diff --git a/notebooks/region_search/Region Searching Workbook.ipynb b/notebooks/region_search/Region Searching Workbook.ipynb
index 440e8e0cc..2679763a8 100644
--- a/notebooks/region_search/Region Searching Workbook.ipynb	
+++ b/notebooks/region_search/Region Searching Workbook.ipynb	
@@ -44,6 +44,7 @@
     "# Import packages needed to run the notebook\n",
     "import lsst\n",
     "import lsst.daf.butler as dafButler\n",
+    "import lsst.sphgeom as sphgeom\n",
     "\n",
     "import os\n",
     "import glob\n",
@@ -66,6 +67,7 @@
     "from astropy.time import Time  # for converting Butler visitInfo.date (TAI) to UTC strings\n",
     "from astropy import units as u\n",
     "from astropy.coordinates import SkyCoord\n",
+    "import astropy.io.fits as fits\n",
     "\n",
     "import pickle"
    ]
@@ -481,8 +483,8 @@
       "src_schema,8\n",
       "\n",
       "Read 46 datasetTypes from disk.\n",
-      "CPU times: user 1.59 ms, sys: 1.52 ms, total: 3.11 ms\n",
-      "Wall time: 15.4 ms\n"
+      "CPU times: user 2.02 ms, sys: 2.03 ms, total: 4.05 ms\n",
+      "Wall time: 10.4 ms\n"
      ]
     }
    ],
@@ -640,8 +642,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.17 s, sys: 89 ms, total: 2.26 s\n",
-      "Wall time: 2.77 s\n"
+      "CPU times: user 2.83 s, sys: 59.7 ms, total: 2.89 s\n",
+      "Wall time: 3.44 s\n"
      ]
     }
    ],
@@ -953,8 +955,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 417 ms, sys: 33 ms, total: 450 ms\n",
-      "Wall time: 544 ms\n"
+      "CPU times: user 305 ms, sys: 35.8 ms, total: 340 ms\n",
+      "Wall time: 400 ms\n"
      ]
     }
    ],
@@ -1031,8 +1033,8 @@
      "text": [
       "Found DECam. Adding to \"desired_instruments\" now.\n",
       "WARNING: we are not iterating over all rows to find instruments, just taking the first one.\n",
-      "CPU times: user 1.82 s, sys: 344 ms, total: 2.17 s\n",
-      "Wall time: 2.51 s\n"
+      "CPU times: user 1.03 s, sys: 199 ms, total: 1.23 s\n",
+      "Wall time: 1.94 s\n"
      ]
     }
    ],
@@ -1061,8 +1063,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.64 s, sys: 309 ms, total: 1.95 s\n",
-      "Wall time: 3.52 s\n"
+      "CPU times: user 1.28 s, sys: 203 ms, total: 1.48 s\n",
+      "Wall time: 2.01 s\n"
      ]
     }
    ],
@@ -1127,7 +1129,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f07219ac6d0>"
+       "<matplotlib.image.AxesImage at 0x7f192d39f790>"
       ]
      },
      "execution_count": 30,
@@ -1509,8 +1511,8 @@
      "output_type": "stream",
      "text": [
       "Recycled 47383 paths from /astro/users/coc123/kbmod_tmp/uri_cache.lst as overwrite was False.\n",
-      "CPU times: user 25.3 ms, sys: 14.2 ms, total: 39.5 ms\n",
-      "Wall time: 108 ms\n"
+      "CPU times: user 47.6 ms, sys: 27 ms, total: 74.7 ms\n",
+      "Wall time: 181 ms\n"
      ]
     }
    ],
@@ -1571,8 +1573,8 @@
      "output_type": "stream",
      "text": [
       "0 DateTime(\"2019-09-27T00:20:59.932016000\", TAI) 120.0 (351.3806941054, -5.2403083277)\n",
-      "CPU times: user 114 ms, sys: 13 ms, total: 127 ms\n",
-      "Wall time: 157 ms\n"
+      "CPU times: user 61.8 ms, sys: 10.8 ms, total: 72.6 ms\n",
+      "Wall time: 88.8 ms\n"
      ]
     }
    ],
@@ -1708,8 +1710,8 @@
      "text": [
       "Overwrite is False, so we will read the timestamps from file now...\n",
       "Recycled 47383 from /astro/users/coc123/kbmod_tmp/vdr_timestamps.lst.\n",
-      "CPU times: user 24.3 ms, sys: 8.18 ms, total: 32.5 ms\n",
-      "Wall time: 30.5 ms\n"
+      "CPU times: user 15.4 ms, sys: 3.31 ms, total: 18.7 ms\n",
+      "Wall time: 44.8 ms\n"
      ]
     }
    ],
@@ -1944,8 +1946,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 499 ms, sys: 14.5 ms, total: 513 ms\n",
-      "Wall time: 513 ms\n"
+      "CPU times: user 405 ms, sys: 19.1 ms, total: 424 ms\n",
+      "Wall time: 423 ms\n"
      ]
     }
    ],
@@ -2006,14 +2008,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1.48 s, sys: 11.3 ms, total: 1.49 s\n",
-      "Wall time: 1.49 s\n"
+      "CPU times: user 1.65 s, sys: 13.5 ms, total: 1.66 s\n",
+      "Wall time: 1.66 s\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f071dd74520>"
+       "<matplotlib.collections.PathCollection at 0x7f19296e7640>"
       ]
      },
      "execution_count": 53,
@@ -2084,7 +2086,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f071f80efd0>"
+       "<matplotlib.collections.PathCollection at 0x7f19296b1df0>"
       ]
      },
      "execution_count": 55,
@@ -2182,7 +2184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 140,
    "id": "2fcdd8b3",
    "metadata": {},
    "outputs": [],
@@ -2376,8 +2378,8 @@
      "output_type": "stream",
      "text": [
       "Recycling /astro/users/coc123/kbmod_tmp/overlapping_sets.pickle as overwrite=False.\n",
-      "CPU times: user 402 ms, sys: 46.6 ms, total: 449 ms\n",
-      "Wall time: 446 ms\n"
+      "CPU times: user 288 ms, sys: 53.2 ms, total: 341 ms\n",
+      "Wall time: 371 ms\n"
      ]
     }
    ],
@@ -2426,14 +2428,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 367 ms, sys: 17.3 ms, total: 385 ms\n",
-      "Wall time: 383 ms\n"
+      "CPU times: user 555 ms, sys: 8.22 ms, total: 563 ms\n",
+      "Wall time: 562 ms\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f071dc328b0>"
+       "<matplotlib.collections.PathCollection at 0x7f19294b4490>"
       ]
      },
      "execution_count": 64,
@@ -2490,7 +2492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 94,
    "id": "fcd39c41",
    "metadata": {},
    "outputs": [
@@ -2505,13 +2507,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 9.55 s, sys: 815 ms, total: 10.4 s\n",
-      "Wall time: 9.86 s\n"
+      "CPU times: user 6.15 s, sys: 482 ms, total: 6.64 s\n",
+      "Wall time: 6.1 s\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -2552,9 +2554,21 @@
     "# To avoid duplicate labels in the legend, handle legend entries manually\n",
     "handles, labels = plt.gca().get_legend_handles_labels()\n",
     "by_label = dict(zip(labels, handles))  # Removing duplicates\n",
-    "plt.legend(by_label.values(), by_label.keys())\n",
-    "plt.savefig(f\"{basedir}/pointings.pdf\")\n",
-    "# plt.show()"
+    "\n",
+    "current_handles, current_labels = plt.gca().get_legend_handles_labels()\n",
+    "\n",
+    "current_handles = list(by_label.values())\n",
+    "current_labels = list(by_label.keys())\n",
+    "# sort or reorder the labels and handles\n",
+    "sorted_ix = sorted(range(len(current_labels)), key=lambda k: current_labels[k])\n",
+    "sorted_handles = [current_handles[i] for i in sorted_ix]  # list(current_handles)\n",
+    "sorted_labels = [current_labels[i] for i in sorted_ix]  # list(current_labels)\n",
+    "\n",
+    "# call plt.legend() with the new values\n",
+    "plt.legend(sorted_handles, sorted_labels)\n",
+    "\n",
+    "# plt.legend(by_label.values(), by_label.keys())\n",
+    "plt.savefig(f\"{basedir}/pointings.pdf\")"
    ]
   },
   {
@@ -2721,8 +2735,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 931 ms, sys: 22.3 ms, total: 953 ms\n",
-      "Wall time: 952 ms\n"
+      "CPU times: user 762 ms, sys: 7.39 ms, total: 769 ms\n",
+      "Wall time: 767 ms\n"
      ]
     },
     {
@@ -3061,8 +3075,8 @@
      "output_type": "stream",
      "text": [
       "We found 95 matches within 3.0 arcsec of (351.0695696334572, -4.336293374423113).\n",
-      "CPU times: user 805 ms, sys: 21.7 ms, total: 827 ms\n",
-      "Wall time: 825 ms\n"
+      "CPU times: user 634 ms, sys: 228 µs, total: 634 ms\n",
+      "Wall time: 633 ms\n"
      ]
     },
     {
@@ -3483,8 +3497,8 @@
       "Overwrite is False, so we will read the timestamps from file now...\n",
       "Recycled 47383 from /astro/users/coc123/kbmod_tmp/vdr_timestamps.lst.\n",
       "Recycling /astro/users/coc123/kbmod_tmp/overlapping_sets.pickle as overwrite=False.\n",
-      "CPU times: user 5.1 s, sys: 618 ms, total: 5.72 s\n",
-      "Wall time: 6.88 s\n"
+      "CPU times: user 3.51 s, sys: 263 ms, total: 3.78 s\n",
+      "Wall time: 4.55 s\n"
      ]
     }
    ],
@@ -3783,20 +3797,1583 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3df17bc4",
+   "id": "9f34362e",
    "metadata": {},
    "source": [
-    "# Next Steps\n",
+    "### Sky Patches\n",
+    "For datasets spanning large spatial areas, such as LSST, we need a different approach.\\\n",
+    "We will uniformly divide the sky into \"patches\" of the sky.\\\n",
+    "We will also give an option to have the patches overlap by some percentage.\\\n",
+    "Patches can primarily be used in three ways:\\\n",
+    "1. Their center (RA, Dec) coordinate.\n",
+    "2. The \"corner\" (RA, Dec) coordinates. Corners are really vertices of quadrilateral patches.\n",
+    "3. A LSST Sphgeom region, which may be hashed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "49bc209f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First we introduce our patch generator.\n",
+    "\n",
+    "\n",
+    "def generate_patches(arcminutes, overlap_percentage, verbose=True, decRange=[-90, 90], export=False):\n",
+    "    \"\"\"Given a \"rectangle\" in (RA, Dec) touple (probably based on some chip size),\n",
+    "    produce a list of bounded regions on the sky,\n",
+    "    with user-supplied edge overlap percentage (overlap_percentage).\n",
+    "    The list is to be iterated over when matching against actual observations,\n",
+    "    i.e., for later shift-and-stack.\n",
+    "    v0: 12/11/2023 COC\n",
+    "    Note: this all assumes small angle approximation is OK. 1/9/2024 COC\n",
+    "    TODO: something to limit Dec range (min or max).\n",
+    "        E.g., at LSST (30.241° S) they cannot see anything above 59.759° N.\n",
+    "    \"\"\"\n",
+    "    import numpy as np\n",
+    "\n",
+    "    def checkDec(n):\n",
+    "        if n > 90:\n",
+    "            n -= 180\n",
+    "        elif n < -90:\n",
+    "            n += 180\n",
+    "        return n\n",
+    "\n",
+    "    def checkRA(n):\n",
+    "        if n > 360:\n",
+    "            n -= 360\n",
+    "        if n < 0:\n",
+    "            n += 360\n",
+    "        return n\n",
+    "\n",
+    "    # Convert arcminutes to degrees (work base unit)\n",
+    "    arcdegrees = np.array(arcminutes) / 60.0\n",
+    "\n",
+    "    # Calculate overlap in degrees\n",
+    "    overlap = arcdegrees * (overlap_percentage / 100.0)\n",
+    "\n",
+    "    # Number of patches needed in RA, Dec space\n",
+    "    # TODO: consider these aren't decimal; so should be ceil for bounds 1/9/2024 COC\n",
+    "    num_patches_ra = int(360 / (arcdegrees[0] - overlap[0]))\n",
+    "    num_patches_dec = int(180 / (arcdegrees[1] - overlap[1]))\n",
+    "    if verbose:\n",
+    "        print(\n",
+    "            f\"Number of patches in (RA, Dec): ({num_patches_ra},{num_patches_dec}).\"\n",
+    "        )  # Recall (RA, Dec) ranges are (0-360,0-180), so square inputs result in (n*2, n) ranges.\n",
+    "\n",
+    "    # Generate patches\n",
+    "    patches = []\n",
+    "    centers = []  # 1/15/2024 COC\n",
+    "    skippedBecauseOfDec = 0\n",
+    "    for ra_index in range(num_patches_ra):\n",
+    "        # Calculate corner RA coordinates; moved out of dec loop 1/11/2024 COC\n",
+    "        ra_start = checkRA(ra_index * (arcdegrees[0] - overlap[0]))\n",
+    "        center_ra = checkRA(ra_start + arcdegrees[0] / 2)  # 1/15/2024 COC\n",
+    "        ra_end = checkRA(ra_start + arcdegrees[0])\n",
+    "        #\n",
+    "        for dec_index in range(num_patches_dec):\n",
+    "            # Calculate corner Dec coordinates\n",
+    "            dec_start = checkDec(dec_index * (arcdegrees[1] - overlap[1]) - 90)\n",
+    "            center_dec = checkDec(dec_start + arcdegrees[1] / 2)  # 1/15/2024 COC\n",
+    "            dec_end = checkDec(dec_start + arcdegrees[1])\n",
+    "            #\n",
+    "            # Make sure Dec is in allowed range; KLUDGE 1/9/2024 COC\n",
+    "            OK = True\n",
+    "            for d in [dec_start, dec_end]:\n",
+    "                if d < decRange[0] or d > decRange[1]:\n",
+    "                    OK = False\n",
+    "                    break\n",
+    "            if OK == False:\n",
+    "                skippedBecauseOfDec += 1\n",
+    "                #                 print(f'Something is outside of valid Dec range: dec_start={dec_start}, dec_end={dec_end}')\n",
+    "                continue\n",
+    "            #\n",
+    "            # Append patch coordinates to the list\n",
+    "            patches.append(((ra_start, dec_start), (ra_end, dec_end)))\n",
+    "            centers.append((center_ra, center_dec))  # 1/15/2024 COC\n",
     "\n",
-    "In no particular order:\n",
+    "    #\n",
+    "    npatches = len(patches)\n",
+    "    info = {\"npatches\": npatches, \"arcminutes\": arcminutes, \"overlap\": overlap_percentage}\n",
+    "    if verbose:\n",
+    "        print(\n",
+    "            f\"There were {npatches} produced, skipping {skippedBecauseOfDec} because Dec was outside {decRange}. Info: {info}.\"\n",
+    "        )\n",
+    "    #\n",
+    "    # produce CSV if desired\n",
+    "    if export == True:\n",
+    "        outfile = f\"patches_{arcminutes[0]}x{arcminutes[1]}arcmin_{overlap_percentage}pctOverlap\"\n",
+    "        if decRange != None:\n",
+    "            outfile += f\"_Dec{decRange[0]}to{decRange[1]}\"\n",
+    "        outfile += \".csv\"\n",
+    "        with open(outfile, \"w\") as f:\n",
+    "            print(f\"ra0,dec0,ra1,dec1\", file=f)\n",
+    "            for quad in patches:\n",
+    "                print(f\"{quad[0][0]},{quad[0][1]},{quad[1][0]},{quad[1][1]}\", file=f)\n",
+    "            print(f\"Wrote {len(patches)} patch rows to {outfile}.\")\n",
+    "    #\n",
+    "    return patches, centers, info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "fd12f4a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (360,180).\n",
+      "There were 64800 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 64800, 'arcminutes': (60, 60), 'overlap': 0}.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's start by generating 1°X1° (one degree square) patches, with no overlap.\n",
+    "# This should be the number of degrees in the range of RA (0 to 360 = 360) * the range in Dec (-90 to 90 = 180.)\n",
+    "# 360 * 180 = 64,800 square degrees.\n",
+    "# (We know a priori that this should be around 41,253 square degrees,\n",
+    "# but our grid is of small angles and does not take into account spherical geometry.)\n",
     "\n",
-    "1. User-specified (RA, Dec) pair:\n",
-    "- from overlapping_sets (DONE 2/7/2024 COC)\n",
-    "- from our extracted Butler data (DONE 2/7/2024 COC)\n",
-    "2. Heat map / histogrammed results.\n",
-    "3. Sky patches approach.\n",
-    "4. Reflex correction."
+    "tmp = generate_patches(arcminutes=(1 * 60, 1 * 60), overlap_percentage=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "6004da63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (400,200).\n",
+      "There were 80000 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 80000, 'arcminutes': (60, 60), 'overlap': 10}.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# The numbers go up dramatically when we introduce even a small-ish overlap. Let's try 10%.\n",
+    "tmp = generate_patches(arcminutes=(1 * 60, 1 * 60), overlap_percentage=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "0dc3d71d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (1440,720).\n",
+      "There were 1440 produced, skipping 1035360 because Dec was outside [0, 0.25]. Info: {'npatches': 1440, 'arcminutes': (15, 15), 'overlap': 0}.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's consider a single slice along Right Ascension, keeping Declination constant at 0.25° = 15'.\n",
+    "# This is roughly one LSST chip FOV.\n",
+    "tmp = generate_patches(arcminutes=(15, 15), overlap_percentage=0, decRange=[0, 0.25])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "c3f1fd78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 2.95 times more chip fields in one Dec slice than all of DEEP.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1440"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many is this, compared to the Hayden DEEP set?\n",
+    "print(\n",
+    "    f'There are {round(tmp[2][\"npatches\"]/len(overlapping_sets),2)} times more chip fields in one Dec slice than all of DEEP.'\n",
+    ")\n",
+    "tmp[2][\"npatches\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "d018cb09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Ground-based observatories cannot see all of the Celestial Sphere because Earth gets in the way.\n",
+    "# The farthest hypothetical dec observable from a given latitude is:\n",
+    "#     (observer latitude) - 90° (in the North)\n",
+    "#     (observer latitude) + 90° (in the South)\n",
+    "# The key fun fact is that observer latitude = elevation of the corresponding (N or S) celestial pole.\n",
+    "#\n",
+    "# Examples:\n",
+    "# North Pole, latitude +90°: +90° - 90° = 0°\n",
+    "# South Pole, latitude -90°: -90° + 90° = 0°\n",
+    "# Equator, latitude ±0°: 0° ±90° = ±90°.\n",
+    "# LSST, latitude -30.1732°: -30.1732° + 90° = 59.8268°.\n",
+    "#\n",
+    "# Note: this is the geometric limit, but in practice\n",
+    "# (a) the horizon is rarely unobstructed/flat\n",
+    "# (b) telescopes cannot (or will not) point anywhere near the horizon\n",
+    "#\n",
+    "# Given (b) above, it would be optimal to further offset the dec range for a telescope's stated lower elevation (altitude) limit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "db66acde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here we have a function to visualize the patches we are generating.\n",
+    "def plot_patches(\n",
+    "    patches, limit=None, xrange=[0, 360], yrange=[-90, 90], title=None, subfolder=\".\", export=False\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Plot the patches provided by generate_patches(), with options for visualization.\n",
+    "    2/15/2024 COC updated\n",
+    "    \"\"\"\n",
+    "    import os\n",
+    "\n",
+    "    os.makedirs(subfolder, exist_ok=True)\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    from matplotlib.patches import Rectangle\n",
+    "\n",
+    "    patch_count = len(patches)\n",
+    "    print(f\"There are {patch_count} to plot.\")\n",
+    "    fig, ax = plt.subplots()\n",
+    "    colors = [\"blue\", \"orange\", \"green\", \"red\"]\n",
+    "    linestyles = [\"solid\", \"dashed\"]\n",
+    "    c = 0\n",
+    "    for i, patch in enumerate(patches):\n",
+    "        if limit != None and i > limit:\n",
+    "            print(f\"Breaking at limit {limit} passed to function.\")\n",
+    "            break\n",
+    "        if c >= 10000:\n",
+    "            print(f\"Row {i!s:6} of {patch_count}\")\n",
+    "            c = 0\n",
+    "        (ra_start, dec_start), (ra_end, dec_end) = patch\n",
+    "        width = ra_end - ra_start\n",
+    "        height = dec_end - dec_start\n",
+    "\n",
+    "        # Alternating fill color and line style\n",
+    "        color = colors[i % len(colors)]\n",
+    "        linestyle = linestyles[i % len(linestyles)]\n",
+    "\n",
+    "        # Plot rectangle\n",
+    "        rect = Rectangle(\n",
+    "            (ra_start, dec_start),\n",
+    "            width,\n",
+    "            height,\n",
+    "            edgecolor=color,\n",
+    "            facecolor=\"none\",\n",
+    "            linestyle=linestyle,\n",
+    "            alpha=0.5,\n",
+    "        )\n",
+    "        ax.add_patch(rect)\n",
+    "        #\n",
+    "        c += 1\n",
+    "    # Set axis labels and limits\n",
+    "    ax.set_xlabel(\"Right Ascension (degrees)\")\n",
+    "    ax.set_ylabel(\"Declination (degrees)\")\n",
+    "    #    ax.set_xlim(0, 360)\n",
+    "    #    ax.set_ylim(-90, 90)\n",
+    "    ax.set_xlim(xrange[0], xrange[1])\n",
+    "    ax.set_ylim(yrange[0], yrange[1])\n",
+    "\n",
+    "    plt.grid(True)\n",
+    "\n",
+    "    outfile_base = f\"{subfolder}/patches\"\n",
+    "    if title != None:\n",
+    "        plt.title(title)\n",
+    "        outfile_base += f'_{info[\"arcminutes\"][0]}_{info[\"arcminutes\"][1]}_{info[\"overlap\"]}'\n",
+    "\n",
+    "    if export == True:\n",
+    "        for ext in [\"png\", \"pdf\"]:\n",
+    "            plt.savefig(f\"{outfile_base}.{ext}\")\n",
+    "        print(f'Wrote \"{outfile_base} (prefix) plots to disk.')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "6e70866c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (45,45).\n",
+      "There were 2025 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 2025, 'arcminutes': (480, 240), 'overlap': 0}.\n",
+      "There are 2025 to plot.\n",
+      "Breaking at limit 3 passed to function.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (50,50).\n",
+      "There were 2500 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 2500, 'arcminutes': (480, 240), 'overlap': 10}.\n",
+      "There are 2500 to plot.\n",
+      "Breaking at limit 3 passed to function.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (56,56).\n",
+      "There were 3136 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 3136, 'arcminutes': (480, 240), 'overlap': 20}.\n",
+      "There are 3136 to plot.\n",
+      "Breaking at limit 3 passed to function.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (64,64).\n",
+      "There were 4096 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 4096, 'arcminutes': (480, 240), 'overlap': 30}.\n",
+      "There are 4096 to plot.\n",
+      "Breaking at limit 3 passed to function.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (75,75).\n",
+      "There were 5625 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 5625, 'arcminutes': (480, 240), 'overlap': 40}.\n",
+      "There are 5625 to plot.\n",
+      "Breaking at limit 3 passed to function.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (90,90).\n",
+      "There were 8100 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 8100, 'arcminutes': (480, 240), 'overlap': 50}.\n",
+      "There are 8100 to plot.\n",
+      "Breaking at limit 3 passed to function.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.88 s, sys: 1.47 s, total: 3.35 s\n",
+      "Wall time: 1.73 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "# TIMING NOTE: takes < 5 seconds to run 2/15/2024 COC\n",
+    "\n",
+    "for i in [0, 10, 20, 30, 40, 50]:\n",
+    "    arcminutes_input = (8 * 60, 4 * 60)  # 8° X 4°\n",
+    "    patches_result, patches_centers, info = generate_patches(\n",
+    "        arcminutes_input,\n",
+    "        i,\n",
+    "        # decRange=[-90,61],\n",
+    "        export=False,\n",
+    "    )\n",
+    "    plot_patches(\n",
+    "        patches_result,\n",
+    "        xrange=[0, 20],\n",
+    "        yrange=[-90, -70],\n",
+    "        title=f\"size: {info['arcminutes']}', overlap: {info['overlap']}% ({info['npatches']} patches)\",\n",
+    "        subfolder=\"generic\",\n",
+    "        limit=3,  # Just show the first 3 patches generated, otherwise results are visually overwhelming.\n",
+    "    )  # , limit=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a039bdb4",
+   "metadata": {},
+   "source": [
+    "You can see the first set has no overlap at all, and each subsequent plot has further overlap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "5f4490c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rather than copying and pasting numbers, we make a dictionary to store parameters going forward.\n",
+    "chipDict = {}\n",
+    "\n",
+    "chipDict[\"DECam\"] = {\"chipsize_arcmin\": [9, 18]}  # 0.263\"/pixel, (2048,4096) pix/chip = (8.98,17.95')/chip\n",
+    "chipDict[\"DECam\"][\"matches_per_sec\"] = 20000  # sphgeom region matching; guess, TODO benchmark me\n",
+    "chipDict[\"DECam\"][\"chips_per_mo\"] = 62 * 1900  # estimate; 1900/mo MAX 12/20/2023 COC (mean 708)\n",
+    "chipDict[\"DECam\"][\"dec_range\"] = [-90, 90 - 30.1732]\n",
+    "\n",
+    "chipDict[\"LSST\"] = {\"chipsize_arcmin\": [14, 14]}  # 0.2\"/pixel, (4096,4096) pix/chip = (13.65',13.65')/chip\n",
+    "chipDict[\"LSST\"][\"matches_per_sec\"] = 20000  # sphgeom region matching; benchmarked; on RSP or local\n",
+    "chipDict[\"LSST\"][\"chips_per_mo\"] = 6000000  # 2000/night * 30 nights/month\n",
+    "chipDict[\"LSST\"][\"dec_range\"] = [-90, 30.1732]\n",
+    "# chipDict['LMI'] = [13,13] # 0.120\"/pixel, (6144,6150) pix/chip = (12.288',12.3')/chip # just use LSST\n",
+    "\n",
+    "chipDict[\"TEST\"] = {\"chipsize_arcmin\": [3 * 60, 3 * 60]}  # 3 degree squares; 12/19/2023 COC\n",
+    "chipDict[\"TEST\"][\"matches_per_sec\"] = 20000  # sphgeom region matching; reasonable estimate for testing\n",
+    "chipDict[\"TEST\"][\"chips_per_mo\"] = 6000\n",
+    "chipDict[\"TEST\"][\"dec_range\"] = [-90, 90]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d486c8c",
+   "metadata": {},
+   "source": [
+    "This would be a good place to simulate benchmarks for region-region matching (visa lsst.sphgeom .intersect).\\\n",
+    "The approach (demonstrated elsewhere) checks for any overlapping regions of two lsst.sphgeom regions.\\\n",
+    "This is especially useful for handling arbitrarility rotated chips/images.\\\n",
+    "In an early design, this was the approach to do our searching, but it was far too slow.\\"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "0a0e7f55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: consider how we link this spot in the notebook to Region demo, etc. 2/15/2024 COC\n",
+    "# benchmarking, prettiertime, etc. would need to be brought here."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ba72d59",
+   "metadata": {},
+   "source": [
+    "### Regions from Patches\n",
+    "Here we work on producing lsst.sphgeom regions (objects).\\\n",
+    "The synthetic patch regions can be used as an interim step to filter out images (with 0 overlap).\\\n",
+    "This is helpful to reduce clutter sent to Reproject, for example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "e2f4536b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def latLonFromRaDecDeg(ra, dec, verbose=False):\n",
+    "    \"\"\"\n",
+    "    Return a sphgeom LonLat object given an input RA, Dec (in degrees).\n",
+    "    We correct Dec values outside of ±90° (e.g., subtract 180 from Dec=90.1).\n",
+    "    \"\"\"\n",
+    "    if dec > 90:\n",
+    "        print(f\"WARNING: Dec > 90° ({dec}°) so subtracting 180°.\")\n",
+    "        dec -= 180\n",
+    "    elif dec < -90:\n",
+    "        print(f\"WARNING: Dec < -90° ({dec}°) so adding 180°.\")\n",
+    "        dec += 180\n",
+    "    if ra > 360:\n",
+    "        print(f\"WARNING: RA > 360° ({ra}°) so subtracting 360°.\")\n",
+    "        ra -= 360\n",
+    "    elif ra < 0:  # just in case 1/9/2024 COC\n",
+    "        print(f\"WARNING: RA < 0° ({ra}°) so adding 360°.\")\n",
+    "        dec += 180\n",
+    "    t = lsst.sphgeom.LonLat(\n",
+    "        lsst.sphgeom._sphgeom.NormalizedAngle(np.deg2rad(ra)), lsst.sphgeom.Angle(np.deg2rad(dec))\n",
+    "    )\n",
+    "    return t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "122c1379",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Dec > 90° (91°) so subtracting 180°.\n",
+      "WARNING: RA > 360° (361°) so subtracting 360°.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "LonLat.fromRadians(0.017453292519943295, -1.5533430342749532)"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Testing cases of invalid RA and Dec values.\n",
+    "print(latLonFromRaDecDeg(ra=45, dec=45))\n",
+    "print(latLonFromRaDecDeg(ra=361, dec=91))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "6e03ae98",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def patches_to_sphgeom(input_data):\n",
+    "    \"\"\"\n",
+    "    Convert a list of patches or FITS files to sphgeom regions.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - input_data: List of patches or FITS file paths.\n",
+    "\n",
+    "    Returns:\n",
+    "    - List of sphgeom.SphericalPolygon objects.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def read_patch_fits(fits_path):\n",
+    "        # Read FITS file and extract WCS information\n",
+    "        hdul = fits.open(fits_path)\n",
+    "        wcs = astropy.wcs.WCS(hdul[0].header)\n",
+    "\n",
+    "        # Extract coordinates from the WCS\n",
+    "        ra, dec = wcs.all_pix2world(np.array([0, 0]), np.array([0, 0]), 0)\n",
+    "        ra_start, dec_start = ra[0], dec[0]\n",
+    "\n",
+    "        ra, dec = wcs.all_pix2world(\n",
+    "            np.array([hdul[0].data.shape[1], hdul[0].data.shape[0]]), np.array([1, 1]), 0\n",
+    "        )\n",
+    "        ra_end, dec_end = ra[0], dec[0]\n",
+    "\n",
+    "    #         return sphgeom.ConvexPolygon.from_radec_sequence(\n",
+    "    #             [(ra_start, dec_start), (ra_start, dec_end), (ra_end, dec_end), (ra_end, dec_start)]\n",
+    "    #         )\n",
+    "\n",
+    "    sphgeom_regions = []\n",
+    "\n",
+    "    for item in input_data:\n",
+    "        if isinstance(item, tuple):\n",
+    "            # If it's a patch tuple, convert to sphgeom.SphericalPolygon\n",
+    "            ra_start, dec_start = item[0]\n",
+    "            ra_end, dec_end = item[1]\n",
+    "            box = lsst.sphgeom.Box(\n",
+    "                latLonFromRaDecDeg(ra_start, dec_start), latLonFromRaDecDeg(ra_end, dec_end)\n",
+    "            )\n",
+    "            sphgeom_regions.append(box)\n",
+    "        elif isinstance(item, str):\n",
+    "            # NOTE: untested 2/15/2024 COC TODO\n",
+    "            # If it's a FITS file path, read the file and convert to sphgeom.SphericalPolygon\n",
+    "            sphgeom_regions.append(read_patch_fits(item))\n",
+    "        else:\n",
+    "            raise ValueError(\n",
+    "                \"Unsupported input type. Supported types are tuple (patch) or str (FITS file path).\"\n",
+    "            )\n",
+    "\n",
+    "    return sphgeom_regions\n",
+    "\n",
+    "\n",
+    "# Example usage:\n",
+    "# Assuming you have a list of patches or FITS file paths\n",
+    "# patches_or_fits = generate_patches(arcminutes_input, overlap_percentage_input)\n",
+    "# sphgeom_regions_result = patches_to_sphgeom(patches_or_fits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "4b8e1b38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (150,75).\n",
+      "There were 11250 produced, skipping 0 because Dec was outside [-90, 90]. Info: {'npatches': 11250, 'arcminutes': [180, 180], 'overlap': 20}.\n",
+      "CPU times: user 367 ms, sys: 9.06 ms, total: 376 ms\n",
+      "Wall time: 372 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "test_patches_results, test_patches_centers, test_patches_info = generate_patches(\n",
+    "    chipDict[\"TEST\"][\"chipsize_arcmin\"], 20\n",
+    ")\n",
+    "\n",
+    "sphgeom_regions = patches_to_sphgeom(test_patches_results)\n",
+    "\n",
+    "# Now we have a list of sphgeom.SphericalPolygon objects in sphgeom_regions_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "7d297ec8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Box(NormalizedAngleInterval.fromRadians(0.0, 0.05235987755982989), AngleInterval.fromRadians(-1.5707963267948966, -1.5184364492350666))"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# first one of our example regions\n",
+    "sphgeom_regions[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "31068315",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\xd6\\xeb{\\xf3\\xe9\\xce\\xaa?\\x18-DT\\xfb!\\xf9\\xbf\\xb9M\\xa8\\x04\\x84K\\xf8\\xbf'"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sphgeom regions can be encoded for portability; these are \"region hashes\"\n",
+    "encoded = sphgeom_regions[0].encode()\n",
+    "encoded"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "70fb95ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Box(NormalizedAngleInterval.fromRadians(0.0, 0.05235987755982989), AngleInterval.fromRadians(-1.5707963267948966, -1.5184364492350666))"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Come back to an object\n",
+    "r = lsst.sphgeom.Region.decode(encoded)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a26fc2a",
+   "metadata": {},
+   "source": [
+    "### Working with the Butler + Regions\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "9431fdb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>visit_detector_region:\n",
+       "  instrument: 'DECam'\n",
+       "  detector: 62\n",
+       "  visit: 946176\n",
+       "  region: ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)])<pre>"
+      ],
+      "text/plain": [
+       "visit_detector_region.RecordClass(instrument='DECam', detector=62, visit=946176, region=ConvexPolygon([UnitVector3d(0.9876086828694174, -0.13336028508776862, -0.08272922024438323), UnitVector3d(0.9873378171284917, -0.13332652431396907, -0.08595389916869185), UnitVector3d(0.9881047366097594, -0.12752395595185462, -0.08594573955553172), UnitVector3d(0.9883760335240734, -0.12755303452468866, -0.0827226676235914)]))"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We should still have our example VisitInfo object from the Butler.\n",
+    "example_vdr_ref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "8f92b9b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Some example comparisons, using regions in our DataFrame\n",
+    "reg1 = df[\"region\"].iloc()[0]\n",
+    "reg2 = df[\"region\"].iloc()[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "5976ab67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "reg1.contains(reg1) =  True\n",
+      "reg1.contains(reg2) = False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Basic reality testing first\n",
+    "print(f\"reg1.contains(reg1) =  {reg1.contains(reg1)}\")  # should be True\n",
+    "print(f\"reg1.contains(reg2) = {reg1.contains(reg2)}\")  # should be False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "06edb382",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking matches of reg1 against all records region for matchces...\n",
+      "There were 1 matches:\n",
+      "{instrument: 'DECam', detector: 1, visit: 898286}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First we will look for the \"contains\" case, where one region completely contains another.\n",
+    "# We do not expect this for DEEP necessarily, but a region must contain itself, so we should see 1 match.\n",
+    "\n",
+    "print(f\"Checking overlap matches of reg1 against all records region for matchces...\")\n",
+    "matches = []\n",
+    "for i, reg in enumerate(df[\"region\"].iloc()):\n",
+    "    if reg.contains(reg1):\n",
+    "        matches.append(df[\"data_id\"].iloc()[i])\n",
+    "print(f\"There were {len(matches)} overlap matches:\")\n",
+    "for i in matches:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "1f8a61e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking intersect matches of reg1 against all records region for matchces...\n",
+      "There were 347 intersection matches:\n",
+      "{instrument: 'DECam', detector: 1, visit: 898286}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898287}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898288}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898289}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898290}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898291}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898292}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898293}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898294}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898295}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898296}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898297}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898298}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898299}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898300}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898301}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898302}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898303}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898304}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898305}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898306}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898307}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898308}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898309}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898310}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898311}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898312}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898313}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898314}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898315}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898316}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898317}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898318}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898319}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898320}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898321}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898322}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898323}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898324}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898325}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898326}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898327}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898328}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898329}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898330}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898331}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898332}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898333}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898334}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898335}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898336}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898337}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898338}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898339}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898340}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898341}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898342}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898343}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898344}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898345}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898346}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898347}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898348}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898349}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898350}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898351}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898352}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898353}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898354}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898355}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898356}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898357}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898358}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898359}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898360}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898361}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898362}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898363}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898364}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898365}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898366}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898367}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898368}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898369}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898370}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898371}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898372}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898373}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898374}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898375}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898376}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898377}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898378}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898379}\n",
+      "{instrument: 'DECam', detector: 1, visit: 898380}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891455}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891456}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891457}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891458}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891459}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891460}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891461}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891462}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891463}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891464}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891465}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891466}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891467}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891468}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891469}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891470}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891471}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891472}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891473}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891474}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891475}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891476}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891477}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891478}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891479}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891480}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891481}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891482}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891483}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891484}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891485}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891486}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891487}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891488}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891489}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891490}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891491}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891492}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891493}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891494}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891495}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891496}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891497}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891498}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891499}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891500}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891501}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891502}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891503}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891504}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891505}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891506}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891507}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891508}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891509}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891510}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891511}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891512}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891513}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891514}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891515}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891516}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891517}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891518}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891519}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891520}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891521}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891522}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891523}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891524}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891525}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891526}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891527}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891528}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891529}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891530}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891531}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891532}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891533}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891534}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891535}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891536}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891537}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891538}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891539}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891540}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891541}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891542}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891543}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891544}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891545}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891546}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891547}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891548}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891549}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891550}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891551}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891552}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891553}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891554}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891555}\n",
+      "{instrument: 'DECam', detector: 8, visit: 891556}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946689}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946690}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946691}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946692}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946693}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946694}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946695}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946696}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946697}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946698}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946699}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946700}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946701}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946702}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946703}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946704}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946705}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946706}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946707}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946708}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946709}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946710}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946711}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946712}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946713}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946714}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946715}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946716}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946717}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946718}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946719}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946720}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946721}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946722}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946723}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946724}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946725}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946726}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946727}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946728}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946729}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946730}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946731}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946732}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946733}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946734}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946735}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946736}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946737}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946738}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946739}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946740}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946741}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946742}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946743}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946744}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946745}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946746}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946747}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946748}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946749}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946750}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946751}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946752}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946753}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946754}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946755}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946756}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946757}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946758}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946759}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946760}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946761}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946762}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946763}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946764}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946765}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946766}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946767}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946768}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946769}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946770}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946771}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946772}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946773}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946774}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946775}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946776}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946777}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946778}\n",
+      "{instrument: 'DECam', detector: 1, visit: 946779}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946081}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946082}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946083}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946084}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946085}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946086}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946087}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946088}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946089}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946090}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946091}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946092}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946093}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946094}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946095}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946096}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946098}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946099}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946102}\n",
+      "{instrument: 'DECam', detector: 39, visit: 946103}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946081}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946082}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946083}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946084}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946085}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946086}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946087}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946088}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946089}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946090}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946091}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946092}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946093}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946094}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946095}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946096}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946097}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946098}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946099}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946100}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946101}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946102}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946103}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946104}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946105}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946106}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946107}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946108}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946109}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946110}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946112}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946113}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946114}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946115}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946116}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946117}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946118}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946121}\n",
+      "{instrument: 'DECam', detector: 45, visit: 946122}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Next we check \"intersect\" -- meaning any overlap of region area.\n",
+    "\n",
+    "print(f\"Checking intersect matches of reg1 against all records region for matchces...\")\n",
+    "matches = []\n",
+    "for i, reg in enumerate(df[\"region\"].iloc()):\n",
+    "    if reg.intersects(reg1):\n",
+    "        matches.append(df[\"data_id\"].iloc()[i])\n",
+    "print(f\"There were {len(matches)} intersection matches:\")\n",
+    "for i in matches:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "aa1e3080",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saw minDec=-6.406214566625444 and maxDec=-2.719246815973079.\n",
+      "Will use minDec=-8.906214566625444 and maxDec=-0.21924681597307893.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lazily grabbing min and max Decs to limit number of patches we will produce for the dataset\n",
+    "ras = []\n",
+    "decs = []\n",
+    "for i in df[\"center_coord\"]:\n",
+    "    ra = i[0]\n",
+    "    dec = i[1]\n",
+    "    ras.append(ra)\n",
+    "    decs.append(dec)\n",
+    "# deep_min_dec = min([df['center_coord'].iloc()[0][i])\n",
+    "minDec = min(decs)\n",
+    "maxDec = max(decs)\n",
+    "print(f\"Saw minDec={minDec} and maxDec={maxDec}.\")\n",
+    "\n",
+    "# add a buffer of about a field radius\n",
+    "minDec -= 2.5\n",
+    "maxDec += 2.5\n",
+    "print(f\"Will use minDec={minDec} and maxDec={maxDec}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "2d4bde26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of patches in (RA, Dec): (1542,771).\n",
+      "There were 55512 produced, skipping 1133370 because Dec was outside [-8.906214566625444, -0.21924681597307893]. Info: {'npatches': 55512, 'arcminutes': [14, 14], 'overlap': 0}.\n",
+      "CPU times: user 5.98 s, sys: 48.1 ms, total: 6.03 s\n",
+      "Wall time: 6.03 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Timing note: takes < 7 seconds 2/15/2024 COC\n",
+    "\n",
+    "# First we want a set of sky patches. We are going to use 14' X 14' patches (LSST-size) as a test.\n",
+    "deep_patches_results, deep_patches_centers, deep_patches_info = generate_patches(\n",
+    "    arcminutes=chipDict[\"LSST\"][\"chipsize_arcmin\"],\n",
+    "    overlap_percentage=0,\n",
+    "    verbose=True,\n",
+    "    decRange=[minDec, maxDec],\n",
+    "    export=False,\n",
+    ")\n",
+    "# convert the patches to sphgeom regions\n",
+    "sphgeom_regions_deep_limited = patches_to_sphgeom(deep_patches_results)\n",
+    "\n",
+    "# Now you have a list of sphgeom.SphericalPolygon objects in sphgeom_regions_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "7ecd13b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For the generalized Region Search, sky patches, we need a function for matching.\n",
+    "# making a function to test iterating and matching 1/12/2024 COC\n",
+    "# non-vectorized wanted > 1 hr to do this 2/15/2024 COC\n",
+    "\n",
+    "\n",
+    "def checkMatches(regions1, regions2):\n",
+    "    \"\"\"\n",
+    "    regions1 will be our generated patches (sphgeom_regions)\n",
+    "    regions2 will be actual images (pointing_regions)\n",
+    "    updated 2/15/2024 COC\n",
+    "    \"\"\"\n",
+    "    print(f\"Starting region matching. Lens: region1 = {len(regions1)}, region2 = {len(regions2)}\")\n",
+    "    startTime = time.time()\n",
+    "    matches = {}\n",
+    "    matches_attempted = 0\n",
+    "    progress_c = 0\n",
+    "    progress_interval = 10  # len(regions1)//200\n",
+    "    with progressbar.ProgressBar(max_value=len(regions1)) as bar:\n",
+    "        for i, patch in enumerate(regions1):\n",
+    "            matches[i] = []\n",
+    "            for j, pointing in enumerate(regions2):\n",
+    "                matches_attempted += 1\n",
+    "                if pointing.intersects(patch):\n",
+    "                    matches[i].append(j)\n",
+    "            progress_c += 1\n",
+    "            if progress_c >= progress_interval:\n",
+    "                progress_c = 0\n",
+    "                bar.update(i)\n",
+    "\n",
+    "    elapsed = round(time.time() - startTime, 3)\n",
+    "    print(\n",
+    "        f\"There were {len(matches)} matches in {matches_attempted} matches attempted. {len(matches_i)} region1s (patches?) and {len(matches_j)} region2s (pointings?) were involved. [{elapsed} s elapsed.]\"\n",
+    "    )\n",
+    "    return matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "id": "94956cb8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import multiprocessing\n",
+    "# import time\n",
+    "# from progressbar import ProgressBar\n",
+    "\n",
+    "\n",
+    "def worker(patch_and_regions2):\n",
+    "    patch, regions2 = patch_and_regions2\n",
+    "    return [j for j, pointing in enumerate(regions2) if pointing.intersects(patch)]\n",
+    "\n",
+    "\n",
+    "def checkMatches(regions1, regions2):\n",
+    "    print(f\"Starting region matching. Lens: region1 = {len(regions1)}, region2 = {len(regions2)}\")\n",
+    "    startTime = time.time()\n",
+    "\n",
+    "    # Prepare input for worker function\n",
+    "    input_for_worker = [(patch, regions2) for patch in regions1]\n",
+    "\n",
+    "    # Use multiprocessing Pool\n",
+    "    with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:\n",
+    "        results = pool.map(worker, input_for_worker)\n",
+    "\n",
+    "    # Aggregate results\n",
+    "    matches = {i: result for i, result in enumerate(results)}\n",
+    "\n",
+    "    elapsed = round(time.time() - startTime, 3)\n",
+    "    print(\n",
+    "        f\"There were {sum(len(m) for m in matches.values())} matches in the {len(regions1)*len(regions2)} matches executed. [Elapsed time: {elapsed} s]\"\n",
+    "    )\n",
+    "\n",
+    "    return matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "id": "f8955306",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting region matching. Lens: region1 = 55512, region2 = 47383\n",
+      "There were 55512 matches in 192160 matches attempted. [Elapsed time: 105.122 s]\n",
+      "CPU times: user 58.4 s, sys: 7.59 s, total: 1min 6s\n",
+      "Wall time: 1min 45s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Timing note: this takes about 2 minutes 2/15/2024 COC\n",
+    "match_d = checkMatches(regions1=sphgeom_regions_deep_limited, regions2=df[\"region\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "809add85",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31.4 ms, sys: 2.97 ms, total: 34.4 ms\n",
+      "Wall time: 32.4 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "matching_regions = []\n",
+    "matching_counts = []\n",
+    "for r in match_d:\n",
+    "    c = len(match_d[r])\n",
+    "    if c > 0:\n",
+    "        matching_regions.append(r)\n",
+    "        matching_counts.append(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "36b31a6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "264"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many sky patches had images in them?\n",
+    "len(matching_regions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "4d2452f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_70035/2951881342.py:33: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
+      "  plt.colorbar(sm, label='Matching Images')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ECEaMGLHXMmvXruXyyy/nmWee4ayzzmrxWl1dHbNmzeKBBx5gyJAhAMydO5eqqiqee+45hg0bxj/+8Q/mz5/Pq6++Sr9+/QC477776N+/PytWrKBXr15tq3SaqMVHRETkAFFfX99iicVivuJ4nsfYsWO55ppr6N279y6vL126lEQiwdChQ1PrKisr6dOnD4sWLQJg8eLFFBUVpZIegNNOO42ioqJUmUxQ4iMiIpJJO+fxsV2Aqqqq1HiaoqIipk+f7qtKt912G6FQiIkTJ+729ZqaGiKRCMXFxS3Wl5eXU1NTkyrTtWvXXbbt2rVrqkwmqKtLREQkk9J4O/vq1aspLCxMrY5Go20OtXTpUv7zP/+TN998E6eNk2YZY1pss7vtv1qmvanFR0REJIOMcdKyABQWFrZY/CQ+f/vb39i4cSPV1dWEQiFCoRCfffYZkydPpkePHgBUVFQQj8epra1tse3GjRspLy9PldmwYcMu8Tdt2pQqkwlKfERERCRl7NixvPPOOyxbtiy1VFZWcs011/DMM88AcPLJJxMOh1mwYEFqu/Xr17N8+XIGDBgAQP/+/amrq+P1119PlXnttdeoq6tLlckEdXWJiIhkUgZmbt6+fTsfffRR6u+VK1eybNkySkpKqK6uprS0tEX5cDhMRUVF6k6soqIiLrroIiZPnkxpaSklJSVcffXV9O3bN3WX1zHHHMPw4cO5+OKLueeeewD48Y9/zMiRIzN2Rxco8REREcmsDDyk9I033uCMM85I/X3VVVcBMG7cOGbPnt2qGHfeeSehUIjRo0fT2NjI4MGDmT17NsFgMFXmwQcfZOLEiam7v0aNGrXPuYP2NyU+IiIiWWbQoEGYNkz+8+mnn+6yLicnh5kzZzJz5sw9bldSUsLcuXP9VHG/UeIjIiKSQV8enGwTQ1pHiY+IiEgmedg/Ndf6qbvZQ3d1iYiISNZQi4+IiEgmZWBwczZT4iMicgD47IOtVtsXleVQXt0lPZWRNjGeg7G8nd12+2yixEc6DBP3cACbhxTnhJM4SQ9j/PfiRvObcAIexvMfIxBOgmOsfoWZ/CQmYHAsv9ACuXEIeuD63x/TJYEJejgWMZygZ31MwrkxnKCLcYP7LrwHOSGPoGNwLeoRDXk4SWM9oNSB5hM+Ddesm8cttNo+khNk7nvfVfIjBzwlPtJh5EVCHJXsgmuR+pREYvxbn9Ukkv4vjIUHb6XPWa+RjIV9x3ACHuGgh2eTbHRtounsGojZDcULxKHyyK14Tf4/7l5BksSxjdDo/7iyJcJBT4KxSZ7CScqOW0WiKeI7RkNtF+BUmpIWx9U4uE05JFy7jKUuHmDLhjw8i3M+iUddMGlVD4B4k0vd501KfDJBXV3tSomPdCgRy/H2UYLkRF1yoq7vGHm5CaL5MaL5Md8xjAcmESaI/3p40SSJLi508R8DwDQ4hLrEoIv//XFzPRKFSSi0u8AGwx6E/d9+4oRcwgWN5BQ0WtWjS8SlS8T/cXXdANtdjxz/uTEASSCIQzAdTT7Sael29valxEdERCSTTBoeWaHEp9V0O7uIiIhkDbX4iIiIZJLG+LQrJT4iIiIZZEzzYhtDWkddXSIiIpI11OIjIiKSSV4aBjdrAsNWU+IjIiKSQbqdvX2pq0tERESyhlp8REREMkl3dbUrJT4iIiIZpIeUti91dYmIiEjWUIuPiIhIJhnS0NWVlppkBSU+IiIiGaS7utqXEh8REZFM0jw+7UqJj6TN2lX1bNnc6Hv7TR9sS2NtREREdqXER9Ji7ap6BvWZTSzm+o5R4oU5jTLA/y+XgGMwBhyLHz9eIojxHJyA/05zLxEEDxyL2wecWACSWH9K3e1hTNLBCVkMAmgMgpuEoP8QJhGwfm/cRICQ5XFNpqEeSdexjgGQwMNgcCzO+WQaYgAYDI3xpFUM8UfP6mpfSnwkLbZsbrRKeqD5IlIWcPAsYhTggOWXSLIpzIa3exAIWeyPgWDQwwn635tgTpzCvyQganNEIFGXw/pl3Qjk+L+ohaJxij6psaqLuyNC49Z8cPy/OcZAfFuu1XFt2FLA1rouOBb1SCYDbNuRaxUDoD5p2BSIEbBIWmK41DgNVjEAPAzBqG70zQSN8WlfSnykQwk6Vo0KhBywaTFq5uDGw7jxsP8IjocTTTa32PiNETAEdoRgh/8YAM6OMO6OHFybOHlN1nUxTSGMZ3thNRgTwFgcVy8exrOsh+s51jEAPNfgOS6exS05LgbXMbi6rUekVZT4iIiIZJIGN7crJT4iIiIZpK6u9qUOXREREckaavERERHJqDQ8pNR6bGP2UOIjIiKSQerqal/q6hIREZGsocRHREQkk3be1WW7tMHLL7/Mt771LSorK3Ech8cffzz1WiKR4LrrrqNv377k5+dTWVnJD3/4Q9atW9ciRiwWY8KECZSVlZGfn8+oUaNYs2ZNizK1tbWMHTuWoqIiioqKGDt2LFu3bvV7pNJCiY+IiEgG7Zy52XZpix07dnD88cdz11137fJaQ0MDb775Jr/4xS948803efTRR/nwww8ZNWpUi3KTJk3iscceY968ebzyyits376dkSNH4rr/mvx1zJgxLFu2jPnz5zN//nyWLVvG2LFjfR2ndNEYHxERkQzKxBifESNGMGLEiN2+VlRUxIIFC1qsmzlzJv/2b//GqlWrqK6upq6ujlmzZvHAAw8wZMgQAObOnUtVVRXPPfccw4YN4x//+Afz58/n1VdfpV+/fgDcd9999O/fnxUrVtCrVy8fe2pPLT4iIiIHiPr6+hZLLBZLS9y6ujocx+Gggw4CYOnSpSQSCYYOHZoqU1lZSZ8+fVi0aBEAixcvpqioKJX0AJx22mkUFRWlymSCEh8REZFMMk56FqCqqio1nqaoqIjp06dbV6+pqYnrr7+eMWPGUFhYCEBNTQ2RSITi4uIWZcvLy6mpqUmV6dq16y7xunbtmiqTCerqEhERySTPwaTpkRWrV69OJScA0WjUKmwikeC8887D8zz++7//e5/ljTE4zr/25cv/3lOZ9qYWHxERkQNEYWFhi8Um8UkkEowePZqVK1eyYMGCFglVRUUF8Xic2traFtts3LiR8vLyVJkNGzbsEnfTpk2pMpmgxEdERCSDdg5utl3SaWfS889//pPnnnuO0tLSFq+ffPLJhMPhFoOg169fz/LlyxkwYAAA/fv3p66ujtdffz1V5rXXXqOuri5VJhPU1SUiIpJJXxqjYxWjDbZv385HH32U+nvlypUsW7aMkpISKisr+e53v8ubb77JX//6V1zXTY3JKSkpIRKJUFRUxEUXXcTkyZMpLS2lpKSEq6++mr59+6bu8jrmmGMYPnw4F198Mffccw8AP/7xjxk5cqTvO7rq6+t54YUX6NWrF8ccc4yvGEp8REREsswbb7zBGWeckfr7qquuAmDcuHFMnTqVJ554AoATTjihxXYvvvgigwYNAuDOO+8kFAoxevRoGhsbGTx4MLNnzyYYDKbKP/jgg0ycODF199eoUaN2O3fQnowePZqvf/3rXH755TQ2NnLKKafw6aefYoxh3rx5nHvuuW3edyU+khZuzOAAbZxDq4WgY76I4P+XTzDoWsfA8TAG7Mbe2e8LkSQ4xvqXYCCUBMcDY9GzHbWvixP07PfH2XmG+Y/RPNGb5Xtjdp7tdu+Na/WJaRbASUdViEaDFJfmWNdH2i4T8/gMGjQIs5dZD/f22k45OTnMnDmTmTNn7rFMSUkJc+fObVPdvuzll19mypQpADz22GMYY9i6dStz5szh5ptvVuIjmZMbCVHh5eFZfJGXBAIU5yXbPAPpl3XJTVBUWmd1h0QwnLRMesAJQCiasLrIh3KSBI7+HJJ2Q/HCTUG6OuAlgvsuvAdOfty6Lk5TkAIHjOs/hvEc3IZIm6fn/7KkgYKCBqsLTTwRZHtDFLtUH/y/I/8SIkClyednc75O96OL973BHhSX5nBodeG+C0raGa95sY1xIKqrq6OkpASA+fPnc+6555KXl8dZZ53FNddc4yumEh9Jm5DlWPkwDkHL4fbBAASDntUVJRhy912oFRyHL7VQ+BAwOBEPIvbfaKFosrnVxq/cRFrqEgi7EPZ/fE0ygImFIej/uDohQyCws0XOn6AbsE6OwbZV8V9CBOjZq5SjTixLT0CRDqKqqorFixdTUlLC/PnzmTdvHtD8DLCcHH8tlEp8REREMikDg5s7i0mTJnH++efTpUsXqqurU+OLXn75Zfr27esrZqe7nT0Wi3HCCSfgOA7Lli3LdHVERESsdMTb2TuKyy67jMWLF/OHP/yBv//97wQCzWnL4Ycfzs033+wrZqdr8bn22muprKzk7bffznRVRERErGVicHNncsopp3DcccexcuVKjjjiCEKhEGeddZbveJ2qxefpp5/m2Wef5Y477sh0VURERGQ/a2ho4KKLLiIvL4/evXuzatUqACZOnMitt97qK2anSXw2bNjAxRdfzAMPPEBeXl6rtonFYrs8qVZERKRDSeNDSg80N9xwA2+//TYvvfRSi8HMQ4YM4ZFHHvEVs1MkPsYYxo8fz6WXXsopp5zS6u2mT5/e4im1VVVV+7GWIiIibWdM81QNVov9lFAd0uOPP85dd93F6aef3uLBpsceeywff/yxr5gZTXymTp2K4zh7Xd544w1mzpxJfX09N9xwQ5vi33DDDdTV1aWW1atX76c9ERERkXTbtGkTXbt23WX9jh07fD/hPaODmy+//HLOO++8vZbp0aMHN998M6+++uouT5k95ZRTOP/885kzZ85ut41Go1ZPphUREdnfNLh5z0499VSeeuopJkyYAJBKdu677z769+/vK2ZGE5+ysjLKyvY94dbvfve7FretrVu3jmHDhvHII4/Qr1+//VlFERGR/ctuPs1/xTgATZ8+neHDh/P++++TTCb5z//8T9577z0WL17MwoULfcXsFLezV1dXt/i7S5cuABxxxBF069YtE1USERGR/WzAgAH8/e9/54477uCII47g2Wef5aSTTmLx4sW+JzDsFImPiIjIgUpdXXvXt2/fPQ5p8aNTJj49evRo1ZNjRUREOjolPnu2p2loHMchGo0SiUTaHLNTJj4iIiIHip23pNvGOBAddNBBe717q1u3bowfP54bb7wx9TiLfVHiIyIiIh3S7NmzmTJlCuPHj+ff/u3fMMawZMkS5syZw89//nM2bdrEHXfcQTQa5Wc/+1mrYirxERERySQ9nX2P5syZw29+8xtGjx6dWjdq1Cj69u3LPffcw/PPP091dTW33HKLEh9pvbWr6tmyudEqxqoVW9NTGRFpsyQe/1yxmbjj+Y5RXJrDodWFaayVtJbG+OzZ4sWLufvuu3dZf+KJJ7J48WIATj/99NQzvFpDiU+WW7uqnkF9ZhOLuVZxgsbhEPJw8P/hcz3wDAQsPr+BgEfzhBb+gzhpiOG5zVPI+5xYtLkexljHAL6Yyt5uf0gEMB44FnO9u7EQeA5OwP+NCek4rslE0DrGDhdcYwhavjlxPAzG6nOTxGOds4PLxz9tVZdoNMjzy8cp+ZEOpVu3bsyaNWuXB5LOmjUr9RiqzZs3U1xc3OqYSnyy3JbNjdZJD4DrGNbTQMDiC3yHCfHxti6ELK4lpYEwp0YTVr9+QpEkuUU7rAYLuskg2zYXfZFE+RPxAhy0Mcf6wTJOIoCxSXoAEkG8D8og5H9/EttyqH2nmkDY//nmOB6R3DhYJE+N9XmsXV9K0OK92ZyA5+s8opbvTYNxWec0Wn1uDMYqp90pFnOp3dykxCcD1OKzZ3fccQff+973ePrppzn11FNxHIclS5bwwQcf8Kc//QmAJUuW8P3vf7/VMZX4SNq4jsG1mD40aQxJ45C0mKkg/sUvecfxH8QJGAIBY3VxxTgYL4Dx/F8ZvWQQvAD4vz43S6bpkXyJYPPiVyyEGw/jxsO+QwSCLuFo0uqYmGQA1w3iuv73JZE0NBiXBsvfDHHADdh9bqTzMwbrh4weqDO8jBo1ig8//JC7776bFStWYIxhxIgRPP744/To0QOAn/zkJ22KqcRHREREOqzu3bszffr0tMVT4iMiIpJB6urat4aGBlatWkU8Hm+x/rjjjmtzLCU+IiIimeQ5zYttjAPQpk2b+NGPfsTTT+9+8L7rtr2/OU2d/yIiIiLpNWnSJGpra3n11VfJzc1l/vz5zJkzh549e/LEE0/4iqkWHxERkQxSV9eevfDCC/zlL3/h1FNPJRAI0L17d84880wKCwuZPn06Z511VptjqsVHREQkg3YmPrbLgWjHjh107doVgJKSEjZt2gQ0P7H9zTff9BVTiY+IiEgGKfHZs169erFixQoATjjhBO655x7Wrl3L3XffzSGHHOIrprq6REREpEOaNGkS69evB+DGG29k2LBhPPjgg0QiEWbPnu0rphIfERGRjEpHi82B2eJz/vnnp/594okn8umnn/LBBx9QXV1NWVmZr5hKfERERDJJT2dvtby8PE466SSrGEp8REREpEMyxvCnP/2JF198kY0bN+J5LZ9X8+ijj7Y5pgY3i4iIZJDx0rO0xcsvv8y3vvUtKisrcRyHxx9/vGWdjGHq1KlUVlaSm5vLoEGDeO+991qUicViTJgwgbKyMvLz8xk1ahRr1qxpUaa2tpaxY8dSVFREUVERY8eOZevWra2u5xVXXMHYsWNZuXIlXbp0ScXZufihFh8REZEMysQ8Pjt27OD444/nRz/6Eeeee+4ur99+++389re/Zfbs2Rx11FHcfPPNnHnmmaxYsYKCggKgeeDxk08+ybx58ygtLWXy5MmMHDmSpUuXEgw2PwR4zJgxrFmzhvnz5wPw4x//mLFjx/Lkk0+2qp5z587l0Ucf5Zvf/Gab9m9vlPiIiIhkmREjRjBixIjdvmaMYcaMGUyZMoVzzjkHgDlz5lBeXs5DDz3EJZdcQl1dHbNmzeKBBx5gyJAhQHOSUlVVxXPPPcewYcP4xz/+wfz583n11Vfp168fAPfddx/9+/dnxYoV9OrVa5/1LCoq4vDDD0/TXjdT4tPJbVq1g/rPm3xvv2ZFXRprIyKd2UcfbLHavrg0h0OrC9NUm+yRzhaf+vr6Fuuj0SjRaLRNsVauXElNTQ1Dhw5tEWfgwIEsWrSISy65hKVLl5JIJFqUqayspE+fPixatIhhw4axePFiioqKUkkPwGmnnUZRURGLFi1qVeIzdepUbrrpJv7whz+Qm5vbpv3YEyU+ndimVTv4yTGPk2hqY+fulwQCSUIBSFrW5aCwx46kQ8Liw3tQQQOhbS7JZNB3jHBRI4RcsIjhFMQh6IHrfwhcoEsM6lxw/deDqIdxDI7t3RohFxxjd9dH6It9sYgRcDzreiQTQTzPIRAwvmN4rgMYbG7/DRj7QwoQMo51nIABD9JyN/OV4+dbbR+NBnl++TglP22UzsSnqqqqxfobb7yRqVOntilWTU0NAOXl5S3Wl5eX89lnn6XKRCIRiouLdymzc/uamprUrMtf1rVr11SZffne977Hww8/TNeuXenRowfhcLjF635mb1bi04nVf95klfQAFAYCTCrJp8HzfyEByI8mqK7czLak/w9vUdEOjj/pDRoacnzHiHTdTvlZ/8RtiPiOQa6LqXChwf/Hw8GlcJvBbQjvu/CehF2SpU1gcUwBcA2hshgkLe5lcExzrmCRDIZ2RDioLg9jEaNpew6rl/cgEGr7E5l32rEjB9ssIeoE6OVGSWL3uXExlJgoSfx/jl0MdYEEnvFflwQetYGY7+13isVcajc3KfHJoNWrV1NY+K/j39bWni9znJafE2PMLuu+6qtldle+NXF2Gj9+PEuXLuWCCy6gvLy81dvtjRIf4aBggIMsGiYAIiGXsqihLOr/yzc316P4oAaKD2rwHcMpbiL8xeKXF4LEQUE4KOG/HglDKOARKvJfDwOYsAMRu4srCXCaPIhYJMkGcB2wuEA7yQDBsAdh/zGCcZdkIgwJ/wllosn/heDLIgSwSK+b64IhCkTx/wFM4LED/4mgZJ4x9g8Z3Zn3FhYWtkh8/KioqACaW2y+/FiIjRs3plqBKioqiMfj1NbWtmj12bhxIwMGDEiV2bBhwy7xN23atEtr0p489dRTPPPMM5x++um+9+erdDu7iIhIJu2cwNB2SZPDDjuMiooKFixYkFoXj8dZuHBhKqk5+eSTCYfDLcqsX7+e5cuXp8r079+furo6Xn/99VSZ1157jbq6ulSZfamqqrJO5L5KLT4iIiIZlInb2bdv385HH32U+nvlypUsW7aMkpISqqurmTRpEtOmTaNnz5707NmTadOmkZeXx5gxY4Dmu60uuugiJk+eTGlpKSUlJVx99dX07ds3dZfXMcccw/Dhw7n44ou55557gObb2UeOHNmqgc0Av/nNb7j22mu5++676dGjR5v2cU+U+IiIiGSZN954gzPOOCP191VXXQXAuHHjmD17Ntdeey2NjY1cdtll1NbW0q9fP5599tnUHD4Ad955J6FQiNGjR9PY2MjgwYOZPXt2ag4fgAcffJCJEyem7v4aNWoUd911V6vrecEFF9DQ0MARRxxBXl7eLoObt2xp+52ISnxEREQyKBMtPoMGDcLsZUC84zhMnTp1r3eE5eTkMHPmTGbOnLnHMiUlJcydO7dNdfuyGTNm+N52T5T4iIiIZJCfR07sLsaBaNy4cWmPqcRHREREOpSvTsS4J34GPivxERERyaBMdHV1dAcddNBe5+zZOReQ67Z9Kgdfic93v/tdTjnlFK6//voW63/961/z+uuv83//939+woqIiGQdJT67evHFF/dbbF+Jz8KFC7nxxht3WT98+HDuuOMO60qJiIhI9ho4cOB+i+0r8dm+fTuRyK5zlobD4Vb3y4mIiIhafNqbr5mb+/TpwyOPPLLL+nnz5nHsscdaV0pERCRb7Ex8bBdpHV8tPr/4xS8499xz+fjjj/nGN74BwPPPP8/DDz+s8T0iIiLSYflKfEaNGsXjjz/OtGnT+NOf/kRubi7HHXcczz333H7tlxMRETnQqKurffm+nf2ss87irLPOSmddREREso4Sn/blO/HZunUrf/rTn/jkk0+4+uqrKSkp4c0336S8vJxDDz00nXUUERE5cBkHPMvE5QBNfL7zne/sdj4fx3HIycnhyCOPZMyYMa1+6Cn4THzeeecdhgwZQlFREZ9++in/8R//QUlJCY899hifffYZf/zjH/2EzTo1q7ZT93mT7+3XfFCXxtqIiNj76IO2PzTyy4pLczi0uu2z8cqBqaioiMcff5yDDjqIk08+GWMMb731Flu3bmXo0KE88sgj3HbbbTz//PN87Wtfa1VMX4nPVVddxfjx47n99ttbPKl1xIgRqUfWy97VrNrO93v/L/Gmts86uVOO51BElD0/Zm7fHMAY2MsEma1krOMYY18XrymIcR2coMVRaQhCERDcZ8m9VMSAofkA++V+UQdf915+SdLBeOBYxDFJB4xlDAy2B8XzHOsY4UgCJ+BhPP87Ew54OI6x7l7ICboEjMGz2J+gAcfY/eCPmgABY9/oAHDl+PlW20ejQZ5fPi6rkh91de1ZRUUFY8aM4a677iIQaP7Mep7HFVdcQUFBAfPmzePSSy/luuuu45VXXmlVTF+Jz5IlS7jnnnt2WX/ooYdSU1PjJ2TWqfu8ySrpAQg5Dj1NGNci9cnFxXESVvVo1vyh28vDfvfJc4PEtuY3f4v7rUW9B389HCfq/9g6GML5cUyOxVP/chPQ0+6XrxN3CNWEMTZJHEA8ALU5dnE8wA1YxXCSDpHiHVZf0BEDdtkkRKMJ/u205SQS/p/YE2uKUPjxoSQsM4WAY3CCHnHXfxLWEA/y/oYuJC2+BxzgKDefuOP/nN9OkrfCW31vv1Ms5lK7uUmJj48YB6JZs2bx97//PZX0AAQCASZMmMCAAQOYNm0al19+Of/+7//e6pi+Pvk5OTm7nahwxYoVHHzwwX5Cik8RHGwuBHbtRV9l+8FzrH6FpzSEMQ1h/9sHXRzHxWmwaPLpYqyPBjQnC07S8gsx6WDcQHMLku8gNDcp2MT4oiXOsTjnArZJ4BdycuLk5MR9bx8KeuSEPXKsa2JwHMgP+T+wIQMRAuw6pWzb5RqLc/7AvO5KhiWTST744AOOOuqoFus/+OCD1HO6cnJy9vpcr6/ylficffbZ/OpXv+J///d/geZBRqtWreL666/n3HPP9RNSREQkK+3s5reNcSAaO3YsF110ET/72c849dRTcRyH119/nWnTpvHDH/4QaH6MVu/evVsd01fic8cdd/DNb36Trl270tjYyMCBA6mpqaF///7ccsstfkKKiIhkp3TMvHyAdnXdeeedlJeXc/vtt7NhwwYAysvLufLKK7nuuusAGDp0KMOHD291TF+JT2FhIa+88govvvgiS5cuxfM8TjrpJIYMGeInnIiIiMgugsEgU6ZMYcqUKakhNoWFLcd/VVdXtylmmxMfz/OYPXs2jz76KJ9++imO43DYYYdRUVGBMaZN/WwiIiLZToObW+erCY9fbRpJaoxh1KhR/Md//Adr166lb9++9O7dm88++4zx48fzne98Jy2VEhERyRZ6SOmebdiwgbFjx1JZWUkoFCIYDLZY/GhTi8/s2bN5+eWXef755znjjDNavPbCCy/w7W9/mz/+8Y+pAUciIiIifo0fP55Vq1bxi1/8gkMOOSQtvUptSnwefvhhfvazn+2S9AB84xvf4Prrr+fBBx9U4iMiItJK6uras1deeYW//e1vnHDCCWmL2aaurnfeeWevI6dHjBjB22+/bV0pERGRbGE8Jy3LgaiqqgqT5nv125T4bNmyhfLy8j2+Xl5eTm1trXWlREREskXzPD62Y3wyvRf7x4wZM7j++uv59NNP0xazTV1drusSCu15k2AwSDKZtK6UiIiIyPe//30aGho44ogjyMvLIxxuOSv/li1tfzRQmxIfYwzjx48nGo3u9vVYLNbmCoiIiGQzjfHZsxkzZqQ9ZpsSn3Hjxu2zjAY2i4iItJ4Snz1rTd7RVm1KfO6///60V0BERERkp/r6+tRkhbt7IPqX+ZnU0NcjK0RERCQ91OLTUnFxMevXr6dr164cdNBBu527Z+eTInY+ob0tlPiIiIhkkBKfll544QVKSkoAePHFF9MeX4mPTzWrtrP18ybf23/6wdb0VUZEROQAMXDgwN3+O12U+PhQs2o7o3s/Qryp7U1sO0WMQwCwmXMqJ+TiJI1Vph+NxnECHsZr05ROu4h0acQJuhjX37NTAHA8jAGrGckDHmAAiyDpiOECHm2cKaul+LYcokkIhDz/QQAvHgQPHIu6eIkATsBYxTAe1u9vIh7C8yBgdboa+/PMGKzPEcBxdk6+4j9OAjAYHMu6WMdIzyHBAdyY3Tnf2ajFZ++2bt3K66+/zsaNG/G8lueGnxuqlPj4sPXzJqukByCKw3Emn4TFrFMF4QTHH76JpOv/KpCX30jPo1eRSNidCjnF26n62j9INEZ8x/BiIRJrS6zqEQhAtHiH1ZeAE3IJFTfYZaWOgY8LIOT//U1uKOSDJ08hlBv3Xw8gHE5QXr2BQMj/Ods8K6yDE/R/QXIcj3Be3C4Z3JHDmtUVBC3qEc2JkV/YYDXhmxMwFBbuwLOeLdcQCNhdtJoCYT53XJvDStDAQZaXgzABKkwenuVMegEcciLZdWnKROKTTCaZOnUqDz74IDU1NRxyyCGMHz+en//85wS++GVhjOGmm27i3nvvpba2ln79+vFf//Vf9O7dOxUnFotx9dVX8/DDD9PY2MjgwYP57//+b7p162a1Pzs9+eSTnH/++ezYsYOCgoIW430cx1Hi09nkECDHYvs8PKLh5sV3HSJJojlxojl2F9dIXhPRgubFr+SOKHWWiQ+AEzQ4WHz5Bj2coGm+GthIBsBiPk8nHiRen0e8Ps+qGtG8Jswhm3Gtktvm1h6T8B/BCbqQm2xuDfPLc3DdED7GM6YEgy7WzRI4BAKGQCBd0+X6jxMMenhOcwNjhqqQErJKv6Q93Xbbbdx9993MmTOH3r1788Ybb/CjH/2IoqIirrjiCgBuv/12fvvb3zJ79myOOuoobr75Zs4880xWrFhBQUEBAJMmTeLJJ59k3rx5lJaWMnnyZEaOHMnSpUt9Pz39yyZPnsyFF17ItGnTyMuz+y7cSYmPiIhIBhlj/6yttrb4LF68mLPPPpuzzjoLgB49evDwww/zxhtvfBHPMGPGDKZMmcI555wDwJw5cygvL+ehhx7ikksuoa6ujlmzZvHAAw8wZMgQAObOnUtVVRXPPfccw4YNs9ongLVr1zJx4sS0JT1g1egsIiIituyf0/WvrrL6+voWy56eqHD66afz/PPP8+GHHwLw9ttv88orr/DNb34TgJUrV1JTU8PQoUNT20SjUQYOHMiiRYsAWLp0KYlEokWZyspK+vTpkypja9iwYalkLF3U4iMiIpJBzQ8ptY8BzU8z/7Ibb7yRqVOn7lL+uuuuo66ujqOPPppgMIjrutxyyy384Ac/AKCmpgZglweTl5eX89lnn6XKRCIRiouLdymzc3s/nnjiidS/zzrrLK655href/99+vbtu8uzukaNGtXm+Ep8REREDhCrV69uMZvxnp6t+cgjjzB37lweeughevfuzbJly5g0aRKVlZUtHhPx1ckDd04cuDetKbM33/72t3dZ96tf/WqXdZrAUEREpBPyjINneVfXzu0LCwtb9RiHa665huuvv57zzjsPgL59+/LZZ58xffp0xo0bR0VFBUDqjq+dNm7cmGoFqqioIB6PU1tb26LVZ+PGjQwYMMD/vnj7dzoDjfERERHJoHSO8WmthoaG1G3rOwWDwVTScdhhh1FRUcGCBQtSr8fjcRYuXJhKak4++WTC4XCLMuvXr2f58uVWic/+1mkSnx49euA4Tovl+uuvz3S1REREOp1vfetb3HLLLTz11FN8+umnPPbYY/z2t7/lO9/5DtDcjTRp0iSmTZvGY489xvLlyxk/fjx5eXmMGTMGgKKiIi666CImT57M888/z1tvvcUFF1xA3759U3d52Zo4cSK/+93vdll/1113MWnSJF8xO1VX169+9Ssuvvji1N9dunTJYG1ERETSIA0TGNLG7WfOnMkvfvELLrvsMjZu3EhlZSWXXHIJv/zlL1Nlrr32WhobG7nssstSExg+++yzqTl8AO68805CoRCjR49OTWA4e/bstMzhA/DnP/+5xWDnnQYMGMCtt97KjBkz2hyzUyU+BQUFqX5HERGRA0EmZm4uKChgxowZe00cHMdh6tSpu70rbKecnBxmzpzJzJkz2/Tfb63NmzdTVFS0y/rCwkI+//xzXzE7TVcXNM80WVpaygknnMAtt9xCPL732YZjsdgucxqIiIhI53DkkUcyf/78XdY//fTTHH744b5idpoWnyuuuIKTTjqJ4uJiXn/9dW644QZWrlzJ//zP/+xxm+nTp3PTTTe1Yy1FRETaRg8p3bOrrrqKyy+/nE2bNvGNb3wDgOeff57f/OY3vrq5IMOJz9SpU/eZmCxZsoRTTjmFK6+8MrXuuOOOo7i4mO9+97upVqDdueGGG7jqqqtSf9fX1+8yuZOIiEgmGc/BWMx7szPGgejCCy8kFotxyy238P/+3/8Dmm92+v3vf+/rAaWQ4cTn8ssvT80hsCc9evTY7frTTjsNgI8++miPiU80Gt3j5E0iIiLS8f3kJz/hJz/5CZs2bSI3N9f6xqaMJj5lZWWUlZX52vatt94CaDGxkoiISGejrq49+8Y3vsGjjz7KQQcdxMEHH5xaX19fz7e//W1eeOGFNsfsFGN8Fi9ezKuvvsoZZ5xBUVERS5Ys4corr2TUqFFUV1dnunoiIpImSTz+uWIzCcdu9t7i0hwOrd73DMYdgRKfPXvppZd2eyNTU1MTf/vb33zF7BSJTzQa5ZFHHuGmm24iFovRvXt3Lr74Yq699tqM1CcRb/uzQb6qeYYDA/g/WYMB1zqGofnhdpbdy7jxIJ7rEAj6f9Ke8cBgcCz2xzP2++PFQxjPwQn435dkU4hAxLU6Hg4eOKbN83N8lTHG+pjEGqOEcxIEg/4vRm48hOc5BCyOq+cGsD3nPS+A8cCxuKc1Hg/ieRBIw32xtu+N4zUfDatnXBr7z55pDmLz1pDEo8ZpYML4p/0H+UI0GuT55eM6RfKjxGdX77zzTurf77//fouHnrquy/z58zn00EN9xe4Uic9JJ53Eq6++mulqpIQj9hMzOQEoDrtWX1Z5OS6FRdvxLAa1BUNJGurzrC7yAIEdUda91JtgJGkRxRAO2R0TkgFitfk4jsXFNRFk26oyAmH/Ca4bD9K0Pc/qeBjX4eBDN35xsffPcTya6vKbkyifttfn8eG7PYlEEr5jRCJxuh2+jmDI/3HdsT33i39ZJKXJIBvWdCVgkcRt2xHlvX9WErbYF4DcaIKe3TdbxYgGHHo6AWxq4gag0XJwrINjlfQAeBjrGDvFYi61m5s6ReIjuzrhhBNST2nYeTfXl+Xm5vqeO6hTJD4HqqDlr8VgAAIBY/ULOhA0GBPA2Ddi4TZFcJsiFnVxCRc1WNbCAS9glTwZL4AXD+PFw75jeG7A+niAIRjyCIZsH9hnMJ7lyeYFiTXkEGvI8R0iJ7cJNx7GtTiuiaaodeuk44DrhvDxUOeUZCJMPBEinugYX6ERy4OSABrTUxXxwTOk4SGlaapMB7Fy5UqMMRx++OG8/vrrLcb3RCIRunbt6nt26I7xqRUREclS6uraVffu3YH986R2JT4iIiLSob3//vusWrVql4HOo0aNanMsJT4iIiIZpBafPfvkk0/4zne+w7vvvovjOBjT3KfnfNG96/ros+5Uz+oSERE50BjzxV2tNssBNsZnpyuuuILDDjuMDRs2kJeXx3vvvcfLL7/MKaecwksvveQrplp8REREpENavHgxL7zwAgcffDCBQIBAIMDpp5/O9OnTmThxYmoy47ZQi4+IiEgG7ezqsl0ORK7rph5RUVZWxrp164Dmwc8rVqzwFVMtPiIiIhnkGScNt7MfmIlPnz59eOeddzj88MPp168ft99+O5FIhHvvvZfDDz/cV0wlPiIiItIh/fznP2fHjh0A3HzzzYwcOZJ///d/p7S0lEceecRXTCU+IiIiGaS7uvZs2LBhqX8ffvjhvP/++2zZsoXi4uLUnV1tpcRHREQkg5T4tE1JSYnV9kp8REREMkiJz64uvPDCVpX7wx/+0ObYSnxERESkQ5k9ezbdu3fnxBNPTE1amC5KfERERDLIpOGurgOtxefSSy9l3rx5fPLJJ1x44YVccMEF1l1cO2keHxERkQwyJj3LgeS///u/Wb9+Pddddx1PPvkkVVVVjB49mmeeeca6BUiJj4iIiHQ40WiUH/zgByxYsID333+f3r17c9lll9G9e3e2b9/uO666ukRERDLIeA4Gy64u78Dq6voqx3FSDyn1PM8qllp8fHDjdgcdIOgYwK65zsFYN2+6bgBjvztg7OviBDxsjwnpOCaJoPUxcUJJcOyCOGk4RwCcQBrONccFxy5GMJS0rkfz+2JZj6B9PUJBF8fy/QUIhlzrujRvb7k/jsGxfW8w1ruSjhhfFosl0xdsP9IjK3YvFovx8MMPc+aZZ9KrVy/effdd7rrrLlatWpV6jIUfavHxIScSpMyLYvO1VxQwFHWJWZ2s0UgCsOvbdd0g61ZXEAjafYlHc2JUFay1qksAiBY1gNUvFwOe3Ve4mwyyaXVXAgH/xyTapZFDjv8ULxn0HcNLBGn6vND+C80xBIMuWMRxQkkO7bYB1/X/Wyknv5HS6o14FjFCW7vAisN8bw8QCBrKq+zqkYiHOB6HZNLut2M0J2Zdl9ztuRSvK7M8TwzFAUhaxNjqevyzyaIKgIODZcNHC9GoLnGd1WWXXca8efOorq7mRz/6EfPmzaO0tDQtsXVW+BQkgP9LGoTwCAbA5udNMAj23xIOrhvCde2iBINeWuoSCBgI+D8mzYmXfT28ZMgqsQ0mk4SiSYj6/8XpxkLEg+lo9TEELOMEQh6hkEso5P9ECYeTBEMewZD/IxuKpOcXfCjkgsW+AEQjLtGIXYxwJGldl3AsZP1dAuAEIGwRo9GyRTCb6Vldu7r77ruprq7msMMOY+HChSxcuHC35R599NE2x1biIyIikkHpuCvrQLur64c//KHvR1LsixIfERER6VBmz56932Ir8REREckgPbKifSnxERERySCN8Wlfup1dREQkgzI1c/PatWu54IILKC0tJS8vjxNOOIGlS5d+qV6GqVOnUllZSW5uLoMGDeK9995rESMWizFhwgTKysrIz89n1KhRrFmzxvaQ7FdKfERERLJMbW0tX/va1wiHwzz99NO8//77/OY3v+Gggw5Klbn99tv57W9/y1133cWSJUuoqKjgzDPPZNu2bakykyZN4rHHHmPevHm88sorbN++nZEjR+La3iq8H6mrS0REJIMyMcbntttuo6qqivvvvz+1rkePHl+KZ5gxYwZTpkzhnHPOAWDOnDmUl5fz0EMPcckll1BXV8esWbN44IEHGDJkCABz586lqqqK5557jmHDhlnt0/6iFh8REZEM2jnGx3YBqK+vb7HEYrHd/jefeOIJTjnlFL73ve/RtWtXTjzxRO67777U6ytXrqSmpoahQ4em1kWjUQYOHMiiRYsAWLp0KYlEokWZyspK+vTpkyrTESnxEREROUBUVVVRVFSUWqZPn77bcp988gm///3v6dmzJ8888wyXXnopEydO5I9//CMANTU1AJSXl7fYrry8PPVaTU0NkUiE4uLiPZbpiNTVJSIikkHGYP18wJ2Dm1evXk1hYWFqfTQa3W15z/M45ZRTmDZtGgAnnngi7733Hr///e/54Q9/mCr31UkEjTH7nFiwNWUySS0+IiIiGZTOh5QWFha2WPaU+BxyyCEce+yxLdYdc8wxrFq1CoCKigqAXVpuNm7cmGoFqqioIB6PU1tbu8cyHZESHxERkSzzta99jRUrVrRY9+GHH9K9e3cADjvsMCoqKliwYEHq9Xg8zsKFCxkwYAAAJ598MuFwuEWZ9evXs3z58lSZjkhdXSIiIhnkGQfP8uHKbZ3A8Morr2TAgAFMmzaN0aNH8/rrr3Pvvfdy7733As1dXJMmTWLatGn07NmTnj17Mm3aNPLy8hgzZgwARUVFXHTRRUyePJnS0lJKSkq4+uqr6du3b+our45IiY+IiEgmGbB+xmgbA5x66qk89thj3HDDDfzqV7/isMMOY8aMGZx//vmpMtdeey2NjY1cdtll1NbW0q9fP5599lkKCgpSZe68805CoRCjR4+msbGRwYMHM3v2bILBoO0e7TdKfERERLLQyJEjGTly5B5fdxyHqVOnMnXq1D2WycnJYebMmcycOXM/1HD/UOIjIiKSQZ4hDV1daapMFtDgZh9iMRdj2TCZcB3rE9V1HTzLWyADAZc0NLJijPH1rJgvSyaCeJ797KW29QiGkuDYHVjj2t+eGm8K47n2t4TGGiO4rt1HPRYLW783yUTIen/i8aD158a4jvV747r25xmAl4a6xN2A/XeJ5+9ZTy1ipOF4BHDS8XUEQDQapLg0Jz3B9rNMPasrW6nFx4dANMDnTswqa9xhAizdmEso4P9szQ15OM7BBIP+vzmjOTG6VW/As7wwOo5HQ30ejsX+YGDt8hyCIf/PeAmGkhR2rbOqhxPwqOrzKW7Sfx+1MbBtdRlOwP9701Cfx0dv9SIUTvqOAZCIh9i2LZ9INOE7RiwWYvPmIkIW7004lGR7fT6hiP/9qd+ewyfrigjafG5y4hQWdiVg8blJJIJs35aH49hdbQKBKIE1WNWldnuUj+oiVsckCBwUxGp/GowH2J2rIQJUmDymzBlIj6OL973BXhSX5nBodeG+C3YAxjgYyxYf20deZBMlPj55jsHmh1oSQ8wNELN8jpvrBnFd/xfoQNBtvphZXNCaGYwJYKzCGEiEcRNh3xFCkQRY1wNC0RihqP8vcc91iG/PxeD/vfESIRKxCIlYxHcMgGQyQKwxh1ij/1+/STdAIhEmYfHeeJEEiViURGz384q0RqwhQtINkrR4f0MhF9cNYfMMxWQy0Hy+W/7KdhzSUJcwCeOQsGhNC2HwAoDFxdNLU5NDiAA9e5XS68SytMQT+SolPiIiIhnUPMbHPoa0jhIfERGRDDJpuJ1dY3xaT4ObRUREJGuoxUdERCSDMjFzczZT4iMiIpJB6upqX+rqEhERkayhFh8REZEMUotP+1LiIyIikkEa49O+1NUlIiIiWUMtPiIiIhlkSENXVzoqkiWU+IiIiGSQZm5uX0p8REREMsiQhoeUWm6fTTTGR0RERLKGWnxEREQyyKShq0u3s7eeEh8REZEM0uDm9qWuLh+a4gmM5WnmYvBsY3iO9YC2eDyE69r3DcfjITzPLk4yGcRY/uzx3IB1jGQa9iXeGMGzPK6JeDAtAxZdz7H+NRhzwbWui7GuRzjk4jh2b3Ao5JKey4R9jGDQvi6BL0aI2PCwbzFI4Fl/LwIYDE3xhHUckT1Ri48PwWiAjU4jAYvBZCEcCELQIka+CXDQxnxCAf9fNgHHUL/jaMLhpO8YAA6Qn5P84qLiM4ZjyM9rIhD0f2ELhZu/MG1ieG6ATZ9WEAz735dYY4SNNQcTjvj/Am9qCrNufRlBi30B8DxwE0Eci/OkPgnLtjlELH4qFec6VFnm2NGIy0lHryWRDPqPkROjonoDnut/Z5oaI2zfnu97+50CQc+6Lnnbc+m5vgzXYgK7mOtQsz1k9Ut4qwM1oQar70UAD0Mw6v/97Yx0V1f7UuLjk+sYXKtfNwESGBIWMUJA0g2Q9H99JhQwxGIRYrGI/yBAMOARCcZIJML+YwRdcqJJXIv9AfDcIJ7r/4vTeA7JRJhk3H8d4vEQ8aYo8aao/xiJIK7bvNgwO9vRLY6r50HMOMQsYkTT0LIIzclPNOK/IuFIojlBt0jSXYtE5ats6xKOh4kEwbblyMOxuvi6OGn4XsxO6upqX+rqEhERkayhFh8REZEMUldX+1LiIyIikkHq6mpf6uoSERGRrKEWHxERkQzySENXVzoqkiWU+IiIiGSQurralxIfERGRDFKLT/vSGB8RERHJGmrxERERySCD/SND1NXVekp8REREMkhdXe1LXV0iIiJZbPr06TiOw6RJk1LrjDFMnTqVyspKcnNzGTRoEO+9916L7WKxGBMmTKCsrIz8/HxGjRrFmjVr2rn2bafER0REJINMmhY/lixZwr333stxxx3XYv3tt9/Ob3/7W+666y6WLFlCRUUFZ555Jtu2bUuVmTRpEo899hjz5s3jlVdeYfv27YwcORLX9oGL+1mnSnyeeuop+vXrR25uLmVlZZxzzjmZrpKIiIgVw7+6u/wufhKf7du3c/7553PfffdRXFz8r/oYw4wZM5gyZQrnnHMOffr0Yc6cOTQ0NPDQQw8BUFdXx6xZs/jNb37DkCFDOPHEE5k7dy7vvvsuzz33nL8D0U46TeLz5z//mbFjx/KjH/2It99+m7///e+MGTMm09USERHpMOrr61sssVhsj2V/+tOfctZZZzFkyJAW61euXElNTQ1Dhw5NrYtGowwcOJBFixYBsHTpUhKJRIsylZWV9OnTJ1Wmo+oUg5uTySRXXHEFv/71r7noootS63v16pXBWomIiNhL5+DmqqqqFutvvPFGpk6dukv5efPm8eabb7JkyZJdXqupqQGgvLy8xfry8nI+++yzVJlIJNKipWhnmZ3bd1SdIvF58803Wbt2LYFAgBNPPJGamhpOOOEE7rjjDnr37r3H7WKxWItst76+vj2qKyIi0mrpnLl59erVFBYWptZHo9Fdyq5evZorrriCZ599lpycnD3GdByn5X/DmF3W7VKPVpTJtE7R1fXJJ58AMHXqVH7+85/z17/+leLiYgYOHMiWLVv2uN306dMpKipKLV/NhP0qKc0lGg1axfAwGMtTPYHBtYzhGfv5I3ayjeO6Dp5ljHgihOfZfeiaYiFcyxjbEwGSlj/htnkuyTS8OTEDrmWY5vfFLkgyGcCzPCaxuH0M17WPkUwG0vK5icdDuK7luZYE17IyjbjW3yUREyCQhmMSjQYpLt3zxVj2rrCwsMWyu8Rn6dKlbNy4kZNPPplQKEQoFGLhwoX87ne/IxQKpVp6vtpys3HjxtRrFRUVxONxamtr91imo8poi8/UqVO56aab9lpmyZIleF98S02ZMoVzzz0XgPvvv59u3brxf//3f1xyySW73faGG27gqquuSv1dX1+fluTn0OpCXlo+ni2bG63iuDGPnEjY9/afflDL9B++RBj/X5x5JsDxTXkWEZqFgw65OUGrOJ4XZPW6EoIBu6vS55sLCYeTvrdPJAJsa4gSDvu/M2FrEt5qCJAT9H8l2G5cPvLqyLP89RT2glS7BYQtwrgGPMuzpDER4s0PuhEO+T+urufgeQGrGDmRBK4XJBj0f541NoXZVNuFgGN3pTcGNv/9OCIW5+vnCViwLUHU4jxpxGVNuJ6IsfstHMbh1tnDOPLoEt8xiktzOLS6cN8FDyDtPY/P4MGDeffdd1us+9GPfsTRRx/Nddddx+GHH05FRQULFizgxBNPBCAej7Nw4UJuu+02AE4++WTC4TALFixg9OjRAKxfv57ly5dz++23W+7N/pXRxOfyyy/nvPPO22uZHj16pG6fO/bYY1Pro9Eohx9+OKtWrdrjttFodLfZbjocWl3YIT6cMccQs/il5gCesW+WDBoHcKx+MxrAdYO4rv/WNMcxOE6AeDziO4brQiwWZi9jAvep0TNsTybZ7v96RiOGbUGPbZa/onMNlBuHmEWcdM0KG0+EiCfsv3ZsYhjj4CZDuBbvTSIewvMCaZk0zm0KEmvy/z3V4Ho0mDgNFq0+cQxNAY8mx36Pjjy6hD4ndrWOk01MGnoA2rJ9QUEBffr0abEuPz+f0tLS1PpJkyYxbdo0evbsSc+ePZk2bRp5eXmpm4qKioq46KKLmDx5MqWlpZSUlHD11VfTt2/fXQZLdzQZTXzKysooKyvbZ7mTTz6ZaDTKihUrOP300wFIJBJ8+umndO/efX9XU0REZL/piDM3X3vttTQ2NnLZZZdRW1tLv379ePbZZykoKEiVufPOOwmFQowePZrGxkYGDx7M7NmzCQbthoLsb51icHNhYSGXXnopN954I1VVVXTv3p1f//rXAHzve9/LcO1EREQ6t5deeqnF347jMHXq1N3eEbZTTk4OM2fOZObMmfu3cmnWKRIfgF//+teEQiHGjh1LY2Mj/fr144UXXtjlVjoREZHOJJ13dcm+dZrEJxwOc8cdd3DHHXdkuioiIiJp0xG7ug5kneJ2dhEREZF06DQtPiIiIgcig8FYT42gzq7WUuIjIiKSQerqal/q6hIREZGsoRYfERGRDFKLT/tS4iMiIpJR9jM364b21lNXl4iIiGQNtfiIiIhkkLq62pcSHxERkQxq74eUZjslPiIiIhmkFp/2pTE+IiIikjXU4iMiIpJBxmlerGKk/k/2RS0+nVhTPGHdr9uIi5uGT8sO45G0nDI9HTG2efYxHAdsv0HCjv2HK0QAy+9CAJJ4eJb70xzBLoZrwLN8b5LG2MdwHTzLfoEmF9w0PCKgyTPWceLG/r0J4KTlohmNBikuzbEPlGWau7qM5SKtpRafTiwYDbDRabS6PBrH8DgxctKQA3epC5FjESZpDAkTJ8dif5rwqIvHyXPsUgbHOPxm9nB69ir1HeP8mIsXsTuuDfEEgahdjI8+2MLV454hZPEeB41DibE7SyIEWO0FCVpcYT2ar882MSJekMjH5YRC/i8V21x4pxEils9Xck3z/oQt4mzHUOM0WKfJAeA3s4dz5NElvmMUl+ZwaHWhVT1E9jclPp2c6xjrFpskLg24VjGCxgETZLtdGMCww2J/knjU41Gfhl+vRb0KKT/R/0WgozAY4o5H3OI3YZiA9a/KIAbXYHmmNbOJEQASyRCJpP8YTcYQMy6xNJxnAbCK0wC4IfvvAYAjjy6hz4ldreNI22hwc/tS4iMiIpJBup29fWmMj4iIiGQNtfiIiIhkkLq62pcSHxERkQzaeWeWbQxpHXV1iYiISNZQi4+IiEgGaQLD9qXER0REJIPU1dW+lPiIiIhklP3t7GruaT2N8REREZGsoRYfERGRDNLt7O1LiY+IiEgGaYxP+1JXl4iIiGQNtfiIiIhkkMF+aLLae1pPiY+IiEgGeY7Bc9TV1V7U1SUiIiJZQ4lPJ1ZSmks0Gsx0NYCds1Bk/heHg+X0p1+IRoOUlOamJVampeM88dLw/ibwrH+VNpLEtbx/pdF4uMauHjHjpu0Xtu1xDaXprI9GgxSX5qQhkrTVzsHNtktbTJ8+nVNPPZWCggK6du3Kt7/9bVasWNGijDGGqVOnUllZSW5uLoMGDeK9995rUSYWizFhwgTKysrIz89n1KhRrFmzxvqY7E/q6urEDq0u5KXl49myudEqTjzmEklDApWMeeRE7U6pdNSlKZYkFLXL6UtKczm0utAqRkeRjvPkow+2cNW4ZwhYXGIDDjQ6SUIWv7caSVITbCBi/J8jQePwT5ND1PivRwyPjcE4oTSkHI5x+M8/DufIXiW+YzTEkzhRu7oUl+YcMOd8Z5OJMT4LFy7kpz/9KaeeeirJZJIpU6YwdOhQ3n//ffLz8wG4/fbb+e1vf8vs2bM56qijuPnmmznzzDNZsWIFBQUFAEyaNIknn3ySefPmUVpayuTJkxk5ciRLly4lGOwYP8y/SolPJ3dodaG+rGSf0nGeuI7Btfh6DhqHhGNI4PqOEcOj0XFpdPzHiJgADV6UBot6JPFIOB4J3xG+xIHKows54sTSdESTLFdfX9/i72g0SjQa3aXc/PnzW/x9//3307VrV5YuXcrXv/51jDHMmDGDKVOmcM455wAwZ84cysvLeeihh7jkkkuoq6tj1qxZPPDAAwwZMgSAuXPnUlVVxXPPPcewYcP2017aUVeXiIhIBqWzq6uqqoqioqLUMn369FbVoa6uDoCSkuaWx5UrV1JTU8PQoUNTZaLRKAMHDmTRokUALF26lEQi0aJMZWUlffr0SZXpiNTiIyIikkHpnMBw9erVFBb+q3V3d609X2WM4aqrruL000+nT58+ANTU1ABQXl7eomx5eTmfffZZqkwkEqG4uHiXMju374iU+IiIiGRQOh9ZUVhY2CLxaY3LL7+cd955h1deeWWX1xyn5dgxY8wu676qNWUySV1dIiIiWWrChAk88cQTvPjii3Tr1i21vqKiAmCXlpuNGzemWoEqKiqIx+PU1tbusUxHpMRHREQkg0ya/tem/6YxXH755Tz66KO88MILHHbYYS1eP+yww6ioqGDBggWpdfF4nIULFzJgwAAATj75ZMLhcIsy69evZ/ny5akyHZG6ukRERDLIpGGMT1sTn5/+9Kc89NBD/OUvf6GgoCDVslNUVERubi6O4zBp0iSmTZtGz5496dmzJ9OmTSMvL48xY8akyl500UVMnjyZ0tJSSkpKuPrqq+nbt2/qLq+OSImPiIhIlvn9738PwKBBg1qsv//++xk/fjwA1157LY2NjVx22WXU1tbSr18/nn322dQcPgB33nknoVCI0aNH09jYyODBg5k9e3aHncMHlPiIiIhklOcYnHZ+VpdpxezljuMwdepUpk6duscyOTk5zJw5k5kzZ7bpv59JSnxEREQyyAPrOcBt7wrLJhrcLCIiIllDLT4iIiIZ5GFw0jSBoeybEh8REZEM8nM7+u5iSOuoq0tERESyhlp8REREMkhdXe1LiY+IiEgGKfFpX0p8REREMkiJT/vSGB8R2aeS0lyiUbuZWL00DOAM4ljPd+KmoR4B61r8SzQapKQ0N23xRGTv1OIjIvt0aHUhLy0fz5bNjVZxkjGPnKjd105DLEEwavebzY155EXDVjGaYklClvWA5qTy0OpC6zjSeTVPYGjb4iOtpcRHRFrl0OpCXaBF9gPjgGfZiKiOrtZTV5eIiIhkDbX4iIiIZFDzwGQNbm4vSnxEREQySIlP+1JXl4iIiGQNtfiIiIhkUDqmWFCLT+sp8REREckgdXW1L3V1iYiISNZQi4+IiEgGqcWnfSnxERERySDX8TCO3dzLnuZubjUlPiIiIhmkwc3tS2N8REREJGuoxUdERCSDvDS0+Nhun02U+IiIiGSQ6xgcR4lPe8mqxMeY5hOjvr4+wzUREZGObue1Yue1Y38xxKwfr26IpacyWSCrEp9t27YBUFVVleGaiIhIZ7Ft2zaKiorSHjcSiVBRUUFNza1piVdRUUEkEklLrAOZY/Z3KtuBeJ7HunXrKCgowHEc33Hq6+upqqpi9erVFBYWprGGBw4do9bRcWodHad90zFqnbYcJ2MM27Zto7KykkBg/9wL1NTURDweT0usSCRCTk5OWmIdyLKqxScQCNCtW7e0xSssLNQXzD7oGLWOjlPr6Djtm45R67T2OO2Plp4vy8nJUbLSznQ7u4iIiGQNJT4iIiKSNZT4+BCNRrnxxhuJRqOZrkqHpWPUOjpOraPjtG86Rq2j4yRZNbhZREREsptafERERCRrKPERERGRrKHER0RERLKGEh8RERHJGkp8vvD73/+e4447LjWpVf/+/Xn66adTr48fPx7HcVosp512WosYsViMCRMmUFZWRn5+PqNGjWLNmjXtvSv7TTqO0b333sugQYMoLCzEcRy2bt3aznux/9kepy1btjBhwgR69epFXl4e1dXVTJw4kbq6ukzszn6RjnPpkksu4YgjjiA3N5eDDz6Ys88+mw8++KC9d2W/Ssdx2skYw4gRI3Ach8cff7yd9qB9pOM4DRo0aJcy5513XnvvirSDrJq5eW+6devGrbfeypFHHgnAnDlzOPvss3nrrbfo3bs3AMOHD+f+++9PbfPVZ6JMmjSJJ598knnz5lFaWsrkyZMZOXIkS5cuJRgMtt/O7CfpOEYNDQ0MHz6c4cOHc8MNN7Rf5duR7XFat24d69at44477uDYY4/ls88+49JLL2XdunX86U9/at+d2U/ScS6dfPLJnH/++VRXV7NlyxamTp3K0KFDWbly5QHxeYP0HKedZsyYYfWono4sXcfp4osv5le/+lXq79zc3P1cc8kII3tUXFxs/ud//scYY8y4cePM2WefvceyW7duNeFw2MybNy+1bu3atSYQCJj58+fv76pmTFuO0Ze9+OKLBjC1tbX7r3IdiN/jtNP//u//mkgkYhKJxH6oXcdge4zefvttA5iPPvpoP9Su4/BznJYtW2a6detm1q9fbwDz2GOP7d9KdgBtPU4DBw40V1xxxf6vmGScurp2w3Vd5s2bx44dO+jfv39q/UsvvUTXrl056qijuPjii9m4cWPqtaVLl5JIJBg6dGhqXWVlJX369GHRokXtWv/24OcYZaN0Hae6ujoKCwsJhQ68Rtp0HKMdO3Zw//33c9hhh1FVVdUe1W53fo9TQ0MDP/jBD7jrrruoqKho72q3O5vz6cEHH6SsrIzevXtz9dVXs23btvasurSXTGdeHck777xj8vPzTTAYNEVFReapp55KvTZv3jzz17/+1bz77rvmiSeeMMcff7zp3bu3aWpqMsYY8+CDD5pIJLJLzDPPPNP8+Mc/brd92N9sjtGXHegtPuk6TsYY8/nnn5vq6mozZcqU9qp+u0jHMfqv//ovk5+fbwBz9NFHH5CtPbbH6cc//rG56KKLUn9zgLb42B6ne++91yxYsMC8++675uGHHzY9evQwQ4YMycSuyH6mxOdLYrGY+ec//2mWLFlirr/+elNWVmbee++93ZZdt26dCYfD5s9//rMxZs+Jz5AhQ8wll1yyX+vdnmyO0Zcd6IlPuo5TXV2d6devnxk+fLiJx+P7u9rtKh3HaOvWrebDDz80CxcuNN/61rfMSSedZBobG9uj+u3G5jj95S9/MUceeaTZtm1bqsyBmvik6zO30xtvvGEAs3Tp0v1VZckQJT57MXjw4L221hx55JHm1ltvNcYY8/zzzxvAbNmypUWZ4447zvzyl7/cr/XMpLYcoy870BOfr/JznOrr603//v3N4MGDD7iL+e74PZd2isViJi8vzzz00EP7o3odRluO0xVXXGEcxzHBYDC1ACYQCJiBAwe2U40zw/Z88jxvl3GbcmDQGJ+9MMYQi8V2+9rmzZtZvXo1hxxyCNB8h0k4HGbBggWpMuvXr2f58uUMGDCgXeqbCW05Rtmsrcepvr6eoUOHEolEeOKJJ8jJyWmvqmZMOs6lvcU4ULTlOF1//fW88847LFu2LLUA3HnnnS3ucDoQ2Z5P7733HolEQt9fB6IMJl0dyg033GBefvlls3LlSvPOO++Yn/3sZyYQCJhnn33WbNu2zUyePNksWrTIrFy50rz44oumf//+5tBDDzX19fWpGJdeeqnp1q2bee6558ybb75pvvGNb5jjjz/eJJPJDO5Z+qTjGK1fv9689dZb5r777jOAefnll81bb71lNm/enME9Sy/b41RfX2/69etn+vbtaz766COzfv361KJzqfkYffzxx2batGnmjTfeMJ999plZtGiROfvss01JSYnZsGFDhvcufdLxmfsqDsCuLtvj9NFHH5mbbrrJLFmyxKxcudI89dRT5uijjzYnnnjiAfOZk39R4vOFCy+80HTv3t1EIhFz8MEHm8GDB5tnn33WGGNMQ0ODGTp0qDn44INNOBw21dXVZty4cWbVqlUtYjQ2NprLL7/clJSUmNzcXDNy5MhdynRm6ThGN954owF2We6///4M7NH+YXucdnYD7m5ZuXJlhvYqvWyP0dq1a82IESNM165dTTgcNt26dTNjxowxH3zwQaZ2ab9Ix2fuqw7ExMf2OK1atcp8/etfNyUlJSYSiZgjjjjCTJw48YD6QSb/4hhjTCZamkRERETam8b4iIiISNZQ4iMiIiJZQ4mPiIiIZA0lPiIiIpI1lPiIiIhI1lDiIyIiIllDiY+IiIhkDSU+IiIikjWU+IiIiEjWUOIjkgXGjx+P4zg4jkMoFKK6upqf/OQn1NbWtijX2NhIcXExJSUlNDY2Zqi2IiL7jxIfkSwxfPhw1q9fz6effsr//M//8OSTT3LZZZe1KPPnP/+ZPn36cOyxx/Loo49mqKYiIvtPKNMVEJH2EY1GqaioAKBbt258//vfZ/bs2S3KzJo1iwsuuABjDLNmzeL888/PQE1FRPYfJT4iWeiTTz5h/vz5hMPh1LqPP/6YxYsX8+ijj2KMYdKkSXzyySccfvjhGaypiEh6qatLJEv89a9/pUuXLuTm5nLEEUfw/vvvc91116Ve/8Mf/sCIESNSY3yGDx/OH/7whwzWWEQk/ZT4iGSJM844g2XLlvHaa68xYcIEhg0bxoQJEwBwXZc5c+ZwwQUXpMpfcMEFzJkzB9d1M1VlEZG0U+IjkiXy8/M58sgjOe644/jd735HLBbjpptuAuCZZ55h7dq1fP/73ycUChEKhTjvvPNYs2YNzz77bIZrLiKSPo4xxmS6EiKyf40fP56tW7fy+OOPp9a99NJLjBgxgo8//pgJEyYQiUSYMmVKi+1uvfVWmpqa+NOf/tTONRYR2T80uFkkSw0aNIjevXtzyy238OSTT/LEE0/Qp0+fFmXGjRvHWWedxaZNmzj44IMzVFMRkfRRV5dIFrvqqqu49957SSQSDB48eJfXzzjjDAoKCnjggQcyUDsRkfRTV5eIiIhkDbX4iIiISNZQ4iMiIiJZQ4mPiIiIZA0lPiIiIpI1lPiIiIhI1lDiIyIiIllDiY+IiIhkDSU+IiIikjWU+IiIiEjWUOIjIiIiWUOJj4iIiGSN/w/FSR8l5qoenQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let us produce a heat map of how many images are in each sky patch.\n",
+    "\n",
+    "# Assuming sphgeom_regions_deep_limited, matching_regions, and matching_counts are defined\n",
+    "# all_corners as defined in your original code\n",
+    "all_corners = [getRegionCorners(region) for region in sphgeom_regions_deep_limited]\n",
+    "\n",
+    "# Define a colormap\n",
+    "cmap = plt.cm.plasma  # You can choose any available colormap\n",
+    "\n",
+    "# Normalize your matching_counts to the colormap range (0, 1)\n",
+    "# This step is important for mapping your counts to colors\n",
+    "norm = mcolors.Normalize(vmin=min(matching_counts), vmax=max(matching_counts))\n",
+    "\n",
+    "# Create a scalar mappable for coloring and creating the colorbar later\n",
+    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
+    "sm.set_array([])  # Only needed for the colorbar\n",
+    "\n",
+    "# Plotting each quadrilateral with color based on matching_counts\n",
+    "for matching_ix, count in zip(matching_regions, matching_counts):\n",
+    "    quad = all_corners[matching_ix]\n",
+    "    ra_bounds, dec_bounds = getMinMaxRaDec(quad)\n",
+    "\n",
+    "    coords = [\n",
+    "        (ra_bounds[0], dec_bounds[0]),\n",
+    "        (ra_bounds[0], dec_bounds[1]),\n",
+    "        (ra_bounds[1], dec_bounds[1]),\n",
+    "        (ra_bounds[1], dec_bounds[0]),\n",
+    "    ]\n",
+    "\n",
+    "    # Use the normalized count to get the corresponding color\n",
+    "    color = cmap(norm(count))\n",
+    "\n",
+    "    # Fill the quadrilateral with the selected color\n",
+    "    plt.fill(*zip(*coords), color=color)\n",
+    "\n",
+    "# Add the colorbar to the plot\n",
+    "plt.colorbar(sm, label=\"Matching Images\")\n",
+    "\n",
+    "# Setting labels for clarity\n",
+    "plt.xlabel(\"RA\")\n",
+    "plt.ylabel(\"Dec\")\n",
+    "# plt.title('Heat Map by Counts')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4aebe1fe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3df17bc4",
+   "metadata": {},
+   "source": [
+    "# Next Steps\n",
+    "\n",
+    "In no particular order:\n",
+    "\n",
+    "1. User-specified (RA, Dec) pair: [DONE]\n",
+    "- from overlapping_sets (DONE 2/7/2024 COC)\n",
+    "- from our extracted Butler data (DONE 2/7/2024 COC)\n",
+    "2. Heat map / histogrammed results. [DONE 2/15/2024 COC]\n",
+    "- nice way to filter by top matches or the like\n",
+    "3. Sky patches approach. [DONE 2/14/2024 COC]\n",
+    "- matching before passing to reprojection / image collection\n",
+    "4. Handling time (manually)\n",
+    "- options to restrict date range\n",
+    "- options to restrict time delta\n",
+    "- visualize time information\n",
+    "5. Reflex correction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "120f67b9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {