docs/source/Adding_Meta-Data_to_PNG_File.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding Meta Data to a PNG File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows how to use the module `tropy.plotting_tools.meta2png`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We often have the situation that an image is generated with a python script. If you find that image (years) later, you like to have an easy way to find the script that made the image. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suggested Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A possible solution is to write source filename and author name into the image meta data. Then, you can search for this info in the meta data. This info also remains attached to the file once you distribute your image.\n",
    "\n",
    "\n",
    "A linux-based command to screen the meta data is\n",
    "\n",
    "```\n",
    "identify -verbose  ${IMAGE_NAME} | grep Author\n",
    "identify -verbose  ${IMAGE_NAME} | grep Source\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "# standard libs\n",
    "import numpy as np\n",
    "\n",
    "# plotting and mapping\n",
    "import pylab as plt\n",
    "\n",
    "# the own tropy lib\n",
    "from tropy.plotting_tools.meta2png import pngsave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Example Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make some random example data for plotting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nrow, ncol = 180, 200\n",
    "x = np.linspace(0, 1, ncol)\n",
    "y = np.linspace(0, 1, nrow)\n",
    "\n",
    "r = 4 * np.random.randn( nrow, ncol )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we plot the random data with internally defined levels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc24c2b4b38>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8fHB64uHY642VOX10mmhlAnpt7dWpWSlpHCamHewiOuLb4bvd6QA+Dg35CDbN/yu2rfVggaoOrHieYeqZ0NPynFkYcbBcdggjxZCZ5xHeLBPTQQXcFtpu68JvaExxzvSQ92L6bUZctm7wcEIgc4puLe6kx4Jy+u3d8xMB/thLdYDbMqiKeRpO5mukCygPwKh9wnph0jGwjfNAJ5qCvw1qWTD8Te9MEs2XN820xkqG6R9Hu39YaOVOQu1kdTaxh1gpJlUrXIfLcJOTnzkHBZMTLi2nXAy2vZsbo3C2Dz75YbwPz6Z8PfZSBR9iEwJUhTc7/jI+2WQcncmwGLdpTxCGxqdp9btsMjnH15ULt3B98dSofRw5i4frFuXqOOjhq3O1as1P63j38it2EYh8ue11fLmFd2txZ4Tz67vIN751u2oz4ewgEufkdJBakB63oO3i2t2NixjnrTSnnVYuoIPCITDXtBkGPXKzrKWhAtMBoU6rSBMDVs9ZyAY3cKTFhmXAZvsLF96eOr0i2mOEwe1sjCY2MF4AFJvUNPFumFjUPqGxttqdhXPk1/PSO1JhxPwE3Y9pq9/y2SrLwiZRT9W0M6GvmrsKaT4rI3Pk6vc9Dbjly8jq6cZs62/nIOQNPHOEs+Y1jLnN8/abM3jjiFYmoPXCU8A7b3SQtvAv1Rz12ELnZGHqTZzjkub3/EeeSTd+zCm7paFkJqualmEKz4V7enzNEfS2L5IFJ/JsaXKaLwuLg3/5Zo0D4e3hj+DPaYdzx4aWGYt/y6nnhGRm9WUClSNh5pJeftGD0niezIkC6VErnweR/xcznRgNOohX2R5swQf0K6k57TTQyKdFbsCzps0hci5tK6Rtm/jX70bpBadr8ebKzCCYWMQATPDbyMPhsqo9u8iTrFxHOkHnhGa577QcxrWn/onxd9lC9SCE0NZivIs0rEXFXEM7zTThsFzc0QOsfDjYrSUAFQfm/Fq7VDN/C//WEyIjT16cXf5Df3QW/YedVrqTkMlpAeyMSCMxNG4y0TxG5hXGZ1SodhBp1UnJsdJzcDYe4G4ihKYXv7M8RtCKxOVqvhefvo0PPvlhABhwbqeV9jRBNymizCuXDvBU55R08IfWjfugteDwQuiw1xB08X5vZyPFq92i1bKWHHSkeWk7tRZjLk8XTufMdHRvc3w+yYRfGhs9CIXzDIq6srbv8HMuS/OZjq8DPoaLhzfzhYS/D4rF6GyjyoqoJTzYGZFdYcVaBYDBe92CQoC2VtHC79yzzIzm/0fYH45iZJUU623xk/EUl+1GftmuMyWNznA3mtzb3B/ddxhlOAgg04IGgST1zDD10Tf0fJLnpt/tmcEkLaeeSxflZ1ZSfK+CRwWyCj129mm9HLEW2sLVnSbds06iP0ZRI7tTjTfLR585p2W2cHC9Yr7POaZ1FVRK+RCA3wfg52utv/nw2XcB+P04uI/wXwP4xlrrL91POSvdSQgkE5cGnmKbVy7cGgnhSHd9WoSlTHuYk16pdSOKu5DUYc3qNFI4hgV5i19us2gLdcBxuuxbjWrRNOq8VL7cgphBVkFZvdjLn8E0ziqJMjNS0zyEQWiOrTHiFvb4XmEG9532EY+V7ACnES3Gx3sysQKgi23MIdX4r8hcYmf9XP9Ea3y2rAolLl+tzxNI3w/gbwJ4np5dB/AdtdZXSyl/GcB3APgz91PIidlJGILIDbxBizadHJEI2W6sGJR64PiyvGl5LWEZg1u38K7vrM3aFTVEV4AdXl57z8p3+LPLX/nMFkxHLu/4JnjQiZ45MdVauLe5j5efKIdb9ttYpRMk8Uz7TvMIwbm9e37YSZrBafyNwlNO69c0vFOV2/nqpRuTxTijDJ5SqEjLiHq4dtH89W9edHhs8VxwfenydGmuXroxivsGjoS1m0cngWqt/xTAL8izf1xrffXw54+jebzaPFrpRhUmxWKBqXNvfWe6Ew3wW3RVEGRa1xzK0g9HYjbSAG3cV6EBFmgtrVOF6nHC3nr8Mo8hPBz2OGDK5OBq8e76IGubOE8l8gxqCarMQshILYNlKauL85PE8xB0IZgCQ1cLYI7DNLOAejyHJm8twBnjyB0tqvh0lrdrc1duzO2Pdbk5cfRHAfxv95vJicCgg3jSZU4QHYAjjAwk6A+/y5wXzqHI+QapoB80JaPVOSxS46rn0EjLbpiN7ndvgvJE5jZQeMA51RyxmZxRCzLgcucKBv7WLRgu+qDlx+i1WSutE87L1GPA85NFROEhJ/RV2OmVa5ngzsaNw5GDR86TseleP8Z73qYePg6G0vjbMcz2Q5O2WZZ+8ZffNPLPtOmHzpVSXqQHz9Van5vzZSnlzwJ4FcDfXZZHpZULaKA9AThSgKEMxZyzU7DmeOpbdE8G3wiLXQifdB6D83TPpZ6GH8I/M/2co4qdqbpNl3G/IA2/435RYT5sfVYnX6PdB14xFSTLCGnmi60RBzdEOqWJ8JM68t9624wTRNldjdkFD8Gr4sYsqCaYMqZCdZmFmvnUBaDnK3D9l+LhspCx0znGDbeLy2NFdPc4t3qXUv4wDpyHz9Ra6/0ysTIB/fgjX5hlyvNkUwdYS2jo91mcsdOeW06PeOa0RoffOV6jDIfbsQamTpzR72TX5dZifNIaa1xjjWTKt3P2WRPVOEBdGiZOz9v3VfPiPDhdq201/yCn+bWI8x1dWMuXIkhEii7YLGx0XLuY8uCT/RPrQIpeDmMA00t9WwubYt7XF36bP2PpWf+6RV4VKcePtkd8c30xPfp0WRjyJFAp5Wtw4BT8XbXWzz+IPFd+5dWcZ9kgdKazCrV4ly0CTlCrBua+nxP+k4VbqcBw8E0sKrxFWM/PcOXp7slRGcZ81egM1tqYV4117pHDsePZ6NJU0z7BS7YABs89XLlXz4izd2eBqzLAFhI/c9+EkG4tHj2+3VhlX8v27lGEjtPGs3wdZVCXpoly+cRHjbxwylB8zxtseLHTuOto22wsnwQqpfw9AF8F4FwpZRfAn8dB1MY6gOulFAD48VrrN91POSuL4rj7+Tc336sZB3jBpr/d7d6a1/rOQZxndmM1l9fydrewVeXZaSYtbzdP1PWdg63JUabTTOIb5cNpLExcDvPIHvr4ltsjc6xlvPE7VwdHWl/tD+7PFt3bPIgx1nTZYsf4fAjvbPONq5uLHdf8GZ5yY9zVKfiN/NXBPJeWiS1284PbhWlZi4W/yyCpExpih1rrN9RaL9ZaH621Lmqtf7vW+h/XWt9aa/3th//uSzgDJwCDbjnoOE0rPI1XfdbONO9h4tEKz0du8kDvDXqnaSzjWMu0A4eHAhiu9sr4a8FEg1CRtO4bztedjdyqE5fleOuZzvHebT+fM1EVl9e2DAdppB36YDM/WS6rC5eZOYlb3/Xq4Yjx/oyPOcLaLdruuITWd/qcoTTXlmFdsHUYFNq0O9XwpGrQrxet/MD+vR1/eSVrJNnE1oE2CmzfnO6wUi2Aza0MV82833pDdxALNZ2wI62Y8upBPeqoVL4ygTEyQXePDv/pXaYapJqW8uHgITWXo38dfsvUsy6Yhx4NODCNIdVSW4uERkhEHvydan3OmuNvWvXmsZSFS2of7+1sDAuYq08rqiJbMF2bRzp3kqPm3bp5XSHEKI93AT+PjbTfTyutPA5atRp37rHzpgPT0Ll4z2FGwxkAxkGjE9eVkQlPp3Ewbjlx9B0+Zydf5iTtOUsYynHavzO/WcNx9VIe1JQPQa8Lk5v8+p22g5Z97sajAMLhfcQ7CwW3IKqlsr6zNuoD5UnbIBtjyn9Q5oQEpg67KKulfEQeLDCzdncL9iDMjXLRGsPcbqMIEnKGsgCPCwkc8dhyh/i7BUr7QbeBc91Pu8Be6XGjjGPqJodMQ40Oi0OBXrjwduvlj4HBglmdcACsthRpndbBad0k0u3CKjCYV8bL3RVGWeTH9u55gL4dtePOxmiRG9qMHFyqEcWioRq3Cqnt3fNDKF38ziAZJ6yyWzjiwHumURQF1XOoz+FvXYBZA84sg2zSM/zVcrRqvbR/4hlbK24Bdtq545n7Qp9rveZYGC6dW5yDAl7L8m5Ck8k83t49P7mNXfcuIPl9mmjlGDSQePNNp6gX/oUL40OBWGjqOQ6sKV+9dAPXcHlwFgJHC4Rb8TPPdOTN5ExUxXuDx+uLp/LdWGbQc1lzHT0jTWvXx2fzbeRaHrdRC0N1QmoZcgLDnSly9dIN4NJY2Gu4nvbhyIJIrB+mDOpqwRSuzuoIjO91I0n2vdOIe7DEXJhOv9Hygk8XUpfl7RzIOmf5f72N3R1je9pv9V45xNHC2picGT/a9i3YIJ9By3nrnn+lZQa+mrit+FXVvuOZu6DUTSitu4vrHqwC0mJdpEWLt1a5rW84fSYQen3rYJb1HX/ed3w3jAERZrpgq6Xk+tftPuTfzCvz52hOuzty0BM/z8x+fj5nkWxBKMclXdCZfy07FtBrBmY8oyNaqQadeb1bWsKQLnGO9Oj5jzwzwh0HzVn4ykwzR5Y/yS/DfHvmZqTV53O0Wm1f5yB0denx40zrIIU29m5uHLVl0lcODolyVPDwotzShAfBM9PyaY2jzILieqtTjusQ77cONUQ+51oXEsXEg3gMZUqECm8WhLHQ3dvMIz2c/yUEaBZJ5aIu2Cp0Gnhr0V/fOd7xCA8rrRSDVtM/yDm83CTubeeeI7zdhGCIZY4gbJWztRhfEstY9bnDa5ciXWYmurKHyZI4hhSL5fcjE3JJs929y7Q6xRUzJy+nZ36dUOD2aR0QpU7Jnk+Bn2UmvxP4TlgyXMb1Hxy7fOB+HA+Asc8gYJBlx52DRZhvdl4HOb9B8MEC3r3nPJ3f55osDvpt1D0WAd36f+YknEGHWxi/B8AbAHxfrfUvyftfDeDvANg8zPO7a63/ayvPtbVXj5wEyLVDNwidQGczWcO5RpNVPN7OMRa/WzeKz9Heox7v+9Szw2/GgeOkNjWvFarowSs9SGSCwQpGO5qsaIcdthbD9R2/k4yjDTSPbJEIzUv7SdMAAOhiAa5bbIdex1gABj/qpBr4EWEWee7d3Bi04JZS4TTi0P63FneGHYw6vrTPHKyn6Zjc4q68DkJvMX0XpNFR7h3XM4OG4lkmmJlaUUmnmboCupTyBgDfC+AKgF0AHy+l/GCt9aco2Z8A8FO11t9fSjkP4KdLKX+31pq28v7+I1bwqbDhAREDG5iGNQXpdzGAeBFwWoaj3tX1zGvmfdd6zSHVnvV3dkgSOzwjn4x35TOb0Jxva9ICR0Kwi8uaw+OVejHY6ztr1smqfoFsjGwtprd16IKosFuv3UcLYaJpx8LBTlBAxrZchKDtq2UGf64ujqJuobVqep5vwEFfxPVkuuU/06p7Y0+FuQu1y+Ce00RzNOivAPDpWuvPAEAp5QcAvAsAC+gK4LFysAH9zTg4yPpVzYhJnYTqzHHOHe7EiNLQTtTvmgLFOCh0ErqBbgfl4ujvZfDM0NhYY7/24uXRQuNwPKf9Mn8901AnUtPDvzjiNdK26jlHY9J3E4ip8U3AG5mTNaMetp7x5vhb31k7vEzgKE3mHOth+Nu750dhk/x9LGRz/QbOUdiCCV158S1r+Q5fd34aBzsNioNJH23I+HNYwXxy4Gm9k3DOWRwbAH6Wfu8ePmP6mwB+I4CfA/CTAN5fa/0VzaiU8t5SyoullBdf+aWjw554APHfKmhisG7vnsfdy6+Mvm393fMqZ9TzbAcvYf7OMc3maANbiztWsGXaL/+L71vCk//N4fV+PPxZGdEPBwvt+Hnm+ddv3fvQ4OP3wSYYDNDCnLHAbRN/u7QxBnsCWPl3FAtw9m12oJPjvbW46NhqKSDbu+dHykKWzlHWjzyv13em0VbZon1aaY4GPY1XO9r2FfROAP8cwDsA/AYcnOZ0o9b62dFHBwdePwcAb9x4a1UsK/4Ocp5xhxVqWv07zlxwA0Y10QyuYBrh3ZRHy6zjszcUfok8QhvUCTZoz4nA1IWBj6PM+FLKHJKuns6Zw+mZr5ZGdZA2Pz1N8x3akhykLvqB2+/lJ8qk36Pd43lmlmdjz6XtjReOfc7gMBWc6mRk4kgQ134aa92C9RTK6Y2XDLfupsnBoKAAACAASURBVDc8uIWLoSm24E4jzRHQuwDeSr8XONCUmb4RwF86PKD606WUfwPgKQD/LMs0Lo0NyhxPvSgKh/mxUMjMfT1TVwX1so4K53RUvjh//oapBz1EZEM2wPdubuDa5nRC9ASIW+xCkDjB0lrEuO4OB1ZBr+b8UK9kTDCf8awnROP2Dqc9trT8ORADU7Td6Bzv2PAhddT208Wsh/f3ymfnKdfZOXIDsjnQvcaK0SRszvDegtzmEo/pnpZ/WmiOgP44gC8tpbwNwG0AXw/gWUmzA+AZADdKKb8OwJcB+JllmdHOVeGgz9VczQY+MBVCwyDLPN4N4dzT+GLVd3w4jd3lrQvECCNs8NJa1DIsMjvrWbHmVvumdVxMn88lZxVpxImLHHHQWBYd0MKKlyXOn30Gezc3cP3po/LcGHCaYsuB13PG8YH86uDM+jvgBoVRWCDzNVeOeMxwxEqrbbkPrly4hesLf0b3aaSugD68QvybAfwwDsLsPlRr/WQp5ZsO338AwP8E4PtLKT+JA0jkz9Ra785loqWtuhW5J4yVMk8+e+uzlV8xMee048mVQSkqbDINyGlImt7BBq0Jy7TsgJ/rZJ0rtIMU3ol8M+2RFwlnhegYyiwyrUPGH1Ok1UW415aRXs8V0SiRwbHd0Uq17JbVsHfz4EyWONaA82TeeSxlfg+2ctyhUJmSEcLd5RnpuB2AseLEdT9zEjao1vrRWuuTtdbfUGv9i4fPPnAonFFr/bla6++ptf6WWutvrrX+nV6eZf9gtc6cJzoRnQOlJxRa5lY40kID27u5YeGEGES9q997EAJTK69lhG7miOH3/Lc7tL7lJHotiM3XnuMso72bG5MNLNGmx8kzw1SvXLg122Tn8dTKNxsnvfEVNLd+DlZiIexC7PSbuWUplu/KzfJcZqE8jbSynYR1rXbx1iB+rvhrphXNxUp7cZ1uAPVCuyaaT3I+biYY5mijytf9CtU5GjhjsuHAap39y+3gtOM5i0WPZ7VY1DnG6fh3b8u78sWOXa5DRrFBJgRvFqutv53F5mANdn5nPGVjtRUJktVr4ME8Hzt922M48wlltAz+/zDSiTjNLiaQTqSedseQAztDHD7N+fEEjcHEwlqdY8ynu7GD66CLRAgyFRxOW2/VVSn7fns3d8y1oJc5E0dN6dCGW1uuW88z/udSy5E6gl8am0bcd/HumumvVmRHvOd2dH3hFIkQ6E7hcKFn3P5Krk/mLIZaJ7fY8aIxQDPmotgMDmpFAHHa44yHh5FWJqDLfhkG2SD0FuMOZmpN/nBcZOcoO3JC2pXlBrbDJN2kd+bu1Us38O7HP4EXLrw9vUSAv28JaefAU/hgsuAl2hp/pxce8MWqQSHUlbQtuR+dsJrjRFJeW1ZTaxHUfJiGyAtMwxNVwLi8WcuOb7ksXTRcHqoxq9aaad6s4MRz7YOWz+ODT34YL3z26Fz1GAPuyjOeZ7xIuLM+4u9JRIhpY60TcHQ70UkU0qWUbwXwx3AQ9vKTAL6x1vqFB13OygT0o29+ZXBgzBHKmbAanBe0SykGSDapdbLx39mkiXfOwacCg29N0fz14s945wahOx+X69gzs3vn+WqbqPMq+FXBrjRnArk87m3uTxapibZlBB47rMI5y3dLav3UynKOsYh8YD51AVPH3mMvVbz8RJksks4abLUVC9AWnJKNUb1ZRXlvheRt4zzeR0FZzIs76kAvg8hoOGeE+N3epXsJTb0Gfg+FcjgYj+ureK2olLIB4L8D8Jtqrf++lPL3cRDd9v0PuqyV3eqtZ3GwVhoTin9zZ87BpJzplJmE+k2Whk3OLK/t3fPTCA+h5z/yTJN3raNOTGeaL4PTZfln/Pa+5291UWWt1uXNec3FpCPPe5v7g3M3Du3PiMeaW4R1YdJ3ihM/9lLF3cuvpIvkBPNOxiPz49pVLaDeYjjXF3H10g285+t+ZHCQcxnKU/DL32ZOzfguO8dm7+bG4LCO/OObmPNXL93A1Us3TqTmTPQIgP+glPIIgDdhujfkgRWyMuLdUEHLOAU0vE3JPYuJkU3IINZiGGNj7WgOlOKgDz0RrfU9t4fT3rO2inJDa5zTpi0cei4meO7GoyOtspVfC0pqkWqWQ7sk8I1i5/wsE2g9fmKb91w6d+NR3L38ypE2f3ijT8sydJtJWjwz9do/qHWs6bJzUReTbGes0uvlBPzsq2+cfRMR8EPnSikv0oPnDndCo9Z6u5Ty3TjY//HvAfzjWus/fsDsAljxjSqZQ1AdNEw9XBnobySJtHO0n1aImyu/VTZje2o9xPtYOBTnzrTP7ED01qDP8FMgN4f5OMisDw7qPj7ASvnOHK3rksZFNuizFgRwv1EtWpZzTk/qkEBqAAbh3CpHKYM6Wg5pzVP7IN658ecW4c/9vV+PdTkvQ+8QVJ41j+AhICE+BInngsJ/Wwt/ScDrRHdrrZfci1LKr8HBgXFvA/BLAP73UsofnBNevCytTEC/8rlHu06wjHreeUfDlVQz0gJeiAFHk9A5vFoYeWhEIxzOTPyMnEa0jaOFjNvECYLs6M3sfUyovScOrBw+2WyuJucEC9CHkhwprusEqDqngtSHoM64SJMJqUxY82W7yuuoXHNaocuXz2uJ72Pc8ILEJ8DFt5kjkduO822dkMgU1hA7A6M8t4gyhYA9gonG70MR0zbXfj5pGDSArwbwb2qtdwCglPIRAE/j4Ez8B0orjYMG+pPURVpwR/YulXTCvBXp0HrOz+Zg2fG3ixJhHvZ2NibmeS9Ot+WIAsZOGgCT4xszR1Pw5yZUdrlskJrlbgFzmi8wjURQrW9rcXANmXMoqSWWjRcuU4VxD+7SsnihdQ5U1+duV53ypXULYudcHAClME1rTKqQdRZqpoFzv2YRIZG/RlL1yEWAOFjwhNEOgN9ZSnkTDiCOZwC82P7keLQyJyFwMOjYCeMcMsDRBGLnnJvIc8lpYUEhKFSYqAOnByMo/z1e3LOec413BkZaPmSdj7CMgX/10g1cuXBrSLe1uNO9SFfrmbVJz7vPzl4nvPV9S0OPOgUPDnJwPDL/c5xuLJzVYdbzHShduXDr6BaYBn9uXGUWnY7JFubP47rlkFRte6QQdZzvrvy5z9z4YYfiSaFa6/8N4AUAn8BBiN2vwuEpnQ+aViqgW8SDiZ/p+0zI9kgjFzLtoSckXBRCT3CzIOpaECbiQH8z/6HlqvDR95xOv3FCz/HihJBuuV4Wd83Salm9RWVuGiYngFqUtVOWN5BHN3CesYC22u7cjUcnaWNcqQBWIcsCzwnpTNBffPr2aKyzMnPx6dvDP2AcaaPlMZ+cl/4+yVRr/fO11qcOj7b4Q7XWe69FOSvdqALkjpAgxvMy549qTz3zH4ANwmfKnFm9aAYV/M6BpXVz6fmZUqZ19zT14MFhj+s7a5OT/ZgcPqj1CVIzOPouw3iD1E8QxGNDNdCRlZH0ew/OCR7dCYQOo83GYA/nH8IrCWaasyC4uXH38it4c5JecWymTBvN5ovzlXAZmj/Ho/OYc1YCx2073qP800wrx6ABDPhiS/jqQHGebM2vRc5Rw6TPW4Ir3qmQ5EHM3mq3rTfSq8WQOajmOLGUWLjxWdj8rcMXWYgxDtzDbZ0AinZwkESmubnncbazcyat76ylh7y7cuOZ3gLeqyOnCx6UnKNUfQysoMT9mcyvzg32I7CSwc475yTNyOHK2ubqIGRNmetxHXmUyai8xmLn+Dutp9mtVINWraylTfPkY8GlE4gHRSa8lZyWwJMohIp64XtaUJQV5wE7oarlsvbv3jHpRMhCkiId79praUXA0VVRHNPMAl3xcdcfrgzdMBKab8aH5uO2xzMfnHcIuswZGPnGs8yJ13Iaq5BWwc6CLrR0109Nq8RoxNk8yazH1lhViC6DFvlZtm0fwNGFEa4u5pmOY52Pp1mLPhEaNAvn+A34jSgZLnr10g1ce/HykRDdPD+KWmAtCfAT3Wlk4fFmeIDLdoKd+eW0gyAl7d7W67DO2UaF+E4ndQYPZJBI6/fdy6+kC4SbMC0hFqSRDUD/bOtJBEEyWTnKYThH4sXL+OF3fA8A4J0/+v6ms6plSWQwVXzLGq5buHgsAUcx3+rDmAOb6YLg6pMtJFkapzjMgQn1e96a/Z6v+xEAB3Hz7LjOhLPjg+t5pkG/zhS3egdpFMK1Fy+PtJsetqjOL9a49SYV3kbe0ijCpAs+WCNjDcZNfBc5MPCxOQ11y0xAXTTiGQuAHkTAPGbQjTWJk1thNE+loQ/hr1tqTXqdmO6qKJc2BBfHmgPAC599+yi9LnKs3YalpFZMpHW8MrUcW3N8J3Mp62/Xtwyt8biew6M7syTru/UdiuPe3B/NQ77dZcTHob9Ix+CkT8806NUSdz5rqDrgM4wUOOpcZz6OJtsi96T3JiEPIHVyZFDD9u75SYxzTyvJoJ9l+NVF5rg051vmw1kanI87wpLbde5kzKAq/Vv5cNZORurz6MXcOyiglS5IfRLOuQwcz9RnrbWnfWvZLZ57Wv8whjGOv1eam89ppZUKaJ2QsQoDSE9P663kTotUDbQXwREaw5YRJq4898xppKExDPU0go3Ln1PHTKiNThNrXErQwwhdG2RYpRPOc6JvuA58vOm9zf1RP2ldeTecW8x6QpUFIvsJ3KLr4KbeQpu9n/R1Iy2nU+otBKEocP4cKdNbUJYRjtnCF2OE+yJboNlRCvSF92mglcdBR0wk4DeQBIbHmKsTDOpN1wHDsICWz1AFp1MhO2jOjdV+zmCag+up8HCLTksjufbiZWsp9MrOnKzcxs60X98ZxxCz1sbpQmioT4DpyoVbVoOMK6i4jtw2GtLF/gd+F32u9XHtnNEymnhQNn7UT6IL6KAwLPzmEgencT5zfRBzxuUyFLzFhjRHijtzW5xmwRy0Ug06w1l1gDmcjTucD/Jhco6OCE1STLilETlNLHNqaX1cPcOb39JeM82T22JwOhnelecWJJBpKmwSX7lwC+9+/BN4H56daDxxCE58s74zjcwIYofQ1uLOxOmqkRTMUxbBkTkv2ckc77QeirdrX/aEhPNDtMatq1f8Xt8Zn6mxvnNwHgoAvPyEH3P87eAcPTwpz5Wryk3Lj+IoiwYZWTAyV9iP4L5Rynwzp5FWelhSUIabqpDKBlFrd1Z2HZM6J2Lguk0MLW3JaR66MAzOmsUYD4z8FBLh92oO98zmEDpXLh1CHLTd232zvXveHq4evOnh7dwmW4uDzRkciqftFfk4Up7Y+Rpm+RxIi8vh/uZ2jXbmLfDcXiqcVXhEHwLjhULThjWh7aELpqsX5zGE5OHA4uBx6TDdvZ2NkfKhygL7R9y77d3zw1Gx7l2QLs5O4dAx2zscTHnkcris00grDbPjzh+ZvBdu4fmd8aH2utLzxGitssuuwM5zHL9bmq8zyZg/vVkivlFBynXUXV8ZTq1t4DTRTFtZ31kDLh0JYL6CylkezMf27nmLUbvIiow4rS6I6ojtUcsCGvKTic9t1zPxVRHoad8Z7j63vMCn9dmWjIvt3fOjW2CcEBzqTe84HFFJv+N+0gWA58TIKmH+MA6njQUvorWyb3vj52GnlWHQGmbnSPEpPlwpKMOXFXPM8u85yRzNOeMh+GQ82znUsm9Vk3c8tSZ4D+eMNPc29/H8R56x0S+c1rWzYr6RTg8FcumWpZYfQc39ZRblTPjzIsFmfW8zEJ9XoTT3uS4a8fs4YzXjVf0L6ztrE0so0iq5BUCx/uPy5r67n3HzxU4r06D396dFh1e+daZBy3ngTCXGp1ULCNKyNF3sKOQVPjP9WpCEqxPvSnR1c6Zha+D3nCsOD2QNJkzkdYwjF9TcdWUwjOD40rTA0aYdhTSGd2ZB5vxapvAkvxlngmeQivMJjMqWOmo/OEtPYbCs3wJKGuqxM75wtdUW3G9sccVcG7TnhA+FAl15A0QmMJirZxBbZFlbrO9Mz8g+bXQinITslXdCNtI6rbCV7/rOGp7fecaaa+xgazmG9JnCFcs6lTInS1aHVj2DGBLhEEKHcztnTS/f6A/OO4tG4HIdpBCCeC6mPBkHOJq4AAYBrqaw61elVh0ij9E4MWlGvAkspmVpGofxRlm6EGictEvXg4L4e948sqzyofxmPiD2vbhzX5pQDMYL0NlOwhVSdMrBvW25oHNOPMayeqTCkTFO1RRapt0c4ZnhxQ7jDkEQ0Q+q8XA+mj9rdSGA3eDnHZrHMUMZ184EcFZ/tVYmGHXS1xl/6rxz6TINOPvt+J5zY4/TDtnqCt56ZWk5GebraFiIqXzlRZ87AczzyTn2sn7KooVYIeDt7XNpjo/pYaeVHzcK5FuDlTIoIk5HYy1ABaDz6Dsh2SqPy4wyMgHhzE1eYNRZpBEIwHSr7RyYgycD88uHPWWmMNdbnbEAJptresKZ6+coHJKOFJ7ItOEW1NJKN3fSD3Uxjqoh0iI2hBCEMIcnzqfFXy+yKHNWtnjNeOOFuMUrl6n8Mk/Kbw+CU57mpn9YaWVOwjgsaWtxZ3J+hhI7IObAA5FXTBaFTHTy8zdzaVmY4OAeOa8NbC3u2C3Qc/NWygS5bqNnx5bmyQ6vaEONKlEBylqvm7A6aZ1wdr6BaJ9sgdZnvRs4Wk5l1ibXd8abb1oaryNuB03TW2xfK8dYC14K2ru5MVEeWhZNELdXb55mi9j6zhquXroxHLZ02mnlOwmDONwGmA5Qd1B7DKT4O56z2Z+Rw7mVnADUgdcyz5nuXn5l+Hvv5sZo1xrXjZ+3JnhrMYv3LYGZCbvMyZq1VRZZ0foGOGoD5SuLgnA7Dh8UhVnvIC4V0pGelYVYxJiyiBemaAPHj37D9Q/IKqIveB4osWacCUxd0NjKizwm2DzNOx6zPSXH4e3M6xwL6TTRSjFodfYEqbNie/couoDTtSIkrKPFmJmKQzvKHDm9cluedE43Fz/nurBmwxZBmOLOShiZppQ20+Ci3bM0CoW4dokIBIeDugiBII7HBmBvz3aH3ms5zFf8vUxUAKfdu7mBrUNHLODPj1ZoqBep4d5ZuEPCFt/9+CcAHByre3B2dx6V1CMX2cNQRk8TdmM++iUWoJaCoXMv5kPPybtqKqW8AQeXxd6utf6+16KMlWLQzgOtMAALFL0lhQdP5hXmd6qhO6HrBgMP1NhtNRKK9J6FsaZZBkKZs2CMJnfjpmVtj4x6EFIv6oH5UEjJ8RQLtC6kAXstqz21FmwVJEy8EGXfc1p1lkZdmLLF0bWhc8wpH7yIb++exzt330/l1NF2+8iT0ytv7N/gUE8+gAqYnrQXfdxypgIU3rq5vHDPxssJpPcD+FcAHn+tCli5Bs1ajZqF9zb38cEnPwwAeN+nnp2Y36yRunecTzYxVKvlb5gi3ctPYJSGB3XkyZMtO5B+riMp6sSxppGXap9ZvgfnOeTeeI2qcGVpm/V+K4+9Cc2UOcy2TFu22lV5cxo7R7cotRzAWq/scgUWPq7e8fya8NnCyLWeLz9RBh+HK6vlNGVhrTCSE54T4ZwsbBEtxM4+4EDgHywm4/ROoz6OxfN6USllAeC/APAXAXzba1XOibhRBTjyMgPjTo8D1zNtSrWv4fYG+ImsAy3TPpU31ljVjGMKDUvN1UyTHXnFZRs55+8caiwonHACDqwO3iHG37g2zTRE/jazdpymxjBKy+JRvi2k1LhAIPMXaFn6fbZ9u+fk4jKcIBru72ssxvq3E1Zal0g3iaLYnLbXoBxkESgYW6Uc6TPBzxdH/hEV0hlt754fIEkedzEeeTxo3bU9H0Qc9P7+I8tYZedKKS/S7+dqrc/R778O4E8DeOwBsJbSSm9UYbOJiSdJFoblLl5tDRprgi6OvstwWEc64ULTZC0hO4C9Rzowg/iAHCdwsvxbJqLiw0GZ4zPK4OultIyRtrSzMfpu7+bGcHKgq2sP62xp7nPaN9MIW+k5fx2vwUP0TRY26BxobN5HP2hdWgIemO7gY2q1h4OCBqwfY8HKlEUa8bh3czrGCi8Grbl6QjTmu7XWS+5FKeX3Afj5WutPlFK+6rVkYlYURynla0opP11K+XQp5duTNF9VSvnnpZRPllI+dlyGWpN0fWd6zgPDIrx91WF5ajK1ygpT/8qFWyP8MP4xqSfe4Z+cr6tX8ONMUdYEWxOEBYHTAOcIwhZ/wDSaQN+3iNvfwVIZP9kCzs81TYyLbMFrHUQV3yvfLp3SlQu3Bk2T68nE7cCREnPyd6QLF49vhZqyvxVCyfLvUU/wLit851oyrzP9ZwD+QCllG8APAHhHKeXvvBYFdTXoQ0/l9wK4AmAXwMdLKT9Ya/0pSvMlAP4WgK+pte6UUn5tL18+i0PNXx5Uo79xfnJ2AHA0Ga/hcuqICVLnYWb+MVThBt2oDBEAzlmm+cQkCK0i0w7nmMysRWvUQHw71xpQUvO7dfJZpM+iYpQHB484WGV9Z625Qca1K/OVCUEWkLGNPYs6yMrm8rJrvpha42/QMo1DUzVSdWxGeqeYqMav9WnNl6zeE54Pywr/C3+nFoRaQy5P5v2kCeha63cA+A7gQDEF8KdqrX/wtShrDsTxFQA+XWv9mUOGfgDAuwD8FKV5FsBHaq07AFBr/fllGWHnR4btAsubxU5AhqbK2ve9zX3r0HNOMJef01RaeF0mAJxwHp4ni8nezY2B95gsk29l0Qv++YopoO/A0zZwxPgnU4Z5tyjyiu38Wra2cRalEWmBcR0VJuBxocI0g4tcmbpY9qI8NB+FQNhycWfBZPlw+8Qcy8akg6kcjs/fcpus76yNzjzP2qTlZ3CL0tbi7CyOFm0A+Fn6vQvgKyXNkwAeLaX8GA5A8++ptT6vGZVS3gvgvQDwhrd8yWjyAZjc/pvdxBDkcFB3LsCQbiHP5azhzIHFA7J10p7mkb1j0gUgEzxBytv27nhrt8I3Wf7x93WMIZoeXNHF6xs3xczVhhjTZD65D5lX1tqzCxrYCTvSehfjE9jWd9aGkDUWakoqgCMPTRvCNW450d89/0TUTU9jdPxEeZlz1PkuMitS8+hh2gzT8ZjliypCyXAKSER3AOM+PA7k83pSrfXHAPzYa5X/HAy6mGdVfj8C4HfgIOzknQD+h1LKk5OPan2u1nqp1nrp1/06zeKIQsPTMC/F1wAMVwIFOfyMzT/NL7YQK9bpcFzFiON9ph3prriYoLELrMV3PGMhlRHXjfFo/p6tBS7L7WRj7JS3WEebtPBdB1W4Z8wPANsuKhTCSaUYb7Rzb4u3q1/swuNy+fquKDfjiSmEe5Y207SzvPh99GXrfsoQzi1cW9s9K0vz5ufLwCF6pZnzKUQ57jzqkwZvvN40R4PeBfBW+r0A8HMmzd1a678D8O9KKf8UwG8D8Kle5oql8UALr7+jQVv6BuBuR8M+jleY8a9skLAm7r5XPtz3GY8qFFtlWb6SWz4cny3Tlumxlyoee+nRUSw4k9alZ220iHF1pgzqcd/2njnKcHwHPzDFb94sEuXqJhiGU1p8MGURFPp8jsapgnjU78kNNtruygvnGQoDLxiM9fO50S04M/t9mmiOgP44gC8tpbwNwG0AX48DzJnpHwH4m6WUR3Cw5/QrAfy1VqZ3P/9mPNpxIvEz1RC3cXSXngb5A7mprtpeOKBaQniOoMscMEwxcNlc1YGv5aiG2hvQ25J3BjlkUQouv0gbR8FyHuwk0sP15wggxoVbF4tO6kVp3USfC6ewRRbf6OLKvLTq1Rt7DCe0FsIWFquCj/nM8szGb88a6KVRfnW8xf/seNXFigV478iF00hdAV1rfbWU8s0AfhjAGwB8qNb6yVLKNx2+/0Ct9V+VUv5PAP8CwK8A+L5a67/s5a0aJONeEUesQljNpxbmG9TyHitmqbhmEGtNqlkHrhgCxjk6HZ6sdXOTQr3kQ1mNeFstq4elMubLDqXI05nKbvGZ1LkjIB2G6/gLamnkOnbc2R+aH+Bj8FkIZtCT9hH/75yL/Jz7UBcSp5lnFoN+17Ms5vhOHP/ZDkitF3DkN9K+Yos0qGehngnrmRtVaq0fBfBRefYB+f1dAL5rbsFlvzSFBnDQgXN3eunZyY64w7POd8/UAaZlsPaX5euEG2vSWdnxPLTVKEvz4u+dgzG0m0zIbe8eORu7iw3BD4G73tuc8tOiaCeOItHFooXhZhoetxVwNC50E0VEhfBN3SxAFGLJzH5gegXV6P1i6iDd3h33obPSHOSg+auQ1O95PPa0ZfeeNWMd8wqHsTMTwMhxrbSM4++0C+mVn8UB5PGOLSzRddwgWJKT07IB1iOdYEzheeZT5JR3x+vcUCOl0KhbDjHHg9MUlSJPdwZHa9daHKUaEzWiIJwmqYtdtoV9He0FlQWxw2u1/qqph4BUJxZTqy9Ys3Q7Wntj2fWNg5pUSOq3GY9ugcnmmVpI27uHRyZcAp7/yDPNreJMCnUwtMFWZtYmvGgDGKI/Wtbxw04rPbA/VvZwhAHTSIfM66vkJns2cYG2ua7vIw1rZsE7n/PM5ExVNYV1knIkQvxmDYk1IRd9MIePOWZuJpyZlL+gi0/fxt3Lr4wEQ8sfsL17fqQ9R53e83U/MlpUswVJNVzXb65/IxqCz6RujTddHFq+CC7b8aTU8n84LBrI459bmrAra2QJCnwYebo+zIR871l827K0rn/mqdElHqdZgz4RB/bH5HQTXoknEA+aczcetR2vgzabLJxXa0Ldz2BhYRsCoiXAWjQ3pCwomxTLeMx5Q4tSLKx88D0wbv8QiD3KLglYxjR2i5+OG1YQ5pKOpYwnx/tcZaNHc2AxJgeNuFBPYLxIZ+eUs4XirBRemLjssMy0vXXux6J9P1FADwut9DzoidmLo8HXOw94ig1OT8dznm51IgXxAUDZVe897UTTMv6nC4BGbmSCUrFEV8f4m79TPvRZCMpzL1Xs/RHtrQAAIABJREFUPbFhj/KMfEfOWByZ905bDRM2M6nvbU4dtTqxGQ9mLHPiYIIXDus7a5PwTHWsKm9znWyuH9xzZ43MFc4tiGSACp5ePjpnyPuwnlcu3Gqe1+zyY0uldQZNz7o7uCD6laE+wHiHJMMuc/0aDyOt9LjRzLMLTB1uHKHAFIPm5SfKKH02wDNMc/A+72zY9xkk0lvhVUg7R5fWm58pX1Fma9BOFiezg+5Im/cLxKh8E23Qmnzbu+dt/HoPFuA+n0Q2LMb1au0y5T6OhdeFYrZ4c1peOE1H9ezUjfPLFgCNlGnVLRZBh0OrM1HzDY2XF10ddyF43SLEPGj76MW2mWP5KK+j71VTdzyd1q3eK4c4WINRYugjfgPeXFQog3HeGDCaF8MLMWAZhlA+4x9rEZFfhls6rTdgAFeGkoYSxj+tP2Op+v+VC7dw9dKNycWrgfU7uIQtGeWtJeAmWu7u0RZs9itovXUB5Xz5OAD+9oNPfnhwKHGZ3MeRRst0ixJDIFnfxfvWAuUwex7LCm2pk65nPeqcUT9FRhmkEU7Bq5duNBc895zL5HpnC37m22H+eDz08OqHnVYKcQSx84wD+TOhoFqUnjsclJmKrA3FRIlTxI5DbPJH+Jaj0Ayv43AwH55TEHxpe7g4bL7ayEEfWmeGHVwat51eb1RZ31kb7uJ7fueZlN9sIs/Fy11/hfasC4WLwnDEFz4oXDQIhMbdjBk54c5tGs8iLY/tcy9V61xWzTOzJgETFYPprTt8yFTGf+SjTjmnMLlnWm/e/TvRtBfjbzPs343p06pBrzTMDqAt2zjo0MCmlFpYVAZbKPUmoBM0biLohGactCcwhu8OJ5MzB7P6HGiTd/By3AZ9qF22hAWA0Wl3Ez4SyrzoGUTj2kzzb13XpeSiC8J/EGU9/5FnmrDJ3s2NYQHsOdYcpsrkFg6XTwYbcDyz7sgc0jT6owU5OMoghlBGoh1j/OhYnvp4jp5vLaY7VN1c0O+4bbIxPvHVnGLtGTghcdBBW4s72L481jZc/G0MsucxvhFcV343gPTvbHC493NJMWA3yPQSUP2Wy9ZFIuqZCR2HYTKumJUX70bnDBPP7mbtTHs/uGn64GyKbCOHUgZ3uXHi8gttm3lr3a+ov90iozTHUlPtdEjT2JHHfdYaN84ZGeVfl2dRb+bv4tO3h4WI4Tkdc2xdsMXZwpb5e/7NEMZk7MGPUaY5kT8PK5Va81PlXktaf9uibv3RPznpkNYlnsO3iZBwThL+DeSam9N6gnqHuLMzih1rDiqIfPjvGLhc956GGeW2NMgwb7N2UceOy1/rrVqzRkI4Z5Xj12nfrQWytTBovnMdt1mdMl7ivYMeXN0mjj/aMel44g0+rl1b9eHy+LlaTtmY4Dq5v+O3kgpdxysrGJm111oQLz59Gx/76r/6E9kVVHNp/W2LevEvfMustC/9kW+/7/IeBK38TkIVRJlpzR0J5BPp6MQ1d0qq126YopwPPvlhvPDZtx8M5sN352708927uTGELunlq0674AkQgtk5c1pO1GzQXzPlavtxlEb8PcEOKV+OJglLh3d+ffDJD+N9n3rWQjfhK2D+e/Vzf/fSzbWE1BcRfITjywlytu5aglPH9jVylioufJTP2FJqmfjcjw7mcsIZGFtBPH6cYjJHSehp0M6/5L5vWStXLtzCse/Q+yKnlWPQmdmpWFZLyPGAizMhHI3wbt0OLjeKv/DZt0/w5MDGM02Qy2HzWk/w0nr1zMZMKwlyGg9PbjfRYsJcozRO01cNSrc1A2MH3gsX3m4n9PbueWxRrHlPK3ftMJecNj2CiRbjg6EATA7852dKLMSDWEhmwibqdB1TWKGnJavW3qLMBzCQQCRMrQUhFpfW9vgJzwQ5ZeetZIt2vDvwI/xQWs7DTCsT0K987gCjzAZmdv5tRstixS1cbX1H7jc0sdf8dzbAgq5eutGEbHpOvgyKCCHLQrmVDzt3dJLHs8zJM0eLinYbhFES96vC+bHDqIZW6FpGTht05UQbxqIZ/otY1KKfW05bIIcCHDleVJPMFlpX/lAmOeiclq1jmmGVliNc/+bfIfA1Ioj5Dh55gRjVyShX8b/6WeKbe5v7pxqDXmkcdKYJZAKHv+sJxayMntDfWtyZDJQMdphTPufBPPVCz1rlqRkOHAmeuYua3vYCjCdMRs5Bxu9aDj7H271Nf57Jg1qcs75TR5uWzdrtXMhpzgITbcRjWLVxl3/mM5lTLtfBtVUGJbk66g0/TrnhsR1btplXbt94tqyCtWoqpXxNKeWnSymfLqV8+2tVzsp3EjpijYzDfpy3WSkz5/nv7MbtQbM0k1efa5muvOd38jAw9x3zwu+ZP6XQVK4R7xnUoRQ3WY+0LYm4WBZmUHN9mfpzBMi6uf6oR85ZpceJZmGQmUbs4CfnH8nq5DTWFh4efegWQjcW5mjvOhacY3Au6bdcnloDOldV8VIe925uTI5Z6ME5q6BSyhsAfC+AKzi4TerjpZQfrLX+VPvL5WnlGPScQTJxnDTOTFATlQdHC2/TwTQSzEIarcBxpZxHqz4tb74emq/8ZOapaoWD4w/jUKXHDs/fCGcmpx1CF3E0aficEq5DxlcPR2YtS6MVANgrtRwUFmNBowTYMXUNl0ftmlG2kClvrO22BHmQ04xd+/QUD+bRlev8Fprf3s2NkcLDC0IGU7Ty4/Txbn1nvEPTCXT2zYycrlRWb0FbMX0FgE/XWn8GAEopPwDgXQAePgGdabqtdEoOvwJybdoJOJ3cQe4dO5syARTpMn4zurd5dIi94z/ev/vxT+CFz7592Nmn9Y6JPDgoCWNkSIHb7PpnnpoKD17gMBZ+1gF3SL0FVxfSluMQODp32014XcCdFha/OZRxBL/MuIrK8cX10TTali24x/kLeHGIRTsT7DrmdOw5DFjTseBUC4Gd6tlu38iP29stKnouikJ2J0QgnyulvEi/n6u1Pnf49waAn6V3uzi45u+B04nYqNLrENZYnfYxrNzwA9xpVvHdxFSVaI74203MbFJyTLSm61kA4bVWAcZ5hZkeArUXOz4SnNJG2fVZTnsPmjuB3GKXlaGkafUCAIVxuB6K77OW3dvpmWnFcy/r3RbhxsQ7ZlVwT043bITyOWsms8YyqMS9y0JcM+tNx6jLdw580sLAo80exFbvOEFzJt1txEG7WNvXZEPJSuOgXaRGpvn0iCGHTBBo3stgcLowLCN0HS8tj31s43YTIP4Pb3q858k1l5f4LivHUc/JlD13i2NAKIA/r4NhoN44cPVwlGmc90MctcCkFoKeRcHfMmUCy9XvOFZaa7y2zvues7Gp1Z7a5q30J0iTdrQL4K30ewHg516LglYOcTC1Bmr8ZnMPGAs6FtKZlpBRb7cgU8vxxmW7c6WXHXg9Z5BShm9OhAX6WmFWz95CmPHlvuE+y7R55SnK0vopLHC/OKaOocwi0PcW7jECPIMdAAwwFzBeqBRHnlsP/lvbqhelwhq6Hj/bCoFTX43CU7zpScnh/CeIPg7gS0spbwNwG8DXA3j2tSjoRAjo6MA5sc8x+CMCQfNhM7IlHFXjmQyem1Mc0x1Sr88z/tX8bTl6VMhmWnTQNZl0KugyrWskcES7cwJ9DjmhnrVNWArRF5Nzjpcsh6nnqFzGMpvzjLXSUVQMne7GaUPQZv4Q4EgY6kLjrJG50SLAFLLgY0KvbeZRKuzTWN9Zs/zrWFE8WuuVwZb67UmiWuurpZRvBvDDAN4A4EO11k++FmWt7CyOx77sQn3Ld37r5LnrFB2I7PDRQ3DCgfbOH33/KA/F3pxTBsAoLCtzCDncLd5l9WgJC61Hy2HkfvfItVNGmnfWH+oE0vdMmfMv+lMvec3aPcoLatWHcenWgtHLL+s35+Nw32m9WmPb/XZ56mWsrv0zwTanf7msVps4q3MO/1m++n3wurW480DO4njjxlvrE9/0bbPSfurPfdvpPotjf/8RrzEsphNKNQR2+GhapzkqZqzvI83W4s7okCNHw7kTtC26p61lzxU/1nfsQOL69wSJCr25wjnSK2wETB2rGe4aaRxfQYxlXrk01vYzbb1VXuZIy24e4Wet28qZVLBFO/F1TS1yWi//r5EMjgZBDw/3ZYLaCeUWn9xGLoJpqL9YvFn0SuQzfEfWnoOQRmV0fAoPO60c4uAOaAkStyK7tGyGzcnHYXLXTdoYPD0B3vPkc5o5wpzPDlkGatD8Mk0p0/SVJpYD2pNLy+d8+LkeKOXKXZZUw2ecVIUInyOiQoMde2rB7N3cGC4ScOMhE1zxu4eROytkEHJmLGgbR0SSjrPAvZ3lkvGr+SrMNowLCjuda60pDn0SIY1V0spPswtyK22PsjCioDBx55h8c01nFiqMOzqHS4ZnM/+OMl5bg5fL39vZmAiIFvziNJc5NII5jNAIRynHYId2posi59mLQNA6ZxDKHMosgMiHD/hRIchWV0uzV99K5N8al3o+SW9MtihznvK4ZyHLC9ocKELLct+5i2GBsZ8hW9yvXrpxak+zW+lZHC1Pcsu0ube5PxwIzxOSowE0/frO+B4/HUCZOcUTac7qrubzlQu3JgJk7+bGUkKIBZrjT4kFgCPV3Fiza+HJwQv32WSCN0i18LibMSj+Dp567Z3V3S38cywwFaItJ1trrMxZJFz+0Z73Nvfx8hNlUobjzy2KkdYK4MO/o+wQxjFWI29XHzc+uCxXF6Z3P/6J9F2L4rvTSCtzEgZgn63Arbv9MieUw9xYi9VDXDLTXp1VzjHI32TaE4DREY0uX+a7hxkqnKD15/RzoIeog9OuMkyb26DliHOkZrVaL708epaA8p3lAbStkTl5OOHofCX8LstnjhN4DkTVqlsLH+Zy9XlvsXHjxeUzZ6xkjvvt3fMP5AD9MyfhEvTom19pajUZjjxngvUcV9u7B+caqxPRmbFRJodQtZwyTCGM3CH4qsUGZhjnFEdZwDReWQW7q+uwMB1ifNpuqlEFbm6dORhrzVEXPU+a28T1k8P/gRwCYgrh7hxWLdJbagKKcX0xaJ8mnJLJOfj4/yB3hgnXRxd+FbYKIQ1/S55aF6egzAkFtcJxiQ1YW4s76dG6AQ3qdWwtxajXD6eBVhrFAeRCbRltqodFR14jHKxxu0OLnCDneihcwFpi6wyDoXxjnmfEWjcT113TKA7M5fBCpOVnAsPlx+UqP7rRQdPGb6YoY5jcpu8yDc1FZ2SCNYvDd5qdHjbfsuJUEANHjr7JxRE4UiD4d5SrdeAyNb37O8P+2XKdc/GxO14ghHCmQQPTi4OzMb6sP+RhpRN1FgfjZ630Sj2zN55lAy9b9TMtomd+hjYcaRkHdHgd4Dc0cP5MVvsm3uY63nTCtha87PzqTDAwbyOBSWk5Pjt4dFBKq4yMotzWZbkaQdASbJpuHe14bSec9HnLunD16eWXkToGW6RzJBOivWim1rdBLWur5UA9TbQyAV32p+f9hiYG+A5SjUW1Ere1mrVSN2l4kE3wPNpd19LwHK7HmuIcau2idBpfK/39Du5l4aMgNyH1uNNM+83e8fvYPOQm8vbuFG6JuszFuEe8HMIIfJGr0jJtPMASD0josKbr6tgSjjqHdFFXiMQJUG7vZSAnzt85ONn5uWy+DyOtPA46o6xTWp71llmYDVY+ZIjTR6gaT6q5AyUTFq3FJ9LOwVgZamDMWvPiMoP0wBuuY4YjA37xY551IdI6um9D4CpP7NjNrkli/nTxnKtlNx3Uh3m4S4IZ4hk9222f/+IsO/c8nqnmy79bsf5qsUVZg0CWkEjtdz7vQy9wcOU4C1OVnRg/GUzj6n9GJwziAKbhWtmgzyIdNA2/b2lTw4Ck7ashPK5cuDXcUeh4UuyWy8t4ZNyT8+TzDTIrIvBW4ODsBBUOgdcCR3GnnI/iwCwEepBHJoDU0RftxztDJzj2YRkOG71y4dZwLoQekRqUbZbh/F3fOAHGFLHzmT9EF6S5loz7JjPrWZCy5ZjBXcyDbsHm71Vzdby7/u35inReaZ8on5llqQpKpHsQx41+MdJKBbQbCK0Bo86ma7veQ94qq+fci2ecL+824/yyyIiWBtdajFR4OQERAqe3tbh1tVMrQqZ764gIxUFYmctotU055NAdFhS0d/PothcHWUW7qdXDTtgYH6yJtnwAk7pvTm9l5/51Qlv51THnHKlcvpJqu7qYZ5T5HFyew7MEGmk5tLNFShdRJo7QaPHv8j2NdKI06GxwOC/14C0mZ5wOSh502UBzg1nNy4wmzk2cT98dh1oTOuK6NVqAv1XKFpChDLOtmdOw6T/S7OQkvBZeO3jxN/224Ujbav9BI6ffutjxAhX59ULGtMzsAHtHzvKLb0fPZREBDqAeZ90t6wDM+IrFPmtjnidXLtwCKBpGx5ZaJtaSXUzLz+CQlgM83l98+vap1aBn7SSce4NtKeXLSym/XEp5dy/PulZx8enbqVDJBI++Y8owxex3i+5tHtxU0vNUa7m9MtS8zEy+WFSuXLg1hDQpLatdpELDOIlaWGHAKICP7tDv3XtHzmmUUUxcXaSiPkrOqezSMGm7ZNq3o2iDe5v7A5/BQ/B7/TNPNS0d5kvHWOT7nq/7kS4v6ztrTaGv7TwnzM7xubW4M1hJ8W6Zxcbxssy3DyN1BTTdYPu1AH4TgG8opfymJN1fxsEZqV1aW3t18kxNVn4GeCyQ07kJqGZtSzjcj7br8o4BGxNMJ1pQxhsvDoybAkfCxB2Y7gRd/GsN9pYA2t49PwlF7C2A6zvTbe1cJy0rtLrYxp+Z6nO0Rk3f+u2+40VmjsMq2s4tdtnfPYHPPLi21ggkptb4YNL5de3Fy00YSH008a81djl/zot9Jtn8OO3OwjkQx9wbbL8FwD8A8OVzCo6NKk4bHuCKzfF5A87pxrGsimtyPvxbb6RQOCJ4YoGkOF3kB8prGe2Kvx/Vw5jYMdjdZM2ck0yZ0yd7PgcKAMaRHcPGhc32t8M524nD9mCStnlXbFedq730bN6zkHBQCdeXzXy3+7GFUc+xGoZdn4268BiM3ZFuAZoj2OZCec5pHN8PvNNRCroZhfnmsjnvVtrTTHMEdPcG21LKBoD/EsA70BDQpZT3AngvALzhLV8yPNcBAEydLa1B6AaMy4d/q8bMz53Qy3bYtUgnfMtBwhqJvo8tssD47j2nebstverM4bK0/rogzhH4W4s7I7OYMeIWhAWM46QdDNFqa+13xVRdFEWmtUfZH3zywwCA933q2YlAZ767+LiJXnALc5YXL9rr8GPGLRQa7aHv2Fmqebk6OMXD4eX3NvdHfdgTtvzelXO/1uzDQnME9JwbbP86gD9Ta/3lUlzyw48Ori1/Djg4uEQFWNapPLDce6BtCmVChvNz3u2Bp0V+uFImBPlvdpCwaZc5J3Vy7d3cwPWnxxNHFwx23ugGm2xByNrK/d0ibQPmX9uFHbvA2GnIefXI8emEUWDVkXevTu/71LMAptaYq2sLcnP86LZu1wfOmaaLwwiyk0ifzDkd7T4XRz+ugGTnoT7XxTq0f7fAtOpymmiOgJ5zg+0lAD9wKJzPAfi9pZRXa63/sJWxEx6ZcNZnLi83QVv5uPI0bQwkDhFz6Vt3+nE5KoRjpxqTaoQhYDJPOJe5t7MxOXPEmehRLz6/wk0O1y5OExzlzQcSmZusnTWQUaYNc3mcb4TwaR4MBagWqRAIgOFQ+6w9elqwbr4Z8bvoL0S96Aituz7TQ6E4emTv5sbgXIw4dbVuehdEuH5jpaM3r3p8j8bZErfUnyQqpfwpAN8F4Hyt9e5x8pgTxfFxHN5gW0pZw8ENtj/ICWqtb6u1btVatwC8AOCP94SzI2d26arbc3oE8Xfh7XZOGh1QmRAPr3tWFjuI5kQLRDmZcM54AcYa1dVLN5oRF/psmUiWHrU0wSAnLI9Dajov8104R50Tak6ekcaldXk6wbYsvjrF5feb4z8TprzAR77ZuSrM55y8dZHSfNiiijqoU7KV/xczlVLeCuAKgJ37yaerQWc32JZSvunw/QeOU3BdG6Mk2WRXTaIHZUQaZ4I6cmlb+KLTqrKy2Bxu4Z9ZHumk7lgC4Txy9dS8NTJDJ5HT5AZTvcGDg3CC5oSWRVmurqEFAmPtaojRhkBYM2/bHsqUslz5uhCrsOH3/H/mQIxn2tYOmmMnbdAExqGFg4/4DF707k61qEZ4dbKtnXlyf/Ozoa2M9RD4NTAeU1/kzsK/BuBPA/hH95PJrI0qtdaPAvioPLOCudb6R5ZhIMMPAdjIBkfq2ALGwnRvZ2PYNqwRHS1tIsiZw4oL8rPsHNt4lh1VqQK5d07y9q4/j1kdTPc29yfHQ6rDiJ1L6hx0Ttzge46DtuWlD14dL8zP5DvkC7Fb5OP7npNP82kJiczM5/dcfvw9R/AwzJA5abntW0fpOiiJnXNXL90YBLjbnduEUhQaMXXr4dFRBx57LTjkdaJzpZQX6fdzhz60LpVS/gCA27XW/6flk5tDJ+Y0O41EAOaHCwG5g03x3KDMQ83US7O+s4bP3fj1B9cTPX27ewjN8B0w0kx0AGcCUcvmuvfqcQ2XxzxhrOEF74+9VAFMMXidfIO2tZie75tFC2ReehXALeeQasV8sI/r654w7Al35bd1Xvkyfg7n8Mu+cYum8srHEfAxrpwXO5MZC49vedzFXFJM2J257Rzmrt+H8WHaLeqjPowHKaTfsI/D8T2L7rZuVCml/BMAF8yrPwvgOwH8nuU5nNKJOc2OBw+TjQI4JHU2OVJH25AXphNAtdDsLIygA1wNkzRaLydoNH02uKPMbKuuq2+8dzxzWVrOxadvYw9jjDPDJN15INu753HuxqO4e3l/JPy1LOaHd56xs1IX2MjL5ePqqsKFeWR8VJ2QGb9uwWmlVd50PM1RDOL7zMnLxL9b80HnkMI1GV4c3w4adgI9ad0crzyWnfOVF48ToElbqrV+tXteSvktAN4GILTnBYBPlFK+otb6mWXLWemVV6pxMS7Ig0MPrXHUcpI4crAF4J2FmnYZp4YKdDep9W8ubxlNX/l21FsAVbtxlGmnW4s72L48NftbC9z27vnRIf6tuvS04ZjYwPREN0eKGzO+PtTH9FOrT6I8t6BmdL9OsonDuxML78g5H10/cP9wO2QLsmu/ObDIFyvVWn8SwK+N36WUbQCXjhvFcWI0aJ4kQNtR5xxcekyn05yCFDeeaw4PGHJyNrLyCcAuOEy9+rbKUdyup/HzM85XD3/XesyZRJkAcLwon5zGXSqr5TirhM1vVw/lU4Vtqz4OXnA0V9PrtWdPCeD6K+QxR9jbRZo01qyurTZwC8GySpPS1Us38PzOM9ha3Dm1hyWt9E7C1pGTwNFAvmYcHErqtV/fWZto6Pp9S2iyhhQmnZ7B7PhRhx+AAat1xJOqBbEor6rdxP/ssc8mlLYFm62ZGe/ghd5k5bSu7J6g6mGz+vza7hT/dg5CV3YrMoNxUa1Di9QprPkHOYhGy3Awi/oJAj9uUcaDUuZEdnxnWrOrg+OHSfvhJMIby9Bh6PGx6cRo0JlDLSib9Kw1z930os4QLh+YesEjnQpZhl4ynDLSMM6qpNpsQDkcxTFnoLKQ0oUva1+FUzIHrX6XwQ8tLNfxG8TmeUZuwZ0sbnIZq4PHHJYf6V37tC6rZf7TsZgoBz18PaujaxNeTJwjT7fSu7Z3zjt2Hr/8RLELCI9TDV8MfgAMjuoWZRbyaaUTd2A/MH8XYPZt62+dPE4LcbhxXIEVxNipCiUuY7hqfsauOSCPEXa4LkMGLLh60QwugkDN41Z7p9EpRkNS7XPudy1yC6FS1H3O0ZlNwbWY9olrJ2fB9ZyOHPrYWwDVotN+5XKuY7oQaH6ZDyajl58oo/sZtc+yPuR5MvF3JPM65smcRf5hp5UK6CxCgwd2/J6kx1SgOsG+DLlJMgjbBLoAgOcPIx+GSSF5Ou2t54nnNuB7B0fOmMXYuZNhqdkAzyJGFHNVSEh5YQ1pSCOLkU5q1sTmLr66QHIb6KLLfAzlya0vDlueONw6eG1Gc/0aQa3FVRckxfBV6XCQm3u3tTg4HOqdP/p+y9PQf8PmkunmleDrIHrnlUn7AZg4Xpm3bHzOhWIedpp1YP9rRRkG2OsYxrq2FneGAfvYSzXVAo+LZalgYZ7jwPXj5r21uDM4ZRw57VYHc3a7dyYoWxQCYmtxZyJguJ3jvOagaIdIw5oPLyB8psicDUKOdHxkuKtb5FVjzDBRXUi0/x08oO/Ymsr8B0zRbhn22hL0DuttzSN9/iAE4d3Lr0x40sVXfSzxt+NPoZvTSivHoPkc2TlpAdgYzIOB4CMZeNXXyddzovH3ysdxBLNqDHM89sq77lRUc5oxwdAcnbnIsA7zF/+Pdjwuji5yBcaTqzXBVUttWUbKzxy/RIb9Z8QwRJafIydknbbrKHs3F2PtWVtz83DfvfDZt0+sCleGWlSRfm9nY2RdZsqCawPF2b/YHYKvBa1cQN/b3B8Jgt5lkhMzWU740tusHelkY2EUv+doLM7jz3zq4JurqWSTKXXasYPGEGvRrbpp+/MOPYVqmM/ehqEML3WOsb2do8ged3qgExquXdnBlUE/nF8mnFXbdnCILhDOzHfvldcWcVvHpQdcLu8e5DnlSK1ArZcjp7Ro22bnm3C52RzgukTbzYnJf9jpxFwaG464jEKIxlbkIB2IvOU1C02yHncRfus7XhtX3G+ECdN7dRrFO9ZyAQCb012OKphYs3RCqeXsCQgkm7AtvDIjFbgaBtmiySKwc3QWB6Rvehpmb9E7d+NRvPzEeJy14KBltGC2TJwFommzxSLznzhH7tbizkigDhEbAN79+CcAYHKWhoMKWvCBW8AzX9HezsZ4nphFRnlpRcSMFvCFt/BOG61MQK+tvTrZ+rqO6ZZocEG9AAAgAElEQVTYoHgfWJcb7DwB2ZHhNFHWftzk4KgNp+GwkFLh3BpUKkyHQUvXW7nIg56Jq4chMfGAD9LFkYV/kB5Tybw6DZCxQ83f8Rzvg/frn3nK7oJrkfIRC1JAXrYt6Ns5wtl95zBiFc4TbVtCAIH22R5KbiGIvnjhwtsHPwDvA+BbupXcHOIxz2NDFZHgPRPmTmnY3j0/sWwz6C14iPFwtlHldaa4k1AHxPrO2jAgJmahGdicB1M24OdM/u3d85MdgC2MbDAx4QdaxkcsTHymRQYTdLHR9FiXKS+KCbewRha4cf7GHMyyJfC0/Lm3SGeQkkvHiy8wvX3cLSK9McFpGF5bVsvrQV4tyCP7xh0bq2NrGR5b/E0W+kU+7tUxGBBWlNHii/0fp5VWGsXRCxEDvMd5rhc6POhOuOm7TACGM2iuIzMzcXsUwrp3IQFDMzHRLz59G9devHx0Iavky3y1Fhp1fK3vjG/l1t+RNiZSPG9FEKhnX+uvfyu526KjTM6XeWTLqtXGvciHnhXDpP3QciyH8OLyddxm/Rq8abpWXdzi2huj0SduMVeFKngfNPhD4vIy4eyskNMMcaxMQJf9g3NSXVSFE8DLaMlBbjLs3dxYeoLGQNta3JkI9uD7fnc99TBcFWwuhCmElApLzYeph8lyOfH/xadvTxYs92wORX/MiZ6YY1mwAFahp4sN18nBP0EqaPg7N56Uz6xu7BBzcB3PBeYz6qH4dKTLyuMxoouBpmP+snQ6V6ONeMwENu7q5eZ5zDOmOQrOw0ql1tnnoz5QWn/bol78C99y8Dd1QBaO03rvSHFVzaNl2rvfzgufwSU9XJMHYO9Qfv0mMx+d+a9QguNVTdCgDAIBMEQMuLJdGZqvOmnd3w7Xd3nrd648l771jnfMOXK8OCGiESDuXfYeyGE8VWS071plBf+tsZ85pHX8Hzhhp+3U6is3d7Q/eEEN+tSf+7afaJ3PPIf+w/Nvrb/xXd86K+1P/O0/ed/lPQhaqZMQyE3uzHHVo8xJ4TzkXH7LmcVeeJ40HInBg34uXBNYt5swSu52mWyRcLhrFn44wlQPKcOVo61aoW+cTkkhkWh3/jvLV/kcTWY5O7g1XjRCgPOPvEPozFkAHTltuCUEszxa2GzrWQbpDfxLRAnz58ax1jdw5Jef6At/V7bLs+UsXWb+P2y0+q3ei+nKDCThag2t1eWdCR99Hv+Hx/jqpRuTg12GNMQDa0hzbh7Wia6Deahb50Zl5qkV68uOLXYWOWuA66PazSDoF9OwL5deyWnI7pm2A78P8zducOE+03K0jfid4qUZr5GW/1btT8cuk/Yrb42OWOaMZ1cHp0C4xVPrm1kLvd8qsHtWQFDPeuo9H+XRsEBOC63USQjMi3rQNC0NRp0ZW4uDbcWsWTEW5wYZa4haRjjKYrvz1uLOcIX9HGotLPyOBVnwrxQTUrcJR/rgNfJ2E/s4OD7Q3z2o5JyxDtLQMliz4n6JdmnRHI2ay+qlc07CljNOvwvcOC5ljXL5nyMnPB0eHP87P85c6sEljqdl4JXec6U5/fIw04k5iwPwwjBIHUm9QcQDvuX5dzypoNTBf29zf3T0IvPknHOthUT5zQQmCytHVy7cmtST719z+ceziSVBi1t8k/GVCVxHrNWxhua0TwszJdqqE1gqAF0+Qdni4Eidc45vBzfx/71IHS2fMdksGsSl599uEZgjiHsLeI9YwLpFpXUNmFpop5FWGgedOYjcb2AapqMTU9PHpooIP8t21Tlz0AlLNjGjfIUPgkZwDMYxtM6Jx/XW39s4P3LMKe3d3Dg8I+NQABBGOORJsaTqeY8bz51w5QsAeCsvY75qkmo9DpxuB3+3BDj3UZCDupR4LPS0rcGyoHhcd0OO839kCy3gF40oj8vVRYrP337spYr1Q/y75X9RLJtP82vBbeqHaGm3PLfYV+CId/c6vF3bxVlNPWvqtNJKnYROOGdCzQlR/Z4FaAhOi6+SsHKwAq/c/H0PgtGBnR0BqcK/56gDjmAX/VZ53tvZGEUhqAasE5gXl5HFsnN0iFVrey5j1Kzd8YR++YmjkEoVeCPhaCY14768izHqwXhwtKVaXtxG2octCKe1q7HlC2E8OMPd4/cIz9484p39M3xzufKgC6wKUh63evcj8+Awc+alpUlH/3Ldg1x7u7ZjwX6/WvvDRCvfSaiOQueYYXJXsjtvs3MG6o6nofysLDNQdHCpVtSjOdEebtK0BDzTxadvY3tz/F2m0YwWIozbKtqPJzV/Y3kzwiHTQtUnAEzPdQbGGrxe5RUCKoi3OjvKtPEMrsje8fOWUFkWfx0JxgXsOBlZjQt/2BELae4P51AdFj6Mr+biNKqAzCV1MDoriH+3zuL5YqJSym8H8AEAbwTwKoA/Xmv9Z8fJ60Q4CZddMRm7unrpBq5eujF0NA9MNdNazsXghalnaqmQYYy6ZWorz5pfTJpIo/wu015zYJTgH5huOIn+YfzS4cNbi4PoFzW/HUyVCRBHIYACuw0sPr6Ls6g5LpvrM7evlXjxcs7XyPvi07dtX3Ierb9dW2YC32nGbsxqPzz2UrWYdfChQl6hQ51LzEvLiafWDOP3zuGqNMcBe0LprwD4H2utvx3Anzv8fSxa+XGjPRphuaINbi3uTCamozle4J4Zp8KppXkzsbk7B5dT2t49jy2zYSETli0Tnq0VhgiyA5paxPXKqCcEtd6tNsCmbMw4/H900JJo9a5dFQZz73qkltskb7oP0R1729S+TRswfOFIx672ZRww5sZMa6NUzw/knjMvDnp0VmrW7r25dYKpAnj88O9fDeDnjpvRyjVo510OGrBcmkw8QMK8bXVk5lxijcBFeegE7kWCOO18cPyYge5OhAvau7mB5z/yDJ7/yDND2Vx+5qxyDqtlB7jW22nvCi+FkLn24uWlyssEnCuP4YwRTiu0jNblNENu69Daoz3clvSot9OEI7/M4dzSajmfFjylFP2XLZxRt6iXs64i+kf7uFW2412jjlr9Eu2xjAB/jelcKeVF+vfeJb797wF8VynlZwF8N4DvOC4TK9ege7ddz9VUMxocKcYhARwKQxxdGOB27AHj08JannLVKll7iclzrSEAOZ8gZyWwMHaHTqnVARxhfO7OxJhgEw3dHNmqk/U4Th1uJz1X2+U3ilQhHnoWlMICfLs1WxDuLJU5lleU4SwVHtNuHKu26caU+iPUgswgQgcjbeNo7GXOOq27U0D4UgWlGG98vRmPKbdQcXSJ4uE4fPaxSUnL0xu+UPGr//W9ucnvtrZ6l1L+CYAL5tWfBfAMgG+ttf6DUsp/BeBvA/jqZfkFVnwWx3/6fV8/gTCC2PTO8Dh1nvQm1MghIfGk6tCId25ixbdue6qdrEIZ7sjEN2RwPrpQZdjdcJHri9Mdka6dsgiSOe3K5WaaJAualmBQszhITfGs/1t5X710Y2iP4OWH3/E9AID3ferZSfqWJtoqG5jeMpMtPNw+jtxCe5yF47gQW/CgbfHYS3V0SWyvTtn4msP/1uIOPvbVf/W+z8Z4/LFF/fJLf2JW2h/9se88dnmllH8L4EtqrbWUUgD821rr473vHK1Mgy77JfWUs/PCCSTWDJYZWO46J54AVy/dAC5hiJtmnJZ54JUdkBtFFmNhkgnQntbJzrGLT98eaRmaV9DIC0+QyEjYmXA0dtw5zLGH5QZluC+/1280H243xZyDXP9nfZJhs/E8BLPDYkMTjve8IGcLuZaldc4W12xMOMslrKaecjEH39d32QKn/gsAo/nBfe8WSl4ogtzipzwf10JbMf0cgN8F4McAvAPA/3vcjFYOcQBTLcFt7HCaaGsAjrS1xRF+2HLwZLdO6EYRHTTKm05YFYY64HhzxsRxtDk9eD3y5LLDkRbfO2Gk/GYWgGsffZ8dop5puXO1OeYxg1V6zkn3fabpRRz21mEfMG/qvHP8LOskVYjDnW/i+muyENI75SkjNyZbmjsv+BOeN6fWBeelPhlXTtaGLSvoi4SuAvieUsojAL4AYBn8ekQrdxJmFIOCsdHWucEA0nCnXjmt3y3i8K7e96zRZDzOEVwqTHlis+Mp2quXR4u2FndSnjLsk3lz2lD8nZXX44fzc/m4Pgl+lbIzToJa4WWajtta28Y5Fl0eTL22cMLQUQtii4VO66h88ThzPMzB/1sUfZk5Cb/YqNb6f9Vaf0et9bfVWr+y1voTx81rZRp0XTvCvjMHiFJmbgfF4eB6zY+bYFl0RywGWkbrQH5nHjuMOyM1A1s0cjAZp5Kauz2NmCeA7kDUMqNdYtI6U7YXEtbjIfLQU/rY2duKbBjVHV5Lc07OlinutHcHBbUWZ+VT/StO2DFvDsvX9Mw3903PZ6Hple8BIkw0ezfG2HLN+qrFj7bD2Z2EKyIebHO0gszBdW9zH+/80fcfi4fQ1rMzk5XPCY8Nk3sCWSTv1QmoJiLv9GsJ28iHha3jTQXw9u750eHrCrEwtUxZVzeOIlHvf2xj1jZyOHMvVls1Q/7ePWvBVAF9cL2GPiIITtP0nGEsnPlskx5FOcP3wKS9dGHksTRa2AWqyQQwk/Yvh9C5eg6+gE0/duYI5jOaCXGUUr6mlPLTpZRPl1K+3bz/r0sp/+Lw381Sym87DjNOa2MHnZpjbDK79PFbB1N2glaYxyx0WmYal8VmGh9v2tIeFO9sCQzmv2W2xv98/oUrlzWzlvaY8Z7xqPV1OKVrN85P27FXBkNhUSa3P/fjXO1esXTgqI94MQeOhFWvDNVkl7EylKeMApZSoRu8s7BWbVv7k2Ez/efSMWWLqaZz81n/Ps1Cu6tBl1LeAOB7AVwBsAvg46WUH6y1/hQl+zcAflet9RdLKV8L4DkAXzmHARaGvTMUmDR9ZvIN+csz1XScqei0kSB3bgQwD0d1FgNr0MxL66YJzY/JXZek9XbUe6/OqqBMU1eh3+pn5kGdU0zxbMB9xWHp4If4zrWVRiGMypHbWqLe1zEeI5w2o6hP5oxWvl1eezc3cP3p/Lvt3fOTXZcDfxiPL10Y5/hjFBJy6aKPrzXaIogXUIa1TrNQZpqjQX8FgE/XWn+m1roP4AcAvIsT1Fpv1lp/8fDnj2PGRelra6/a1TTICdyWVhrfu9U4c8C4sxVcuoy3uU4Ql59S5nSzg393HPrnrAYl1SKddTDXWmh9k+XR0npZQMxp00yQLDupnWDqWU3xnToFXT5ZHTPc2JWT4cL6vfYpa8tOMz4uzRn7czRsRwz39cbzaaE5AnoDwM/S793DZxn9NwD+D/eilPLe2Dr5yi99HsD8znMDtedd57TOjObfqonwTigd9Py98sYaS1avOVo2/+12Ci5DavJHPnygP7df3NAyh/cWLOJ41e2/vK06zGmmTOjyZiHWjLXdma8sqiWDJhgGypy9A75+GGE0V8BzPj1YKHumxPCdUqYAKF9zqOUHWYaOM+9PG81xEhbzzG4/LKX8bhwI6P/cva+1PocD+AOPfdmFUR56UDuTTjgWMkEsKNZ3DrdsJ6ZuEMMfrQGXmYKxSLBp1vJO8zbjVh31t+OtZ0pn+Qat76wNh+hzXe5t7o82e/TKU2dZy7pxUMPg7Dos9zoOHHR8Y3S029VLN4ZtwEGOV6ZRmZhCFVlERiYQ+RsdixZuoHbi/zM+4+9lHLNcR3YgxjxQPD3mBo/VnnWk7Tj8vSsbigTXzuCQlv/iDN44ojkCehfAW+n3AuZ0plLKbwXwfQC+ttb6/x2XIYcTZgOoRTrJVHjy3yqAGV/uasKLcR5u8rAQ4zRMozMi5L1qiL0BzItFdmNKS2vjkEItL+PF4rcYL2ZRTwCTm1kAHF3Uu7kPgBa8wzYe+Drs28deqqMdlyrYGFvWCJJIrxqnLr4tJ6WzHtxCrb4LBwGw4G8t8o4P5n10nsXOVCHgg/h1Hrj81cmswlafZQuUfp8tMkxz/BUPO80R0B8H8KWllLcBuA3g6wE8ywlKKZsAPgLgD9VaPzWn4P39R+xqmk34bOJkWm38rdRybgSpg0356wmDIBVYTqMaBrk5lCgoJhxTbxKzo8Zpr87BFcIzaz/WmK5cuIV3P/4JvA/PTrBXwIcFcj1ddIf+djgkt/HLTxS8HAJPhKVbjFRQKfF45BvElSaCVPLKHNjcFiOfBqawWTYPerS9e9TncURAK4a/5QjUOjmrdK62yxaEE+iuni2r+rRQV0DXWl8tpXwzgB8G8AYAH6q1frKU8k2H7z+Ag0Op3wLgbx2cDYJXeweNlP0j5MSttBrkzmYlU0uzbJmcGkuqk8qt9i7PzOvcwyxbESCalzs0qUU8sEMohtABABjhEzCD8u8WpNDU+Pqp4dqrnbGQd/XSBYHTqVbn/nZnYsS3WdSL5j2qtyG92V39Ew7TVYGti3UvIqfH0xzNWp3Z1+n5YKE1rvLiZwwLRR165WaKU4/n1sJ1mmnWRpVa60cBfFSefYD+/mMA/tgyBfNOwrm0jDah0Rwh7Hs4qU6iyYQWHJMXjYtP3x4dRhT/Z3hyJqydKclCcg7MoTQyf2dsT88WRP3GacKtqITMHHYLDx/Gn/HZGg/OWsqgJCeE4r2OCYddr++sTS/mpYOWmDIrK96psHJjNrOKXJ5qoS0DmamQdtYu4MM65+Tv+HW+itNKKzuLY23t1fSd65TMO83fxOBpORHD68+HrIdgdgPbEQ8gLTO0VMUdgz8V1pmm3SqTyQlDjijQcLCot7M2WoJD6xsU7RZ5bi3u2HZn4rSc54AX0yH5wEGbqmao57Kotq24cPzLBLoKZhfxEfkrxKH1UuJLdbNIEibXF9rPWaRNVr+txfTqtGWI880ur2DSPuxRjAMd58v6nh42WtlW78cf+cLot9Ma///2rj/Wsqsqfyu071VkoIQZeMM8JgNKGZSAjsOPNIwiZcqPfwhYEsXQiFo0WqNGDAhBTTQGTDQqirRDiGkM8sdYBC1ChhJgFKoM0BawlRScPl/pSEeJFQgzlC7/uHfd95111tp73/dj7n1vzpdM5t17z9l7rX32WXvtb629N9zDYg84G12zQAaXA/RTqzzl4T0F31E69ZA8x9g4BHzd4spCJ4Le0bcATzNk/Kfp5PUM5S7cZ2C6xLfDhKul36ITwDPu/fTq2pFVxpOyDHZCi/WFyFPMjKXpbs+3JQDnDWGNvjJ9SsdGRWgJQHL9EaJtCbJrvcdfogJbKAYfmPV1sY5+xunriGINPIBf7JiZgX7wocvwxOTlKvFXvpNlHY9fAm9gLLOBwRu587VWh++sfnrMWQI9Hs6dppxNE1n/EqedeWq+Pfg+plMiT4qn8dx2mQEG0NnwZ/LbmYMj45EEp5hiWVxZmGRuRIYzCmh6WHv6YJg3tlE/i8rNsgl8OZ1n0Djz4vK9werFDVa72UX3r+zrDAQl2scPgpEe177yVgDjfu9oOj8D4Xs770FARXRoQuprtdmZIaLzDiwPmyXNFJGxBdqCItN4LsYTnjhzMA0qes+vlgHh5ajxhQZvqCIOr5dpgT6HmBlkAw8SPHW263afvHT0+35HwYyDeBxMNB7bPvtdA+23Y4lsQH+PhsxLtTKjwSRr69Lg5IOei3QPXxv1uey3bOCI4gQ+aFrijHuzJJpN+muiNugcTEGzCK7bH4Zh/WPy3XJ/R8ZM5xYqg59FOFum8g1ZYPdiwswM9Pnzl/SNMW1enr0MWadu8YZ8kM0buqicKGiUUSysg13vOVn73qfN8W8mQynqz4bUX1ObgfBL4Hew6/C46L88/PL7hTd87WihSazfNOAXuxTVP3HmYGhIpxlMfaCPEenonw/3zd7zCJ5TdKgq98viczcsx4NJpoNt2ep30ov6rh/ESuVPgvErXY496sORYxSVP21f2YmYqQedGVug/3BqnrIZHg7IZPfYFpf8MtbKL3GdJWTeAa/w8i9qS3u0ItvSMuOwO0Z3uW98vJyRUVtcGa1SzAKN3jMuURhcV4+3L+SO832s2wFH5TBqgWiTv1QnD97cH3nG5O+NvMSoTVr7Xuq9J/3d1+857YnxTQKcfNpQDdPMeAfMMIvDwN5bqxFqMZRRwMdnXTDH5eEDijXu2KaHkyl0YtCmeem4TTjNyXsj/OJk3lfEBR5dursXoMna1mSPfrPsBKNL7PoWRG3iYc/OZw9w8Nc/m6gvTWscan3M1+Pbh+vzhx/758R7kvjnad/bdXZf6Z3xcpzbP1oy7+MCJY/d2tVk8zNersPkihDN7iJY+00bQN/JmJkHvbDwUCcYVcouiIxdRlF08lzdgoFsE/EI0bTReyWlYFw09fd8n/3G9M1icq+XIdqDIjOs3BYm4/2f3Ddphygw6vXNArk8pZ2c8hxE7qMyo8EqpGqwJ5U3Q0RftKxMY466FI/I6vH68P1RPfyso+A1zzaM3y+9K1FdmbzRc/eeP7B2iLLPPjK6xA8+vJw/0tvkYGSDv323nYKEIvIqAL8L4OkAnqOqp8bfHwXwVgALAM4D+E1V/WixLNXpF4xsBhafvKwHfvY3Jp+9Uco6fumFWO9vTfI23F8ydD6VyfQtLcXlzAhPC9RWzFk9hmllrw2WpaAoT4mjgatEbXWyABrjD62wqTgHv6JYx0bqWC9KAehIJt+evXgOusbdZ2dk9JPdVxqoS58jtLZpVKc9s4+/6I8+U1udXMOjdy3rsw//ctO1H/3Ym9Zdn4g8HcDDAG4A8Hoy0D8M4L9U9asi8gwAH1bVYmL8TDloP5IDeeTe3xO9yNO8VNMYL74nMkylTARgbTXW7pOX4uyR73TkPoE2jxCoG2UfSGOjFnlOXBbTPTU5IvooakMzCsxns4c9DUoZG1H7RTqeOHMQN1zxHuAK4Be+9OqQQiqBVzZ6r9PqnOQnE99c45OZGsuCrr4tAFoAQ7OYqA9MBnog3N0u8l69Hiwrt5VfNu4zLyKu3+tSmqF673w7QFXvAoDxthf8/efo4xcBXCYii6p6LitrZgZ69yO/0fuuZJyzEd17WRmyjBHjiyNPJLvfw+8NEUWiLXCW6RR9BvrcpS+3hsxIR/V6zynyfKJ2Yn68FAzzyMpng7cZLyfrd3zpUPh7KUOk1re4zSZ54ATf96Ll9kAepOwNRG7ZNlM3ofddCIYzvch894nlgymvzI4Jl196XiW6L8Jor/IdvZvdTwD4XMk4A3OWBw20e7MtfGKpw/CKt2wK2WLwfZAt6lCZ0fZUAL+8tT0zWGbL1GjJhLBysmBNi0GcdvofDajeC6/V540Ib6YVeV6+jQ1MJ3mutYaIa43y11uOerL7azw9UKYVuE0yz9sfk5WBHQzju0tycPncDi3piozoXbMzNTcT8u3zWLh7tfXy3SJyij7fON7PflSWyEcALAX3vVlV31+UQ+QHAbwNwNU1IWZmoM9+61HY674rdUTe7KizuXvCJ3Kn9Qn3HGzJjGotGGSytbxktQwQg3lep1f3AEt39xazRN5PJKtvx5p8Jf7VB3BLsYHTyPO7WbZJvRWqwxtZ/3JndEAL52nPvWUGZv2ut9thspy9VB4bvkV0KYaMZrK0UKYTOnIE/cm3wWQrWdIpel98HjPLFQ2EWf3eI/cGu9Y/SzGOC4SzJQ5aVV+0nkJFZBnA+wBcq6pfrl0/Fx50hg6X5ZaNdjd373JvvU6TGLDWQBj/zV7vYv/WsCzvPfcCU06njn4kT0bFeD494mJrujK4/dg4Gp/Y4xxRz/5gj8+3TZZNYPCDYOb9R88tmi14Gufc/rX9Oux+G9j9smXPl0Z7YtTahK8xmsBvCXss6PuLKwudTfh9H/a6dtrWDYYdvhw5V+6dndOrFks53/ne2uKY6yN+IIxQM8LTxivmFSJyOYBbAPyWqv5zyz0zNdDekDIHbQ/5hiveg+MPHpp4HWnGwMq+auDI15MF/Axelk5Qicpr6WCTFyQIiER1exk6U+nA488OGYg8uxrsnohG8t5QyQBGRtnKMKPo7/PUTjZz4XoMWSaDN/Asc9b2p1f3pEemsQwR3561Cfc7DtoxsgUfkW6R9+q56MxTPb3an+1EMxI/cwDW0in9XiwnzhwszrAiOaLPrOd25KBF5BUA3g5gD4BbROR2VX0xgOsBfD+At4jIW8aXX62qX8vKmul2o36hBNBduHB6dQ+OP9gP6jDMaEYvfASbQprHxN6p1e/l2UiwKgqw8UBk8gAjg8gnjVtnL/GkGY9q7eK9pmhbVZPNt6GnPqzdOgPVWB++nmHPmPW0NvXP/vTqnomBOrD8QOrR+b+zQZh188+Z6QTTy9DJUghmWtEAcd3hkx2daobT9LRn7h2AjKutoTa7YNS2BOU2M93sb9bBcHTp7nDvbO53PvbQkhSw3aCq71PVZVVdVNUnjI0zVPX3VfV7VfWH6F9qnIEZGujz5y/pPWDu4Pbgbrr5qs5WlP5lZyNrsI5eM6qRUfK/s2yGGsdZepE8b5zJWBts2IAz2HDWeNWMw85k8dQFD3S2P7OnMLyHWTMy/Oy80YyedQbPm7Mu00yZ/QDXYjgsHc/QOovJBi0DP4eof2dcvR+A+NmZJxzRMdFgHX1niE7+maats/bdKRTHejBTisM8PD7M07aqvGnlqqYyIs+nZHBqBvHA8gO9DWjY2wL6mR/ecLRO4bxMPe9pnKlw/8q+0EsuGfmIFoi8s2mmkDWDz96jz6YAumce1tLavA4lGsj44pZr+XduD39tS1/iKb5RcGacvaFpGZh63P5qPzea5eays42XSihx89zXrY7oqCz/f23W4+Hl9tvbzjBIOBeYmYGW8zKVpwlgEiSx73veRiUroOSdGKKgj107WaiArmErcWhZPZlcHHziHdoimUuDQlRHxI1OgxqPO5Eb3fYpBaqsLF+Pf44cpATWdOf+wFNy/p6vj1Ay7h26ww0qNrPja25auapHi0V1e4McBQCzTAk/eJzbf754Kq7girUAABCxSURBVHzt8zEaqCzQ5wdz092fyu5jAr0NrAJHoddfxrMs+5uzVloC8TsZM1vqvetpS/q4N/16GAQCXOpPsC+tXes7buvBnLWXIOro3qBEskb1TsNhR4FORmRM+Nqsrewav5AkywxheaLyWgefErJ2ydog8zIjOaNMlxbayPeF1mBrFGCLruPrDbX+wc9o2nv5ukgP/5tv0yjoXtIdoH1iXM65vy5LUY0cj3t/5o0bXur9mEsfr1fuflXTtR86844N17cZmIs0O+6A7EUY/DSoNTjky4+Q8au+k0YdtJQHW5LJBpLsHi7fR8s5jzWsw3mf2QvMxvm6wyd7p3wbskGx5I23ZLUw/cHX+3tL3l+pzf2g0zJNrsUFImPLBj0yOBkFx/0p67OZfPxMawNJdK2/PqLzgLXMKF6yb15yaUYSZaJw206co/Fvtt8G90kv53baLGkzMXMD3THGwWb39jeQR3aj++37KNiUeZcRnxYtOZ2WEyt1Zi9PRN1w59175X3pUuFJ508GjtOre8Iz7DhzouaNRQYy8nhKHnlmxKZpp2gQje6P6isNCJkRZVmZLjIDV+OYS/0uM+yZZxuV7U8Un4YjjmYMJkOtDM6K6cmUecfLXTqktpf0RmZp2x0zzeKoIRrda8a6hpKX5Mvce+V9eNRPfRVAPcgTBZnM4+AUI4M3kiVYhkR0bcsJyyYbB9MOLD8w4dRLL0BLO5tuLRRCDbvu1TBjwwyWj/RnZbbEN/zf1saZB5/p16JXLXAWBeiyzxFnH50YXpLLz4KuO3yyk31SGgxaqaIItZRQfs6LKwsXtXEG5sCDZlrDPgPtXipHl70nYwe5WnklHi7iDY8eXts/uuUlLBnR0unL3jsqcZinV7t7JPj9EqJpIutu33vDXvKGWc5IxhK4zWu0B7C2B4P3iL2RstNR/FmLfI+nkTxd5T1X/1st0+T+lbU9UNaTBcL1sp4+kwGI6QmDtW3EDbPeHj4mAYz03nWvYpGOQstktc+eq87azfPP0UKgyIM/sDwcGjszZNNL+xy9ZMAa/2sdpPQyRbSBz2qIDA4HOXg/3RZDA6wZAH+IamTooinstIMV39N7adzMw+eset3M6PHy+VbUKCQva+l+MyImMzA2XEb1oJ4qGA1yAMIMg+wZWxlGMdmg7bMcMh2yYKaXiQdgdj64rAylOIvnle1vOyDgWKdN+n3It0VkkK2OjDLh9y0LPkbP8ujS3fh4qvXOxswNNNCfWvILFXlA/hrvBUXeTPTgS7xe5tX4JeWTchAbn5DjLGwcw3JFBjZDZOyilzkL7mXBn8lnt7w7qic0wAE3ysYiiw+YPN7DiwyZ3WvGExgNrjyj8v2iFIyMjJIN0Mbjl6iV0oBqcvg+6Q2VvQ92HQdWa9kv2XM4vbqns3cNxzO8/uvh1Fs2Dju6dDeuefRncXzp0OQZcb2+LQBzlG4pyrNTMRcG2u+nDIwelo/uMjYSqMuCRiXvxvYtYI/ee/MlREY88zJa0AtmLXfbL1q4EMq0Wl9xmcnKZbBcvJH86dU9nY3uOzomwUK+xu6JXn42EmaYTrjfs/IzZIONp6AW6e9oWs5leZSoo9q1XpZo1hkho/UyKqUWRC0Fz6PgvvXNE8uj5+kD0y3c9sWIuTDQHmZ0fHR3172Kc/vXPq/n4WaBuloZvLNZyShN4AwmoxaUi4xDZMRZXluBeQxHJsGVkgdd4rkBOq2jcDp6NKX1WFxZAMbZpMw7+gwF1svuY2+Qt+Y0lOIKNmjZb9nsy8tqlEJpQPffeT7VgrE89S+1ERuyA8sPTBbZdDhb2lSp0xcC52DXvYr/cxxyNlj5DI0e9ResHhx596EqPZ2iv0v7ZbfMFi8mzPxUb6CbnZFxtOf2n8fZI98J7+d7vEdhU8XRiB/jwHK8V4GVN/GUG+gQj2jhjJ9qt3TIo+P9obncmofmXwyL8pf08Jsp+eyJzJNmeK/82KkjOHbqSDh1bvUyTbbs3tqU3O7l62yDIwCdE8lrdIevO6NEovI4Q4X7VgZ/onY0g4naMGo/+3+aQwo8/+3fz+gZRMY50pP7fks7X4yYKw962mlOZiy4MxrOHvlOyG1vZgeIvDhbOgvEaVzemC6uxCd7+9V/kwwTogKyaayVy3IB/VWJWQaBgX9bz2Blv1sGRM0D5u8ib71zD7VjNMWe3Et87jWP/iyA0dL9b+CJPf67ZDw9Hed/8wNaNFvzz8RTPxHPzrx95Nkuriz0TiOZzPDGszqfVRSV5Wd/PPuIziEEYi+/Rpl4DN5zFzNb6n3Zvifp0h9c3/muNL2JXmSmOAxZ9NzA1zKfPDFOhWOmIrQOKtnL7GcLPtPDXgD2nksZGJ1pcWHZdyabTc+z+2rPyBvIaGodPa8SD9/y7GvG3k5P94FIrtO3eaRnVkcNrfdFwcLoGUfBvQhZf/L9h+tm+N/4/syAZ/0GcCcjuXsjuQ0X61LvmVEcutAdGKzT+SkTT5uywI33mLNpH3uwxm9yR+EObwbRl2PX2/cl4xfJUZvSRlNIexntH8PrwB4Qe+vcljy1Z3rnwPLogIRrX3lrR9YWmieShXWyf1ZfJFep3ExPK58HMc8hA/3VahFVYDRS1ndYlghMBUUUhungv2c6zdN9DJ5tcfDcl/XhF/5p2AasZzRgWN38L7q/5BRF9ImXo8U4Dxhh5ieqTHhR7AHGL5iPztt3/nNEFQDx9DzrrBxIYkTLT+36Vq/Fy5HJXvPiS6dkW1CJue7oOq4vSluzv48vHersohe1u+fVo+lxVG8J7FlFi4uyLA5Gp53I84yMOSObnTB8INm3S28GEMzkahkWvl19oNTDUzFGPxxfOtRpA7/oJjo3MqK9WL/onWL4mQnrwEY7Csj7mUI0y7xYF6rM9EQVgz0QWxhSCoAZ9l55X+qJAH1j1uJd2+98eGuLMc4i5IbrDp9MAzNZ+aW6ax5Gre3YI/Xy2zOwdpomJgCUDbIPiFrAik+RKZWbxRq8XjWEHDb6xjmqL2qXaeIY08wepml7NnInzhwsBjG5nSId+Z+VbfdldU/zfes1gyc9Dxv20+faC+gRRcqj4FI2ZfRBJm+cGZ4b9CsZIz4z6tC1TueNBHsu3sNgmiU6WJW90qgta0YxQ4sRjOqMvE2eMUTBr9L+w3xPJFPGh/t6eMXoNGD9Ik54YtjQ739R22f9lr+z69hD9TBZfL/xyBya0vXMhftr+b0wZLs2Mt0YzXQY0/L9OwlNHrSIvERE/l1E7hGRNwa/i4j82fj3O0WkfJAg1jZL8h3VP4ySl8GpZ9lDjrjLaLppnTXieWuwaSiXzSlt3gBEL2dLECnz7vzfZtjsJYrS0/xMIqJcDLW0LNO75LVFsjKXyihxwdMi6hORYYpmY5nRZ11bPeBpdGHDyv9Hnm/kdNx08+g0Iv9+ZOXVqAv73zs6vryWAdn/ZnL6k2g402W7edIi8ioR+aKIPCwih91vzxSRT41//7yIXFYqq+pBi8gjAPwFgKMAVgF8WkQ+oKr/Rpe9FMBTx/+eC+Avx/+nMIojC3AZFxnxjmYQ+bh4AB1vvDPCu70CzAvNPMvMELaO5N4AW/Dt+NIhHMORjkxZvf57e8FKAxDLynwmp1X5NKnMiHA9tgjmxJmDnTQ1z622rKiM5Of6ewNaQwZKpIP39PhvP8NhY8BllDxJS5/0fdHDym09Wiy6NnJaSnt/sEysb6STfc4GAX7emQz8rtb6BBt8T212ZKqkOc45vgDglQBu4C9F5BIAfw3gNap6h4g8DkC8uGOMForjOQDuUdWvjCt5L4CXA2AD/XIAN+koZ+82EblcRPaq6v1ZobzdaGgkgr2d/TTejsbxZdQ4s0lnpA3xj5060vOKfBoe/9Ypp8LNWeAmOskklCuYmmcGvRZ4WlxZwDGUp/BRvXbvuf3ne0HKaGl+CdlAGAVLo2k0YzLQncoHOtOp9JJ742yHFkTXeFnZ+EXXeh0zZEHPTPcMe6+8r5cayQMxG2XvzdbekesOn5ysTs0CebzqNKJzojaIfueAKH+33Qy1qt4FACLif7oawJ2qesf4uv+ulVXNgxaRawC8RFV/fvz5NQCeq6rX0zX/AOCtqvpP48+3AniDqp5yZb0OwOvGH5+B0Uiz07AbwNlZC7HJ2Ik6ATtTr52oEwA8TVV3baQAEfkQRu3TgssAfJs+36iqN05Z38cAvN7soIj8GoAfAfB4AHsAvFdV/7BURosH3RsGAHir3nINxgreOBb21Dwkgm82dqJeO1EnYGfqtRN1AkZ6bbQMVX3JZsgCACLyEQBLwU9vVtX3J7ddAuD5AJ4N4FsAbhWRz6jqrVk9LQZ6FcCT6PMygK+u45oBAwYM2BFQ1Ret47ZVAB9X1bMAICIfBHAIQGqgW7I4Pg3gqSLyZBFZAPCTAD7grvkAgGvH2RzPA/C/Jf55wIABAy5CfBjAM0XkkeOA4Y+hG8vroepBq+pDInL9uPBHAHi3qn5RRH5x/Ps7AXwQwMsA3IOR6/7aBmGn4nO2EXaiXjtRJ2Bn6rUTdQK2kV4i8goAb8eIZ75FRG5X1Rer6tdF5I8xcnoVwAdV9ZZiWbPaLGnAgAEDBpQxF/tBDxgwYMCAPgYDPWDAgAFzii030FuxTHzWaNDpp8e63CkinxSRZ81CzmlR04uue7aIfHecIz/3aNFLRF4gIrePl+DO/SHSDX3wMSLy9yJyx1inlrjQTCEi7xaRr4lIuD5iO9qKDUNVt+wfRkHFLwN4CkZnud8B4AfcNS8D8I8Y5VI/D8C/bKVMF0inKwE8dvz3S+ddp1a96LqPYhQYvmbWcm/S87oco2j6/vHnx89a7k3Q6U0A3jb+ew+A/wGwMGvZK3r9KEZpZ19Ift9WtmIz/m21Bz1ZJq6q5wHYMnHGZJm4qt4G4HIR2bvFcm0EVZ1U9ZOq+vXxx9sw2XV6rtHyrADgVwD8LYCvXUjhNoAWvV4N4GZVXQEAVZ133Vp0UgC7ZLTe+FEYGeiHMMdQ1U9gJGeG7WYrNoytNtD7APwnfV4dfzftNfOEaeX9OYxG/XlHVS8R2QfgFQDeeQHl2ihantcVAB4rIh8Tkc+IyLUXTLr1oUWnPwfwdIwWjH0ewK+q6sMXRrwtw3azFRvGVu8HvWnLxOcIzfKKyI9jZKCfv6USbQ5a9PoTjPZY+W6wEcy8okWvSzDaI+EqAN8D4FMicpuqfmmrhVsnWnR6MYDbAbwQwPcBOCEiJ1X1wa0Wbgux3WzFhrHVBnonLhNvkldEngngXQBeqg27Vs0BWvQ6DOC9Y+O8G8DLROQhVf27CyPiutDaB8+q6jcBfFNEPgHgWQDm1UC36PRajDYwUwD3iMh/ADgI4F8vjIhbgu1mKzaMraY4duIy8apOIrIfwM0Y7fs6ry+5R1UvVX2yqh5Q1QMAjgP4pTk3zkBbH3w/gCMicomIPBKjvczvusByToMWnVYwmhFARJ4A4GkAvnJBpdx8bDdbsWFsqQetW7dMfGZo1Om3ATwOwDvG3uZDOuc7jDXqte3Qopeq3iWjrSjvBPAwgHep6txuhdv4rH4PwF+JyOcxogbeoONNeuYVIvI3AF4AYLeIrAL4HQCXAtvTVmwGhqXeAwYMGDCnGFYSDhgwYMCcYjDQAwYMGDCnGAz0gAEDBswpBgM9YMCAAXOKwUAPGDBgwJxiMNADBgwYMKcYDPSAAQMGzCn+H7VUhlSll+BMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(x, y, r)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving the Plot as PNG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard Way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('test.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I implemented a functionality for adding metadata to `tropy.plotting_tools.meta2png`. We use the class `pngsave` similarily to `plt.savefig`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class pngsave in module tropy.plotting_tools.meta2png:\n",
      "\n",
      "class pngsave(builtins.object)\n",
      " |  pngsave(*args, **kwargs)\n",
      " |  \n",
      " |  That class is designed to save pylab figures in png and add meta data.\n",
      " |  \n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  *args : list\n",
      " |      other positional arguments passed to `plt.savefig`\n",
      " |  \n",
      " |  **kwargs : dict\n",
      " |      other optional arguments passed to `plt.savefig``\n",
      " |  \n",
      " |      'author' : Place yeur name into author keyword \n",
      " |      \n",
      " |      'source' : Specify your source filename if needed,\n",
      " |                 if not set, it tries to automatically find the filename\n",
      " |  \n",
      " |      'notebook' : {False, True} if you are working in a notebook\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, fname, **kwargs)\n",
      " |      Call self as a function.\n",
      " |  \n",
      " |  __init__(self, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  meta(self)\n",
      " |      Set the meta data, esp. the Author name and Source file information.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help( pngsave )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... save image to  test_withmeta.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pngsave('test_withmeta.png', author = 'Hans Wurst');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Author: Hans Wurst\r\n"
     ]
    }
   ],
   "source": [
    "!identify -verbose test_withmeta.png | grep Author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Source: /vols/fs1/store/senf/proj/2018-05_pypackage_devel/tropy-tutorials/docs/source//vols/fs1/store/senf/.conda/python37/lib/python3.7/site-packages/ipykernel_launcher.py\r\n"
     ]
    }
   ],
   "source": [
    "!identify -verbose test_withmeta.png | grep Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the place were the source filename appears.\n",
    "\n",
    "__Bug with Jupyter__\n",
    "\n",
    "* It is well working with python scripts,\n",
    "* but not with jupyter notebooks. Sorry for this ... there might be a clever solution in future..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving the Notebook Issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I give a solution to the notebook issue. However, it is not a nice and convenient way.\n",
    "\n",
    "1. You need to run the function below in a notebook (unfortunately it is not working within a python module)\n",
    "2. And then explicitly provide the name to `pngsave`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the Notebook Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.display import Javascript\n",
    "from IPython.display import display\n",
    "import os\n",
    "\n",
    "def get_notebook_name():\n",
    "    \"\"\"Returns the name of the current notebook as a string\n",
    "    \n",
    "    From From https://mail.scipy.org/pipermail/ipython-dev/2014-June/014096.html\n",
    "    \"\"\"\n",
    "    display(Javascript('IPython.notebook.kernel.execute(\"theNotebook = \" + \\\n",
    "    \"\\'\"+IPython.notebook.notebook_name+\"\\'\");'))\n",
    "    \n",
    "    #\n",
    "    nb_full_path = os.path.join(os.getcwd(), theNotebook)\n",
    "    \n",
    "    return os.path.join(os.getcwd(), theNotebook)\n",
    "\n",
    "\n",
    "def pngkws(name):\n",
    "    \n",
    "    kws = dict()\n",
    "    kws['author'] = name\n",
    "    kws['source'] = get_notebook_name()\n",
    "    \n",
    "    return kws\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.kernel.execute(\"theNotebook = \" +     \"'\"+IPython.notebook.notebook_name+\"'\");"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/vols/fs1/store/senf/proj/2018-05_pypackage_devel/tropy-tutorials/docs/source/Adding_Meta-Data_to_PNG_File.ipynb\n"
     ]
    }
   ],
   "source": [
    "source = get_notebook_name()\n",
    "print( source )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.kernel.execute(\"theNotebook = \" +     \"'\"+IPython.notebook.notebook_name+\"'\");"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'author': 'Hans Wurst', 'source': '/vols/fs1/store/senf/proj/2018-05_pypackage_devel/tropy-tutorials/docs/source/Adding_Meta-Data_to_PNG_File.ipynb'}\n"
     ]
    }
   ],
   "source": [
    "print( pngkws('Hans Wurst') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.kernel.execute(\"theNotebook = \" +     \"'\"+IPython.notebook.notebook_name+\"'\");"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... save image to  test_withmeta.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pngsave('test_withmeta.png', **pngkws('Hans Wurst'));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Source: /vols/fs1/store/senf/proj/2018-05_pypackage_devel/tropy-tutorials/docs/source/Adding_Meta-Data_to_PNG_File.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!identify -verbose test_withmeta.png | grep Source"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now it works! Yeah!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The module `tropy.plotting_tools.meta2png`  help you to \n",
    "\n",
    "* place meta data into PNG files\n",
    "* when `savepng` is used it automatically places name and source info into png."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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