Rename_trial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from night_light_utils import *\n",
    "\n",
    "city_cords = {\n",
    "    'blr_x_start' : 1781,\n",
    "    'blr_x_end' : 1870,\n",
    "    'blr_y_start' : 1640,\n",
    "    'blr_y_end' : 1738,\n",
    "    \n",
    "    'milan_x_start' : 2150,\n",
    "    'milan_x_end' : 2262,\n",
    "    'milan_y_start' : 1021,\n",
    "    'milan_y_end' : 1117,\n",
    "    \n",
    "    'pune_x_start' : 880,\n",
    "    'pune_x_end' : 957,\n",
    "    'pune_y_start' : 286,\n",
    "    'pune_y_end' : 377,\n",
    "    \n",
    "    'paris_x_start' : 428,\n",
    "    'paris_x_end' : 656,\n",
    "    'paris_y_start' : 227,\n",
    "    'paris_y_end' : 350\n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan01_h25 = week_loc_mean('h25v07', 2020, 1, 1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cloud_loc_mean(tile, year, day_start, day_end, month):\n",
    "    clouds = []\n",
    "    for i in range(day_start, day_end + 1):\n",
    "        day_file = (dt.date(year, month, i) - dt.date(year,1,1)).days + 1\n",
    "        day_file = str(day_file).zfill(3)\n",
    "        path = 'A' + str(year) + day_file + '.' + tile + '.h5'\n",
    "        cloud = return_cloud('Data/' + path)\n",
    "        clouds.append(cloud)\n",
    "        \n",
    "    return clouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan01_cloud = cloud_loc_mean('h25v07', 2020, 1, 1, 1)[0]\n",
    "jan01_cloud = return_cloud_filter(cloud_data=jan01_cloud, threshold=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(jan01_cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_cloud_free(tile, year, day_start, day_end, month):\n",
    "    clean_tile = np.zeros((2400, 2400))\n",
    "    clean_tile = np.matrix(clean_tile)\n",
    "    \n",
    "    average = np.zeros((2400, 2400))\n",
    "    average = np.matrix(average)\n",
    "    \n",
    "    for i in range(day_start, day_end+1):\n",
    "        tile_light = week_loc_mean(tile, year, i, i, month)\n",
    "        tile_cloud = cloud_loc_mean(tile, year, i, i, month)[0]\n",
    "        tile_filter = return_cloud_filter(cloud_data=tile_cloud, threshold=128)\n",
    "        average = average + tile_filter\n",
    "        clean_light = np.multiply(tile_light, tile_filter)\n",
    "        clean_tile = clean_tile + clean_light\n",
    "        \n",
    "    clean_tile = np.divide(clean_tile, average, where=average>0)\n",
    "    return clean_tile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(return_cloud_free('h25v07', 2020, 1, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(return_cloud_free('h25v07', 2020, 1, 31, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.multiply(jan01_h25, jan01_cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(jan01_h25)\n",
    "img_rad(return_cloud_filter(cloud_data=cloud_loc_mean('h25v07', 2020, 1, 1, 1)[0], threshold=128, scale_for_img=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "jan_h25 = week_loc_mean('h25v07', 2020, 21, 27, 1)\n",
    "cloud_jan_h25 = return_cloud_free('h25v07', 2020, 21, 27, 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_no10 = np.multiply(cloud_jan_h25, (cloud_jan_h25)>10)\n",
    "img_rad(cloud_no10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.004166666666666667"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 / 2400"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan num pixels is 4307\n",
      "Feb num pixels is 4083\n",
      "Mar num pixels is 2400\n",
      "Apr num pixels is 1546\n"
     ]
    }
   ],
   "source": [
    "#>40 version - commercial\n",
    "print(\"Jan num pixels is \" + str(((jan_h25)>=500).sum()))\n",
    "print(\"Feb num pixels is \" + str(((feb_h25)>=500).sum()))\n",
    "print(\"Mar num pixels is \" + str(((mar_h25)>=500).sum()))\n",
    "print(\"Apr num pixels is \" + str(((apr_h25)>=500).sum()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan num pixels is 18986\n",
      "Feb num pixels is 18743\n",
      "Mar num pixels is 15760\n",
      "Apr num pixels is 14936\n"
     ]
    }
   ],
   "source": [
    "# 20 - 40 - administrative\n",
    "print(\"Jan num pixels is \" + str(((jan_h25>=190) & (jan_h25<500)).sum()))\n",
    "print(\"Feb num pixels is \" + str(((feb_h25>=190) & (feb_h25<500)).sum()))\n",
    "print(\"Mar num pixels is \" + str(((mar_h25>=190) & (mar_h25<500)).sum()))\n",
    "print(\"Apr num pixels is \" + str(((apr_h25>=190) & (apr_h25<500)).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan num pixels is 146053\n",
      "Feb num pixels is 154095\n",
      "Mar num pixels is 129856\n",
      "Apr num pixels is 118554\n"
     ]
    }
   ],
   "source": [
    "#<20 - residential\n",
    "print(\"Jan num pixels is \" + str(((jan_h25>=30) & (jan_h25<190)).sum()))\n",
    "print(\"Feb num pixels is \" + str(((feb_h25>=30) & (feb_h25<190)).sum()))\n",
    "print(\"Mar num pixels is \" + str(((mar_h25>=30) & (mar_h25<190)).sum()))\n",
    "print(\"Apr num pixels is \" + str(((apr_h25>=30) & (apr_h25<190)).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jan num pixels is 5590654\n",
      "Feb num pixels is 5583079\n",
      "Mar num pixels is 5611984\n",
      "Apr num pixels is 5624964\n"
     ]
    }
   ],
   "source": [
    "#<5 - Noise\n",
    "print(\"Jan num pixels is \" + str(((jan_h25<30)).sum()))\n",
    "print(\"Feb num pixels is \" + str(((feb_h25<30)).sum()))\n",
    "print(\"Mar num pixels is \" + str(((mar_h25<30)).sum()))\n",
    "print(\"Apr num pixels is \" + str(((apr_h25<30)).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2GwnszrlZS2gTEZE+kviEqpmdBvwU+JK7v2tmHe6ap83z3N8CMsM2jB5dmkMgmgZYRMpVop67mQ0kE+wPu/vK0PyH7HBL+P1WaG8BRuXcvBp4o/19uvv97j7R3SdWVVX1tH4REckjydUyBiwBtrn7PTmbVgP1YbkeeCyn/dpw1cz5QGt2+EZERPpGkmGZC4DPAU1m9kJoux24E3jEzG4AXgeuDtt+DswBdgJ/Aa5LtWIREelSl+Hu7uvJP44OcFGe/R34Qi/rKinZa91rDv6kyJWIiCSj6QdERCKkcBcRiZDCXUQkQpo4LIHc6901iZiIlAP13EVEIqSeey+oFy8ipUrh3g2Dx2YmvvzztjtP2KagF5FSonDvgdw53nXtu4iUIo25i4hESD33XlIvXkRKkXruIiIRUriLiERI4d4D+o5VESl1GnMvMF0iKSLFoHBP0bGTq61FrUNERMMyIiIRUs89Bdnx96Zdrxe5kvTULa1rW26qbypiJSLSEwr3XtBJVREpVQr3Qlg0JGfl2AebdHJVRPqKwr3IssMfJTP0kX1h0rsSkbKmcE9RvrH33OkJoG+nKMg7bp77rmJR6wkvLvmGmnr8AtTusZLQuxuRdCjc+0jutzl1KhuICcMwVzYYs1MTpy0b8tkpj48L3x4E+Qm3bXc7Bb1IzyncCyC399vRFTS5verc9ba+8XHj9sffb24POjcAO62p3f3X1Y6GdjUkdezFI3ng1jSsyQR0b14EUtDR8dKLh8RG4V5ghbiipqZhTc5wT2aYJ3f4p46OH7Ojetq/2CTRneGa5sprYFG7xjwvYMftz7FhrI568Ul690lfAEVionDvYz0ZMmkfyNngq6sdzWAa2oZJSubSzE5Cu5AU4iLHKNxLTE8CevDYhk5768WSZHgqV1fh3Nabb9C8+SJdUbiXoZLpoQd1S+ugdnRbgOe94ibBp3jbX1mUZL9CXH2kE7kSA80tI0XR0bTJmk5ZJB3m7sWugYkTJ/qmTZt6duNFQxQGEWjf68/3LiDp3D199VmC7p7Y7S69g5CumNlmd5+Yb5uGZaQk9XRopyudDf1090Who3MECmUpBQp3KQmFfPeVdCy/ELp7qaZeDCQtCneJVm9Cvf119mlL0uvvqF0vAJKExtyl7HR0VU5fzaffl/MDdUVB379pzF2i0uGnbDtoj+lLVDqj3r3kUriLlDF9Klc6onCXfqVQV9yU0lCNCCjcpR/o7LLKrN4O3XQW+IX+RK1IPgp3EQpzcrZULsHM1dEHrzqisfvyVZBwN7NZwPeACuABd7+zEI8j0ld68knZzuTrzRf68kvo3Rh9ZydsdTK39KQe7mZWAfw3cDHQAjxrZqvd/aW0H0ukUDobm++ql9/ZbXOna87u076H39UwTnfH/HvzoqETtuUr9evczWwKsMjdZ4b12wDc/d87uo2uc5eYdTZbZlf7dHbb9tuy8/pnJRkWyg38ng4j5buPtncj3RwGSiKtL2tJa/9i6uw690KE+1XALHe/Max/Dpjs7l9st98CYEFY/TCwvRsPcwbwdgrlFks516/ai0O1F0ep136mu1fl21CIMXfL03bCK4i73w/c36MHMNvU0atVOSjn+lV7caj24ijn2gsxn3sLMCpnvRp4owCPIyIiHShEuD8LnGVmtWZ2MjAPWF2AxxERkQ6kPizj7ofN7IvAL8lcCvlDd38x5Yfp0XBOCSnn+lV7caj24ijb2ktiVkgREUmXvkNVRCRCCncRkQiVXbib2Swz225mO82sodj1tGdmo8zsaTPbZmYvmtnC0D7MzJ40sx3h9+mh3czs++H5bDGzCcV9BplPGZvZ82b2eFivNbMNofbl4UQ5ZnZKWN8ZttcUue6hZrbCzH4bjv+UcjnuZvbl8O9lq5k1mlllKR93M/uhmb1lZltz2rp9rM2sPuy/w8zqi1j7f4Z/N1vM7FEzG5qz7bZQ+3Yzm5nTXtJZhLuXzQ+ZE7SvAGOAk4H/A8YVu652NY4AJoTlwcDLwDjgP4CG0N4A3BWW5wC/IPP5gPOBDSXwHG4BfgI8HtYfAeaF5XuBfwjL/wjcG5bnAcuLXPdS4MawfDIwtByOOzAS2AWcmnO8P1/Kxx2YDkwAtua0detYA8OAV8Pv08Py6UWq/RJgQFi+K6f2cSFnTgFqQ/5UlEUWFbuAbv5RpgC/zFm/Dbit2HV1UfNjZObZ2Q6MCG0jgO1h+T5gfs7+bfsVqd5q4CngQuDx8B/y7Zx/+G1/AzJXRE0JywPCflakut8bAtLatZf8cQ/hvjuE3IBw3GeW+nEHatoFZLeONTAfuC+n/bj9+rL2dtvmAg+H5eMyJnvsyyGLym1YJvufIKsltJWk8Hb5PGAD8H53fxMg/H5f2K3UntN3gVuBo2F9OPAndz8c1nPra6s9bG8N+xfDGGAP8KMwpPSAmQ2iDI67u/8OuBt4HXiTzHHcTHkc91zdPdYl8zdo53oy7zSg/GpvU27hnmhqg1JgZqcBPwW+5O7vdrZrnraiPCczuwx4y9035zbn2dUTbOtrA8i81V7s7ucB+8kMDXSkZGoPY9NXkHnb/wFgEDA7z66leNyT6KjeknseZvZ14DDwcLYpz24lWXt75RbuZTG1gZkNJBPsD7v7ytD8BzMbEbaPAN4K7aX0nC4ALjezZmAZmaGZ7wJDzSz7gbfc+tpqD9uHAH/sy4JztAAt7r4hrK8gE/blcNw/Duxy9z3ufghYCUylPI57ru4e61L6GxBO6F4GfMbDWAtlUns+5RbuJT+1gZkZsATY5u735GxaDWSvBqgnMxafbb82XFFwPtCafWvb19z9NnevdvcaMsd2rbt/BngauCrs1r727HO6KuxflN6Lu/8e2G1mHw5NFwEvUQbHncxwzPlm9p7w7ydbe8kf93a6e6x/CVxiZqeHdy+XhLY+Z5kvGPoacLm7/yVn02pgXrhCqRY4C9hIGWRR0Qf9e3AiZA6ZK1BeAb5e7Hry1DeNzNuzLcAL4WcOmTHRp4Ad4fewsL+R+XKTV4AmYGKxn0OoawbHrpYZQ+Yf9E7gf4BTQntlWN8Zto8pcs3nApvCsV9F5gqMsjjuwL8CvwW2Ag+RuTqjZI870Ejm/MAhMr3YG3pyrMmMb+8MP9cVsfadZMbQs/9n783Z/+uh9u3A7Jz2ks4iTT8gIhKhchuWERGRBBTuIiIRUriLiERI4S4iEiGFu4hIhBTuIiIRUriLiETo/wHjpkeanWXwegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "light_hist([feb_blr, mar_blr, apr_blr], ['Feb', 'Mar', 'Apr'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_val = 200\n",
    "#filter_res = np.multiply(cloud_jan_h25, (cloud_jan_h25)>=100)\n",
    "filter_res = np.multiply(cloud_jan_h25, (cloud_jan_h25)<=250)\n",
    "#filter_res = np.multiply(filter_res, (filter_res)<100)\n",
    "filter_res = filter_res * (255 / max_val)\n",
    "img_rad(filter_res)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_jan = (jan_h25 * 255) / scale\n",
    "img_rad(scaled_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "feb_h25 = week_loc_mean('h25v07', 2020, 20, 26, 2)\n",
    "cloud_feb_h25 = return_cloud_free('h25v07', 2020, 20, 26, 2)\n",
    "img_rad(feb_h25)\n",
    "img_rad(cloud_feb_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EJFIoiIhIpFAIKmtXFLsEEZGiUyiIiEikUBARkUihICIiUbYfXitpDWVXFbsEEZEjivYUREQkUiiIiEikUAjibbRFRI5iCgUREYkUCiIiEikUREQkUiiIiEikUBARkUihICIikUJBREQihYKIiEQKBRERiRQKIiISKRRERCRSKIiISKRQEBGRSKEgIiKRQkFERCKFgoiIRAoFERGJFAqHqKxdQWXtimKXISJSFFmHgpkda2ZvmNlzYb7KzNaY2RYzW2pm3UJ79zBfH5ZXZrttERHJrVzsKXwX2Jwyfw/wE3cfCHwKzAjtM4BP3f1rwE9CPxERKSFZhYKZVQCTgF+GeQMuAJ4MXRYB3w7Tl4Z5wvLxob+IiJSIbPcU7gPuAL4M832A3e5+IMw3AgPC9ABgO0BYvif0b8bMZppZnZnV7dy5M8vyRETkcGQcCmY2GfjI3demNqfp6h1YdrDB/SF3r3H3mvLy8kzLExGRDHTJ4rmjgUvMbCJQBpxAYs+hl5l1CXsDFcD7oX8jcDLQaGZdgJ7AJ1lsPy8ayq5KTMyDyqYliba7JxWvIBGRAsp4T8Hd57h7hbtXAlcAv3f3q4FVwJTQbSrwbJheHuYJy3/v7i32FEREpHjy8TmFO4HbzKyexDmDh0P7w0Cf0H4bUJuHbYuISBayOXwUuftqYHWYfhc4N02fJuDyXGxPRETyQ59oFhGRSKEgIiKRQkFERCKFgoiIRAoFERGJFAoiIhIpFEREJMrJ5xQ6q3jLC/YUtQ4RkULRnoKIiEQKBRERiRQKIiISKRRERCRSKIiISKRQEBGRSKEgIiKRQkFERCKFgoiIRAoFERGJFAoiIhIpFEREJFIoiIhIpFAQEZFIoSAiIpG+T6EDKmtXxOmGuycVsRIRkfzSnoKIiETaU+iAg9/ABvoWNhHpzLSnICIikUJBREQihYKIiEQZh4KZnWxmq8xss5m9ZWbfDe0nmtmLZrYlPPYO7WZm95tZvZm9aWbDc/VLFNS8nokfEZFOKJs9hQPA/3T3M4GRwCwz+zpQC6x094HAyjAPcDEwMPzMBB7IYtsiIpIHGYeCu+9w99fD9F5gMzAAuBRYFLotAr4dpi8FHvWEPwK9zKxfxpWLiEjO5eSSVDOrBIYBa4CvuPsOSASHmZ0Uug0Atqc8rTG07ThkXTNJ7Elwyimn5KK8vNGH2kSks8n6RLOZ/T3wFPBP7v6fbXVN0+YtGtwfcvcad68pLy/Ptry8SQ0EEZHOIqtQMLOuJALhMXd/OjR/mDwsFB4/Cu2NwMkpT68A3s9m+yIiklvZXH1kwMPAZnf/55RFy4GpYXoq8GxK+3XhKqSRwJ7kYSYRESkN2ZxTGA1cC2wws3Wh7X8BdwNPmNkMYBtweVj2PDARqAf2AddnsW0REcmDjEPB3V8h/XkCgPFp+jswK9PtiYhI/ukTzSIiEukuqRlKvXNqZdOSIlYiIpI72lMQEZFIoZADDWVX6X5IItIp6PBRLqUGwzx9GY+IHHm0pyAiIpFCQUREIoWCiIhECoU8qaxdoZvmicgRR6EgIiKRrj7Ks8raFQc/6KYrkkSkxGlPQUREIu0pFIm+tU1ESpFCoYB04llESp1CoYB0Ez0RKXU6pyAiIpH2FPIkda+gPa2dX8jmvMOhh6qKet4ieU+oPFx9le3Y6dyOSHMKhSJJhkZ7h5EOhkvKG2qaO7JWNi3p8Jtam2+EHb2p3yH9OvTmmqsbBqasp6Es8ajDcSK5oVAoshZ7FPNSl6W2t31r7sTtuxPTad8g8/jXerMaIFFHe9tJV88hbc2C5jD2vDKlvQYRhcJRLTVIkn/tpwZR8k2y2RtyB0MlPre9N9d0YRfamoViFrJ9s1dYyNFEodAJdfTQVDPzenboTfhgULRsS1dD6p5PvnXkkt9sLwtWQEhnp1A42nTwzR8Kc8gmHzIKxUPoMyVytFIoyGFJFxSlEB4drSs1KHL2uZFwuCu5Du1ByJFModCJlcKbdalpbUwy2btIdyittT6Q3eXGOmwlhaJQEEnR3t5DLg5NHaq1Q1WtvfkrICSfzN2LXUOrampqvK6uLvMVtHMZp0i+FOpzEwoFScfM1rp7TSbP1Z6CSB4U4z5X2oOQXFAoiORZeye8s6GrpCTXFAoinVBHwkJ7E5KOQkGkCA733lf5PgRVUjdQlKIq+IlmM7sI+BfgWOCX7n53a311olmOJvFzDu1cSlyKN/9TiJSWI+ZEs5kdC/wM+G9AI/AnM1vu7psKWYdIKero50o6+qG8YoVHrk5468R5cRR0T8HMRgHz3P1bYX4OgLv/KF1/7SmI5EZH90IyWU9bn+dI99xM+jV7Tgc+v9GR/q1p63zMkRJO2ewpFDoUpgAXufs/hPlrgRHufktKn5nAzDB7OvB2BpvqC3ycZbn5Usq1gerLRinXBqovG6VcG7Ss76vuXp7Jigp9otnStDVLJXd/CHgoq42Y1WWakvlWyrWB6stGKdcGqi8bpVwb5La+Qn9HcyNwcsp8BfB+gWsQEZFWFDoU/gQMNLMqM+sGXAEsL3ANIiLSioIePnL3A2Z2C/ACiUtSf+Xub+VhU1kdfsqzUq4NVF82Srk2UH3ZKOXaIIf1lfQN8UREpLAKffhIRERKmEJBRESiThUKZnaRmb1tZvVmVlukGk42s1VmttnM3jKz74b2E83sRTPbEh57h3Yzs/tDzW+a2fAC1Hismb1hZs+F+SozWxNqWxouAsDMuof5+rC8sgC19TKzJ83sz2EMR5XY2N0a/l03mtnjZlZWzPEzs1+Z2UdmtjGl7bDHy8ymhv5bzGxqHmv7cfi3fdPM/s3MeqUsmxNqe9vMvpXSnpfXdbr6UpbdbmZuZn3DfEHHrq36zOwfw3i8ZWb3prTnZvzcvVP8kDhxvRU4FegGrAe+XoQ6+gHDw/TxwDvA14F7gdrQXgvcE6YnAr8m8RmOkcCaAtR4G7AEeC7MPwFcEaYfBP57mL4ZeDBMXwEsLUBti4B/CNPdgF6lMnbAAOAvwN+ljNu0Yo4fMBYYDmxMaTus8QJOBN4Nj73DdO881TYB6BKm70mp7evhNdsdqAqv5WPz+bpOV19oP5nExTD/AfQtxti1MX7/Ffgd0D3Mn5Tr8cvrC7yQP8Ao4IWU+TnAnBKo61kS93p6G+gX2voBb4fpfwWuTOkf++WpngpgJXAB8Fz4T/5xygs1jmN4YYwK011CP8tjbSeQeNO1Q9pLZewGANvDG0CXMH7fKvb4AZWHvHEc1ngBVwL/mtLerF8uaztk2WXAY2G62es1OXb5fl2nqw94EhgCNHAwFAo+dq382z4BXJimX87GrzMdPkq+YJMaQ1vRhMMFw4A1wFfcfQdAeDwpdCt03fcBdwBfhvk+wG53P5Bm+7G2sHxP6J8vpwI7gYXh8NYvzew4SmTs3P09YAGwDdhBYjzWUjrjl3S441Ws1850En99l0xtZnYJ8J67rz9kUUnUBwwCxoTDkf9uZt/IdX2dKRTavYVGIZnZ3wNPAf/k7v/ZVtc0bXmp28wmAx+5+9oObr/QY9qFxO7yA+4+DPicxOGP1hS0vnBs/lISu+f9geOAi9uooaT+T9J6PQWv08y+BxwAHks2tVJDIV8fPYDvAd9Pt7iVOorxGulN4hDWbOAJM7M26jjs+jpTKJTMLTTMrCuJQHjM3Z8OzR+aWb+wvB/wUWgvZN2jgUvMrAH4vyQOId0H9DKz5AcZU7cfawvLewKf5Km25PYa3X1NmH+SREiUwtgBXAj8xd13uvt+4GngPEpn/JIOd7wKOo7hZOxk4GoPxzRKpLbTSAT++vAaqQBeN7P/UiL1Ebb3tCe8RmKPv28u6+tMoVASt9AIqf0wsNnd/zll0XIgeWXCVBLnGpLt14WrG0YCe5K7/rnm7nPcvcLdK0mMz+/d/WpgFTClldqSNU8J/fP2V5C7fwBsN7PTQ9N4YBMlMHbBNmCkmfUI/87J+kpi/FIc7ni9AEwws95hb2hCaMs5S3zJ1p3AJe6+75Car7DEFVtVwEDgNQr4unb3De5+krtXhtdII4mLRj6gBMYueIbEH3OY2SASJ48/Jpfjl6sTIqXwQ+IKgXdInG3/XpFq+CaJ3bM3gXXhZyKJY8krgS3h8cTQ30h88dBWYANQU6A6z+fg1Uenhv9A9cAyDl7ZUBbm68PyUwtQ11CgLozfMyR2lUtm7IC7gD8DG4HFJK72KNr4AY+TOL+xn8Sb2IxMxovE8f368HN9HmurJ3GMO/naeDCl//dCbW8DF6e05+V1na6+Q5Y3cPBEc0HHro3x6wb8n/D/73XgglyPn25zISIiUWc6fCQiIllSKIiISKRQEBGRSKEgIiKRQkFERCKFgoiIRAoFERGJ/j9IyFo6X2nKhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clouds_include = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], feb_h25)\n",
    "n_clouds = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], cloud_feb_h25)\n",
    "\n",
    "light_hist([clouds_include, n_clouds], ['with Clouds', 'no clouds'])\n",
    "light_hist([n_clouds, clouds_include], ['no Clouds', 'with clouds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = (feb_h25>800)\n",
    "temp = temp * 257\n",
    "im = Image.fromarray((temp).astype(np.int16))\n",
    "im.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Cannot handle this data type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m   2648\u001b[0m             \u001b[0mtypekey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"typestr\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2649\u001b[0;31m             \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_fromarray_typemap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtypekey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2650\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ((1, 1), '<i8')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-2d77015646c8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m   2649\u001b[0m             \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_fromarray_typemap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtypekey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2651\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot handle this data type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2652\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2653\u001b[0m         \u001b[0mrawmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Cannot handle this data type"
     ]
    }
   ],
   "source": [
    "Image.fromarray(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(feb_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 2.28571429,  2.71428571,  3.14285714, ..., 19.71428571,\n",
       "         15.42857143, 18.71428571],\n",
       "        [ 1.71428571,  1.42857143,  2.57142857, ...,  7.        ,\n",
       "         11.28571429, 12.42857143],\n",
       "        [ 1.71428571,  1.42857143,  2.        , ...,  5.42857143,\n",
       "          5.71428571,  5.28571429],\n",
       "        ...,\n",
       "        [ 2.71428571,  3.        ,  3.        , ...,  1.85714286,\n",
       "          2.42857143,  1.71428571],\n",
       "        [ 2.42857143,  2.85714286,  3.28571429, ...,  2.14285714,\n",
       "          2.71428571,  2.14285714],\n",
       "        [ 3.        ,  3.14285714,  2.71428571, ...,  3.14285714,\n",
       "          2.57142857,  2.42857143]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feb_h25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Cannot handle this data type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m   2648\u001b[0m             \u001b[0mtypekey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"typestr\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2649\u001b[0;31m             \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_fromarray_typemap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtypekey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2650\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: ((1, 1), '<i8')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-cc2946f720fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg_rad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Night-Light-Research/night_light_utils.py\u001b[0m in \u001b[0;36mimg_rad\u001b[0;34m(matrix)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;31m#Display the matrix as an image - useful for visualizing night light or cloud coverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mimg_rad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m     \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m   2649\u001b[0m             \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrawmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_fromarray_typemap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtypekey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2651\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot handle this data type\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2652\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2653\u001b[0m         \u001b[0mrawmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Cannot handle this data type"
     ]
    }
   ],
   "source": [
    "img_rad(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clouds_include = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], jan_h25)\n",
    "n_clouds = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], cloud_jan_h25)\n",
    "\n",
    "light_hist([clouds_include, n_clouds], ['with Clouds', 'no clouds'])\n",
    "light_hist([n_clouds, clouds_include], ['no Clouds', 'with clouds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Renaming      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      .      Renaming Data Finished\n"
     ]
    }
   ],
   "source": [
    "rename_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_file(tile):\n",
    "    hf = h5py.File('Output/test.h5', 'r+')\n",
    "    HDFEOS = hf['HDFEOS']\n",
    "    grids = HDFEOS['GRIDS']\n",
    "    vnp = grids['VNP_Grid_DNB']\n",
    "    data = vnp['Data Fields']\n",
    "    data['DNB_At_Sensor_Radiance_500m'][...] = np.array(tile)\n",
    "    hf.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_file('Oct_h25_trial.h5', oct_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hf = h5py.File('Data/A2019298.h25v07.h5', 'r')\n",
    "info = hf['HDFEOS INFORMATION']\n",
    "metadata = info['StructMetadata.0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 group \"/HDFEOS INFORMATION\" (1 members)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(jan_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_h25 = week_loc_mean('h25v07', 2019, 25, 31, 10)\n",
    "\n",
    "nov_h25 = week_loc_mean('h25v07', 2019, 23, 29, 11)\n",
    "\n",
    "dec_h25 = week_loc_mean('h25v07', 2019, 23, 29, 12)\n",
    "\n",
    "jan_h25 = week_loc_mean('h25v07', 2020, 21, 27, 1)\n",
    "\n",
    "feb_h25 = week_loc_mean('h25v07', 2020, 20, 26, 2)\n",
    "\n",
    "mar_h25 = week_loc_mean('h25v07', 2020, 21, 27, 3)\n",
    "\n",
    "apr_h25 = week_loc_mean('h25v07', 2020, 20, 26, 4)\n",
    "\n",
    "may_h25 = week_loc_mean('h25v07', 2020, 19, 25, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_city_data(city, city_cords, tiles):\n",
    "    city_data = []\n",
    "    for tile in tiles:\n",
    "        city_data.append(city_from_cords(city_cords[city + '_x_start'], city_cords[city + '_x_end'], city_cords[city + '_y_start'], city_cords[city + '_y_end'], tile))\n",
    "    return city_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_blr2, nov_blr2, nov_blr2, nov_blr2, dec_blr2, jan_blr2, feb_blr2, mar_blr2 = return_city_data('blr', city_cords, [oct_h25, nov_h25, dec_h25, jan_h25, feb_h25, mar_h25, apr_h25, may_h25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(jan_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], oct_h25)\n",
    "\n",
    "nov_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], nov_h25)\n",
    "\n",
    "dec_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], dec_h25)\n",
    "\n",
    "jan_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], jan_h25)\n",
    "\n",
    "feb_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], feb_h25)\n",
    "\n",
    "mar_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], mar_h25)\n",
    "\n",
    "apr_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], apr_h25)\n",
    "\n",
    "may_blr = city_from_cords(city_cords['blr_x_start'], city_cords['blr_x_end'], city_cords['blr_y_start'], city_cords['blr_y_end'], may_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quadrant_city(city_tile):\n",
    "    h2 = int(np.round(city_tile.shape[0] / 2))\n",
    "    w2 = int(np.round(city_tile.shape[1] / 2))\n",
    "    h, w = city_tile.shape\n",
    "    q1 = city_tile[0: h2, 0:w2];      q2 = city_tile[0: h2, w2:w]\n",
    "    q3 = city_tile[h2: h, 0:w2];      q4 = city_tile[h2: h, w2:w]\n",
    "    \n",
    "    return q1, q2, q3, q4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_blr)\n",
    "img_rad(oct_q1_blr)\n",
    "img_rad(oct_q2_blr)\n",
    "img_rad(oct_q3_blr)\n",
    "img_rad(oct_q4_blr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_diff_filter(apr_q1_blr, feb_q1_blr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_q1_blr, oct_q2_blr, oct_q3_blr, oct_q4_blr = quadrant_city(oct_pune)\n",
    "nov_q1_blr, nov_q2_blr, nov_q3_blr, nov_q4_blr = quadrant_city(nov_pune)\n",
    "dec_q1_blr, dec_q2_blr, dec_q3_blr, dec_q4_blr = quadrant_city(dec_pune)\n",
    "jan_q1_blr, jan_q2_blr, jan_q3_blr, jan_q4_blr = quadrant_city(jan_pune)\n",
    "feb_q1_blr, feb_q2_blr, feb_q3_blr, feb_q4_blr = quadrant_city(feb_pune)\n",
    "mar_q1_blr, mar_q2_blr, mar_q3_blr, mar_q4_blr = quadrant_city(mar_pune)\n",
    "apr_q1_blr, apr_q2_blr, apr_q3_blr, apr_q4_blr = quadrant_city(apr_pune)\n",
    "may_q1_blr, may_q2_blr, may_q3_blr, may_q4_blr = quadrant_city(may_pune)\n",
    "\n",
    "\n",
    "q1_mean = []\n",
    "q2_mean = []\n",
    "q3_mean = []\n",
    "q4_mean = []\n",
    "\n",
    "q1_mean.append(single_loc_mean(oct_q1_blr))\n",
    "q1_mean.append(single_loc_mean(nov_q1_blr))\n",
    "q1_mean.append(single_loc_mean(dec_q1_blr))\n",
    "q1_mean.append(single_loc_mean(jan_q1_blr))\n",
    "q1_mean.append(single_loc_mean(feb_q1_blr))\n",
    "q1_mean.append(single_loc_mean(mar_q1_blr))\n",
    "q1_mean.append(single_loc_mean(apr_q1_blr))\n",
    "q1_mean.append(single_loc_mean(may_q1_blr))\n",
    "\n",
    "q2_mean.append(single_loc_mean(oct_q2_blr))\n",
    "q2_mean.append(single_loc_mean(nov_q2_blr))\n",
    "q2_mean.append(single_loc_mean(dec_q2_blr))\n",
    "q2_mean.append(single_loc_mean(jan_q2_blr))\n",
    "q2_mean.append(single_loc_mean(feb_q2_blr))\n",
    "q2_mean.append(single_loc_mean(mar_q2_blr))\n",
    "q2_mean.append(single_loc_mean(apr_q2_blr))\n",
    "q2_mean.append(single_loc_mean(may_q2_blr))\n",
    "\n",
    "q3_mean.append(single_loc_mean(oct_q3_blr))\n",
    "q3_mean.append(single_loc_mean(nov_q3_blr))\n",
    "q3_mean.append(single_loc_mean(dec_q3_blr))\n",
    "q3_mean.append(single_loc_mean(jan_q3_blr))\n",
    "q3_mean.append(single_loc_mean(feb_q3_blr))\n",
    "q3_mean.append(single_loc_mean(mar_q3_blr))\n",
    "q3_mean.append(single_loc_mean(apr_q3_blr))\n",
    "q3_mean.append(single_loc_mean(may_q3_blr))\n",
    "\n",
    "q4_mean.append(single_loc_mean(oct_q4_blr))\n",
    "q4_mean.append(single_loc_mean(nov_q4_blr))\n",
    "q4_mean.append(single_loc_mean(dec_q4_blr))\n",
    "q4_mean.append(single_loc_mean(jan_q4_blr))\n",
    "q4_mean.append(single_loc_mean(feb_q4_blr))\n",
    "q4_mean.append(single_loc_mean(mar_q4_blr))\n",
    "q4_mean.append(single_loc_mean(apr_q4_blr))\n",
    "q4_mean.append(single_loc_mean(may_q4_blr))\n",
    "\n",
    "months = ['Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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   "source": [
    "trace1 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q1_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q1\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= 'Q1')\n",
    "# Creating trace2\n",
    "trace2 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q2_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q2\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80, 0.8)'),\n",
    "                    text= 'Q2')\n",
    "\n",
    "trace3 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q3_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q3\",\n",
    "                    marker = dict(color = 'rgba(255, 0, 10, 0.8)'),\n",
    "                    text= 'Q3')\n",
    "\n",
    "trace4 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q4_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q4\",\n",
    "                    marker = dict(color = 'rgba(0, 0, 255, 0.8)'),\n",
    "                    text= 'Q4')\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4]\n",
    "layout = dict(title = 'Night Light for Different Months segmented by quadrant for Pune',\n",
    "              xaxis= dict(title= 'Month',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
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   "execution_count": 18,
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[0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Night Light for Different Months segmented by quadrant for Bangalore\"}, \"xaxis\": {\"ticklen\": 5, \"title\": {\"text\": \"Month\"}, \"zeroline\": false}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('3223129d-2038-452d-81f4-32043996638a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q1_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q1\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= 'Q1')\n",
    "# Creating trace2\n",
    "trace2 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q2_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q2\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80, 0.8)'),\n",
    "                    text= 'Q2')\n",
    "\n",
    "trace3 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q3_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q3\",\n",
    "                    marker = dict(color = 'rgba(255, 0, 10, 0.8)'),\n",
    "                    text= 'Q3')\n",
    "\n",
    "trace4 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = q4_mean,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Q4\",\n",
    "                    marker = dict(color = 'rgba(0, 0, 255, 0.8)'),\n",
    "                    text= 'Q4')\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4]\n",
    "layout = dict(title = 'Night Light for Different Months segmented by quadrant for Bangalore',\n",
    "              xaxis= dict(title= 'Month',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = oct_blr.reshape(98*89,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8722, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def light_hist(tiles, legend, bins=100):\n",
    "    for i in tiles:\n",
    "        plt.hist(i.reshape(i.shape[0]*i.shape[1], 1), bins=bins)\n",
    "    plt.legend(legend)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(feb_blr2.reshape(98*89,1), bins=100)\n",
    "plt.title(\"Distribution of Pixel-wise Radiance values\")\n",
    "plt.xlabel(\"Night Light Radiance\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], oct_h25)\n",
    "\n",
    "nov_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], nov_h25)\n",
    "\n",
    "dec_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], dec_h25)\n",
    "\n",
    "jan_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], jan_h25)\n",
    "\n",
    "feb_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], feb_h25)\n",
    "\n",
    "mar_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], mar_h25)\n",
    "\n",
    "apr_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], apr_h25)\n",
    "\n",
    "may_pune = city_from_cords(city_cords['pune_x_start'], city_cords['pune_x_end'], city_cords['pune_y_start'], city_cords['pune_y_end'], may_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_h18 = week_loc_mean('h18v04', 2019, 25, 31, 10)\n",
    "\n",
    "nov_h18 = week_loc_mean('h18v04', 2019, 23, 29, 11)\n",
    "\n",
    "dec_h18 = week_loc_mean('h18v04', 2019, 23, 29, 12)\n",
    "\n",
    "jan_h18 = week_loc_mean('h18v04', 2020, 21, 27, 1)\n",
    "\n",
    "feb_h18 = week_loc_mean('h18v04', 2020, 20, 26, 2)\n",
    "\n",
    "mar_h18 = week_loc_mean('h18v04', 2020, 21, 27, 3)\n",
    "\n",
    "apr_h18 = week_loc_mean('h18v04', 2020, 20, 26, 4)\n",
    "\n",
    "may_h18 = week_loc_mean('h18v04', 2020, 19, 25, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_blr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "oct_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], oct_h18)\n",
    "\n",
    "nov_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], nov_h18)\n",
    "\n",
    "dec_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], dec_h18)\n",
    "\n",
    "jan_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], jan_h18)\n",
    "\n",
    "feb_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], feb_h18)\n",
    "\n",
    "mar_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], mar_h18)\n",
    "\n",
    "apr_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], apr_h18)\n",
    "\n",
    "may_paris = city_from_cords(city_cords['paris_x_start'], city_cords['paris_x_end'], city_cords['paris_y_start'], city_cords['paris_y_end'], may_h18)\n",
    "\n",
    "## MILAN =+++++==++====\n",
    "\n",
    "oct_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], oct_h18)\n",
    "\n",
    "nov_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], nov_h18)\n",
    "\n",
    "dec_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], dec_h18)\n",
    "\n",
    "jan_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], jan_h18)\n",
    "\n",
    "feb_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], feb_h18)\n",
    "\n",
    "mar_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], mar_h18)\n",
    "\n",
    "apr_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], apr_h18)\n",
    "\n",
    "may_milan = city_from_cords(city_cords['milan_x_start'], city_cords['milan_x_end'], city_cords['milan_y_start'], city_cords['milan_y_end'], may_h18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lights_pune = []\n",
    "months = ['Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May']\n",
    "\n",
    "lights_pune.append(single_loc_mean(oct_pune))\n",
    "lights_pune.append(single_loc_mean(nov_pune))\n",
    "lights_pune.append(single_loc_mean(dec_pune))\n",
    "lights_pune.append(single_loc_mean(jan_pune))\n",
    "lights_pune.append(single_loc_mean(feb_pune))\n",
    "lights_pune.append(single_loc_mean(mar_pune))\n",
    "lights_pune.append(single_loc_mean(apr_pune))\n",
    "lights_pune.append(single_loc_mean(may_pune))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lights_milan = []\n",
    "months = ['Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May']\n",
    "\n",
    "lights_milan.append(single_loc_mean(oct_milan))\n",
    "lights_milan.append(single_loc_mean(nov_milan))\n",
    "lights_milan.append(single_loc_mean(dec_milan))\n",
    "lights_milan.append(single_loc_mean(jan_milan))\n",
    "lights_milan.append(single_loc_mean(feb_milan))\n",
    "lights_milan.append(single_loc_mean(mar_milan))\n",
    "lights_milan.append(single_loc_mean(apr_milan))\n",
    "lights_milan.append(single_loc_mean(may_milan))\n",
    "\n",
    "\n",
    "lights_paris = []\n",
    "months = ['Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May']\n",
    "\n",
    "lights_paris.append(single_loc_mean(oct_paris))\n",
    "lights_paris.append(single_loc_mean(nov_paris))\n",
    "lights_paris.append(single_loc_mean(dec_paris))\n",
    "lights_paris.append(single_loc_mean(jan_paris))\n",
    "lights_paris.append(single_loc_mean(feb_paris))\n",
    "lights_paris.append(single_loc_mean(mar_paris))\n",
    "lights_paris.append(single_loc_mean(apr_paris))\n",
    "lights_paris.append(single_loc_mean(may_paris))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lights = []\n",
    "months = ['Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May']\n",
    "\n",
    "lights.append(single_loc_mean(oct_blr))\n",
    "lights.append(single_loc_mean(nov_blr))\n",
    "lights.append(single_loc_mean(dec_blr))\n",
    "lights.append(single_loc_mean(jan_blr))\n",
    "lights.append(single_loc_mean(feb_blr))\n",
    "lights.append(single_loc_mean(mar_blr))\n",
    "lights.append(single_loc_mean(apr_blr))\n",
    "lights.append(single_loc_mean(may_blr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(months, lights_paris)\n",
    "plt.plot(months, lights)\n",
    "plt.plot(months, lights_milan)\n",
    "plt.plot(months, lights_pune)\n",
    "plt.legend(['Paris', 'BLR', 'Milan', 'Pune'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.offline import init_notebook_mode, iplot, plot\n",
    "import plotly as py\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "marker": {
          "color": "rgba(16, 112, 2, 0.8)"
         },
         "mode": "lines+markers",
         "name": "BLR",
         "text": "BLR",
         "type": "scatter",
         "x": [
          "Oct",
          "Nov",
          "Dec",
          "Jan",
          "Feb",
          "Mar",
          "Apr",
          "May"
         ],
         "y": [
          159.04001375831228,
          202.08877387230976,
          222.17586071346676,
          248.54014478985815,
          247.5970779965277,
          216.23074655223243,
          189.33245978969438,
          167.59047728240574
         ]
        },
        {
         "marker": {
          "color": "rgba(80, 26, 80, 0.8)"
         },
         "mode": "lines+markers",
         "name": "Paris",
         "text": "Paris",
         "type": "scatter",
         "x": [
          "Oct",
          "Nov",
          "Dec",
          "Jan",
          "Feb",
          "Mar",
          "Apr",
          "May"
         ],
         "y": [
          115.00012735089759,
          95.61743790370235,
          120.87885873219635,
          175.75980092507692,
          120.77705442467958,
          192.6545683313976,
          216.4861391283086,
          77.94088371334843
         ]
        },
        {
         "marker": {
          "color": "rgba(255, 0, 10, 0.8)"
         },
         "mode": "lines+markers",
         "name": "Milan",
         "text": "Milan",
         "type": "scatter",
         "x": [
          "Oct",
          "Nov",
          "Dec",
          "Jan",
          "Feb",
          "Mar",
          "Apr",
          "May"
         ],
         "y": [
          157.23243516156464,
          108.28526254251702,
          255.53934151785714,
          226.99734268707485,
          207.12470769557825,
          204.56781462585033,
          176.28071853741497,
          157.9680325255102
         ]
        },
        {
         "marker": {
          "color": "rgba(0, 0, 255, 0.8)"
         },
         "mode": "lines+markers",
         "name": "Pune",
         "text": "Pune",
         "type": "scatter",
         "x": [
          "Oct",
          "Nov",
          "Dec",
          "Jan",
          "Feb",
          "Mar",
          "Apr",
          "May"
         ],
         "y": [
          73.75128952679974,
          115.88344308752471,
          90.7361414096108,
          131.5972394951987,
          133.8640747008094,
          106.3609859528227,
          128.3231666292891,
          114.17498827702909
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
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            "colorscale": [
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            "type": "contour"
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          "contourcarpet": [
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            "type": "contourcarpet"
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          ],
          "heatmap": [
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            "type": "heatmap"
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          ],
          "heatmapgl": [
           {
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            "colorscale": [
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            "type": "heatmapgl"
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          ],
          "histogram": [
           {
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            "type": "histogram"
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          "histogram2d": [
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       "var gd = document.getElementById('71495f70-10f6-49bf-ae69-5d25491d539b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = lights,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"BLR\",\n",
    "                    marker = dict(color = 'rgba(16, 112, 2, 0.8)'),\n",
    "                    text= 'BLR')\n",
    "# Creating trace2\n",
    "trace2 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = lights_paris,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Paris\",\n",
    "                    marker = dict(color = 'rgba(80, 26, 80, 0.8)'),\n",
    "                    text= 'Paris')\n",
    "\n",
    "trace3 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = lights_milan,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Milan\",\n",
    "                    marker = dict(color = 'rgba(255, 0, 10, 0.8)'),\n",
    "                    text= 'Milan')\n",
    "\n",
    "trace4 = go.Scatter(\n",
    "                    x = months,\n",
    "                    y = lights_pune,\n",
    "                    mode = \"lines+markers\",\n",
    "                    name = \"Pune\",\n",
    "                    marker = dict(color = 'rgba(0, 0, 255, 0.8)'),\n",
    "                    text= 'Pune')\n",
    "\n",
    "data = [trace1, trace2, trace3, trace4]\n",
    "layout = dict(title = 'Night Light for Different Months',\n",
    "              xaxis= dict(title= 'Month',ticklen= 5,zeroline= False)\n",
    "             )\n",
    "fig = dict(data = data, layout = layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_diff_filter(m2, m1, p_decline=0, p_increase=0):\n",
    "    diff = m2 - m1\n",
    "    inc = np.multiply(diff, (diff>0))\n",
    "    inc = np.multiply(inc, ((inc / m1) > p_increase))    \n",
    "    dcr = np.abs(np.multiply(diff, (diff<0)))\n",
    "    dcr = np.multiply(dcr, ((dcr / m1) > p_decline))\n",
    "    \n",
    "    gr = np.zeros(inc.shape)\n",
    "    rgb = np.array([dcr, inc ,gr])\n",
    "    temp = np.dstack(rgb)\n",
    "    temp = temp / np.amax(diff)\n",
    "    rgbArray = np.zeros((gr.shape[0],gr.shape[1],3), 'uint8')\n",
    "    rgbArray[..., 0] = temp[:, :, 0] * 255\n",
    "    rgbArray[..., 1] = temp[:, :, 1] * 255\n",
    "    rgbArray[..., 2] = temp[:, :, 2] * 255\n",
    "    img = Image.fromarray(rgbArray)\n",
    "    img.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_diff(m2, m1, p_decline=0, p_increase=0, inc_dcr=False):\n",
    "    diff = m2 - m1\n",
    "    inc = np.multiply(diff, (diff>0))\n",
    "    inc = np.multiply(inc, ((inc / m1) > p_increase))    \n",
    "    dcr = np.abs(np.multiply(diff, (diff<0)))\n",
    "    dcr = np.multiply(dcr, ((dcr / m1) > p_decline))\n",
    "    \n",
    "    if inc_dcr:\n",
    "        return inc\n",
    "    else:\n",
    "        return dcr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "apr_feb_h25 = return_diff(apr_h25, feb_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_file(apr_feb_h25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_diff2(mar_paris, feb_paris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_diff2(apr_blr, feb_blr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_diff_filter(mar_blr, feb_blr, 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = mar_paris - feb_paris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "inc = np.multiply(diff, (diff>0))\n",
    "dcr = np.abs(np.multiply(diff, (diff<0)))\n",
    "gr = np.zeros(inc.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([dcr, inc ,gr])\n",
    "temp = np.dstack(a)\n",
    "temp = temp / 370"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgbArray = np.zeros((gr.shape[0],gr.shape[1],3), 'uint8')\n",
    "rgbArray[..., 0] = temp[:, :, 0] * 255\n",
    "rgbArray[..., 1] = temp[:, :, 1] * 255\n",
    "rgbArray[..., 2] = temp[:, :, 2] * 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.fromarray(rgbArray)\n",
    "img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7ffb68e66278>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Om/dq3Bfnh893js3UgHjySaoR4723rH35op7lCBvExk9gEBydnrUBCkWM10XP6J1qlyp1yMtjl7Iqj6b99ll1BFM/c+66q830WrWTRzajhnwaNWo0EWovn0aNGk2EtlfsKjF63blyWdhsWvgNscMVtMDfBcHpZbCdvTrPC/ed722HKiuBNVfAoYY1p4WZUcxym8PifFzKUqrr/e5T20jceriGfYVdbzOZ6LQaO62x3a5xeN52Er0zDZdrOZDUkT6fj90/SJgTMR6Yw/t0KB0NrJo4drkbDKPqGLuZ164/4ntUcoTN01bnP6hERMMlwvcHa3YNuYf59EnTwxbUOfcwYauQD4kD+PyAAn4pF7XoiF9XvA6bRc+9rom57lniMXWn8+EjxmvEXK1VvLj7i0SckyfJqvRHq+8a1vFr2Yu8Mx+IlaXhNV4SLqqiRWAe9qQyYvxb/nTUqSGfRo0aTYS2F/msxeiVaVBhJmUcRAnrL/2VFOjEUcaD3ufT3NBUOtfJJ9/Z/WvSCp5Hk0msWSWnSzaXvuQcli+5T1zkiKMSfU6nN0fE+Ahnug/qc8U5yuiTKg+pvQ3HF4cpJJ2yX2kt8x1jSknM99i9M6kqSKyS3qg31z5n11jrT6ncl8qIMWfN0e1/zOq8Nz2sD9o37SffketnsebJqIHMoSGuwJJpwU7AwS5rZ4WFZH/lZO0xdqRkXp525KT5L9oYB4TArtu65ON0UPQ6WFmhvtpeqnl2svnS4YMR42TyJN2nr752WVF9LX0fMTwau0YN+TRq1GgitK3IZ0+MHK8e83zEKuH0J/OhaBEbUETNlMpnIt7z8csREQc5CkTlssdn8Ay4l+q4qZ23Oo/HLaAzDruiz48xILHcWUM+OAMidzu3wHEwM003+yJvw6DRg3genPO6cZnSY0lSproNuWoixoOkA3qom1Jh8IdSVXcShAnD8X09ABboBJh67yqcnS4TJX/S6uCOcJojiVL/IiKu5UyIvneAtKDepJOLiF6xlJu55KgiJ/RJx/VEjIE56PWy/RZYV9w1eNattkHYM7gOXKGS9yMr4Xwc6gzoij67mwaXchqf4/YZPRuola3jqPVCbE0N+TRq1GgitK3Ip4vRS/gBY5/I/XNZ9qyZmXLknumLcsAh/zuqyAnrajlh8rPWvYH1YRW67A5vOCtGsqg5R4AhvUml50DpcxapRMdRC9YEKZxPZUTEXSpBQxcdFmE6oqFaZGyaY46vdiQI4qkZyyAsHqyLB0mCUL5dJcz7N6wOY6IdOPQJqwOSBBXBjV2X9kT2qqsEW/YwK6O+iB4hbLjkerK8wVTJf2RMK6jApx7ER5PM9ZVaGksmnd9SRXczjp0Yf1XQ4yRrqAMnDpHk55WSckbEGPV/Wp09oTE7WvJ83zVqyKdRo0YTofbyadSo0URoW8WuXTGCyCfTdxExhqr5CJyIjY6CQE5TSq+nWKQVwdOBv1Q2O7vSUfA1nT0YC/Zs8powaZ9XOTgcTUpsYrmyGTpiY6yMp1g9qZKYLEQK143TFlITUNdjiFBMMv5bbaxTkvMupTihgSiicc+qcRD8HVYFRS/iDs24A1tWXj5h11i+N6tExHqF1WH8RHjjTlDTr/IM+ujP6uW2fEKhd5xrPMRFfy3AFSY/K+2tHSL/iZ2rRXrTfxdTkv/gONuBP0MbavfysO195mdBKt6c5tbpojbhCf3va8a2YO7Z589W6tyuecAg4vFciNjPR50a8mnUqNFEaFuRz2qMuJvnncEse1ivzDkpRped68BlQCHZ9T9ig/J4XXVead8fFAwgGttNgTmIG6Sx15TamBozo/So9F3iQCAOUI4jBtqGI3gGwnwU9CmO3rFxMGxQVW06TqRnvMmu/TeVR4n01tz7FB4VS855eByBZdd6OKObZGHIcEY/GID+PqbycSCPQcldF4ZtZtTn36FTZ+4HnFUXp5MrRsTGo3967u2VcsQ8ZJO2V4NlPpa0QSoAezCP/TXaVocu1FwgcCdg8tXggj9EAyC8YtHgCAdVglrZy55pk/CWPGQXQHJiS/awI1IU6A35NGrUaEfRREztzrUwCcMFMVUvGxQh0G8uhwHkc24ixpxIr+sLVgd3b26/YK/pTqxoTZwWudnVIFTnfhiKm49zxkHy1zgagLOACh6zazmP76zGs2wrtXy36n5hVDJ3HqnCKTrfotJleuYcjoSuyLkWiG1f+t8RFG1iioUZu5Ph8/RbbO4Fe8gxoSuQyl3y33/R6sA9aZO8xr703I/7Pxx1EPTJkcr613UT2XWBxw8CQlHACJpmNwN7RI9qaq4H9Bv9ls/5gi726XhqDrf5qJza0TfaM2Vl4zPQ33AUU87jHTH+DV5MdfxlkQ8LpDuer+ll5/MppdxbSvlwKeXzpZRHSyk/ou+PlFJ+vZTypMrDN2qrUaNGjaAbnl5RSjkeEce7rvtEKeVgRHw8Iv5ERPyPEXGx67q/X0r5sYg43HXd39qqrb2ldPfHUL8CUMknEDtS+G2ViyhOUIwYi0WPBAPYnb6PGL9pQQjud/eUkNKUuE/m+P45e9G7gY7Z/EMqax7u6D3Qg7j8/1/pePZmc/bFQBi/BnSLPYTnMo9uQTqpEo5Pn91aRtM4k9Edl9+zlY35dKfLDE5XKp/zWYy1jIw4rP2ESud03wHk0qBfJZb9dCXC9RXp1IaI8dSmyJp40h+SrFu3CxX5/mD6mVesTDWUdq22MegIHdA6e0oM9hxtruqefWbWZdhXdP8Ru5+5pd/ofp4cV4lL+QjmSi7onL2y4s/Y74/VL/X0iq7rTndd9wl9vhYj5HZ3RHx/RPyMqv1MjF5IjRo1anRT9EXpfEopJyLiLRHxUEQc67rudMToBVVKuWOTe94TEe+JqHvxN2rU6KuTbvrlU0o5EBG/EBF/reu6q6XcRIbxiOi67r2hlCtTpXTPxFAhCWJGFKk5kQH5F0nwrQbutTrcB+zDWutHxtAmR6qcd3N+ErcQhVyE6JXJaqgI6l60qTgoiPpR+gy8tmftmhs+yxNzg8tpu6tFYWdtsGivmaiZD8SklJxu1LZKxAR3PUDiQEGJ4tdFonzYIF30Oc8xdq6QTIn/eujuTConoEf8Gij2sfNqN89J7LrVxATEFBTWLj66+B1hsXYuEmkAu9Uh9rDrpNnLWYzzviLmrKwNmo2IiJfUaG9PSeehR1jyf5UHlobPirDk9rrfjQ15f+MeccmV2ik7/pT6tW77bXp+2B/WxZu50XGkN2VqL6XsitGL5993XfeL+vqM9EHohc5udn+jRo0aZboh8ikjiPOvIuLzXdf9Y7v0voh4d0T8fZW/fKO2uhKxuCsGGjjejkS3E1H7O45KpNE8IMhSyzfCQOA6vO2dawAMzsNa/RkpJy5tO2JYTx8WYB/2kCKu2zspijVcMnS0IqhDLpYrzprEOog67tlgJf/MLeI+VzV4R2kgDMISasHXlLOVOnASFOeEUDg3Q85m7uF+nWeIFBw4JFjlLvpvVQmq+i2VjmhhwidU/t8qBwkwgTFaV5CXp6r+XpVMtT+DMAy2xWLtOBrt2ZxnuTOl9GVtmntXh3Vre0jeEv0x0BEbj9Gm/y8ZFJxKWvrr88N7/TN7wH8n+dDFPveyaYr7o4hcRImIA+Yjw/5mW1a9X2rzaHQzYtc7IuKHIuKzpRSyFfztGL10/lMp5Ydj5GLxp26irUaNGjWKiJt4+XRd9zux+RFy7/qin1hiINTPrww70h/k54L/a3SrkA9OfQNdiQhuztv+iqGbfXqTH9AzFyzhCG9yXtZrqYwwMyfu63306bgO6gdCOF6r8s2mHMj5b06PL8WLnEagSnAhRzWLmpurPF+dXjCdz6NqB7na9TlwXbg/0+CnRsB1U2qaAeLg2rOsFSjA106Qibm7zy5hewWpvE2lgzwQGA6EPGKAfECJQllrGvQ5S3D0hOYjH0AZMXbH6JcTfZuhGsIycprmWw21XkkWFZ5RC69g7q6ZsmbPwrBOf58pR9ArzcwPL7muhfVkDT18B0J9w75wXR7Pp80lPWRvxZUD1NzvL5+DTRAP1MIrGjVqNBHa/nO7dsWAba2qB3v1mocL7jNBdVXKAL5CV+CpLBRpMD6gAo5iFo9eZL01NtCVnKA5H85lfVyBbcAq7RV+irZ135w6+WGrc1wcBO5xytgWpyFkd/HBAQQ8o1eyqLTV7AgW1TV3tsy6CER7149lHRooybl4P42wTTrtKex07aKQn+dnfn8MCSvTt9p3rCdrB5hxQ99+siwyMObTJu2ZFORZS/wHx8chc8lQzUtav2mt3d50T0TEMc053WAOXXXyrL5cXt14kfXISRdfZ3swI+qPqHT1JXq62hHGD6W2r+vG260fWcwB8bheqZavOyJixn5vbPlaFJRfb9SoUaNtpfbyadSo0UTohrFdX9aHTZcu9scQ15FkW+LJVlAt6zUdaiJWEGcEdHUJ4OMqV3TjtGFGZmEdW+SGbPPjKOEOPIsS2cU47kvR3MfsWUB+pJXHPNVcPq9E8sV9JgIgbqL/QxRxXd8KIof6MWsyKiZyYsvovkfn43iHeIEU6kf45ITn3O+Od8QMofS8y66hEAXCI1K4MhnxgowEKKAfsTonsRbw4FrqvvPDSy7G0m+e8YxKzzaQj8pmD/pYc04bymdtYWZSqHfOghAxdivA+PLqSh3miqOyXb2AiMy43IkSxTBzTv/deENT19komvQ3WB36jZGCGEI30PDc619qbFejRo0a/X7Q9iqcuxi9Vh1sCaKs7hr+75qs7G/H29tNiDB2WeV7N3o/TmZFb/JjepOfcZ90KfUOnR+27Qq1HmnkE9Ic+cAS6LSeccY6cr/q9CDH50OD3KMHLwrSPfu6cZUDvzcqe64rJNd5Z4FXmtd1Qz48F1d/xuVhEVmxS/jL3VaH9UDZ/yJJYuxc3fs0NgChu8ET5pJPMHJU8ttq8416CMjWueYBTQRK6fXKmS10jWlwhTXzcVIlIPP1VgfExN77jEr/AfH8Pm+2yj2GSqjf52s2G/fxFHaD4tvRBGizn+KV4fcR4/kEwV1wSJzcM2j8kosa7GEtFmjVDTxsrxz24vMBsq0d4x3RkE+jRo0mRJPR+bjcy2s6e7N5t3KSYqGK18xtrEL+mZMq7xlX6TlJDjKMGLv9/18qYQy1Q/LQNcFtHF2dSvbN3WKDDo7gDi/qwy4T/FeSQxfT8+ZxlR6NMVVw49Nb6BaOxEZCTq+AzQ0HvjFnrq6DI3Lf48BFf1g3rDRtSiOqsUafZIKNC99JMLH+f1Dl/2ePADxwCgjI5UWrk0908BxKTBU6CtbX16xLdWjHuXqfUVJo9xYpiByVgPzYV+5SwXPR2bCVXGeD3hIkxv6ooQjmxeehd55lAChLXxjXybHMWUcZMUZ3OLru1wS5syN9azqfRo0a7ShqL59GjRpNhLZX4Qw5dkfpm10mK4ex9aRrT+RDACPiaeHBFWF5DsiLGCsJ/6hKf/PyGWhJRPBDXkk4EhhLMPUgixraS/UNVHvRqvQSkcYxyP1NxLzGfFB1HjK/glnNEcrf03TWRK1VPssm61IsY0Thi4LTdY5A5ixmePL/nNPmoLTK+y0QDSX2mtZ5zTS9a/PDdr5bst632TMQ//6Lyg+q7GwPLes8mJdkfyd+zOPhGDPKcRdlENWR+lhCV8D3ylMm4vZUOSJWNZ7dErdykvWIsSjFdv8au4YZn6WbS99HRHSS4a5J1l7MCZMi4oA+M3eu0F/LB0VqYtzjG5Guj49Up+dt8W9nAGpvVs/0vdzOam/UqNGOpG1FPmV9dJTsiiOffPYG1/wVmt6y1ToLw6oo0Nwcf1IlSjvnSEzEebGAA8AAc1jbk9OlCd1ctIZQnp7Tdzhm/Yp15HaxZMCdm1LnsQmLRcOxZ00bzJE/z8KugGI+IG6U59w5U/QuXx1WZ+odSOb4omt7NlbigMdu97DONdfC6to3ims6coLzofjmWb6s36CSmKTe19I4/XUhHs8OEDFEcqAY2naHSlANqATdq2/T/pA8EtBfq1TS+Gc1yDOaxNevbajS7zdHYBklswZu0LhbiKdXIrM/DWbMLw/v9y7eqXkjw8M1JTPydekPWGCBuGjjOB8EuPkAACAASURBVJdC9hmXj4et7MnpnRryadSo0URoW5HPbIxcxx83uZ+8wwvYHPP5NBG90LpvefBvLJod/bA8qi6BBlT3k9YMb/Qn9Mo9bcLwCZW7c1yHJcLJuVggD/M4JfY1I6SBjuFNpoAAhGBSfWJ8Kfbphnmx6BUpAAbyM6wapYDGfLspB+4TZ35C5WVL3TejvrAMAJWUEnr0fJXkzXYE1nv2E8EvVjttHPKIuOZD6uPrbH5zHh049U9Vnp/1Uu4CcVuqAyhxAHY9HTHj94OYQFW1PFG9R8je4ffFdD5w8mvqyB0au3sekFmylkwTJAoiBvH4luQ7tv5tQuOu3wIVseTuVkC91fS/63z6ecven15Ji3VIDZ3WIh63H4ePrUYN+TRq1GgitK3I55YYWTK+1djOr6t8CraDycEF/5Q/F4555plxFRjQLtVd0f0LlTNbj6Z8OhHjt/CSPOfOiX18k3FxmD6caL1ycsB1NbSqpEOPnhyVB83cdY0QCnEJZyi9VUX9fqkWrUkH1Ld7NJ+erRBLFmOcseMaQAj9AXayoKxaA9mvcyXdE2Ho4cDwi8pZfb0OzfMK5SOHMUw6xwTMgSB7dOPHYCi68Y/EkP6bfb4rIR53IGTvgEhh+LVDFPvF0sT6D4ixHhPiqSS63HCIpO9BgjRfRF/Hjf4Q3XBR157XQu0ylIL17q7hLRER8QnVP6j66Nu8j5sec+wQTIvM0s9o8K478hNCatSQT6NGjSZC7eXTqFGjidC2il37IuKNMc7REjE2az6FZxg2Ucd+gnhLwo/PCSNOdxuqjA/pgyq4+G6JKya19WIKsgvihR+x8p9phqAwBYRd8SCxkyofVSnxYMrErin1A6TsjmZIPi8Jeu9RxxZNlpmSCIa4h4L0lAURdWoI8cIdITGH8vxjqutxRngV5CNRPOH5Bg21Jmvx5Pir1YVhFRcNgeiINCjVLcyo7z9KZabhuy3bPWIT++p3VHosEmNFanKzPJI1XgRsIe9Hf3gj4m8ZthsxnmPuh7NbkP/AcTFiuM17MzWbj0Gbiwf9n2NwmjyXQrmN+XXl+h1JiVzJFtz3m58O8+OKa9QZOceWj4c9s5mzYUM+jRo1mghtK/K5HqOE13664Ek+zKfKhmCKuE2fN12v2zXjwuuyGWLm5dq0KWrh4k+pdMaNQxiveaKnXWnWie0c+dyovAgr8DNneAiaY7GCK5Vshc9pQEeNs/U6PaWv2/3pUblobGJdDotzMnPeo7n7vCkEYYxvVPnR8aV4UX3cKwQG16rlawFVPfmqSiVM9no+BwO+aOuymhIjHciuDDE2cWcE4oRJmGX6tF2jPvuDDAXuvAmHBp24uwSgBgT1uYrZ+Jz6v4rGXB1aM3h1WvEut2t/YCvw8bDnvsBBjxZGhIL4OTrLXJsGfrf6saw553huB5isGcjeEQYIh3ng5+FGAuZ6nf2NQ6JPqBo9o+9YZlfk81NwCcOpIZ9GjRpNhLY3n08p3VQk3yUC9Hh1c9HfsnqFHiC7n77+Q1aFtzW6m7k36YN58HEo22J/Ruz42pQEU8yUcCjXDcCI5mEt4Eb38NJ0cjwOw/LcxZ/rH6qyZvOHFXH4nyHDdaVrvENjO5uCUCPG8jlDdFUNeoecx8eRIJ/JH4xTm8v9qLWySfUxZ6MaD6buFy0V4h1SqpxlcjTZrsujj5i9AYmeTxhuDoLi8TUzOJzeHf+Yo8/xgfX1Y6yZEBqvxcbo2t26j72zbvNxj5BwH7Rp7J8cTqxHh97P0GafR0hIevb8sDsREd+lkts8F/VpQdrXC4GCWDxy6Jl8JBQ5pio2+AVduzUdUhkx0C22fD6NGjXaObStOp/pGHGwC85i0QHk5LbTG+tc5zWtV/qTxnXI9Pc0uY6lhzlownBvqBBreauFCryYSoDGW8dVeq7/CbEtmvauoiuha8j9nzPIsFsdWarFM8B1YGViD+uVhNWoH0AO02ZWQRXBmD2lB5QPeXCrBHqbpwQZ/qLm6mSlPuqgX+WCg2mtHcM5YCaks0IYhzXplwRHBmk3xD5XNeeAk0fMsnfQTWgxBimua8GBcUGDvmzRmqzx/RrjMyyiR5+yT9kz+cSMGB+dDHg/wS2GGPgOQP5gDmGIcSAm+qRbashH3/11/f+zdj/oH+vfs3YNnRv7gX3uyImUHBxguaRy3iQFfkOvTgjfc3y7nrFGDfk0atRoItRePo0aNZoIbavCeaaU7lBEXHCtpey809Ja9qZLh6MZmgL/XNlHgnLhxzVpSqefGleh6auSk47a/Shkv0Xlh1S6ohgxg8PlPlbpHlAf+Akqr0UvP47clN0MIizTd2o4oh/rGwWhP6M53G8KUsaTfNEiYiyWIK38sMrfsjroSPuzvTVn+23O/nuVKE//SzrPPGI8V5Bb6mnqRYk5xypm+DNq8x61iWLU55zxoPfHdcCby7rkmvmZuXqCIDHzurztfcM2uf+yNXSnOoUNhTXw5P/82si28LCZ6osmB8dWJG0fK/fj3bEsUfXbTa7+vMqVdE9ExEva0Mck6mJQ8P1JHB0i3mOM0UV/dA1MOvvTROb9Wqy5pnBu1KjRTqJtN7XHdMQtxj2z0rYPI/D0a1A6grimLt8reMIL2DOrrcL2ainzgAYK852SZ9SbrMo3q0S5x8veu4pic03c/C6xSnerf5vK30ah6Rpr0MtWbIEjdzRWjttxrnObIBdKRze37knVUVBeO26VgBisFezcWOSPCq2CZn5aSuAZUwBj0gZd+FyxDHkP+HFHfJfBoVt9aecl+UnMaO1WbV6PaBz5+KSIMTolSuYDms9XG+TwzAMRY2c6TzaAsYIpQqHvRoucsNNdOUAcPAsXBjcWsOeWQCFC+rM2QcxfbzL3dWXPC66+VY17RoCfEmw/IeMA41q239uUnstXy3TazwnixzffkE+jRo12EG0r8pkqpdsdwyBJWMAMJln9f6d1C13LIrqiWjIU2AxstBZVl835LsP2nl2prnGUt+naJ/Q/ep2XxlV6NEFziPSunsKsiWzvDl7Uf5YPKVfuoDHqnIhhxyJ6tvIK1XXktaB5e7WuMWXejxU4WE4E7A6VyUvxDvXxrNnw75BJm2n1YWSnQHIwDdaVCZWu4ugjw+74/ae1P2YFRzx3EM9lyT3Yk23FkJlWDxX4HpWoENEJursCyCWDREc+IC7cEjygEx3RSaGIu8lNZXWoz3dsD58yHDLxalh2D1Ot1VT62Xv2xiva2EfUwEVN8G7ryJJg4iF5zKI78jAPUNonms6nUaNGO4nay6dRo0YToZsWu0op0xHxcES80HXd95RS7ouR7vVIjAD/D3VdVzGWWht7Sxevjph6ZPwdcBFEV9Mld+kaUteKvTr36EvgNdDZY2dmVWcZrHzf+BqY+bhkD6Crz85j4HNhy30SWzwPDhNA1T5C2OqgpATWu0gG9EcsWAYP18Su5eHzL5mmdubU8JrlOY9FtTmjL4lwdtGMR6ynCHgXEw6lulc0n280l1rWFYfic66QRDGtNXqzJulTnnwI7M5Easz7Dd8z5310ujbPfTZnKE2RQDxPE3sFCY/bfM6+WyVeu8yVe0DQNpI+bgYe1Y2IyP0u2uHZfCjV8R8V+5G+XlLlY5c31ukPWrD7SfN7QBdZu0r6rFhQaoc/KmuFazD4zP0oxc/a2t2l39KLXwax60di7EIQEfEPIuInuq57IEbi+w9X72rUqFGjCt0U8iml3BMRPxMR/2dE/GhEfG+MXsx3dl23Wkr5poj4u13XffsN2hk9zLRjff4d6qgcHCGsclUsalqv3TXXOmLTVZ3bBR08fAoQgQ7Z9c1wsIcEWQ4IngyUfXSKXEHixs6oQTxwK7ioIzm6PZfKiIh55gbTJc90W3PKAH9QpUf583wAgh88x1ShIO1jdlxrqedOi6MyVz7lKFYfp6958DHOuriutt3NgiHhevC7KjtPyyektOejg24N5myd52vt3yxNtgHsXvmcT+eOGK9ff7SSSh8rCJL1ZL/42udpZN96zBzPAPG4K8jTQuT3arEwwztCIOvCSZX8et0Zlr5iYfejmRjTE3rWfj3LERyfWfPHK4iWecxB/q64Zh4+8DKRz09GxN+M8XwejYjLXdfxzFMxjCnrqZTynlLKw6WUh2/yWY0aNfoqoBtGtZdSviciznZd9/FSyjv5ulK1CqG6rntvRLw3ImJXKd3hiAHbWU7lgrjYmnFPEA7u52t6NRdTQBTpD16n8lG9kvcYdOlS6dwP+f62ZKb9gtVZSkon3vIut/M2Rq8DU/a3PJOOLO7qnB5O0ABoz0zca3r+fRo/ugHP7odai3E5qFlKJR/2dRvrEO6yMrexHegdWqszEvwdZV3mBpVX3VtP6/o7OQGPN7Ay7A/6kGv7N9a5TYiHR7gZHC6edYzeNtx8MTnZRUScBG0KrvF4z2XEfiCxQtbPRGzME/20bwy1DRo6NPw6IsaID3TDM3w8n9DFN2o+PDifPfdazTFg1R0Zn33LqDyrGJDbnx0+KyLiIbw1syPj58Z1DrhbRoVuJqXGOyLi+0op3xUjZHlLjJDQoVLKjNDPPWHHRzdq1KjRjeiLcjIU8vkbsnb954j4ha7rfraU8i8i4jNd1/2zre6fKqXbFRHL9rYnmz7c6hTsx1js7UIByMv0eM64aBH3/Sb9j1XCOXVWSQyyrtGW2NY+wQj3z7rAP0qMfEhRl458YNpvSv87ukF2JRTDD6c7D/Khk/2pf+M6t8iKgJyL45vL2+gf4NDOPeGSMKbFmtktnxJIJ31CBau+WX3lKOAzFTf8dVi+K/M8wDhiPNmOjoB1Ys19eIG3I9PVwWeGtw9CawRlH9TJgL4e1If792tmY71Dc3NWHSAzo3Nv5pO5r1kR0eMw5W5Ry2EZTIejEtaYpeoDXK3Oa1Idt7Sy52jnaR7qClBN3H4OWmSCvCEa0AAI6H6FVXm2X6wvv5Ph34qIHy2lPBUjHdC/ehltNWrU6KuMvqhMhl3X/WZE/KY+PxMR3/Dl71KjRo2+Gmhb06h2ISho8A3oBcQskq06g9WXU52rgutTFlK8rovXdD/m5LMGnUkHybE4fihcrziTBhFY/gNW5acFP//cQ8N2ftPqcB9xUvjUuQgA9N2b6kSYrjUn8TY5ATSLuNUrDW8f1+knTeKTSzK9wjsfhGfeknv03WIOTnMvQ8m/5AHiGcX6uk7/eZbvuBxrR589cTvrJ7EAJeZdpmG9JnHrvCZm2vvIoyRundx4qV8Huk1Xp0wMPatFmpZBw039EPFNPD4fiuiNL+tLX7Kc0IAh+trhl4lB5AnEYZN3pj4zKtlzrngnzuyDfIH4ZJ2c0QB6P07cJMwItKDOrmifrqkfz/r61ibJqIVXNGrUaCK0rVHtu0rpjsSQE/AyhcFe4EMtPFY3YhJ2ez9ManH38IujxoXhGsiK3o/PCGndl0y7nlsGznRXuvarVue8tIxF0AcE5nras7K93opp1TXWsLkcBm5sYp80kPM5kt+1432HVLppmvrietNikZ64vZ9c+sYAatyM9oBgtTNz6ZsrLXs/fpXZFyKiRwq7tHhwfAc36+k7pmpg6f16lYzjM3aNORfXvkVz5tNBF9kzSynsJGIYsuH9esps3TNKgYAy+/WGrlAs00Wm3qecDAocHd5vfN+oOXmSbz403vRJk1XMVs245/RbmlJ767YHyVZJ3T7pgcMZvlxrUe2NGjXaQbTtOp987hgMGebDAWl/1OrsFdtBhibAzE2Qc7xlxYnuEWJwt3GYxNPDqiOSfXJFJ+FxKJ0HjWKN/CDpAcUhj/vRLUIRHPi2qvG4Of1sRm7OmZigfcP/iw123n36I8YIxhILrWUztiFAgnAXhUrWUDy4Lk5jWmfSaifx0W/gHazMUOseLdoikMFN5CzA8XTNWb0maUXKilO4+BssmZbyLjsS+pxfVcJtDvA7ZhvxDKhB83hVc33VkOQd6hNfrajvrq/LBxv2ekNbl59HMSSFnfnk9fuBvcezXAjol4i54wf0UqVSCgfyZ3TsJ61ZJUV47BbiWeLHaTqfJGD0v999tocw+X8q6tSQT6NGjSZC267zORzDFAHZWQpymZ44QxBPd0If3IQk9rBLr21e9t6Ov90zXYX7wRFqJwKK2+6XMmFOD5ky1rQu7jmL85W+dyCCGgaryoIrC2CbdBzo5SYLrEIMCOTlnnNMKMGnsZFg/ouwKLcycQNtwzId+dDHnO3Q54w+wYV9zfKBkSh0fByw4YwI3ZFRXB8dCVPmmRnZDwxx1Z3qyO8sdMNa+f3nNTYyANYOWSGFBk6FL/AM2/DHNZ6UTSQiLMPnyrA9fwbhHCz9NTUwZZXWT3Cx0gBrxEQw546cWD/ti+Of31ilYx0S3NxjJmRTEzadT6NGjXYOtZdPo0aNJkLbKnZNl9LtjWFM1j5BUiQX3oau6OUz1sA5cvaYYhKpaSXVreTO3hjVHRErwHjEm1pQFjhSHbr9/MYql5IzHIh33rOZy+GtD5J3nwEFbL1CnoiMx49oQVwDzdcim2kSeO5Ky965kGT9CxufcT05B+6aH7YbEbGMmPBKBhIbKTv8ufxHp5hzFs1t3MwbyXkYrAWr4Qy4jiwkhfyDln8Gx85qJgEc9LQut6vP7tz3AhPJ8zUvU2bPX09rf1176pA97LIS8nA0k5uvUby/XaILc/0Rq8IUM408vrK9htpwiPVgPMiYHp5P0KAm7dZu+KyIiC6fvihx2pduHkPExSZ2NWrUaAfRth8aOBMRq3bw3B6xIoDHdbiXm2STCfc+la4QxMyJ/gtm6pyacAQ4gwdx5yNqeLF7Ny7yWlf//xAmWavD807CisgTfdIqgaDEhXZZ/hrQC9Z8hn5qXKVnXtlB050ms3J9/pbYSDkr0/WN127XfJxjQl2ZjGY1n0V8xOpobHtUd9HWfkMCYWCqWyQ4zc5t0hFDOzqaUPXtXilTPdL72r5hnQGL1k9gl2+oGOrWe+SY96dDayl4CfsBEPoxPWso94U0dhkkXVFbDywMu+o6+tPqVJ8tQMjlFlu73kWglryIa2wsNoon/UEZzX2147xz7AZzaCH85ifbkE+jRo12Dm078ikx5CjZ6ZAXsPuZXUP4li216O3qzO8Sdlax/1nBA2fCMH8YpSMfGHKydg5UFtfJqKgp63TTKwxyPIe8DBdV9Omdnx3XQT/1MJzJYMot4jKACERy5+JnNB+7hRBwArvVEAPTAfD66PhSPycwuHlx84M26dfgkCCEWhIkGsdlfyGVEWO9A/l4rI9LXEOpx/Md+bAAR9M1T4STD1bkf4etXKulsdS6Hrw6fMRS7fho1ox5qIWtsGjX0vcxzvGDR8fA5K/+kpWz+9pROfvxcRXUU09xHxu15t7AQFwXd2uqk70FI8ZQnnXZys0iiwi2docF1y815NOoUaOdRO3l06hRo4nQtp/VPhNDFLgGvE8nnR12eK5yXhCRmBNHmqDyjHT9WSu6gcP+XPeJCEKbq/TLTcMo0wQ/j65sfAZ6vxVkwmySjOjH+hppIp9w6J6S1D8gUcSVyUgnF/WMWySTuWX1JI9SoNCMnSODFIv5/9iZYd8jIuaQf/MZPA69kSEQFSV+XXM/iWRqnzHl5SoaWSA7e8AU4XtlJVg4muq4mJAT8mQ/A39GTXzUOh56adj0NR+r2uS88qVKut9pTeAaci0ika8vC8l82nlH9+l8FySjT6GctoV5ndwRUGLPIYe5qJs9z12MZfHxqkcH4Un7szKaH8oJq4MBgPEgopoi/4jW7mITuxo1arSTaNsVzpteU9lnMDRYsktwJof3uDKZF/hsKt0MTn0YQUXn2DN26jpDyaFIdNGZBtdoGyW1Kz8PqiGOfymm6O3SaXL9vLhpWIPbK8RDbNhd5ijWZ1RUezNmr11Vo7NajWWQipuN3z4qDvy8xtGn9xtX2SsUg371HBze1m5WytPeIdHhak5YwwL7LmEgjL/Wzp5Ul8l31JqVsKawPqrn98kJmAc/iY6FeCr9/zVW52Rqm1SXtvazMjwss0G/aXyt10JLUbtXcHyhBhFOjIpb5Kw4OJIIPw2N9djj40tnEircJ+Qyb2t2h747m88uNwS3T30D/OIKsmr9mNLz1xvyadSo0U6ibUc+0zFkbHzO3t7nnQvDyeBe0oe4SE5KGiyia9JZ7LVQ3AU9hCNB3MrJSz3nYvFgcgi1BWodV2vA5L5V5X/gHuN++8Th51OYQ0RE/HmVv64STu3IR5x1v3QsczmdXMSYreS0eBGxWychoqtaqZlSNefHVOnM4eH3owGoPJxKX+C5VPcBu4a3p3RpBythHiDBXUJuK+ii3AzO3J5Umc3QEeO5IQ2loYF+TILJezXmhddYnZTju5pBEOiECwJ7r+ZbwtgNwe3XpptjHpmIWrYCoZsp6V4GvynSQJxU6crNfBRTnpeI8dhAy+g6HUky58kVYuapcZX+6KGWybBRo0Y7iSaCfJxBw33pBW/Dmj4Gpvd6lR+yOorX63U9j+nDjGn6MZiAbjpPl/h7KsVlTpDjxao8phImfl1w64i5lH9vqgOj+yU/9E/PqOUjxup2Dc5CEK15GZ6DMwoBzaq9ZUdHvdlOpXM/Jhs3fnVgd63KTPqidtSGnouL/1V37qNPQNrK2b1/RNkjf5sJce9RJlJH+N77vlH5/GutDs9gHVBEeFwD45cua+Ck+FL6jk00iMZN34ECfF7JZZMtYZ5ycxDTE0MLEm3TH7X9DWbq/CioTnNN5kC3hq4jBrCZHL4znyl/9rRZIdewQqbc6YOxgq4+PCpmVGfV9wfjmW/Ip1GjRjuI2sunUaNGE6GJRLXXsjpmf6ianxpIlx77gYD3C86CcGvHS9M2otQjf9wuSgl7XIrI0zqz/XWfH1f5PPCejqhjxUSiLpkn36KHemDw44gyiBeuhEURmoO7/nBsJOTOfEh4xBhyJ+fNiLGj3yoTwQT7ET6IIEB2oLiftMhnLdaU+r7uZl9geM4jE9Gbol8j6P6ERKuB2IVzJGICItUzVuc7VT6UnuWJ/bPJ3v001tM11tNP9ON+5hXxx0POWeQktu0zmeie4aV43hW9/BgQHxmz1bnjyVHJsp5jXTwfD9YT9qI7GdJvxoPi3l8DElf3aw+wZJ5llyEu5cMgXaykzcUmdjVq1GgH0bYenRMxejF2zhn1Bt6/Pvh3gBR4Q8KQlvTFQXvL8na+LK45rco1XdsjUmxO/5r1S1zvtLjMHUI8rsPt2ZW4L0fw+jNeIqm8TMwwJGfCRzRIwiPCopb7/DWfUAln+6DV+TaV+QA/O1LnHeJev/tD+uID42urwEomFG7oClY4OtyYLINu9p0fluviojOnx1Vu1ecLKGF9x31yVPTeEJwZ45OFxp6xgc6+y+rMpDqM5zGrwwZhsXwcjDU7J56xOjzjhMr+7G6rw/0pR868JWN6gvtAnZ6VnX4wxnyKYFgOK/VnjxD7ov+mQMvqz5ShM7It9s84qdJhiJDSnJ4xt76xzhKoBpRJHftNFn3eTLZqyKdRo0YToW0PLJ2NiCWT6QkRgBGtSM6dNQcxmC/i5RUF491uOXK4di2V7hfVnxryJn0wEzny/Z5PD+8755z6zSold8Ph9lgGvD45H/oCuJZ7NGKipkOGOI4JRZxR28fFWU4/OK4T0keBHPrBuj5Fjm6v/d1R+bg7w8H1WHp0Tr9jdRgIOhv18ZAdq3sZszkcGuc631L0jQV2v4L+dDqVrMvvjqvcpbZeVBjD7kc3NtMjHpQSrJNXYs1ABY58spPlO1X6aXegvHepfEKlZ1hkzzJ36FPeYHU+lp5lsTn9WuM3gquAoxq1/bWq+3E2qqNWkG0tKVU6UwqEvm6hE7sTol6qnbvE0dKac25xN5pV1nWp6XwaNWq0g2j7A0unhgec8VIFDKEjccMNb1OU9ividJ5zF8MAaTdm9GZ3qxmM9hrI4Qvja3eIs52VzueIOLy7+i8TaChOPy1us+aKITqSOj1IJQEqgvu5E5rG9jWaiEffqe/N6rbhlAjaea99hy4C50lDif3zcsjDSauD1Q1OL4vUAF2BOOCsjNlPSFSb+8WF5wxJzgh9rMprdFoowodH3OXv0Uf0SR70yfyDsuDibq1im6ND+w92jc3GJgQxOAIAMeAICdJwqxkoV2jmkHROl19pddSPPUI1i+6QCTrO6TR9rCAlfjhs0EHuGJXM9Vzl2v3pf1/X/jRLlSdTvyLGc45ejLnzlJtAm5WGfBo1arSDqL18GjVqNBHadlN7dENUDvJHR1ex6o3FLb44n/6PMWpE3OJ4npXFjXWuSVk4ZWbBXswTtLxYU0wCKRG3svk1InZLdOASfVz2bHa0A4S3bHZMxKM08MTw+0HjmFCJc/ppq4MIgGjksUyC4bM6VK/3QfOFwbGNg/eQX10eRuGMQlRi8B6PGBflgwIiIlbTJDFEl2J/70/qA3NWSzLPDcwnimczcd8qEejK96jLti/u1Ofn/pS++A2VrpE4nb5D3HG5njl+etjVgewuE/siIo2vK2Ki1vUeciGZ6wGSD7aSZebQ1449yx5wZXQSxWalevDpZI37eC3Eac80ofVAUryq9u6smNrN82JADfk0atRoInRTCudSyqGI+JcxMhp2EfE/xUgl+XMx4vsnI+JPd113aZMmRu3MlC4OxIALzwjqgHhqx/vCWJe/UR947Ztya0ZK1N6jHA7tZkZx6gN6FTtD6m/kvvn0f0TsFSeAafYzZ4q4KfI669gTkMM+UypTHSbhY+3RFEpG2Kc73mHChVPCkTyKmxy9KOUrh+RtgJs+ISwEXBSUdUelDu3Q90djI6Eg9fBrOPOR9L+7QOTsiLU0lLgR0B84tc/Zerrm85HN1TlvdcQY3cLqQVVuW8ZaAgJLBoqIGMMA1tCdFJlr1pH0DS6f9HEVKvnFuTn8TKp7rtAN0gAAIABJREFUr12jHmErjMfcX0BDTPHaq1LdiHin3Dwy+P6l2EiLLzOT4U9FxAe6rnswRt4Yn4+IH4uID3Vd90CMoox+7CbbatSoUaMbI59Syi0R8emIuL+zyqWUxyPinV3XnS6lHI+I3+y67rWbtaN7uigRu+yR+LvBZOZqOYfhiEAgcZu9Zr7uY9h4s+eTDCI2uoI7F+d5yM50yHMFw6HRbej5A9d2uC8H+4n7LvlrHs5IP9yNXxxxz+nUtgdb8gxQAe24/kHfHZFTmwPAOVYJ87VOTRggH1DV5VT6fL5RpRwz79EinPKxilsevLzxER2IJQedmiNjP1fol/rjQawOyI9DHcnu59kOWVc2igfR5kDMnMEvYjzH2UHTn4HTJ46ZIB8/6xr3DhwPXT9G6AdjpF++eKAYEFQ+8jpiPA5QpukbZ4SqAOb4SPbuJxEbPXUZh+l8Xv3x4SNOycG0PDuu0+cdfxn5fO6P0TL8m1LKJ0sp/7KUsj8ijnVddzoiQuUdtZtLKe8ppTxcSnm4dr1Ro0ZfnXQzL5+ZiHhrRPzzruveEiMtxE2LWF3Xvbfruq+rvfkaNWr01Us3Y2o/FRGnuq4jW8rPx+jlc6aUctzErrObtgBNRcS+iFXD3ueJ7tXdfaJwU+IeF8S+JFFoUa/MwZEiKQP9EcFSd8o8MzWsU/VgBXIjYvkMeS6biFjM6SYjxiKUlJa9XtQ9YXVt+kKqE9ErJxcRexAta7lnidpGNDtpdQThLyICuLcvSs4sbrzd6gD1gd6Vo3N6he6JUXG3ILd7sB+WuIV+813jS/HBv6I6f2tUXiJZvN2/wvPx7AVf1xI1SezscnLziPHuJCm8i3bEpFGf+z2eTubzXqn8DZV2EJuYF/bZ3xhXefAvjsp3SlT9F39+fG2DVzr/u5sEagXGn1QRg2uMw49Ukhj7kPbVYbV90Dzor7nZPqI/LuhtZo//iNbhAPOqfT/IWHGDt8sNkU/XdS9FxPOlFDQF74qRqPi+iHi3vnt3RPzyjdpq1KhRI+hmTe1vjpGpfTZGOeT+QoxeXP8pRm5Tz0XEn+q67uKmjYza6aYiYt2dleC+KDRRFLvpEOVvf86vSm+H42x1bUlIw03cvX46J0WPGCprI8YIyKDTYTVwie/Ux1l7xjKvc7ig7tlraG8hH+9bOS6ZNpdBLM7FN8u27wnxmTO4mLMGUBhzDPr8hNV5tUoUpLTjmffQWhL3RbuedfGfjYrbxWE9zOlhFJmgERTQbq/NJzN61keIfQHiyCcORIxRIUiw5pRHvBPc3NFqcjDdcFyPt8PzU0aAiIg/LYXv2/T/jzoUBKGgxUWp7qdSfr1KYvXur9QpqTwxvjTz2+oqUKI/G9ruZ85Za/JNuRiRjp9+lfa5T9nHWM9TdYXzTXk4d133qYio6WzeVfmuUaNGjW5I2xrVPltKd2dEPO9vUORTuDDmOZcd4QAye0+LezjzusL9oIFKNrpp3Pgl499uZsFzoBG9ug9Lv+P6mKuYfeEo+Qhe75SQTpHc3jlLgBOqr35cMsOfg/uIexyxw9iw/mPlhVHX3NhBGu5vlxnjNZCPowpQ0AmV6D+esDqsHX37vlFx378fV3n2x/UBx0MPz3i/SqG7NwtlfcodADEtuztCxNC2uiH+RnQ0NhJOeWZGJ9RiGbSqvfeg6XOYv3OatH362Xiappd4Hm4RQG3X6/xXlehY3mrXQB/qW1HZuVsASJgc1qAT14GxL9FFGjLnaGiWoVcr1U7Q1HzuE6qadyRJn3Stdylx15Rx/vAW1d6oUaOdQ9ufzyeGivmON6641/4UwhARsSY0MSs0sax7dplres7507/aPTA0Oys6YgGNIac+Nmw3IqITZzx6fnjtvAeGet6cGL/d3dh0BgR1pvIMEJsg135xzzlDA/fOD9vGmPKXY3O6zz7DYPv+w8Xc+geCo6/wLc83rfpHpatgyl9wdCOE8teEVn/y++2aTt/oDxtkfd5odVi/HKnrjnvo69gPIDkPj2CtGZflcuqRSs7B7CdkEOrAPBB86nOG4+DT6Zqt3V6hqQVQ1v3jaxt0meiZHPmg+2P8tUMhaZv80L7B+Kz9fkx1asZDmp5T3d2GLJcEu2cFt0HhtSm//DLDKxo1atToy0rt5dOoUaOJ0LaLXSXq/lC8Bddr9je6mJXSbl7M8Hwr0Sqb7iMsO71KKQ9n7Bmr6UDAvl8/YO38ih6Fs2TFVD4t8WBN8H7Krq2j1CPdpkSzRY/tymk+1a93G3bG+g2C92kYZ7cc0UuVOr0oghL19MZKxzXnp//fUXngu0fldRdDiRli0T9m11BsIqqiHV+p1MlR9p4/QeLaHok0i7gJeNJ8DbJInO7c5p/FFMQ/l5WRVXMGgRNWhz6h4M0ZCiKG+oSIevpT6HClDnv/sVTX9wcLjBjqVpMUe9iLdu4om3MW1Q58hPLv1Z+FseDJJnY1atRoB9G2ZzLsIqIz6FP0lu1f7quxgXoQw1taXGjW3rK8rFfgTHCYWtQvqMK40CIzIQXg3qeH7UZY2IC4P4zxBVcyy6S+AtepRNCvJY6yfmx8reewhJKAyNyEyYTo2n0yc37YqsDEs8e/d4WxMWUOBp4E4aDc13y472fPqOWycJ3jjt1ZMdM322c6BdfOcxYxRj5z6dqrrI7WbvFwuuZcmNAL9oMPhPnU2t2i9bxqB/rdrfnIAd+du43wJdpX3ANcuU3/MbT4L5B+gKBA6D4fOfznUPo+YoxUKGtHGeX8Rj5XzDljY++5U2+2WvAMH4/nKqpQQz6NGjWaCE3E1D60LedKG7+fTlX6l3Tl1Tm9nurUnlVBQ7t1LYvd/iJfVv7fnmN/dFT4sc3Xco4anMA8KBUuDspzUyrfwcW/JT0zYnyo3uqwnLX8RsuYhknY4h6ZQnwc25wjBiIirsLl0Pn8w/TsiJ773vq/j8orSPXfbXX+bgzpZ+zzT9JZlXB8Q6RH9N1FOD2l6x9yaAxmaHcyBIX0J0faNdBDPq7ZzODl50Zln8II3Zy3gz4mH6vjukV0iKALX3vGzaajPQ/Z5lpC4VF37lPn7Vree5T2W5rVfcv0m2f6nPsROU41s/5a0/k0atRoB9H2n14RMUA1GehMd8PvB9dSuZYPz4uN4KZ0G6/VwB5NrSf5dtkDOj+oEj2M2nHE0HOEzKGNQ+4TUponuM/T+7EioCkSmbhTHfI2WR/nh7dERH88MM1N2TMY60XpHW6VbO7Wrj3i3vv03It/WxccwX37qLjCnMHbviM20ANakCf/kX2JnkLXdsmi5v5y9P/VsiDNqfQI5iX3oIwYo5ya7gjkUXHue8MHRuUjclI8YZa5f6KSE51fmTNmRozRLjqfmnUIlJYDqiPGsBtIgIXRdS3Mdd773o8ceOxwfjaVeb9FxDLoaLCxY2ipy/my11IZEbv1eXC0tVFDPo0aNZoItZdPo0aNJkITj+3KEdY1y3i+xhvTe76WvquNKqe/GdTJjoP5OJiIjbmGaudcA4sxnwOrLeScJleVOXD/742voftEx7iCYtMhL3AYW/9JlSav7JFcwjCcyywBuaXQPKo+XrBKx1E66v+aFRwJgtvOIsq4cx4Kb/rv9nzy1ywNuz9wqCTLgOKsOLv9V37Q6hBxT8Q4CmM39UrsPaYBeeA8oWBKLtiP+RGrgxsDkhjS8Cdvi43EhDDPnisHESo7IkaMJzQrg2s/mNikbq0fLnZpf96pPr3kCm8om+j53+Xh/oyq9H/th9fOam/UqNFOom1XOE9FXZm8mcXdP2f9Xe38udyec+oNiMcVeVTEPEvj/nqGk8JlaNBz9cD1P5M6a2hglWfJOW/OTMKL5HXGJR/FpCmMDwmpXOYarNty/vDVEzzX89+gvJbC+ZraO2CT1QM15ggOaTldzn9K/dH/dwmdvOgaRuYG5bK5DOxVvQUhhFVx0T2mhOVY4a/R/32q4SftGfTpIyqZO99EcjU4o3ncY9r1ZzWP5FXiyJfXmuvCCi4TGuzrMqKL2Bi6ADm6YI7pYy2D4KVU183oeRPXzOkbrDh2Taj5Jf3y914ZfB0RY6DUH0elcRUbK49Yz2P1Z1UMQk4N+TRq1GgitO06n4x8sj6HF+fgIL6U46YWNAq3zEkGnTLTeIVd+wKciDZ5pmW823Mt9a2moBL3KleGX3fu8EY2PDLEuY4k5catmmt5LkjsaPo+YsxRNaG7LVSAJgFT61mZFhH7NVlzoApQlus4MLvD4Y6n/yPG81c7LjmHTmRTc8TYbE6WRaCPK20UyHqvdGfPq4+3WlDwFdY3K9UixpsOR0w2jx8ayDywruwBT2DD/SCmms4FWWMp/R+xUam5kr6PSt2a6JB/BK6rYW3Ux1u0Hp5GnC6hlpqr6YXYe7TNj6vW16Wm82nUqNEOoomk1Bh8p5KXK+J+5+EAWEPECXrm6Xlnkx5mpuLZlNGQM895/fOgOMJj+NG7+zxcQhxxjuf7s7InJDoTz3inPsJ1rjrKS2dOzajTq869xMXvULDnWaCMLyW6lhOpPxFjrvXZ4aU1n0+QCusA8qjlcMZKA1LwRZ5Pdf+kXWOxQVNkDvSzsGDDzB8IxJEkLBqkw3x6O+i82AQ+V77G3o7r8hgj64NSzCHD/uG1onY7D/9grWpIIetxWBjfuOvD76YFX9f8VBHGzRpaHw8IAdMltpWfc45KEOtffxZXjnPy/mwRwN2QT6NGjXYUtZdPo0aNJkITie3qKp85iK8/3sbjnXYPir7TxZzIQMPc1psCY2OdXszwaxw2yBc4rlnC9Fk5xaFX/cLVjc9Y61MyjopDUvxedhlPHbgKHPbcMjSmjqymY6AjoldEnkWUyMceR8S9Esl2CWY/U0tULnFnDfHCxQ/aQsZFuXx7pQ6KUcbhzn05++Rv2zWwPvl/+oW1OoityAcoeF2MZc4Q3xDDfM6ZxxMqLQfTbolJS9Qnut3N+cyR3Bn2qR/znk2TrI1qp9cxuGiGoptN6Mrc7LjHOrmbBONWf9b4/81WR2LS1MlRuW7Pz8kJ0b+/394Ed6ym/lcMEhvM+LUf3A2oIZ9GjRpNhLZX4by3dHF/DCKj98h0yZu45qW9K5UwBmeQU6ms6RVji+/Qqc3RgLjXcUNgp0EfdBaOX1H20Qx60XPGvabE/XoTt2cyFProHfDgWq7UVuM4yi3CPd13gL6BLswBsVf0amyHtQaX3NOMseY8Oh76DlLAjO9OmxD3wRH9OBomnf6j1D5hdUBFQie7Hx+VS54nGggrNDJth0H2Vd6hD2QVdPM3inY9g6OtF77W6tAP5oix+7rwHXPEenjkOnNFmMmr7dqnUn1t1Gnr6xroDgQoZHrAYPz15BB68PHxtWtaoyndz7I4sL6ekc50+j9ijFKzGOE/rvn+u6ZwbtSo0c6h7Q8sLTG064nbTCWnvK0sdxndeP2cguQmExn213rGDld2J7/+LGOV5FA2uf/U8FIPajwRIamOASM+jhc1uP1iSXPq7LQtU2+RzSc6WJ19un8+O4FFxHFx0tPYW1kPQwwzcu5bRSlAzhx3qruaSvQ7D1oddBzc9wa7Bkd2Z76I4RHC2RGxlrkPDouuh3lxh8h3qSQi9Gm7Bjpjrn5L5dusDpkh/61K0NL3WZ2fS33ELcDPsWbO2V+D0zFVsolBF6aLO6B5PKH/2UOLrt8CVaGz8nxHWqui9e203zy0Jic3nAdJuT4n63yWNtY5pHlohwY2atRoR1F7+TRq1GgiNJkE8pUcKNMSXWqiURaTtsrnk2PEthpdTfyClvWQYjCyA0YD7xFJXGw4oRLIjILVRM3/ThHR6B4/Mr7Ui2kvpc4N0sGmNK4HBHld33sOsUBiy70G/Z93iB4xnghfF8QkTOuIxcetDspbDoeTovReE2meR6GKeOExVTkJO8/yfuRTDxFb3C3A3TIiIr5epR9pxAZhPVwJnEzkvQL8c1YH0Uwi616JNAuu5OfwAOaFNfBnMdeM3ebjmGT2M4iNlK7kZ4O4+T5ieICm+npUBwteqMVmMR/MXS1yXnv4gOSw6w5Vsrcza+b7A0/rl5rY1ahRox1EE0E+/sZDt7aeyq2QTy3rYQ6LWdWbeb8pWmEgtSSFm6VHWfW4K3Hh40IBp0E+pjSc1uc1OIM41IPmeIdedwku7pwJxAGa4vm+TKCBnHjFuZGeOy14teacMkdU10z+0pDPykmSOfNHLKPYFBJ8per2GQIixpMOCrDo+g3cEjTj6IgHszD02ZXScFg0paAK9yIFxTCfbiJnPpl70JrPGf3GosAYbe1Jtj+P68U96Z6ImFJfAVnXfc71/FkS+uck7RGbx325pSXFOd5q83AFdCmkBKL2ww8PrgwfhdHjNtuDmOaxA5xjDm3OOGp8tSmcGzVqtJNoW8Mrih5Yeygv8nxoH/fVqGb5683W5KNxjpBMmd3SxkvrujZDR7yzelWfzq7kxv3WYGnUEed/zDsuFHCHOP1101/Mw5nz0SwOOWAZcDGFfUwZh1s/of5ghnYuzthygmfXBb04rLImdLZm3G+XdBsr0nt8gXbcHE/9jBgixiZy9BWshyuvQE6gJJ7hRxDnUw9xYvWodKVCLMr50/05u6bDH/u1Bnl5lH/Or0Rdy+z4gMb4afwqcsbLGGf+W6Y9DxPR+Jezv4hv9PxDoa7/SLTWs9pXAy8W8jtpXXr0bZXYgkz545rz8/ZbmF8adHncD9sfg6OcKtSQT6NGjSZCN6XzKaX89Yj4n2P0XvtsRPyFGEnqPxsj/vKJiPihruuWN20kIqZK6XZFPXlb9pPbqle1/My18+4z9f5qshbtM8Qyj/4m559xLkx2wJRJcaB7gpNIKP5moRJPx8OJw6Cyowb3iP+EsffqD7MiHJPl6kzKA7zL9EorhHOo7f2GrubU/93q/1LOMRMx1kMxfjijIxd1creQB2O8co/VwZrCojm0Zbfcmuq6Diy773OP6+JQQDB+FtryIx+VXuoCSMVRHm1jlaI9fwbrSpgFjpRnYiPl4Ep3VGUd8mkp+bP/X9P55DUzGHFIaIot7cY2Hstyrui+vdZnACOoht+m+yoybKacZbrip5Mwf09+iTqfUsrdEfFXI+Lruq57g57zZyPiH0TET3Rd90CMpvSHb9RWo0aNGkE3K3bNRMTeUspMjHjG6Yj41oj4eV3/mYj4E1/+7jVq1OgPKt1Q4dx13QullH8UEc/FSLX3azHKcnO56zoQ2akYAvJ6WzFCzYMIWpXZguhvxc3M8K6DBaHOpLpep9ftST6Yt47skdKz18UCy22GaGtW0J8jXzpX5gL1NTO/BfatpSiVeHLBlI4XUorTXjFo2PkMTn2IRBrsijt4ISZIcTznqTy1Ukvq6x9XNPWveSyT+jvzaxoOz3KHSk3WBlHZx+q5fSKGJuGl9N2JYbsRMTZx543hR85k0a6SOP0CfZJIt8civfu4KMbIBvOAPGLSEHNwNLXjdTaw8pTbKSLG4hJjrB01k0snxshzc9reiLisfXkZg4QdbH9CIjLDuKT9vWBz9UJ279A6fdKeQSzmelbA+/7YUglzc2LX4Yj4/hiJfHfFSKL+zkrVqpqmlPKeUsrDpZSHb/SsRo0affXQzZja/1hEPNt13bmIiFLKL0bE2yPiUCllRujnnhim6+6p67r3RsR7IyLKrtLF0YgFU9L1+ky9bdeWN3Zss1NCasrlnOu6FvnewXXsWN5FGoWjCpXss4Tp82pgOXfAzaUk9sY5j9d7DgGIiH2PjkoHLE+nsIj+aGNXfKOVVoT2u6Q4/5A3nsMZzJmtKJtgpzZ/Da7ufVTofZ+4nud7JkNdW9ZYl1m056xO5vC+IDm5PhPhfgk5lQGo0RHHQip5hkeMMw71Z7DxSbqEMlpGgr2G2hbEOqel7F+rhYKAlEByjL1yjtMuoYkVn888R0upjOj3HGE/RKX7gX49CmDtTeGNIYODEWc1j7sMpcyB5NVQUR0//aEHZfRtLZUR9YTzRjej83kuIt5WStlXSikxSk7wuRgdX/0DqvPuiPjlm2irUaNGjSLi5k3tfy8i/kyM3smfjJHZ/e4Ym9o/GRF/ruvcba/SzlTpYibqZxhL/7JH3Nfzk5BfOecZ8Z7nTIa1Orzc+xOIPRseX5IDRVx0xjjsagpHmE46j4jxyx5GC0NacVabZXrnENmjshZvArck89/3qvx/rE4OSqzlM8beymS5fkao8JYP6hIhA66zUZ/2nxyVcyn4ctBvFs/zVYNUcuCjhSP097M+jMP30JVUR/mM93x4XGWRfDy4IFg2zX4BQcIgGNdZ8BlTOX2uHZu0mK5VHFVrJnLmaHf6SQ5+VNmJlXYcXWVkXolDohmmzJd+KW9eyNrpwzJiCxrnYqqa2m/Kw7nrur8TEX8nff1MRHzDzdzfqFGjRpm2NbB0tpTu9hiK63zmxb0u+Xu3WRp482Occu99KPtj1fyz8mnL7vgHA7rM61icba/1gz7mU31ruaShHvEMIjJ1X417ZKe6fDpAbiuiRyOvMJ3Ncxrk/VjmrPppdCvotzj5wPMJv2/Yds2q0i8IeiopFPZZ8ChzteKTDcEZv0Uluh4PLJVlb+aTo7JmAOq/Q//yrSo/YZVOqCTA1MIi+oyK36xSzxpADvRCoAH0VG51uzNdo64vcIbvHgLCXtud6phO8eBaemzN9JuUoYN0LOm2DMSc6MZS/lFVHrtacVSdXu2/aoGljRo12jnUXj6NGjWaCG1rVPtqjHyQXGfZH4eDIlLwbcnzggh2XgfmS1abMQyec/xsdXQOJuFB9DDyAQ1cHH7tl2opdjYjzlovFXNrzZesrA2/m9b/1Zi15FH5nIsS6uwz1HneriFCcI14HDeR45SIg9oJlTbou+Sc+CKaP4ltnYtdKHpR1HqgEWIJ4hbr4c59nFWfTwjwnct3KIyJUj+5sc4r5BDynC/eX1D5K6PibvX/BX+G9uA+ibHziJouTqrf/YGC2sMzJjatsq/ZWK7pXUklSm1bfCRBpmNG41jfYn/VcmNlsct/CujW+ybzgY1Ria+kY9Mb62xGDfk0atRoIrTtmQzz2y6/iWEozhBW+VKv2bukWPW0tfktz/8+ut46eUIf7E2+V46PC6o0tYUpMcPFWtDxZo6RtfsqTKsa9Ayt5RiSWmpHcaAT4kgn3RmOw/BoB6ThE5q16dhmPWqZvNBoPwnP+HdWR7mrdz0yKlc8tiZlB+gV0D4OoYCiPrqj2wbKeXAcCeLQyFkzJ+xazhrJvLqpHSU9e4Y581CSbBFxZTTUh3+rrC0wUoDaOWAbJB+uSVdryHiro6IyrW+VnxmqmPPz1tskSL8pnBs1arRzaFt1PhGjt10NjfDdxXzOcMT4TSw49GL6mna9nS3zAv1hlWaKXUC3IVayLjm3mLy+WdtbHUyYowOcalbSPgd1jZVA6JHS14OqQgEnM0qKGB+YR4AqHNuOdI5Pq8xZE91JkA5gon+/yhNWRw6DKyCEraBkQrgR0es9OvQ56JNq/ha9v4ZK1y+BYkhK4/3QuA8LFV06kO6JGOvMsuNdxcnwdm3Qc+hs3mF1OJAwIYeIiE4Bv3fIAfKs5t73zop0orv13Fq0SS1mFcquCn36b7vA55zMs4bQN8uvXntGpoZ8GjVqNBFqL59GjRpNhLZd7IoYvvGymLILsefcxjqUe9P/3maf7kQjmzKsSHL4+LhKjyFCYydYe5vErYHiO+pUS2SfoWZNIVjzkO4p2UIHyeFT1a4mouVzv108QLGcY7tcmcxheHhNY3p3czxi2m+oRKnt+Y34juwAW7mnS9Tea6coLkh2OKzxIPX4HPRDS3LGA7Z4TxLVX8n1g1L8EvPCfGyVQpd2fKxKNHPuA/qfcbkifGrYzCDljZTaZ/lVSu656usqUZL4qyUmxDeRMjKsPT98VkTd/O5d9To5R1ZtD+dA/JpKZTNqyKdRo0YToW01tU+V0s1GHSlkGjDxDGsUD7PLnNF6kyMm4ZxnJMw8STyNHRdyNMXMLItdTBlrqkXK1/73url/TtktIGIcT7OYWGMts+OWlI577l5n1zbThjur4vko4ol7cq9LkEIO0POI8ZzPyNEJCmbWkTxFlWfsllkfBavrkldz/xlXTTmuPnocXH/mIOioElO1IccOdayvPIKpBiz+rh+pDPLTvBbbnz3gYe7JDWr5r8iywD6ZQznuSE4XD2gdPE1T3sO1PYitp8/+yG/Ale1ZC62Gb7GGqL7cTO2NGjXaSTQRnU9NFszfuXwJ9+590MQp3Yerv598JWqgpmLYXwmdyG7rNcSxpWn7BrRVO04wmZKev2bOedPXh+2sontxJ0GtbJ+18Wm7Btv8Iyo5v9lPlwMFnFQJG3+71QEZ5Mj3e60OurvPp3a9T9+hEhbtR83IxL4kOLH03/S9s+rNch5XskeyaM9lh8SIMQKr+VDkaPT19H9EIER8bl+6Zjk+Z9XO8tTwURG259E1gYDMjk5apR4MMVem37pFe4fIlo+YI+SU6vU6GvVjn80dW+1COqLadUcragB94571NIbxbZumcm7Ip1GjRhOhiYRX1BL3baVHyUihdrQy/lzZgONv4pt5027l25cNSDXL1mYRD1uNp6bP2WAtq3kyfqNKHO8seHS/Gpq7Ld1jD5wSKunP2HPEAhQEVT2j8i6rw3ewODJDeggFn9Ed2eGFvSWMQeNI6LoWJodgURQJjo5grbXTJKGt4GZetK02ys08o+JAmG/PaZsixhEsWGpnK+ZV7l9hw6sfd5sYAADup8VOLpm+tPG5EUODHI6MkTJT3mHI+uxm2Q4dHo2dPpvOp1GjRjuH2sunUaNGE6FtFbumS+n2x9DfLT+9FqC9WSzVVibuPkbKru1K17aCxbVnrEosKLLz1iLPa/3fjGpiW3bs4v/lN1gllLgoIgV1ZzzlqyYCp80Vd5jLR7sgA3hKz2xKZUCu1AYeR91rAAAIWElEQVR6Ez3O8x2KI0Ix6W4SRhGKbZeI81dZHezWbBocQ5+xOsyDy+GZXo61oNbOVoF9ua6b/JkHZF1PXSt3glmNbTkfyBexUfHN3LnDLG1rX+wzURcDTKc1LxXPwT7HFvuiMlfk0qIu3dpE4dzErkaNGu0c2lZT+3oMo28jNmdIW0WB1/7fEKZhz4SWk5KsdjBhJtedXpbptlPjh8Rp3eS/CDdPid+n7cjarZgwn+EkKCGXHcIJ+dymwZ0Xqln1zooFrYjVzZrPQdHnHoFWMtX1iAUFc164iJgW0lm7NPx+yiZ9PZu0HYGxyFgJ4OKedZFFQqlOp31h89HWeCDWFnWL42SgHhg6SgNN4BzIrZWk6n3SKvVnt5nBl1AUkyXgUXuGNhI62zWNdc2OPZqeGz5r9fLw/4iIu1XnMhlAfQ16L1b1tQLxe9N47XifGD6PumtaV99nNzgtuSGfRo0aTYa2PbxidwzfiDeDfDZ7Q9Z0LX3bB/MXsSH6zTl0zq1TiwmE+ec0vO46sMBFcZZdaxvb6R0J07Oc8rw4E6bbvUU659zxzna58jjvb4pfjDU7PrrvMB3hOBsbLPqCvv9beI9i+veEil8gkFUec/s1Z3PmiLhH1xja80IOxRwiOxAfE9MPqNKfWl913x4cVPW1I9oVfflXNK+/qe8duPQ6POYIlOc6LBYfhOBxIvqcY1fdq4DbiNgAJHY1vRBj9OdLr3RM++EM9w2SBtFobE6MbWH4qM43+kxfp+l8GjVqtHNoIoGlbpTY7O23lRFhqzq9amRfKiPGbFcc9paT40sAhWwt2yrVQE75GxGxmpRNuzI6uAHlerVg1mwlgzMu+LHD4r67CCVx6LSZB2TNEVET0+tuKlAwJ0sc+N8BWbDQeXjF7uG1veKig5AYdGgEWWKRc0dEcknn8IitIphrRzqg35LlyFHa+W/ng0pZ5g4aAruW5rUP6vVUJcApdIBmCduTcj4vMj++h9Efca2ii+sVlfzQ3NqmPTKl0Jb12mGO2WpYQ5Bp7/QnI9fSJ6415NOoUaMdRO3l06hRo4nQtke1r8fWZvStEr9vJbr0bYL/qOxmYOCkRIer1hEg8lbOgZuljfHx7NrE0W2rpPlbKddrluEs5vSP9CjuXemameqnN0vJaNRLLog9+VBF68habs/ie3ZJpEISuOTzg+ig2KMFBuaiBOKVDhbc3w27E2EhRClgaqvsCeu+IPRX5nzC2c7+JauTrAxF2TAH68V4lJGwoyF/Fop73Aps7hY3E4d9D+dr2VEzIoqyFPRJ912prT2yjmMoYpyLVnljbiX7i+ZqiYFu4GnbkE+jRo0mQtsd1X4uRrzs/I3q7jC6Lb7y+hzxldnv1uftoe3s8yu7rrs9f7mtL5+IiFLKwzXN906mr8Q+R3xl9rv1eXtoJ/S5iV2NGjWaCLWXT6NGjSZCk3j5vHcCz3y59JXY54ivzH63Pm8PTbzP267zadSoUaOIJnY1atRoQtRePo0aNZoIbdvLp5TyHaWUx0spT5VSfmy7nvvFUinl3lLKh0spny+lPFpK+RF9f6SU8uullCdVHr5RW9tNpZTpUsonSynv1//3lVIeUp9/rpQye6M2tpNKKYdKKT9fSnlM8/1NXyHz/Ne1Nx4ppfzHUsqenTbXpZR/XUo5W0p5xL6rzm0Z0T/Rb/MzpZS3bkcft+XlU0qZjoh/GhHfGaOzzH6wlPL6re+aGK1GxP/Sdd3rIuJtEfGX1Ncfi4gPdV33QER8SP/vNPqRGB/PFxHxDyLiJ9TnSxHxwxPp1eb0UxHxga7rHoyIN8Wo7zt6nkspd0fEX42Ir+u67g0xyh/wZ2PnzfW/jfFxjNBmc/udEfGA/t4TEf98W3rYdd3v+19EfFNE/Kr9/+MR8ePb8ewvQ99/OSK+LSIej4jj+u54RDw+6b6lft6jDfWtEfH+GEXWnI+ImdoaTPovRlFJz4aMHvb9Tp/nu2OUw+tIjKLb3h8R374T5zoiTkTEIzea24j46Yj4wVq938+/7RK7WDDoVIwztOxYKqWciIi3RMRDEXGs67rTEREq79j8zonQT0bE34xxaN/RiLjcdR2hizttzu+PUSaffyNR8V+WUvbHDp/nruteiIh/FBHPxSiT0JWI+Hjs7LmGNpvbifw+t+vls9UpNTuSSikHIuIXIuKvdV139Ub1J0mllO+JiLNd133cv65U3UlzPhMRb42If9513VtiFPO3o0SsGklP8v0RcV+M0o/tj5HYkmknzfWNaCJ7ZbtePqciwg/jvSciXtymZ3/RVErZFaMXz7/vuu4X9fWZUspxXT8e4+QIO4HeERHfV0o5GRE/GyPR6ycj4lAphUQVO23OT0XEqa7rHtL/Px+jl9FOnueIiD8WEc92XXeu67qViPjFiHh77Oy5hjab24n8Prfr5fOxiHhAFoHZGCno3rdNz/6iqJRSIuJfRcTnu677x3bpfRHxbn1+d4x0QTuCuq778a7r7um67kSM5vY3uq77HyLiwxHxA6q20/r8UkQ8X0p5rb56V0R8LnbwPIuei4i3lVL2aa/Q7x0710abze37IuLPy+r1toi4gnj2+0rbqPz6roh4IiKejoj/ddLKuC36+YdjBDk/ExGf0t93xUiH8qGIeFLlkUn3dZP+vzMi3q/P90fER2OUcfg/R8TuSfcv9fXNEfGw5vqXYpRWbMfPc0T8vRilN3skIv5djNKI7ai5joj/GCOd1EqMkM0Pbza3MRK7/ql+m5+NkSXv972PLbyiUaNGE6Hm4dyoUaOJUHv5NGrUaCLUXj6NGjWaCLWXT6NGjSZC7eXTqFGjiVB7+TRq1Ggi1F4+jRo1mgj9/woZTHkq7R3PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image.fromarray(temp, 'RGB')\n",
    "img.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_pune)\n",
    "img_rad(nov_pune)\n",
    "img_rad(dec_pune)\n",
    "img_rad(jan_pune)\n",
    "img_rad(feb_pune)\n",
    "img_rad(mar_pune)\n",
    "img_rad(apr_pune)\n",
    "img_rad(may_pune)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_paris)\n",
    "img_rad(nov_paris)\n",
    "img_rad(dec_paris)\n",
    "img_rad(jan_paris)\n",
    "img_rad(feb_paris)\n",
    "img_rad(mar_paris)\n",
    "img_rad(apr_paris)\n",
    "img_rad(may_paris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_rad(oct_blr)\n",
    "img_rad(nov_blr)\n",
    "img_rad(dec_blr)\n",
    "img_rad(jan_blr)\n",
    "img_rad(feb_blr)\n",
    "img_rad(mar_blr)\n",
    "img_rad(apr_blr)\n",
    "img_rad(may_blr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(months, lights)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[159.04001375831228,\n",
       " 202.08877387230976,\n",
       " 222.17586071346676,\n",
       " 248.54014478985815,\n",
       " 247.5970779965277,\n",
       " 216.23074655223243,\n",
       " 189.33245978969438,\n",
       " 167.59047728240574]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lights.remove(159.04001375831228)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Nov', 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[202.08877387230976,\n",
       " 222.17586071346676,\n",
       " 248.54014478985815,\n",
       " 247.5970779965277,\n",
       " 216.23074655223243,\n",
       " 189.33245978969438,\n",
       " 167.59047728240574]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(months, lights)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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