfV955ZUyALhz5w73xIkTVwsKCviPP/544AsvvFChe53k5GRBQEBAvY2NDR0/fnzlnj177GfPnl35v//9L3/ZsmXusbGxITY2NsrPP//8xpAhQ5o8q/n5+RZeXl4N7e3fyJEjq7ds2ZI/Y8YMn59++slOJBLJf//9d7vk5ORMlUqF6OjokJkzZ1aMGDGidsKECVcBtUDfsmWL08svv1ymVCrJpEmTKj/99NNC/XPfvn2bf+TIkWw+n0/79u0bdvLkyUxnZ2fl3//+d689e/bYNzQ0EFtbW+WBAwcyDx8+bP3zzz83eZxra2u5x48fzyorK+OOHj06KCkpKdPS0pL+7W9/C0hKShLu27fPYdWqVYWTJ0++o/X6Hj161D4jI+Myn89v4QlesWKF+7Rp08pffvnlsk8//dR5+fLlbps3by5QKpXk2WefLd+4cWPh0KFDg5KTkwXR0dH17R1PRvfFnPKwLgCeAuCs/khAKY3vdMsYnU4zAfl/ahEiVUSBQk9wmFh41WrcqxG0gqPvZBugTCPuXMIB6cdA/nG1UK2vAOYXAMVngJwDapHqMQTgW0PZawDKZ9xAWUUlqqqqMChgEPa8m4gr8ksARwVrTg2eDVdAEfQO3BAI8aA4qGxrkb6aIvX6BQgd3eE81hE+j/UBb8Ad+MYQOPoKIXBU/0p4xtjBM8aupeFGUnSZrLi1dpBaiOui8VSWlrhAdwJE5q0WvFX9LqL4cXUBA/tL/ZBxaSQo5SLj0kg4+AtQkVMPKw/SNP5qAUtQSns1915rxHbl9TMAOKjMNf/vvMG4V/04ZgPjof1S5DlBhEJktvpsNH0BeCsNRemKNotQUzG4jIeLGTNmVA4fPlxkb2/f6O3t3TSHnZ6eLjx9+rStVszyeDwKAB999FGvhIQEBw6Hg7KyMp7Ww9e3b18Zj8eDr6+v4s6dO1z962zdutU5IyNDOGzYsCC5XM5xcnJSzJ49u9LNzU25efPmAgAFCQkJNq+++qp3cnJypradl5dXw6VLl4Tt7V9sbGyt9jylpaW8uro6TlZWlnDw4MEiAKipqeFeu3bNQi6Xk2XLlnk2NDRwSktLeXZ2dmUAwOFw6KhRo2oNnTsiIkJmaWlJi4qKeIWFhRbjxo0LBACZTMYRiUT1VVVV3OjoaBkAjBw5stk5oqOjazgcDtLT0y2Liooshg0bFgyovwTk5ORYLFu27GZ8fLz7tm3bnEeOHFm9ePHi0vj4+IJp06b5cjgcvPXWWzejoqKa/jhlZ2cLXn/99dsA8Mgjj9R89913jgDA5XKp9stAnz59Gm7fvm3Ouh1GD8ScG78fwAGo87wyehDNPFYaESLW9SBqMbHwqrWFPwCAhhrg9vm7nlOf0Yh7cwjiHIcAdaXAzz5A8FNA3GrAPgCw8wXs/QF7P1BKkWcVhzKPUJSWlkIIIYYJnbFvzx7cvn0bLi4ucLRzQvGFalQkWMPaOxB1PnloUNjg4JrBsPO5CQc/Jwh97CDny/HIKk/YelqAL9T8P0pcBE9t3waYWzGrDQuSEhephT8I4NwXKLuk/lkzpmLeWKQ0RkNbuI5XY42i8QdRlEIBDoHMNw+xNlyg7BIysicjdNBVZCSHAQBkRRTyczWgKtpU904cFQOM1rkfaV/gZNIbGscvBVUSnFyT18J2Q/3V9arbX+yn6fcSxC01HcOs/VKkOhCI5UnTWx8jDaK/W0HyXtdOLcXo2tjY2NDHH3+8om/fvs2+pYWEhNQPGTKk5rnnnqsEgPr6elJSUsLduXOny5UrVy43NDSQwMDAcJVKPUNCiPFCkiqVCn/++addWlpaBoej/uI5ZMiQ4JKSEm5RURGvf//+cgDw8vJSaM+n5amnnqpau3at2+XLl0u1oQM//vij3ZNPPmnWGhJduyil6NevX13fvn1lhw4dyuFwOJDL5cTS0pI++uijAatWrSoaPXp07YIFC/pQSom2vdZmfbTxwW5ubo1eXl7y33//Pcve3l4FqBef7dq1y/7333+3A4A//vjDWq8tAEAkEsm9vb3lp06dytR6VCmlqKur43z99dd5KpUKfn5+4c8991zFE088cWfmzJlVv/32m83bb7/t+dtvv+VozxcYGFh//Phxm/DwcPnx48dtAgMD5YZs1vaL8fBhjngtp5Syoo09ELOKAQDGCwJoaagGaooBp2Ag7yiQ9QNQdV0tVif+pN7/5xLAwV8tSvk2AIcLzEoCrN2gVKm9kvW1tTh/0wmlpaUoK0tBeHg9Bg0ahJMnT8LGxgbOzs6wanDGpd234XFtMGyLGjBuUSAy95fhr7UFcHSzRUO1HeoARDgfx/j/XAdHk6wfALKzS+AUqHF6aKf/qQoAbeZVTkhIgDZvsK4H+uSaPKR/p57iN3tqW7uojHCA2S0XPUvSvoB9ztM4I1NC5nMDjXbV0CpRAhXEMTGIuzIB8KeIi/4QeEPVFI7g8SgfKcorTcKVENJSaH9/RiNcOdCNwdW33VD4h26Wg4yvWpbs1Y6VvujV/VLEsg8w7jfx8fG39Le9++67xbNnz/b+7LPPelNKMXbs2KqVK1feCgoKqouKigoJCgqqd3BwMGu10W+//WYTEBBQrysC4+Liqrdv3+5YUlLCe+aZZxyFQqFKpVLhww8/LNBt6+zsrNy+ffv1BQsWeMvlco5CoSCTJk0qN1e86hMdHV0/atSoOzExMSIul0sFAoEqMTExe9q0aRXz58/39ff3r7ezs1Pa2dkZX6GlB4fDwQcffJA/duzYQEopOBwO1q1bl//MM89U7tu3zyk6Olo0YMCAWj6f32IxnJubm/KVV165FRsbK+JyuZTH49Fdu3Zd//TTT3sdPXrUjlKKYcOG3bGxsVFpvbNyuZz8+9//LtY9z8qVK4tnzpzpt23bNhehUKjavXs3W8jFaIY5RQo2AbgEQArNfz9K6enON818WJGCziM7OxuBAQFAdZ7aa1p1DRA4A0GTgEMvANcOAIpaoFd/YOYZIO8YUJqm8Zz6A45BANdCvZhIJsOxT9JR/BMHrpMUqPG9hrKyMlRVVeG5556Dk5MTTp86jcL8YuQV5iJQHgsPXhAqc+shdOLjsXWBuPD2F6jOuQFH9zo48M7C428DwRnzqcnUTid1xJ7kvQj1Rm3xAuDuQjGNeG1K4A8gKiqq6Xza5PwAEPaUGWIscRGQuhEAbYpxNcSXMVIUjUsAOBRagQkAUTY5sFe8i4x9xeAMPYBCJ67aswq12HRR+KKEe71JvOra2txeCkJUcOhdhcpSJ4NCUjt+nkQE1YFATRy0f1OBh5MN15qLUI34j69eBgoCQgiWL1/eom8fztmmLvCQ541/bZ1tsP8G708PoDv8LWBFChhtQevZPXz4sPV7773nfuzYsewHbROj53KvRQr4AAZoXoD6v2uXEq+MDqC+ArCwBWqKgCu7mjyn1r2fAPzmA3tGAHY+akHqM1rdJmYpEPcuYO2mXlgFoNFjGMoFfVFWVgZ6myLMxQKHDx/G+fPnQQhB420BnFWxKDgsR9SyvhgR6wo7oQPO/LcQlddvoLrQBZX+pUAwcDP/NsRTBiJ0Si84+qkXGkU6LwKcdJwIl5KAMZ+aXDimncYuTFQAj2o8rk6hSCjygVQRBXFUNOxT+iHj31KETu7VLI5X91y6HkXzva5ULY5NeK5DJ/dCZaY3ZD65iOInQ9oYpS5BW+MP94QSUMpDgYMFAAppSjJAOKCUqoUr1JeIilYLV11P5117e7dqr9bjrhW8unHQiJiPuNF6Xnpt9gR+CqSNMUbjWmW+eQCo5t0wze4Pm+NhMLosTzzxhH95eTmvoaGBfP755zcetD2Mhxdzsg3MuR+GMDoZZQNwJ+/uQqiQ6cClrcD5T9XbqBJ45pz6XVYC9IoAAiehXuYIcPnAi7nNTtfY2IiCOxYoKytCaWkaPD09EX5zIzac5oNraQtn7zD4+PigvqIR5VfqQbKcETbRHdyCYOTTO+DV2ODCkkZUjqzFqP/0RsDfHOHgK4C9twC/HSmGVEoQ8rQjQiQuzfuhjb/VLYUK0wvHtCLO41H+3cVn5RmQNj7VtDjN/aBbk2h7canhON64qPfVWRki5gNoKUZbeH91YoVNTZ/HLfVGHGZrvJm/AdQJ0hp/iN3qYa+xvU9lg9rzyksBIl+6u0hLL+JLKwTTvytB2FO92ly+1VActEE0fZPE+EAyuqXHVUtUVOsL+prdHwaD0WXRjUtlMB4k5oQNBAJYCsALmn+VlNIx7b4gIU4AvgZgD+ACgH9QHSMIIX0AfAt1kB4HwKuUUpMxASxsAOrV7FSpjuG8+r1akFZdA5zDgKg3gN3Dgep8ddypc19g1Hqg/CrQUAXY+QFC5ybvqZbGxkbk5ubCwcEBGRkZKCsrQ2lpKUaPHg0nJyfs27cPzs7OcHFxgZttH1h8PQvlVT6gKgtEfrUVJ9/PQ/Yv5ajhl6LRphrV/dLxr9eXQF7TiD2TLwMqtVOyrQKrvWRnZyMw9+MmQZkgH98krO4uSDLhVdWGGhBuszKsWrThBoam0LUezab+6qUfMxT20Ezw8n3unuwN9a/Lzp07kZWVBQBN19SNy72fY9sRdIdp9rbQHfrDwgYYDEZX5V7DBrYCeBfACgDxaJmEsq28CWAPpXQHIWQLgLEADunsrwbwFKX0NiEkDMAXAIbd4zV7Boo64E6uekrfayRQkQmcXnlXqI5cD/R9Hsj5WZ1Wyn0Q4KoRL9P/aCFO4RQMSilqa2shKylB7969ceHCBaSnp6O0tBR37tzBlClTUFdXh/r6evj4+KB/vwGQXxUgO68WvtdHwsXSChETXZHw0lWgbAkcOCfRK9INADD41T4Y+qYXfvnlF0ilFyEWiyFw5EHgyEPYlAeUm1PHmygB7npYJWg9FKCVcrfmeH+b+quXfsxQ2EPzVGYvtbh2TEwM7O3tm11T2weW95TBYDAYPRVzPK9HKaWjCCHHKaWPEEISKKXmZ6Nveb4zAMZTSisIIU8CiKGUvmXk2AAAmymlI02ds8d5Xm+mqKfEK68BjXXA8DVqkZr03t240zFfqVewF51uSisFgYPRUzY2NqK8vBylpaWwsbGBt7c3du/ejdzcXHA4HAQEBGDKlCnIzc1FTaUMrh69wKm1QsqO60ClAJW59Yha4AHvYfY4+vZ1OPgK4OgnRK9wKzj6tTttoXEMFEa41/RVXcoT1lbPq36/EheBpn0BYqBwhBZTi9i0dKVsAF3q/nQA3aE/98Pz+kHyBx470ne4az9ve2xbBgDMPjQ7VLvt2bBni9+MfrNo+O7hERXyCj4A+Nv7y36e9HPGv/74l8+h3EMu2mM60taHlUOHDtmcOHHCJicnx3LZsmU3w8PDDaaiYjAeJKY8r+aI10QAj0E9lX8OwAxKaWR7jSGEZAIIoZRSQshIAE9TSlu4sgghXKhzzH5MKU00sH8egHkA4OHhIf7jjz/aa1KHIpfLYWlpaXQ/aawFX1YApaULVFwrOKd/CL4sH/zafNS5xKCkfzx6pb4DTmMtFFZeaLDxR43XRJDGOlCuhXou2AQKhQLl5eW4c+cO7ty5A19fX1hbW+PHH3+EjY0N7Ozs4OPjAx8fH5QUVIIjs4SLyAq3TjWg6JgCskIlFLUUg9faQllPUXyqHnbeFrDy5MDKnQMO//6k1QvYHwJClaCEi5wnrgBQT5Nrp+VnzpzZdOzRGVWACgAHGLXL3ug5W7s33QlD46OPsfHSxdyxux/0pPsDdI/+BAUF9aiwgSNHjlgfPHjQ/uOPPzYocletWtW7d+/ejQsXLiy/n3atX7/eeevWrb0IIVi/fn1eXFyczJxjDG0rLy/nPProo8F8Pp/W1dVxVq9eXTBx4sRqQ9d9/vnnvZKSkmytrKyUAHDo0KHsXr16Na14XbNmTa/+/fvXjRs3rqaj+2Rqf1pamuXAgQP7JiQkXB07dmwNAJw4ccJq6dKlno2NjWTAgAGyzz//vED/mh1FXFxc0OXLl61efPHF2x988EGzNF1SqVSwcOFCHwBoaGggubm5gsrKyguLFy/2OH78uB2fz6f/+9//8gYNGlQH3B1jW1tbJQCMGzeuctWqVS3StZniXu9zZ9CRzyxgeMyLi4t58+bN8zpw4IDBVGj3GjYwHeoMA4sAPAPA8H9BHTTC85SBXQkAKqCOd63UvBv7I/IFgF8NCVcAoJRuArAJUHteu4qHw6i3Je8YkPC0OuepvR8w/APAbxxQOxCwnwLY+8PC3g/2FjZA4C6T11CpVOBwOCgoKMC1a+p0U2VlZZg5cyaKi4uRmZkJZ2dn9OnTBwH+geDXW+PZ4YvAF/LgGWOH4ytycWp1JQiHD9d+VhgsCYS9ohZegY1w9BPAxs0ChKMWqdZ9HpD3SDNFTyLmN11fd1pe16abU+56DwMDu4nn9V6JmN/keTXWJ2PjpYu5Y3c/6FH3Bz2vP92BNWvWuG3bts3oKvgLFy5YLVu27Ob9tKmkpIT7+eef9z537tyV3Nxc/qxZs/ykUmlma8ccOnQo21A7e3t7VVJS0hU+n4/09HSLadOmBUycODHD0LUvX75s9dNPP2WLRKKG6dOn++zbt89eK9y3bdvmoFKp0B7h2lqfWtu/fPlyj5iYmKbr1tfXkyVLlngmJCTkODo6qvSv19Fs3749NyEhwa6goMBCf59YLK5PSkrKBIDNmzc7Hj161Pb06dNCqVRqff78+SvZ2dn8WbNm+Z09e/YqoB7j7777Lqe93mtjY9WW+9zRdPQzCxgec3d390ZbW1vVmTNnhLGxsXX6dpjCnGwDZZrYU2eoPa8urTQBpVQJYLChfYQQGwDjAezUvP9g4JiPABRTSj/V39dtcYsGnr3QLK0UAED8msHDKaWoqamBUqmEg4MDTpw4gby8PJSVlYHL5WLRokUoLy+HXC6Hr68vBkQORG2eCshzRP/Gx+DpbYde4Vb4elQqLGy5cPQTwnekAzxjgMjZbhj0micEDrymii29Qq0N2vHAMLDS3Vg1ry5ZHlQnLKDNC8MMhEwA+mEAG5DjuxiBgYFGp/7NqX7WJceOwWgHFRUVnPLycp6np2ejSqXCnDlzvDIyMqyUSiUWLlx4e+7cuRXZ2dnC/v3712/atMnx008/dQWACRMmVL777rs3ASA8PDy0T58+8tLSUj6fz6fHjx/P+uGHH+zWr1/vWl9fzxkxYsSddevWtSl04c8//7QeNGhQjUAgoCEhIQ01NTXcuro6IhQKqaljTLXTVrSqrKzkhoWFtfCIaSkuLrYQiUQN1dXVnCtXrggXLFhQAgBbt2513LhxY+/HH3+8IikpSRgTE9Mm4dBan0ztP3r0qLWrq6tCW9ELUHvMra2tVVOmTPGXyWSclStXFj322GMGRbWhe2RpaWl6ClmPgIAAhTnH7dy503nJkiU309PTBZGRkbUAEBgYqCgoKLDU9icvL88yNDS03WEX93qfjT3ru3btsm/vc9sZz6yxMR8/fnzVrl27HDtcvBJCdkItXLWJGimAP9tyET0+APA1IWQhgDQAhzXXWQf1wjAfAK8COEUIOQ6ghFL61D1cr2tgYaN+6aEbi+rq6gpbW1ts3769SaRGRUVh5MiRcHJyQu/eveHi4gJOjRBXfi6FLNcJyutWCPinF+oqFDi28gYc/ARw8BOAZ8UBl8/Bs4f7gydsXg7QQZMz9b5hRIy1BXPiN1sj8ysZjh2R3p/4Tp0FWdIa16bFWLopuYzaoLeYS4uxXLbNFnYxIcp4SElLSxP06dOnAQAuXLggyM/Pt9R60JRKJWQyGeHz+TQtLU3w1Vdf9frrr78yNf9kw956661blpaWNC8vz3LHjh3XBwwYUD9x4kS/xMRE63Xr1rkeP348SygU0tjY2OBbt25xXV1dlQBQU1NDRo0aFaxvy/jx4ytXrlx5CwBKSkp4jo6OTVP1dnZ2ytu3b/N8fHya/pkbO8ZYu+vXr/OnTp3qf/36dcGGDRtyDY1HVlaWRXV1NTcqKkqUmZkpXLFiRcHIkSNlADBnzpyKOXPmVOi3Mac/5vTJ1P7Vq1e779y58/qiRYu8tPsLCgosMjIyrNLS0i5XVVVxR40aFZyTk3NZv5StQqGAoXskkUhqzLXdXG7evMm9du2a4G9/+1vN+fPnBRs3buxdX19Pzp8/L7h165ZFSUkJVy6Xc2pra7mxsbEiALCyslIeP368TYUbTI2VOffZ0LOemppqaey5fVDPrLH+h4WF1W/dutXZ7AHTYE7YgAel9JG2ntgYlNIyqBd6629/TfNjCdSFEXoUMpkMt27dako3NXz4cNy8eRM7d+6Eo6MjnJ2dERsbCycnJ4wdOxZ2Vo5wcLHFtd8rcHJNHiqv81Fb0oBp3zshJ6UCRSnVcPQTQPSEMwQOPNj1scS07/u2uK6+cDVJZ4lMI2LMGIY8iaaKEJhLYaICUN2jyDMxRs36HjFfXV2LKiF2rUXKTWsIr3ujbnA6Khyvow9PBOBuVoJmfY4ykNUgcRHEvBvqogriqGbXCp2s9uY6+ArwZcx9EucMRhcmIiKiPioqqnbcuHH+YrG4dvXq1beSk5OFISEhdXv27HFYsGBBiZWVFQUACwsLKhAIqFQqFQ4fPvzOgAED6gGAy+XSY8eO2ZaWlvJHjBgRDADl5eU8lUrVNHVmY2NDtaLBGC4uLo1JSUlNixWqq6u5vXv3bmztGFtbW1VlZaXBdn5+fgqpVJqZmZlpMWrUKNGMGTNa1J5OTk4WTp06tWzLli35GzdudDp16lRL74ke5vTHnD4Z27979277gQMH1rq5uSn1zycWi2ucnJxUmldjcXExz9PTs9k4nTt3rsU9srW1VbXFdnPZtm2b04QJEyo4HA7EYnH9U089VT5s2LBgX19feUBAQJ2Hh0fj3r177SUSSfnevXsNhqo0NjZCLBaH6G8fM2ZM1Ycfflis7buxsTTnPht61nfs2OFk7Ll9UM9sR2OOeD1DCOlHKW1ZmJ3RDO3it+zsbBQVFaGsrAz19fWYMWMGUlNTceXKlaa8qIQQeHl6Y+GsxbAU8mHtysef/7mBc9lXUJlbD88YBcZ8ZIsGmRL23pbwHeEAB1+1xzRwrBMCxzp1fAfaKDINYVBktpJiSh9DnkRTaaiaaEV8e47mo+iIAqGTe7XZk9skLv1tEBetVAvT0RuaXVMqvethlSzfAKR9gc9q5qGkRgjeHWvYp/VD8ePqMrCFNLOZYNUtLgAsQdxSPfvTvoDEUgmJ4BAgaWzKKSuVSrF8uQRxS72bV8di4pXxkBEREVGvjaeTyWQc7aKQoKCgvq+99lppcnKyVWRkpKy4uJgvl8sJAOzdu9fOz8+vnsfjITk52Ur7Nzw9Pd0iPz/f0sPDQ/HWW28VzZ07twK4Wx5Ve01zvFgjRoyoXblypadcLid5eXl8a2trpe70q7Fjxo4dW71mzRp3/Xa607cODg5Ka2vrJiGYlZVlERQU1AAAqampwr59+9YBak/r6tWrPbXrJYxhrveytT4Z23/+/HnhyZMnbYcNG2aTmZkpzM7OFvj5+V3THq9QKFBTU8MpKyvju7q6Nur2B4DBezR06FBZW2w3l7179zpv3rw5V/t56dKlJUuXLi1JTk4WrF692p3H4yE1NVUYGRlpNGxDc4zhFbWtjJWx+6w/Joae9YaGBmLsuX0Qz6yp/qenpwvCwsLqTR1jCKPilRCSBXWIAAHwNCFEofmZUkpbdPxhQRuLyuVyIRAIcPjwYZSWlqKsrAyenp6IjIxEYWEhFAoF/Pz84OLigsY6FYIc+6OXuwgeUbZQyJQ4+HwO7hTIYeXCx8C57hA94QLPQXYQPeECBz8BBPbqWxMysdUQ446jjSLTEAZFpqlKTQZokRMV5sVvthDfemJW9HcrSN5TL6CJj98GSilSklOQkpwMECAqKtroNbSJ/9OzpiIu+gOofzWaX1Ms/rl53yPmo+RULwAEjXY1IFygD0+EQlUGxLxkZHw/DlTFaeqr9hpnMo/iaHxec2GtuTcnc7YiIzoZnkMaUOjEg1isXiiekJCAIokUVrneiBWN6lIpsBiM+4Gjo6PK0dGxMT8/n7do0SKv/Px8S4VCQaZPn17q4uKiPH/+vNULL7xQ5uvr2zB9+nT/L7/8speLi0vj119/nQsA58+ft3JxcVEMGTIkSKVSkc2bN+daWVnRGTNm+G3atKk3j8ejixYtujVr1qwq7TXN8WL16tVLOXfu3NuDBw8WEUKwbt26plrJTzzxhN/+/fuvGzrGWLuUlBTh4sWLvbhcLm1sbCQffvhhPqAWKBKJJPDq1avpAHDp0iWrxYsX3wIAgUBAAwIC6v/44w8rbeiAIcz1XhqzzVR/AOD999+/CeAmAEyZMsV33rx5pcHBwQ0AsGDBgluDBw8WKRQKEh8fX6BUKpv1x9g90saFtsXz+vTTT/ukpKTYNDQ0kHPnzlklJibmaG0H1MK4oaGBDBw4sElUDR06NEipVBJHR8fGzZs352nHOCsrS/DTTz85afpUvmTJkhJzbGhtLA3dZ/17DADPPPOMj/6z/sorr5Qae24fxDNrbMwB4JdffrF/+eWXb7dlzAAzUmV1Bzojz2tjY2PTFH9AQACqqqqwf/9+lJaWgsfjYezYsYiIiMDZs2fh4OAAFxcXCIgNMk5fh43CBVX59Yh5xROZ+8tw6v082PWxhIOvAFELPGDrYYnKG/Vw8Ba0bVr/AdCtVkzre171KmLp9iUhIQEpSVKA3C2xaqgylpZNUVKNXqWYN2vw3fK0TqFIKPSGtDEKnjIHqI4NbyYYP/vsM5SUlEAgEEBeXwcxPwUSywQAwMnkJTgjEEHmewN96uyhPDIcoEDxEwkAKKAieFQ4u5n4bKrURRoxd+ZQEE2lL/3qXl/GSFEZehEy3zxExbQ/TtgQnSWMu9WzZgbdoT89rcJWYmKi9cGDB+3buqgKAGJjY4MPHz6crZ2G7m4cPnzYOjk52frtt99usxDoihjqT3e/R/dKT7vH95Iqy5w8r/8C8JEmLysB8E9K6Yf3anRHci/itaamBiUlJSgtLUV1dTVGjRqFv/76C4mJiXB0dISLiwvGjBkDoVCIkpISuLi4QCgUojDpDsqz61BxvR48Sw6G/NMLJ9fkoeB8OdxCHODgJ0C/mb1BKcDhEnB49yc/akfTHf4BG0VPzOr35eSaPJy5cgQy3xtqzys/BZIYH4NFA76bdhkVOfVwDBDgqb19mwnj+Oq3QcEBVATuByUGy7I2iUuosNzmP+oCExHzEX/KpUVbjxU3kZKk9qI6ZPTDi/+3pakfJ1OWIOP7WwgN+B4lQ+W4WOHY5OnVzWiQvq8ExRJ1iAIBxXLb1WbHMrcWUqFf6rajxGy3ftYM0B3609PE670QHh4eeunSpfuSiojRPtg9eri41zyvj2nFqkbAPgagS4nXeyExMREVFRVNsaiUUgwcOBAxMTEtYoO8vJoWRyLrl3LwhBw4BwnhHGwFQJ12KDu7AYGBvm224+SaPJzJPKr2lLnVQlL733taONXd6CgBpJ9OytT4xS31RhzmqD9oxWgaV12uNSUZFATSlGRIJBJU5qpnj7TvuiEW4px6SG8K4FnvABUXBsuyehIRCpSZ8KxUAkMXNtnlcm4ZSuoIrJV8EE3bOIkY9r+UI+OyCKGD0puFJsQt3aAZn2i9uNflTULzy1VqL7FVnjfq/PIg5iW3KZa5tcVx+mEdLNsBoyfARFHXh90jhhZzxKsVIURAKa0nhAgAdLGEoPfGpEmTWmyzsGiRt7gFj6z07VA7Mn4ogWxcHgAK6U0BJDb3tnCqu2GWADIj/6mu8DIrp6oWvXhfMS8Z0sYoiHlqj36LOFydOF7JaAPpM/RQHQiEuzIQKqIEJmxv2l5Sp06sUctXIHTFTcRJ1F7UuIA5QNkbyDg7BcBW9We9WOTAwEBkZ2e3WMSmtTVWNErd78RFOLl5CTJypiC0Ia/VsWhtcZx+blhDMcoMBoPBYHQW5gRcrgOQRAjZAeAvAGs71aKHlNDJvWCV5w2AQOxWr56TvYeFUw+ExEVqD2biolYPTUhIQHx8PBIS1PGfoZN7NXke9Tm5Jg9fxkhxcrPtXQ+iDrqCVSwWgxACsVjcTBC3yugNdwVs4iJIBL9iuU08JIJfAagFm8eKmzjasK3JZqN2rslrsS90ci8QokRo4PdA6mfAfwnwXw6aFn4BSElJwaoV8fjqP3uAiPnIyJ4CSrlI/ysEX+46g5MpS5qNn1a46npHjXmwM7InNy0Oaw2JRNIU+6t7j4wRt9QbLyaJm65nahwYDAaDwbhXzKmwtYcQ8juAAADZlNIWiY0Z9456Cnu2zpb3H5Qp7acNqbb0p6ZNVXpqEqE5UxAX81ELUa/rKdTNSnDyYl7bPIK69vdf2CLzQmvT6fpiWRtPG+VeB8nS9xEX9b7mGgRq0aoXb04BcCgKGjOB0RvAmbgHBY2ZsMr1gv2liGZeaa0tKUlqD7N2u0EPdtoXCA20QUb2FIROdjNvLMzorzFYGAGDwWAwOhOzlrpTSssppclMuD7cmPKoJSQkIL56GRLkkhbJ9Q15Y3U9pK3R5JWd4gq83miwbKxuzKcWfY9gq0TMv+vxHr2hxbVas1nXe5zxQwlkPvkAIZDe1FQ0056z/8KmNlG8ZBCoEMVPRp86O0BFNAUMgEKaCXAoZH43UNUvrZkIF4vFgIrAKtcb6ftKDNoAQD3uVIm46A/x4prtBsdC3wtuqL9t8aaa8qIzGAwGg3GvsFRZHUx3WGHcFnT7o7/KXBf9VE1N6KWrepDcz3ujm8kgyr0OkvkGPOnaGF6qyTGuN0YJCQnQPteG0nhtEkubfp4nNfIlwIzxN3rvdDB17zuKnvy701Vh2QYYDEZXxVS2ga6dZJTRpTDlUTPqldT1Zj5ExC31xr+2zcGKlSsNC1dAxxP7ksExkkgkiIqKMurt9RyjzlAQ9pQJD6cZ42+OF5x5UxkMBoPRVTAnz2swgLkAnKFJ504pfaHzTTMf5nntPHpSf9rVFzMyHJizYKoz6En3BmD9eRAwzyuDweiq3KvndTeA8wC2APhK82IwHg600/qpnzWL221aMJWS0ixWNH2ferGSbhwqg8HoXDIzMy2GDBnSrGy5t7d3uLnthwwZEpyZmWlh6DztIScnh8/lcsUHDhyw1W47d+6cICYmRhQTEyPq379/yKZNmxwBICYmRpSTk8MHgLNnzwqHDx8eFBMTIxowYEDIypUrXU1dp7U+rl+/3rm8vJzNsDJ6HObkec2nlO7qdEsYjK5IxHy1cAWQcDYXKSdXweqGDzz9RSigVwCg+Wp87URG9w8lZzAY7WTbtm1Os2fPvv3NN984TZgwoRoA3nnnHY/4+PjCxx57rEalUqGkpISr26asrIz77LPP+v344485ffv2latUKvz4449292LHN9984yKRSO44OTk9lOVUGT0Xo9/ICCEzCSEzAVQTQtYSQmbpbGMwHg5Gb1DHpAKQNkYBBJB550F1INBgPGrYU71aj0NlMBj3jQMHDtgOGjQoWCwWix599NEAmUxGAOA///lP7/Dw8FCJROJfWVnJ1W+XnZ3Nf+SRRwIHDx4c/MgjjwQWFRXx9u7dazdnzpymUotDhgwJunr1aouqNr/++qvDJ598UpCdnS2oq6sjAGBlZaU6fvy4za1bt7gcDgeurq5K3Tbfffed/dixY6v69u0rBwAOh4MpU6bc0T/3/Pnz+0RGRoZMnTrVV6FQEACQSqWCwYMHB0dHR4tiY2ODi4qKePv377fNyMgQTpkyJeD555/3Ki4u5sXGxgbHxMSIBg4cGJKWlmZ5r2PLYDwoTHlegzTvWZp3bfAW8ykx2oZe3Kh+vOj9jBNtF5pYV3GSFCkN0bDK824q42ooPVeX7AOD0UVZvOeC188XCntPjPS8/fH0yPz2nufSpUtWMTExIv3tI0aMqJ0wYcJVAFi4cKHnli1bnJ588smqnTt3uqSlpaVXV1dzAgIC+um3e+2117zeeeed4kcffbT2m2++cVi5cqXbxo0bC1atWuVZV1dHbty4wefxeAgODm7QbZecnCwICAiot7GxoePHj6/cs2eP/ezZsyv/97//5S9btsw9NjY2xMbGRvn555/fGDJkSJ22XX5+voWXl1eDvh26nDp1Spieni68cOHClczMTIu+ffuGA4BIJJKfOnXqKpfLxfvvv99r7dq1vT766KPi0NDQul27dl0LCAhQyOVycuzYsSyBQED37t1r95///Mf9u+++y23ncDMYDxSj4pVSugoACCHjKKW/arcTQkbeD8MYPQi94gX6ye+7RVL70RvMKgPLYDDaxs8XCnurqPr9XsRreHi47PTp01e1n7XxoFKpVLBs2TLPhoYGTmlpKc/Ozq4sMzPTUiQS1fH5fDg5Oan8/f3r9c935coV4VtvvdUHAJRKJXx9feVcLhfjx4+v3LFjh0N6erpwzpw5LRambd261TkjI0M4bNiwILlcznFyclLMnj270s3NTbl58+YCAAUJCQk2r776qndycnKmtp2Xl1fDpUuXhKb6mJ6eLhg4cGAtAIhEogZnZ+dGALh+/brFq6++6lVdXc2trq7mRkZG1uq3LSsr486dO9f79u3bfIVCQaytrZX6xzAY3QVzArn/pff5pc4whNGD0UvXpJ+aiaVhYjAeXiZGet7mEPV7Z5x/9erV7qtWrSpKTk7OHDt2bBWllIhEIvnVq1cFCoUCFRUVnGvXrgn02wUHB9d9/PHHeUlJSZlSqTTz22+/vQEAixYtKt2+fbtLYmKi/cyZMyt126hUKvz55592Fy5cuHLixImspKSkzDt37vBKSkq4qampTdP0Xl5eCpWqeRjqU089VXX48GH7y5cvNx2nH/MaGhoqv3DhghUAZGVlWZSVlfEAYO3atb2nT59enpycnDl79uwSbRYhPp+vamxsJADw5ZdfOvXv31+WkpKS+e9//7sYmuxBDEZ3xKjnlRDyLIDnAEQSQg5rNvMB1Blrw2AYZPSGZmmmdEu4Ag/ZVLuJEAr7i/26dvgEg9EJfDw9Mv9ePK6tMW3atIr58+f7+vv719vZ2Snt7OyUnp6ejdOmTSuPjIwM9ff3r/f09GwxXb9+/fr8efPm+chkMg4APPfcc6UvvfRSua+vr0IgEKjCw8OrLS0tm4XR/fbbbzYBAQH1HM5dv1BcXFz19u3bHUtKSnjPPPOMo1AoVKlUKnz44YcFum2dnZ2V27dvv75gwQJvuVzOUSgUZNKkSeVPPvnkHZ1zyYKDg+sjIyNDQkJC6nr37q0AgMmTJ1cuXrzYe8+ePU7u7u5NfZk4cWLl7NmzfWNiYmqeffbZ8lmzZvmfOnXKNiQkhP0fZ3RrjOZ5JYTYA3AA8DqAtZrNDQBu0i5Wlovlee08elJ/ukRf9Cpe6Va3cj8oaVMVqy7Rnw6E9ef+w/K8to8xY8YEfPTRRwURERHyB20Lg9FTaVeeV0ppFaX0BoD/Qr1Ii0LteXUlhLDpBgajPZgIoWDhEwxG10Yul5MRI0YE9u7dW8GEK4Px4DAnz+s+AL4AMgGIAOQDsCOELKOU7mnrBQkhTgC+BmAP4AKAfxjy5GqOy9bs/6at12EwuiSmQigkYOECDEYXxtLSkv7xxx/ZD9oOBuNhx5wFW1cBBFBKhwEIAHAFwEAAb7bzmm8C2KM5nzWAsUaOewvA6XZeg8FgMBgMBoPRAzHH8yoCUKP5uRZAMKW0mhDSInmymYwA8L7m5wOaz4d0DyCEeANwB2A0kJUQMg/APADw8PBAdnbX+DIsl8u7jC0dQU/qT0/qC8D609Xpaf1hMBiMroI54nUXACkh5CKAvgB2EkJ4AI6085pOACo1P1dqPuuzAsC7AKYbOwmldBOATYB6wVZXWRjRHRZptIWe1J8H1pdOKtLQk+4NwPrDYDAYDPNoNWyAUroW6qn9DQDGU0rXUkobKaWrjbUhhHAJIX8ZeL0DoALqeFdo3sv12vZTX5ZmtLdTDMZ9J3GROpNA4iIAQEJCAuLj45GQkICTm23x5c5TOLnZFgCaFWkA0KxIA4PBaDuZmZkWhBDxjh07HLTbtEUKAODTTz91HjhwYEh0dLRo3Lhx/vn5+U2OG5lMRmxtbSM/+eQTZ/3zzpgxw8dQ1a7Wjlm8eLFH//79Q6KiokSDBg0KBoCDBw/aTp8+3QcAFAoF3njjDfeoqCiRWCwWjRw5MjA9Pb1FmVktn332mdPrr7/uYar/3377rb2x/QxGT6NV8UoIcQHwFNQCdgEhZHlrbSilSkrpYAOv/wD4A8B4zaHjNZ91EQMQEUIOAXgGwL8IITFt6BOD0Tb0hKcWXQGqy8k1efgyRoqTa/LubtStIobmAjUjZwoo5SEjZwoAVqSBwegM/Pz86j/66CM3/eT/hw4dsvn6669d/vzzz0xNEv+y6dOn+2n3796922Hy5Mll+/btazYLWF9fTy5fviy0sbFRZmVlGRSWho45d+6c4OzZszapqalXUlJSMvfv35+j327FihVuNTU1XG0BhC+++CJPLpebswbFIFlZWZb79+93aG97BqO7Yc4vy36oPaTXAORoXvfCBwBmEUJOAFAAOAwAhJB1hJBelNJtlNJhlNLHAHwD4ENKadI9XpPBMI6e8NSi7yHVYtBTaioF1hRXtTid4gpAnWFg+fLlTVkG4pZ648UkMcs0wGD8f3t3Hl5Vfe1//L0SwoxAGGWSMQkogwajtEC5ffyJkrZaqLXWoanXOlyv1dJ7LbbVIq0DLUXrUH9VWoWrFrTS1hraWq+CEZBAFJCCgShUCIMxgKBAyLDuH3sfDCEnOZLxxM/rec6zh7OHtc4JPsvv+e7vtw569+5dOnLkyENPPfVUl8r7n3jiiW4zZszY1bFjRwe49NJLPzx06FDi1q1bkwAWLlyYfPvtt+9u3bp1xbZt25Ii5z3zzDOdL7zwwv1XXnll8eOPP15d97Zqj2nfvn1FUVFRUk5OTvvy8nJ69ep1wjSsixYt6vbLX/6yMDKZQUpKytEzzzzzuClq8/Ly2o4cOXL4pEmThr7wwgvHcrrhhhv6nnPOOSkjRowYPmfOnO4Ac+fO7fXyyy93zsjISM3JyWk/c+bMXueee27KGWecMfx73/te1BZbkXgVS/G6193vcfenIq+63NDdi909MyxQb3T3inD/Le5eVOXYmRomSxpclcIzomoLaUS1LaXnPQzTy44Ng1W5QB0/YwDfuft3jG89+ITW3YhorbwR1bb2irQEi6/tz51d01l8bf+6XurOO+/cVbX1defOna0HDRp03Axa/fr1O7pt27akPXv2JJaUlNjQoUNLL7/88r2Vi9SFCxcmX3PNNXu/8Y1v7P/HP/5R7U/y1R2TlpZ29Cc/+Unhj370o779+vUb+R//8R99q5535MiRhEgxHc2tt97a97777ntv6dKlBaeccsqxAnj27Nm7Vq1atTkvL+/thx56qHdJSYlNnz59zxe/+MUPc3Nz8ydMmHBo+vTpRa+//vrm9evXb1q6dOkp0VqOReJVLMXrTjP7rpl93sw+Z2afa/CoRBpKdV0EqhSekUISOK6FNCLSUgrEXlBGad2NiNbKG6F+sdJivfVsT7wiWNbRkCFDSkeNGnXoySef7BLZd+qppx7dunXrccVbYWFh64EDB5bOnz8/edeuXa0nTJgwbN68eT3+/Oc/dwUoLi5OXLNmTcerr776tPPPP39oYWFh65UrV7arfI2ajrniiiv2v/rqq1vefffdDW+99Vb7v/zlL50qn9u2bduKjz76qMbJfrZt29Z20qRJHwNkZGR8HNk/Z86cHunp6akTJ05MKS4ubrVz584THrxesGBB17C/ber27dvbRFqZRVqKWIrXJOBM4BrgO+FSJD7VUkRC7YVkxKcqKKO07kZEa+WNUL9YabFGXvI+lhAs68HMmTN3zZkzp3dk+6qrrir++c9/3jtSLP7hD384pW3bthWDBg0qfe6557quWLEiPycnZ8uKFSs2Dxw4sCQvL6/tggULut5yyy27cnJytuTk5Gx55JFHts2fP/+4B7qiHbNnz57EwsLCVgDt2rXzLl26lFXth3vppZcWf//73+8b2V9QUJD05ptvtq18zGmnnVby6quvdgBYs2ZNB4CioqLEp59+uvuqVavyly1btrlTp07lFRUVtGnTpqKsrOxYMXz33Xf3Wbp06ebXX389v1+/fiXurlkxpUWpdagsd/92YwQi0ihGXffJsFVRpKenHxvKqibDp/Y4NsRVrarMrFXVcTNtVWP8jAHqEyst09RHtzP10e31dbkhQ4aUjhkz5tDSpUtPAcjMzPyooKCgeOLEiamJiYmenJxctnDhwq2bN29uXV5ebt27dz/2k/z555//4eOPP95tzZo1HebNm/evSvs/uuWWWwaUl5fvSExMBGDRokXJ1R1z0003FWVlZQ2sqKjA3W3UqFEff/nLXz64ZMmSY62vd9555+4f/OAHfc4+++xUM6NTp07ljzzyyHE/4cyePXvH1VdfPahr165lycnJZQDdunUrHzZs2OGxY8emDRs27EiXLl3KAMaOHXt427ZtbS644ILBs2bN2nXhhRfuz8jISBsyZMiR9u3bn9DnViTeWTUzsx5/gNlY4H6gE8FIAHe5+w8aPrTYjR071tesiTqfQaNqaWM7tqR8WlIuoHyau3jIx8zy3H1sfV5z3bp120aPHv1BfV5TRD571q1b13306NEDq3svlm4Dc4GpBA9ulQFn12NsIiIiIiIxi6V4LXX394Gam2hFRERERBpYLMXru2Z2K9DZzG4G8hs4JhERERGRasVSvN4AfAisBI4CNzVoRCIiIiIiUcQy2kAZEH1cIRERERGRRhK15dXMtpjZ5iqvLWa2uTEDlGakugH+61Fts0zVVf5vD2mWKhERkTgXtXh192HunlLlNczdUxozQGlGYhjgvy5inRzgZBW+VKpZqkRaoPz8/NZmlv7www8fm97161//+ml9+/Yd2VD3fOedd5ISExPTK8+e9cYbb7TNyMhIzcjISB09enTao48+2hUgIyMj9Z133kkCWLVqVbuJEycOy8jISD3zzDPTZs6c2aum+wwYMOCMmt5/4IEHuu3duzeWLoAiLYb+4CV2tcwSVVe1zTJVV33PS9IsVSIt1IgRIw4tXry4K8Dhw4etsLCwdWJiYoONkvPEE08kZ2Vlvf/kk08eK5hvv/32PrNmzSrMzc3Nf/PNN9++6KKLDlQ+p7i4OPHKK68c9Mgjj7yXm5ubn5eX9/bIkSMP1yWOJ598svu+ffsS63INkXij4lVid97DML2sxpmi6iIzM5M77rijxpmm6iL139vzndx0zVQl0gJ17ty5PCkpyQsLC1stWrSo8+TJkz+MvFdcXJw4ZcqUwePGjUs599xzUzZs2NAG4IYbbuh7zjnnpIwYMWL4nDlzugO88MILncaNG5cyZcqUwSkpKSN+97vfda3ufn/961+7/OpXv9pRUFDQ9vDhwwbQvn37iqVLl3bcs2dPYkJCAr169Tpudqtnn3228+TJkz88/fTTSwASEhKYNm3agarXvu666/qNGTMm7Wtf+9rA0tJSA8jLy2t77rnnppx99tmp48aNS9m5c2er559/vtOmTZvaTZs2bci3vvWt/rt27Wo1bty4lIyMjNSzzjorbf369W3q6/MVaU5UvIqISJO5Lee2/qMXjE6/Lee2/nW91rRp0/bNnz+/6zPPPJOclZW1N7L/9ttv733xxRfvW7ly5eb77rtv+3/913/1BZg9e/auVatWbc7Ly3v7oYce6l1SUmIABw4cSHz++effffHFF7fMnTu3d9X7rF69uu2QIUOOdOzY0adMmbJ/0aJFnQEefPDB7R988EGrcePGpY0aNSptxYoV7Sqft3379tb9+/c/WlMOy5cvb7dx48Z2a9euffuuu+7aWVRUlASQmppasnz58s2rV6/Ov/jii/fNnTu3x1e+8pWDw4cPP/zcc8+9M3/+/O3Jycnlr7zyypbc3Nz8GTNm7PzpT396al0/U5HmKOpoA2b2I6JMTODudzdYRCIi8pmxZOuSnhVewZKtS3reM+Ge7XW51mWXXbZ/4sSJqZ07dy4bMGBAWWT/xo0b261YsaLTvHnzegK0atXKAebMmdMjOzu7S0JCAsXFxa127tzZCuD0008/1KpVKwYOHFh64MCBE36Sf/zxx7tt2rSp3YQJE4aVlJQkJCcnl2ZlZe3v3bt3+bx583YAO7KzszvefPPNA1avXn1sbPT+/fsf3bBhQ7uq16ts48aNbc8666yPAVJTU49269atDGDr1q2tb7755v4HDx5MPHjwYOKYMWM+rnpucXFx4jXXXDPg/fffTyotLbUOHTqUVz1GpCWoqeV1B1AY5SUiIlJnUwZNeT/BEpgyaMr7db1Wx44d/Utf+tK+66+//rinMtPS0o5Mnz59d25ubn5ubm7+yy+/vKWoqCjx6aef7r5q1ar8ZcuWbe7UqVN5RUUFAGYW9R4VFRW8+uqrp6xdu/btnJycLbm5ufkHDhxoVVRUlLhu3bpjP9P379+/NHK9iEsuueTDF198sfM///nPY8f98Y9/PKXyMcOHDy9Zu3Zte4AtW7a0Li4ubgUwd+7cnpdeeune1atX52dlZRW5B21LSUlJFWVlZQbw2GOPJY8ePfrQmjVr8n/4wx/uAqInIhLHora8uvv8xgxEPuNeujEYxWDUdTX2qc3OziYvL4/09PQa+8a+du97bFpcxPCpPdTHVaQZu2fCPdvr2uJa2axZs/ZU3XfXXXftysrKGvDrX/+6p7szefLkD2fOnLln2LBhh8eOHZs2bNiwI126dCmr7npV/f3vf+84ZMiQIwkJn7T9jB8//uD8+fO7FhUVtbriiiu6tmvXrqKiooJf/OIXOyqf261bt/L58+dvvf766weUlJQklJaW2sUXX7z3q1/96oFK1zqUkpJyZMyYMWlpaWmHe/bsWQowderU/d/73vcGLFq0KPnUU0891vXgoosu2p+VlTUwIyPjoyuvvHLv5ZdfPnj58uWd0tLS6vQgmEhzZpH/e4t6gNmXgHuAAUAFsM/dBzdCbDEbO3asr1mzpqnDAKCgoIChQ4c2dRj15mTyiRSOCV8uoNDzSe/1MZkf/7LmwnRuq2AYLksMHgqLYtasWbg7ZsYdd9wR9bjHMvKOXe47ueknnUtzpnyat3jIx8zy3H1sfV5z3bp120aPHv1BfV5TRD571q1b13306NEDq3svlge2ZgJfAN4AhgLP11tk0iJtWlyEl8OOsvxg3NbdbWsfHzbGYbhiHU5r+NQeGhZLRESkBap1eljggLvvNbNEdy82s9ENHpXEteFTe7BpcRH9WqWGLa9H4ONaCtPzHo5pCK7MzMyYhtIaP2OAuguIiIi0QLEUr++ZWTtgpZn9FajT04tmlgwsADoDa4HvepW+C2Y2GPgV0AHY4e5X1eWe0rg+KRwrt47ObqpwRKRxVVRUVFhCQkKDTRAgIi1bWBZG/W9Ird0G3D3L3Q8DM4DbgC/XMaZbgUXuPoGgOJ1czTEPAde6+xdVuIqIxJUNRUVFnSsqKvSku4iclEOHDrU1sxMevoyoteXVzKoWj6MIWk5P1hf4pBnuL+H23yrd7zSgPfArM+sFPODuz9XhfiIi0kjKysqu2b1797zdu3efgSbCEZGTU15eXv7jaG/G0m0gMuuJEfwOfIS6Fa/JwP5wfX+4XVkf4ExgBHAQWGFmL7v7vsoHmdm1wLUAffr0oaCgoA4h1Z+SkpJmE0t9aEn5tKRcQPk0dy0tn1ilp6e/D3ylqeMQkZar1uLV3e+qvG1mf6jtHDNLBJZX81Y2sI+gv+v+cLm3yjF7gbfcvTC81lpgGJBbJa5HgUchGCqruQxJEw/D43waLSmflpQLKJ/mrqXlIyLSXMTSbaBPpc1+QGpt57h7OXBulOt1BKYAT4fLxVUOKQDam1kn4DBBC+y/aruniIiIiLR8sXQbeCpcOlAM3FjHe/4cWGBmNwDrgRcBzOx+4C53LzKzW4G/AknAY+4etdOuiIiIiHx2xNJt4N/q84buXgycMFCnu99Saf0l4KX6vK+IiIiIxL9Yug18HrgZ6Ebw0Bbu/sUGjktERERE5ASxdBt4BLgOeK+BYxERERERqVEsxesWd1/Z4JGIiIiIiNQiluI1wcwWA3mEU3W5+90NGpU0nZduhPW/gVHXwXkPRz0sOzubvLw80tPTycw8oQvzMa/d+x6bFhcxfGqPcMpYERERkZMXy+wnfwL+DOwACsOXtFTrfwNeHixrkJeXh7uTl5dX43GbFhfh5cFSREREpK5qLV7dfX7VV2MEJk1k1HVgicGyBunp6ZgZ6enpNR43fGoPLDFYioiIiNRVLKMNfBe4BSgjGG3A3T2lgeOSpnLewzV2F4jIzMyssbtAxPgZA9RdQEREROpNLH1erwKGu3tJQwcjIiIiIlKTWPq8bmzwKEREREREYhBLy+sI4B0z2xRuu7uf34AxiYiIiIhUK5bidVqDRyEiIiIiEoNYRhv4F1AO9AH6hi8RERERkUYXy2gDPwHOBwYB24CDwOSGDUtERERE5ESxPLA1xd0/D2wGPg980LAhiYiIiIhUL5bi9aNwaUBrILXhwhERERERiS6W4vUFM2sH/BbIB5Y1bEjSIrx0I8xtFSyB7OxsZs2aRXZ2NgCv3fsej2Xk8dq97zVllCIiIhJnYnlg6z53P+zuC9x9oLt/vzECkzi3/jfg5cESyMvLw93Jy8sDYNPiIrw8WIqIiIjEKpaWV5FPb9R1YInBEkhPT8fMSE9PB2D41B5YYrAUERERiVUs47yKfHrnPRy8QpmZmWRmZh7bHj9jAONnDGiKyERERCSOqeVVREREROJGrcWrmU0zs9VmttnMtpjZ5sYITERERESkqli6DdwBXODuu+rjhmaWDCwAOgNrge+6u1c55jZgKuDAQnefWx/3FhEREZH4Fku3gbfrq3AN3QoscvcJQAeqzNZlZp2Aq4Fzgc8B15tZh3q8v4iIiIjEqVhaXruZ2WqCVlIHcPdr63DPLwCzw/W/hNt/q/T+YWAn0I5gYoTDQGkd7iciIiIiLUQsxetP6/meycD+cH1/uH2Mu5eZ2RKCCRESgJ+5+9GqFzGza4FrAfr06UNBQUE9h3lySkpKmk0s9aEl5dOScgHl09y1tHxERJqLWotXd19mZolAT4KW0FqFxy+v5q1sYB9Bf9f94XJvlXNTgGnAYILidZmZ/cndC6vE9SjwKMDYsWN96NChsYTW4AoKCmgusdTmtXvfY9PiIoZP7XHCsFXZ2dnk5eUxdOhQvvnNbx5/4ks3BpMPjLruuOGwmrt4+m5ioXyat5aWj4hIcxHLaAPXABuBfwIrgOdrO8fdy9393GpePyWYXnZKeOgUTpxu1oCD7l7i7oeBEqBj7ClJrGqa5SoyI1a1LUdVZs8SERERaSyxPLB1PTASWA+kAuvqeM+fA5ebWQ5BX9YXAczsfjPr4e75QK6ZvW5mq4Dl4T6pZzXNchWZEavalqMqs2eJiIiINJZY+rx+6O5HzSwBOAqk1eWG7l4MZFaz/5ZK67fV5R4Sm5pmuYrMiFVty2uV2bNEREREGkssLa+rzawdQXeBt4AdDRuSiIiIiEj1Ynlga0a4OsfM5rn7/oYNSURERESkerE8sDXYzJ41s78DH5nZ9EaIS0RERETkBLF0G3iUYKzXJHcvo5r+qiIiIiIijSGW4tXdfX2DRyIiIiIiUotYite9ZvZ1oI2ZXQTsaeCYRERERESqFUvx+h3gTIKZscYRjPsqIiIiItLozN2bOoY6M7Mi4F9NHUeoO/BBUwdRj1pSPi0pF1A+zV085HOau584S4mISDMWtXg1s3eBsqq7CfrApjR0YPHKzNa4+9imjqO+tKR8WlIuoHyau5aWj4hIc1HTOK+vAP2AJcBCd1dfVxERERFpUlH7vLr7vwNfBrYDvzKz583sgkaLTERERESkihof2HL3o8CrQA6QDIxojKDi3KNNHUA9a0n5tKRcQPk0dy0tHxGRZqGmPq+XA18DSoFngefdvaQRYxMREREROU5NxWsF8AbBEFkAxw509/MbPjQRERERkePV9MDWoEaLQkREREQkBjU9sPWvaK/GDDBemFmWma0ws+VmdlZTx3MyzOzMMP5XzexlMxtsZm3N7CkzywmXbZs6zk/DzFLMrNTMxreAXNLN7EUze8XMfm6BB8N8XjCz5KaOMVZh7A+Z2UozW21ml8VjPmb2dzMrMrMfh9vV5mBmyeF2Tvi+NW3kIiLxK5YZtqQWZtYV+C4wCbgCeKBJAzp5u4AL3H0iMAe4E8gC3nb3CUB+uB1PbgeWhetZxGkuZtYauBeY5u7/5u63ApOB9mE+zwC3NmWMn9LpwOnuPg74IvAz4jOffwf+u9J2tBxuBRaF+zuEx4mIyElQ8Vo/MoAcdz/q7luBTmbWpqmD+rTcfbe7Hww3SwgmqfgC8EK47y/hdlwws3OA3cCOcFfc5kIwNfNHwNNhq/gE4jufncBRM0sCOgF7icN83H1HlV3Rcoi73EREmisVr/WjG5882Aawn2BosbhkZh0IWsJ+wfG57Se+8voRQWtlRDzn0gcYDVwOXAk8RjD9aOV8ujZJZCdnH7AF2AysJfh7q/r9xFM+EdFySA63I/vj6W9PRKRZqemBLYndXqBLpe3O4b64E7aELQJmu/tGM6ucW9zkZWaZwBp3L67UvTAucwntBVa4+wHggJl9ACRyfD77opzbHP0/oC8wlCD2HOBF4jefiKp/Y5Ec9oXb+4m/vz0RkWZFLa/1YxUw3sySzGwA8FE8jolrZgnAk8Cf3P1P4e5lwJRwfQqf9B9t7sYAk8zsbwSF0hxgE/GZCwR/Yylm1srMOgE9geeI33wM2Ofu5cBBoDXwEvGbT0S0fy/x+u9IRKTZiTrOq3w6ZnY1cA3BeLg3u/uaJg7pUzOzrwFPAJHY3yJ40OR3QD+CvqPfdvcjTRLgSTKzJ4B5QB5xnIuZXQlcByQRdIf4M/AgMAo4AFzl7sVNF2HszCwR+C1By2sb4H+Ah4izfMzsMeBzBDlsAKZSTQ5m1g1YAJwCrAducveKpolaRCS+qXgVERERkbihbgMiIiIiEjdUvIqIiIhI3FDxKiIiIiJxQ8WriIiIiMQNFa8iIiIiEjdUvH7GmdlAM/NwGKbIvt+a2damjKu+mFmWmf24Aa8/xsz+u/Yjmy8zm2Rmoz7lOReHYxpHtp+qhzguDYecw8weMbNcM8sKt9uZ2e8tnHHCzLqb2dN1vaeIiMQfFa8C8AbwNQAzawP0B8qbNKI44e5r3f0XTR1HHU0iGJf0OOGkFdFcDBwrXt398nqI43rgf8wsmWD2rc8TjGsLwXjDsz0c28/dPwAOmtnoeriviIjEERWvAsHUlaVm1hP4ErAk8oaZdTazZ8zsf83sZTMbGu6fbWavmNkbZnZtuG9SeNwzZvaWmV1S9UZmNj28zmozuzPcN9DM8szsyfB6t4T7s8zsT2a22Mw2mNmEcP9SM+sXrv+4Uuvc/PC9N8zsK9GSNbP2ZvZXM1sWHp8Szo42L8zpNTPLiBZvlWtNMrN54fro8HpLzez34b6RZvZSeI1nzKxdNdeYbWYrw3tPtsBvwjhWVIrlCTN7zMyyzez18PuKtFi+Hp7/g3DfJWaWE17jjmjfT1goZgE/CuNONLMCM7sb+F8zGxCes9TMloef1QjgAuBBM3s2vHZBuOxV6bNdYmY9Iu+HeS4zs4XVfAbDgT3uXgqUAO0JZt06YmaDgE7uvrbKaUuAE/7GRESkhXN3vT7DL2AgwbSclwL/CTwD9AYKwvfvBb4Rro8G/hCudwiXbYDNBLM+TSKYxSoR6AOsqeZ+kfMMWEHQejcQ2ElQsLQFtobHZBFMVQvBLEaRey8F+oXrPwayqly7G7Ch0jV+XCWGs4CnK20nELT6zQi3ewHLo8Vb5VqTgHnh+kpgRLieGC5fjZwD3Az8Z5XzpwBP88mEIYkErZq/C7cHA7nh+hPALeH6D8PvqxvBTGgdKp3fFXgdSAr3/REYGe37AWYCV1SKaRswLlxPAlqH6xdWiusJYHylcyJ/L/cTzCoFcBUwt9I1x4TrLwJnVPkcLgF+Vmn7O8Dvw+/9MSAFuA+YDbQLjxkO/Lmp/w3ppZdeeunVuK9WiASeJyhi97n77rBrIQRFzxfM7Ppwuyxc3mBmFxN0L+gZvgDWejBf/U4z61LNfaaZWWQa3cEEXRQKgU3ufgjAzCp3WcgLl+8RFGqE50ZE+kAmAD8xs8+FMZ5WQ65vAnlm9iRQDPwkzPNzZnZBeEznGuJ9L8p1u7v7RoDwMwA4HVgQfp5tCT7jys4AXnH3yM/h5WaWSlAo4+7vmlnXSsdX/jyGhK/17v5xpfOHhvn/I7xvl3D7I2r/fiD4Tl8P17sAD5tZb4KW0INRzolIJZjmlTCHb4TrZf5Jy2nl77Ja7v4Y8JiZTSb4n4JvAQuBQcDlBNP9iojIZ5CKVwHA3Q+b2R+BjVXe+iew0t3/CGBmrcNi6tsE/SSTgHzCIpLjC8vq/BRII/hpeHkM551QqAJ7gX7ADiAd2E7QKjzK3cebWXfgnRpiaEPQIugWPMx1ZZhngbvfF8mzlnirU2Rmae7+tpkleDB3/QbgMnffVeW6ERuAbxK0LkaK8HzgK8A8MxsM7K/h8ygARppZu/A7TADeDfef5+5l4T4DJlD953yU4/9b4JFiGrgCeNPd7zGzKcD0KOdE5BO0lhaEy/xqjonEXtkGYNpxB5glAZcR/K3dT/C9tQY6hYekhOeJiMhniIpXOcbd51Sz+y7g/5vZTQQFRzbwS4Ii9zVgE0HrZawWExSBbxO0BJ6MBwgKu80ERSUERVKSmS0D1nJ8wVfVCOABMysj6DLwLYJC+EEzeyU8Zg3w358y3huA35iZA7sICq8bgSfCQgzgHuAfkRPcfUnYF3UlcJjgZ/HngUwze43gJ/6bot3Q3feG/VOXmtkh4G/uPtvM7gdeDluxSwl+wo/mH8D9ZvYl4OtV3nsReNrMJhIU+BEvALPMbJO7X1dp/73A/LC1+lAt962cxyYzO9XMWrv70XD3TcCD4f9k/JagtbW0UoxTgEdiub6IiLQc9kkDi4hI0zGzbxD03f1tDMd2JyhsL2v4yEREpDlR8SoiIiIicUNDZYmIiIhI3FDxKiIiIiJxQ8WriIiIiMQNFa8iIiIiEjdUvIqIiIhI3FDxKiIiIiJx4/8Am1CfkcsbNiMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "# Retreat vs SMB\n",
    "x = X['SMB']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotLinReg(ax, x, Y, dataname='Surface mass balance')\n",
    "ax.set_xlabel('Surface mass balance')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs SMB')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.173\n",
      "Model:                            OLS   Adj. R-squared:                  0.169\n",
      "Method:                 Least Squares   F-statistic:                     43.18\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           4.02e-10\n",
      "Time:                        16:31:30   Log-Likelihood:                 71.990\n",
      "No. Observations:                 208   AIC:                            -140.0\n",
      "Df Residuals:                     206   BIC:                            -133.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0960      0.012     -7.987      0.000      -0.120      -0.072\n",
      "SMB            0.1620      0.025      6.571      0.000       0.113       0.211\n",
      "==============================================================================\n",
      "Omnibus:                       20.579   Durbin-Watson:                   2.275\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               65.074\n",
      "Skew:                          -0.281   Prob(JB):                     7.40e-15\n",
      "Kurtosis:                       5.682   Cond. No.                         2.08\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "# Retreat vs Snowfall\n",
    "x = X['Snowfall']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotLinReg(ax, x, Y, dataname='Snowfall')\n",
    "ax.set_xlabel('Snowfall')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs Snowfall')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.017\n",
      "Model:                            OLS   Adj. R-squared:                  0.012\n",
      "Method:                 Least Squares   F-statistic:                     3.487\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):             0.0633\n",
      "Time:                        16:31:30   Log-Likelihood:                 53.946\n",
      "No. Observations:                 208   AIC:                            -103.9\n",
      "Df Residuals:                     206   BIC:                            -97.22\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1056      0.013     -8.119      0.000      -0.131      -0.080\n",
      "Snowfall       0.3151      0.169      1.867      0.063      -0.018       0.648\n",
      "==============================================================================\n",
      "Omnibus:                       12.091   Durbin-Watson:                   2.118\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               26.648\n",
      "Skew:                          -0.165   Prob(JB):                     1.64e-06\n",
      "Kurtosis:                       4.722   Cond. No.                         13.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "# Retreat vs Runoff\n",
    "x = X['Runoff']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plotLinReg(ax, x, Y, dataname='Runoff')\n",
    "ax.set_xlabel('Runoff')\n",
    "ax.set_ylabel('Mean annual length change of glacier cluster (km)')\n",
    "ax.set_title('Retreat vs Runoff')\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.186\n",
      "Model:                            OLS   Adj. R-squared:                  0.182\n",
      "Method:                 Least Squares   F-statistic:                     46.99\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           8.14e-11\n",
      "Time:                        16:31:31   Log-Likelihood:                 73.568\n",
      "No. Observations:                 208   AIC:                            -143.1\n",
      "Df Residuals:                     206   BIC:                            -136.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0943      0.012     -7.894      0.000      -0.118      -0.071\n",
      "Runoff        -0.1683      0.025     -6.855      0.000      -0.217      -0.120\n",
      "==============================================================================\n",
      "Omnibus:                       23.230   Durbin-Watson:                   2.298\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               83.039\n",
      "Skew:                          -0.301   Prob(JB):                     9.29e-19\n",
      "Kurtosis:                       6.036   Cond. No.                         2.09\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Multiple linear regressions"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cases 1-3: all available variables"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "# Case 1: Using dataset means (instead of multiple datasets for each variable)\n",
    "# -- this is the basis for following cases N.1\n",
    "\n",
    "x = X[['ECCO_depth', 'Mean_SST', 'Mean_ASIC', 'Mean_SISL', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.205\n",
      "Model:                            OLS   Adj. R-squared:                  0.181\n",
      "Method:                 Least Squares   F-statistic:                     8.645\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           2.26e-08\n",
      "Time:                        16:31:31   Log-Likelihood:                 76.075\n",
      "No. Observations:                 208   AIC:                            -138.2\n",
      "Df Residuals:                     201   BIC:                            -114.8\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0598      0.121      0.495      0.621      -0.178       0.298\n",
      "ECCO_depth    -0.0406      0.029     -1.419      0.158      -0.097       0.016\n",
      "Mean_SST      -0.0515      0.034     -1.527      0.128      -0.118       0.015\n",
      "Mean_ASIC     -0.0015      0.002     -0.743      0.459      -0.006       0.003\n",
      "Mean_SISL     -0.0097      0.012     -0.834      0.405      -0.033       0.013\n",
      "Snowfall       0.0861      0.162      0.533      0.595      -0.232       0.405\n",
      "Runoff        -0.1458      0.029     -4.989      0.000      -0.203      -0.088\n",
      "==============================================================================\n",
      "Omnibus:                       19.726   Durbin-Watson:                   2.315\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               61.028\n",
      "Skew:                          -0.265   Prob(JB):                     5.60e-14\n",
      "Kurtosis:                       5.600   Cond. No.                         743.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7f207b20>"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cases 4.x: Remove sea ice season length (SISL)\n",
    "because it is correlated with annual sea ice concentration (ASIC)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "# Case 4.1: means\n",
    "x = X[['ECCO_depth', 'Mean_SST', 'Mean_ASIC', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.202\n",
      "Model:                            OLS   Adj. R-squared:                  0.183\n",
      "Method:                 Least Squares   F-statistic:                     10.25\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           9.05e-09\n",
      "Time:                        16:31:31   Log-Likelihood:                 75.716\n",
      "No. Observations:                 208   AIC:                            -139.4\n",
      "Df Residuals:                     202   BIC:                            -119.4\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0039      0.100      0.039      0.969      -0.194       0.202\n",
      "ECCO_depth    -0.0360      0.028     -1.283      0.201      -0.091       0.019\n",
      "Mean_SST      -0.0474      0.033     -1.423      0.156      -0.113       0.018\n",
      "Mean_ASIC     -0.0020      0.002     -1.016      0.311      -0.006       0.002\n",
      "Snowfall       0.0911      0.161      0.564      0.573      -0.227       0.409\n",
      "Runoff        -0.1464      0.029     -5.015      0.000      -0.204      -0.089\n",
      "==============================================================================\n",
      "Omnibus:                       20.315   Durbin-Watson:                   2.338\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.448\n",
      "Skew:                          -0.271   Prob(JB):                     1.01e-14\n",
      "Kurtosis:                       5.673   Cond. No.                         734.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d98149250>"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cases 5.x: Remove annual sea ice concentration (ASIC)\n",
    "because it is correlated with SISL"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "# Case 5.1: means\n",
    "x = X[['ECCO_depth', 'Mean_SST', 'Mean_SISL', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.203\n",
      "Model:                            OLS   Adj. R-squared:                  0.183\n",
      "Method:                 Least Squares   F-statistic:                     10.29\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           8.45e-09\n",
      "Time:                        16:31:31   Log-Likelihood:                 75.791\n",
      "No. Observations:                 208   AIC:                            -139.6\n",
      "Df Residuals:                     202   BIC:                            -119.6\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0025      0.093      0.027      0.979      -0.181       0.186\n",
      "ECCO_depth    -0.0367      0.028     -1.306      0.193      -0.092       0.019\n",
      "Mean_SST      -0.0314      0.020     -1.564      0.119      -0.071       0.008\n",
      "Mean_SISL     -0.0121      0.011     -1.085      0.279      -0.034       0.010\n",
      "Snowfall       0.0631      0.158      0.399      0.691      -0.249       0.375\n",
      "Runoff        -0.1432      0.029     -4.942      0.000      -0.200      -0.086\n",
      "==============================================================================\n",
      "Omnibus:                       18.698   Durbin-Watson:                   2.288\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               59.074\n",
      "Skew:                          -0.213   Prob(JB):                     1.49e-13\n",
      "Kurtosis:                       5.576   Cond. No.                         114.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7e3f0100>"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cases 6.x: remove SST\n",
    "because it is correlated with 250m temperature"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "# Case 6.1: means\n",
    "x = X[['ECCO_depth', 'Mean_SISL', 'Mean_ASIC', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.196\n",
      "Model:                            OLS   Adj. R-squared:                  0.176\n",
      "Method:                 Least Squares   F-statistic:                     9.843\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           1.95e-08\n",
      "Time:                        16:31:31   Log-Likelihood:                 74.876\n",
      "No. Observations:                 208   AIC:                            -137.8\n",
      "Df Residuals:                     202   BIC:                            -117.7\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0876      0.073     -1.202      0.231      -0.231       0.056\n",
      "ECCO_depth    -0.0339      0.028     -1.195      0.234      -0.090       0.022\n",
      "Mean_SISL     -0.0072      0.012     -0.619      0.537      -0.030       0.016\n",
      "Mean_ASIC      0.0010      0.001      0.809      0.419      -0.001       0.003\n",
      "Snowfall       0.0442      0.160      0.276      0.782      -0.271       0.359\n",
      "Runoff        -0.1447      0.029     -4.938      0.000      -0.203      -0.087\n",
      "==============================================================================\n",
      "Omnibus:                       21.032   Durbin-Watson:                   2.280\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               69.002\n",
      "Skew:                          -0.276   Prob(JB):                     1.04e-15\n",
      "Kurtosis:                       5.767   Cond. No.                         724.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7d7c50d0>"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cases 7.x: Remove 250m temperature\n",
    "because it is correlated with SST"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "# Case 7.1: means\n",
    "x = X[['Mean_SST', 'Mean_SISL', 'Mean_ASIC', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.197\n",
      "Model:                            OLS   Adj. R-squared:                  0.177\n",
      "Method:                 Least Squares   F-statistic:                     9.922\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           1.68e-08\n",
      "Time:                        16:31:31   Log-Likelihood:                 75.039\n",
      "No. Observations:                 208   AIC:                            -138.1\n",
      "Df Residuals:                     202   BIC:                            -118.1\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0073      0.115      0.063      0.950      -0.220       0.235\n",
      "Mean_SST      -0.0441      0.033     -1.322      0.188      -0.110       0.022\n",
      "Mean_SISL     -0.0065      0.011     -0.570      0.569      -0.029       0.016\n",
      "Mean_ASIC     -0.0010      0.002     -0.489      0.625      -0.005       0.003\n",
      "Snowfall       0.0814      0.162      0.503      0.616      -0.238       0.401\n",
      "Runoff        -0.1610      0.027     -5.909      0.000      -0.215      -0.107\n",
      "==============================================================================\n",
      "Omnibus:                       17.954   Durbin-Watson:                   2.294\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               59.761\n",
      "Skew:                          -0.134   Prob(JB):                     1.05e-13\n",
      "Kurtosis:                       5.612   Cond. No.                         741.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7ec20f40>"
      ]
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "source": [
    "# Case 17: Drop Snowfall\n",
    "\n",
    "x = X[['ECCO_depth', 'Mean_SST', 'Mean_ASIC', 'Mean_SISL', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.204\n",
      "Model:                            OLS   Adj. R-squared:                  0.184\n",
      "Method:                 Least Squares   F-statistic:                     10.35\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           7.45e-09\n",
      "Time:                        16:31:31   Log-Likelihood:                 75.929\n",
      "No. Observations:                 208   AIC:                            -139.9\n",
      "Df Residuals:                     202   BIC:                            -119.8\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0513      0.120      0.429      0.668      -0.184       0.287\n",
      "ECCO_depth    -0.0403      0.029     -1.411      0.160      -0.097       0.016\n",
      "Mean_SST      -0.0484      0.033     -1.460      0.146      -0.114       0.017\n",
      "Mean_ASIC     -0.0013      0.002     -0.654      0.514      -0.005       0.003\n",
      "Mean_SISL     -0.0100      0.012     -0.856      0.393      -0.033       0.013\n",
      "Runoff        -0.1486      0.029     -5.183      0.000      -0.205      -0.092\n",
      "==============================================================================\n",
      "Omnibus:                       20.135   Durbin-Watson:                   2.325\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               64.725\n",
      "Skew:                          -0.257   Prob(JB):                     8.81e-15\n",
      "Kurtosis:                       5.684   Cond. No.                         560.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7d55bca0>"
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "# Case 18: Drop Runoff\n",
    "\n",
    "x = X[['ECCO_depth', 'Mean_SST', 'Mean_ASIC', 'Mean_SISL', 'Snowfall']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.107\n",
      "Model:                            OLS   Adj. R-squared:                  0.085\n",
      "Method:                 Least Squares   F-statistic:                     4.825\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           0.000344\n",
      "Time:                        16:31:32   Log-Likelihood:                 63.932\n",
      "No. Observations:                 208   AIC:                            -115.9\n",
      "Df Residuals:                     202   BIC:                            -95.84\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0002      0.127     -0.002      0.998      -0.251       0.250\n",
      "ECCO_depth    -0.0931      0.028     -3.304      0.001      -0.149      -0.038\n",
      "Mean_SST      -0.0474      0.036     -1.330      0.185      -0.118       0.023\n",
      "Mean_ASIC     -0.0003      0.002     -0.144      0.885      -0.005       0.004\n",
      "Mean_SISL     -0.0112      0.012     -0.906      0.366      -0.035       0.013\n",
      "Snowfall       0.2332      0.168      1.388      0.167      -0.098       0.564\n",
      "==============================================================================\n",
      "Omnibus:                       11.225   Durbin-Watson:                   2.240\n",
      "Prob(Omnibus):                  0.004   Jarque-Bera (JB):               22.003\n",
      "Skew:                          -0.200   Prob(JB):                     1.67e-05\n",
      "Kurtosis:                       4.542   Cond. No.                         734.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7ecba400>"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "# Case 19: deepwater and SST only\n",
    "\n",
    "x = X[['ECCO_depth', 'Mean_SST']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.094\n",
      "Model:                            OLS   Adj. R-squared:                  0.085\n",
      "Method:                 Least Squares   F-statistic:                     10.63\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           4.04e-05\n",
      "Time:                        16:31:32   Log-Likelihood:                 62.465\n",
      "No. Observations:                 208   AIC:                            -118.9\n",
      "Df Residuals:                     205   BIC:                            -108.9\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1078      0.013     -8.576      0.000      -0.133      -0.083\n",
      "ECCO_depth    -0.0887      0.026     -3.417      0.001      -0.140      -0.038\n",
      "Mean_SST      -0.0301      0.016     -1.873      0.063      -0.062       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       12.248   Durbin-Watson:                   2.291\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               28.369\n",
      "Skew:                          -0.135   Prob(JB):                     6.91e-07\n",
      "Kurtosis:                       4.789   Cond. No.                         2.15\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7ecba220>"
      ]
     },
     "metadata": {},
     "execution_count": 31
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "# Case 20: sea ice only\n",
    "\n",
    "x = X[['Mean_ASIC', 'Mean_SISL']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.047\n",
      "Model:                            OLS   Adj. R-squared:                  0.037\n",
      "Method:                 Least Squares   F-statistic:                     5.016\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):            0.00747\n",
      "Time:                        16:31:32   Log-Likelihood:                 57.168\n",
      "No. Observations:                 208   AIC:                            -108.3\n",
      "Df Residuals:                     205   BIC:                            -98.32\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.2343      0.072     -3.266      0.001      -0.376      -0.093\n",
      "Mean_ASIC      0.0030      0.001      2.344      0.020       0.000       0.006\n",
      "Mean_SISL     -0.0030      0.012     -0.247      0.805      -0.027       0.021\n",
      "==============================================================================\n",
      "Omnibus:                       11.376   Durbin-Watson:                   2.123\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               23.156\n",
      "Skew:                           0.184   Prob(JB):                     9.37e-06\n",
      "Kurtosis:                       4.592   Cond. No.                         303.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7ebc1d00>"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "# Case 21: just runoff\n",
    "\n",
    "x = X[['Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.186\n",
      "Model:                            OLS   Adj. R-squared:                  0.182\n",
      "Method:                 Least Squares   F-statistic:                     46.99\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           8.14e-11\n",
      "Time:                        16:31:32   Log-Likelihood:                 73.568\n",
      "No. Observations:                 208   AIC:                            -143.1\n",
      "Df Residuals:                     206   BIC:                            -136.5\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0943      0.012     -7.894      0.000      -0.118      -0.071\n",
      "Runoff        -0.1683      0.025     -6.855      0.000      -0.217      -0.120\n",
      "==============================================================================\n",
      "Omnibus:                       23.230   Durbin-Watson:                   2.298\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               83.039\n",
      "Skew:                          -0.301   Prob(JB):                     9.29e-19\n",
      "Kurtosis:                       6.036   Cond. No.                         2.09\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7ec4fa30>"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "# Case 22: drop both ocean temperatures\n",
    "x = X[['Mean_SISL', 'Mean_ASIC', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.190\n",
      "Model:                            OLS   Adj. R-squared:                  0.174\n",
      "Method:                 Least Squares   F-statistic:                     11.92\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           1.02e-08\n",
      "Time:                        16:35:01   Log-Likelihood:                 74.144\n",
      "No. Observations:                 208   AIC:                            -138.3\n",
      "Df Residuals:                     203   BIC:                            -121.6\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1145      0.069     -1.650      0.100      -0.251       0.022\n",
      "Mean_SISL     -0.0048      0.011     -0.417      0.677      -0.027       0.018\n",
      "Mean_ASIC      0.0012      0.001      0.935      0.351      -0.001       0.004\n",
      "Snowfall       0.0453      0.160      0.283      0.777      -0.270       0.361\n",
      "Runoff        -0.1579      0.027     -5.805      0.000      -0.211      -0.104\n",
      "==============================================================================\n",
      "Omnibus:                       19.151   Durbin-Watson:                   2.268\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               66.255\n",
      "Skew:                          -0.165   Prob(JB):                     4.10e-15\n",
      "Kurtosis:                       5.745   Cond. No.                         724.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7e6a8730>"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "source": [
    "# Case 23: drop both sea ice variables\n",
    "x = X[['ECCO_depth', 'Mean_SST', 'Snowfall', 'Runoff']]\n",
    "linreg(x, Y)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.198\n",
      "Model:                            OLS   Adj. R-squared:                  0.183\n",
      "Method:                 Least Squares   F-statistic:                     12.55\n",
      "Date:                Tue, 21 Sep 2021   Prob (F-statistic):           3.82e-09\n",
      "Time:                        16:37:15   Log-Likelihood:                 75.186\n",
      "No. Observations:                 208   AIC:                            -140.4\n",
      "Df Residuals:                     203   BIC:                            -123.7\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0974      0.012     -8.061      0.000      -0.121      -0.074\n",
      "ECCO_depth    -0.0288      0.027     -1.062      0.290      -0.082       0.025\n",
      "Mean_SST      -0.0174      0.015     -1.131      0.260      -0.048       0.013\n",
      "Snowfall       0.0601      0.158      0.379      0.705      -0.252       0.372\n",
      "Runoff        -0.1430      0.029     -4.931      0.000      -0.200      -0.086\n",
      "==============================================================================\n",
      "Omnibus:                       19.067   Durbin-Watson:                   2.306\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               62.819\n",
      "Skew:                          -0.199   Prob(JB):                     2.29e-14\n",
      "Kurtosis:                       5.663   Cond. No.                         13.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x7f3d7db96670>"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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