Edge Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Edge detection step by step, following https://towardsdatascience.com/canny-edge-detection-step-by-step-in-python-computer-vision-b49c3a2d8123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'visvis'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-011ed21619ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mndimage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimageio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mvisvis\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mvv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'visvis'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import ndimage\n",
    "import imageio\n",
    "import visvis as vv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = imageio.volread('/Volumes/ElementsSE/thesisData/FCCbatch/FCC_Sigma0_HHHV_20190412.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gaussian_kernel(size, sigma=1):\n",
    "    size = int(size) // 2\n",
    "    x, y = np.mgrid[-size:size+1, -size:size+1]\n",
    "    normal = 1 / (2.0 * np.pi * sigma**2)\n",
    "    g =  np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sobel_filters(img):\n",
    "    Kx = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], np.float32)\n",
    "    Ky = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]], np.float32)\n",
    "    \n",
    "    Ix = ndimage.filters.convolve(img, Kx)\n",
    "    Iy = ndimage.filters.convolve(img, Ky)\n",
    "    \n",
    "    G = np.hypot(Ix, Iy)\n",
    "    G = G / G.max() * 255\n",
    "    theta = np.arctan2(Iy, Ix)\n",
    "    \n",
    "    return (G, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndimage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "filter weights array has incorrect shape.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-f36226a0a514>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msobel_filters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-14-58755cae58d6>\u001b[0m in \u001b[0;36msobel_filters\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mKy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mIx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mndimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mIy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mndimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/scipy/ndimage/filters.py\u001b[0m in \u001b[0;36mconvolve\u001b[0;34m(input, weights, output, mode, cval, origin)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \"\"\"\n\u001b[1;32m    827\u001b[0m     return _correlate_or_convolve(input, weights, output, mode, cval,\n\u001b[0;32m--> 828\u001b[0;31m                                   origin, True)\n\u001b[0m\u001b[1;32m    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/scipy/ndimage/filters.py\u001b[0m in \u001b[0;36m_correlate_or_convolve\u001b[0;34m(input, weights, output, mode, cval, origin, convolution)\u001b[0m\n\u001b[1;32m    658\u001b[0m     \u001b[0mwshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mii\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mii\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mii\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    659\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 660\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'filter weights array has incorrect shape.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    661\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mconvolution\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    662\u001b[0m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: filter weights array has incorrect shape."
     ]
    }
   ],
   "source": [
    "sobel_filters(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2 as cv\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "im2 = cv.imread('../processed/Sigma0_20200418/WinsvoldGRD_Sigma0_Subset_Orb_TN_Cal__S1B_IW_GRDH_1SDH_20200418T101605_20200418T101630_021197_028396_5FA1.data/Sigma0_HH_db.img', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print (im2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "cannot identify image file '/Volumes/ElementsSE/thesisData/FCCs/sigma0_HHHV_12Apr2019.tif'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-659e35cde105>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/Volumes/ElementsSE/thesisData/FCCs/sigma0_HHHV_12Apr2019.tif\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#, cv.IMREAD_LOAD_GDAL)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2379\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mdocstring\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_dedent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_imread\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2380\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2381\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_imread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m   1354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mext\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m         \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpilread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1357\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m             raise ValueError('Only know how to handle extensions: %s; '\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mpilread\u001b[0;34m(fname)\u001b[0m\n\u001b[1;32m   1332\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1334\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1335\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mpil_to_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.6/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2588\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2589\u001b[0m     raise IOError(\"cannot identify image file %r\"\n\u001b[0;32m-> 2590\u001b[0;31m                   % (filename if filename else fp))\n\u001b[0m\u001b[1;32m   2591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2592\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: cannot identify image file '/Volumes/ElementsSE/thesisData/FCCs/sigma0_HHHV_12Apr2019.tif'"
     ]
    }
   ],
   "source": [
    "fp = \"/Volumes/ElementsSE/thesisData/FCCs/sigma0_HHHV_12Apr2019.tif\"\n",
    "img = plt.imread(fp,0)#, cv.IMREAD_LOAD_GDAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio as rio\n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio.warp as warp\n",
    "import rasterio.plot as rioPlt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = rio.open('/Volumes/ElementsSE/thesisData/FCCbatch/FCC_Sigma0_HHHV_20190424.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rasterio.io.DatasetReader"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19114, 21189)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_orig_firstBand = img.read(1)\n",
    "array_orig_firstBand.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_orig_firstBand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fccf71f98d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Lp0nvLGAO2LQ2WqRSCRzXJWmaFLcNoGsaB3ZMkn/bEKau05qvcXF1jXv/7YcAsEtJEvkEg/kcpYND5LIpyht16htN8qlUXILTC/jv/vABhBBoqsrejxzENk2Avnf1E0TfSF3jVFpxZr4iBHXHYaxQwNA0xg+NUXMcVlfixEyAkXyeIAzp+j75/SWWKhXCXrwqa9u4lQ7vO3QIp91l4OYR2q0OhqaRsiwKUwOc/8Y07W6XWrvN5jPzzJ1cpHluk/yhYVK7C5jFJKOTJRKGgZ42qJ1cJ5IS6Uexomi3i7PSxBpK0qq3OfkPL6HpGke+8AJ3/e67WP7aBQQwP7NCZa2OOWwjI0mt3mLqnTuIpCRhGAyk02QSCSA2RsV0GgGcfeBl2t0ul75yjkhKzn35FOe+fIpL36fQus/VQd9IXeNIKVEUhaRlEUQREoikZOXlFZKmyeBAjsxwhmbPuOiqSi6VZOCmMaSU5JNJbNOk0mrFcaKRNIoQGAWb4ZvGaLsudcdBaPHy7tDte9k7NU7muhI7bpvCGEjQmqsh/ZCFr5zn9JlZDF3DLNq4TXcryJ5OJNhsNml0Opw+cZHcWI5mt0sURqQti+m/P0F308HUdUYGCiRNk+RkDhnBrg/to3ZinYtra5h5i3KzSdI0GUinqTkO4+/fheN57Pvp61BVFVVRiKTE9X0MTaPjecx99Tzzj0xf6X9Xn+9B30hdw9z/Z1/jp95zG13PI4wicr30g1a3S7OXI7VRrhG2fLaVSuwYGiI/kWfbL13HM//vkyi9L/NGo0HWtpFA2PaRgAwi6ic3GM3nySeTOPMNXN8nvbOAnrdozVSJvJDmhSrJySxepcvEB3azb9ck4x/fR2e1RRRFGL1lphcEjObzjB0cBcCeyPC2u/axUq3ihyFeEJCcyDCUzWJvz2CkTRrnNnEu1bBKKV48P8M7770JoSp4YYgfhqiqwthwkekHT5OyLBYemUZXVVzfJwhDhBBsNptA7Gm6vs/JLx3n9P0nr9j/rM930xe9u4YpN5u45Q66pqH6PuVmkyCKyCQSrFSr7B8fZ61eZ7NSR/SKi2uLNYJ/Ost4oRDnH0lJyrIw0iaDtkGj3MTxPCI/JDGZIZ0yaE1XWF6PBe+0pE7YDWhUWhRuGiHyIyIvZODWMcpHlsheN8jsF06h2wamoYMQ6JrGRr3O/t3beO6Zk+weGUFoCpsnVpkYLRG5IdVWG7/mkpnI0ZiukN1XBCkRQrD25KU4JaFo05qpMTY4QP6mEbrrLTbPrJO1bTabTYxeUmoURVsZ97qqxmkYgKIoaIqCoijMPzIdZ8nrOuPv33Wl/5Vvafqe1DXKo599mnQiEUum9PKhNppNMokE22+fYsfQEAD5Xm2eqijU2m0anQ5z86tU2238Xua4rmtUNurY27IEUUQpm2HhiRmsks3iC/Mkd+SZGB+k6/u0F+rMzaxQ3F3CHkkjBNROrHHpgTMELZ/WTAXdNvAcj1qrjdPpogiBEILZuRX2jo4ys7rK0jdmyW7PY42mQAiGdpQI2j4zp+ZpdbtYQ0k6yy20lE5iLI2paWw+u4gMI6SUtC5VmX7hEqN3TCIBVVFIJC1MTYs9xCiWNNZUFa3nXSlCxMmuUYQfhgD4YcjMw2c50/eurhh9I3WN4gUBpq4DYGgadcdhW7EYZ5W/vBGrH0QRqWKKiYEBhBAUi1nqjoOiKOy+ew8za2vUHYdO1yVlWVRPr6MpCmEYB7kjLw6ya7aGljLI59I48w10VcUrd7j49yfJHhjEyFvs/qXrGXzXNlZnN9jYqLL9Z/aTy6ZiL09R2D02SsIwaHQ6ZG0bRVGYO72EUASza+vIIMIsJrBNE0VRWH9mHj1rYk9myewpsmNoiM1KHWMggdvxEGps+MovrJDamWNkzxDlaoNkKm71ZRlxXpahaWRsm7BnmLq+H8ethMAPgp7hSqCpKifuO8bp+09y/sG+HuOPk76RugZ57LNPo6kqN+/cSRRFFHJpdk6Okk+lsIdTWMNJTE2j4TicOz/PWr2O5/vIUDKSj2WBhRBsL5UIe8sjezRNaiTNkZkZGp0OpUyGznKL8akhFr41B1LidjyqyzUAOvUOrW6X/O5JgrZP5egyl758Bj8MabsurfnYM4O4o83s2jqKopBJJNh/5x42Gw0mdg4TBZKxQoHqUg2hKgxvK6GrKvObm5gDCZrTFRStFwgPArSkTiJtIVSFhGGQ2Vck7AbUZqtEUcTaZhUhBIoQNDodpJR4vo+mqihCoPYaq6qKgmUY+GG81NRVNU7P0PUt0b5+ztWPh76RukaxdB1d19A1jXK1ge94OK7L6qUNuqttBt42RCQlhVQKKWUcUG86mJpGciBJ7cQ6xb2DRL3dQdXSSO0uMJrP0/E8hu7ZQRRERJ2A4YPDVBeqFG8YRu9le9sFm32fOMS5z3ydoOERuSEL5TK6qmLqOpsvLDP+gT1Y+QRCCKbGh/GCAF3TaJ2Pg+WnTl7k5LfPIYB02iYxmkLLmKzV49Kb9nwD1dLolh1c36eQSuFuOAhDRUvqFAdzVE+vI4RAAMPbSnhBQNqy6HgefhDgBgFBL0alKkps6JT/9rUIowhDVVEUBSkl7W53q4wojCJO33+So198qa/I8CbSN1LXGA9+6rGtMhffD6g0m6zV68ysrRFJieO6hFFE5eV1DE3D0DRMXWcgnWZmbY0wiiCCqBuQ2VcE4iry/KEhom7A/r3bUITg4oNnUBMajVqb6RcuIYRg8cgCqqKQTyXp1roIRWAMJFBTOi+/eIHDdx3Yin+NvGc7M397jPTuAvliBsVQSFsWiq7guh6ZRIJ3/sad7Nk+hqapyFASdgPc1Ra7905QymSYn11FBhGRF5CyLFL5+O8ahQSl2yYwh2wS+QTdlTblVouNxQo79o1TODxCsZgjnUiQ0OPseb8XJBdC4AYBXi8mFUYRXhhuKUQYmoamqhi9ImtdVWn3VCGO33esvxR8E+gbqWuMywHfs0tLWDmLjG1jmybDuRxez9twXJfNZhNdVUmaJooQlJtNDk5MUG23Se8foO26VI+usvOOHQBoSZPaiVjUbnLvKEnTZPbsEq7vM5BKYRkGxe0DmPkEQlMwbIOVr8wQNFxOH7/I7olRjn/jLLt3jzN163bskQz2eJrMrgESE2n0jImuaVRqzTg+ls1w9HMvcubiAkvlCkIVLL20SOHWUbSUjqFpjBUHqM9WkaGMA+EKDNwwzMqpZS793Qnmji1glmxaTocgDDFUFW+zQ+QGBB2fXDZFzXEIoyiOczUa+EFAGIZYuh57UZpGGIY94xvL0ARhiON5qIpCx/MopFKEURTL3nQ6HPvHo/00hjeQvpG6hnjqr7+Bqihx0FzTiLyIru9jGwblZpN0IsHAnhK1dhtFxKUtihEvzzqeR7XVopTJcOnpGYIwpLXcoDVTww0CqidXcTsejbkaWlLHHLTJ2TaloTylG0YwcibpXQXMYoJ2q0Or3mbgjjHWlyr4YYieM0maJkJTGL17J/NfOI1fd1n7+ixzR+Yw8halW2Jp+yiKKDea5JPJWFfdMDCKCSqtFn7DpTZTIbNnADWhkRpOU35uCaP3XuyxDINTJQI3iHWnDJXCtgKTxSL1Tge9YBF2AlRNpeu4+EFAq9Nhz4cPkDTNOKCuqvHGQG8ZLITAceNjL0sm+2GIAPReLEtX1Vh7S9O20hpmHj7L0S++xFc/8+QVPS9+0ukbqWuId//anXhBEDdByOdRLTXejet5Ch3Poz1bZ/voENsOjFF3HJI7cziuy8G79uGHIamxDI7r0vV9/CCgWW+z66f203h5g0ZPcjh7YBAZSYIool1z6K626VY68bpQCNLFFKWDw6w8PUsumeTgzkmayw0KqRTNlQbTn3mJzIHYyJw9dom646ClTIJW7J1AnCnvuC6WrnNpfZ2xD+wmCEMqL61SvGEEezyDNZoi8kKMvIWmKDRXGjQvVlmZWWPg5lF2vWcPesYCCUEvtmTkLTpLTaIwwtR1LMNgcmyI4/cdwwtikT8JOK4LxG9JVZQt4+QFQRynEwKlp3Aqe3pYQRShKQqO520lzAZRxAd/+z19Yb7XQd9IXWMYmobrxyqcfi2OlUS9QG/NcVgql1nbrLJ4diVOWnwhbim1+MI8+WSS5elVLF1HVRQypTRJ0+TCQ2dITGYY2z3MgV++kbl/OsPypXWKkwWGbhpjc7ECwMwj5xi4cYT0ngEa58sksolYP70TkBxMUbxhBMs0OD+7iLPUpLvSxgsCqu027bkaRj5BaXsRLwwZHC4QyXhnb6xQIOj4tF2Xs0tLBG2fzkqL2tlNsgfi/CmhxPWHycks+V7rLXejzUsPHsMej2WPW66LmtDRMyZGzkQocRMJRVMYyeXoeh4QGyhT12k4ztbyudnpYOj6VkwqjGL1Br33+QZhSCaRQNc0TE3DMoytLPYjn3+BQz9zA6cWFnjuH57n+c8d6dcL/gj0jdQ1Rsfz2D8+viVuJ4GUZXH9PQe3AsOXFQKiXq7UZU+h0m7jhWEse5JOE7YDzEGblGXRXYq1n/ymx1OnTjE8XqS10mT9xSXyhTT2cIqdH9iL3/KovLDC8N1TAAzdMYGiKgzdtY32TJXkrjwHD+0gaHpEXsitP3UjH/nXH0RNaCQnsqgJjWwuhQzjTPeUZVHKZHj2T59hrFDgPb95F92lJpEbkBrPsPrNeTL7BnC9uKlEd6ON5/uUX1gm8iP237STi8/MULp5lMFMhsKhYYSmIANJudEkiiIqG/WtpdplaeTL2luxcyi2yokuF2CL3nK53Gxu5Vhd1mG3TXNLsnasUOCWn7+ZI59/gb2joxiaxkA6HUvK1LpceOjMFTlPfpLoG6lrDF1VeXl+nk5vF88PAlzf5/ijp7BNk2a3i+O6VFotisUcdcdh7z37SG3PMloqYBsGS0sbsURvUiNoeui2jtAVktuyrD8xyyd++b0gJWYijt+srVdRLI2Vp2YpH1lCS+p4lQ5By6P8/DLTS8tYxSSRG9K+UCXsBtiTGRITadz1NstfPk9nqUX5hSW8SofNzRrSi4PVcXKlwnDP03E329TqLRQjXrrZpSTOYpPMthwD6TSpbTk6vo+mqdjjGYKWx2qthrvh8I7/9bdY+uo07lqbRrlJ1rZxPC9Oi8jH2fkQ7+jVHQet1/Qh6nXDEb3HTE3D6D1m9XYHLzeICKKIhuN8lxLqLT9/89brVVstCqkU3bVYoeLCQ2e49JVzHP3iS1fqtLmqeSP67s32Gi8cE0K80BsrCCEeFUJM967zvXEhhPjPQogLveahh1/xOr/eO35aCPHrr3deb1WSloVlGAzmckDsWQFkEom4TVUiwXihQNa2OXdpgaRpktqep3Gphjlob4nZlfYO4lQcCreNUSk3GP/IHqIgInNggJcee5nmZovU7jyF64dIWRZLp5djTalIYpZs3LLD0dlZNhoN9u2aZOGBuOmnYqqEnYDnnzqJYmp0V9qgCtbWK3iVDmtLZVrdLkJTsEtJSvuGcLqx0oKp65x49BTb7pxCTxs8/sBzKIaKmtBQjDjrffrzJ+LGEUGI1xPbe9uBHUReyPn7v4JQBIN3bydd+G87ddmRLItzawxls3Q8b8tbulzj5/r+ViPIZi9PKmEYdHqF26am0ex2t9pvJQwDp6cZr6nq1v9m70cOkk4kkMCXnn+e6qV4mWxoGoXJAmEU8dIXXmTlsYs/npPlJ4Q3ypN6j5TyBinlzb37fwA8LqXcDTzeuw/wIeJWVruBTxI3EkUIUQD+CLiNuE/fH72i83GfH4EP/Na7AeJaNEUhaZpbLay6nkc+meT8ykqctKiqGJrG0b/4NunJLO6qQyGXptXtcvrIBartNke+8AKlyQGWvzaDDCXPPvwSt/z0YQZ2F/FqXdZfWiY1kGRwuICh62T2F6lMbzJ3eomUaVJttzFKdizlkjNp1tpYQ0lufffb2Di+Qr3VZuev3cC26ydRbZ1yT//KGk2R3lXA23DiBqW99ltD2Szrzy8x8/QFcraNt+6Q3lUAYGlpg4GdRVzfZ6lSITGWwSzZpHbmSe0sYA3a+A2PM/efxMhbZAbThFGEs9EGYgM0WSyiKAop06TtuluGyO9IsamgAAAgAElEQVQF1Y2eLLHjeVi6Tsa26fo+qZ6YHrBVxF1ptbY8qctMr6ywXKlw1/798fvUdcbumiJoeWwfHyZpWbR6mldnH3iZ2vG1N/mMufp5s5Z7HwX+qnf7r4CPvWL8r2XMt4Fcr0/fB4BHpZQVKWUVeBT44Js0t7cEhqbh9b5MtmkShmHs8VQqZG2bye1DHLx1d5yNLQTlmU22/cJBQi+kmIklUTRFYWJgAKOQ4Oy5ORaPLvD+33sv699aYProLAtnluMlTsMDVZCZynHpsWnW6nUKqRSjhQL7t43jrrboLLfwKl3ShRTuuoPQVIYOj5IvxEFtNaGxcmGNbcUi6USC2oUyVinJ7NxK3OVmOEN6NEO13d5aiiUtC6Nk8/J9x9FsjaFsloUzy5xeXGTXrjFaF6s48w2OPXKS7N5irKfuR1iGgdAUZi+ukE4k0E1tS/FASsloIU+51Yo3H8KQpGluGSg/CEj0ymW6vk/X87Z2TpM9QxVJiReGbCvFzVVfyQd/+z1MDQ7S6HRouy6rtRoXHz/P5nqN9J4C227bTi6dirvwCEFjtcH0g6ff0lpXb4SRksDXhBAvCiE+2RsbklKuAPSuB3vjY8DCK5672Bv7fuPfgRDik0KIF4QQL1TrrTdg6tcmiV4i4uWtcoiTPDVV3TJAZ87OsXluHUUIwigiiCJaCzW0RBwM9sMQXdPQNJXm+Qq7x0YpjuRZfugCfhAwnMtx8++8A9PQyb6tRKfi4JW7WL3atlq7TW4kC6HE3p4luS1Deu8A1nCS9L4B0jvjpgrmUJLVZ2YJGm4slaKqTN0ZJ5DWTm9w6N5D5CcLIEBNaExsj5eXmqKQTyaRQcQNv3oLEEvTBGHIu289hFG06a60UDSFfddPsfrUJZzFBgvrGxiaxsVjc1i6TspOMHDbGHsP7yBhGiQHU4RhRBCGNJy43KbZiZNB646zlbUPbDV9CMOQbDKJ0yu1CcOQ4VyO1Wp1q0nEKzn0MzcA8Y7h5ZyscrNJ5Ic0Xt5gYX2DgUw6Xhq+omHG6ftPviVbzb8RRuodUsrDxEu53xNC3PUqx36vPn3yVca/c0DKT0kpb5ZS3pzPpl7bbN8CuEFAJpEgmbG3ugdf3s0LwpAgirB6OUKXi20ziQT2SIZyuc7UrdsJo4iBsTxBEOL5PsV3TqCaKiiQLqVJlJIsPzKNmtJxFhrk9xW3MtUtXafV7XLm9CyZQ4NUT20QtHxSUzmc2QbdtTYbz8yz9uISp49c4NxzFzhz9CLF4TiO9uJXT1B3HPSsiVlIYAwkkEFEYjyDMZAgmbMBsC2T0PGZ/vwJ1ISOpqoM5XLMz63RulAlc7CEktBozNco3TZOZa4SJ4ZaOqPDRYIoQk1qlJ9dJLNnAKOYQCix0d5oNGh0OtQdh0qrRd1xKKRSpC1rS275cr+/UEqqrRZBGJJLJlEUheVKHG+6bJD+OX4YYvU69+STSXLJJEHTQ6gKxUyG3I1DGLYRqzQMZhB6XHydMAwuPHSGxa9deMuU4LxuIyWlXO5drwNfIo4prV1ut967Xu8dvghMvOLp48Dyq4z3eQ2EUZxpLv2ITq98w9B1FCVWBqg7DgnDYLFcJowiMrZNEEVUT64yv7mJnjYZ3jnI2TNzrNXr2Dmbr/zlk1QWqrRaHRbn1vCrXdrrLcKWT3pvEbfcwW94DI4Vtn79NVWlu9Ki4Ti01po88udPMP6xvTQWa8zOrcRdkLNZbv4Xt2JoGtZwCquQYO/uCba9bRy/7lI/u8nq0SWG3jNFajKPVUpi5K24ELnTRbV1kgkLerlKdcdhcmIIezKDNWDjVboUrx+mdm4zDopHEaP37sbelmXq0ARCEagpndXHLqFnTYJGHDh/93UH6fo+6UQs06IoCsdmZ/F6f+OV/QpbPXmZIAxjxdNeoqeufX9Nybt+5e1kEomtOsEgDCnPbFJrthi5fYL6yQ1UO37+xnJs8IQQDE4OsO3OqVgwENj8xgKdleabfEZdWV6XkRJCJIUQ6cu3gfcDLwMPAJd36H4duL93+wHg13q7fLcD9d5y8BHg/UKIfC9g/v7eWJ/XwOXlEArk8mnUnuKk2St/uVzWcbljsewtC2UQMTU4SPNCheS2HKqisH33KDKUHJqcZOyObRR3lxgfHyR/yyi5XQMU75qkfmINRVMgilBMjbGBATRVpZBKoegK4wdHKRwcIpdM4lY7FPaV2HPzzni3cTjDyS8cZffN8Y6dnrOQfsQzTx7DuVTDLCbY/6uHmb/vDOUXl1h7doHibeMkB1NxzKjhIjSF44+8zOQNE5RG86ipuERm/ak5irePEbohfq0bd1VOJvnmX3yDjZMruJsdUARGIcHkz+6Pm5WmDeykBWqcSX45N0pKST6VYrPRoNXt4vo+uWSSZreLqeu8vLDAcrVK3XHYbDRiiRv5XYuB76DWbqOpKgldZ+rQJOlckiAMWfn2AsldeZyN9tZS0BhIYFg63Q0HZ7GJjCQNx2FloxJ7pk/PY+YTbDw9/2M6y358vF5Pagj4hhDiOPA88JCU8qvA/wHcI4SYBu7p3Ye4lfpF4ALwaeB3AaSUFeB/B470Lv+uN9bnNeD1JEi+fvI0Xz9+iuemp7EtEyEEacvim+fO4QYBHc9jMJNh/AO7qPZ21QZGcqzMbaBaKg3HIWh4PHfqHGPv3YFX6aDoCl7TpXFqg8gPUXSVxEQGr9wl8iP0jEGl2WQol2Ps8DjthQaVC2XOfWsax3WpHV9j6eQyqe25OGM7a7HnvXvRsxahGxJ2A7y2R8qyMIeTdJaaPP3HTzL+03upnd1E11Re/psXKS9VKe0oEvkR5WqDtusyf2wBp+ywcmGN5nSFxY1NvnnfEWQo2Ti3jqlpBGHIWKHAjf/Tu7CGk3iVLl61S+3MBlraZGOhjFAVKrXmlrxw2PMMlV7uU8qyaHY6zG5s0HAclioVdFWNkz97PwBqTynh1bjxE4fZPlji0sYGkRuS3JFjbO8ImqpSObkWJ6f6PqVMJtaUb8S7kPXFWhycDwKGsln0jEl6/wBupcO5xSVCN7im0hhel5GSUl6UUl7fuxyUUv773nhZSvleKeXu3nWlNy6llL8npdwppXyblPKFV7zWX0opd/Uun319b+utjdFTuzR6GdTFdJr7vv0ctmUSRBF37ttH2rIopFJ4YchDn3qcfCpF5Uz8ZRFA2A05MDFB4AbcsnMnmR0DhI7PwE1j2GNpTk/P4ddcaifX2Hh5lcRkBns8Q2u6GhfmRhGdhTgbO4oitk2NMJjJsDa/ydCOEs2ZCq1uF7/aQTU1UpNZmqc3cZabJAaTHNy9jdp8lcqlMjd//DAv/s3z5A+WSB8ocsMnb2fXRw6AAD1lkEsmydo2I1ODGHqstJneXaDZ7XLrPYfozNUBtjwfPwg4/h+fieNYCY3Bd07izNVpTVfiWjwvbvywf2yMVi8vKogivF6vwsvZ6Vov0J9PJjF1fas7tKYotLtd2r0A+6txZjFO1Zg/v8zsc7PISJIoJGi7Llqvs42haQQNj9FDo7FhV1VUIzaKErDHMySGUqw+cYnBbJaFJ2bQTI3qsVXOP3iatSdm37yT7cdAP+P8GuPpv/kmmqLg+j4Jw2BbqUTH9xkrFHj8xEkyhRS6qvI3Tz+Nqijs+fAB9o+NEUYRds6mcMsoxWyG9lwt1jjXVErvmmTzhUUUQ2Xpy+eoXCqze2QERVeYP7fM/t+8CaKI2rE1ur3OyI7nsblew8pYqIpCZbXG0NgAkzdPUrhxBC2ps2vbKMPv20H52UXWnpql0myxWquh56w4tylrk0wlqB9fj72JUBK5AdVTaxBJkhNZ7O2xuuf4SImL5xapttqUm00qZ9a57YM30Flq4gchXc/DSJtbipunl5Zont7EKNmsPTlL1I27x2x75w5QBSNDA+y4a1ccK9N1NEWJr3uxNr3nJV3OIjd7mecQJ3Nelnr5Qdz1K29nKJfbUl1AQuSGjOwZwlBVStkMyVSsyeU348TcuuNwcSmusfR8nzMPn+LlLx2PKwOIey2GXkhjtUHSNDEHEoSOT/XY6ptz0r3J9LvFXGMUUilOLSwwks/Tdl0iKbl5xw4G794OwPSDpymkUnzynntwfZ9j/3gUsyd8V91s0HysFXtfwOgdk9SOrdM4V8bIWzTmanF8S1W5sLJCpm5z02/cRuXYCmEnoFPv0Ox0GB4ZYHO9RhRFW9eKorC2VMabX0d8Cw5+6DpevPgy0f0RiYk09YtVsrbNbf/m3Zz50+dYrFS4+WcPs/zEJdr1VtyOywtZPr0S70SOp0nvLNC6UEXPGGhpgyl7LG5TtVghkpLuWqzOYJeSpCezOItNBtJp9KzJzu4QSkJDS+o88+RR3v3+m9BsnfZsDcWIDU/95DprtRrD+TxRFJE0TYIwJFJVGr38syiK1RQUIai0WmRsm1ovj+vVYlLP/cPz6KrK4Z+7aav+r9JqMViNteM3ZzYpbCvQWKhhJ2yCWhx7i6SkWMyhVuLblmUSScmZpVjba/+de4i8kM5SvCM5vj/O/nEWG2gpg9mvnCNhmhTvGEdN/GR8/fue1DXGqYUF3rF3L5lEglImw97RUWY3Nlh4ZBoZRuQLma1EwcuKkpqqsl6v4wcBmcE0pVIeGUnWn18id8MgznKT6qkN5jY2UI14p+veP/owN/3Gbaw+fhHF1PAbHg3HQdc01lYrqL3WUOlEgmwyiRCCgXQaQ1XJJBJUX1wha9skxjO05xqM3j1FpdWiW3GQwN3/8z2sPx13hnFcF71g0Z6tM7x7EDNvMfnRAwhNoV6LdxjddQe33GFldp2UZTF4wyhISeH6IfSciV9z0RIaesYgqLvksik212u0zlf5qf/uvSimRuD4DL1rO6V3xBvN7VaHj955G6vVKgnDAOKldBBF2KaJ31v+Bb0+f4VUClUI8r0Ezjt+6bbv+3+67Rdv5fDP3QTE3ljKspgYGKBSayKBTC4ZN7fYU0L6EWo6NsSWrhN142qBHe/ehQwjkhmbsUKBqd1jdNfa2GMZrOE4CI8QWINxuk7Y9hm6YZT03gGCjk93rbVVP3g10zdS1xAPffpx9o+Ps1qv4wWx6FsyZzM1NMjc5iZB2+fouRkGDg6RsOOlz+VkxUangx+GVFbrLK1uslKLPaDTj59lenWV0m1jbB8cZL1cI7evSGuhzsznT6KlDJpnNqlUGgRRRNfzqDsOzU6HPR/aTxiGmEmDfCqJ0JWt2rVm00FTFE49d54wimjP1dl+0zbKLy6x7WP7mP7MS/hBgBcETO0fR7U0VEOlcOMIRtGmcmKF2a+ci8tKPraX7HUljLTJ+MExup5H+2KNyAtpX6pTn64gNIGa1CEi7qrcdEhZFgBLj86QOzCIu+aw8e0FFr96gXY1DlILI27A0HbjZFOIG4kqQqD1xO3MXqb65STPIIq2aiZ/GA589G3c8LM3st5ooAjBer2OOZyiu9Ii7PhUak2G37Od7qYT7x42mwhg7pmLhFFE2A1ina+DJbSUQW7/II2LVUYmSlhFm+rRVTL7isgwYvPEKpqtoeixbr1iatSOr1F5ceUNPBPfWPpG6hrhgT9/lFa3S7vbRQA7x0fYc2g7xkCC5UqVqcFBzj9xjht2TZE7ECtXFos5bNPE1HWGc/FuW7unj671uhdXWy12Dg1x8akLuL7PyESJyAtpzdXIjGWJ3BCn0YkfKxVwe6oLuWQSoSkk83GGuYwkibF0LKxnJ8gNZdA1jW2lEsmRNH6tizPfQEZQObYaNz1wXSZ3j8TpDSKu52vOVOjMNzj7ldM8c+YMZsLgwudOxt6CAudeukjHi1tayUBybmaBertN2A3xqy6oAr/hMnhodMsTSo1mWLz/LKHjo+gqqfFMrBEVRdTXG0gpGUil4sJn4kTMWru91buv2m6TMAyyvUTOhGFsxax+WJYfu0jSNOPXlZLOXB09a1JfqCGA+vkyeirePSxlM3hhuFWe03FdPN+ndnSN7P4SD/zh/SRH0tgTGQLHJ71vgI1vLODVXI7MzJCczNE8V8Zv+fj1LkJXsMfTaLZOenv+qjNYfSN1jZBLJuP24b1EzmqtSXpXgfL0BmODcd5S1rYxSzb1c5u0XZexD+9heMcgg/ncVu6UoWk0Ox1q7TYL5TIQl5uYmkZp7yCTH9uHNZgktT1H6AS011tbygBL6+Wt+rXcUIah669j8D3b6Sw1Yy9GSrKDGVzXI2jGHtxGo0HQ8lhfqrCyVoYoQgYRRtpkdG+s/SQ0haFeEW7jTBmjlEBVFD58563oeYvBG0dxlhrIULJ9dCiWS6m06NQ7XH/nPiIpOTUzR73Vxhq0UW2doOWSHcqAlAzdtY303gHCMMLdcIjcOCs/CEPCKGL3yEjcX7AXNDe1uAuPEALLMEiaJs1OhyiKcFwX1/fpfo9ymFcjNZGh2mpx88/fzFA2y6W1dUp3TOD6PlnbprvaolFpxbWAQpA0TRL5BADpXJKMbeM1XS59+Qw33rIXBLTOV0ARZHYVUG0NKSU//29/mof/89cgAnethV932bi0iTWUovLSChf/6jgAMw+fZePrV0fOVd9IXQM88pdPUev9mmu9YteEYbD4+AyGrtN13K1f6KF3buOJB5+PmwisNvEqHV6auYgXBFx30y4GshmCMNxqA5WyLGqOw0azSX2mwvw/nomlXc6XCd2AWruN6/tbDUiHhgsMZbP4NZfVl06w9sQlrOEUy8ubTB+dxSgkKOwvgRLL8lq6TrPSwtTiL1Fyew6/2sUYSOBV444z3dU2c39/kkvnl2JJ4/k6Q9uKKFYs07L20hLdlRbFt08Q9nSoTszNUWu3Se8aQBGCw3fsJ5tKEvkR1lCSlfNruNUu6f0DrD0zR3clLih2ag615RrF7QPomkZhKMvU6BAX19aweh6nEAKt5zG1eikNoZR0fZ+MHZfsWL3GrD8smb1F3vHLdwAw/N4pHNfl4udfZmjXIF4QkNlbJJNLxlLQloqha3Rrca1ku9EhZVlbEjNepUtzsY6UknNPnaM9X2f47imMnEWilGSsUCB0fDaXqyRG0wzuir3j0tsnGfvoHmQQp1EEbsDG0/OUn1si8sM3+rT9oekbqWsArSfJEkYRI/k8AjATBp2eTnnH8+IyGSmZ/dwpRvN5Vms1OitNdvzK9Vi6ztSt22OvRRUcnJpk9+hI3Biz3cZxXaIoYnplhUa5ybf//Bs4F2tsVOqoikLQ272LorjL8OQNEwhVQagKkx/fz5P3P8dwIR97HoNJEiNpGk2HwWyWdCJBKpsklbYppFKsPDlLu9Fh+F3bqW42MPIW7YaDnjMZLxUxNI3Fchk9Z2ENJXFm6wghCB2f9GSO9O48ru8zWSwysX+UU184RjqRQLU0tLROfaZC0PIoDuZI7y5QPrFGalee5moDXVOxLJPsUIbmYj3Whao4SD/i3sOHiaKIxXIZU9cxdT0ugSEuMlV7ahKu72P1SpBeD13f5+zSEgtnl1EVhZnHzzNwxzij79sBkURGElVRaPTmEIQhiXSsZNqst2m7Lo2mw/YD4wzcMMLm87EY4dznT1EqZKm12hRKWbSkgT2epfL8Moqm0LpUxW95JDN2/BptB2skhT2SwW+6V2Qp2DdS1wCRlDQ6HSIpmVldZSSfZ+i9U1RbLbwg2AoQe0FA5kCRg+/dz96dEyimxrP/6Sm8IKCz0ERoCsU7Jzhy7gKZ60pkbRvX9+NMccfB8TxWajVa3S4rlSq6qpLuiegNlLJsK5WoXiizemoVz/MJOwErT17k1uv3slmrMzFaorvW4pm//xZDewexijaqplIu1zk9u0C51aLjeaSH0sz+3cm4yPdsmeFbJ9BzFpu1OnrBYveOccKOT/N8hfnNTRQhGLh9nPv+8EuYAzbFdDo2ZmdXWCyXKd08Rut8BTWpY5eSCEVQvH2c0PHJ7ixQPx6XlioJjdJdk9TXGuR2FjCTBpZtbskti16w3A9D1ut13CDYahxxuWj7sgLE5d3A18oHf/s9vPc37uLgxw4x8r4dNBwHgGf/f/beM1iy87zv/J18Tufu2zfHyXkwwCQMMAiMYKYoUhSYFOgk73rL9gd57Srv1pZrvbW2a3ft3bVdltaSVhQpmhIpiSJFUowABnkADCaHe+fm2DmefM5+ePu2IQkUSWBoCev5V03NzOnu26dvn376fZ/nH77wHF7Ho9bpYPcoJtt2O826aPbLve1gQtcJmh5LX72OOZSkuVRnc72CMZRg+kFh7zz33ZsopkLgBthbbdq36yQm0si68CDb8+FDJMbSVC+sQxQTuSGlp4QEp3r+v4y89m6Reovj67/+XY7tmBErmjCk5TiYus7cH10ll0wSA6qhih6GrpMYzzD75CwvXbmFrAupx5lHjjI7v0r9RpmbX7vKe3/pUZaeW+DAw/s4Oj3dZ1Pnk0mRjKwoxEDasii3Woy+cycDp8dZ2Noiv3eQsfsm6LourZsVVq+vE4cxBz58hKFHZ5B1hXtP7ROOnBJIivhATReLggTquhiDYoQ/ls8LftalEsQxxUIWvWCR3JFF1hTsrsv08BCT793LwjdvcP/DR5BUmcxEjqFclqRhcPqhI8iaLIr2fJXKag1nvUP90iarN9aFXi+rk8gl8NseQcejsHOAKIgYfWwXsqWKhrkic/TgTuI47hvZJXvGeNu/n+0eliLLHPvovXf0fT7xceEnaXseo+/cyfm5ObaaTTRFofuaSaIfBCSSJmZCnFur2ibs+tx+YpYojhmZHsQoWLRv10nOZLmyvExrtsrUR/bTmhX5hu5Wl2devkrLtqm+tEbkhYRdn+5yEy1noBcs1r45i2KqdFeabHx3/o6+1j+Pu0XqLY6kYXDu2nUMTaPjugQ9k7ZKqyV8zIdyxL6wwW10u1z7g4sMFrLcd3A3T/3RCxz68FG8qs3uXeNYeYt8Koma1BkYyZEYS+P4PvdMT5NLJPpRV0avUG01RN+jdmGD5vUyhyYnUUyFS09cI5dNkdyRY/fb9yIbCmvfuY0kSwzcN0Z9oca+v/MQqZ151Izen6RZus5IIc/C+QVmPn0URZaJvBBzPIVsaigJjc5sjc5ig8gPyYxkSMxkqL28zo4PHMAt21z5+mXM4SSVZgtL13E3OnhV8WG7ub7OwHiewUemqK/UGZksiomeKmM3bNqOQ/3iFuZwCmMwycb35sWovrdaqm01iXrxVZIksVarIUkSni9CIJq2jaaqpC2L8186/yPeuTeGd/yScEL61D/4EGP5PHLPZz2bSpIdypAwDIig0eyQsSzBhHdC0exXFCJX9Jbcmk12/yDvft9p7NU2nZUm4+/eTdgNUAwxdS2kUqR25ik9uyLixpI6YduHOMaaEAqAypVNJEli+Vu3uPW1q1z5w4t3/DXfLVJvcUiSxEP3HGIgleKe6WngP6vrAbyWSCvWe5OpKI5ZWN8i9iPGCwXmvnGdazcWSe8RFrxt2yE9nSd7cJDahQ0GBjLEcczkzhGOzkxz+PhuZnaPofTy/ABevTiLNZZmvV7Hq9ic+vhJJEVGSxtEbogxmETTVDa+fZvKK+vs+NABajeWuPGd64QdH8s0GBgXPSvHcZGAW7/1CkOnJoh8Me1LTmRolVqsVCoMPTKDNZomMZWhMyees35pE8VS2X3/TpLTOUZ3iymfpEiUrm4iqTJLpRJxELHwjRtkRjKk9hREfmBHTO4GZ4p0ml3ccpeg7WFNZKit1tn1vv20uzYD43nuPbibxz77KB/5u4/xyb//wb6dsCRJKD2niVqnw4mPn+jHV/0oPP/FF/7S21/8IQVv4t27mX7PXhzfJ/ADgoaLOZ4iDkXBlxQZ1dKEpCYM+3H1QdcnOZGhOVeler1EciaLnreoXtxAVmVWVraY2jfGwP5BvLpDakcONWNAHIMioeVMvIrYfm5z6iVJwtJ1MpbF/DdusHYHBc7StifOWw2H983Ev/8f/se/6tP4K8UX/s0f89ipe/E6HqqmYo6nqM9VqHU6falGLpXEdj38MCSTTKCkNCrrNdHslWUanQ5+GLJrakzwkRSJoYenCW0fWVeovrzOrcuLzEyMMPGhfVz4jecZGikQhxGVirAkaTkOB0/uBkli5dIK+VRKpPvmDIKWL/obmkxtq8n4iUmIY9pzdbKHB9EyBqUnlpAUiXK1Qco0GX1ohkt/conxgQLmRBp3q0voiNAETVGQZInUvgJBxxcrJccXicx5EzWl4zddkjM5qhc2aNo2uqpycXGR3SMjjM0M4ZS76GmDsOP3z1HSZGIvRB9K9PRzAePv38v6d26jZXSQJIhj4lhk+/2XxPNfFNPYEx8/wbO/+3yfyf7il85zsrcN3PreAjFCcC2pEnZVbEklxHa/1mj35U9Rb8taHMrhtVx2fuIIyDJbTy3QWW6y1WiQSybJTedJTGbRUjq1VzfQCxaduTrWZBqvbOM0HcbeuZPlb90imU/SrnWwPU/Yz/TCKIIwZP+HDr/u6/rY3/lnXL6x8HqGl38Gd1dSb2G8722nqFVbwj2y2aK71MRKmqQti0I6RQxs1RtsNhqM3TvOwuYWxmACU9Mo5NJkc6m+O+fS6ibdepexx3Yz/5UrlJ5conG1ROQEHP/YcarVJrNfeJXJeyYYODMhpneIb9DxQgG9YDF/YZGVSgW9YCIpIttOL1oM3D9BoyS4Vq2rZRpXylQqDVIzOVo3KxTPTgCgqSqVdpvZ79xAliTWqjWCtgdRjGKqGJaOMZIkimK8qsPKlVVarS6piQyKqqAkNJAltIzB+ovLZPYOEPVCPM/ee4h8KkVkBzRtm7Dj4wUBV68tYDsukgRqRsfb6rI5XyIxmcGpdIncgMiPaN2sUr66RXu29l/8fT79+Kl+T+q1UpvtAgUw9PYZZE2mVm6ycHudzXqdWrvd52slDaHxMwZEEvPIriG8liv87V9eo35lk+rtCqjOpSQAACAASURBVH4vATtVTKGmdNqzVZo3ysiGwuJLi4y+dxfEoo8HsPKns4w9NIOWE/1EqRc5H4Rhn1ay+u05qufX2HqDbgx3i9RbGG7ZptbpkLasfihAo9khO5imbTtkEwkUWSbX4+5kEwkuPHWVwv5B5lbWyRwYYGggR77HlE6PZ6ld2qDlOGSPDuHXRcMYwPE8ojhm7dIatZeFoHh495BwOGi3mX9qjtmNDQ5PTdHcaKIXLeyGzXPPX6Gz3CCVEATM7RDPM//9u2gv1rn44k1kXSV7dIiJk5NMTg/3+ydHPniE2ItQUhqxL7aW3lYXWZExhxKM7R5h/P4p3I0OtVabxqLoVz1z7hJLlQpBy2Py6CSZhGiKdxyHZr3D1IkpvCDASpvs3znZTynulEW81sBgVgiXUwZa3iJ3ZBhzKMHIqYnX9bn+SbGdr/d627gfZ3v4wx67slnu+4LJskypZ9C3vFFitSqscW7dWOb25iZzV5ZwfB8toeFsdVl/cZn5rS0qvYlw7IWULm9QfGCSKIiozQvRtt/20PIWtfkqnZ4hYPWFNcpz5b4Tqev7KLKM0puErlaryJrSF7n/pLhbpN7CaHS7GKpKpdViOJul7TgkdB0lodF13f4FmzAMIi8kiiJqnQ5ezWHH0BCSKiPr4ltvcmIIv+HyzLdeYebEDAtPztHYbKLlTTrztT6jvTCYJb1/gNx9w0Inlk3hBwGbjQYndu7EDwIWSiU2boux/uTAAHpeUCAGjo8iyzLZXIq179/k1W9cYrxQID2dQ7FU3IqNltb7STC1lzdAhtJKlcSOLMWzk1hTGUYe28n6+RU66y0ar26R3J3H0jRMU3DF7r//EGd//jSKqRB2fTRVhB1sG9O1b4gPrJIUhMtsJomkK1hZi25HyIISM1nWvnELo2gx/5Ur5I+NCKuYOKazWH9T79u9HxNxk69dCW3j1M+f/LF+xus91u59kYCgmxycmOhLf2zP6xNPk6aJ4/tsNRpcu73M1esLzG1u9i2lDU1DTevkdxRwNtsoplgRHfzAYdxSB6NgUe92GRjNC+tmIGWZGKpK2jTRVVX4Ycky6/U6u3eN82baSneL1FsUt752FT8I+j7ccRzT7QVbWqOp/oTPCwKsQoL80REGh/PcMz3NxmKJwUenccs2kiqRTSaxGzb6gMk9u3dQvbJJ2AvGjJyAwfsnGdg/SAxEXkh6Js/KuQUkRcJ3A6Ynhjm8d0bkzU3kOH72EAldJzOdY3zXMOZgkrH376Z1vUJqMoPbcbHX24zkcuz71L1sPLnA7/7brxN5Ic5mh5EHp0mkLSRVxqk7DM8U8WsOsqag5Uxm/+AKfi/NpfjwFJIikdtRwBwXXllh16d+cYvVq+skJtJ4vgiisD0Px/OotzsMZNKACCuVTYXF+XVWV0vkZwp0613Cro+a0ukuN8ntKGBvdfAbDnrWYP3yOq3Zv1rj2Et/8Crf/a0n/8zKS5KkvtD55MdPML+1Rbcn+I56PK6EYQiNpuvi9gI6JOgPQqaKRTKjGaIgYuSRHcRxjL3c4pmbN2n1iLClp5YYGyig5wwkVWar3qBjO4S9rXXCNDB7EfRTAwNEXkT+2Mgbfq13i9RbFAulEpqqkjJNMWrvOTZOvGMXXl1QBQazWQbSaRRD4eoXXsFpCmO10Zkhlr89y/i7d6PlTJrdLre3tti8tSWcCiayoregqrSWGtilDonJLMWRHJVak9qVLRZLJWEXbKi4HY/SpvD39so29Vtl5kslrHGR7lK7sMHcl6/w/JWbGAMJUqNpgoZH8dgo69+eY/3iGid376a53iSzfwC30sVpO0R2gJHUufzqnPAyVyQ6t2sibktRGBzMUXtlA0mW8FsekSt4SoUTY6gpjYl7Jgi6YntZ63TY+cBOqu02V1dWkFSZK5duU91oUNtqcvixQwzncsxdXkJTVVavrpPZN4Bfd8kfG8EsJvBbHkFT+EjZS807/p7+JLSFIx+5h3f80sP9ldeTv/MMCcNA7lnkgOhlnf3UGZKmSdI06bhu3y001+O8bYd0GJrG/t1TjBbyNNebgrT5wgqtGxXaXZv3nD1B0PapzVbw3QDX9XA2u3Tadj+pOZE0SRdSmBNpho6NEgPDE0W0rPHDXsaPhbeG69Vd/AVsW7FIiB6AqeuMJyzat2vYKy026nUOTU2iJDTszY64GHWNRDpBc6MpTNYub5A7NIS93OpvBwpkCW1hkTJ4apzOfJ3WbJWg6dKtdpk4MoFXtXnn3307RDHVa1s0bVtIT2RZhHxubnHk4A66Kw2qtyvkxnJkRjK877G3Yw4k6K40cRyX+guLjB0dJ+eFqGkdxVLxasIrffj+SVrXK0i6zN6pcZI7crQX6oJrNZgl9iP0AQu/7tJdaaGmNPSciWIqzH/jBjves4/6xU30AZH2Ymoai88vMjlYZP+ZPSQmMox/cC+zX7zI7k8c5ervvMJWo8Hh47tpLNTIJ5OsfX+etuPQ/vJldv3MIcJugJrSUWVEIOodxonX2cL9OHj+iy/w8Kcf4PkvvsDpx0/9hdu3C9m3f/MJVFn+M2Jyr7fCCqOIZqWF1usHqmmdxs0Knu9TmCrwg6de6QedjuRyJHSdyAlEn8r32ajXSRoGA2mxQk3vLjBz3xSd240+N+uN4u5K6i2K9/+td3Bzfb0fLhlGEUOPTuNuiQBLQ9PIHB1ifa0syJJhiGKpdCtdOo7Tl21snVuiVG+w78wehnM5mtU2rVKLfDLJ6tMLVFZr+HWHrdUqyWISe6nJ8pVV3FIHLW0w9tAMuWSSWqdDNplgdn6VjGVhjqbI7h/E0DSuXplHL4jehVPpYpe7hFHEarWKnjcxx9Mkd+RpLzRAloj8kNbNCm7TQTFVtKxB+2aV7mKDTrlD9tAgru3RWW7SrLSI7IDafBV7tdXvG1VfXqdb6tC8IZwcBgdzGKpKoyMmdq2bFaoX1mk7DpvfXyBpGOzbNYmaMcRKrSDy9QrpFJmBNGpSwxxNsbFeobJRJzGd+WuTzLJdmF6vQL0W7/rlR3jbLz7U907fTmWOYvE7W61Wadk2Rsbk/AvXePn2bZIpCyTRW9R6jys3myxXKkia8COrtFp9NQKAmtSoPLuC3/SoNJoUH5h4U6/vDRcpSZL2SZJ04TV/mpIk/QNJkv4nSZJWX3P8fa95zD+RJGlWkqQbkiQ99prj7+kdm5Uk6R+/qVf0XxGShiEEuqZJ23G48OVXqDfaDJwaY2ZwkNL5VY4+fh/P3LjB8J4hZudXaTsOacti7D27aV4rCxte0yRyA6yCRSoh+luJgSTpXBJdUXjp0i0GxwTZc7VcERdyMUHp+RXsjTZmxqSQTLKwVcLxfYpDOdo3qjSulzDyFrtHRkhMCm6RmtAw0ga257F7ZIT2rSr5I8NsPL2IFwRUZ8sMnBxHH0hwfVWIYtWkhu15xGHM5GN7aN8WTO9qu00Yx1TrLWRJolPu0F5qkElYSIpEYjCJnjORJYlSqU66mCJjWaxd28BveZSubjJzegetjSbWSIpOrUPrapmhQyPI2n/2gyqenaRxrcT6lbV+Bt/shUU6XZv5b9z4K3nv/zyuf/Xyj33fUz9/krOfOsPZnuvCdnisrmk4vs/i0gb5ZJKzp49Qa7R59eVbtByHBz55P2c/dYYHP3UGVVFY3igxPFVkvFCgkEoRRBH5XQOC12YHeFX7h3KkfhK84SIVx/GNOI6PxXF8DDgOdBHhoAD/x/ZtcRz/CYAkSQeBx4FDwHuAfydJkiJJkgL8W0QC8kHgE7373sWPgK6qeEGA30vOBVG4grZHYizNVqOBvdHmbfcdIWh5nPmFM32xcfWlNayxNJeeuE7LcZh/dYnCyXE8z2fo6BiRHRDHkMglOP3AIfS8SeSGjA8NsGfnBFtPLhL7IZEXEnR8wijC833SpknQ8dGHEjQX61y8Mkdih0gmbt2skNs5zcpqCb23xVhe2KSzVCc9nmXm/ftIZRNsfm+eF5+8xLt+5e0oloZsaqTySZSEytYPFvuTypHxARzPY2AgQyKXIDmUIjmZwRhNEnYDIj+kXW73o6acukOp2SRlmgw+MInVCymQJInQ9knkhD950PbwGy7J0TSNTpfNb8+T2pEnl01hqELLNzk2iCRJ6KrKza9d5dYdTBO+9keXfuLH1DqdN/RcYRQRRhFnPnEa1/c59tF78YKA+37uOPO3VtlqNHj40w8wmMnwxOee5sUvnefc559FlWXBdG95JIdSpHfm6bouYcen1Gwy/uF95O8bfUPn9Odxp3pS7wDm4jhe3I6gfh18GPhiHMcuMC9J0iwi7RhgNo7j2wCSJH2xd9//OjKk3wQe+OT9/W/yeqfD1Mww+XtHOfeFZ9kzOsrkyCDNyyVq9RYDIzn8lkt6KousygQdn5vnbrH34BR+06NRb1N5Qfg1OestnK5Lbk+R1lwVv+31/aWM4SSRHxLZAY2FGjs/doitc0s0lrrsGR2l1XOv3Lpd6jsH5A4NErpiu3n7958jn0yy2Whw8P49TI6mWP7uHNmxLJvfXxDZgHsL7O66eE0HNanjlsXIP2h5qGkNLW+RPzbCc7/3IsfedojQDnDW28RBJNjiXojTdpBlmeLBIbyaAyVQVEUwqfcXufr7rzK2b4TF78wy8cA0oRsSuQFFVSZyAmI/Yv76Cjv2jeOVbSovrqFlDOg45BIJQjsgiCLiOGbswAjdxSb1i5v8L//xi0wNDjJWKFDvFY7P/upHeeJzTwPwyGce/JHv64EPH/mJr4U36roQ9fzat4MhQHz5XfjyK8LhFDj3+Wc5+6kzfYb7y7/3En4v/PTqwjJqr/nedV0iN+TYZ07y7X/7XQqp1A+Nmf9JcKd6Uo8Dv/ua//89SZIuSpL0G71EYoBxYPk191npHfthx/8CJEn625IknZck6Xyt8dffQP6niSd/5xme/J1nKDWbff3YyuIWledWOHx4pwgvyBqk9xXQFIXV5RKh7eOVbLqLTSRZYnLnCEHLY32zQjaXorHVJD9VQElo7P7kPaR35Rl79y52/cI9IosvigiaLnEQkT5QJFFIsPCVa2QPCVuXpm1TabXoum4/E240n+f8514gDiNkXXwIXN/ngc8+QO16iflv3wIgjmKSO/PoRYvQDTn0K6dZ/8EC608t8EffeFq4IbS6eHUXv2Zjr7c484nTtK9XiLwANaWJNBUvJPIj0uNZ4bd0s0pqV4EgDAn8AF3XqF8vkzJN3K0uScPALXfxmy6yoVJer1HfbIIMU9PDeDWHRkesrrS8ycDeQVRFYeSxXaRNk2TKQjbFNsmvOvzqR36Gv/dPP8nshoiP+uyvfhSAtiNWcV/5d9/kic89zdd+7Tv8yf/zPZ75wnN897eeBEQxeKOQf/ji4Ifi6h9dImmaSIDZc3E49/lnBT2hR8isdTr94qX0nqPdY5YndL1fHD3fR1EUXr02x9f+zbfww5AXZ2ff8Ov5M6/tzf4ASZJ04EPA7/UO/XtgF3AMWAf+t+27vs7D47/k+F88GMe/FsfxiTiOT+SzqTd13m91bPNhvCBgo15nICVix1fXyyzNrQsh8a01yhc2SKYshrNZGlfKtFtd4Ye0UMYpd2nVO4wU8ugFk+xAmjiIyB0eon51i5e/9BKNqyXWvjVLat8AetrA63iEnYCtF1ZITmeJ4xjFUFFMEeGeNIT/kh+G5Mdz5FNJpmZGqL60jr3awivbYpI4XxMf9kMjIi1lhyB0KpZK0PZY+cZNdE0lkUvw0Z97G90VIZJ2fR+natOZq1M9v0YQhLSWGnS3OpjDwisq6q2K9JxIdNl4elH8zoAojNBUBSMpxuJqRseru0KneHGTw5+8l5Ztk943gNcQhb7tOEROKCQxcYxVSNCaraAkNbGl6cXU266HktBozVZJWxaf/dWP8s/+8a/xH/7XLyFJEm3b5mf/m/ewWCr1E2Kato2haTz7u89z9lNn+PZvPsEzX3juJ74e3siKpdpuU242iRGKAkvX0VW1/+f046copFKcfvwUT3zuae77ueM884XnePjTDxDHMZqqipTrKKLSIw4bmkbGsnB9n7/xjz72E5/T6+FOrKTeC7wcx/EmQBzHm3Ech3EcR4go9e0t3Qow+ZrHTQBrf8nxu/hL4AUBCcPo96UWSiVyiQQZyyKfTPZ1VAtbW/zg1ctIioh/2hYWA6R35ESIZ71BalceLWdglzqUnlyiebnEWKFAbaGKV3FoXhf6LdVQQYZb6+t0FhsYlhDfho44n9GxIqNHxlirVjFHUljTWSRFIrIDwm5Ao9kRDpznV2g7DnMvLWDlLToLDfyGi6QqaFlT+EbtKZA/PsLmpXW0lPB82tYLWtNZ1JQuJlSaSnI0TXu2Rv7eUbSMjlO1cWuCPZ4ez2JYOpqmYrsucRQT+xGyJSx4k1MZ7KUmVsGieavC8V88LbZ+908gGwoze8awHRctpQuKhB1QvV5CyxlUnl/F2eyQHs9S2CMsjetzFd519Cj/8z/5dR45dIiWbfO+v/l2Dk1NcetrVzF6RMeWbSNJEmc/dYYznzjNd3/rSTFZVFVe/NL5H+mO8GZh9MidiiQhyzItx0Huae+2NX+bDZH+vL2aeuCT9/Ps7z4viq7joMgymqIwkssJr7GeXm/bRvlO4E4UqU/wmq2eJEmv7ZZ9BNgeO3wVeFySJEOSpB3AHuAF4EVgjyRJO3qrssd7972LH4Kv//p30VWxcun0pA4Aq9UqRm9CI0sSuqL0E1lur29Qbjb7jGNL1/FbHsc+c4KRySLrP1igPFdGkiQ2yzXW6yKlJGGZRGHEszdvYo2lUSyVyI84NDlJ5EUkduQoP7tMEIZcXFzEbthUrm1x5udOQRxTvSmi2zfLNdIHi+SHMhiWTjonsvgWSiWKZyYxR1PoeRNZk3FLHTZeWqE7XycKIop7BgldwVFK5BIkJtKEXV8UxkICFInOeovs4UG2vr/AwOkJVFXBCwLR0C/bEMYQxyRTFkvlMuZ4Crdmi8J0pYxsqWh5i6Dt0V1roqYNof6fF8xyyzSEhjCMCdxAuExoClpGxxxJYRQt3FKXoO1j+z4Zy+LtR46wUqmQ6G2Jrq+u8uriIpVmE0PTaHS7PPbZR/vvqaaqTBQKdF0X2/Mw36S7549CFEUEUUS7x1BXejbU24k35790nvc//jCdXijsNgqpFLqq4vccMEq9KC5NUWh2u2IQEd45T/Q3VaQkSUoA7wK+8prD/1KSpEuSJF0E3gb8Q4A4jq8AX0I0xL8J/Le9FVcA/D3gW8A14Eu9+97F6+AHv32Oe6an2Ts6Sto0SVkWSVN4W2uqyo21NYIwpO04xAinRlUWxm1py2K1UmH6PXtp2TZBy6N5q0JlvU6lJQidWkqn63mc+ptn6Lou5mgSNanx8LFDtGdrKJaG43moKZ04jIQiPi2mZ4+ePoqmKCRz4lu0dbNKZiQDiNVb+aU1nLrDwvoWQ4/OkNB1jkxNCVsYTSZ0AmRNRlZlho6OoRVMvKqNYmkolipSZHpJyl7ZRtYUYcIWxkRRxNaLq6xtVegs1EAREhFJkmh3bfwgJLmnQOiJ3832BDRo+ehFC8VS8Ws2G9c36S43ac/VaC/Wub25SXI6CzLY6yKBxhoWwZ2dhQabq5W+EFs2FJSE0K8lZrIcOrYLXVXJJpP89v/+h0KyE0WkEwlKzSaf/PsfBOA//V9fFxKmXsqMqWn9/s9PE1Eco8oy+WSSH/z2OdKWRdt1CXuM/iPvPMTsk7OYIykG0mm2vrdA9YU19jy2n/GBAmc+fJxTu3czXiiQSyY58elTvOOXHmar0eBtv/jQHTvPN1Wk4jjuxnE8EMdx4zXHPhPH8ZE4jo/GcfyhOI7XX3PbP4/jeFccx/viOP7Ga47/SRzHe3u3/fM3c07/f8Z3fvMJxgoFup7HyNt3cPj4bvJJkf7hh2Isv+3lM5rP43geuqYR9MbMje1vua7Pzod2kzlQZPWlFVzfx/Y8BgtZlpY3cX0f1dTwwpDQCejWu3SaNkurmww9NE0cx7Rrwsepdq1EHIgisbK4hTmewm7YLH9vDi1rYI2lSExnGds3wvi7dmHmTI6+7QDXvnyRIAyFxuu5VSRZQjFV/KaLkhQrCHMwQRxE+A0HYzCBXxc9ovw+EXRpjqZAkZAtVaz8JImJ6WHcqthGJSwTRZZJJUT0U+XyBi3b5uQjh1ETGpIifKm6G2Iy6NddRg+PQgxxENG9XeeB991H5fw6kiLjBQHZmTyKpXLr9gpRFKGpqvBW2hA9mdzRYbL7ixDHhG2fM3v3kk0k2DU8zLGZGcHtCkOK6TS/8a++zOf/9VcFjSQIcIOAruvSdd1+n+hO4fWa8n4Y4vbCOpKm2T+P+37uOH4YMvfULDMnZig/vUIumyJzqMjyyhbN62XGf2YfS0/Nc3tzk4MP7mPXY/s495tPUz63zId/5d137LzhLuP8LYVt6cmrCwusf2+ezL4iu96zj9y+IjNDg9xcX2e6WCQ/LYiXR993FL1nn2H02MDFdJr578+ipXWczQ56zyM7l0ziOT5+GPLQLzzI2nfm0BSF5bkNBg4OIUsSKcuicn6VwUMjJHMJIV7OWsJdM2sxkE4TOSFxHIvnjcGt2vh1BzVl0LxRplvtsvKiGObm9xQZPDNJak+e+oUt7JUmkqogyaClNFo3qng1h6DliZj0rE710haLF5aZ+vghjLzgOFnjadReb2Tg5Bh6ziRwAyRNRlNVojCi4zgoPXeF2ZcWWL62hprQiIOI4Yen0bIGxnBSJCVbKlrOQCuY2GttOq5LrdoklReBp2raYNfEKImxNGPHJpB1GSThYxXaAdZYGnu5hT4ouFs7h4c5sH+GKI45u38/hXSaZreLqevMb27ymX/4YTYbDV65fZuO66IoithOvsnEmddim7j5WsRxjKIoBD1hcKXVQldVnv/iC9ieRxhF3Hj2luBSeSGLLyyy/9F9JCYzRH7EyOFRJgYGaN2osPnEIjuGhiienXydZ39zuFuk3iJY/OZNxgsFTE3jnhkRvBC6Ad3VFq1bVTw/4MjUFImMRXu1iSLLjJw+wM7Dk/iBMHqLokgksHS7KKaGlhXWJoM9vZWVFVPA8jPLOBsdCpN5dp2YwVnvcGVlheEdgwQtjziIKG3WGDs5iTmeJg4j1KRGeiZHa6PJ0PFxkpMZ1he2aC83RS9pPE1joYbj+4wfFzIJJSEKZ2ehgTmeIgoiypc3UCwNe6ONMZRAyxrkjg1jjaf7LPCErnPr8xdwa7YICXACIaROaoRugJLQ8APBdVqtVKi1BaGzcET4XyV0ndHpQVq3ajibHeoXN1ET4vch6wpaWicOY8KOTxxGQpM2mkdJqBTuHaV1rULkhVQXq2xdXKPbtKktVXn+yUt0Fup9uoKz2iJoeQzmsrTLbVKmydjIAAcnJtBU0cQfzGb59X/xe5iaYNW//2+9A1WW+32pO42v/odv9/9t9txbHd8nCENSPZsVQ9P6cWb3fuw+kmNpzKEEBz5yRKRX367zR//6G7z05BXBiWs02Go00BI/Wdbgj4u7ReotArnnod1xXWRJ4vLSEms/mMdeapLZN4Dtediex8Wb8xhJg50/f5jbX34OJanjBUE/C84PQ0byeaovrdGZq/enOi3bBkVi5MQEa2tltJRO5uAgiy8v4bVczjx4GL/hUrx/gqDtMTIzSHuuRvN6GTVjoGUNuktNEa10pczi1VUmDo0z/u5d6DmT0lNLJHIiIuvyE9cZPzJG+0aV+e/fwhhM0Fls0NxooioKQdtD7iUXd+cbbDy5iGKqIIGmKliGQbKYInIDFEvFXmpiex6KpVJ9cZ3Gq1uoioLreowPDJAyTXK7BP9rm7wYhzGyIhO2PBRLo7vaIg4j4hhCJ0BSJPQBS7DuJ9LImkwcxdz6TxeRTYXM4UEmHpim3u0Kn6Yw5MTJAzRW6tRf3SRyQ5SEiprWMYbF685O5NAHLNKWxXuPC0+pUqPBPTMzwrTQ9/kffvXfs1ar8chnHuThTz9wR6+hc59/lqlisU8s9XvpNrlEAl1VkSWJoz97TLQNekLki1+5wPWL86zNbRK6ARe+f4Wr1xcYzeUYyeVIDiTJJhJM7RrF69z5ogp3i9RbBqVmE2soSb3TYbNe58DEBImMReHUGF5FuBBMFYvkkkkkRWL1qzfJ7CuSnMryyCceEKunnpThyvIyV6/MU643WK/V8IOAXXsnSO3K01moU2230QcTlJ9appjNkD86JD7UhkIci79rq3X0AQtrVBSL3KEhWj0/cc/zGR0s9L3JIz+ECOYW1jj0M0d54G+dpXq9hGwqFCcKeGWbMIpEfmC3y8DxMcyRtIjcsoSBWvWFNdG3Sgk5DXGMW+qCLOEFAYV0iqDtkzlYBEBL6P0VQqnZRNIU/IbIqSseH0PPm6hZHXMijZbR0QsWkiIT2gHIMsZgEiWhYY2lMAoWkiwhKTK7P3YYopjuQoPN51eYHBtEURR0VaW6VKWwe0C4mcoit+Dm9SXaK00KAxlSuwWv2RxKEAYhZw8c4LPveAfjhQIfvf9+3nXPPaQti5tra3zt175zx6+hs586w7GP3ssjn3mQc59/tj/N25bUPPDJ+/v39cMQQ9NEJqPnCSVBry+3Z3QUNxCe82paZ/T+SdyKzeg7d97xc4a7ReotA1PX8Wsupz5+klMfuJdSs4ma1rn1reskprMUjo8QbttudHw6bRu3atO8UUZNCD6MJEn98Mq263JzfR1NUZgaH8Yud3G3Ojh1h1wySdjxkHWZSrNF6ZV10nt6JmeKTOSGpNMJGrerws7XVJF1QQAMoqhPiUjuyOKUukReSKPdIZdM4rdc7K02pmmIvk9aB1mMw5OGwfBUkdqlTRRTwV5u4rc9hiYH+OPz50UYQ9PDDwIiLxK2KUkNK2vh+QGyKlM5v0al1sQcTlAYFG6lVtsBtgAAIABJREFUY4UC9RtlZF0hOZTCXm5iDiUxigkUU0XWFeIg6k8rAVHQ/Ig4gs68cGcgjln/5hzIEuZ4GjcIWFkvk00kSFgmhqZRulmiUmng1h2cms2enRNYBQs1a9BZqBNHMZImgjeHJgdodDr9YnFwYoJPnj3LcDb7U9nqvRYJXcfxPFzfp5hO4/o+P/jtc4AIOk2bZl+ft61oADhyYAeVVos90+NM7BtFMVXKL671JUA/DdwtUm8R5JJJAj9g8/sLfO/LzzE+UqR4ZpKNep3m5RLOZoeB/YNkk0miXuPaXm72nRTveXC/mNopSt8KeFvRryRUMjtywoeqVmP/o/torDZQkhqSJDHz4QPEQUx6zwAb35qjtd5ESfSKUhAiyRKlZ5ZEaGgckxlII2ky3YUGQdslCmIsXefILxxHSxukpvJs1eoUjo3itzzMYRFiagwlGH5EBIjaay2a9Y6Qe0jwkUcfQDFVrKkMCdMgdAL8potXcVAsVURSPTRFFMdkLIvZC4soCUEs1FI6Zsaku9wU00NLhFq2b1ZRDIXIj/pyGlmTxbYvjCCKkFUJLWcQBxHOVpcfvHqZsffuxl5pkhtIMz5SpNpq4TgumqrQ6HbRVZUYyOzMs7FeQdYVQjsg7IgEnvw9Izg9V4diNsNWo0GikCCRNFmtVnnvffdxYteun+r1JMsyCcPgwU+d6fuRPfoLZ7nw5VfEl1kU0eyFoMY9qsLm9xZw6w66qpKYTKMXLGo3ymimxti+N+68+SPP9af2k+/ijuLl27eZ39pitVJh1/AwsRey9sc3Obx3BmsyDbKE33AxkjrVdhsjb7E4t05iPE2pVOfGC3OcfOweDk5NMpBK9b2E6p0OW8sV5i8uoxVMdh2cYuW5RXZ+5CDWaEoQIp2A6vk1bn/9OlrBxEqatEttJn52P0bGRE0bJKazNLpdrLwlHEDtgEqliZrU0bMGuq6x8b15mjfKbHzvNpP7xwi9UDTUb1S4ubbG5/7wO4ROgL3SxF5rk0oJ/lJ7o8Wzr15j5cY65mACNaOT3JFDS+sYIwmxorJUZv9Q0Ou2P1xazhTs9rUyAI2tJuulqhAkrwn2vd/yRECoLmyHtwuVrCuiP+WGYvWjyGgZnQ9+/GFWvnKdyAvRcgabW1Uc38cNAmzXYyyfx9A0wjCkPV8nbZooSV0kYgUxy5dW2Pr+AqOnJ2mX23Rsh+FcjsiLmF/b5OzfPEvHEQ6q1796mS/+n1/7C9fChS+/8qavp7bj0HYcnv3d5/HDkK7r8uKXziP3wk6ThkE2IZKFhnM53vNP34esiBVgqdmkebOKV7VJjaRJ7MgRRz+9aLy7Reotgg/87XdSzAhi5MiBEUbesws/COnWuzhrbfSCxe0bK3SaNvlkEr1gCXV6wSJpGOw+Mk1m7wCyKZwJCqmUIHzGMaWmcOoMuwHtNZG7FgURalKn7Ths/WAR1/bIjWa5cWkB3xEe2fZmW9ANsiLoIWEYlNZrLJZKaHmTsUNjaGkRUqnlDVqbLfyqQ9jxcUtdyueWmH16js1Ggz2jo7z3vvtwSh0RpVTr4LtiW2dYOo+8/V7+9NVXqV/cEtYwHY/MviJqyqB8Y4vs0WFSlkm1N8mzdJ3IDTDHUuRTKfy2h66qTO0eFSuvrIGW0bGXmrSXGoRdn6Dt46y1CZ1QRHbJEt35BmpSQzEVMvuLeDUHJaGJ2yWJgx88zP6zexneM8TY2Rmatv1neE5RHGMvNgSXLIgYGspTODmGMZAgmbH6gQiNepuZ0SEufv4lhnYOkh/OkksmefjgQW788RV+/V/8Xv9auBMR7rIkkTJFeIIkSWQTCTzfF15hvTTspm33M/pe+b+fQR8wieOYPZNjKKoYcLhlm+REhsz+4ps+px96rj+1n3wXdxzP3byJoiisXF6luyKCOXVNQyuYbL24wvHHTwrrV1WhNVdldKxI7cImuqYhGyrVV9aZ/Jn9TI0Pcf977+XgxARN2yah69ie118VnPjAMSIvxK057Ns7ReAHdFyX5HSWg6f2kBhJUSxksdfaxH5E+3YNohhT00iZJjuHh4n8kMK9IyBL2GtNnJIQNl+/vYzdsIn8CLfjkU0kGB8okExZhFFEdm8RcyRFZihNYjBJGIQoCY31axt8+u2P4Dm+6It5IVrGRJIFm736/CqSKhOEIV6vJxY6YhWkqYLpHYQhm4tl3K0uQcPFa7h4fiCCQp0A2RCrqdD2kWSQNRlJkegut3DLNvZaSyT5Al7Xw93qUnl+DXupSWpnnvVzCwztHuxHcvm9c4mjmM31Cm7HJblT2CAvfeMmaxsVRncNo6sqYRTR7TiYuk56dwFCUfwTg0kKuTQfOnmSG398hdmvX7sj19IDn7wft0cg9YNAKP17vvbbpE5L1zn18yeJe4Xq9vUV6p0OWs6g1mqjJDSGHp7i3O88c0fO6YfhbpF6C2Hf2BiDmQxRHLP8/CKlZhPX9/EqDrldA1SeXyGzp0AUxRgZkyiI2FwpI8sSet4kaHm05qp0mzbLzy9yzy+fYnJggDCO+xISRZLo3K7TuFpi7oXb6AXB1h4czbP0zAIXnrlGFESYYymClku3I9jgTqmLpIkMPj1nEDkhr/6/LzL18AM0btfQUjqNTod6t0ujK+yDrYKFmTKRLVU0ozWNZ//dU2KVospEQYSW0DCKFrlsioH7x4WD5IBJ2A0oPbNE5EcUDgz1bTNSponr+4wfHscoWhjFBKl9BdGcziQoDuUE0VOR0XMmuq7Rqrb7jgfWZBotK4z7ANS0jpoS/B9nvYNXEVtJIy2K1XbIQPNmhfzeQfyq2EYBOL7PwFAOLWeILxNFoXGljL3UZOyhGXbfv1OIky0Lo1ccLF1n9fu3iYKIsBOQnBH+X3Eck9B14jj+obHrPwn+47/8fVK9LZ2l6zS7XdReT3E70EFTFF780nkkSepbtzi+z+VX52jbNvWlGn/6208S/pRT0O8WqbcQTj9+qk8lyBcz3NrYID0sRuiFe0cpb9Rx1tpoKV2M0qOY6ePTvDR3m6DlktyRp3GtzFK5zNCuQTafXODej9xL0jD6vkK25xG0PNZubTA6WKAxW6HcaqFYGkO7Bzm4XzS2i6cmCNo+C6USes4if3SYTtummE5z8+Yyt5bXOPnfPcTSk88QRRGvXJ1lZnqUs2eOMFjIiolXSkcxFRLTWcrlOpahc/CdB3DLHZI965bcPcPYq20kRab64hqyqeJXHdZLVZCge7suOFQg+kpRxI6TOyASqcOyJhO0/nMend/2UFRFNMYBJamSnyqQnM6KtGRZRtZk/KqDs9XFKXeJg0g0vU2F9poYRkiKhKwrRG6AMZKktdKgM18njmFkskh6R47hbJb1tTJXri3gBwFKUkO1hKd49aV1ll9aJnd0SBRrXWdgNI8XBOR2DQgS6e48btVhfnkDLaHRdhxURWGq+Oa3Vn/jH30MZdsfv2e7su1h7vp+fxuo9MiekiT1WeiGptG0bVYqFYIwvON8rj+Pu0XqLYZap8O+Dx5CsVQ+8OGzSLKEljWY+/3LYttTEWP78Q/u4dbyGl7F5tDEBNVrJSrPrxIHESceuwdjwMLZ6GANpxjMZclYQmJi6jqNZgdVUdiq1Om6LtMHxskeGiSzr0jY8tAyBst/cA0loXJgcoLaS+tUXlyj3u1Sbbe5520HOfOJ0ziVLl7NwQtDOq6L3/bwyjYb5ZoIPGi6IEs4ay2GRgoUH5pk6dw8kR/RXWwQNFzcmoPTdnj+yg3iGPyuh6TJjE8N4ZVsOraDV7ORNZnKVh1D07jx7C3stTahF9KZr+NsdjCSOoHto6V06q02akroA7edF2RdQcsYQmjcdNEHE+h5E0VXcGsOakrHbbkYlo6kyZTWRQR8ea2GmtJJjaTxfJ/Yj4jcELdso+UM5F7/L9mjgJx79SqqoRL7EYMTBazRNHEgtlOxFzJ0bJSlyys0mh1WX1lm5dJKj/smC47XriKJiTTf+o0fvOFr6JkvPMfFr1yg0e32JUxGz9UgimNOP36KMI6ptdsiqNUwCHrNdT8M8Xvs9CCK+nbUP03cLVJ/zfBkb3+/bXz255f2Zz5xGoBb15cpX91ka6nCwssiMnuzLj6kI+/ayda5JfZMjqHlTWqdDolcgtubm0R2gJrW2Xp5jeG3zVA+v0ana5NOJxjbNyLG53HcJ0KamkZ1vkL1pXX8hoPnB9SulShXG6R25pF1GdlS8WsOScPA0DTM4RQDhyapvbJO82YFPwh44MB+vn/5Mtljw7w0Nyc0cqaKNZamuiEa1+VzK7w4O4s1msZvesQxxGGElbU4PDmJklCFNch6TchkLBVZlgnaok81enCUli2IoVrGwPE8xh7bLbZLvRQZv+0xMJRDL1qYw0mQJCRNIfJCiGMiL0SSQJIRtsMIR9JuvYtuaoLjpMgUcmnUjM7RXz6JaqoMPjiFaRqY4ykCO8BrudhVW8h1ZBk3CNhYr3Bq9248RwSPEsVUX1ojva+AoYot7+pLK32XTUmSMHrbxFa1zcEPHRF8LVninunpN3yNJU1TFMU4RpZl6p1O39tq+5rrui57x8ao9W4DKKTTjOWFl3ml3UaSpB/LDvnN4m6R+muG7aXzNvv39eK0t483bVts1QyDdK9hnRnJcO63n2b40R2oaZ2Ny+uMjw8SxzG5RILkngKbTy2RHEjSulUh7IrxudN1MQaTpMYzZJMJhrNZLF2n2unghyFBw6V1q4pVSFBtt5k5vQNnqwOyRLvcJvJCLF3H1DTMYpL2Wpn2UgPH95nb3GTvr5zkoQMHUCyVB/fvJ+z6FM9MEtoixKFjOyR35dgxNCSa4IrgJw2emqBeaQn/pfUGwzNF8c3edLHG03Rdl+pGneKZCbya4PCMFwrk7x0hN56j9MIq2SNDSJqQeaR25JANBb/uELlC/iLJoBgKoRsiazJaxiDyhR5RUsTjErkEalqsvrYz/wYfnKLyyjobTy8RugH1Vpu5VxdRdOFlFfWEuynT7Dfut/s7blOsziInxCl1SRZTpPcWKE4P9AXIXdclaRhC4hME1C9soKZ0yrdKBG/Sr8l/zbnkkkkKqRR+z3/r5MdPCCPFrS1MTSNG6Pzu+dljjBwf59TZw8RxfEc9o/4y3C1Sb1HM/cl1TE2QLZWe/i6KY1obTfaMjrLylWuEToDr+0ROiN2wGds1zOZF4TflNz0mP7AfczRFyhSWJrIm4250SO7KEfYsgK2e97Xr+9S2mlQ36owOD+BVbJzVtsjps22MkWQ/6n3pS1dY+eqN/hZhvFBg7nOvMP3h/dz85jV2vXsfkRdSOreENZZm/MgYLcch6Hjs2SMIqnrexBxKUr9eIooizs/NMbCrKBrKUSREvCmdweE82UySOIwwilY//WTrewtkDw8hazJew6W0WRNbt80OdqlDHEPQFsZ5cRARdHwkWdjYhG6IYqpIqkzsR0y8fy/mWAq/t7LKHCqS2V/E3mihZQxSo2k6Sw2G9wwJdnbXJZUVnlqKIqyKM5aFpijUO53+B7+91BDJNHVHeGJ5IZIqVjMj+TzHf/E0LcfB+v/Ye88gya7zTPO53qXP8qarfTe6Gw1PEIYEKRI0EkeEuCRlqCVDox1JsxMxP/bPamMjVhHaiI2J2H8TOzEaqyElihJFCSJFSqK38A20977LV1b6zOvN/ji3kk2IFMEGQIJCfxEVlXmrsjIr8+bJc77zvs9bFo311lqXcNOlNl/9AQjdT1pBFFEtFFBlmUdzMWeSZfQ8D1VRePrPnqVkWT8Q7nDvR+7jyGePcOKbZ3nx6TPsmpx8TZlR/1TdHqR+DuuZzzwHwP4PHyaKY2bftZMT169jjzujoEc/CLmxuM62O+dYbbZEdLoqVMZ20WL2l/Zw6b+/hL82QFMUNvt9vJW+iGF/5y52fuwuAbDLd3rcMMQPRcR44sesXdlg4IqZ3NRkjf5SF7tkUbZtJEWm0eqy2e8Tpyn3fORekmFM7/wmG70ew2sdwraPVjHondlk9dQqVcch7gr1+cxEnagXYE0V6J7cYKXd5q6FBbG71vIp1gootkY8DIVUwgvRisYoiXnyrfOUDo3jbwzx1waETZfJbWO0On1h8N1RIQsTMYvccOleaROsDQVWOMlG9pi4F2BM2fQvt3C2lVFsDcVU6Z3dpPGt6yiGSuLHJIFYIi6eXRn1bNqtPsWZErqpEbohqqpg5jPN4q4a7cHgBwZ/e6HE8FqXuBui10wKcyXCjke55AjJRs5Rj4cRaZiMBKu3Wlt46af/7FlUWUg3JkolgW/Jg0O3GuZ+GHLsr44SRBESwv3w+u7n/WDdHqR+zuro514iSVN2/eJ+zn7uBJau0zm+zrseuptvPHWUbY/vRivoOHWHqUqF5dMrFC0Lc0oEV+z+rXuQVZn28TU63QH9JYFJ2bZnmrDh0ej1GC53Wf7Sec4uL2PXbfx8t6dWKIjwhygizRXrsq6wtLrJ9GPbSbwYWZaIohg/DFlsNlFlme7ZBvq4hbvYZ+/0NJdP3qB0xxj1+2ZRLJVGr/f93bdegGypGOO2CB6ddDgwP4dpiBj2rxw/jlrWc1RviDFhEycJ3fObqCWdsm2jVy38jSHecp/2Ro+oGxB3Q+r1suBSFQ0iN8IfBAwGHmbJRC2JWYOkSMiGKnpVqoy/PKC4q8bKVy5jL5QE3cBQUUs6fkP41RJXkB1sQzTK0yzDMQzctQGSIhTcUSyWRrIs073UxNSFAXrg+5Qcm+UXl0j9hKDno5UMzKkC3uqA7b9+SIQ2WGL3NeyL2CjHMGh899YSlFsDIcIF4dML4ngEPtw6vsUh22Lpd12X9lDYlK6sr1N5DRnmP65uD1I/Z3XPh+8dAczWOh1kXWHQGLB0fYMn/tXjAhPixTjbK/R9n/2/fEic4JKwiVz+46P0WwO+8Y0X2fuESJe1pgokbow79BkvleicaXD5ygpjxSKJGzNWLFKaKNF1Xcycoe6YQmMkmwoV26Z7qkGUJAw9n7V2m2uNBo/ec5CybVO7RwyAWZgw9uAsk+UySZCw/KULWLMlDt23G7tooVUM7PkSWtnAXxsy8dZ5/JUBi41Nmr0+g/aQX3zrfaSBoCr4q0P0iklpvIhRt+mv9qgUC/yHP/w0q5fWR/qmwAuJo5h+d8jGWou1F5eEg19RKNUKKKYYFBVLQysZhC0PsowszVDLBu5qn/JIba6ilkUvqX+pjbfSZ6PZwTYMCpbgRZUcm8L2Cpqp4fX9kY7IMYSmKklTQfVUFGrFImEolN6bna4Y4BbKGFWL3vkmx/7oWWRZotHtjW7b6wwxDJ3OLca6lW17FKBg5IgWVVHIYISe8aMIN8cCaaoq0DGP3c32hWne+4GHmPiF7a/iLP7J6vYg9XNcb//Nh4kDoa6enR+n9dwKLx6/QOnwBOsvLjM+I7hRm90eiqESdXwmH99Jezjkg7/7Hk597hi1g5N0jm1gL5RQFQWjanL12HVqhQIL4+NkccrYPdNE3YCybWPoQlU+9H3CMGLYHArrR8sVjeJckPjo/v3IhsLMO3bQObWB5wfoYxaSLLPtif2QpgT9gP7FJu5Sn9X1pgDQHVsn9WMKOytc+ZPjuH5AvVikXi1Rmi6h50hhZJAtFW+5jyRLmGM2pmnQ7Pb4lQcfxA0C1i9uoMoyVtlCLxoisivLRkuVIBJsdUmVt+AHJGGKURfZf5Is+kPeUk/oz3Lh5uWTN+j3XXqeR5ZkGJrG7O4php6PpMnMPbGP7pUWOz52mJV2mzCKMKsWkiaTpkKfZeakVDcI0DUxSy1alrAhffsG7eNrVA9PUBsvE0YxFcdBkWVM28DUNAaud8vkziR/DPd/9H60LZO5LGNoGkXTHD22omWhqypOHh6aBgkvnb7ItZeu39L93mq9ov8yD/nckCTp1E3HapIkfVWSpIv592p+XJIk6d9LknQpDwi996bbfCL//YuSJH3ipuP35eENl/Lbvv4U+n8mNfWuHdgVWyxr/Ih779zD8FIbXVWp3TeDpMpMzo2NQGybTy8yXa1y/Mlj7H5sD2QZlbsnAHCmi5w4fYXxUolyycGZKCAbChsvLlM5PIFmaigFTXi1bJthEFCYLDJ55zTORIFhEBDnmihztoCkSNgzJcYfnBNRTWWD4vYK7RPruNfFzODGlTXiJGHbnmkx4CaZMDQfX6fd6gv5hGmglQyibsDi6WXcpota1Im6ATPv3S1y9uKUNEmpFQo8e/Ei46USlYKD7ZhkUYpiiSiuXW/fTWW6DEBpuiS29CWIB8JoDCBpCpIiozpC7a4WdLSKQe3uGZAlZmo1attqHPqNezAnHSb3TdJZbLPw+B7ingD21Q5OsnlEaJx2vHUHsirjDr8/q5IkCV3TsHQd1w9Q85AGQ1VRHU2YoBsu/daA+uFJkiQhTBIazY7YOcyyWw7ffPRjD/Hgr72F7/zp08Q54C5JUyTErrKl62iKQpzrpoZBwM7pKU6dvMK28XEOPnH4VZ61P1m90qH4fwDve9mx3we+nmXZHuDr+XUQOXx78q/fQYSFIklSDfgD4EFEFt8f3JRu/B/z39263cvv63b9E1V/cJaNrtju1yom/daAC6urdE6u424MiboB1lwRSZEI+mIgKVkWa88tYtRtZF0ldgVKpGzbqJaG7wZcOHeDNE7xokg0leMUc9Ih3PRG4ZqD9b5gCi2J6KetwAejZiGpMt7GgKUvnGfugXn8DZfrf3Ga4bUu7U4fzVApmCa7f+Mw9nwJ3dGxFspkSTZK0Z0ol4njBH/TJfQjpqbrOJMFslDMBoK2R/nwJP3LLYwxi2EQMFOtUt5eRauZmJMO9YfniHshcS9kcKWDVjKo7h/Pd/OEMp80Q1IlslgIQCVFEjqtOEU2VPSqSfOlFc6duIo1W0At6nTPbxK2PIINFy8MiQYhU+/dRfPYGvV7phne6FEwTa48e5V4GFHdXqPk2HhhiBuGpGlKGIt4rDDHpRSKtrjvKMVfG6KrKutHV/CiCDUns+qqyq5f3M+v/dsP/MTnyrc+9b2RBq9kWaNlbxAJKch3/vRpmoMBaZaNZDBpmlLYW6VeKIziuX6a9YoGqSzLvgO0Xnb4g8An88ufBJ646finMlHPApU8i++9wFezLGtlWdYGvgq8L/9ZKcuyZzLRtfvUTX/rdv2Q+mGojod+/UEROHmtg20aHNg+T7jpUdlXp9XvoxYNsc0OXFpbo1h2sIsW1lSBk393UnDKyzoT5TKyoVDeXuXu9xxCKxvUx8sEmy5zH9qPvyFUypIiMbGtTppldE9s0BoMaPb7ZIBtGPgbrsimzjKGPU/wxNtiR2pLHLjZ6qLKMjf+8gyKrVG9b5rLR68x/8R+Br4vxIw5iSGIIqyaLXAqvkDz2nNFuuc2kXUZSYK4JxKUt09MUD4wQRomhE2P//D/fIYkTNDHLPSqmQ8CCf2VnggujVMSP2aw2CNouMiqLGZVaUYaCa5UGgu21F3vPIBetUjcSOwEphnrGy2qhQLBpou73GPhffex9q2rIu5LlhmvlMnCBMVSqT88S9m2Kds2fc/Dsg36noemqiKifugzaA7p912CSNAmgjhGQvSLbMNAkWU6x9dv6dx5x8cfxQ0CALquS5plQreVEzirjoMqy5Qsi6Ofe4ksy9h/eAfWdJH2cDhaKv4069X0pCa34qry7xP58Vlg8abfW8qP/VPHl37I8X9UkiT9jiRJRyRJOtK+xabhP4f6UaiO+z96P8utFkubTYFk0RW8FaEM7h5bx+/5GFWLg7sWsLeVqNw1yeXPnqReKDD3vr14ywMUWWbx+jr++lD0W8Ztwr44qf3GEMUUs4pkKAYLp2Qj6wpRHCPLMhIwValw7fwyhZ1Vrn/lIpqi0Gx2MUumEIYmCZVigZk9U2iqytX1Dfy1AcNrHQ69/xDtE2sjfRZArS4QNbIqUlniQYi7NiDY9CjtqaMVDfz1IeZMAUmTOb+ywupXLnP92hppmPKrjz6COSlwwMaYjeroJEGCVbbEzLIfjvx/iq0R9QKitk/UF3TMLVaSpCnIhookSyJcYq7E5lKLiWoFraSTDCNKe+pc+JPv4K8N6W8O2PHxuyjeUcfeWUE2VJ79yxdQdBEoMb19gq3zOM2XW5YhllqyJDHMUTq2rpNmGVXHEbu5rsvGYvOWzp3v/OnTI/1TJQckBjk/KklTFFnmnZ942+jyrm0zkMGNL1/kLb98Lwvv23tL9/tq6vVonP+wflJ2C8f/8cEs+89Zlt2fZdn91XLhVTzEf771wEfv554P30vt/hnGHp5DUiT6vo/rB5y8cYOTZ65gL5SRNZnuqQ1OLy1RnCqx/OULWLMFbmxuMlWpsPDRg4Qdn+6ZTezpIuGGC8BwtU/ixlhzRVAktJJOYU8VJ1dVp1mGpigc/uBhOic2UGSZDPEm7Db7XN3YoF4ucW19g95iB93W2TE1SfPMBmpRx13uoddt3CAQmXm7qySeWBJ98+ljZInoL6VZhl4zaR9bE2LMXoi33Gd5o8m1jQ1US6NeLArMjCcYVv7ygMGVNnE+S8qSDM1QkRQZf8MVwLcNMZOScqRw4kYIn4wYHLM4n0lIEkFjyNx98zi7KlizRfS6RfOFZQDiJGHswCTuSi9fPsr4awMmymV8N0Ar6bRXOiMdWpKmQpYQhCMPnZn3hrY0UVt9K/jhb5pXUqos45gmT336GYa+j6lpGJomxLySRGsw4KW/fHHU95INhd5SB9sxR37Hn3a9mkFqfStSPf++kR9fAm4O35oDVn7M8bkfcvx2vQZVu3+GBz56P4Wqwz07dnD44E7OPX1BOPj9hEceuwtzyiFs+zz39GmmKxUKu6s0X1ph9dwaK60WaSSYTsXtNZyZIs6OMmrJYOqd28XAcr1HvVBAliTcIGC10+HyP5wn7PgYmsZSs4kfRex4/z4OHtyBWtQ49Pb9lGbLIjjLAQfBAAAgAElEQVQh95CFLZEsHHb8kYHVXe6z2umQZRmPveUw5mwBY0Jot2RdoXrvNHrZFL68IEaWJCxdJ4tSClUHe6bI9APzYukXRqRejCRLGBMOiqlgzRQENTPLUFRFJMoEQui5vtZCcTQRCKFIaEUdxVQJWx7JMCTsBkKvNQzRKxaKrSGbqtA5FQwqB8ZpvbRK5e5JmqfWiPsh5ZKDVbTIkoz5t+9k9vFdTO6bwDYMimVHxJ77/og4ECYJek4kaA9ELNZKq8WeDxy4pfNBkUWYgq5pOKaJpigjXdfWoNj3Befe1LTRoPx65Om90no1g9QXgK0duk8An7/p+MfzXb63At18Ofhl4D2SJFXzhvl7gC/nP+tLkvTWfFfv4zf9rdv1GlX9wVkmf2E71Xunufcj9/En/+VLLG82uXTsGv0LLf7hqRdHHKTNM+u0Tm2QpmK3LO6HTL9vN9EwIGx4rL+0AllG+9QGWZyiFDRMQ6dgmqiKMvKApVnGSrtNrVCg7/t0TqwTd0M66z285T7OjirDK20kVXyKR22foOlR2TdGpeAgAZIsMTNeQ5ZFAIRiqrQuNSmYJuMPbUNWZS7/+clR/8qPRGqwWhYBD6V9dZaev0GnOxBCyzhleLnD4EKLxE/QSgbNZg9FVdDHLKxtJVAkSGFqti78bRtDsYGw3GdwuT1KWE6DZCQUbTy9SLAxFM14P6Zy9xSSIpPFKd1TDYI8B1AxVKzZAtV7prj6zYucfPI4sinQJ5EbYmoaqqIwMV4deSHLjk03R/QMg4CZWu2Wz4MHf+0tIyhgkCvKtwI0tmB9tmHgBmLHUXU07JL1qs+/V1OvVILwGeAZYJ8kSUuSJP028O+AxyVJugg8nl8H+DvgCnAJ+C/A/wqQZVkL+L+BF/KvP8yPAfxr4L/mt7kMjCLYb9frUwfm5zm3vMz5FZGuUi8WqToOkiTxpRdfJM25QYWyw/R7dqE5OnrJJIpiWoMB3TOb9C+2sLeVBdKkqI+y2wqmSdVxcIMATVFY63TYOTGBNVNkcaNBbVuNsQdnGVxqkQYJoR+hOwKB8vWTJ+ldaaGWdPY+spuoHUAKsiwh67JQk0cRE+9YII0Sms8v40wU8MOQ9Y02jV5vNKAVdlVpPrMsCAOANe5Q3FsTZmNdof6WGcJuQJIIXrk56TC81EYxVCGhUGWCTY/eao/h1S5h0xdBDrrMzHt2k8UpgRfi7CjjzJfIkkzMqmoW3TMNGk/doLnWob3epTZextpWYurxnegVkxN/e4J9Twij7rmnL1ApF2gNBmi2jq3ruH0PZ7JAmqb4QchEqcTA9+m5LjcajVt+3b/9J0+NdFBZlgkfZ97788IQSZJI05SSZdHo9egud4m9+NWfcK+ipC0Z/M9bHdq3Pfvcf/q/ftYP46dSX/vjb/Pu33rsNft7J588zpPPPcdd27ePdpX2z84KhHCnw+U1oV36yMMPo8gyURyz7e553KtdJF2huLeGOe6IbfkXV9HKBme+cRZD03CDgEIuCFQVhUavx9zkGJIi02x2GRsrE7kRY2+do/3SqrDotPrsfeIQ3/hv3+YdH3+UxI0Y3ujSuLLJ3APzxF5M3AuIeiGtVo+p3RMYEw79MyJgIYtTWv0Bf3/0KI8fPszc7ikSP0avWfSutNEMlSgQSNzytNi9tGaKJF6E6uj0L7Uwxh1SPxJEzgzCpjcazGRNZvyRbWx8+zqJG42eg6gf4q8OsLeVyFLh9UvjVPS70gxz0kEt6Fx76uoIIleaLhFuerQGA2GOPjjOU0++wN337aW0r871r1wkShL6nke1UBgpw/2cjAmMZqlZlrH7l+74iV77b37yu7SHQ6qOQ8VxKNk2q+02Zt6XkiSJKElEyo4iePjzM+PUH5r78X/8J6wP/+4fcur8tR/bXrutOP85qNdygAJo9vs8sn8/K+02WZZRME0WNzcJo4izS0vsnp5GVZRRWoiqKFx44QpX1ze4srjKpe9dEvl2R1boXmryvS+8MDqxtxC0GTAMArEb1R2QhQn1aglJVyjsrHLtW5fQqybWvBCOBi2X+x4+gD0topLSIEGVZY5/4wyXj1zFXRvQaLQBuHZmiUvfuUgaJgwHHq4vQgMe2b+fqcma0HRFKc0LDcyqBSmYJZOp++co7hFhpGkQEzRcTv7DKfrrfbpnG6PYqfZKhyhJclSLxOwv7aX5wjJaTXj8IjdELepIMug1k6gbkCUZS5dEE3/ul/cLXdYwwlsZMHfXLMWxAtMPbcNZKKOWDaZ3TwqzcCSMvdZ0Ab1iMXnPrBDJWhYF20ICVFUswQpVR/SnAD1/nk8+eZzP/9FXXtHrfvRzL6HkZM+tHbzWYDAicm6lYKdpOqJrBFFE8DolE7/SUn+m9367fiZVsiw0VRXkgTSl73l4YUhzMOBfvfvdZMC1jQ2ev3SJ+3bupDUY4IUibWXLjX/p0tLIkFq0LDGLCkVPJUwSTIRXzc9FggPPxw9DkZZy1yQrp1foLHXYdsdu9IpJPAxxFsos//1F4n7IcOAR5aLTomWNsuG8MBwNflvaLClv2B+Yn0fSZJJhhFrUKWoytfumyRKhc0rcCLVoEPoR1569RtGyBGdroUrQcAk2XAr76gAkXiwGyqJO/0obc6aIvzYgaPvoRQNvuY9sqISbfRRbRTEVsZvYC7n26ZMU9tVxtpVxl7r4qwNSP8Go2zS+J2Lr1YGKFwSo55qM1yuCE/XMIiuX19EURURiuR6lWgG/JzRjg80e5UoBPcvEYJJ/wNyzY8cret27N+msvvfpZ6gXCvQ8jwP37uLMS5dRFSGNMDWNwx+6m+f/4gUMTWPmdUomfqV1e5B6E5YfRURJIpq5OUeoOxzytgMHsMYd+ms9/s3vfYj+2SZ/99JLzNaFaDOI45FJ1s0Hiq1GqxeGSCC2zjUNPwwpTdXwNyNsXSeIY+YPzYrlkCQhSxKVuQrd0w1SPyEafP/TemvQ3PqEv7G5ORpYwzgW2+Z5qu5EuUyl4KCP24QNV/jwkgx/w0Ur6Zz9/Elmdk8RNlyyNONao4FjGMztnUaxNKy22GlrtntUCwU6ZwW/ypkooJUMVEejuKNK4/kl4m5AZzikmIpsvuZqh/psFVmV8dddhkEgbmuaRG2PS8eXmdg9zrUrq0xVKqiOhtf1MAvC8K34MmpJoG8a311ErxiMj1dZXNlAkiR2PrwTvS7i7rNEJDwPe6KBbuk6ac7OeqUCS0PT0HNMsJ0P8o987CGBa1EULF0XAMIg4OjnXkLLB62fdd1e7r0Jq+o4ABRNk7Fikbfs3s27Dx9GkSQ2l1qYplCn+1HEr/7me3jgvv3smJggiCL83NKR5Z/mfhSNlMiKLIsBKac9gtDlAJQdm+LeMbIk49SnX6Ls2CLx5UaTsC8sMMvNJu3BYPS30iwjShKBX1FVdrxtJxPlMiXbFrt5qipU2wtlIT5NM6yZIgBaQYcUJqZqrFxaY+j5DIOAiuPwwqVLDFf6or+kiNiqiuOg10ysmoUsyxhjNrKholganbMbYhZWFnjkJE2J+xH7P3KY2j3T4v4qBmMLdaq1kkgyXu5Sn61y8fg1wjhmudVi/RtXKS2IwWp9o40sywxWeoQdnyRNOXf+BoqtCgrmE3fhLvdZ+uolskQQPrfkFVGSCPxLkhCnKTvev+8Vve5+GBLny/J7P3IfqqLw1KefEYNqHI/iwBzDIIxj/Ch6TTL+Xm3dnkm9CSuIY4o3+bZubG6KlBLHYX5ugsvXVij3hfrcu9FDNhTq42UenaiQxilHz18WdIH8DaPlYkRNVbFz3K2qqSiWSqleJOj5WPNFumc2kHVFvFlik6AjGEZxkiDLMtVCAS8MCcJwtBQ7u7zMo/cdZOKx7Vz/m7OYJZP+hjfqmey9dwfOtpIYcFSJ/vkmw4E30hspucWjtnuM7pUWxbLD43fdJQbBKMlV7GCO2fTXejhVB2emKCK1gpgsSRlcbJMEMb2+S3VM3FenP6DxmZcomCaWbTD20ByLX7+MU7JQayat6wMmZ4uUlx3s/Hna/1uPc+6TX6Ows8qsJkMGK5fWKNk2VtlitzFNI/dAaiWT1Pu+lMPIETlbDe2+51G6BaZTEEVYus63PvU9yraNLMsj/dWWG6AXxyNEyxuhbs+k3mS1/NXLIrlW0yjbNkmaUi8WGSsWafX7fP35YyxubmLnS7mg5498atEgxJotCP2SJBHEIjRUycmO9UKBvuehqyqlO8eZfMd2zGkHvWhw6dh1gjWX9fPr7Hl4N0ZVoH5tw6BgmtzxK3fSHggtk6Fp9FyXgmny6P2HBLvpcgurbKGVjRGQTVMUirvrdE83iHsBG5caXFpaHSm0d925jXqxSLFWIO6Jx5n4scAtex7+pjtCBes1k8quOsaUg1YySLyYqBMwuNgmS1LqD84yfdcsiRuh2Bql/Lnb+cQB9LrFyjev4lRsIjeiv9pDliSWnr9BEEVopoZl6Axbi6RBwuozN/BWByiWSq1YHJm1FVsjyzIm58Yobqvi9jyq22ooijyKktpSo9uGwcD3mX181yt+7evFInGakuS3D/PlvpFvjkiSRJzDDLMsY/Aq6Z+vVd0epN5k5QaB2H3LMga+Lz45XRdNVTF1nR0TE5Rtm7PLy3ihwN72O0P63SFaQedvnhRhkLIsC4uFYZBmmeiR5MszLwwZu2eG9vE1Tj9zEUmVqdg2y5tNdFWlfWqDG9fWWMuV5Hf+24exp4sipDKPKa8VCqiqQnejR+CFNI6vohSE+rtgCSa7qig0vnuDwVqfxZUGXhiye2ZaZAhKEsGmR2G+RJIzt/SigVbQUQ2RGGxULWGxCRIkVbwVJAmBaKmZwnuiiHSW9ourLB1dRLZU3I7LZq/HwPdZ/YfLIlTi7inUgo5RNZm4b5ZKuTBaonW6A1w/4Monj9NqdLEdk5n37hIK9EPjdDsDFFvlxrU1kjTl2pUVOuc20DWNybcvgCShqiIjz8h39+I0Zd+/OPgTvfaNXg/bMDDyQUiWZcEFy8WdEiJ+XZVldE37qTHMf1zdHqTeZBXeFKktSxJxkmBoGl6+MxdEAtdi6zqNXo8XL19BlmXqO+qE/YBzy8ustlps9nrM1+vM1WoixkrXmX+X+FSfOjDNtc+e4umvHSNOEi5eXORGs0mWZbSHQ3RTxDTt3TtPbbzM6rev0r3QJE5Fc1jJmepBGH1fIDpWoHbPtLDNRDHlkoNZMOn2hqM3X5pllA6OjZaikgSpL5Z0cS8UotOCsHrU62UUQyH1Y5Al0ihFUmSQZaJ+SJoI35rXckmGEda2MpM7xjHG7dEyed+eeay5As21Dt7qgKgbiKj3oWBKFU2TgmniGAblmTJazaRk21y4sUzQ8li5toG31KPvCdxLxbYpmCa7Dm5j7etXKd1RZ/3b15B1GT8IUSSJDPCiiDCKfuLX3soTkMPcDB7FMdVCgVqhwND3BZ89jsVs7WdAO/hRdXuQepOVpqrCVZ8HO5YdB9sw0PNdOkPTsA2DiXKZyXKZsWKR9W6Xp586yX/92td4aN/3m7TfOnOGYsXBUFUqjkPY9piZqrN5bp3NjQ77ZmZGoZOaolBxHKYrFTaaHcZLJYK2z+Qv7KC0u8Yzf/0CSZrihSGb/T4rrRaKLKNrKqqm0m/0aR1dxZx0qB6YQNYVWi3xBvejiFqtxEy9xuByG0vXRc8pFEGdWQZa1URSZaJOgDHlICnCNJz4ifidJEPWFRRDQTFV+mc2mf6FnSx8cL9IsukHVA5N0L3colC0Gfo+xoSDtzJgbKbK9Lt3Eboh3ZUuxV21Uc5fkFuEAPxNlxubm+zft0A8DEfBmvse2YPiiKVcYaxA2PSYeGwb8TAizkkMYZLg2GKJ7BgGd3zwzh/7Wj/16Wd+4PrDv/FWvDAkzJnmmqoyzFXsSa5xc0yTII558Nfe8lqdcq+63hidsdv1Uys71xoZmsYw92fpqoquKIRJMgoT2Oz3MTUNTVXxwxBVltk3O0uapoyVSqP02//0xS/zW+98J24YsvbiMovNprB1hCFl2yaIYwqmSa1QoNnvU6oVqGWCYNF1XaaGIUc/JxrQUs7a1lWVQ3u3o5UNEWmuyridLjPzJYKGi1G3aHVFv0SRZerFIqErJBDOpC08crZKYXcNb6WPs1Bh7XvXiVcTClWHxI3otwYYfQ01NxUnwxDFEo3oxIuYfNcOon7AcKlHueQwWOqRJRkTD85x7uvnKFoWl49cxdJ1/M2I6vo0miq27K996bx4wxcsvKHIAmzcaFK2bfYeWmCw3EMxxP+ZBglZnKJVDKpJCbcp0qPXvnlNPB+GijsUyzzyZfXUu16ZLuqRnIUPgkF2M/BWUxSU/PmWZZmq49Do9SjbNm/51Qdei1PtNavbM6k3WQ3yaf16t0uWu9+3tFJb/So/3wFyTBNVlnEDwRkv2baA3Lku7cGA88vLjJdKfO/cuZGmabpaxdC0kcyhVihg6TqLm5v0PI9Wo4teFI3ifR84SPO5Zfwows71V1OVCn3PE8kp6y5hN4A0Y8+79hEPQtbOrmFOCrtImu/gyZLEeqdD13VJ/BjZFNyn3tlNintqXPvmJexxwWJyOy7rS00KRRujZJKmGf7qQMx8wkSEhaoKesmg8cwSw6sdlIJGcXuFuBew9NQ10bNRVcF81zRmZsYYXG1TvKOOpglZhG5qeENBgZBNVaQG+z79pS7VAxNEvRBrqkAaJLg3enkKszD7GnULzdTQywaSJmPZBoah0x4MX/EA9fKK86XcVtiolKfaBHmI6cD3KVkWq+32P5qB/azr9kzqTVQXvnhGYDpUlalymTRvnm8pjbe8WqqisNnrYeQKc8c0cYOAqUplZErdcuRvWSlOXL/OvTt3Uh0rUXvLDBe/eIbWYMDMzJiINq8KcF3tQQFRkzWF9vE19KrJdKVCBrhZxpPPP88HH3iALBE9IqNqMmgMcJsus+/excLbdtI9vYGqCluIpesYqkp1dgJrrkgapUS9ALKMwq4qUS8QM6F1kYJsmgbFqRKyoRAPQgq7KqSREGcqRv48lHTWvnmVNExIBhHO7ir+al/gahIb0xfUAl1VKZZshh3Rt0qzDM3WBKXTCylOl+gsd1DjWAg4ETO/4RVhYh6uCoRy5IbIvoxeEzDBLMmI/AgtJyqojkY0CNl7i3gWECLbMCd8OqaJrqpsdLtUC8LEnKYpgzxt+Y201IPbM6k3VUVxPNIXeVFEmAv4uq47CoM0NbENviXG3DKgW7pOq98fNd7jNBVamlypPFWpsN7pIBsKF794hpJtM1ur0d7sMfbWOQp7RZ8mcSN65zZ5/rMv0F/vs3ZhneVWizBfFr7jwIFRU1irGBgTDnf8Lw+IePeXVpFkCXu+TJKko8eQpCmyoQgmeZqRBgnmVAFzssB3P/8Cl5dWsYqWaIxnGVEnQJIlMVtruKR+jGJppJFIMgbQqoIPFYZihgOgFnWMCRs3DKmViiiyzPqGeOw3NjdRFPF22vWxu9j3L+8j2PSwDIPlVkuEiOa9KcUQoRC6qUGSYY7Zgnaw6SLJEs5CGVmWBeDPFMbkW51BbdWW5KRk26OwjHqxSNd1ibdAepJ0ywk0r2e98R7R7Xrd6uATh7nzV+4aqY4zwNRFoOZWDHiz3x8ZhVsDgR6uOA5+FBEmyaivkWUZuqqOdq/CnJF9/IRIMPHDkJ7niZy4sw3SMEFxVJpHVjl98goTpRJpmmLpOjsnJ/GjiNZgQNGyKNWLwoPnx4w/OMfSF8/TXG2jFjRqh6cAMTPYWnJJkghUkCRQDMFAknWF1osrbBsbY/+h7VhzRbIkJY4Fa9xfEUu89kYPJImoF5BGKVkqBtI0iJENlfKuGmP3zTCV+9f0MZv6bJVOf4Blivh0VZZZmBhHsTUkRaZ/pcXy318gTVPcfLYZxTF2RWirkiAeLS+3IHuaraOqCl4QMrjYEsZeL8QYs0b45ldTbhAw8P0RJ2priWeoKrqq0s+Z8g989P5XfV+vdd0epN6EpeXYkCzLcIOAKEnY7PcpWRbz42MjLY2VW1zcIEDLr2u5+34r8aTvidRjU9NGvrpGr8fC+/Zy4IN3su9fHGR5scHgQouwK3pe82NjI4aRmyvOtdydP/vuXQzbQwq7q3hdj+ZLK5w+dYWSbaOVDDrnG8RuRJKro9tDkahiTDmkYcLEIwtYcyXWn11k5dqGWMrKEt6NHpImC23YIARJIktSKtUCWtkYIVn85T7xIETWZHqXW2hFHb/p0j6+RvviJr0TG2RZRn26SqvXx7SNHPSX4HZckKF/sUnUC5FlmVKtIOKiJIles48sy8RBLDIHfZ/EE7NbSZEYej62Y+IPxEBilS3SKH3VsyiAim3z0K8/SJKmYhfRNMmyTMgf8iSaW5E1/DTqdk/qTVi7f+kOzn3hlEgi3jIMhyEZEITRaACZrFREPJWmUakWRr2SLW9dmNsnTi4u4ueJI84P6WlU8oa7oshkaUY/Z2v7UUSQWz2GOYdqeLVNnKaU942Rhgn9s03uf//dpEGM6uiELY/OqcZIGS1JEuX9Y9TunibxI9a/d50//tSXuHNhgfFSCccw2LjSoFYvEQ4jxmaqXLq0zPaJcTquy8zCFGmYoBX13AqTEGy4mLNFSjsq9C8KPIwx5lCaiyDLkDSZ0r4xsjBBNhSSMMGadMiSjNVrG1QcB6NkIhd0Ei/m4Pw8iipy7LbY7UPfp2hZdPoDVFdsOliGQavTxzEMNE0lSzLKB8dfk9d8GAS88NkjVB1nFBdm6TpxHqv1RrLBvLzemI/qdr3udfOOWpKmzFSrWIaOFwgky1aPassZnwYJXhDy3MWLvO89D9K52hp92t8lL6ApCqcXF7myvs7WBva1vz8/6neE+cwnzb1oBdMk8jzaw+FoxrbW6bDxYhddVVn7xlV6jT7VhRqrz90YNfe3hJ5b/Z2CKXhOK1++RNwVXKn33n039YkKyJAMRYiD4mgwDMnSjN1750jcmHq9ROInSGq+hE0yxt6+jc6xNSRFxr3eRVJkGkdXMQsmwTBg5p0ikdld6gmiA6CXDdy1ARkwOTfGcHOA6sUk8feJl9OHZvGPraLmADtJkph/7x4ufvEMSb4s3GqsmyWTNEwYe/i1A81tSUIG+Q7uCFCYnwOyLL8hzMQ/rG4v994kdfnvzv3A9bv/p3vwcyPvFvAsTTNsR6S+qLIs0nRlGVPT6A7FjOodhw4yXOxhV0Szd/zt29j7iXtoD4fcdccuHtm3jxc+e4QTf32MKJ85eGFIEEXEeU8rTlOSXOm+MDZGwTCYrlZRZXmEkLlyZQVFlrl+bnlkWjYsXdAtbQsrx8F0XZfr55dpr4tw1JV2m4md46KRnmQkcYKTM7pLu2pkUSrCBVKhKI97AWsXN/DXh3jLfcbu3EZjpY231KN67zShG2IVRQqyPe7gLvdIvZhg08XZXiYYhgQdX5ATigZxN0DXNMiDHQTAzmT96AppltEaDEbJwKeePE5tssy2h7djGwYZgkxBmvFaRrYd+ewRoYnKm+JRngRTzg3Kcu69fKPW7UHqTVJD3+fcF079wDFdVVnvdoWoU5bxw5DNVlcga1VVQOwMfcQakkCY24Bmo4tpGwyvd1j71lUcw0AfF27+7dMTmLoutrzzN0dnOGRyrErJtqk5DpZjUi45REnCxERVKKlNk4XxcYqmScVx0FSVerFIlCT0PI84RwCD0HuFcYyaI29LtsXzly6xe2qKxIsJmh7xMELRFewdFRRbo32hQfnOCfyOL5AoM0UajY6gAZgqYw/N0Th6jZkD0xT21OidE7TSYc/D8wNSL8bZVqHXGTJY77Pt/XePFNytfp9oIIB8SZoSxQnIUKkWUIpiWdUeDimaJrIkMbt7it2P7qZ85wSDKx2MnDve931OXbr+quQGLy8/ikZgQkWSmKvXv7+xkW9A3P8GbJhv1Y8dpCRJ+u+SJG1IknTqpmP/ryRJ5yRJOiFJ0pOSJFXy49slSfIkSTqWf/3RTbe5T5Kkk5IkXZIk6d/nyTBIklSTJOmrkiRdzL9X//GjuF2vtg5/6G72//Kh0fXn/vx5gjimZIlZxuRsnYm7pqk4DpZt4IchiiyjFHUUXYRVBlEkdsjyxivA4HKHlfNrFCaL+KsD4mFE5e7JEaSuYtv0fZ/9+xaQTQXdFDlvWZZhTNgULQtrvoQsSTR6vRFMr1ItYo7bqIoyIi1UDo6L2caBMSZn66JhnTfd9Qmbxw4dxB53SAaRCG5QZPSqSTwIhf/OMWk9v0JxewWtajE432JyTiQ8Rx1fJL40XcK2z+BiC8US3ZCtQImri2sUFiosvGs3Tt2hcewytaky5ekylq6jlw3KEyXMqoWmiRBVxdYYbA5wDINaoYCmqsRJgl636J3dpPnUElE3EMZeRWHo+zz2Pz/ymr72eg4IXGm3SbNsFFaRZRmGqtIavLGDdl/JTOp/AO972bGvAoeyLDsMXAD+j5t+djnLsrvzr9+76fh/BH4H2JN/bf3N3we+nmXZHuDr+fXb9RrX+b89/QPXbcPA0vWRYvv8xUXWj63Q8zyBDAHGttVI+iFZlCLnzvhhEHBlY4P6vdOEfkTsRUzM1giaHm7fQyvoaEUDRZGpFoTWKoxj1pc2iXohSSh0Wca4TbAmrDVpKGBrU5UKXhhSni4DoFVMvLyHkqQpw6tdnJLFi189IWLZ83QbAHelj7OzLGZP+eASRzFxPyQNxFIm8oUwU1JkWhcaeH7AtSurTG+fQLE0tKJOsOESdwMUUyXqBBQrDgXTRLN15sfGuPTHR+me2uDG9XVWvnmVLIPuahdT19FrFv1GH3PCptnt0bjeFIyqkkUYx7hBwNg90+x6bA/NE2skoVj2bSUaX1pb4/CH7n7NX3tJkkSunqZh6Tr3f/R+jJz9BfDoTfaZN2L92EEqy7LvAK2XHftKlmVbOTfP8oPhnvPzEesAACAASURBVP+o8vDQUpZlz2RCHfgp4In8xx8EPplf/uRNx2/Xa1jBy7aX7/yVuwTtIO+F1ItFHNuiYts0Vlqoskx3uctiYxNzpsCRy5cxxkT/arpSoXFkGXvcQbU0jMkCRl0sJ8J+gKzKRHHC0PMJclmCLMtiRqbIVArCC6ePWURxjLfYFwblfCmXuBGpH3Pj6I0R8nZsooK9rSQYS4UCpBlOSVADipaFUTG5/tINEj8mDROBWAEkTYgiASbeto0syTDHbTRFYbnVYn5mAq1igAS9qx2inuihVe+eQq+ZyJaKpqosr22iVwwxCzHEDLF+1xRey6UyX8UPQ9oXNxm7Y4L+xTZl26ZccEQwRZKNdlLXjizRPbmBWRASAM0WYQ9T79rB23/z4df1HNjicJ3+mxPse9ue0eD/Rq/Xoif1L/nBnLwdkiQdlSTp25IkbQFpZhFJxVu1lB8DmMwDQsm/T7wGj+l2vazCOObSl85y/K+PAXDir48x8EU4gqYodIZDhq4nBJ65sbhQdShYFlHb510ffCtZ3livFMWyRdYUGo02cT+ges8UPddFUYUuycj9eVshCrqqImkyrueTJKngGLU8qnvGSJMUs2Ri6zqWrjPsCfNw2XFQZJmxXWNCAJlmOGMFxopFsYzqDkfLl2FzyPT2CazZwghc50cRw+YQtWgI7MrKAFmTR/Hvu6enUHN0iz1bxBoTWizbMQm7PmHLp3bfDFmWMTtRp7vew9JFMGicpvhrA3RdI+4FTByeoTUcEg8jSnfUR8+7lPOkJubrwpIzVqDbGyJJoJkapy/feF3iom4uQ1Xx8uX7Vp39zgUyGPXC3sj1qgYpSZL+TyAGPp0fWgW2ZVl2D/C/AX8mSVKJHx5d/xMH/kmS9DuSJB2RJOnIa7n78c+9zv/t6ZF4b9v2SUD0qHb94v4Rr9zQNNrDIbIssfC+vehlMcgUTBNzpsDykUW0oo5sKji7KjT7fdo3WkxMCbsLkoQXhsi6jLcimsiFos3A93FMk9q2Gs1Gl+kH59EKOrXxMuVD4zTOC3Fk0PPRc0xMlmVs9vsEedJMsOlBKvL1FFNFq5kkXiz8c4oiljFFgRuOuyH+Up/ualdYbcYLyKokVOiqhLNQQasIhXx36OJtDMky8NYGIyyKO/TpntjAmi3QenFFaKCyDMe2qM9WiXsBJcsShmRNRlJkrh+5BkC44dI6tYFRMiHLRJ+q4IyMz8kgYnyhzue+/TRjj86/7rMn+L46/+aMTVPTUGWZA68A+fKzrlsepCRJ+gTwAeBj+RKOLMuCLMua+eUXEWnEexEzp5s/LuaAlfzyer4c3FoWbvyo+8yy7D9nWXZ/lmX3V8uFW33ob7raEloOfJ+N5RbP/8ULo59t2SBsXSQQd4cuF794BntbmcUlMYC0zjWY3DdB1A3Qaxbute6oGdtsdCHLGF5rM10XA5Z7o0uaZXS6A9I0ZdvCJJ1FkfH3zBdfotPsk4YJvdObrHU6AiJnqKRpShTHhHlDX5Yk+r5P5IaQZXQuNYnaPuakQ29J7Mp5UYQsy2glXQxQPZ+eK2Zi4zM19LqFWjTwlvr460MSP0J1tBGPfa3TQa+YyKo8yvtLs0zk63XE0hVZEtKJKB71uzJE7JViKmystSjZNq1+Hz8IcSYLYrmpSHz3yClWmy36LRFrBVA+NMHv/v5Hf2qv/5YFyo8i3NwpkP4chQLf0iAlSdL7gP8d+OUsy9ybjo9LkqTkl3ciGuRX8mVcX5Kkt+a7eh8HPp/f7AvAJ/LLn7jp+O16jSqIIqI45p4P30ucZ9ndXLIksecDB4iSBF1VqdZK/Pv/7y+ZqVVRZBlnrMDVE4vodYssTpEUSXjsbFvMZGaKBA0Xzw9IwgS3KWKXdFWlWikybA4xdREfPlOtoikK7tBn0Hc5eO8uDEsnjdJRsGgpDwjQVJXJ7WPCV6bKWEUTSZNBkghznVWam4zTMMGcLWBWLSZ2j4+4UWHTQzFUZj+wF8XW0Msmw8sdojimPRiwY88s3nKf9qUmvaUOWsWgtlCDDBI/JthwsaYLuEMfzdQIVodIipBrZLnXb6xWpjMccmB+XnDIWz7fePYY42/bxgd/7z00ej0ur69zfmmZK2vrP/XX/54P38tdH7qbt/zqAzzw0fsFuz6KOPjE4Z/6Y7mV+rGKc0mSPgO8AxiTJGkJ+APEbp4BfDXfXXk238l7O/CHkiTFQAL8XpZlW033f43YKbQQPaytPta/Az4rSdJvAzeAj7wm/9ntGlXZtvHCkDOfP4mqKPQ8jxc+e4QHPno/575wakR5TLdU5onBb7//3SRejFE1yTKYnR/HW+wT+hGapuIslFg/v4FjGPRONvD9YMRNz7JMaJeKNv2+SxAJjEllrkJJkojaPsaEzfK5VcEXV2QUSaLfd/HDkCTnXAEMVvuM5Sk1kq5AkhEPQkpFm1anj6oonFla4u1zNWHubfSpmFWSYYRWEyC91nPLop82V6JzuoFaNjCdAuqyQm+th5oD/9Y7HSEarVqsL21SKxap3D1J90SD0nQJd2OIURQhDfV9E3jXu7gtF7tmM1EpgyTxlaPHmCyX+fC/ef/o+a8Xi6Rpyr0fue9n8vq/vN6oyvIfVT92kMqy7Nd/yOH/9iN+96+Av/oRPzsCHPohx5vAu37c47hdt14vB/af+fzJ0eWbtVP3fuQ+nvvz5xl4HgXLYnymyuK1dWzDoFxyWFrfZLZWI01Son5IpV4k7Ae4no8XhtTLJQauwPmmWUZrdcDeQwtsXmuiKgru2gA3p3x2ugMMTaO4p87mM0skaUrBEpyjYRCglw2SYczUYztov7QKMWwsNxkbqyBJkITJSFf0C+95AK2gMbzeFUQEL0KrGDjbK7ReXAUg2ByyeXaDbe/ejWqruEt9VEPlyvo6+3bNk/VEErMzVyJq+4zXKyIA4sgydt1Br9uYUwUGF1rEUczgShtFFXRNQfaMOX7lKntnZvDC7wedvvDZI+iq+oYZoH4e67bi/E1Q38tJi1s7e0EU/ciGqanrjJVKRHHMhQuLqIrCwtt2srbZplYQfUAvCJh57x7cjkucJBTHi8Ic7HooubVlGARMVyoMV/pMHZxCkiQ0W0dXFAxNY2yuRmW6TPvoGq1+n9qdEyRJiqYoOIbB5lqHOIppH1uj3xgg6zIbvV7eI5Lpui6mplGyLJrnNxhc6QgDdJKg122s2SKDSy20go5Rt/AW+0w/OC/Io+tDUi+m0x0wXa0SDUJ0TWV+1xRq0RDcqCmHqUe2YVfE32qcXCVouCBLfPP0ac6vrPDCxUt87eRJvvu94xy/cpXVTofVdhtNUfj8H30FED2/5A1sOfl5qNuD1D/zOvHXx0bq8CCKOPnkce758L2A8HQd+ewRjv3VUQBOPnmcNE1p9vvIORzP0nWWn7rGRLmMGwiukanrNJ5dRM5ThjdX2yJpJEnY7PUE8qVeH80o1k6vYTg6g+4QJe+HpHEqaJkTNmO1MkHTQ6+IgMrOcAiAqqnE3ZDCWAHFVHn7xx5GLekEG0PGxsr0XBc3J4MGfZHUUrIt4l5A+Q5BD0iGEXEnYOYXdwtZwvqQcNOj2e5xvdFAAuzZIs6uitBXAXrdQi8bdE810CoGcS/AKdmUD45z/MIVaoWCoILmg6Sah1iAMAhvZRlu1e1Z1Kur24PUP/PaYo8/85nn/v/2zjw2ruO+45/f7tv74E2a1H2ZtmxZsi3LR2vHceJDSlrZjuHYQWE1R50ECdCiKFAHAdqgaNCmQFKgaJrESZw4cWr5ki05tmDLimsFkSJbB3VQEiVKok1KlCje5N7H9I83fFnJpE5K3CXnAyx29vfevp3fe48/zsyb+X1Z9vlbWPTQYmdbTied87jd7F7TxKKHFiN6KkE6l7M142IxugYHGdBinZXLGghOi/DiyxvtzJ4FaVsaFjVQHY2iwFHdzeZyWC4XQ/0xyhvK8UV8+L1eBroG7d/wuPDVBrFCHrzlPnwBuyXnsyysiD3R0Qp5QITjG4+AgFguxOt26p7KZsnlcqT7k4TmVxBZUMnQ4V7mPLaIwUQCV8ACETrfbaP/QDfZfJ7+WIy5dXWUTysn05ck3Zck2Z+0Z7/3JskMpjh1so/I1VVErq6i+o7pvPOL9zjW22unCXa78Xu9eCyLaCBAXyxGfXk5IZ8P0ed203ObJ+y6TyZMqpZJTn88zvTKytPmyIzgtayPPeFRSjlSRxWhEFWRCEe7upy8T81v78Nyubhp7lwOdnaSzeW4Zd48BhMJUifjjminS4SGT80l3j4AIqRO2SIJw30xXGILDgRrQvQf7CFYE6Lyxnra17UQmhEl1Rkjlc0ilgtPpZ++tl4iNXa63nzSDmz5dM5pqfksi/I5lQx9NIC4hYpFV9G15SMGD/dSFg6RT+Y4+nIzodowCBw/2sWsmhrKFlbT13zKzvhwVRh1cph0T5zKpQ0ANM6rBGDtj992lt/4PB76hoediZEd3d0MxONcN3OmPXFV69Z5dEqWHS9tNy2pS8QEqUlO0Odjxv0LmIE9YJ7L553WVKHE0a41TQwlElSFw46EusvloqOnx2lduURY/PASdr68g2gwyJLP3cgHL25jy8GDXD9zpr2cJZ8nVB1m6NQQPZs7yKbstMIz7prLUEs3Aa8Xl9eNtzpALpm1Z7t3DlDWX4O/MsBw+yAAFeEQuVgGl89NxexKssNpPGF7MqnK5HHp9W5ej4e6a69i6HAvH546RW1kOj07jnFkyxFmXj8NX12Q/g/7qLqmhv1bW6krK6OutgLxuEi0DyFA7T2zAQjPKR/1HI504dwuF5FAgLDfj1unnKkrL2fRrFlksvbk0oDXCzozg8eyODU0dNmu7VTBdPcmOV63mwPr9rLjpe2OgvDI073CR9H9sZiTFG0omSSjRS1zuttWHgo5EwAjgYDTMounUo5ow6mBQeLpND2dfbhEaD9xipMDA5TXRflg3Q6ysQxW1Mvxrh7cATvzZGh+BZWzKhnc3424XYTqI3h8FsmU3UrKDmXoa+vF7bewyryonMJbHSTQEKGuvopoMECiY5DuoSFu+/wyYm39pLrjVIRCuPwe3AGLqmtr2fy7XRzr7eVgZyfvbNtF5dIGKpc1UK9zl58Ny+22dfS0unIul6M8FOK6ebMQEWeS5Ej30y3iyINVhcNFJxFVapiW1CTH5XI5UxCaXtnpLMNofm23veQkm+WWR5dSrtfJ3fLoUna8tB2/184jNSIm6nG7nYHz+Z+51jl+fUWFPcguQtDrxa/XtiXTaWqiUTt/t+VifkM93qoALssW88wlbKkmlVN4Il5brXcwjdstpBJpWywgm8db5YduRezEMGVXV+Gt8JPsHKbvaK89oB7xEuseZmZjAz1/PEZ4Xjm73t3HzOpqMv1J4p1DHO3qQoCVX7vvgs/fup9soD8Wo1r74vd47GkO+TzHTnRTW1ZmJ5HTY39Z/QChLBRy5od5S2B9XDFjWlKTnMa/uI7deurBks/dSNMrO9m3dg/XPXgDNzy8hFseXcr7L3zA4oeXkMpk+MNvthAJBE5L9Rvy+Zzk/VtXv0/za7ud43vcbvJKMXt5I/1xe0pCcHqEgF6D53G7iXfFyGVzeKK2IvHJ/n4Sx4exIj5ysTRde0/gqw4SvrqSZFfcSRznDlj0tffhuypkL4Bu7WP4cB/eCj+1S+pJ9SVweVy4XC4G2vtxeV0MHewlkU7z602b+LefrWZvezvxVIrceS4DWfeTDad9ttxuplVWEvL5yOl84CP52fuGh51z5LUsUjols6VbUxGtJJPP59n24rbxu6hTDNOSmsTsX7uHVDbrdOva1rcQ1kKfza/tJpvPs/jhJUQDAZpe2cmNj9zE1tXvs0BnhWx+bfdpTwNH2PHSdqc8Z3kje17dBdjLa5RSDH7Yb6uo5PMo9GB8yEumP8mHR08we24DLo+LdE+c4Iwo4nHhqw4ysLfLln7yelFKkRlMU3tDA8kTw1RFIvjqQohbGG7tI9xYRTaX4+D+j5yV/CcHBhiIxTh04gRz6+oIa6ktESGvU5K884v3+PQXPzHmOfvLr9572ucVX7mHTc9tJpPN2t0+Pd6U0aIT2VyOsmCQk/39hHTamJgOiimdLC+vFJRASpRiRUZ76lMKXN84W738k3+a6GpMClpebyalxQE8brcje7Tkczeya00Ti88zEdvhNw84GT2rohEQwVcXJH5siLxS1N89m97tnfaSlXIf7pCHVFec8IJKovMrOfiSPVZ2or+fmdXVJDMZauZV468NkToVp3xRnS3UKULfzk72bm915nJVhEJ0DQwwmEgQ8vvZ3dZGNGjr3EWDQZRSfOZvPr6w4Y2fbkREWPGVey76/L3zi/coC4XI5/P4vV5b4MDvJ5nJkCgYq0plMkWnDjyRPPLVf2FvS9toGVJOw7Skpjgtrzdj6cC0cOUipysI9tNAwW45jXQBrx1lpnrL681URsJ0Dw5y62PLaFvfQiabw+OxyCWzeMv95OIZgg1R2jceBqD+jvl0/7EDf12Ivr1diEucltg1187C7bco81l4ynwkOocZaO+3FYV7EjTvPULX4CBXlf/paVwslaK+osJe5Z9KMbeuDhFh8Q3z+f3WPU4LadNzm7nrr+5gy/NbHYn57sFB/vCbLfTH45QFg2SyWT656s6P+TkWZ2uZNb2y05GMMlwcpiU1Rdm1pgnBziu1b+2eMZfJdLzdyvT75rPn1V24RM65cn7L81sJ+/22KnJ9FBR4K/1YER/eMj8nfneU8NwKwnPK2fPqLuYsnglKke5LMnBqiOp51Xgr/5SlobfpBA33z6ftjQN82N1NQ0UFpwYHUUoR9PkckdNynTlhJLtnLJWie9BePDxWzvCNv9xEJpcj4rcXIt/xhdsu+Dw2vbKz5BbsFgumJWUYlV1rmogGAqd14c6W+Gz6ffMBRh2bGo3bH7+V4+8cIZPN0vVRD9X1FWx6eweV4TAfdXezdO5c8od7QSlqy8o40vQh06bVIHqCZ/L4MOm+JFbYy4b/28Yj31iOyuXZcfQojQ0NDMbjTl52sAe2w34//XF72oGl5bkAaqLRUXOG//bpdwh4vU6CvZHJqxdDMQeo3WuaEJHzvnbFiglSU4zzHV8ajf1r93ysu/fRW4eYef+C0/fr6CDg9VIZDrPvQBs+rTQ8t7aWA8ePs2xRI01bDtA4cxr1lRWkh1J4/B7C06Ls23WEroEB5l91FZFAgE3PbWZbayvV0Sh9w8OE/H7u/eInePfZ3wP28pOhRMIWS7AsGvS8p9Y39p82VaKQzz756Ys+B6WEpRdzlzomSBnOm9HGo84MUIAzVjSYSOD3eKiJRp3JjfPq6jjSdpxrZk0nlUgTKAvwxqbtzKqpwd/upSwYJJXJsLe9HYD7v3Q3bT/o4om/P12f45Or7mTTc5vtfOx6jlL34CCp9S3MWd44ZoCaSvg8HuatuGaiq3HJmCBlGHdGZNT9Xi89Old5Jmevtcvm8/g9HoaG4pTVRtndfJj59fVkslluf/xWANRru4kGAgzE47z36z8Q05NIC9n03GY7p7lWRU5ns1SEQo6s+1Sn5fXmi+7CFhsmSBnGlV1rmvBZFv2xGEGfj5DPR1bLtldHIgwmEiTSaXweD13HeriqvJy8UsRSKbY8vxXL7Saiu26xVIpIIMANM2fy5s9+56Q9zuXzRAMBJzh5Leuy6NWVOmV6uVKpY4KUYVwZyV3lKVAnsdxu8jpFr8+yCHi9DCUSTorgZDpNRcieVR7wehmIxykPhbh+xgw6+/vxuN2sKHIBy2JkMnT1wCyLMYwzSilbDHQktYllq8C4XC58lkVYJ4nzaTVdt9vOC5XN5YgEAuTyecpDIXqHh0lls8yfXk/kDOEIw7mZLF09MEHKMI4c/O0+PG43w8kkmVzOVsjV41Ney8Ln8RBPpeiLxWwdOOBqvQTH7/HgsyxHTPT2x29FKUUinho1F5bh7EymHFbnDFIi8oyIdInI3gLbd0TkmIg06deKgm3fEpFWEWkRkfsL7A9oW6uIPFVgnyMiW0XkkIi8ICLe8XTQcOW4+rMLnWUgfo+HdDZrSz9hq8MKUFtWRlkwiOV2M2d5ox3YLItsPs/s5Y0MJRLO+JLL5SKeSp0mFmGYepxPS+qXwAOj2P9TKbVEv94EEJGFwGPAdfo7/yMibq3F90NgObAQeFzvC/A9fawFQB/w5UtxyDCxeCwLr2Xh12rEfr1YOK6DVSabdcat2ta3ONMHQj4fR9e34PN4aFvfwrENh50864apzTnvAKXUJqD3XPtpVgKrtZLxUaAVWKZfrUqpI0qpNLAaWKmFQu8BXtbffxZ4cJTjGkoEt84Q0Ds8bCsBa/WYyIgYRDbLYNzWkw0F/E4gGojHyeRsmSrA6eL5J8FkRMOlcSn/pr4pIrt1d7BC26YB7QX7dGjbWPYqoF8plT3DPioi8qSIbBORbX0Dw5dQdcPlYP/aPXYXLZ0m6PM5KXbdLhf+oM/JkDCSZymTtcUeMrkcIZ/vtIA0nEySy+dPS3FsmJpcbJD6ETAPWAJ0At/X9tHuKHUR9lFRSj2tlFqqlFpaURa+sBobLjsey7JFPv1+p1WU1aoz6WTGfqpnWU72yuFk0s6nnsk46WECOrunz+Mh5PPhscwsmanORQUppdRJpVROKZUHfordnQO7JTSjYNfpwPGz2LuBchGxzrAbSpCR9LnBqqAzrUApheV2O1k6/UEfPo/HyfiZKkhjEvB6SWcyuN32benz2pJYhqnNRf2bEpF6pVSn/vgQMPLkbx3wvyLyA6ABWAC8j91iWiAic4Bj2IPrX1BKKRF5F3gEe5xqFbD2Yp0xTCxzljc65ZH+/9H1LU6wUsDAYAyfZRHUudAtl4ucy0U4EkTl7GUusUSSkM9HOpM13T3DuYOUiDwP3A1Ui0gH8M/A3SKyBLtr1gZ8FUAp1SwiLwL7gCzwDaVUTh/nm8BbgBt4RinVrH/iH4HVIvKvwE7g5+PmnWHCKQxcI7Stb7FnoefzJDMZexnNwDB+j4dUJsOCzy6k850juETOS83FMLk5Z5BSSj0+innMQKKU+i7w3VHsbwJvjmI/wp+6i4YpwOxRAhdA+1uH8FoWHW+32gnsMpkrXDNDMWJGJQ1Fw4xR0r4YDGamnMFgKGpMkDIYDEWNCVIGg6GoMUHKYDAUNSZIGQyGosYEKYPBUNSYIGUwGIoaE6QMBkNRY4KUwWAoakyQMhgMRY0JUgaDoagxQcpgMBQ1JkgZDIaixgQpg8FQ1JggZTAYihoTpAwGQ1FjgpTBYChqTJAyGAxFzTmDlBb/7BKRvQW2F0SkSb/aRKRJ22eLSKJg248LvnOziOwRkVYR+S+tXoyIVIrIBhE5pN8rPl4Lg8EwVTmfltQvgQcKDUqpzyulliillgCvAGsKNh8e2aaU+lqB/UfAk9gyVwsKjvkUsFEptQDYqD8bDAYDcB5BSim1CegdbZtuDT0KPH+2Y4hIPRBVSm1RSingV8CDevNK4FldfrbAbjAYDJc8JnUncFIpdajANkdEdorIeyJyp7ZNw1YxHqFD2wDqRoRG9XvtWD8mIk+KyDYR2dY3MHyJVTcYDKXApUpaPc7prahOYKZSqkdEbgZeE5HrsBWMz0Rd6I8ppZ4Gnga4vnH2BX/fYDCUHhcdpETEAh4Gbh6xKaVSQEqXt4vIYeBq7JbT9IKvTweO6/LJEdl23S3sutg6GQyGyceldPc+DRxQSjndOBGpERG3Ls/FHiA/ortxQyJymx7HegJYq7+2Dlily6sK7AaDwXBeUxCeB7YAjSLSISJf1pse4+MD5ncBu0VkF/Ay8DWl1Mig+9eBnwGtwGFgvbb/O3CviBwC7tWfDQaDATiP7p5S6vEx7H89iu0V7CkJo+2/Dbh+FHsP8Klz1cNgMExNzIxzg8FQ1JggZTAYihoTpAwGQ1FjgpTBYChqTJAyGAxFjQlSBoOhqDFBymAwFDUmSBkMhqLGBCmDwVDUmCBlMBiKGhOkDAZDUWOClMFgKGpMkDIYDEWN2CnHSw8RGQJaJroel5lqoHuiK3GZmew+Tnb/4OJ9nKWUqjnXTpeaPngiaVFKLZ3oSlxORGSb8bG0mez+weX30XT3DAZDUWOClMFgKGpKOUg9PdEVuAIYH0ufye4fXGYfS3bg3GAwTA1KuSVlMBimACZIGQyGoqbkgpSIPCAiLSLSKiJPTXR9LhQRaRORPSLSJCLbtK1SRDaIyCH9XqHtIiL/pX3dLSI3FRxnld7/kIisGuv3rgQi8oyIdInI3gLbuPkkIjfrc9aqvzuaIvZlZQwfvyMix/S1bBKRFQXbvqXr2yIi9xfYR71/RWSOiGzVvr8gIt4r5x2IyAwReVdE9otIs4j8rbZP/HVUSpXMC3Bja/bNBbzALmDhRNfrAn1oA6rPsP0H8JQuPwV8T5dXYOsTCnAbsFXbK4Ej+r1Clysm0Ke7gJuAvZfDJ+B94Hb9nfXA8iLx8TvAP4yy70J9b/qAOfqedZ/t/gVeBB7T5R8DX7/C/tUDN+lyBDio/Zjw61hqLallQKtS6ohSKg2sBlZOcJ3Gg5XAs7r8LPBggf1XyuaPQLmWor8f2KCU6lVK9QEbgAeudKVHUEptAnrPMI+LT3pbVCm1Rdl3+q8KjnXFGMPHsVgJrFZKpZRSR7EFcZcxxv2rWxT3YAvqwunn64qglOpUSu3Q5SFgPzCNIriOpRakpgHtBZ87tK2UUMDbIrJdRJ7UtjplS9Gj32u1fSx/S+E8jJdP03T5THux8E3d3XlmpCvEhftYBfQrpbJn2CcEEZkN3AhspQiuY6kFqdH6sKU2h+LPlFI3AcuBb4jIXWfZdyx/S/k8XKhPxezrj4B5wBKgE/i+tpesjyISxlYh/zul1ODZdh3Fdll8LLUg1QHMKPg8HTg+QXW5KJRSx/V73PW+WAAAAYZJREFUF/AqdhfgpG4Oo9+79O5j+VsK52G8fOrQ5TPtE45S6qRSKqeUygM/xb6WcOE+dmN3l6wz7FcUEfFgB6jfKKXWaPOEX8dSC1IfAAv0kxAv8BiwboLrdN6ISEhEIiNl4D5gL7YPI09BVgFrdXkd8IR+knIbMKCb3G8B94lIhe5i3KdtxcS4+KS3DYnIbXrs5omCY00oI3+8moewryXYPj4mIj4RmQMswB40HvX+1WM07wKP6O8Xnq8rgj63Pwf2K6V+ULBp4q/jlXyCME5PIVZgP3k4DHx7outzgXWfi/1EZxfQPFJ/7DGJjcAh/V6p7QL8UPu6B1hacKwvYQ/ItgJfnGC/nsfu7mSw/2N+eTx9ApZiB4DDwH+jV0oUgY+/1j7s1n+09QX7f1vXt4WCp1hj3b/63nhf+/4S4LvC/v05dvdrN9CkXyuK4TqaZTEGg6GoKbXunsFgmGKYIGUwGIoaE6QMBkNRY4KUwWAoakyQMhgMRY0JUgaDoagxQcpgMBQ1/w+a52vevBPI3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(array_orig_firstBand, cmap='pink')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cv.imshow('Pic', array_orig_firstBand)\n",
    "#cv.waitKey(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "General configuration for OpenCV 4.2.0 =====================================\n",
      "  Version control:               4.2.0-dirty\n",
      "\n",
      "  Platform:\n",
      "    Timestamp:                   2020-04-04T15:48:58Z\n",
      "    Host:                        Darwin 17.7.0 x86_64\n",
      "    CMake:                       3.15.5\n",
      "    CMake generator:             Unix Makefiles\n",
      "    CMake build tool:            /usr/bin/make\n",
      "    Configuration:               Release\n",
      "\n",
      "  CPU/HW features:\n",
      "    Baseline:                    SSE SSE2 SSE3 SSSE3\n",
      "      requested:                 DETECT\n",
      "    Dispatched code generation:  SSE4_1 SSE4_2 FP16 AVX AVX2 AVX512_SKX\n",
      "      requested:                 SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX\n",
      "      SSE4_1 (14 files):         + SSE4_1\n",
      "      SSE4_2 (1 files):          + SSE4_1 POPCNT SSE4_2\n",
      "      FP16 (0 files):            + SSE4_1 POPCNT SSE4_2 FP16 AVX\n",
      "      AVX (4 files):             + SSE4_1 POPCNT SSE4_2 AVX\n",
      "      AVX2 (27 files):           + SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2\n",
      "      AVX512_SKX (3 files):      + SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2 AVX_512F AVX512_COMMON AVX512_SKX\n",
      "\n",
      "  C/C++:\n",
      "    Built as dynamic libs?:      NO\n",
      "    C++ Compiler:                /usr/local/opt/ccache/libexec/clang++  (ver 9.1.0.9020039)\n",
      "    C++ flags (Release):         -stdlib=libc++   -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Winit-self -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -Wno-comment -fdiagnostics-show-option -Wno-long-long -Qunused-arguments -Wno-semicolon-before-method-body -ffunction-sections -fdata-sections  -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG\n",
      "    C++ flags (Debug):           -stdlib=libc++   -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Winit-self -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -Wno-comment -fdiagnostics-show-option -Wno-long-long -Qunused-arguments -Wno-semicolon-before-method-body -ffunction-sections -fdata-sections  -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG\n",
      "    C Compiler:                  /usr/local/opt/ccache/libexec/clang\n",
      "    C flags (Release):           -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Winit-self -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -Wno-comment -fdiagnostics-show-option -Wno-long-long -Qunused-arguments -Wno-semicolon-before-method-body -ffunction-sections -fdata-sections  -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG  -DNDEBUG\n",
      "    C flags (Debug):             -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wuninitialized -Winit-self -Wno-delete-non-virtual-dtor -Wno-unnamed-type-template-args -Wno-comment -fdiagnostics-show-option -Wno-long-long -Qunused-arguments -Wno-semicolon-before-method-body -ffunction-sections -fdata-sections  -fvisibility=hidden -fvisibility-inlines-hidden -g  -O0 -DDEBUG -D_DEBUG\n",
      "    Linker flags (Release):      -Wl,-dead_strip  \n",
      "    Linker flags (Debug):        -Wl,-dead_strip  \n",
      "    ccache:                      YES\n",
      "    Precompiled headers:         NO\n",
      "    Extra dependencies:          -framework OpenCL ade Qt5::Core Qt5::Gui Qt5::Widgets Qt5::Test Qt5::Concurrent\n",
      "    3rdparty dependencies:       ittnotify libprotobuf zlib libjpeg-turbo libwebp libpng libtiff libjasper IlmImf quirc ippiw ippicv\n",
      "\n",
      "  OpenCV modules:\n",
      "    To be built:                 calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio\n",
      "    Disabled:                    world\n",
      "    Disabled by dependency:      -\n",
      "    Unavailable:                 java js python2 ts\n",
      "    Applications:                -\n",
      "    Documentation:               NO\n",
      "    Non-free algorithms:         NO\n",
      "\n",
      "  GUI: \n",
      "    QT:                          YES (ver 5.13.2)\n",
      "      QT OpenGL support:         NO\n",
      "    Cocoa:                       YES\n",
      "    VTK support:                 NO\n",
      "\n",
      "  Media I/O: \n",
      "    ZLib:                        build (ver 1.2.11)\n",
      "    JPEG:                        build-libjpeg-turbo (ver 2.0.2-62)\n",
      "    WEBP:                        build (ver encoder: 0x020e)\n",
      "    PNG:                         build (ver 1.6.37)\n",
      "    TIFF:                        build (ver 42 - 4.0.10)\n",
      "    JPEG 2000:                   build (ver 1.900.1)\n",
      "    OpenEXR:                     build (ver 2.3.0)\n",
      "    HDR:                         YES\n",
      "    SUNRASTER:                   YES\n",
      "    PXM:                         YES\n",
      "    PFM:                         YES\n",
      "\n",
      "  Video I/O:\n",
      "    DC1394:                      NO\n",
      "    FFMPEG:                      YES\n",
      "      avcodec:                   YES (58.54.100)\n",
      "      avformat:                  YES (58.29.100)\n",
      "      avutil:                    YES (56.31.100)\n",
      "      swscale:                   YES (5.5.100)\n",
      "      avresample:                YES (4.0.0)\n",
      "    GStreamer:                   NO\n",
      "    AVFoundation:                YES\n",
      "\n",
      "  Parallel framework:            GCD\n",
      "\n",
      "  Trace:                         YES (with Intel ITT)\n",
      "\n",
      "  Other third-party libraries:\n",
      "    Intel IPP:                   2019.0.0 Gold [2019.0.0]\n",
      "           at:                   /Users/travis/build/skvark/opencv-python/_skbuild/macosx-10.9-x86_64-3.6/cmake-build/3rdparty/ippicv/ippicv_mac/icv\n",
      "    Intel IPP IW:                sources (2019.0.0)\n",
      "              at:                /Users/travis/build/skvark/opencv-python/_skbuild/macosx-10.9-x86_64-3.6/cmake-build/3rdparty/ippicv/ippicv_mac/iw\n",
      "    Eigen:                       YES (ver 3.3.7)\n",
      "    Custom HAL:                  NO\n",
      "    Protobuf:                    build (3.5.1)\n",
      "\n",
      "  OpenCL:                        YES (no extra features)\n",
      "    Include path:                NO\n",
      "    Link libraries:              -framework OpenCL\n",
      "\n",
      "  Python 3:\n",
      "    Interpreter:                 /Users/travis/build/skvark/opencv-python/venv/bin/python (ver 3.6.8)\n",
      "    Libraries:                   /Library/Frameworks/Python.framework/Versions/3.6/lib/libpython3.6m.dylib (ver 3.6.8)\n",
      "    numpy:                       /Users/travis/build/skvark/opencv-python/venv/lib/python3.6/site-packages/numpy/core/include (ver 1.11.3)\n",
      "    install path:                python\n",
      "\n",
      "  Python (for build):            /Users/travis/build/skvark/opencv-python/venv/bin/python\n",
      "\n",
      "  Java:                          \n",
      "    ant:                         NO\n",
      "    JNI:                         /Applications/Xcode-9.4.1.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.13.sdk/System/Library/Frameworks/JavaVM.framework/Headers /Applications/Xcode-9.4.1.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.13.sdk/System/Library/Frameworks/JavaVM.framework/Headers /Applications/Xcode-9.4.1.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.13.sdk/System/Library/Frameworks/JavaVM.framework/Headers\n",
      "    Java wrappers:               NO\n",
      "    Java tests:                  NO\n",
      "\n",
      "  Install to:                    /Users/travis/build/skvark/opencv-python/_skbuild/macosx-10.9-x86_64-3.6/cmake-install\n",
      "-----------------------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cv.getBuildInformation())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "error",
     "evalue": "OpenCV(4.2.0) /Users/travis/build/skvark/opencv-python/opencv/modules/highgui/src/window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'imshow'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-b8a15a1c3c20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'First FCC'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitKey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.2.0) /Users/travis/build/skvark/opencv-python/opencv/modules/highgui/src/window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function 'imshow'\n"
     ]
    }
   ],
   "source": [
    "cv.imshow('First FCC', img)\n",
    "cv.waitKey(5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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