your_own_colormap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "import math\n",
    "from pylab import meshgrid,cm,imshow,contour,clabel,colorbar,axis,title,show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the Matlab txt file into python notebook\n",
    "cmap_vector = np.loadtxt(\"S:/CubeHelix-master/CubeHelix-master/mycolormap.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02659871 0.03093882 0.03424073]\n",
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      " [0.41968227 0.10511353 0.4782688 ]\n",
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      " [0.45334686 0.10853414 0.4864765 ]\n",
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      " [0.48719991 0.11264306 0.4927252 ]\n",
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      " [0.53784793 0.12032    0.4984511 ]\n",
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      " [0.65153541 0.14694216 0.49581039]\n",
      " [0.66686819 0.15189409 0.49378911]\n",
      " [0.68189717 0.15716007 0.49140885]\n",
      " [0.69659838 0.16274756 0.48869075]\n",
      " [0.7109487  0.16866337 0.48565735]\n",
      " [0.72492587 0.17491363 0.48233261]\n",
      " [0.7385086  0.18150373 0.4787418 ]\n",
      " [0.75167661 0.18843832 0.47491148]\n",
      " [0.76441074 0.19572127 0.47086946]\n",
      " [0.77669298 0.20335569 0.46664469]\n",
      " [0.78850654 0.21134383 0.46226723]\n",
      " [0.79983593 0.21968714 0.45776821]\n",
      " [0.810667   0.2283862  0.45317969]\n",
      " [0.820987   0.23744073 0.44853466]\n",
      " [0.83078464 0.24684957 0.4438669 ]\n",
      " [0.84005014 0.25661066 0.43921095]\n",
      " [0.84877528 0.26672103 0.43460203]\n",
      " [0.85695345 0.27717682 0.43007589]\n",
      " [0.86457968 0.28797322 0.42566883]\n",
      " [0.87165066 0.29910454 0.4214175 ]\n",
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      " [0.91479497 0.47002957 0.39716079]\n",
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      " [0.91241296 0.52966515 0.40853287]\n",
      " [0.91098636 0.54483276 0.4132432 ]\n",
      " [0.909287   0.56006269 0.41875418]\n",
      " [0.90734762 0.57533339 0.42509004]\n",
      " [0.90520257 0.59062281 0.43227335]\n",
      " [0.90288776 0.60590843 0.44032494]\n",
      " [0.90044058 0.62116733 0.4492638 ]\n",
      " [0.89789985 0.63637622 0.45910699]\n",
      " [0.89530576 0.65151149 0.46986957]\n",
      " [0.89269982 0.66654927 0.48156454]\n",
      " [0.89012475 0.68146548 0.4942027 ]\n",
      " [0.88762443 0.69623585 0.50779262]\n",
      " [0.88524382 0.71083602 0.52234059]\n",
      " [0.88302886 0.72524156 0.5378505 ]\n",
      " [0.88102642 0.73942804 0.55432381]\n",
      " [0.87928417 0.75337109 0.57175949]\n",
      " [0.87785051 0.76704645 0.59015394]\n",
      " [0.87677445 0.78043003 0.60950098]\n",
      " [0.87610556 0.79349797 0.62979177]\n",
      " [0.87589381 0.8062267  0.65101479]\n",
      " [0.87618949 0.81859299 0.67315578]\n",
      " [0.87704313 0.83057404 0.69619772]\n",
      " [0.87850531 0.8421475  0.72012083]\n",
      " [0.88062666 0.85329156 0.7449025 ]\n",
      " [0.88345763 0.86398502 0.77051731]\n",
      " [0.88704847 0.87420732 0.79693699]\n",
      " [0.89144905 0.88393861 0.82413045]\n",
      " [0.89670876 0.89315982 0.85206375]\n",
      " [0.90287638 0.90185273 0.88070013]\n",
      " [0.91       0.91       0.91      ]]\n"
     ]
    }
   ],
   "source": [
    "#Printing the loaded vector for reference purposes\n",
    "#cubehelix(128,2.64,0.61,1.74,1.47,[0.03,0.91],[0.02,1.00])\n",
    "print(cmap_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the dimensions of the vector\n",
    "cmap_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding a 4th column of 1's to the RGB array to make it an RGBA array as numpy takes in a Nx4 array for the listed color map\n",
    "\n",
    "#Creating a column vector with teh same number of rows as the RGB vector\n",
    "vals = np.ones((128, 1))\n",
    "#Appending the column to the end of the RGB Vector\n",
    "cmap_vector = np.append(cmap_vector, vals, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.02659871 0.03093882 0.03424073 1.        ]\n",
      " [0.02539983 0.0321071  0.03770819 1.        ]\n",
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      " [0.02412807 0.03578254 0.04749766 1.        ]\n",
      " [0.02390932 0.03796741 0.05348178 1.        ]\n",
      " [0.0239712  0.04029357 0.06009395 1.        ]\n",
      " [0.02432822 0.04271767 0.0672842  1.        ]\n",
      " [0.02499766 0.04520569 0.07501061 1.        ]\n",
      " [0.02599772 0.04772986 0.08323592 1.        ]\n",
      " [0.02734661 0.05026692 0.09192575 1.        ]\n",
      " [0.0290621  0.05279712 0.10104745 1.        ]\n",
      " [0.03116121 0.05530351 0.11056946 1.        ]\n",
      " [0.03366004 0.05777155 0.12046087 1.        ]\n",
      " [0.03657364 0.06018871 0.13069111 1.        ]\n",
      " [0.03991591 0.0625443  0.14122985 1.        ]\n",
      " [0.04369955 0.06482926 0.15204678 1.        ]\n",
      " [0.04793602 0.06703601 0.16311164 1.        ]\n",
      " [0.05263547 0.06915838 0.17439413 1.        ]\n",
      " [0.0578067  0.07119146 0.18586393 1.        ]\n",
      " [0.06345718 0.07313159 0.1974907  1.        ]\n",
      " [0.06959297 0.07497624 0.20924412 1.        ]\n",
      " [0.07621874 0.07672398 0.22109392 1.        ]\n",
      " [0.08333776 0.07837445 0.23300991 1.        ]\n",
      " [0.09095186 0.07992825 0.24496206 1.        ]\n",
      " [0.09906146 0.08138697 0.25692055 1.        ]\n",
      " [0.10766555 0.08275312 0.26885582 1.        ]\n",
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      " [0.14695689 0.08737188 0.31578644 1.        ]\n",
      " [0.15796875 0.08834211 0.32717609 1.        ]\n",
      " [0.16943951 0.08925196 0.33837443 1.        ]\n",
      " [0.18135847 0.09010931 0.34935563 1.        ]\n",
      " [0.19371362 0.09092268 0.36009464 1.        ]\n",
      " [0.20649168 0.09170119 0.3705673  1.        ]\n",
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      " [0.23325732 0.09319276 0.39062164 1.        ]\n",
      " [0.24721231 0.09392665 0.4001599  1.        ]\n",
      " [0.26152515 0.09466725 0.4093451  1.        ]\n",
      " [0.27617677 0.09542605 0.41815833 1.        ]\n",
      " [0.29114712 0.09621488 0.42658196 1.        ]\n",
      " [0.30641515 0.0970459  0.43459957 1.        ]\n",
      " [0.32195891 0.09793156 0.44219614 1.        ]\n",
      " [0.3377556  0.09888451 0.44935797 1.        ]\n",
      " [0.3537816  0.09991764 0.4560728  1.        ]\n",
      " [0.37001257 0.10104394 0.46232984 1.        ]\n",
      " [0.38642351 0.10227656 0.46811978 1.        ]\n",
      " [0.4029888  0.10362868 0.47343484 1.        ]\n",
      " [0.41968227 0.10511353 0.4782688  1.        ]\n",
      " [0.4364773  0.1067443  0.48261703 1.        ]\n",
      " [0.45334686 0.10853414 0.4864765  1.        ]\n",
      " [0.4702636  0.11049609 0.48984581 1.        ]\n",
      " [0.48719991 0.11264306 0.4927252  1.        ]\n",
      " [0.504128   0.11498775 0.49511656 1.        ]\n",
      " [0.52101998 0.11754265 0.49702346 1.        ]\n",
      " [0.53784793 0.12032    0.4984511  1.        ]\n",
      " [0.55458399 0.1233317  0.49940636 1.        ]\n",
      " [0.5712004  0.12658933 0.49989777 1.        ]\n",
      " [0.58766963 0.13010406 0.49993552 1.        ]\n",
      " [0.60396442 0.13388667 0.49953143 1.        ]\n",
      " [0.62005787 0.13794744 0.49869893 1.        ]\n",
      " [0.63592351 0.14229618 0.49745305 1.        ]\n",
      " [0.65153541 0.14694216 0.49581039 1.        ]\n",
      " [0.66686819 0.15189409 0.49378911 1.        ]\n",
      " [0.68189717 0.15716007 0.49140885 1.        ]\n",
      " [0.69659838 0.16274756 0.48869075 1.        ]\n",
      " [0.7109487  0.16866337 0.48565735 1.        ]\n",
      " [0.72492587 0.17491363 0.48233261 1.        ]\n",
      " [0.7385086  0.18150373 0.4787418  1.        ]\n",
      " [0.75167661 0.18843832 0.47491148 1.        ]\n",
      " [0.76441074 0.19572127 0.47086946 1.        ]\n",
      " [0.77669298 0.20335569 0.46664469 1.        ]\n",
      " [0.78850654 0.21134383 0.46226723 1.        ]\n",
      " [0.79983593 0.21968714 0.45776821 1.        ]\n",
      " [0.810667   0.2283862  0.45317969 1.        ]\n",
      " [0.820987   0.23744073 0.44853466 1.        ]\n",
      " [0.83078464 0.24684957 0.4438669  1.        ]\n",
      " [0.84005014 0.25661066 0.43921095 1.        ]\n",
      " [0.84877528 0.26672103 0.43460203 1.        ]\n",
      " [0.85695345 0.27717682 0.43007589 1.        ]\n",
      " [0.86457968 0.28797322 0.42566883 1.        ]\n",
      " [0.87165066 0.29910454 0.4214175  1.        ]\n",
      " [0.87816486 0.31056413 0.4173589  1.        ]\n",
      " [0.88412244 0.32234444 0.41353023 1.        ]\n",
      " [0.88952538 0.33443699 0.40996882 1.        ]\n",
      " [0.89437746 0.34683238 0.40671203 1.        ]\n",
      " [0.89868427 0.35952032 0.40379716 1.        ]\n",
      " [0.90245328 0.37248961 0.40126132 1.        ]\n",
      " [0.90569378 0.38572816 0.39914138 1.        ]\n",
      " [0.90841696 0.39922302 0.39747383 1.        ]\n",
      " [0.91063585 0.41296038 0.3962947  1.        ]\n",
      " [0.91236537 0.42692558 0.39563944 1.        ]\n",
      " [0.91362233 0.44110315 0.39554284 1.        ]\n",
      " [0.91442536 0.45547682 0.39603891 1.        ]\n",
      " [0.91479497 0.47002957 0.39716079 1.        ]\n",
      " [0.91475349 0.48474361 0.39894064 1.        ]\n",
      " [0.91432508 0.49960045 0.40140954 1.        ]\n",
      " [0.91353567 0.5145809  0.40459741 1.        ]\n",
      " [0.91241296 0.52966515 0.40853287 1.        ]\n",
      " [0.91098636 0.54483276 0.4132432  1.        ]\n",
      " [0.909287   0.56006269 0.41875418 1.        ]\n",
      " [0.90734762 0.57533339 0.42509004 1.        ]\n",
      " [0.90520257 0.59062281 0.43227335 1.        ]\n",
      " [0.90288776 0.60590843 0.44032494 1.        ]\n",
      " [0.90044058 0.62116733 0.4492638  1.        ]\n",
      " [0.89789985 0.63637622 0.45910699 1.        ]\n",
      " [0.89530576 0.65151149 0.46986957 1.        ]\n",
      " [0.89269982 0.66654927 0.48156454 1.        ]\n",
      " [0.89012475 0.68146548 0.4942027  1.        ]\n",
      " [0.88762443 0.69623585 0.50779262 1.        ]\n",
      " [0.88524382 0.71083602 0.52234059 1.        ]\n",
      " [0.88302886 0.72524156 0.5378505  1.        ]\n",
      " [0.88102642 0.73942804 0.55432381 1.        ]\n",
      " [0.87928417 0.75337109 0.57175949 1.        ]\n",
      " [0.87785051 0.76704645 0.59015394 1.        ]\n",
      " [0.87677445 0.78043003 0.60950098 1.        ]\n",
      " [0.87610556 0.79349797 0.62979177 1.        ]\n",
      " [0.87589381 0.8062267  0.65101479 1.        ]\n",
      " [0.87618949 0.81859299 0.67315578 1.        ]\n",
      " [0.87704313 0.83057404 0.69619772 1.        ]\n",
      " [0.87850531 0.8421475  0.72012083 1.        ]\n",
      " [0.88062666 0.85329156 0.7449025  1.        ]\n",
      " [0.88345763 0.86398502 0.77051731 1.        ]\n",
      " [0.88704847 0.87420732 0.79693699 1.        ]\n",
      " [0.89144905 0.88393861 0.82413045 1.        ]\n",
      " [0.89670876 0.89315982 0.85206375 1.        ]\n",
      " [0.90287638 0.90185273 0.88070013 1.        ]\n",
      " [0.91       0.91       0.91       1.        ]]\n"
     ]
    }
   ],
   "source": [
    "#Printing the RGBA Vector\n",
    "print(cmap_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the Listed colormap with the consolidated information from above\n",
    "mycmap = ListedColormap(cmap_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EQRCohGAFQRAEQRAEQRCMRAhWEARBUAkDOz9a9kEEQRAEwUoSghUEQRAEQRAEQTASIVhBEARBHczg7YhgBUEQBIEkFrkIgiAIgiAIgiAYkRCsIAiCoA5mICJYQRAEQaCyLoK1sewDCIIgCIIgCIIg2C9EBCsIgiAIgiAIgpVnXSJYIVhBEAQrw04schEEQRAECrHIRRAEQRAEQRAEwYiEYAVBEAR1xCIXQRAEQWASghUEQRAEQRAEQTASIVhBEARBHczA1sFlH0UQBEEQrCQhWEEQBEEQBEEQBCMQi1wEQRAEPWBQzMEKgiAIApUQrCAIgiAIgiAIgpEIwQqCIAjqYAa2Yw5WEARBEGiEYAVBEARBEARBEIxECFYQBEFQBzOwtbXsowiCIAiClSMWuQiCIAiCIAiCIBiREKwgCIKgEgbFHKwgCIIgUAnBCoIgCIIgCIIgGIkQrCAIgqCOmIMVBEEQBCbrIlgbyz6AIAiCIAiCIAiC/UJEsIIgCIIgCIIgWGliFcEgCIKgHmZQpAgGQRAEgUoIVhAEQRAEQRAEwUiEYAVBEAT1bEcEKwiCIAg0QrCCIAiCIAiCIAhGIgQrCIIgqIMZtLW97KMIgiAIgpUjFrkIgiAIgiAIgiAYkRCsIAiCoAqKCFYQBEEQmKyLYMUXDQeHFER0EhGdvuzjCIIgCIL9BhGdRkQvXPZxBPuXLk2w5v8yCMEKekNETyCizxPRlUT0dSJ6NREdtYD9XqMVpX8loh8Q0YVE9HoiOm7qfQfBpDCAre36/0EQ7BuI6FFE9Mn28+2b7fbvEBEt+9iCYNmEYAX7GiJ6OoA/AfAMAEcCuDuAWwE4i4iuMfHu3wXglwE8pt33HQGcC+C+U+2QiCKdNgiCIJiU9rP15QD+fwA3A3BTAL8F4J4A5j5biWhzoQcYBEukj1yFYAVrAxFdH8D/APA0Zv5bZj7IzBcCeAQayXocEV2LiH5IREe3fZ5DRFttXxDRC4noZe32aUR0ChG9j4i+1/617t8b+74fgPsDOJGZz2HmLWa+gplPYebXtW1uTkRnENG3iegCInpS5lx+mYi+QESXE9GHiei2Sd2FRPRMIjoPwA+I6LC27BlEdF7718XXEdFNiej97bF/iIhukIzxzja6dwURfZSIfiqpO42I/oKIzmr7foSIbtXzaQn2A+0crNr/QRCsP0R0JIDnA/gdZn4XM3+PGz7LzI9l5qvbz41XE9GZRPQDAP8fET2YiD5LRN8loouI6KRkzPcR0dPEfs4jol+hhj9ro2RXtOW3b9tcm4heQkRfbes+RkTXbuvMzzXlnP4jEX2u/Yw9m4juMMW1Cw4dQrCC/czPAbgWgHenhcz8fQDvB3B/Zr4KwDkAfqGt/nkAX0XzV7ju8UeS7o9GI203AHABgBcZ+74fgE8x80WZ43srgIsB3BzAwwD8MRHNRbeI6NZt298DcGMAZwL4GxGBezSABwM4ipm7b4D9T2gk79YAHtKe87MBHI3md+p3k/7vB3A8gJsA+AyAN4vDeCyAF7R9P6fUB0EQBIcG9wBwTQDvLbR7DJrPyCMAfAzADwD8GoCj0Hxe/TYR/Urb9g0AHtd1JKI7ArgFms+7X0TzWXzrtu8jARxom74YwAloPu9vCOC/A9hp60qfa92+fgbA6wH8FwA3AvCXAM4gomsWzi8ITEKwgv3M0QAuS4Qj5dK2HmgE6hfa9Lo7AHhF+/haAO4C4B+Tfu9m5k+1Y74ZwJ2Mfd+o3YcKER0L4F4AnsnMVzHz5wC8FsDjleaPBPA+Zj6LmQ+i+UC5NpoPlI5XMPNFzPzDpOyVzPwNZv5aew6fbP/CeDWA9wC4c9eQmV/f/hXyagAnAbhj+1fKjvcx80fb+j8CcI/2HIJDEQZoa6f6fxAE+4K5z9Y26nN5mxHy823xe5n5n5h5p/2c+zAzf759fB6aPxx2f9x8L4Djiej49vHjAbydmX8E4CAaSftJAMTMX2TmS4loA8BvAvivzPw1Zt5m5rPbzynP51rHkwD8JTN/sh3jDQCuRjOlIAh6EYIV7GcuA3C0MS/pmLYeaATr3gB+BsDnAZyF5k3/7gAuYObLkn5fT7avBHA9Y98H2n1Y3BzAt5n5e0nZV9H8xU5r+9XuATPvALhItNUiZd9Itn+oPL4e0OTGE9HJRPRlIvougAvbNkcn7XfHbyOA326PKwiCIDi0OADx2crMP8fMR7V13T3bzOcSEd2NiP6BiL5FRFegmbN1dNv/agDvQJO6v4EmK+NNbd3fA3gVgFMAfIOITm3T+I9Gk6XyZXmAzs+1jlsBeHoriJcT0eUAjkV8xgUDCMEK9jMfR/NXqIemhUR0XQAPBPB3bdHZAG4D4FcBfISZzwfwY2hSGNL0wBo+BOCuRHRLo/4SADckoiOSsh8D8DWj7e6cJyIiNG/+adshv5mPAXAimrTGIwEc1+0qabMbrSKi66FJxbhkwD6DtYaB7e36/w6I6AFE9KV2XuKzlPofa2/SPtvOxXjQ6KcXBEGO7rP1xEI7+bn0FgBnADiWmY8E8BeY/Zx5A5p09PsCuJKZP747EPMrmPkEAD+FJlXwGWj+SHoVAG0utOdzreMiAC9i5qOS/9dh5rcWzi8IVPrIlUewpvh8DMEKqmHmK9DMl3pl+6I8nJol0t+JZu5T99exK9Gs7vcU7AnV2WjysXsJFjN/CE0k7D1EdAI1C08cQUS/RUS/2c7NOhvA/6RmoY07AHgi9BzxdwB4MBHdl4gOB/B0NB9uZ/c5NoUj2vEOALgOgD9W2jyIiO7Vzvt6AZp0w9z8siCohpqVxk5B8weQ2wF4NBHdTjR7DoB3MPOdATwKwJ8v9iiD4NCGmS9H89n650T0MCK6HhFtENGdAFw30/UINJkbVxHRXdFIUDrux9HMn3oJ2s9nACCiu7TRr8PRzOO6CsB2m83xegAvpWbRqE0iukc7d8rzudbxGgC/1e6DiOi61CzIcUSmTxAslKk+H0Owgl4w85+iWdjhxQC+C+CTaP5add8uT7vlIwAOB/Cp5PERAD46YPcPQzNB9+0ArgDwLwB+Fk10C2hSII5DEwl6D4DnMfNZyjl8Cc3k31ei+YvdQwA8pM1NH4M3oklB/BqA8wF8QmnzFgDPQ5MaeAKavzIGwdjcFU1a7lfa1/fbMP9XcgZw/Xb7SEQkNQgWTvvZ+vtoFpX4JpoU9L8E8EzYf/z7HQDPJ6LvAXgumj8eSt4I4KcBnJ6UXR+NBH0HzWfVATSf6QDwB2hS+89B8/n0J2juGT2fa925fBrNPKxXtfu4AMATrPZB4GGCCNYkn4+0rNzEIDjUIaLTAFzMzM9Z9rEEq8Edb3VT/sAfPqq63zG//YqvYm/uIwCcysyndg+I6GEAHsDM/7l9/HgAd2PmpyZtjgHwQTQreV4XwP2Y+dxeJxIEwUpBRL8G4MnMfK9lH0sQ9OV2t7sdn3766eWGghNOOMH8jJzq8zG+PDUIgmD9uYyZfzZTr82PkH9dezSA05j5JUR0DwBvIqLbt+lCQRCsKUR0HTRRrkj7DdaenoGh3GfkJJ+Pk6QIliaLBUEQBArMwNZO/f8yFyNZUAXALTGf4vBEtKlF7ZyNa0FfGSwYSHxGBouCiH4JwLfQpBq+ZcmHEwSDmSBFcJLPx9EFyzlZLAgOeZj5CZEeGCyIc9B8F86PtwuqPArNqmMp/4ZmlTEQ0W3RfIB8a6FHeQgQn5HBImHmDzDzdZn5RNa/uzII1oaJVhGc5PNxihTB3cli7YF0k8XOn2BfQRAE+wcGaGv8ebHMvEVETwXwAQCbAF7PzF8goucD+DQzn4FmFc3XENF/a44ET+CYpDsF8RkZBEHQk7E/lqb6fJxCsG6B2S/BuxjA3SbYTxAEQeCEmc9Es/pmWvbcZPt8APdc9HEdgsRnZBAEQU+m+LvfFJ+PUwiWZ7IYiOjJAJ7cPjyh+Y7XIAiC9aJNQRjvDcw3pypYX3p9Rk56REEQBBMy5mfkuiRWTCFYnsliaJdHPBUANjY2+PBrXHOCQ1kCIYpLpub6r8cv6b5lTd4kSxz80dXlRkGwR/VnJBFN/suyqD9yLvOPqfGH3LprsC43slOyzGuwqH2v2/O8Lsc7hWDtThZD80V0j4L4VvGVZ63ehNfpWFeNuHazLPhNa8jv2Zq8wdZCE83BClaKpX1GjiUYY4vKlOKzbKla9v6HsGrHvowb6/QaTLl/bewh17/mWHP7WTWZcS5asRKMLljWZLGx99OLpbxZrNAbFE2yKn+wylR9hdHUr9UR3xS9v8tr8kYcHDos6jOyz81Z3xu6ITeCQ2/ix5SAQyGKty6MLRw1+7Eo7X/IjT8RDeov+3qvVWmf2jjLFpxl79/LJF80rE0WWwqTvokt6A1ypaQoPhQWz8A3kjFfP4O/b9b7+plQxFb9jZkZiAjWvmfKz8iam9Cp2i6i/dB+Q/suYrwpWJcUwbEEoXY/yzjnbp9DzrlvFKrP+Xd9lvX6OKQFa6mM+gY3wZvlQoRpRd/kV0oWF8TCpKSWHm9Qtc9f73PPnfNQ4UzGXpM36SDw4L0587Qbc6yadrVt+7Tv22fM/osety+LOJ6xBWnouIuUhxqxSo+nVqZqolA1KZHLEq0QrEUy+E1ghDeR0eRhwje0lRGcVTmOKWnlYtnX3JScMV5nhTc577mPksbYRxhXULYYEcEKqindoC27ftFt+rTt035ov0WNt4rURm2843UMjYD1EbfaaJA3LZCZ3emJ3uP2HKtXtoamN9YSgrUIev9iDviFHnzDPNIb56Q37mssQDXXZXB0KcfU19B57H1eJ+7rUnotO98ErWMcJF6Vb8Dde8mavHEHQUfuxmvRdYuoH7NNn7Z92o/VdwxWJUWwNmpTO563b20UZmwp8kSjPNJUilKVrk9JpkrXaVGSdUgvcrEwqt+ker6p9RaZZUpcx4Q3+cuOzIzBqp6DSyz6HrtjbM91cR3jwGiTPI7ewlXxZky0fMnaXu7ug/WhjwjVlq9anad+zDY17Yb2mXKcVdh37Y3xkPS/PtEk79hjkJOrnFiNJU2eOku0VkGy1oH1E6yqX/jKN4fqG+4ebz69b+pHkIGphGJVRWWdSOVh0oUp+oytiM0gCes5x0rbZ7XoOd6YI5oVrDE1AjWWhK26zHnqvW1q2vVtP3b/VcAT0Sn17+gT/eqbOudpM3bEp4+oWNfXkjXremrlNQIW7LFegjWFXFXdzE4tbD1vrMe4IY+Uw4kpiMCQ65+TjFHmQeXGqNx3UYgqU/3SfbhlyxtBW4FoVhAYeIVpqHCtg7ANqfPUe9vUtBvapy/LShH0ziGq6e+dD+Vpt+g5Rn2FyFM+VJ68QmW1nVq81kXs1kuwXIwtVlOIGlAtHX1vwEcRp8UIEq1hJIzdaWtDz21iicqNUS1fY0pXRfSpG9stcSv4Jh2LXAQjMkSiFlG2iPIhdZ56b5s+bcfsu6z91c6Fqum7zhI1tDwnVjVS5S3ryldBblbhGDysj2C5ftELbVw3omMLlbNdrVz0kpFhN/kLESDanH4fY8Hbk1+TPYEbYc5VXxEr9XOnIRri455r5YxuuaNajmhWRLGCFcQjMH3ajDWut9+Q8cYuH1JX06am3dh9F82QtEBvX89qhGPMaeozZ2kqUbGEq1TmkTJ5zqVjX0YUKwRrTIq/ZI5f4OLN5RhjAK4b4Zqb8qob+Pqb8FEEYYWkiCquAXtX4jN3NuJ5s76yQc3zo0fTSv0r5ldZsuKOSsl2jvFc0a3CvK3BYyxQshjA1mJ2FewfphCnRcnYstMac+WlOk+9t82Q9lONPdV3VHnGzsmO1a7UpnaulCUKNQIxVDLTY9TGq5Emj0Rpj2v7TEmsIrhQhkatxoh6Fdp4b5LHjophgECNJA81wrNoFn1sWaGrud49ZaxOwJzi5ZWu4r6HyFZBklypg45oVhCsIWOL1CJkboqy2rZD6jz1te2G9unDkP0sY05VKRUwJwKLlgRPtMkjUn3EqvZxV7YqYrMqx1Fi9QUr+wueqRsiVsW+A4VqrEgYKgWqhzSNISG0QhGuZcG8Pfha7gpaTxmrEzDnwhbeaFdRuBzRraJsDRWtjGQtKooVEaygkpys9K0b0naKx31LFImPAAAgAElEQVTb1JT1KS/Veeq9bcbosyj6RG08fUrpgJ5UwNo0QEvsLAnRyqZIofMImfW4RrSsc1l2FGsdWH3BMukrSH37TS1V5Ztvl0xV3Hz3veEfS5gI+0+82PgSo9prxkqUyvN8zUXJcvsV+9BeX76oVw8ZKspSIbqVlSWHaPWRrCBYccaQq0UI2xSPvWX7Pbo15ph9b2Rz+/Ck+lntSlGsXATLqrPEYEphGCJHpcceWRpLpJYV1QrBWhZ95KqvWA2SqnzfMWWqRqT6ytMUsrSKqwqWVg3scx00KfM8DzUSpqYnavvoJV0F4RpNtvqIVh/JWjLxRcNBhqHRDq8kLVq69nOUy1PvbTOkfS19xl/U3KpcfUm0+kaUFiEUQ2Wqtm4qkZpaUNeB1RYs85e7VpRGFKtcn55CVRSJws22V6Bq5KmPKIwtRIdCaiHzTvW17oSsRsJKr5Fi+mEh1TCfXphJKcylEpoiVyNamWiWKVmGmBEtJk0wCJzUytGQ7Sn719SN8XhoWZ/yUp2nfmj7RVB781tqn0sdLKX9ecr7lg1dOGLsuWBSoLyi1UfAPNtTEYtcLIPR5GpMseopVQOFyismNTf2feVp0ZJUc05WOl92/AHno0acHNdVRoxy5yjPyTpeeSzyNVVMNcxEuvIRLkOocpGtWtGqkqbVimQxAxxzsIKRWKSATRklG9LW83hoWZ9yb/3YbGz4P8t3durfG4dI4liRqlx537IhlKJSVtsxRGlMaVqEQHlYhWPwsIaCVfPLO4JcWW17RKv6StVQofJIR61ADRGNVZl7NeVx9E37AxT5qVicwjqnkngNEq5eslURucqK1giSpVLTNgiWS04+vG1rtlc5+jXGY2+bPuUeaiRoSkHb3KydO+x/z9TkzfvaHSJaixCqGmpFqbbPVDK1TPEKwRqK+otWI0xK20nFqlKqjJvtnEwNFSmPRE2dRli7j7Wl5+8/Y7taxHLPayc4JfEqRbq016WZVmikE9pphE6hUssLwqamDPJ8W6+QEU2bJsiIOViBi5IU9BGjKSVr0dGxKR5bZRaWKK1aVGssao671Na6id7Z2XFLrZXqp0XFcpEi7+NFLAZRK1djiFiNhC1CtkKwFsUguaqQqIr+i5KqnOCUZMpzE18rUEPFaWNFIls17AxcNXA+cpTvl0aiPOl/pflScn+5SFcuyjUT4TKiW9WylROtmsjYIMkKgvXGK2JTCNmipWpK+dKoFaih4lQT2bKoOYYxbmStNMO+0aouqpYTsNw+ciLkeewllwY4BkPlyitRHcuM8klW5ThKrL9gzTFArtxipfcfS6ysG+e+QjWWTNUK1FjCtAoRL23+FFB/jlLISufmETDPvKucdHmFyyNbZiqhkUaopxA6olE1aYNeyVoFGOCt9fzrdbAcvFGWWpFalIQtYrv0uI9MDY1uaWP2HWuqG/kxZExLM8zdIEshK8mqJmCyrDRmjTz0EY0+86WGylMfoSqV5a7BogQsFrmYBI84LUKunGKl3OSOJVR9ZaqcQliWhVqhGEOOViGytTPC5xfzdtW57DjSBZsvMC4vepGTrlyUyxIuaw6XOXerENXSI1oOUTKjWT0ka51ELDgkyd3weqSqVpoWIWPLFq2UkkT1jWrVPm+1jBHZGkrNghjWOTOzOe9Lu6m20gXTMjmmJV/aa8aTDjjFtnX+8lxr5Moqq5GsWrGaSrpCsIbgecNxfU9UT7lagFiNJVV9ol25fh1eEagRqEWIUq3ATDGutY/aaJW1z9KYM9GqTMQrt9CFlc6XjueJbqmRLSWq5RYtK5rVW7LQow1NOg/LCJYGwS6eG3ZNYKw2pX5W3bpFvlKGypQnouUZJzfm0PGG9q+5ka2NVHWUUvrSOm+6YG5MOU4u4jVVZLAjt5DFWGU10uQRqVwb2XYq9o1gEdHrAfxHAN9k5tu3ZTcE8HYAxwG4EMAjmPk71DzDLwfwIABXAngCM39mmkOfO1Lx0CFJrqhVP7HySlWNUPWJTuX6lKRhiojW1JK1URMxm/h3dAeVESvHe3kpCpaLeu1FmXxRqa6PlVLoGUeLbM1FtfqKVjFl0CNZEaEKhrEun5FDBGoK2ZoiwuWNTnkETPbR2vaNaOX6Tn1TP/Uy7Smlc7EiVp75VdZ+PBEr63ns2krpKkW5+kaovCIyRKq8bWqiVstm1Y7HwhPBOg3AqwC8MSl7FoC/Y+aTiehZ7eNnAngggOPb/3cD8Or250BKbzieN6SCXDmErG/EyitWNVGqsYVqrIhWtXAtcX7V2PvecUagdttj/IiVHCPXtyRcaZ+a6FaxPzYGRLRqo1mKZM1RkqyQsCDLaVjSZ6QnWlWq98rVushWx1ChGhrRsvrUCpRXiKYQM+8y7d4b3inmV8lxayNWVt+0n7dPjr6SVNu+VqpqolaSdZCxZVIULGb+KBEdJ4pPBHDvdvsNAD6M5sPjRABv5OYKf4KIjiKiY5j50rEOGIAhQ7n6CeRqYrEaQ6r6ClVJDNyy1VNgVmHOVQktvdBzvjsZGbLG19p5xEnra/Wz0gpzUammfF6EPKJVkzqYlSxgXqpKklVKA1zmioIM8Pa0f8EOxmUVPyNrhatGqvqOMWaddUy1UtVXqMaIbGnjePtPHeWqRTse7UY7F1nq8KwCKPuXFrbIXa8+8pQ+b2OkE2pSlKsbKkp9ZEsbZ1msi8T1nYN10+4DgZkvJaKbtOW3AHBR0u7itmxcwZpBvCgHy9X4YjW2VNUIVV+RKkqWU576yNIyo1puevx+72DbLWEl+cpFu3IpgpZweaJb2RRAJaqV61sjWvMS50wZrJKsiFIFo7L0z8i+EjXWz7H7yO2O7ma3RqiWEdEqyW4NQxa1qNnfkBvZUnqhJWWelMFcmp82jhQoq2/ar+tjpQdq0a2cbOWkxCMsuQhViT5yVStfi4L50F1FUHvW1StBRE8G8OTqIT2LW+wyrVz1iVjJMYdIlXWTXRvRyorWiPOwhorTMiJb1kIY3nPpFbEyxu7GykW0aiNWHuHqK1syqtUrolUTzaqVrBzLEjCmWKZ9fzP6Z6Qn2lTbdxUlLEUTnykjWkOiWaX+ubFq+48d2fKMZ93sSlHqG7HqymXEShvTEhxLiDz798zH6ugb2eojT1NIU408LVO89rtgfYPatAYiOgbAN9vyiwEcm7S7JYBLtAGY+VQApwLAxsbGOFerRr4mlquSWHn6aG2aIx0mYH2FaqyolmesvmxWjLvtWEWwz3F6ZGm3bUaaSmPl5K0kXFbKX9deEzMtjVBKUy590NVHm581RLJyxJcLB9Mx6mckEQ36jKyRsGVLV05sasXKG/3ytC31KfWTfUt9xpal3DFYeBa56CN/mizJcnkMnqjQmPOrShKeEzNmNtt5JGpKefII0apFrSSrdCw5+grWGQB+HcDJ7c/3JuVPJaK3oZm4e8WoueWe773aJROtysjV1GLVR6qmEKq+IlUShhohqZEhL8teRXC7YuXAUspgLlUw11frlxMuLT2w1M6KankiWlK0itEsV8qgIVlVUSwRpVqCgDFiDtY+YfLPyFykJCcfQ6NKixQswCdVU0S0cpGpIRGtGnEa64uI+/bru4/cDbD13VXa/nKpftZY3WMt3c/qVxIurW2uXTqejGpNJc4WJbmqEbCaNlOf0zrgWab9rWgm6x5NRBcDeB6aD413ENETAfwbgIe3zc9Es/zsBWiWoP2NCY7ZONDMX2WccjXfzy9XixArr1SZolWbOjggmgXUC9Si516NsT+5cmDpnNOoWSlqpR2fFfGyIl2l9ml5LrolI1ueqNacODlEqyaaNUuNZEFvFwQ9WPXPyFrZGipgteOVpMkT0aqVqlJ0orTfUvtcH6u/p9+ib87HkDh5M+yNWGn7T+UpHasUgZI3/aVUvpwgpW21dp4UwiELY9RGp3JtrPFSVjGKtSrHUcKziuCjjar7Km0ZwFOGHpQPZ/QqmzaYSQvMyNXYUaspxUq7SR9bqDwi1UdmpohwDUWmFdauHGidU068PGmCpkApspW211YrzKUHekWrlDpozc/SolmWZLlWGNztl4pURRRr0TCwE180vFaswmdkTp60spwsaWW1wjRW1KpvvdZmaLu0rdZmSFSr1N87roea/fS5kfUuxZ7W1cyvKo3p/Q4rLWJV2ocnPdCSqFRSNjY2zNTB0s8UT9taAbPKtGuXK5sKPoQXuRhOn7+YzAiNM5LlTAscK2o1hVQNEaraaFZJdjyiUStMqyhYtWw7Vg7c4e1e52qlCVopgpqcqe0cUS3ZRtZbqYN9ollqyqAnXTAbnfK2MyAC1uRNPthflG6QSzfvJaGqFaaxolZTSNUY4uVpK9tr7dYxotVn/FwfK2JVes16UgW1vlaaYC7tL23rSQ+0olqyjbd+KDVCJsvS49PKUkpSNaV0hWCNwsAXnJka6EsL7CtXNRGrklh5IlVDpKqPUOWEwSMKQ8RpkdKlLYLh3X/XN9d+25EKqI2xraT3AfmolSZNuXaeFMJcVMuKaGmilYtmmSmD3nRBb6qgiwVEt+J7sIIKamTLK17pdimqNVTGOnJi5BWvoW20fVntrDFLfWQ/q+2Qm+0hS7jXoi2C4RVCmeaXlsnxtesro15Do1Ylybba1bRJ662IlnYcNREqq722X3ldSiK1TKlKCcEamxlBIqPcmxqYNDPSAr1yNVXUqo9YDZEqSwSGyNTUsjV0vNIqgrXHlo7nSQXU2nikyyNcfWXLEi2tTArQBjZNCctGqJRoVq1kzacLGlipgjPliUjFHK1gTSjJUrptyc7QqFaNcAE+ebKOb+w5WmNHtHJ9PH21Mfr0H9KndCPr+c6qFM+XCJfSBYcsxa7t0xONAvrPsUrFJydSpb4dfSVLlsnz69NWbi+KEKxVwpMaaMhVLhplRa1qxKokXbVSNUSoLJnKiUbfuj7tXGPVLNU/4u/oNvql+XnHsFINtfRCbSVDT4qgbKPVA7OiZaUOekQrF81yz8syJWtgCuCSYAA7EcEKFGoEqnZbilBOvkptPWPViFWtNNXWa21q2qVttTZeAcu1zY3RR7K81I6tCUFa5/0SYes4cqmCWjqf7ONJ/bPapW1KqX2pLMn6rr9HtErXPydg6XZJonJtx9weixCsZeJIDayVK29KYN90wLHFaohUWTf5Q2TKLVtVXyQ9jKH72k5u2j2pgFa7XDqhVadFurQIlye6ZaYHluozEa3atEFLsgB7XlZvyVoj4QqCKRhLvmqjV0D/dMCxxWqIVC0imtVXnJaZIugVwfQGWd6ALztqlVKKWvWp70SmNm3QEqOOkjil26sYlfISi1xMSvKi8s6rGvsIHHLljVrViJUnBVC2GSJVk4hWpdSs0kIXMqWwdC6dgJXSBfuIlSpRlbJVEqmuTY1oafOzctGskmRNjyNNcJEwYWd7ce9lwf4jJ0xTRsHSbY9ceaNWNeLkkaZVi2h5RGrMNMGa9rUpgn3TA1MBSMvkPK0p5ll52lkilcqQt79Wp/XzStaUESf5vKyKhIVgjUnNTfkI0SvPfKt86qAuRV6xGhqtkjflHqGqlSmz3PFc1UjTYSskWDVsY7t8LYz3CCtV0JuGqKUTylRCmUaopQcCs/JkpQ+WRCsXzbJSAdN5WVZa4cKiWBHxClYcb+RAPvbImLZdE9HqyAnUOkSzamWqT2Qr194aY5mC5W2vpQdqbXMRMSkX3tUB05+eFMGa9MCurtS/wxKtXNpgaQGMErloVnpuuW3tccqiZSsEazCFF1JV9KouNXCvrCxXQ6NWNRGrXN1YUjWmUJVkoK88bS4gWrmtfE+S53i3CqsHzqQLimtnRbxqo1me6JZXtnLpg7mIlpU2qEWzSgtgaPOyOqolS6Vmvtb0kS3e6ZcaFBxalKQqd4Pvkam+dbLd0KhVTcTKW6ddk9o2sr42sqW180a3SjfafW7Ea8fRbnKlEOXK5Y0+MJ/mB5QjVel2KU2wJEq5NrI+F7XSok+5ujRt0JIsbV8eeepbZ8mV9rylLEK2QrCWgRm90trqcqV9/9RMN4dceRex8EatcnUlsfJIVJVoGdc1J1MeMRlLmmqkbUvcrKfUHE8qY9b+c+K1K0oDhEuWeVIJZRrhtiJImmi56gyZqolmpXO2JJ75WHrHmIsV7E88N919pEs+zo3jlauxUwXHjHbl2ljjeITKe+2t8UpCvWjkPuVNb0m2cmlo6ePaJdjTbc8S6kNSBIfW1cqU1qeU2ue53n1lSus/NTEHazK0N5Ge0avdEfORqdrIVW3UaoyIVUmq+gqVJlOWSOXExiMrY6UCLmsVwUlQXu7bvKMKVylaqEW2rKhWKaLlqUu/sNgTzcpJlkwXLC560dI7irU3Alb/RRIEe2g36n1FKn2ci1RZ4tUnJbAkSF4Z89Sl5TVCVZKpnHymlAStRppqFraoGbfmRlZb/c/ap7VIg1VfioblVhDsflqi1rWvSRGsrRszNdCTMphbEMMrtpp4pfVam2CWNRMsJ5pIGamBe/W6PJXqp5CrRYnVEKmyhKgkU16RWsRqgn33sS2iHqWoFaBfly7yJftr0S4tyqVFuEqRLOtxKklaRAuwUwettMHalEFvJKsY6Sp9P9YKR66Yge2JlmknogcAeDmATQCvZeaTlTaPAHASGrP8Z2Z+zCQHE4yC98a8JF6lx3KMUnsrcrMo6fLUyTFz9do1rYlqeUVLw5KnXL8aicqRG0feWOe+MLgjJwa5yIkVodLESNtP1y7tO2SFwLGiVlZqYK5cG0deK+vaedunj7UxasqmYIp9TPH5uPqC5b0JVtspZT0XtdDr/XJVK1aybiyx8kjVGEJVEqkauTlsgStEWmwl0Q3v6oHldEGREpgRLjVq5ZCtsUVLSw+0BExGs/pKVkfue7Jyi17soUSpvLK1wlLmgZo3pVMA3B/AxQDOIaIzmPn8pM3xAP4QwD2Z+TtEdJPlHG1Qg/dmeoh4pTeqJRHLpQXmJMojUN5UwD5iVZIkbRxrTK9caWPn2lr9a4Sqpm0pNcxqq0VMAH01QMC3DHtpOy3zpP1JOasRra6vV7S85d55WTJdUNbJx9rzMUSkNBYlV8D4gjXV5+PqC5ZG7ga3FL3qypyLWpQiV1NHrSyxGiJVniiVJgeaTGVTAzPPU400rYJgVZH5/Nrmnap0yG3Mt5fCpa1YqKUSzrQ3RKx7bIlWabGLXLlnlUFNsuTqgu5FL7qy2ijWkmVqoi8aviuAC5j5KwBARG8DcCKA85M2TwJwCjN/BwCY+ZtTHEgwPZpkWOW5Mo9slSSqr3CNUZ6WeaTKurnW2tSIlTZeqf2qC5a3rVbuna8F6Mu7lxZ3SIXLkxqYvjZLKYDdGKloaemBHTWpftY4JSkrPadSqtIyeV1zZbnyRTLBvif5fFwtwar4xW/I3Xjno1d7u9TKfJGrGrkaS6xk3RCxGlOqhshUrUAtQri2xE16aZ9pe61tV69dJy3ipUW5tAiXJlvpfmRkyxvVKkW0XFErUe5NGayJZO2V5VMF98jNxSrN0xIQNTl968EtAFyUPL4YwN1Em1sDABH9E5o0iZOY+W8Xc3iBh9ob7D6i5RUwiSYjNXJVuyDGmHOzLLGqkSrrOfAsolH7OLe/MekrbrVzd9LHsn0pGpWLusifQDk1MN1nLqo1VtQqLfdGreT5aMvbewXKKq8RK+05yJUPZYIxJ/l8XC3BypL5Ra+cS5OLXuW+68pTNqVcWfJUjGYl59RHqmqEKiciY4vW0DGkRPUZZ2tXevT2uXpLurQoVyNS9bLlES35WJOwNCI1I2CFqFVa7pGsjlJZJ1neKFaWbMSKsMiFLpiBnX7LtB9NRJ9OHp/KzKcmj7VB5YkdBuB4APcGcEsA/0hEt2fmy/scULBYLJnq6mrLSwJWI2MeudJEZepoVk0br1TVLHbRR6w0Vm2Ri5wQ9llkoduWKYbWXCu5X022St9ZBczLXdpmzAUqpDjlJMu6nrKsVqCs61cqt8aaCubeqwjmPiMn+XxcI8Fq6Zse2IqKtmqgd1GLXOSqlBI4tVgNkaq+QmVJRW15bZts/wWuIriFnd7Ha/Xdwo5LuCQylXBu3lby9iHTB3NyNRO1GhjNyq0y2MmTVuZd9EJdVbCNYq1bmmAPLmPmn83UXwzg2OTxLQFcorT5BDMfBPB/iehLaD5Qzhn1SINJKUlTTpxkfam8Q86j0qJLpWiWJTtjiZUlRbmIVk6y0vGtNn0kyxo/175GmqYSLG1crX/NlwmXtrUbfOu7onI/cysAdj89KwF6ymvS/bS2Vllp0Yv0Gmvl8jpagpbWLytNsOd+c5+Rk3w+rqhged4AyumBWqrf7h4yqYG1cjW752FyNTRiNaZYeaXKW+ap222zgFUE++5ji/NRK6AcuZLlWnstwiXTCWUqoYxsyRUJraiWli7oFS1vNMuKcHXk0gWt5dtdqYK7dV37oWmCU0a1CNvTfNHwOQCOJ6IfB/A1AI8CIFdA+msAjwZwGhEdjSYl4itTHEwwjNJNsiZSWv0QGevwyJV1XJZc5STKK2Jpf+1YxhCrnFSVyiTWyotWP89rYCjWGLloRlqvlWkLXVipf1279GZeu7FPyzwpgBal1EA5fo04yfG08pqolRQyaz+10pSrt45LazOlfE0w9iSfjysqWBV4bpIVEdqtEqmBObma6ZeJXGkS1Tdq5YpeZeZW5aSqJFRDZSorWY7nbRUXt9jCTvHYt9iObA2JegGY/9tD5n0mF9WaWxiD0375iJZMG/RGs8wyJWo1c8oOydJSBfcG2FTmYsk2axexqoKZt4joqQA+gCZ//PXM/AUiej6ATzPzGW3dLxLR+QC2ATyDmQ8s76gDjdpoRa1s5aTKI0hpn65N37J0H/LYvMLlrS/JmNU2PWerTBur1HaoZI0hWBa5G1yZnqaVycfWjb2MKMm+clGLtEw+1sTFimqlgiAXtOh+SqFKy4csXuEt08TJkizt2qbno9WneNpofaaSrLHHnerzcf0Fq2P3ZjETtcKsRGmpgXtt7ehUKS3QW9Y3auWJWB02074uUiUFYLBojbCa4GGuqOY0bCX24YpaKedrRb1ykatcmYxuaZEtOWdrtm52jtUmbZgRLbldG83qKJXJqJUa5cqIlEwVzM/FaiNVKyZWzMDO9jR/WGDmMwGcKcqem2wzgN9v/wcrTu0NtFe2vG1T8ZFlJWnylg1JH6wRK0suvfOyrP7pGLl2Q0Wrhpr5WtaXB3vFru98q65OWwFQXisZ4bLILbeu9bdEShtXi0RpfYbOq8pFrbq22qIXcqxuvNx16yNVXb+pUwenGH+Kz8f9I1iCXHqg1dYTqapNC9QiVzVzrRYlViWpGkOo8mmDdR8aU6YQbokb7tKxdQKWW8hCHm9OuEpl2uOcaAGzKYSWaKVjeCJYnmhWLpLlSRfUVhaUbdT5VQY1bYNgHehzwy0FqnTDnBOu9GavjzQNjWZ5olaWTHnEaohUWeLUR7Q0crI0VMSA2S8PTsnd4OYWu/CKleyTphdaESkpS9bPXAphTg5y0aEaoRpjXpUcT64sKNt05GTKG82yWIRYAYMWuVg4qy1YuzelJB7XjNFFq/ToVW5Jdi1S1SG/QNibFqjJ1ZRiNaZUqZJVtfiF/YZfK0yTRrRqX2fZG3ZjLOXwtdRCj1zN9EnGlYtjpFGtIaK1O74SzZIrDeZWE7TSBa0ol5YquHvaIlVwLorlSROU7Ea3CAAvLNo10RysYB8iZSEnBLVj5MRKkyRPm3Q/sl+pTOsryzx1uXPK9cld075zsrSx5Ji5diUxnorS/JtSe0vCtFS/dDu3wEVugQr5M21j9ZXtuteFFLy0jZQ3oCxfubKSeHX79kS2cniigNq1rB1jLPaNYBHRsQDeCOBmaGZ/n8rMLyeiGwJ4O4DjAFwI4BHcfLsxAXg5gAcBuBLAE5j5M+MedvvGQ+LngLkt8+JVjmZlxUuRM29KYEmuasUqlwI4hlTVClVJpobI0+EVfQ8aE5g8+59JG8ymBM6OZUW75NwuLcJVI1tyNcJUtmpEK5c2qJVpKYPWaoKWSLmiVkKk+iHSBHcFqi0PggKr+fmoC4ElYLVjWpEqTxtNruQNplWmjS/HzNVZ515a7GIMqaoVqlL7Wtnq0w7wzcmR7TRBkmXWl//K89Ykx4pwWUuq1/T1LPveoQlSrq5vWd+oVa5NDo9ALUqkLPaNYAHYAvB0Zv4MER0B4FwiOgvAEwD8HTOfTETPAvAsAM8E8EA0Sxcej+aLul6N+S/smhRraXYreqXJVEdu3tVum0xaYEdOroqSVYha9YlYVUevXAtgzL/R5Odf5d/oa2SpD33GP7grSHrfXYEypMsjXCXZsuZjpY+1uVq5eVqdaG1JicpEs7wLYGxLkVIiWTucj1ppQtZhLXhhRbFWOU2QOSJYa8bafT4C/qiVR8b6RrNKbbxlpbpc27SNtq2du5W+mJM07ZrJflrb2sfeOi/WGLlUtFzKn5Smrt5aWVATLo8wedIAtf5WtMkSilLaoDdlsE9EqtRGuy6e18QqCFSJVT62lKJgMfOlAC5tt79HRF9E863HJwK4d9vsDQA+jOYD5EQAb2wnhH2CiI4iomPacVYaKzUwF7my5CoXufJGrfqKlTcNMCtcxYUvfDKVkyiP4CxiuXYvW7wzTPq0c1Fv9EU7ucvMe4uUK0/6oFwQY6/MThu05EpLGfREsqzvydIkK5cqGASL5FD4fMwJTOnxkDbdvjUpK7UtiVVJqCz5k+1yP2V/WWe1rxGuHDWLWdRi3cgD+eiWfGzN+9Gky1pRUP60JE6LaqXb3U8Z0dJEK+2bjiWvizZOLu1Pk7EhbaTQrYuYlFiX86iag0VExwG4M4BPArhp96HAzJcS0U3aZrcAcFHS7eK2bOYDhIieDODJvY56ZiCZp9wvepVLDbQYW67GTAf0RqtyYljm6dwAACAASURBVDWmVOXExCtRy1hJsJQKuNuuFSXtPLWolxbp0tIKt8BmdEuLbFlypaUPphGtdDEMT9qgJVdayqC1wqAnkmVhpQr2jmKtzGqCk30PVjAxY34+tuON8hmZi9B4/wO6GJVu+IcIWDq+FbUqpQPK/Wh1lohZx2JJmXbuVptcmfVYG1viEbAaSbNuZNPFL6w2VmTHEqC0ThOgPgtcyGOWaYC5/QF2RMvCShuskaP0vGqiVGm/3Bge5Fw4+Twtk1RwVx23YBHR9QD8FYDfY+bvZp4srWLuajDzqQBOBYCNjY36qzV3szvuX2tK0as+aYGlyNVuXUauaqNWfSJWJbGaj275hWpIyuDsOO6m1Wwlr0b3CoKZeVjyWhwEq6mCOdmSqYQ1otXVye00KlWbNrgtZKRWsjpykaxcFGs8xHyrlRGtYJ0Y+/MRmP2MJCLXZ6S8wbJu4GtutiykQHiiW9oYVhtNonL708RJ7sdqZ4mY/FkSq1xfefye52PIQhdjPMfWGNq8IFmupf0BuqxogpMry8mWNW56XFpULIcmP9rYaVt5/Fq5R6C8Uapu3JKEebCE0xLiRbOvBIuIDkfz4fFmZn53W/wNalMbiOgYAN9syy8GcGzS/ZYALul/iPLFXy9SMipVil7VytXs0c2Kk5SrbARLSQnUolY1YpWNXpl9xxGq2pTBGmmadH6W716mgTPHYUiXR7jm+s7d9Oupf+lKhNaXGluLYWiipUWzZr6kuL1UpXlZWrqguciFU7JKUazdy9NrIQwhXt1KgkEgWO7nox4hqL25tkSlRp5yj9PjrOmTluXKc7Jk1efEyitVfYRKe26sOVnWWFa9p3wMrBtcrVymBFrRL23VOy31T7vJTx/LlLhUArroRyojXZ/uuZby0LXvHpcEEZiVHkuopBBZAiX3o7WpfZxef3n9PGh9liVbq4xnFUEC8DoAX2TmlyZVZwD4dQAntz/fm5Q/lYjehmby7hWj5Jc70sis9MDdekWIatEiVVK4SnLlnW+Vi1oNiVj1ESuPVNUIVUmm+grUYRUfKlvGm0Fp3wdn0ga1cdu6QpSqaTsrXFY64Xy0ai+yZUW1rCiWFemSomVFs2aiVEnKYG5eVk6y1EUuKtIFLeQcrV6LXSw4osWIRS7WiVX5fPRKlZQSS4KGYEWq0vG90S5N9Kzz0MZP22rjaWNq2+lP7XxKMimvq2eRi5xoaY+tsilI96NFs6wbb02Ouu3clwprsqSND5TT+jSJyomWxJqfJbGOwzMHyxOVmiJSpVGSpj6CNpR1ETlPBOueAB4P4PNE9Lm27NloPjjeQURPBPBvAB7e1p2JZgnaC9AsQ/sbox6xij+qNTR6tbdHPZrliVx1SLnypgRqcjWWWJWiVX2lyhKqkszUCFMttWN3QmYd864ciWpNuKw5WDnZ0tIIa0Urty1XHLTmZuXmZckUwFrJkpGtsaJYeWS0KgjcrPznoyYVpfbpz77RqiH9teO20vw0YcpJlSVruW05tnad+kiVR648YqWxqEUucqLnEStrOz1+j2zJck2ESvvV5MeSLe/cJq2dJqW1USh5bWtTCXMSmWId9zJZleMo4VlF8GOAeRd8X6U9A3jKwONyRaxybazFLdS2cymEdfOuaiNXucUscimBNemAQ8QqLS8JlVem7DlZBcHK1i6Y0geb9TuvpRxqqYXFtMA9ZFRrqGiZ36HF3f7KKYPp4hcdfSJZ3lTB3cuWSQE0F7tQGzuiVRNHtDiWaV8rlvX56JEmb70mKrmb+iHy5I1c5SJeljh551nVipXV3irT+mnX0CrTxtLGLbX1ilgfrLG1+VZWP5k6KLdzkTE5H8pKAeyeHznnqka05PHLeWaetEEpO2nbIal+3lTAnIxqWAIjr7dVn2szBvtGsNaOilUApXRpc6p260aSq5ShcrUssfJIVa1QeV6Ih0/4gWFxMPlFto5xq6sXx2dFvA6CZ66XFeGSaYHdOOmcrbFEa2+/+WhWJ1nN8W6qkpUi5aqvZGnIaFfVsu1tmmAQHEp4xaxDLjCRi4oMSQPUHltyZUmTJV/W+eTGk8eTEypNrHIyVZIr7ZpqbbQ+HqmqEa/aG2l5A259v5UcQ5McKQUp1hcDW/01ccqJVoc1l0riiWZZESQrHdAjUaVFLeTiHPLxskWpD51ErwNrJljdHKu60Lfni4W9qYEeNJmSdZZc5VICvVGrscSqFK3ySJUmVLkXnVeiFrWKYO54OvnSzmcLPuEqyZaWFjimaGnzs6wl3T3zsnLpgh25OouaVQWtJdvd7EaplpM6uBURrKAnHoHy9NP6lwTK6qfVybGs49Dkqq+AWec5hliVFrsoSVnuuvQVq9rXgIYnwqFFNWRZ+lj7fiu5LykhlnR5VgSsFS3Zx5pLJfvUSlaHtl9t1UINeW5yH5aApddTO44SyxawEKyF0b0hzUtX7jutPN971Tc1sGOz/Ze2Acpy1TdqVSNWfaSqJFQ1MmWJi1ecphQsP/pBHGTWz1s7Z/k+IdMJk1TCuUUyrFS1CtHaSwHMR7NKKYOedEEAM2LVN1VQPWXHlw9rqYR7c7ViHlawP7GkqyRjHoGyZKsUrUp/etMCc/Ilz0fKTx+xysmVdU41QlWSKc92rmxKtP1pN70y9S8VLe3aaLIkZU37mc656vp3fVMxk9uWaEkhk5ElKVdd2648t/hEKV1QjtknqiX7p/uxIl456dPKl8mqHEeJ9RUsx/wrta4ieqW1GbKoBVAvV7VRq0WLlVeqNKHKSVJfgarpt2X8jpbG2I0yKe22eP5ctUiXlla4xTwTnUr3scWzUa2mbk+ktKiWV7SGRrNykiUZuugFsBfN8kSxNAbPw5oQBrCdW/o/CAwsgdLaWTfkVp0lUNr+a2Ur3e4jV5Y01aw4qPX3iFVJqLR+Vn2uzHqsUVrcokbGSjeynsUutCiW3JZCk1tRUEOKlCfqlBMt2bajtEJh2i7XJrfgxBhSZbWV1wzQn2OrTivXrtPUhGCNiTclMBexcka4rIUttDa1i1qMKVfaXCtNrmpTAS2xKkWr5AupRqhKQjNVtKqvjGWXZqf5ck24crKVihYwm0ZoiVY6TytdfVBLHUy/tFhKVjPuzpxkpe1rJcuah1X1HVnGghcp+QiVgnceVnz5cLDC5IRJa+sdo69sleSqJEq1cqUJUZ/olhxDa5trowmS7Jf7mdvWnoOU3PM6BK2/TNHTygH7C4A1mdCEK22XE6CSpMh+1nlqUtO3XXfMXsmyymoWsaiRKq3MEi2PzOTGmIIQrN7UvyHk5ElbPVAtc3y/Ve47sKaSq75RK02axpSqklBpEtJXsA7fd3/QFyck3yySayllayaFMI10FFIHZ9IL2/o0mtXtIo1mlVIGp5CsFM+CF6pUKWmCcjXB2TFqlnTfG3GKLx5mppiDFWTx3DRrwmMJlCVRWlutnRQbq99UcqXtP9dGOxZN1rTx0jqtTPbx/JTbchytvvS4VD4E6+bWinZo9daXCssbdflT1ntSAdP6ro/WvmvXlWlpg3LOlRxPMoVk5fp7parUrpQi6BEvr5zVEotcTEkhmqWlB2qLW3jSAWfKjNTAlKnlKhe18qYD1opVTaTKK1Xmd2I5PgsWOfdqdrELvc3BTORKlltl3TWUqYRyoQyZQmhFtWTqoLYYhpY26I1mjS1ZKa4vIXakDQJ78qUJVDZNEIioVbCWWCJUaqNJiEeaUvrK1phyZUmTJVeWYKXt5LF7xEpex5xcyf6yPidi2jie599LLn1M1kv50R5326UUQClA8ma9e+xdgCLFE31K28lz1o6hVqJK9VNKlRTPjtxzqeFpMxUhWAuhJFt10SuvbFmpgVqbseVKW8hiSNSqj1iVolWlx80Y82W59nq78X/Jtji9Brl2zU/tPA6yLlw52ZKphGkaYS6FUBMtmToo52dZaYOpZO3V6XI1VLJSrFTBFK9UdeSiWDarsdBFzMEKLGojEp7Ihywrza8q9U/HKPWbQq4saaoRMK1/jVhpP3NSpUlUjVjVvi681MhaTqy6x3JbEy4pNVII+opWul1a3KJr35VPkQ44pH6IVHVt5RiaMFnX20ttey8hWKNQ8aZR+P6r3HyrdDtNB9QWtvDMu5parjxRK02saiNWllTVCpUlU3bKoO+XZ5polvcXV995I0pjHEcySPJmkka1LNHSUgdT0dobaz6a1f3RQlsAY0zJAspfQgxgbsELEMy5WLWLXTQNar4PizBFWmAQ9CV301dzU14SpXRbPpZ1NfOu5OMx5MqSJqteO5+cXHVtZXlJxGQ/7ad3W3ueNKYQL+vmtpRyZomWHFfWpRKUlntSAdMymRKYLqahze2S6YA5GZNtNPpKVOmxrMv18yx2UZKqnDRNJVSSEKzRsd5AfItXWPW5uVeze/enBGptppKrXNSqb8TKI1Z9pEpPF7R/UfoIVE1Ua0uJFPhXEJzfzxaTmSqYXg8Z4dKiW1pkS0a1aiJaVtqgJ5o1pmSleFIGcwtepGipgRZ187AWG9ViAFvLD6IFa4h14yblwJKpXHtJLsqltdMkRu5vCrmaIm1QXiNLsrQ+uZ+5bTlWrk1JqmqkqyZFUPtSYcAfVeq25c/cd13JtmmZFaXSjsOzuEXXfspIldynN3JVik7Jdt7XiCW9uf0vghCsKTHmYaXzr+SXC6dlQD56NVOWyJb2fVe5eVceuZKLV/RJCfRErTSxGlOqPEKlSUl+qfb6X6LNfNboLDt9fklzb0zaeCJqVIhwSdkyx0rfYNI3y7ZYi2ilEatSNEtLGdROb2YxDEJRsro2ObnSUgW1ZdtLUaw0JVD70mFzHlbMvwrWgNzNeO5GMd22BKLbtkQlHSvXLidDXjlKBSU9vs3NTbWPPH5rfK1N7rpYQuWVKu3aaX2tMWRfq9/UglWql1EpbaXB3CIX8mdXp8lW199a4EKLUqV1Wn/P4hZpmzQFrxtne3t7pr7bz87OTlay0mPV9ievmRW5kmJkRbGseWalxSxysjq1cMUiFytGKWKVksqTXNgiLfPMuwLKkau9drOyBfRLCcylA5YiVmOIVV+pyslUlTRVUDPudvt+Z0Wumrq0bL69jHDJ6FYa2dLmaOWiWuk8LS2ilS6EIdMGZTSrlDLYkS6E0VGKZKVtPPOxmiOYX/BCi2Kl1ES0VglmPbIaBF5KN+BD+9SIl9yHbCvLPaIm67UxvfJm7V+egyVh8np552PJfvJ6aW1qRStX7kXrX7PCXCpIaV1ukQspPOm+0vKh865kfYeWNigpLYCh1afCaEmWdW21sWtESqOPFC1SpHKEYNWSvphS+bC2UxmpEKjZXeYjWimlVQOteVcppbTAvXYb6hLsgC5Qllz1iVpZYjVEqmqEqiQ9UyxsYZHe6GrHZUmXlSpYki0rsiXlSopWKXUwlzaorTSYCljHTDSLZ6WqlC6Y4pmPpbVPMSNWFSI103ZmHlaSEphGssztkKFgMVg31N5tS1aG9umQ6X85YfJExLT/mvBY4mTJUW3UymqjtdWum/Yz7WfVe7Y9jyU10lWTIugRq3Q7bWOlAUqpkj+teVRyn+njXARJCpn1nVM7OztqtCltkwqdhleUSsdSkw5YknTt+fM8l97tsQjBWgFq0gOB8twrLXo1s60sauGZc1U736pmrlVOrEoRq9y8qtn2eaHSpMgSqZJAHbaxwF+sUpbYhvFBpaYeyrayzV59KlvaioRdm73ypEH7xjOzxLtIG9TmZnXCMDc3S3xnVipZc8dkSNZsm/lFL4BZkcpFsVKs+lKaYBAcSngECphPr7O2PRJmperJx90NYk6uurHGmI+VHqfWRhtH9tGuhUeu5DFYbWpkS3tcwloQQyMXAdH2LW98Zbqg3LZu4NPHVipgKlnpdU+lJ22j9fUsXNEdQ9pmY2MjK2JdvRQ6eT27uq69lRooRSqXNpg+ttIBS2mCixKl/c6+FqwcpehVuoqgJlQzUatMtCqlFLkC+suVJ2o1RsTKEqu+UmUJlUekNieIZqVLZFvH0H0RrBa5AmbPU4tyadGrrj6tk6LVjNMvoqVFs6y5WVbKoLb4RYoVyZptk4lqGdGs9Lux5CqCQP8o1irCiEUugtWgdIOvRYvS8lSetJu/ktiU+tXKVUmavBErz0IX8rgtGbOurVeu5D4ktfIlSedPdWg32Va6nHWDLkVHK+t+5lb6k9sdnnTAVKys6JAmSRq5+lLaX2keVFpekqRUqkrXfp1Zl+Nfa8GajVDp0aqa+Vc5tIUtUtLolTbvyrNa4Fhypc21ykWtphQrKVXqnKyMTNVK1GEV86q0G9nc/jr50o53a4dU4crJlpy75RUtS646+dLmZ5XmZvWVrC5dMLe6YNNmdj4WgLko1u5zoCxu0QdrHpa10EVEuIL9Qunm3bMt++W2rfFlXU6iagRpiFx55E4ej9Yud/26ttpzIdt5xCultJJgSapqpMubIlizgmDXTpOstJ01NyodtyQR3lX7ZLTIimb1kSwpPRalfpaolepy27nnJt3OifEyCMGaCmMFQUDKVP/0QEumrEiWXj+bGthsZ9IDHXLlWSXQilp5xapWqmqFSpOTnNTUCFOz/5pfvMq/7uVWHdSOU96ry5TCufHSejbK59FWHpz54uLuzah9LKNZlmTtjuWQrL2285IF7EWuOskC9LRArUxuW4td1KYJZqNdS1pJkBGLXAT9yN2gl8TJmx5obacyI8uHSBIwv1pgrp+24mCNfMn26TlZ7bRrmWtTI1TaSoIloZpasHL1mijJOhlVSutk6qBWrkW15I2/JXAy1a87Bi1tUEPOqyIiM60vLevG71YX1Mb1rBSYPpZ1temAuTTBPpHGRaHJ76qyJoKVuct2RqWsSJYpXIZ8qZKlLMmebqfLsWvpgd7IVdo+t0qgJVeleVaHK2UesRpLqnIyVSdOPrxjbncpgcrxdVGw9Hy0SJeMcGnRLSt6lYtqaRGtXNqglTIoVxmcXeDCny6oRbLk8u1NO3vpdiAjVIZEWWmC7pTB7BcOJwtfBMEKUYp2lOqsdpYg1GxbdfKY+4pXKaUwJ0m5/poslc4hbVu6PtrPdAztOcg9R9Zjq6wGrb8WLalZ3KJmBcHcDT5gr/gnb/q7x30jVZ1IyePQrlEuWuWNcqVRpNK17rtdmzKYa7do2QrBWhJWeqBkjOgVkF+SXdbvtVMky5EWeJgYqyYlsDZqVStWJamqEaqc+Ewx78pie3dO1fw+Lena2pk/xm0mt2zViJaWOphLG8ylDGJ37OZhuviFJ10QmJerlNkFLeyl25v6caJYEitNcJVoIljLPopgXfGIVu6GP90eI6plCY4lQnL/OWHKiZd2nrLeinCV6rwSVrrm8rpp16D2xll7LKmRLm+KoEes0m1NkoDycu2yLF3gojSXqkObn2WdnyU8pTGkSKXHb82Ryl1jz4IXtdvW6y2XIqhdq2XITgjWAqFchCtBLr/eJ3o1s8iFsiS7lRpozr2aiW7Ny5V3vtWUYuWNVqUS4REqS6TyKYMLjCTsZF5XZtXsG6UmXGnfVLZkVGs2hZDaNuk13yuTS7zrX1jcFhRSBuXiF2mqnCVZILRlSeQqPR5HqmA3X0sueAHUR7G0xxbmFw4HwZrjEakUa35PTiDkdt/UQJlq6B3P6lfan1ZvtZHnWpM2KMdP92E9J6ssWLn69HFpW6YDpsdmLWqhRbU86YByjFw0q6uTbboyAGp9d8wdWl8pOVa59vzmUgXTdqVtKZulJd6XmQ5osQrH4KEoWER0LQAfBXDNtv27mPl5RPTjAN4G4IYAPgPg8cz8IyK6JoA3AjgBwAEAj2TmC8c97LxQyflX3oUuPNEri1Jq4Gxbn1ztth9JrnLpgDViZUlVc84ykoXZekdkq+lXvuGdIm2wi0zljmFrZ0M97m2mmWPa3qGZ85fphDKyld7jW1Etj2jl0gZrUwZToQLy6YJSslJKqYI5PFEsiUwTBKNiufYlpgRyRLDWjWV/Rpb+Cl+q1yJFVj9P9Mo6BksyPIJTK2tD69NjLh2fPD9NstJrZElU6WduW3tsldWg9femCOa2PTftuUUtNAlIty2BkueWipF1rWoWt9Be49qcs7QuLe9kTBOnXEqfJ3Ilx5PXzZqHlfbLHcMi5WtdBMsT+rkawH2Y+Y4A7gTgAUR0dwB/AuDPmPl4AN8B8MS2/RMBfIeZfwLAn7XtelD6K0xJsvzpgX3nXs1v66mBuXlXTbkuV2la4FhydTjZcnUYzZdtbuzJ1WHEe203eC5itblbt/e/GYN3/6dtN2dEbWfm/+64SV/5X2Njk93/NTz7M49VnJfsO3dd5q7B3jVNr7V8DqznKv8cQ7SZfd10r6PDxessfW3u1jn+GNCUz7/u09+Lmd8Xx+/XRmbupFUnKa0kWnpfKb0vBYckS/mMtG74coJUqk9lo8MSMGtbk5a0vLuZs0RHtl20XGnH17XZ3NzMtrX6deV9f5a2tWOyzrmr8/zP9c+Vlbb7/NSihrlrnz5fpdeflRI6Rn0u3TTXR7bVxvL8Psq+6e932kb7Pdbw1k9FGmms+b8MihEsbo7s++3Dw9v/DOA+AB7Tlr8BwEkAXg3gxHYbAN4F4FVERDz0DB1CRZkbq1x6IOCXKWthC2tJ9tJy7H3lqo9YdVg35mmZJ2I1Kwd77UtRKi0ylItGWTKkMUVUCwB2tikzthLtkOmFcy/f5KInqYBpVMtMH9yZnYvVUE4bnKdtIFIGwZkvJc7MyUJh+XYA6tLtTfn8ghcAquZiNZfZnyZI7f6yi2AseDXBmIO1fiz6M7LvjY28sZP9rPTADi0Vz9pOb8K0G0urXLt5l+PKG9ecPNWKlTw2zzlp19a6EZY3p6WfpTLv477kxtHSA9P2Vlpg10ZLFbR+apET7cuE0+20T1duLXme9pUrDcqxrS9fTqNV1hcKa68RK8KVPrbKZRpiblueb+7Y0rayvdZGqy/1G8K6RLBcc7Co+bPvuQB+AsApAL4M4HJm3mqbXAzgFu32LQBcBADMvEVEVwC4EYDLBh+tlKzSX6MLqX0bFemCpYUt5rcLESx1FcG8XA2JWs2W2+mAY4lVSapyESiLseXJM16XMqgd1842qeM0qYHJvCWRUpimEqZphOl8rT6i5Ukb9KYM7vkauyULQHHRC23pdrktF7wA9LlYktJ3ZhUXtpArCS5pqfZg/ViVz8g+N2i5cWT0yho7lZkOTW60ctk+HTMnSdpYlux5+uaOKSdVfcWqj2TltjWs53cIuS8VBvILXaRtpNjIn3JsOZYUFEucLJnQ6mUbjbQ+N+/K6tfRnauWKqgdqzaOR6DS8tpzk2Na5YtmXwkWN3/ivRMRHQXgPQBuqzVrf2rP3NzVIKInA3iy8zhN5AIXuRQfEumAkg0pUpm2NamB6bacd2V9iXBaNhthGkeuclGrklh5o1UeqdLEpSQ+U0WpUjqxsva1vUNzx64JV0625JwtLapVEq1ufpa24qBcbbBWsrZ477WSShYA9cuIwTvZ+VjptraqYLO9F52SyCgWADM6tYFN7GSiU3Ie1qosdBERrPVkpT8jjRthrV13E1aqzwlaThAsiZKPS+W14jWkvnRs6XWwjl1elxqp0uokpeijt05izY3S6jRJ0IRKHktOskplmmil42rnnoqRRzRK9bKNFB7tGOR1ko+lfEnBtM4xF7myziF3nKsgUxqrchwlqlYRZObLiejDAO4O4CgiOqz9C90tAVzSNrsYwLEALiaiwwAcCeDbylinAjgVADY2prtj1qQqN38DyKcL5qJXVmqg/DLhpnxepMaIXI0tVrXRqpxUeWWqJFATvlwmRcpWmkZoRbVmVhLcjXQly7x3X17sSBvU6BPJ2o0ki0gWMCthe+mDe+mBVqpg03c2itWtKAgU0gLbRSyyqwlmZCsIxmKqz0ii2ZSAkjRpN35aWVdu3aBb0mXJglc4ZLn2ZcLdOCW5kjKUSye09pVel04gLAFL23qEUdvOXUN5jVOsuTOlx946Se5GVpMlrT73xcLptiVUWp2WDph+J1a3r9xcnM3Nzd0v/tXaEtFMvZX2t7Gxsdsnt7hEWq695tIvIc797nZSaa02mJ673G+pXntONdEqydfUMrZvBIuIbgzgYPvBcW0A90MzKfcfADwMzSpJvw7gvW2XM9rHH2/r/55HvRraX8w2s4/VUaRUFaJVnoUtAE8a4Oy8q1LkyiNXtVErbXXAdPGK3bpCxMoSKylIUqzmol5WuqBDpDY3x4s6bG+nk0D1fe8Yka3tnfkVBIG9c5dzuFLZsqJaWvqgFtGS0SxgPm3QimalFL8vS6QLArORLBjzsXLpgx1WFEumCErmZKqQJgg07w+pbOlfRryBZawkyGiem2B9WLXPSEuitMe5tjmpkuQWG7CkK+2bltfIlbdPrs6ql8eUOxevWOUkS7u2mlDlJMzz3NdgSVNaJyMrMpVPRrxykSEpV+mNemm7e6xFtKz9WF9U3FGql6IjX9faCn6aHKXSJK+ltY+0vbUvea20Y/b00YRpaomy0NI6VxVPBOsYAG+gxlo2ALyDmf8XEZ0P4G1E9EIAnwXwurb96wC8iYguQPNXuUf1Prriil6ZrihLl5Qqa0GL3TIRvdIWtmgeO7aT9vK7rpptJNv5yFW67ZUrT9RqiFj1kSpLamokqmZBjC6lz7OfTr7kMWrCpclWelypbNWKlkwd1KJZMm3QkzII1KULNtuNPHWSlZYBtlDJVEFtwYuubptTCbMXvgD0BS3mZKo0DytHzMkKdJb2GTnk5tmTVqbd1KX9czewteLhlShr/DEXu7COX2srj8dzrtq1lc+LVj9EtDx1klyUqiRW1rb3i4Vr5Co9vjR10Pourb7zrnJ18jUqr0+6rUWU5HZ3DWS5vN4pqcBp5+URpVLqYI5FSde+ESxmPg/AnZXyrwC4dh2KKQAAIABJREFUq1J+FYCHj3J0ALSIlYa2oIWUqg0o6YJymXaypUt7XFrYAsjPuzpciWaVVgv0zLeSC1nkFrHwiFWtVJWESpMpS3BqpGm69EHrDWfMScR6+mC3CzV1UKQNaotgADS3AEYuktW10dIF5ZcRI5EsANn5WEBOuma/D0vK05xMiS8eBmFuNcHmiuYjVX7hWkxEi9Fc72B9WPZnpHWj3pXlbugtebDKcn1y+/EueJGTLsCfSuhd7EKuRNhty2PQxtGOU7vmObmS+8v99G5rj711kpoUQS3tz9q25Eym96VS5NmW4tX91ISBiNQFHTwiJce0VgkkomLqX/rTu7BFKl/pa9mSKau+1EcKb07QFiVXwD4SrFWEaAPaCoLl77DxoUmVNfcKmF/YQluSvXlM+rYSuZpNGWzLKuWqFLXKpQNqEatasSpJlSZUOZkaW5484+0UVhGU57C9vTEz7o4SzUojU1oKoRbV0iJaWtpg5wDzaYOtAPHe49LiF1KyAMx9GTEgo1b2fKx06XYA6oIXTT97LhaQXwijBvULh2kTBMyXB8EaoN3Ma/Vyu3b8NJJlSYg8nlzbvuWpTHnlSkpV7n9HKQLmkSx5vTWZ9chVrVj1eZ6tvt4UQbmdu2lPf2opealApY/ltta2w1o9sJs/1bWRka/ccusy0lM770or16RHylT62Iq8lSJyOeTrKzcva5FSlRKCtSJsYBNzkSxl4QtVqrIphBtz0atcaqA17yqNZmlfIjy1XNVGrWrFqiRVllDl5KcmotWXLn1QOw5LuqRw5WSrRrS01EFP2qC10mBfyQLtzcdKI1na8u3afCxgPoIFYCaKBcBcUVCbj6WlCcpoFtEmNthecTAI9ju5G61S1ConVZ7FL7SI0FhyZY1b6uOVq5p5Wbmy9Fpb16lGsnLb2mNvnWSsFEFNuKyfANQ0wlSMNJGybvbl+B7xyAlKaaU+7bmwFqXwzK/qjjsnXvLYcudi7TeXGrgskVp3VkywjBd9RWTKWuBCW8RCWz3QkxJoLcve1M+uGliad9X0oblyKVd7Y+z9zM23kgtZ1EStvBErj1R5hEqTmGwkawGCVcuOsmz7fEqZ/3U8uyiGkjoo0gZz0Sxs0MwCGJhJGZz/nUslC5idkyUlC5iNVOXmY6VLtwOYXeRCLNve1OdTBufSApUFLqwvGtYXtjAw514RlNW1B8GIZdoDmz4327kxUqQsyTJNqqTcaGUeGbHa9invjltrI+u6Y5FCmUa5rOO1zid9nF5HS6rkc5cTq2ULVq7OkxYoZSwVpvRxui2jWprAWemCmoR1SIFJH3erA2pfOizr0jHlSobpPq15V9riFmkb+dq1xEuSk61SWTq+hfY85LbHZF1kb8UEqw7ChipUpEStAEOyCpEqoLn5K829OkzcPMvUQM+8q5xcydUCPcuwD4la5SJWY4iVlCozklWQKRpRtjhZ9CL3xcJa5EpGqtJ2u9+P1V6TNLJlRbXSsa2IVk00K10AA0ijVPOLXwBpBGt+4YtUsuTy7bn5WM1+96QL0KNYUqxAmFvsQotsSZGyFr0AY27e1V55pAYG+wfrZl6LUKXb2l/a5bjWvjw3iVq5R15y5TL1LydXOVnKRdG8x9PhEaucbOWek9L2GHhEzXuTrd1sp/Iiy+R2TrRkW7mPVJRkKmBHV15KwevK5E/r+ljya/2edNdUkzh5vtbzI69FN5aMOspr3f3UUiq1a74MtBTQVWWtBasPMmpllWspglr0ykoL1B+nkaj8ioFAfin2dFuLXHnlyhO10lIBPWJVK1WWTNVI1JBVBHP74W0tQtXKlVghUMrW3AqCTtHSUgdnvkvLGc2aWwBDWfwilSxApgnOSlZTn19ZEJiNbDWPZ5dmn0sXTETKimIBMBe7SPEs176SMHavaxAsAxmhktuaiKRtchKUG0vrP2YqoSVeNQLmPT9NrEpyVfNTbueeQ0mNhOVuZD1fLJxuW+mAXT+tXksHtETLmosl51ql5OYwadEqeZ6eOVCanOVkSpu3lns9SEEqLVwhj2UdCcEaG9qAlWKlrSAI6BGrrlwTrdLS7B1ykYvSwhbeeVfpioE1kSvPfKshYqVFrDxSVStUluDUSFOVjLlbTrWGYDpq3Ug50UIn8slKg/Z3ZnVvuHuSlaJJFpjNlQWt+VgyimUteAHMR7GA8pLtHfJLh2fqrHRBcyXBRvr2y9LsRPQAAC8HsAngtcx8stHuYQDeCeAuzPzpBR5iMADrJr57rJGTKa3OinxpspWb25X+18THaquVeyJX1pcLy2Mo7Te9tp763PPSR7Q8z53V3lMnKaWIWW21VLg09a/0syRZUkK0VL50/1p9J1BaWl9HJ1pEs6sBWgKWSqd33pWMhKXPuSZeMk1QK8sdgxXNSuskmvAtM6o1xf6m+HxcH8EqQGSkCyoLWnRY5VrKoLZyYC5ilS5s0VGad9WUJ+M5I1fpdipXtVGr2oiVR6z6SNUYkSwPnvE4kxYox5BRrlx0a0dGp0RUqxTR6iilDXbRrN2X5s78CoNb3Mm8L5IFQF1ZsCnX52N1dch82bCMYjVl5YUtOiyB2sAmdghzc66694tVWjGQARycYFxqTvYUAPcHcDGAc4joDGY+X7Q7AsDvAvjkBIcRTEDphjl3Q99tl6JWktJiDd65V9Yx1sy76o4nJz7WWLn60v/0uKUcjilZ8prnniPt+aoRqlJfbT5TLp2vw/N9V/Im3jOnp3tsRW08EayuXBMl2dYbyZLlVvvu+slzlNde+/3qJEnW5eZlaftJj0c7hvT5yTHkdVbL2II11efj2glWaSn22mjWZi6aJfYlVw4E5udfaQtbyNRAz7wr60uES5GrmpRAT9TKG7GypKokVJpMjRHJGouayFX/JS3a/jOrENq9O9Gy0gYtydpdAOP/tXdusdJkV33/r+7+fOE6GAtCZqzYFlYSiwSZWIbEUuJgRMxFHh5MGEKIQZb8ghNyQWASCSTDA5AohkjIYYSdGEQYYAJiRByMZbDyElu+IYJtLEaOhQc7GGPjQICZ75yz81C1q1etWmvvXXW6+3T3+f8+tbpq195Vuy59av+/ddlu8gvfkuVNRowgs6AXjwV469uEF8M6plYsYJtREPBTtnensXbLu6s412rVb/dSuJ82LwDwaOrmZoKIPATgfgDvN/V+AMCPAPiuw3aPLKE2oPa2t4opKx6i+p7FKG+vCZ2lSTDmLnv7KfUp96HFalXqe2ndW/buk3fNDyWwLN7AVpd5yyl1iSp0mbW2WOtVtGytWZ5AK7m9afHkCSltTbL7yvdal2sh07Ls/cY84aTrtrj86bYt220fWq1ZJbEble2DPRxjL+/HkxNYrURZA1utVqXymvWqW59ar1rjrrTVao64GvbniKtWq1XJHXBXwuo6VqxSm13RkuzCHt+zXuU61gqmLVs2ZiuXRRYtLxFGZhBajsugF5cFwE3jHoksYJq+HSjHY2VXwUxLXJa1YgGx1apkzbJZBnP5LubQ2hcJe4vBuhfAR9T6YwC+XFcQkecBeEZK6VdEhALrjGgVWlp8tNT3lj1rjv2eK8S8uiUxFpVHx5kbezUn4cUcYeVZBqPrvVRYzRFc3gDalusyuxyVeWnY9f5K1ivPgqXJ1zByC9TJLPSyFUT22bNEwtiWe7FU9jjeudrrp9e1YPLEUU0o5TqRC+HlpfX2uPnkFhn7DOyIvbwfj1Rg1f8ASOF/+KNU7ZlIaHkxWMA0ucW2fJ71qmtTj7vq2m7b3FHLkVtgyXI1V1yVrFaeK2AkrErWqlZRVRNSlVs9izwWj47puQvWXAWtGLPbrNDyXAftfFrA1G3QWrOmExRPRZZnybLugsB2nqyufJy+vRaPlVlixQL8ZBddeRCDFQgrTSlFu2BVtGzlWkfI00VE+4M/mFJ6UK17nR5ukHSuAa8F8G376R7ZJdGgrlbWsg0oW61KFh67/0g02TLbplV0zSmvWa7miKu5oipa19fbXsel4qrl2WjFtrWD+UjsaDEQleVvK4h03ZprYFTmuQ3OOdeSJUtvL4kmfS4l98JIONlnrpYEQ5+zXbbHqyW4KAmq6G+NJ7r3xUKBVXpH7uX9eKQCq4IaVUcugcBWaNXcA91JhoPU7JH1KkrLDmytV6W4K5vUIrJcbev44qol3uo6wqpmsWq1VLWKqaqA2uzufzKkOnB2RKFTq5S6wttmY7WiPXpCq9TGE1ndpjR0QKdx1+6CpXmyMjbpBTCNx4KxYnlp23PCi0wWX135NGW7dQeMyjPZTbAsrJTLoKy3avvAJGAkTGfwiZTS8wvbHwPwDLV+H4CPqvXPBvAlAN7Wvxz/EoBHROSltUBecnyUBuWt24CtmLCCq1VUzC3zklHYPuQBua5rxZOXCKN0jJoQ08ulc4i222W7b+/7GASWxQ5sS+vavc8ri2KPsqDRwsG6Bup96E/GChEtTuxyRluzctuc2MIKEi0KbcKL3M4mr/Disdbr9Sh5hr2vLc+Ffea8/ZT6YL89QWZjsEoC7BAsPHbpHbmX9+PxCawdTCqsyeIqSs+eiVwEbczVtrzNTfCOtWCJH3eVt9UsVyVxNdQtxFu1iqu5FqtIPNViruz28DY1Cqk5Fq3JGDo6xkW2Uk3bW+sUgGbrVd7mCS3PomWFlnUbtNasQc4FcVkAJnNleSIrsmQB46QXkGk8VkbPjTVcbs89EFMrVrdtmlkQiF0EMzldexSLpdnN5MNHyzsBPEdEngXg9wE8AOAf5Y0ppU8DeHpeF5G3AfguiqvjozRgnjO41sJBf0ftSgO4jCcaatYrLWxyu5oQK1muSuJqTvKM2vFaxJYuj65RSWSVvkvL0T0slXvUXAS99ZIVy+7Ls2gB5TmvavvzXMi0BcgmtrAugqV2kcjy7rGX9EIv5/62xl15VizbtnQf7LWyfW2x+Nl9zam7S/aw3728H49PYC0gJ74oWbMAhOnZM5GLoJfcIuOJqo0Ria6roHowo6QWebk0zxUwdgvch7jyrFZzhNVsUVUQU7t0Caztaxhre/25kFF7T2yVYrJahJaXddC6DdZElo7LmkxMfFWOybKWLJtZMC8jiMcCpq6BXZ2xFaurtwqFFTBOdgHELoKZUrr2TLZaHVNCi4Q0XMud7jelCxF5FYA3o0tD+4aU0vtE5DUA3pVSemTnByUHZY6YKtXzhJYVGna5NLiPBpf52xMomZqrX6sIqm3fVTt7Hq3Calciq2V9DrZtSVjldbs85zvvq0Vo6T62xOXUBIRXboWNd23tfiPxo/uQy+x91NfDXgtbN7LYeaLQe26sC2F0zbKFreQyeGhr1q6Pt6/34/EKrBmWLGBrzWq1akXZAwEM7oFRavaJqBrKtw9vtl7ZxBa1uCvrGrjULbAUb7UvYdVqxWoRVNFtlCN4YtMlJn2euhea/0FTy5GboBVaUNumroH1bIOeyMpNtMgadhNkF7TugjbpBeDHY2kr1kZZfC60C6GJxerK+36Kn7LdS4LRdXs9XPZWa9UsqxVwipargZTSmwC8yZR9X1D3RYfoE1lObaAYDbRbBuBWyETH8YRPqQzw3Q5b2toyb9krA8quh1551KfrCKuSqLqOsKoJqJqonoOXVU6j3fesCGv5jpbzIN5zHbTCrPSx+4lcBHXCC+0uaGOyPNc7b1nfq5rFyrMolQRaSURZdP2SVUtfT0/M6e3eMfYpuFrE9ML97vz9eATD1QozhRbQCagWoQXEVqvtdv/4WWg1WaswtlB1ZbrN1DUwsmh5lqtuOW/3xdVSq5UVVsBWMLWWbdf1SVdEFypiap8p23PiCuf46WLa14ngUtYtmzjDs17VyjLWbbBmzRqdkkl+oUWWdRcEtpYswLNYlV0FczzWsL2S8KKrtxrEkWfJWmM1sWJtt5WtWRqRNVYVy5bf8DDCKmFxDBa5pUQCqbTdE0/RAM2zvETuhC2iwy63iivvGCWhtFS8RW3miC27j9K1sdd+jsiy98m7n5aaINPYgaxNt57xJrnN9exAPRq453aRa5sWViWh1UJrEguvzHsebRsrLu11KQmvfG1anpPoetWEndcnXe6dvz3nmkDbJzcZ/zWH4xdY16A1/grYDtLs3FfAOLmFtV512/1Jha340tYrwHcNtHFXOsFFNInwLsVVi9VqjrCaI6pCQVUSU7t8grMy8I4XiC4ruKzY0pYt7UY4ElXD9rjMcxsEppkGI5dBoCyytLugnowYV9P07cD2OS25CuasgpkobTsc90BtxbLJLoDud3qZLqsxWMC8OCxCzonWQbln5fCElt1eElet8VheW1tWEl2HFlderFjpvGvCao64iu6HN+CP1udg20Yugt7Ewrb/JZFVchPUdTLWdTDqeylTXim+Sm/X+/PcDL3rHQmbkvDyykpxV6XfjL5WkbCy56oTc1js/bppjqEPLRy5wMqDrPwHbZk1y/v2GETW8L3MenXHuArqxBZdme8amAet2h2wW58vrkrxVnOtVlZE1dwDI1FVFVSRkCo8pbt0GSz/ZP2tMnENtK18saX3N7gJqq22rO42OK3piaxcd4nIAsZug/o/AzxXQQBDwgttxRp+CwusWBfYiqqSuMpiyn7PYYjNGqxX2o9yPyTsbR4scoa0DMS9+nP20fI/6Z6YahU2XlKKKFGFtQpFboJ5W7Qfrz9Rm5aP7cuuBFZGizrvXu5LYFnswFavR8kPcvnSyYX1cs110NbTbn/eJ/crPw+ei6B1F9RtIvdA/Ux4CSTy/dTXxCvzRJ333WKdKv2uSxbFEi372AcUWEdItmRF6dk1Od4jSm6xrReIMM8SJuWsgblsvH0stFotV8BYXC11CYzElWexWiSsPFHlPJXNQmo942Vyaf6YBMdIF06fPGvXpQz7SP32katgvibGhbDmJuita2tW5DI4OlVVB8AsSxZQTnSR1wE/q6BmY0TVUB4kudhu77IIRtkEgWm69my5IoSMqQ3krVjR7bzBvt1vq3VFl0cCzdYpCbZI7JTq5za19O16P7Z/pb7MFVn2XpQGy0uE1RzBFQ24PUuWXbYWqOtMLmz7YMsiy1LJgpXbicgoVbo+L71vva0kerz6peuV738tnkkvW8uW18aLtbKCr9Vt8NigwNo52Zo1TmaxxKqlWat/YR3lHui7CE7nudqWt7sGandAG3elhdZaLze4BbaIq51ZrQJhVbRWzRFUcwRUjdq+egHmuQW6giufkxJauX5JaHmug252wf7bnXurEpcFoMldsLssncgCMErf3q0D2VVQP6M1V0FrxQK2sVg2o2C3TZ2l+EktgPH0Ci0xWBGdtSonvbjCdi6s/VutLCWhSUhE6X+4W9tG22zMVklEtNTxBA+AiXiJhJG3v1L9kiULaBNXUX9LfZ8jtDKegKsJqiVCqwXbNnIR9ISAJ7j04B+IJxfWy7ovXqxVFlLWbVBbsGpCy85HZYVHKTGFLotEsBVeVgTZ5ywSb1aMlZ4xXSdKbGH7WsLrz024De4rycU+OCGBtUUwTsuuMwi2uAJG2OyBxTmwnMyBgD+p8LbdWFxZ18CubBp3FWUM1IkMrLgqxVuVrFYlIWXXPYtVKKqspWoT1BvqBz/4gz6xwf8AOu6CqbAmwX6y+6A4roM2u2AkvOJJin2RpdEiC8jPiuDiCoPo0nNk1eKxAPQZB7uz0q6CGW/y4aF8NMGwn7L9ElfVbIIlVr1b4QprXAmgMwkOaduxQqLIISdMSWy1xF3VBvZ6vSRerBBpEVzaTU/32RNE1nWw5ALolc+1dNlzKomq0vnaaxq5WNrrfWiBZWl1EfSWo1irvM0mrtCCKnIVtK6AeX/WmhW5CmoRpQVabuNZdzyXQPutBZsnrrTAtCLLHs+KLk+IeceI9uV9a7x4LHtPDi2qTpWTFFgeeg6sLLiuJbb6IW5OehG5AgJ+5sCufGu9shfacw2M4q6AWFzZea70YFuLqxaXwJo7YMli1SSszEWYWrXsRUGMvYC7ZOTnZrdh3E9j5ZpYtwLL1siqNQQeTS1akfUqCy0tsqaTE29b6eQXACaWLD1PlieyABTjsTIT10EAkK0V68L8TfYyCo63d66DXVKLq2I2wQgbh6VTs2dRRci547ntlQZaltogzRNhkUgoCS7b55pYK4m20qcmDL36peO39MNeJy8hhr2mLQNl7x5G93SO4NqFi6Bua0WWrhclrtDtvHPxxFYu99KxazwLl2eZKi3ndXtPPEtUKeGEfcZLGQX1dfOeCeuCWBNDXlvrjnhMnIq4axZY0qmWdwH4/ZTS14vIswA8BOBpAN4D4FtTSk+IyJMB/BSAvwXgjwB8U0rpwzvveSM57mrVW7d0/JVNahGRxVWU3CJTsl4B48QWwNR6lcuiuCsAQ9wVEIsr6xZYEletsVYTq5XjCtgirBaJqoKYSjsUWmJzkWs80eWILesaONSvCC3tOpgtWjY+K7JmjebYCuKytMgCfHfBLLIATNK3l+KxgGlWwa5M3ElzIyvWsN0ku6jFZ+mkF/0F3BoRBV0GwROJx0pI7jUhx82pvh8BP+5KfwPjQWNpPS97IqJVQHiCJ2+bG3dl23iCprTfUlKNlo89jr0m9hpE16j0XVr21udg2851EfTEllfeIrRs+xZXMW1Z8qxWtp49jmetstelJR5LCyVb37r7WXHk9cX73WRxqScGLom8WjzWMXN2AgvAdwL4AIDP6dd/GMBrU0oPich/BPAKAK/rvz+VUvpiEXmgr/dNO+zzTtHp2bN7YJQ9UJPdA7PYumNElRd7lcttcossqry4K5vUoma5ahFXXrzVddwBXWEViaqaoHLETVFE7VBglX6y/lFsC/NiU9tdF0ITqwUooaUSYUzcBvtvve7Om7VQZG0ff5kkvfDisbTY8uKyunLfinUHXVzWRmXqq8UgjX6fgt6yVU/XTsge2fv7sWVA7S1HA+7Wgbe1fJUERIur2xIXwlpslrdc2x4JqyjmqzUJhr5mpbJDCaxdEgmuaLnlO1rWostmCRSRMHOg/YjIJL7KE1vamqVFni7Py7mPXpp3K2qiMk/02OclSkCRz8u2s+LM60ONOX835i7vilMRWE0ZIkTkPgBfB+An+3UB8JUAHu6rvBHAN/TL9/fr6Le/WPb5i98Teu6rbr3gIoj8P/t9XUdD2MQWwDaxhecaaJNa7MpyZV0CZ4mrTQI2CbJ2xNU6dZ8NBtEkG71dtuJqg1E9qzrTRkafSR1PpW4vTvvHUjhGtU/eeanjjK5FrpOvmbqOw7Xtr7W+/iUro72Xek6z4flYX21j8fSzomL2NqurURKVzWqcVMV7PvN/DnjW2DsSu8cOv5dCqnv9++t+j/PdfU+JBOAiXc3+kJvjGN6P1xnozB3A27beNk+seNtbRQ5Qt1C1xk7NFVer1apZXHn90X3X8WV23S63fEfXKh/T+6zX6+ZPtI/afZxzHq3XJh9vzv3On3wurffbe15a77e3fekz3/p7Kv0W7e9Xn5f33dK2tHwIaqLa+9wErRasHwXw3QA+u1//fAB/nFJORo3HANzbL98L4CMAkFK6EJFP9/U/sZMe3wAjF0EtutxJibsHLVu0vMFli2sgMI67AsYZA/O6t2zFVZSCXS97g/cmq9Uci5XNUa+YWKkiy9QNZBGc9OUijfor28wOw/axa6AM+/NjtYxroHUbNNaskssggFEqd421ZAEYxWWNlp3Mgjoeq7sssatgvgxD3f5S3IHgLhI2kGnmQFnhIl12vy/BkOyi5iJIyA1zI+9HbxAWbbsOViTYwaf9jrZb0RENYL2ykmtg6yDbO4fa/muDeG/wXDpedI2iaxJdY4sVPd79W4Jtq9349DoQp16Pvu1xrLtg5HK4i8mFbZm1Zun7V0tAYa+Td39LboTevj0LlWed0uvRds96dl28++Vt2wc3KZjmUhVYIvL1AD6eUnq3iLwoFztVU8M2vd9XAnhlYz8L/duqgBXW0AkubPxVXrap2bN7IBBPLmwZrFb9xMJumvb+gc7WqyixRck1EMDgGhhNJFyyXLUms6jFWs0SVg2iqiqoAvGzy5irGq1HKjsKJndLdh8sCa1tab+PQWhNXQYHkdWXeckvgLHIslxdZvfBcfp29EkvADS7Cm7Lu/Z3U8JGxvNi6VgsAJ2roCO8PPSkw52b4OU4lrIvW/XbcxwWEoZEF6u0Te0+pGe/YRLQdP7kONjX+7Hfd/Ud2TJo0nWswMkDc69cb9f70YPPvO6JEb2tVTjpvkTtIstQJIhyP7UVqmQBstekRWjNPYdISJUElr6Htjxa9tavgx3Y2oG1LZ8zubAWVnq5JrSspUK7AOqP58IXJbfQcUki4mbVs/VsRsDruAV6oisScVaERWLO66/ui3eOudzuJ2Lf4ipzNgILwAsBvFREvhbAU9D5mP8ogHtEZNP/L919AD7a138MwDMAPCYiGwCfC+CTdqcppQcBPAgAKzsragOjrIFKWAHjrIF62Utmoct0enY991VXVncVBOLYq9F2V2zFroFeOnZg7NoFzBdXrYksZG2tUmNxtVhYNYiqUFBt2oTwIno1YY89JMGwWQSN9Sq3G1m2AqvWRGjlZBh2Li1jzZokwOjrZWEVZRgEYktW3gbATd+e47EATFK3Z6IYrO12CTMKjur1Viv93V+w3rI1nnTYi8HSZTmDoF2WXnTlTILMKkhmspf3IzB+R4rI/HdkIKzyelSvtJ9okKe3lQSYXrfHKAkkT8i0ih59/nPF1VwLlj1W1J9IeHnX0V7L2j3xlqP7WcOzNOlyPaD2llsmF9YiKdqPZ7WIBExLxkBPaOh9Rsul628Fjy6z+4vEkd6nJ5hKAso+R6X7452v3u4JLS/Bx01xNgIrpfS9AL4XAKT7H7rvSil9i4j8AoCXocuU9HIAv9w3eaRf/5/99l9Pe7oa151kuBXrEqgnFrbJLcbtxtbSKFplAAAgAElEQVQrT1S1uAYCftyVnutKa1QrriI3QL08ElfXsVrNFVZGVLmCqiCm0g6FlmzNNNONF1e+4IrE1hyh1bsOetasidBSVi4vyyCAkfugl2EwLwMY1i/NXFleZkGg7Cqo58bqykdXy50XKzP8jtTEw5s+ffuhXAS7yYZv1oqVkOgOeUIc+v04d3DTWr82GPdc+7x2JQFmy+ZaeVrKIxFUE26eVay0P68/UflcoZWJRNUckVW6ty3YtnqwntdLy/rbiq28H09MWdFl+2TreZMLW7c/e05aZHkWLlvX9tvur/Sse/VKvwubuKLULrIi6uNlS1Ttd17DE2m7rN/K2QisAt8D4CER+UEA7wXw+r789QB+WkQeRfc/cw9cr4sKWQ+TDB8TWWyV5r3KeIktgDbXwK5eLK7yJMI1ceUtN1mtrMUKKAqrFlE1dRWc3t+qiNqFyLq4Kh7H+/NjJ6ON/kS5ToJaaGFszRq3ia1ZMuxt6zLoWbJ0hkEtsvRkxIAvsgA0uwoCWmwBdz0LFgDI1oqFJCjNg3VTCFZIgsF9kZCZHP79CN86ZLe3rNcG75FLYat4sutRm5qY8cRTzUJVE1dzXQM9N8tabJZXZtvZ+1m6NyWBFZXNRQsoXWaXrdCy3yXLVuQWaD8i4lp+ovgqu1+9XMoEGFlytCjzRJjn6ueJJ32sFoEWibC8HsVu6WuZl/W3PmZp3W7bxXO1hLMUWCmltwF4W7/8IQAvcOr8BYBv3EHfQkSmrn6t6Lmv1r0roE7Pbt0DtfWqlprdYq1XUWKLtRnXa9dAG3flTSQMbMVVZqm4qlmtWixWs4VVq6hqEFJzLFqDxaq078FdULnUeJYuZeEaWbYiq9bQbiq0phatgjXLuAx6LoKeyAKy9bITWcA4ycX2WZrGY2lXwa4dgKtOWEUJL/L5efNiDfWdlO0QDFYstRs/8C1fpj4262qhq5+eiPjQJIAWrBPlUO/HlkHNdQZOUVxWSSzpMr3/SCh5rnU1wTVH9MwRR5G4am3T0hfv/Oz1KV3DJeIqusdzBsW1GCtvAD+3TAsTfU7WgmXFVhY5wFZIWWFRsmDldvrYnvXKu1el+ag8EaS31Vz87Hm0tPGOH63reKscX1W6RtF6TeS01FlCZNk8Rq5jwToMsoIXkF9jZWKy8gTDQH1iYYsXd6VTs2vrVbetsC+ZJrboypPrGgiM464APx27l4odOJC42pGwmoijJdasGdT2JRdX0z4YS9dEbF1XaEVugyY2a8g0OFNkAVvxdDXMg+XPkZXR8ViZQXCpCYiBOOGFRmcUtFYsN7vgTBfBHHuVk1oA2MZfLZpwOGc1pPAhx4cdZLVi47AiN8BSO92+VFYTG96xW4VKrZ61WkWWspoVTq/nNi19WSKsagNoe41rwmrJ82HbeWKr5BbotbGufbrcuuHZNq2Dai1GrPuf3q7X83Ht/S3Fa9l7WirT2+y5WQHVsr8oNs5eh5KAssduaRex9G/QUiiwdkxLvJWobIEARpkED4WXOdCzXnV108g1cGut2lqvorgroCyuSgktqvFWuxBWDaKqJqg8AZQ2NzsPkr2NniFlu+3KLR/whJZJNjYkwTDbBTIWWbmsIrJ04gvAF1kAwngs7SqoRZaX8AKIrVg2o+C+0RkEgT7RhaD3t4w5dFwWLVhkKd7/MntlnrDy9hMJDb2tJkRa2tt1r03+5EyAkSCy1rdIVOnzLrkF1mK3logr3b/StbD3ouXbew6isrl4g9rIRTB/azHgrdtl7T5o47TmfHIb7fKn47K8uCsAEyFVE1tebJYuW6/Xoyx8dn/efYwEVsmq5a3n6+Bt18fMaDdDva6J/qYcWvBQYN0gq9kWKpVp0EwuPF4eJ7fQqdlr7oLA2HplY7CGOjPirnLGQGCbrABYJq6ubbVaKqxKggsFQXWQLILjY8vF5aSPo7uurFsj61dk1RradUJrSIbRaM0SLctUXFaryAK652YQVSp9+5x4LI1OeFGyYmUGt0CTsh3KTVBnERyWBbjoxdHGySBYYnUN90FCzoG5g3Bv0Ge3lQSD/rZWm5oAKw06vTqtrn5e3Vq764qrOcKqZfDt3ZsangUywpsbSqMH13pQb61YtrzkYpjXtdBaKhAjkeBtj1wVbTsrokr3KkpQ4X1b98aaCIv2pet452yp3ddj5Jj7pjl5gZVTtLeKqlEMlpn/Sgst7RboTSis2WoMcTMHemnZAUysV1ODjh935aVjt5araI6rqkvgLoXVAlE1EVSBkErr/Vmyoj9FE2uVEVwS1PWsWp7QSpP6DdYsJy5rK7y2WQT1zFf+LFjTObKieKzMJsdeKSvWpRFdfpZ9AVICpLNiZU0ZsVFJMLS7YP69XuJqmA8LaRuD1SK6tpYsHEd69pSOLuEHOR08QaKtOLau/o7KS/VqLm67sF7lY6zX62pclhZMpe+8P++Y3rK9jkuFVXTOpWvYej/ycex9tMwRK1HdKLGEFlX5Ww/WS9YrvaxFWW4fzXtV+tiEE/Y41pqV75POuGf341mvSvFWAIaYJy24oliu0nOR3fe87TouzXPNbPk9e+X6fK01bs6ztCsYg3Vk2JiruTFYGjuZ8Ky2ynplE1tkrGvgqLyQjj2vixJj0XK2XAEzxJWXct2Iq6XCqiaqamIqba7/GMvFNoG4dzy5vHSSWqi52JTYmlqvpuUToZVjtKw1q68dp3Qfx2UBQJ4vC8Ao9souW0sWnHVgGo+VXQV1eUYnvACmLoOtdLFYy7Di6joJLwg5N2oDc289Ej96W24X/Y+63u4JmJL4aLVILRE8rfVbrFu5D/r6Re1sf+21LIkse5+iwXK03opu5w3agU6o6u1esgr7bfehl/W6LrMucFqc1Oa+0v3ynku7XLIeRd+lexcJMLuPktufZymc6xZoz0+v27irmxBPrVBgXYec2KJgORJZYU42wZWsm4TVyHKF1WTuKwAj90Cbml1br4BtDIrnEmitV9olC/DjroByxsC8Ptst0BNXjVYrzxXQE1bXFVVNQmpOjNbF9g9KtO8svHR/amJr1H5Uxwgt7Tpo2g22q0FoKWtWQWQB7e6CgEp4EWQWBKbxWONyGVmxrKDaSCey7vRWqu2jsrVi2ZTtSFe4q7IJXqC3Iico69UKl5VYpWzVak1qkf+ehBkE+z502nE/gi0Bs9wdye2k5X+Q5/4PsxUGVgDp8ugYrQPPaB+eGLHtloimkhtgyXo1Z78t/fHOpXSd7PXO+/WudWk5un81vMQWuTyv24F/lILdiiy7rOvUkmNoy5O13ESZA60FCpi6CEaT65YElxU5ubwmpkoCqOX50PfAHsfeL++c9Hm1CpbWvzn7FmcUWDdMTnDR4jqo07MD49irvL4Eq0e09SoLrmhC4da4K5uO3XMRDN0CvXirkktggzvgIKIca1VJVFlB5QqeBvE0x5rV8ieg5Wc82k8gtnSdiStgL7Qit8FJbJZyGbSp3GXkYFh2F7wCRunbATTFYwH+BMQXkO657hNeoI/BGrouZVfAiLFb4DYWa50FD4CLiihZKRdCWrPIbSIaqEcCKqpb+y4JlyXr+aNd+SKBky1MLcfwXP0it0B77JrYAqbugNaNslVwZSJBdWiBFZWXEl1YEeZNMBwlqNCf3MZaa/K6FQ2em2Det3bX81wE7Tl4osve21yvJLa8hBd5n57roCfConV97fT1Lf32LZ4A03X3LZrOkbMSWCusmzMH6vmv5nBHWbJsavY7/QPox51MyZMKe4ktRn2txF0BY3Gly7YHc9wCc8dzmSOuWt0Bmy1WBUvVRBwFgmoXLoG1/Qwug7YPF5ejdnJxMZxDZNly3Qe1RSv3J3AbHAkykwBjJLL6Lre6C2qRBWAstIJ4LI1NeLFZJVw49SxZaN2RzoqlU7Z3QixbrtrV2PA7FuCyYl3S6dtpLSKnzpz/NV4SjxWVefFXdh/eQLN03BYBEomsJWXRR1+r1n16beaKqkhYXVdcLR0c63aRi6C3HH3ruCWvjyXrlbZUZbRVKrJgRVab2nObvz3rlRc/Zfcb3Vd77yMXStu25XfmJcfw9uWdp3fNPNdBy5y/P7uCFqw9IbKGXCOGKk8w3IKdXFjHX42WRS9vv3NyCxt7lV0D3WMa65V1D6wltdBlAHy3wJrlqtUlMLBa1YTVXFHlCqH1Hh/dy4vJMV3BZcTWqL4SW1t3v8uJqMpCy7oNhrFZwxGSL7KMJavkLgh0z8pg0bLWrEtloZo8r9O5sYD+Pw1U2vbBbKR6bq1ZOmW7ngdrnFkwZw6sJ4DIv++l4kl6a9ch07NnEhLTtJNrMWew49WL3AKj8pYBoF73RIZd14LF29YiwGqJJaI2QNlyFbn+zRFwNWHlbWsRV6XrXyv3aHURnCOytBuhtUjp9q3JDDyrkhVb1t1PZGvJsinZ9bJXVoqn0ut2uxU/pefYXkeNdyyvH7ksskpdJ+7q0ILKQoG1B2Shq16Ov4qEleceONct8I562FotWDr2SrsGTvqv5rvyklp4LoLdOopugdV4q4XCauQi5wirkSjZNLgGOmJqV1YsD+/2eT9nW8+t4wgtbx/WbXCwZjnH8OKyRhkGvTTucEQWti6CWmQBOsnFeH6sTPecjufGwlUauQ1GaHF1R+bNh6V/l9pN0IvHWs+wankIVseRWZCQRloG2FE7K6A8geJ9631EIsSut7jeea58kdipCSA9f1bpG2jLLljr+xxxla+1vYZ6vXb9DyGwonLrUueVWXfAvKyFlU7mYEVW7ZPre9YrzxUQwOCyZ90CvYyB2r0vX0Mtlmx9uz2vRyLLluXjeaLOq+/dX+/bCqtIgJVY+jdmV7QK72PgpASWR07RPifhRUSLqIqSW3TbxsktStar7A7oCiplvbJxV3m75w44dhlE0S1wkimwP4EWq1XJHdBarEJr1aZgxZorqHYhtnQWQc96ZftkrFxycbE9p8CyNbZabV0HQ7fBwJo1cRkExhkGgdBdMIssYGu9sm6DgLFeKVdBONuB7jm+uBJ38mFrxbqbto/XNqxMJsku7lZEkY7NWorIGqsEXEkhucUBSeBEw2S3eOKpNDAq/e+0HqR567ZeTRTYwV+pbEmdOcIIwCxBZevbOvraR+dYslYtFVne/V06ENbtai6CuW7JehW1tVatqC+1zIFaeOh96fOw1iwrVDwLlW035z555XabrR9dW1vfO2/vOnjXsvY3QC/PFWH7hgLrhskJLiKrVY6/WhdElTe58C7JOsUmtvDw4q5yuRVatWyBJcvVaFmLqwVWq5rFalRuBIyf5KL+uKb1nWqdjFzere/7InAX1P1VYisSWjlWa7BoOckwxgJsNbJmeS6DXb/HlqyRyCpkFwTilO3ANB7LoufGygkv+tPaGcPvzUw67KF/xzVrVmtWQULODTvQ9MSRruuJDbvdq6vrtCSeKIkfu/+aAJqzbzuwbrWI6es3pz/e9W8VVi3iqiSyrosVW56gyvXmiCu7bEWPF4Nll2sxVtZ6pS1OwDQmTIsKfW1L7oH6ObDxWVYotVixonV9nnmfJYGW29q6Fi/uatfP0K6gwDpS1sod0GNwEaxMLgzAtV61xF7pSYW9xBbWelWKu7Lp2GsxV5G4GlmuGqxWkcUKcITVElHlCJ4m8bRpF1gtP1Hvz4tuZ8XWaFvhWNVtxprluQy6yS88kQW4MVmAk74dfjyWnZ7YJr1YS8IlxJ18OIrFytxVVqy7lT+cm1HmwEBsDW6Dpyik0on2m5w60SDdG2R55a2CIBIVUVkkkjyBowVS5AZoy4B5kw63Wq0iUead8xxR1SqmSsK5lWggW5pcWC9bFz5dFi2XXAf1x1qTovalWCxvcuHI1c8TZVag6efAE1Ba+OTv0rNv6+rtkWBq/c16z9GpQIG1axwXwCjZRUtqds1a6nFX22yBW/fAYdvCBzS7B3qJLVZBEoxaUovMyHLVdfz64qrRHdCzWEXCapGomiGiipT2c3F3cvzB4rUJBJUWW55Vy3EdtBatwW1w6IeyZnnp3HXyi0hkKXdBAKMpnGpJLyz+c9klvFivkpttsMYdJaxyNkEkGbILRtkEh9+rABczrFKrYOJhwXoacyXr3npGyHHjDcJbBtnRAMsTWt5grmRtsu2twLD1SwKm1GaJSNPXqFVcRcdu6XMkuuYMsEv3zVoOo3s5B91OD2r1XFeAnxUwt7eiR5fpOl6bUlY8a80quRZ6wipajn5HnpVL1yvdM6/cs2KVjmutUra+dw2i32J0nWp1snXLO6dDQoG1Q8SxJnkxV1GK9jXKSS4ibPbAFqtWV08vTzMH6kmFN6t48GatV9Y9sJTUwptEuFs/kLhaKKxmCaoZ7oDNDCLK7Pvi7qRvcnl32/cLI6g8oTXsqxNakdtgFJu1RGR15wJM3AULSS8yU3fV8QTEms3qCpeX62Hy4e6U0siKlSch3khntaqxkRUu0tUom+BFvdmA/r1bQZVTtdtykTVs9kCR1aRsXyQsS8hBbifewM1us9u1yMl41p6ISFDpY3nCw263+4r24YmZljot5bV6tv9z5sWqiatWoRXd30hQ1QbKcwbEVvjoMj3o9wSXPWbkLhitayKRZetYd0GdLEJvj+KpbLnn7qf7o8vtM+y5Kka/UyuS8rrn6helmPcEV5Ri3bPKWbdA7xpFy4cUW0xysVOWZQ6skeOv7DxY2T0wsmZ5c19Z98CuXnKzCWqXQJ3owktsYbMGAmNR5SW1GOnLSsxVKK60mLqusFoiqqy4iYTU+kl++SEwgkvf6pqboDj1Wsu3y1dNImuUXbAXWUBvmFEiCzDWKxixZbIKWi77BBc5i6BeznSPWye07kx+GwKk6ZxYlk2frr3LUn+lyrcP/n5c7PJVIeQ42PegJhr8e4PJXKdmzaqJE8/1z9uH5wro7bfk4leLuWqxbJVEFtCeur10vTVWHEcDdm+5VBbhDWQ9V0C9rBNWAJgM0j1XPs9NsJQpULezboM2Nssmz/DEhT5+7rOtZwWOFyNl2+bz9u5XyZUwmqzYE1Elq1XpGds1hxZZp8ARCqzd3Zy1Y9HKEwzPZTIPVuNDtB4MQOPMgaUsgpZVIKoATOKuAGzjrpZYrhrFVVOcVS+umoRVTVSVxNQuLFnZcuUd5/KJ8TEu7477q8RW1arVW7R0Mow4BmtqzUqAWm4TWdaSJZBJZkGd9AIYiy3AdxfUdFkEt5MP64yCF5eC9Qq4aNA9dk6slmyCk77081hpS1BksVrOfl4inAeL1NjlAMYTRPZ7rjXL9tUTIna7XrftWsRMNBidW29X4kq7FHpWupY+6Wsc3RvvO7oPS9DtbPxQa+ZAK7by+ZXcBPXxrYAqZRu0gqPmDhhZsvJ3bTna5oml6Bh2P61WK6+vLVar0rmW2s9ln0KLAuuA5FTtmjlxWNHcV3OyB0bJLbpt24dhnCui/7EXrFdeVkHrGrgtn4qr0STCfUcXi6vrWK1ahFWLqKqJqTlWrcsnyvv1RFdJbFWElnYdHKxZJj5rcBvst3eC6hoiC3AnIxad9CL3JXAVBKy7oJ+2fbPaTj6sMwpupLNadb8DyWc1tPUsVRqdTVDHWunfq5c50MPGX62wxpVgfCEIORM8odT6P82tg8howGmFWSQmdP9qgqMmnFpd9tbrdVPdVqGlz7fVahWdUyaKVyt9l5ZLZRGtLoKRuLLfntXGunxZUaXR1iYtPLzserWYq+ga5j7qeaii5zaK2/Lusb1Oeh/aShc9+/r8vHiw2vNl72OEdz08V8GbhAJrj+iYrFYh5cVg6QmGx+Vja5VGz32l3QMjsriymQOtuAKiBALTrIHFuCvtNpjFVcFyVYu38lwCa8LKs1g1iSorjjzRUxFQMkNg7fMn6rkJlsrcOn1s1jiRxtWorJMrJsOgFllmMuLBcqUmIo7isWxWQcuqT2qRn9/Lq05k4Wo1yih4gU5cXQa/qe3vR4CUAJHeBXB6h7rf5TRduzfhcP69t7gMrrAe6nUxVzcnuJhFkNSYI45KFiiNtd7YfWkR4R1/qXvgnHipUl17DpEboRdHVXMlLPXJDpZL51S6JvpeRQPkmxRYXlnkIhh9e+LMc/mz6yV3QM8tEBgLLS/+ynMR1EJCTy5s3f/0snUjtM+H7lMu80RZrmvr1H5PntWpJo708VqtVl6cVsS+hRgF1p6IMgdqJm6BTpto/qsokUXOHhiRrVfdPrbJLQAMyS2AsUugt+wlttiKqanFahJ31XW2yXIVias5LoGhuGoVVjVR5QimOSKqRGk/6fKJ6bE965XrUhhYtfJ2a9GyboMm22BnjRrPm7UtM8kvhmMULFkAdLYILx6rK+8TY6gJiLd0CS905sBoeZMnIc4p3CGdB+PW6ObSTfElbmKLnADDssYKF0agrJV4Arq/D7W5sNyMgoQcMd5gO6LFmtUyaI+Eg91eE1/RvqI23va5FqnS+lxXQWCaAKP1o+9JbUDd8u3dz6UDXt1uqYug/bYiy4qJ1iQGWpBoa1OLBStyEbTipxRLZeuUnttS++g3oNc9q5Wt7/2easu2TJ9TTXTN+XuzS5jkYk94mQM1K8dV0MMmtujK/MQWd/pMgu5+BBML1saZ1wrwXQMBI7LWsZuTnQPLXgo37gro1u0kwiPhVZ/fqiiuWqxWNWFVEVWhENpHkovebdAecyK4WsSWI7QG18GLaXbBzvWv4DLopXIf/PCUT55yF+y2dXaukVLx4rHQi3Y1P9ZqncL4q/X6CldX3bOwXm3r5YyCuVve3+nsNjh0r8fLLrgZMgiON+bfq7VmbYyg8lj1cVoloeVlFNw3ifNgkWtQGuxoYVBrb0WL3q4Hfd4xI0Fl63jCRm/z+tEqnJbWaanXGocVbbfn2Gr58+5PNEj3lktlEbt2EdR1bayRTmChj2mtVh5eXFbJeqO314RQyZoU1Slt166AkcXK+73o49pYs+i87Hln4dQijnL9kpiZ8yztkrMSWCLyYQB/AuASwEVK6fki8jQAPwfgmQA+DOAfppQ+Jd0V/zEAXwvgzwB8W0rpPU29aUyDvu1XXXBZ65VnzQozBjouTdo9MMKbWBiYJrfIoknPe+VZrzzXwO5AaRJ3NckYWIm5Ci1XXrxVyWrlxVhlceG5ARZcAyeCKhBSsn6yW34dop+tvdtLf97WJTCXZWuW5zJYjMsy+7QxWeP9ddYjm1lQlKsgLvo/vIGroObqUobn9vJKhuf5UiW40MteynbLHZHBTdBOOrxBYM3yXAaxnroLSjkFeieoZogbWTF2iwA43DsyGtCUylsGUXoftUF7dNxIJNQEia1v69Vc+PR52P17FqlSme5LzZoFxHNoReejr1nLdfCu69J7lWl1GQWm6dbt/pe6CFp3PyvErBixbn+eu2DeruOytECwLoL6emgXwXx9bJm1tpWsYF7dvM9IsOVvK6KidV3fuzfRPYueHU+02X0s+fuzD85KYPX8/ZTSJ9T6qwG8NaX0QyLy6n79ewB8DYDn9J8vB/C6/nun1FwFWzIFrp107Dn+ylqt7OTCFp3conrcgrjKeMktMqW4q/7EiparOeKqyWq11GKlymqiqiSmZHV9oZWuHg+Pky4fn1ivdH9H1q1s2dIWLV3WX5tB+PQWrUhYYbMO47KsJWsruKaJLwBsLVk6syAQxmN5aIvWap1webmNHdQi66Ji+Mkp2wEJ58Ty0rVna5aNw8rp22sTDq+DSYYzN+8aSAvWCXM070g9qFu6PX97AqK0zdYr7RMYW8lKAqVVwM352L5mN79SLJUWV9Gxo3J9vi2iao7I0th7u3Sgu16r7MBmUBtZm6JvXce2sWVaaOXziUSV3Z775lmwrCjS82RZEaP7ovdReg718aKkFbadtmLpvkT3Vx+vJWarJOg8alar2vZDcNPHb+U6LoL3A3hRv/xGAG9D9/K4H8BPpe4KvF1E7hGRL0opfWz2EWZYtGqugWuT0MJzE4yIEl7Yua/c4/bJLbL1quYeqDMHetar0GjnxV0hiykx6wW3wF2JqyXCqkFU1cTUHKtWuny8uF9PdJXElh+3VXAdDNwGtTXLi8sqiSwAGGUXBDorJrBdB8J4rOF6aFdBjOfGys/n1ZVgvd5OPjyOu+qSXXT/fyFDsgsP/fvx577yLVceG2W52ia+iAWLzSpYZBB3hDSx93ekJ2SW1m0ZfHn1a2084eFt84RGS51duA/q/USiquWYpWPV+mrP1d6z0gBZC6rafZkjtkougt7kwrp/JXFlhZVe1/uMYo/yNs9t0BNKLWKrds1LIsYTMvZetT7T+ppE7n6W1t+i16blt9u6vznP1m2gVWAlAL8mIgnAT6SUHgTwhfmFkFL6mIh8QV/3XgAfUW0f68vmC6xGWsTVeN2zWq0mVisv/kpnDxyVq9Ts2j3Q7a/JHOilZY8mFO4OVnANzJ3MroEzLVdzXQKrwspxBYxElRVHnuipCahdWLPmMLIuBeWt+5hYrwKXQX28qrugOU62U2XFIpuyqyAwnhPrCgCUyOrottqMgpbNKo3cBHMvtbtg97sSIKVxyJgTh7XpBRQwjsOyiS5skgvLqiFmi5AKR/OOjAajpfqRxaM0mLTHqgkOr07UzmvvuQZai5OuG7kW6u153css6LXRxywJqlZB1npt7LWO3DpLy6WyCM9S4LkF6v16GfnmuAlG5TbbYGTdyvsW2Sa/iNwF9b3yMgZakaN/J577nraIeQLOCr25z4G2Wtk6+h7U3AntM9BitYoyFd4E52bBemFK6aP9C+ItIvI7hbreVZ9cDRF5JYBXNh4/Pphj0ikJrprlKrsHWqtVTs8+qivj7IHu/oz1KsoiOOp/MPfVHNfAZDrVarnq1p15rNabstWqFGc1Q1hZgbTEitVKaT/p6vHJsdPl46M2us7IsqWtWrmydR20STKMNWskrHSWQWC0PMuSBZj07VJ1FYSZgAdiVWEAAA8XSURBVBhAmPjCZhEExlasKCmRflTH7oACm649Tz7cia2YUqILz2olsj6KeKoE4JJWslPkKN6RrQNq73+brXjxRFkktloEkz12SaB4+2pp29qXqM11LFDeNt1Gi6LSx94fK6xsuTfA9u7tEnQ7bVnJ61o85eX8rRNP2D7WLFe2D56lSQsrbcWy18s7fv7WgicSP3pd70MLnlJZaX/eMxQlqKg9K62ugNoV0Yu7ssf2rmupbN+UnpVjo0lgpZQ+2n9/XER+CcALAPyB9G4NIvJFAD7eV38MwDNU8/sAfNTZ54MAHgSAlTcBlLEcSZCIwtKSoj0TZQ6c1HOeodrcVzq5RYlsvQIwJLbIy5PEFhvXf2o835UTd5VGLoIrI6Zit8DZ4qrFatUgrFosWQCw2oO16iq7BZp9W8GlxVZNaA2ug1ZU9XWGsshlENiKLKDoLghArXd/OG369q7OYP+KXQU3aZSmPVuxLo2bYLe8zSgYoZNdlObEsm6CXtmkXf87jiYcXpKqHej+7oxisugmSBz2/Y4Umb5Mlg6iPWtV1NaKhwg96C0d1xMHS8TMXOGky2qCqmSVahVj+hxaRJu9Ft419bZ5A+louVQWUbJQZXGky3WZtrRYq5Zur9e9ObFyYgi9j9Kzb0WTd1xdT19Dz1plRYvXLn9H4kcve66E0b3P9aOU6VosReR96Hvl1bHbSse1bUvr++BsBJaIfCaAVUrpT/rlrwbwGgCPAHg5gB/qv3+5b/IIgFeJyEPoAnc/vSj+qtgnfyCnLVe1DIJ2guGNl/DCs2SJPx+WnftqOI6xXmX3QJ3cAsBo2c531ZVtvyeugVHcVRZXauLgasxVFG9VcgkMLFZdf8fCqkVUWXETCanV+ilu+T5YMqQuuffNbRctA/AnJJ7UvRrvI4rH0q6Cl93zZnWIrLdWrdUq4epquzxla8W6UG6Ddk4sTXYTvFB/RLPl6q7qzDaxhXIPlNWQ6KKWSTCKv7qJ9OxbUntMGDkKjvMdWf9faD2ItO289p6w0PssDSpbBVAkZmz9kjDK56TXIxfC2rH0viK3Qbusj107z9I1j6xx0TWP7nHpGagxx0VQu9/ZbyvKrPugFlK6jf3YfWiRpq1ZOj7LCqVcVwspLd48YaXLrOufJ5g8t0C7L8/Vb44Aq/3+IlFV+o1bi1b0/BxCRJU4G4EF4AsB/FJ/QTcA/ktK6VdF5J0Afl5EXgHg9wB8Y1//TejSzz6KLgXtt9cOkFL60yf+4s8/uKD/p8rTAXyiWus8uE3nCtyu8+W5dvyVQ3aEHB17f0cC+NPLy8sPtvyP8hlwm/6uALfrfG/TuQK363wP9o48G4GVUvoQgC91yv8IwIud8gTgO2b244MppefPbHOyiMi7bsv53qZzBW7X+fJcd09COeshOT74jtwtt+nvCnC7zvc2nStwu873kOd6KgJr3sy+hBBCThIReYmIfFBEHpVuXia7/V+KyPtF5LdE5K0iQsscIYSQoyFyH619auzj/UiBRQghR0PCVbqc/akhXeDqj6Ob5Pa5AL5ZRJ5rqr0XwPNTSn8TwMMAfmTHJ0cIIYRci10LrH29H49FYD140x04MLfpfG/TuQK363x5rqfDCwA8mlL6UErpCQAPoZvwdiCl9BsppT/rV9+OLrsdOQ5O/fmbw206V+B2ne9tOlfgdp3vwc51DxasvbwfW+fB2it9Otpbw20639t0rsDtOl+e6x6Og7Q0BuvpIvIutf6g6bM3ue2XF/b3CgD/fUlHyO7hb+18uU3ne5vOFbhd53vIc10Yg1V6R+7l/XgUAosQQsi1+EQlwNjLq+u+pUTkHwN4PoC/t4uOEUIIIbtiocAqvSP38n68cRfBWmDZqSEizxCR3xCRD4jI+0TkO/vyp4nIW0Tkd/vvz+vLRUT+Q3/+vyUiX3azZzAfEVmLyHtF5Ff69WeJyDv6c/05EXlSX/7kfv3Rfvszb7LfSxCRe0TkYRH5nf4e/+1zvbci8i/6Z/i3ReRnReQp53RvReQNIvJxEfltVTb7XorIy/v6vysiL79uv65wOfvTQNPktiLyVQD+DYCXppQev+65kOtxbu9HgO/Ifv1s/o5qbtP7EeA78ibekXtKcrGX9+ONCixpCyw7NS4A/KuU0l8H8BUAvqM/p1cDeGtK6TkA3tqvA925P6f/vBLA6w7f5WvznQA+oNZ/GMBr+3P9FDpzKvrvT6WUvhjAa/t6p8aPAfjVlNJfQ5ea+QM4w3srIvcC+Gfogjq/BMAawAM4r3v7nwG8xJTNupci8jQA34/OneAFAL4/v3COjHcCeE7/8n8Sunv5iK4gIs8D8BPoXh4fv4E+EsWZvh8BviOB8/o7qrkV70eA78ibfEfuQWDt5f140xasamDZqZFS+lhK6T398p+g+wNzL7rzemNf7Y0AvqFfvh/AT6WOtwO4R0S+6MDdXoyI3Afg6wD8ZL8uAL4SXZYVYHqu+Ro8DODFff2TQEQ+B8DfBfB6AEgpPZFS+mOc6b1F50L8VBHZAPgMAB/DGd3blNL/APBJUzz3Xv4DAG9JKX0ypfQpAG/B9IXU3ickXKWL2Z+Gc70A8CoAb0b3N+nnU0rvE5HXiMhL+2r/FsBnAfgFEflNEXkk2B05DGf3fgT4jjzXd+QtfD8CfEce/B3Z92unAmtf78ebjsGaG1h2UvQm4OcBeAeAL0wpfQzoXjAi8gV9Ne8a3Ivuh3oK/CiA7wbw2f365wP44/6BBbbnA6hzTSldiMin+/qnMtP5swH8IYD/JCJfCuDd6P5n8uzubUrp90Xk3wH4PQB/DuDX0J3vud7bzNx7GZUfHSmlNwF4kyn7PrX8VQfvFClx1u9HgO/Ifv1c/o7emvcjwHckcHPvyAaL1JJ97vz9eNMWrObAslNDRD4LwH8F8M9TSv+3VNUpO4lrICJfD+DjKaV362KnamrYdgpsAHwZgNellJ4H4P9hax73ONnz7U349wN4FoC/DOAz0bkAWM7l3taIzm/H5532FYNFTo+z/k3xHTniHP6O3pr3I8B3pMOB3pF7cRHcCzctsJoCy04NEbmD7sXxMymlX+yL/yCbv/vv7MN5ytfghQBeKiIfRue+8pXo/rfunt5kDozPZzjXfvvnYmp+PmYeA/BYSukd/frD6F4o53hvvwrA/04p/WFK6S6AXwTwd3C+9zYz916e8j0mx83ZPlt8R57l39Hb9H4E+I7kO7LCTQusamDZqdH71L4ewAdSSv9ebXoEQM6e8nIAv6zK/0mfgeUrAHw6m1+PnZTS96aU7kspPRPdvfv1lNK3APgNAC/rq9lzzdfgZX39k/kfnJTS/wHwERH5q33RiwG8H2d4b9G5PXyFiHxG/0zncz3Le6uYey/fDOCrReTz+v/R/Oq+jJDrcnbvR4DvyHN9R96y9yPAdyRwA+/IJdarm7rMNxqD1fuh5sCyNYA3pJTed5N92gEvBPCtAP6XiPxmX/avAfwQgJ8XkVeg+2F+Y7/tTQC+FsCjAP4MwLcftrt74XsAPCQiPwjgveiDXvvvnxaRR9H9z80DN9S/6/BPAfxMP+D5ELr7tcKZ3duU0jtE5GEA70GX9eu96GZq/284k3srIj8L4EXoJiB8DF2mo1m/05TSJ0XkB9ANhgHgNSmlxf8rmQC6/BEAZ/t+BPiOBM73HXkr3o8A35E39Y7s93md5gdDTqWjhBBy7mzufFa6556/MbvdH33i7e9O5YmGCSGEkJPmqU99anr2s589u9373//+g78jbzqLICGEkExKSIkWLEIIIcTjVAxDFFiEEEIIIYSQo4cCixBCyGwYg0UIIYRMucmkFXOhwCKEEEIIIYQcPRRYhBBCZsIYLEIIISSCAosQQgghhBBCdgQFFiGEkFkkJMZgEUIIIQEUWIQQQgghhBCyA5jkghBCyCJSurrpLhBCCCFHCQUWIYQQQgghhOwICixCCCEzSUiMwSKEEEJcKLAIIYQQQgghZEdQYBFCCJkN58EihBBCppxSkovVTXeAEEIIIYQQQs4FWrAIIYQQQgghR8+pWLAosAgh5EhISHQRJIQQQgIosAghhBBCCCFkR1BgEUIIWQAnGiaEEEI8KLAIIYQQQgghZAecUhZBCixCCDkWEmOwCCGEkAgKLEIIIYQQQgjZERRYhBBCZpMSY7AIIYQQDwosQgghhBBCCNkRFFiEEEJmkgAwBosQQgixMMkFIYQQQgghhOwQCixCCCHzYQwWIYQQ4kKBRQghhBBCCCE7ggKLEELITBISaMEihBBCPCiwCCGEEEIIIWQHMMkFIYSQZTAGixBCCHGhwCKEEEIIIYSQHXEqAmt10x0ghBBCCCGEkHOBFixCCDkm6CJICCGEuJyKBYsCixBCCCGEEHL0UGARQgiZSeo/hBBCCNEwiyAhhBBCCCGE7BAKLEIIIfNIYAwWIYQQEkCBRQghhBBCCCE7ggKLEELIAk7j5UEIIYQcGgosQgghhBBCCNkBp5TkghMNE0LIMZHS/E8DIvISEfmgiDwqIq92tj9ZRH6u3/4OEXnmjs+MEEIIuRZZZM351NjH+5ECixBCzhwRWQP4cQBfA+C5AL5ZRJ5rqr0CwKdSSl8M4LUAfviwvSSEEELK7Fpg7ev9SIFFCCFHQ4Is+DTwAgCPppQ+lFJ6AsBDAO43de4H8MZ++WEALxYR2dmpEUIIIddkDxasvbwfKbAIIeT8uRfAR9T6Y32ZWyeldAHg0wA+/yC9I4QQQhrYg8Day/uRSS4IIeRISCm9+fG/+POnL2j6FBF5l1p/MKX0oFr3/qfNvnVa6hBCCCE3xZsB7PoduZf3IwUWIYQcCSmll+xp148BeIZavw/AR4M6j4nIBsDnAvjknvpDCCGEzGJP78i9vB/pIkgIIefPOwE8R0SeJSJPAvAAgEdMnUcAvLxffhmAX0+nkg+XEEIIWcZe3o+0YBFCyJmTUroQkVehc69YA3hDSul9IvIaAO9KKT0C4PUAflpEHkX3P3MP3FyPCSGEkP2zr/ej8D8oCSGEEEIIIWQ30EWQEEIIIYQQQnYEBRYhhBBCCCGE7AgKLEIIIYQQQgjZERRYhBBCCCGEELIjKLAIIYQQQgghZEdQYBFCCCGEEELIjqDAIoQQQgghhJAdQYFFCCGEEEIIITvi/wNibYqu2XYGogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using linspace to create the plot used in the previous exercise\n",
    "def f(x,y):\n",
    "    return y * np.cos(x/2)\n",
    "\n",
    "#Defining the range of calues x and y need to take, along with the length in order to correctly set the dimension of the image\n",
    "x = np.linspace(-np.pi, np.pi,1024)\n",
    "y = np.linspace(0., 1.,512)\n",
    "X,Y = meshgrid(x, y) # grid of point\n",
    "\n",
    "Z = f(X, Y) # evaluation of the function on the grid\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.title('Own Colormap')\n",
    "\n",
    "#Plotting the function using the custom created colormap\n",
    "plt.imshow(Z, cmap=mycmap)\n",
    "plt.colorbar(imshow(Z, cmap=mycmap))\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('Grayscale')\n",
    "\n",
    "plt.imshow(Z, cmap=cm.gray)\n",
    "plt.colorbar(imshow(Z, cmap=cm.gray))\n",
    "\n",
    "plt.suptitle('Own Colormap vs Grayscale Comparison')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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