test_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sc\n",
    "import cv2 as cv2\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io as scio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comb_data = h5py.File('../Aditya_data/combustion_img_13.mat','r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'test_set_x',\n",
       " u'test_set_y',\n",
       " u'train_set_x',\n",
       " u'train_set_y',\n",
       " u'valid_set_x',\n",
       " u'valid_set_y']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_data = comb_data['train_set_x'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = comb_data['train_set_y'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54000, 1)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test = comb_data['test_set_x'][()]\n",
    "y_test = comb_data['test_set_y'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 18000)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25000, 54000)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testimage = train_data[:,5039]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testimage = np.reshape(testimage,[250,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x52baf10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M7IjzVCjf6CGP+qxzpdSs9CyUiG5phY95FI7yvNExAlFcuXK73q9R3+p1FGqpGr3vZlSt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\nd3R0dNwQdIHe0dHRcUPw/wGvh/KCdxE2ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5272950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(testimage.T, cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img = testimage.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KMlbHnRLwaDTKkgUdcpoY0mq1cswzj6chfED1Kd20hPf393Hjxo2sU7NdRofU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\nQETbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5272e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edges = cv2.Canny(img,100,250)\n",
    "plt.subplot(121),plt.imshow(img,cmap = 'gray')\n",
    "plt.title('Original Image'), plt.xticks([]), plt.yticks([])\n",
    "plt.subplot(122),plt.imshow(edges,cmap = 'gray')\n",
    "plt.title('Edge Image'), plt.xticks([]), plt.yticks([])\n",
    "   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa796f90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAACoCAYAAADw6BWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7b9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(th2, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ret1,th1 = cv2.threshold(img,40,255,cv2.THRESH_BINARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa8d3ad0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAACoCAYAAADw6BWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXZIGwkCXpIEw0CVpIP4f6W3Wb3AKvh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8a43d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(th1, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xaabd2d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JDbgR+AzwWeAdXc23DzfgJcC/NbdH95YfeBGjvfiPA38PXL7ptq5p+T/EqJTwP4zqnrdM\nW3YgjEZEfRZ4BDiy6fZ3sC7+slnWTzEKWleOTf+OZl2cBl676faveF28ilE55VPAw83txl3tG6u4\neei/JA2EO0UlaSAM6JI0EAZ0SRoIA7okDYQBXZIGwoAuSQNhQJekgfg/eo3dVsSNsScAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8ba810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_th1 = cv2.Canny(th1,100,250)\n",
    "plt.imshow(edge_th1,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xab507d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAACoCAYAAADw6BWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0kAY6JI0EP8HoQCw7WKqb8cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaaa0990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_th2 = cv2.Canny(th2,100,250)\n",
    "plt.imshow(edge_th2,cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb2e5c10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAACoCAYAAADw6BWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXZIGwkCXpIEw0CVpIP4f6W3Wb3AKvh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf20d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "params = cv2.SimpleBlobDetector_Params()\n",
    "     \n",
    "# Change thresholds\n",
    "params.minThreshold = 0;\n",
    "params.maxThreshold = 256;\n",
    "\n",
    "# Filter by Area.\n",
    "params.filterByArea = True\n",
    "params.minArea = 1\n",
    "\n",
    "# Filter by Circularity\n",
    "params.filterByCircularity = True\n",
    "params.minCircularity = 0.1\n",
    "\n",
    "# Filter by Convexity\n",
    "params.filterByConvexity = False\n",
    "params.minConvexity = 0.1\n",
    "\n",
    "# Filter by Inertia\n",
    "params.filterByInertia =False\n",
    "params.minInertiaRatio = 0.1\n",
    "\n",
    "detector = cv2.SimpleBlobDetector_create(params)\n",
    "# Detect blobs.\n",
    "keypoints = detector.detect(th1)\n",
    " \n",
    "# Draw detected blobs as red circles.\n",
    "# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob\n",
    "im_with_keypoints = cv2.drawKeypoints(th1, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    " \n",
    "# Show keypoints\n",
    "cv2.imshow(\"Keypoints\", im_with_keypoints)\n",
    "cv2.imwrite(\"testimage.jpg\", im_with_keypoints)\n",
    "\n",
    "plt.imshow(im_with_keypoints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xadbfd50>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAACoCAYAAADw6BWzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXZIGwkCXpIEw0CVpIP4f6W3Wb3AKvh8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad488d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im_with_keypoints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from skimage.feature import hog\n",
    "from skimage import data, exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KxQa7TU1NWX8oTevlctmbZ/p8IBB5QMCLB8G0vgzgRoTTl6vLqRSqdaufmWXR\nxNvMtOtqCO6SMR9cMzvP1/u4Jm+Wrb56Su1tbW0NS9IoIPCPWnY+n0exWEQ2m00E25HIqQnzdxVi\nXLOm+uJpQi8UCsjn82hvb0ehULBETs0ilVrIQV2tVhMCRCqVQrlc9losXIsC2wycm8QaAUnoGF2s\nf5fK/LVUZrHFjtOFpSlMNX1oNptFd3d3YhmXZgpzswT67u1LjsRsZV1dXTYWxbcWmm4k3TkMWAgQ\nNcagp6cHhULBmq1ZL9bpdPK8czmpgqbwSy+9FKOjo14/OLAQfOdLkXq6WLt2LV72spfZyH1g4bnx\nvcxms4k0rxs3bsSqVaswNzeHH/zgBw3PeMOGDRgcHLRWxhMnTtggQJavmSdb6R0Pjr9zAF1eRdOw\nmopVM+d3JVEeIwHpnyZPce9D/y7rsBjcbTrVF6wmbSV6jSwHYNulGdFU6GCblPjy+XzC9E3C7ezs\nRGdnJzo6Oux/brygGnUul7PX5XK5RJ1pQletnOTOl533bm9vR3t7e+K5+DZB0Uxx7np0/lcBK+Dc\nYqkJVIWp53P9UscLhUKCxIHYvcRNRgA0rMVm7m6CMSLufQkug1IoaWez2UWzl/n8uvp+q++ZmJub\nW1TQd+vqM3vrHNOMxImzIXEAuPXWWxMkDiSFZ90ilVhqnfjevXvt8ZUrVyZInOX7LJitgDAbnSFc\nDVkDyZR4NaBM/+jT5V8+n7daJQmnUChYLdNdPqWSt5JJM5+TrgN3/dmuIOEKGiQxJTXel4Tokr97\nHv/cADsSOttIEm9vb7fky2MkYfZLoVCwv6nQpKZ7fS7AgrVEhRDVjDS4z7dBimrpGgkfcO7g0xSb\nWaJ8CYZ81y1FXu71tAy5ZFyr1ay1CEhuHQosuMyo+XJJmlsWBQmuc9a2GGNQKBQalrdpHZm0BUBD\nXebn59Hf32/XSPO+K1euTJTl9jHXkGtbgTjKXHHgwAGsXbsWF198MYB4uZwP/F3vWywWG85bu3at\n93qfIMI20hLnrsHfsGEDAGDHjh1eYY19fP/992Pbtm148sknvccZw9RK2jjQoqb15xMoodc0u54v\nTLOy3SA0V0PWc0hm1AoBWJMNj+l3d3mZG+WtSVlYloJR3XotiZ/1UgJ21127ZVEzrVaryOVytk9Y\nrrvTkboHOOnNzs4mtF/2DUkaiF9QkqebZMatk7t8jGZ0avvZbBbFYtGSNvtY19azL2ZnZxOahwo8\n9IfTr66U3dNEAAAgAElEQVR10En3hQzYeamAY5Y5A9wJlO9boVCwMQx8T6MoSoxdjiMlfN2dr729\nHdPT04lJW4XQNWvW4MCBA3bsEiTlyy+/HBMTEzh48GCiniS2DRs24Pjx49i9e3eiDVwjfeutt+Lw\n4cO47777EkTF+g0ODlp3koJtHB4expEjRzAxMQFjjJ0LKPACMSkfO3bMav3VahVTU1OWSF/xilck\n7r17927kcjlbx40bNyZI8uGHH05YEG677TbrRtBguV//9V8HANxwww3Yu3cvdu3aBQD4t3/7NwDA\nY489BiBe5/7qV78aTz31VKKN11xzDYCYaO+66y685S1vSRx/5Stfaf8fPnzYLl1jpDoT0+zZswff\n+973cOutt9prd+7cacfD3r178fu///sNa+11/4ilNmVZjmjZYDclXVeq9m3O4U7q9fs1+Hh9/iu9\nn5Ijy9OlV/rZNaG79VHidTf3YLQ1/dC6+5YSu0a28zpuaagmcX6naZykms/nG4LQ2DYADYKDm2VN\n66gR+epiyOVy6OrqSlgZ8vl8oq90KVuzDG7sO9a9s7PTtkcFE/YtzYl8KblOnH559uno6KjNaDUx\nMYFjx44lAuAmJibsd+7CxnLOF15MwW7ukkrfcX3X3HdUo6ybCeM6HoCkD5sWsVKp5M2xn8/nMTg4\niCNHjiCdTts8CgStaTT19vT0JEgOAK6//nrcd999NnDV1brVVVQqlWxWQ/c4V1i4dcxms1i3bh0O\nHDhg5w5aF9x+XrNmDebm5hKpTLu6upBOpzE2NmaJe2xszB5fsWIF1qxZg1KphI0bN+KrX/1qouyV\nK1eiWCxibGwMpVIJlUqlwQWimrbPNH7o0CFcc801Ddo4sWbNGpw4ccLGKjBQ1cUf//Ef44knnsCe\nPXsswWs9X/e61+Eb3/gG9uzZ4x1vnEsXS0t9rvGSD3ZzI7gpZSvBq/bkCgCulk7Cc5eDsRwSJ6/T\n4C2WQzAwg9e46UwJ1fJIsm4Uue/+eh3QGPTmmhVdgUfNyW6deL72i2teZLIWwrem3GcqVTLmS8Oo\nfJ202QbVwtWUXywWE1ncCBVutEwVphiNX6lU7PpWCkAkeZK3/nEiXU4CcCvDZy5vdpzjFUgmS9JY\nh2ZWN1cwJSlyU56Ojo5Ewhe9nsFR1PbduYOkTEGBGjCJ6tJLL8XOnTvte8S9wan5UgOkBUvr6zvu\ntpFlFYtFKxDPzc0hn883EF0URTh69Cjm5uawcuVKHD161K4j5y6FJPCenh6MjY1hxYoVGBkZwWWX\nXYZ77rnHGwNw9OhRDA8PY2RkBB0dHbYv2N+uudzto4985CP4i7/4i0VJnH55Wgp9WnM6ncaOHTtw\n8cUX4xvf+AY2bNiQIPNLL70U999/P175yldi9+7d3sBh5pRwFb/ljJYjckZ9Ao3kqb4NNXU3Iyn9\nz8/68ruRzHoez3UtAm70uWt2pqTn+moBNJh/WR4jrnVA8Tqt12LmXjU3uyZw16Lg6y+3z1gf1X7d\ne6tfnwSspKwBd0zgoqZOVwBzXRbt7e2J85eCJn/h5JjJZKxJt1wu236ltk5Nnd/pPw8m9XODpSKE\nmbzH1e50DPuud+NB3OMsj5uFuBqyXu9uNuISpJIygAby/NGPftQg4Gt9SNAkpmq1mvg+OzvrDXBj\nHavVaoIo+V77tFW2PZPJWI18YmICqVTjSgES+sjISIJIfejo6GjQsPWZ+Qhaz3/LW97S1GcOxNq6\nEj+tkc3GDQPfdJ340NAQjh49ikwmgz179jSQvGKp8pcbWorIdYJXUuNE70Zm63VKPixDiYWkpMtD\nfIPbhQoM/O9qAj4/LV8mnynQXerF/3pchQgSXDPJUaOs1UTO+rprtNva2rwRnK7wo5Ooz8LA8mi6\nV4sCgMTv9NXrDmV6LwCJ9fg026vFQ/uRdfKNFU66NJ3ncjnMzs5iZmYmYSXw+c8DiZ97LPWOLbU/\n9VLXL3XcjVJ34UapuwS52C5exFJjRrVLCpKKpbKPkSibRZyzXKKZYNQMpxOlfraR6s20cUKJv1l9\n+azdzWQA4NixYzh27Bi2b9+OBx54YNHr1frTClhWRN5Mm/SZtl3Tty+gidcASPiW9F4kedUoSWYk\nS18gG393NXofXFPcYmSgv8/Pz1tfk6vp+8rRoDLCjbTW1KpAcgJh+6ix0helpn1NTEOTpkummj2N\nS8cYaU6fvEbJM/iNvkrWXyOD1XLgRtyz7j7LCLAgAKRSqYR2zeApthOIx0lnZ2dC41Nz5vP1m/nG\n6EsNzdZY6/vE70qO1MgZrZxOpxNlcMy6Wfzc8lmWW75bx0KhgImJCfubGxOj68aBOIiObheOSVc4\nYB2KxaJNLKRgKlEKCe5cSKsA06i6MRocv+wHl4i4Fp4C8KpVqxJBec3ihXz40Y9+hDvuuAP/8i//\nYn/r7+9HuVzG9PS0jV+ZnJxs6FsqMD6i1OcIxOvzVThQd8jc3Bw6OjoSAs6dd96J66+/Hr/3e7+X\neGbE3XffjTe96U344Q9/6G3XmcLnulgKL5SpflktP1uKxH3mcF6nGpNrgtZJ3ke2+vI3M8FrWYv9\nzgGk2r9eo5/VLK4+PmqNNPnSP6cBWoRGjbtlELr0y627nqv/NaiO5k/fkjoNzlOCZuAZJw6NLNfA\nPo0J0H5Qoua1uiwtl8vZqPVCoWDXoXNZWkdHR2IZX3t7e2I9ugoPrJPWUf+zvoTPl7sYtC1qhTld\nuOPHtZQsd2h/+ZbuuUFbLskqafvM7G7QpzuP6HvtW7qkoL9Y4Vr6XBIulUqJdeadnZ0N5Wq8yuDg\nYMNxzQfuU2jUKpDJZBpIkKtLmh0vl8tWML7ooosaIuvVTXc6ZKMkDgAnT55Ef3+//e6SOAC7/SqA\nhEJAqGm9o6OjQcPX5+5zNWi0u89Cefvtt+MrX/lKsyadMc6UxIHkONBd684Wy4rIm2m3JAvfBOZ7\nYC4RuWZp/lczs68MX918Pm+FS97u+b66a7YwfidhU2PUcmq1WmJplOtn52++Nd2qoetmI25f63ct\nZ7EkN7oUTwm3ra3Nrg/X89x+o0mR1hNX4HDvqWTLSPjOzk4Ui8XEWnOSta4g0HXy+luzpDw8zxUM\nF4NaD9wcA2dC5vpMfH3XKiAJq6VFAxJ9yV6oabLP3fer2ZJNJW4FLVDNhDHe36csqMvHJWtqmlwH\nrvdVSxm1ee6dDSyssW72XLnOm8srXV8061SpVBK+dbfcKIoSvuzh4WEAzQmFS9JcvOY1rwHQuM58\nenoaHR0dWL16NYCk0LZt2zbbRxwH3BcdiJelqUXDHQcsi8qCe3xgYABA7Gv3CUKvetWrAKDpPuS+\nAD5gYX26L4kPAPziL/6i93cfNGCYwpLbh88Xy8q0DjQ3r7vroVXidKPM+b+Z9qzmeLds19yuUPNf\ns/r5ftO6uEKJTtKuRk2yVqFDyVtN9q75kGWrz9kNHpubm0vUUTVkJTdex7Sn7nIg7Sdq7yRazbpG\nzVeFDAopjBQFFnyOlUolIRwoiZKcNWCOn7Uv3XSsuhyN4PUka9dywJgB3Uud/eszlbvm/8WW9y1l\nZmcZi0V2twJSqRT6+vowNzeHUqlktRm6LqIoQnd3N6anp5HL5axGNzs7a5cU5nI5zM/P26VHQNyP\nJE2NPNcc+tSWGXXOvAi+qOdyuWzdSXw2Skj9/f0NWjtzIXR0dKC3txfHjx9vcONxHKxduxaHDh1K\naKylUgk9PT12nTvbwP/j4+Po6+tDsVi074wqCly9ASzsbQ7EWvGOHTvsvDU7O4sbb7wRx48fx/bt\n27F//34AMUlRW56ZmcGmTZsAxJHobvYzAPjd3/1dPProowmNuKOjA+VyGStWrEBPTw/y+XzC8rF7\n925s27YN3d3d9r1WK8Tx48exadMm9PX1ob+/325J+vd///cA4k1Z+D7edtttlgDf85732LauW7cO\n3/rWt7zK3eOPP47Xv/71DevHiS1btlgyr1ardgc3BssNDAzYfhkfH0cqlcKtt96KL3/5y7aMX/7l\nX7b9plu0UnBSBaKvrw+pVKrB+vN8seyIXDVAdxmRSyJA0gfi+sX0dyUDn/mNBOEGkrmaYbM6s4xm\nvnPXz67nNHMVuCThCgYalKdw0zFqvfjH8ly/mNvvrKcKB+pvpwCg9yEhqlZOzbmZdYVChcY00B/K\n+7kBeEyX6loJWI8oiuy6b5246aOjeV8JmwIHJ6JqtWpf8HQ6bcmBY4iWEy1PBQv65+m/5eYwFCjU\nLeKaQ10BRgWwVgE1176+PoyNjTWYQ0nC/f39mJmZadA22V5uT+te75rqXXOn+tMHBwcTa6eBpEY/\nMDDg3bWLz3/t2rWYmZlpKIPlDw8PY25uzhv0NT8/j+3bt6NcLjdohalUygoww8PD2LlzZ+K4MQZj\nY2Po6enB2rVrLQETmiXu2LFjDZHYqvkeP37cJlEhVCu84oor8MgjjySOq8b+9a9/HbfffntijTmw\nEDC4du1apFKphudQKBSwe/du/MIv/AJ6e3vx4Q9/OHF83bp1qFareOc734lSqYS3v/3tieMUUG65\n5RY88MAD+MIXvpA4Xi6XceDAAQDxvKSuDvb5pz71KXtcs9KdPHkSMzMzduXCli1bsG/fPltvICZ3\nujfe+ta3IooifPSjH01o8t/85jcBxGvVb7nlFhw+fBgA8OCDDwJYsJCsXbsWY2Nj9vi5wLJKCJPN\nZiMgaT70rTN114zqWmjXtMbrmkVgu7+5JnNf8JwSsEuYzcz/auJWn6uWR/LRNcxqIVisPq7mmslk\nbNIV1VabEQzLVFLWsjXpiusvdlPAuiZvbjSh17mkxQldyTifzyeWxNFMzl3P+vr6bOpWbZfGC+ha\ncGppJFSuRR0fH7dRt6Ojo9i3bx/Gx8cxMTGByclJnDx50pI615/PzMzY56XmWPYX+96NrNekMhr7\noIl2VDhTAVCtNJVKZdmr5saYiM+wVCpZ363OOerz9gVB6TvJMaLrsHmcWj2vZ7mcP+hz5fk0cfP7\nUnuG83pep2Z8IBYSjh8/bsc+AymBWIjp6OjAwYMH0d3djVKplBDctI2+TVkU3d3d6OzsxHPPPWfX\nedPcns/nMTs7i0KhgMnJSdsHw8PD2LdvH7Zv346dO3cik8mgVCrZ+w4NDeHYsWMNa6fZZlogPvOZ\nz9gMbj5ccskl2LVrlz1f/dy9vb2oVCqYmZlBJpPBmjVr0N3dbQPPbrnlFjz00EMYHx9HJpPBBRdc\nYMkUiMmV2eDomvH5qKlAzMzMwBjTsHSQ/X3ffffhmmuuwate9Sp861vfaroUjePsF3/xF/HlL3+5\nIcBtqf3MdUvXrq4uzM/P2z7RuepsE8IsWyJXKJkpefMY0KiZqwYDNK4Dd7+74G+MZldi0zJcbbvZ\nmnUlJJIisKBRqEuAa5d91gB1C7hL6TQ6W33gSuaalpWE4/aB2zdqIlaNnte4AXg0e2u0Otd8E7oe\nm/2rwoKSINvrBqD19vbaYDcSRnt7e8KqUqlUEhOnxk5UKhXMzs5aEi+XyxgfH8f+/fvtnuRTU1M4\nceKE9d3zGmb3UgFGTfu6cQstEboDG5e+kdB1jbo+a30XKDREUYTp6emWIHIgGa3ssyZwsiTRG5Pc\neUuj1mnR0HL0naSf1R2PtVotIXBp+V1dXZiZmWlq6aBJXyPn9TkxEUupVEqc65ZRqVTQ3d2NyclJ\nr0WwVqvZiOyenp6EYME2URBwyZ7WIE18pKbtTZs24ciRI1aIzOVyCUJaKmr9jjvuwIkTJ3D33Xd7\n+4ibIB0+fBhdXV1oa2trsDywDc1WL7hR7atWrcJzzz1nj5M08/m8vVbL4LyVz+fxoQ99CDt27MBd\nd93VUNdUKoWenh587GMfw+233+5tjw/PJ0p9MWgfv6iIPJfLRUCjmdsHn//afUEJJT4Ocp0kdbmU\nq/Gq71b9xfRbaqYxDSojfMFjqn26LgASBDVHn/nc55tVIme51ApVk1b/b1tbGzo6OhJ1dn2wrvDh\nuj2UzCjNM7jNjRZXCZSExGvdoDJ+1ix1/GNZXV1dia1M1cRObVctGyr0KZnTZzszM4PJyUk899xz\nlsinp6ftsiCSr2r22m7tR25247oGSNwU1rQfKpWKrZdq5CQoFUZaiciJZkR+usfdZV9nev25wFJ1\nWOr4UhqcL2r/TK53hRgXP4084r7If8XZjoOlCFU3lvGhGU+cT5wtkS8rHzknRDWNKpH6tFPfd592\n6T48nxbuu15JlIRD/7RrhtagJtf07d7f1ebd9dh6Tx/J+oQcCica5e72g04Srt/e119KpKqdu64P\n/a4kzCVonGCaxSa4bdNnoAF7KkC4ebFJ8sBCClb6vTiefIKKEi7/aKas1Wro6OhAW1ubjQqm1qf9\nrO0wxthAPPYVNXIlemDBmqGWGVcbZb1V82xFLEWySx1fjCBP5/pzgaXqsNTxpRK7LJVcZqnrl0pi\n8tPYDGSpAK6zHQdLacVLJfhZbiR+LrCsiFw72E34AiyQerNIYYVOfBqtreX5JLPFfNHu7+6adfd8\n19xNqH+axFSpVBKmcpqPqK1xcKv25yNydSkweQKQTGRCE20qFQfz6R7bPkInEfEc1p/l0c9O4meU\nOoUe3qeZlYXnaN2p6bIuSrZsP/2t1FLT6bTVavm7xhloDIXWQT/T3UChiFYN9W1HUWSflyYa8rlb\nKNSoSZ2WAraTQgehz4p9rhaEVso4BSwIexTImpFJNpv1ZjXT45lMpiGdKu9BP3mzHNwc6z6ypabb\n7DjzkjfTeLnsLJ1Oo6enp8GsTC21UCigq6urIWBO29jW1tY0SxotXG6wGQC8+c1vxuc//3nMz897\n+6izs9OuvPCRLfvAt8QNWOhDX7IXICn8+8iY1oB8Po/f/M3fxD//8z8njjOnO91lo6Oj3j5oa2tD\nZ2enN6Zh1apVWLt2LXp7e/G1r32t4fiVV16JXbt2oa2tbVGh6Fyb0Yne3l6Mjo5618mfDZadaK/k\nqH5jNYueyRIcJVHVjlw/nN4LaMwUplAfLCdid603y9Cy+MeADI0i53f3Op9AsVjCGxeuFkzi0cAp\nfid5aaS0qy2z/ho4pxHquiyM17KPqtVqIhpfo9S1zW48gd7XXbvu9oP6sl2CdPMGqM9ZfeeMLWA8\nAQPquD49n8+jWCyio6PD/mcyGk1Co591rbvutc6+U9eBm1hHk+ao8NcKIDkTbnIVty2uYExLBduu\n6YT1eg3S4vl6PcHxwOOMJKYg6B4nmMqX41H3G9frKQhwLTXvwQC9trY2VKvVxLpkdbX5BAXei+Oe\ndaRVhxHYe/bssRnctA1ch873m+WzXEZ4uzne2TduX3BpltsHhG6pqqAla+XKlTZVKtHX12dzzvsE\nAdaB80szy8WRI0dwww034Pvf/z6ABYvWTTfdBCC2aGgbOY6arSMfrq+115UBigsuuMD7uw8DAwO2\nDwhdUXA2WFYaOeGaVl2cjl/fNVOrL9N92X2CgWvadrX5ZloRze9qNVBzsJKzq6mzzj5Nu5k5SAne\nzeCm11ODZfkkLwYZ+eIDeK1LmO5/viy6/IrEw/v4+lO1fBUYNBBPYxA0SE8j1Nlvqqmrdt/MJ6aC\nmEaPs80sP5fLJcaTS/yun1zdKRqboYILsDCGdD0/g32WcgO1Cubm5tDe3o5qtYqurq5EjAGQ3N+e\nWopO1LOzs3aSZepUXUeuwVMdHR02KpoWEGrhQKOAzHqQcHp6emzUtEbPc7IdHBzEzMyM1b6JrVu3\n2rYC8XugS4tmZmbsGvDBwUGcPHkyoY1VKhUr7HGLU9WKeU9ulzo2Nma3XQXiTU1WrFiBXbt2Jbbx\n5bicnJy0ZF8sFjE5OYl8Pm/bkMvlbBtV8JmamsLU1FRCUUin01i1ahWOHz+eaIOO8xtuuAE/+MEP\nGjRqCnG/9Eu/hO9+97uJZXBRFGFgYACpVAqdnZ22HsyJzjp0dXUl+tIXac7gQwZHAvGyvN/+7d8G\nEKdqPXbsmM0PAQDbt29PrCP/+Z//eQDAs88+i3379qGrq8te/+STTyY2qmFAHi0A73vf+xK53lmH\n7u5ubN682Vr3MpkMxsbGmloezgTLksgX8wW6Gpw7QesSCrdMXabW7J5uljP3OKEkrS89JwElQCUU\nYMG0rVGaPmiwm68PVDtzfdckWPcl5DGWSw2WE6aSuc+a4TumL7+rRZOstR3sG32WbmQ8/dRK2u3t\n7Qk/uCuoqbahz0eJnXWkRq7xGFpHtTxwclRBUK0Z7uoB1tm3kkKfmy6TohbFvtN6adxBrda4z/Ry\nBjXyFStWoFqtNvgvOXl2dHTYJX4K9lN/f7/NC+C7HljYb17B2AQgNm+75lQSHbCQHYzXESS8wcFB\nrF69umGdNTcUuf7665HL5fDggw/avbmBeCzNzMzgqquuQq1Wa0iPyjEwNDSEXC6H48ePN7ShVCph\n9erVKBaLDeuPU6mUNTO/+93vxp//+Z839BFJd3h4uGFzErqkgFjDfPrppxvKJ6655hrs37+/wbTO\nc37lV34F3//+95tmUPvABz6AT37ykw1r2bkD3c/93M8hlUrhS1/6UuI455iNGzdifn4ejz32WNM6\n7ty5s8E98vTTT9t2feITn8C73/1ue2zXrl3YvHmz/X755ZfjM5/5TOL6arVqn/ttt92GbDaLr371\nq4nxx4x373jHO/COd7wD73vf+wAA99xzjz3n2LFjuPjii7Fu3Tp84xvf8PbR88GyilrPZDKRqzU3\nw2Iaqmp6+jsnR1cTdq8n1KfqlqP3UpO5+r9JpHzYbBsncv1MbZD+8FKp1KDxsU6uKV3Lkr5MaMra\nJiXLjo6OROQ3yZPtYZ1JJBQQNMubJmFxs5gpMfmejwbGqdk8lUolItGZnpL15+8qMLH/uHRPyVDH\njfYpdz2bmZmxvuvJyUnrB9dIcwo+Sij83W2Xa9Wghsj/1WrV3pO+fHcJlFqB2H9cLvfMM8+0TNQ6\nzc0cd80CwnK5XCJdMaHvF98V9YNzDPJ9ddeiq5WKS9zc6znWBwYGrCZKFAoF65vnskI9ToLdt28f\njDHo7u5uIOPh4WEcOnQIHR0dyGQyGBkZSRxnAiK6XDTDG4/Pzs7aPQQmJiYSVgG+F1u3bsX69evx\n/e9/P3EP7kfApWvT09MJP3uxWEQul8PExAQ6OztRKpUSQlehUMCKFSswOztr+0fn6J6eHlx77bX4\n9re/7V1+BwDvete78A//8A92uac7x1966aX48Y9/jEKhgIGBAYyNjSX84KtWrcLIyEjiGbg5CQqF\nAn72Z38WX/ziFxvuzzmlWCzi9ttvx+7du/Hd73438Yzy+Tx27dqV0M4JmtGpgV966aWJDG4u/vRP\n/xR/8zd/k/iNPvLu7m6sWLHCZo570S0/0+VW7iTsM0v64PpZm5nDWZ6rQWuksOtLUdAsSDJyTcOu\nqVstAqnUwo5kGtGtE7sSOOvjmqE5wbE8l8w1eE01aL74JG6SY2dnZyKTmdaRZdHEreeo5u4GFBLq\nN6fZnNe7G5RQI1dTemdnZ4MAoAIdSZzJOGhpcJ8ZgEQ/T05OYmZmxmqMnGRI+pyYNE6Dz4W/qztB\nTZs8RhM+nzEJ2f3OevM+Ok7pCpmZmcGTTz7ZMkRONAuC4u/NfJ/0aWazWZtVT8shOfM8d06joKum\nb50L9P1Mp9Nob29PELWue06n0+jq6kqYQ4vFohXOtCxfG7h7mdtOBlfRbUAy13tMTU2hvb3duis0\nYI3vQzabxcDAAFavXp0w7zKFKpPVMB0soWlrOY8okff39yObzeLIkSPo7OzEzMxMoo3XXHMNHnvs\nMSuw+dyBTDxDocTtIw1IrNVqKBQKCQsK+4DzlxsUWCgUcOWVV6JareLgwYMNWfoKhUKifQMDA4n9\nyl+oADcf+DzEGvjiWn4GNAZ2KYHrS6jnuVHR6s/kubqUTa8nwbp+XJ7ji7LW+rjHVIuiGZv3UXMp\n6+kSBklE/7uR6q5rgf4pTjY6+auWqMcI9p0SH02iasFgPTRQ0Of79rk9lJApdJCwab7mZwoValp3\nrQC6pp/tqdUWEmq4Wro+H/1OUmZbXZN7M8sPwUlLffTuMjm9tztm+XzokqHGqVql/sbJ9sWEpZYb\nuXt1u+f7AhoVLmEs9n1+fr4hYtu9n+vTdM/3KRnaBp+wogRSzxPQ9B6uRYLXzM/Heej37t3b4DvW\n8mq1WkP5qv37yndN5W4b3f29fe+N5m339ZH+xoyLCu0DnxtmZmYGO3bswNVXX+1NtUsLBqP23cj9\nn7bL6lyuPllWRO76IXXSA/y+c12mw//utepDdU2eem8XLEf9na5Q4fq+FRQq2B6VUtW3rktmNJLa\nR46+IDpfe4AFTULJXLV6krOvHCURH1nzOTFQy60n0Lh0TX3galLnbzSZuRuWqC9c/dBqSmV76XfW\nNeZ89hqk1qzffBYQCjoUdijMkPRd14YKSxQ82Y8qoGWzWZTLZWvdyGazVtOgdqfPmm1pteVnxFKE\nvdQa6rM9fi6wVN8vddy3LEyxVBuWWiPtWxZ2JtefDpa6x9muA19qLf5SguxDDz206PGlnsFPA+dy\n6RmwzJafUar0TaZuQJhrYuZvwIIZ2D2mpk9foJUSnJ7n83+6flA3WlzN4m5EtPpnubaYZl4lcZbD\n/3qtBlrpBM97qSmX17tpLbVMlqvpQrU8rRfPoe+YaUt5P9Vq+V3N5VzSpduLcsma+sjVnE+yY7+7\nplH3eWqMght4BjQGtrnP0R0H+rxVSPQFVer56s/X+mqmPf7nn+4Up24LVxhsNVBQc+G6fFyoy8t9\nz3gdz2N/+8rnZ/d4s/vpd9aL7h73/upW8wWvqmDn6wOWyZTGutUrfwdi8/TKlSsblksVCgWk02nr\n63a3WeVuW+57RXR0dNjNU3zPgAImAFx44YUNy7FyuZzdXCWfz+PlL395Qxm8Z29vL1auXNlwnP2i\nm4hHhHIAACAASURBVCEprr76altXbousGBoawrZt27Bp0yYMDg42bGHb09Njxwdz2rv3Zx0GBwcb\nnoHej++ngvEPvJdvi9Jt27YBAN72trclNnY5WywrjVx9xT4N1DWjuho7z+Fxfnc1W/7Xc0ms7qSt\nudapWSl89VTTv/tSqJldtTTVyAklYbe+qVTK5p12tUGd7DnY6IN0y9Q6MxiLxEJTHetKbbRarSba\nRQICFtaQ6oTR1tZmA+nYDp5HAtekNLp+29VutU9VcFDNm6Z/nXxUiOKY0QmVPkxdksJ2qItDg/d8\nAYhqGSLcyVtjMtgG1pd+4Ewmg0qlYv1oFLIo+LUK+Lw4hjRSH0imJFU3lF5P945aSHQtMJ+bvku8\nh/ve001Bi4rrt1cLii5x43uVyWRQLpcT1/H+XIFiTJyGmVoX3ztadmjJ4ljkcjZjDAqFAqamphJa\nK5eKtbW1YdWqVXjqqafsOxhFkd0kRPccmJyctOVu2rQJu3fvRl9fn83zrs+BiWBI5jQ5q2WUAnxb\nWxu2bNliI7Z1y2EgJrNsNotHHnkksRENU0HPzMxg1apVOHHiRKKPKXTr8ksd58ViEQ899JBNFjM2\nNpY4nk6nren+oosuwsGDBxOaPTdP6enpsa638fFx20cuL0xMTCSeM5Pg6DwxPT2dGAfcjx2IhZ0n\nnngiMZZzuRyeeeYZbNu2De3t7UtaHs4Ey4rIgQWTM6H+boKaTq1W80rXronXZ5pXM76+9HqefvaZ\njZUYfISu9/LVRe+npKrXNjNTqbleJynWwS3HZ1rXCHY3ypsvCeurPmlXG3DjEli+pjvVwDXVUjXq\nXc3qvqQvrkBDQcYn6HAtP188Ncnr8+I9+HLrpK0TGI/5nhnPVS3C1crUxcLvfH7UMnkNXQBu2/W6\nVgKfcbFYtJH5uulFe3s75ufnUSwWrZbCiGp1uTBQDFiwcnEc12o1G22tkyuFI4LaWBRFNmBLhV0K\npBpboYlPenp6rDDAe1D7jaIIfX19KJfLCX8uBWIuoaR5W0mQWdeY0UxJjkRCgZlzFcdBFEWWQIaG\nhnD06FG7LSwAnDp1Cq985SsBAM8880yi7+fm4g1ahuuJT9rb2/Hkk08CiMcaV8/ouL/sssvw7W9/\nO0FEzLmQz+dx5ZVX4pFHHklEnFcqFbtf++DgIJ5++umEwMx7tLe3o6Ojw45/CjTcs3379u14+umn\nvTEC6XQaIyMjuPTSSxtIcmxszO653tbWZiPHKWyp0M93W8cbAHt9uVy2K2p0HFx//fUAgBMnTjRY\nRPic2tra7Bg6l5a1ZWVa12hgIGmuVhLVyU9N4JyU+bsvsMsHl4x95myfqZvXKInrfx8xA8lIbzXf\nav0Jdy24luNrI8tz/cuqHdMU6N5bBzJJXYPw+JmBJhp5qs9Es5KpyZh/mjmNnzXaWwUTNw7BdQ/4\n4JI0SVLLV/M9oeNGg+vU2qFm3sWeif6pAON7XixTid59F4hWI3G2vaenxxKDTsLcFa+/vx/pdLoh\nCIkWm66uLqRSKbt0iSSo46NWi3e70zS2zMaWyWTQ09NjN6vhRK877PX29tplVz5f9YoVK2CMscsG\nCb4Xw8PDyGazGB0dbdAW5+fn7Va+TFRD1Go1TE1NYXBw0DumGYA3ODiYiNfQscg5SgO5+K6Uy2U8\n+eSTePLJJ3HTTTfZ/qBV7Pjx43ad9aFDh7BixQqsWLECg4ODiTrWajW84Q1vwNe//vUGoqRgdfPN\nN+PUqVMYHR1NjPO5uTmMj49jy5YtVshRYZcKHJf6ucFs8/PzGBsbw+7du3HjjTd63QN8Dr4sbFEU\n4dixYzh27BguvPBCu+RUrX2cgy666KKGcVSr1TA2NoaxsTEMDAygu7s7IdgDwI4dO7Bjxw5s2bIF\n69evx9DQEIaGhuzxarWKiYkJPPXUU7juuuusa/FcYNktP9PvrunSp5m72hjQmEebx5oFovi0Xpbt\nBoNxQvb5V3k/jY5X0iCUSHkOfdC6jtbnx9XrmmnXqjnrEi/6opXgNVCN7VLN3dVmgOTyPJrNNe0o\n/VfUwtva2uzWhq7/m23R4DZ3OZr2vbZdTeo6KajWVq1WE1nE1N/PF3V8fBzVatWmdaXGxGfAl1p/\n0/vQ+sD+aOYn1Weruds5Afl2WKNpneQzMjKCiYkJHDp0qGWWnzHDFiPzXW1Kl49xLOokrmZroDHt\nMIUuBgi6JKwuC7U+ERyLzLYFJOcEvjdM0+pGdXNt9+joKFKpOAOhGxDGdeJ8p9yAK2OMdT0xWNNd\nJz40NGSXhXHbXYI53Pkbl1NqG9j2l7/85Th58mQiMY0GcK5evRpTU1OJqPFsNovLLrsM09PTmJub\nw969exN92NfXh6uuugrf+973bDIcd15dtWpVwtrk5pvnsjRaRXQpJsEtka+44go8+OCDDSsGcrkc\nXvva1+L+++/H0aNHE3N+oVBAT08PRkdHsWbNGoyPjye2gs1kMrb8U6dONcSjcBzQwud7zl1dXejp\n6cHMzAx6enoakv/QwvT+978f73znO+0KiLNdfrasNHKXFN1AI5/QoRO7ape+Mn3aua7Z9S1FAho1\na/Vr+v60PPdaN9DOPeaa/932NStDNXF+J2G6fn+VQjXJC39T94b2ufpq3fzyCpesXTOS9pHrjyaa\n9T3rr/XSNfzaZ2wThQF9/ipp0w+oVgG9F+vmaskaUKfafrNx7CbHYd9p4KBaQVyyYT2aWSKWI0je\nbmyGHudY0gBQQteG6zulz1hT3LpzhOvOUFcIsKAtqxnbzbbo5uOnj5Rg8hYVEDVQSi19bKMKyKxj\nqVSy/10Sj6IIR48etYJouVy296Cfl+OIghDvoX1cq9WwZ88e7N69G5s2bWroI/bx+Pi4DQajL3zj\nxo3YtWtXYiMi4tSpU5iZmbFuBbcP0+k0BgYGcPToUVt/ugPYR4yl0HnGRalUsoK1b2OXoaEhPP74\n49i6dWvCPcU+4vMdGxvDiRMnbDY/9lFfX59N5tNsnFGIm5ycbDChr1u3DgcOHMDc3Fyijwm6Vp54\n4gls3ry5ISDv+WJZaeQdHR2RG4QFoIGA9HclPHfS17ZxIAONuct9mrorDKj2r5OGTzhw3QO6/tln\nEqNGzmtdgYB11ntrvZppuTQbqqTPoDI19apv3Wf6JegHc03XzI3MfNDUvnVdeKFQsNqIBo2x3pR2\nqd1SctVIZdZdrSRqseBnDVLiBEctihOuriSYnJxMRPuXy2WroavmrM9B+5/SuZrR3XFBIuBkWy6X\nrQmXkwvNiawfgw91FcGJEycwNjaGgwcPtoxGTiy117YGsPnQLKEMoX7lFwpLJQ1Zqg1L1dHN4+6C\nSVWaodnOZYRvnjxTXHjhhdi/f3/T40v1EbObNcNSe7ovtXMYg/uaYalndDpYaiz39PR4d6gj3va2\nt+Ef//Ef7fez1ciXHZFrEJrCZ8L2aa/6uwYU6Wf3GMH7+jQenwbc7Bgld/XfueZ5JeVUKpXQEEns\n+rtbF62HErlqfhoZrn5aflfSZLmuNsrx4as/P2ua12KxiK6uLrS3t9vydJmZLhXSIDESNa+hqVEt\nBYVCIeFC0P7SIDCSX61Ws6ZA+vTL5bLVqvgbfZ5cSscMb2qadzUQtYDQ36iE7vPf6ZK+crlshQwl\nd5I4zeuccObn4y1MT548iVOnTrWUaT0gIGBxvKhM62oeVbgTqGtm9wWiuRq9Eh+PuaSt9+Vnn2as\n9/Bp5K55360TScZdb02o9tesb5q11dcfrr+dxzRYToUL1ezdwC3VQvW7/u4L8mM/a6IWt40kOo0X\noFbsrsVXE7RmPVNtm+SspmoN1uPyLhJ2qVSymnK5XLZ+SPob6bOm5q4krP5vt46sA7Vy1stXR12W\npJYFFU7OVqM6X3BjRQj+xl2t3HPUgqVuJYIuDXeXM8J1S7kBRu676n7ndQCspalZ+b5rFe7aZIJ1\n7+npQSqVSpidtdxLLrkEnZ2dDX1AgXrbtm2JNMtEd3d3QqFwTbrZbNaue2ZkuWLr1q32+MUXX9xQ\n/2KxmCh7sbX0zeB7rgo+15e//OUYHh5uMGvz2V588cV41atehdtuuy1xnHMd4282bNiQOD40NNTA\nNc3qZ4yxO8r5sGrVKgzXVwL4yvit3/ot3HzzzU2vP1MsKyJXKDkolEBdbdqnAbuDw9WsXO2W5/i0\nc5K5nudCTb2+SYt110nbJbdmaGZWVx+xK7S417FNGrDmmtgJH4HzXPUNa2wCz6NFgZo325ZOL2wQ\nwzq61hH6EVUrJYlq1LGSu5rBlcgpLGlwnPrhSP4qGJDIfcl1WEclbhI5z+WfCib6u+vvdeMzXOsC\n+6RVCVzHX7PlooQbTwE0bqDiWhHVH9vMxcY6+EyiXFMNLATeLdYG1yzLGAttw2LX+46rKdlnOl6q\nD9Qf7Guj+rx9pmv1qQ8NDTVs+rJz505s2bKlod7E1NRUglh9LhB99j5S135hgKNCn2tXV1dDoJk+\n59tvvx1f/epXE8e1jzZu3NiQxvbYsWM2ynypcQSgYeMbF/v27Vv0+Nq1axc9fiZYVuvINW+1Tr5A\nIwG7GjfJ0TVZszw+QDXpugE4JDn3eve+StYu4bsE7gbXAMmX0tc+11TsEzZcDVrr4U4UGiDkat1q\npnbry0hiNbkrYbMM3aWMz1GXefE+2j9uYJi2US0mlUol8axYJt0DFODcZ8r6G2MsUbvarJrx+cw1\nkI/lcRMHX8AZCVvvQ1+5Pj9aGNTi4FoWXKj1hUKNu3SpFcD3hMu7UqmUXRnAsRBFEfL5PEqlUsKP\nTA0qiiK7rzmw0CdMEALAWjf4PtRqtUQ2OfZne3u7vZ5jCVjQ7JnAhX5eHucSMm6qw+Pcz5wR1xxD\n9HVznM3Pz9sdyOju4fFUKo5l6enpweHDh71k3NPTg+7ubuzatcvbx1NTUxgaGsKzzz6b2Nikt7fX\nBnVp/MbU1FQiZSv3Nd+3b1/Cz22MQVdXF3784x/juuuu86Zo7e/vBwCsXLkyIbjyOa5btw6bN2/G\nvn37rJAQRQtbyA4PD2P9+vU4ePCg3XMeWNhpTAWxbdu24ZOf/KTXF1+pVPD+978fr3vd6xJ9yIQ0\nAHDnnXdienoa1157LX7yk59gx44d6Ovrw+WXX27bwnqNj49jbGwMXV1diViiV7ziFThw4ABOnjzp\nJfTf+I3fwAc/+MGmwsCb3vSmc6qRLysid6FSp06cSqD8zQ1+Urgm5WbHVEvUgDZX81eNXcE6anIZ\n3710YlGNiy+ZG5nL7xocx/twEgQac6u7AgTrQOJQU7hr4idhcslOFEWJdeYkc4121WfECZVbM7pE\n6xNgmgk81J5ZB04UbIMmyPC5HEjq2pdqqgaQ0N5dIdFH4qpdc9JnMKCaNXWcaGQ6CVkFDDWr81o9\nR9PgtgqY4KVYLGJ6etq77Gp+ft4GQ7qapu5XboxpmLh1c41169YlNsuoVCoJZaCjo6OBhFTgKhaL\n3iAsjsnu7u7EckaCZNHT0wNjTMMGI3xvuru7bVS62wdRFFmyWSwQaylNNpPJNLSxXC7bPchvvPFG\nm4ucgpNi3bp1ePTRRxO/MYodAH7yk5/gTW96k00aw75jYNfQ0BBWr16NycnJxHs3OjqKBx98EC97\n2ctwySWX4Ic//CGAhbztY2NjeOyxx3DFFVegr6+vYc933mf79u3Yt2/fosFwf/mXf9kgBF1xxRX2\n8+OPP95Qfq1Ws3uc33zzzfje974HYOHZ6zt9yy234LHHHvNuzAIAb3jDG/DZz3626Xv6xje+EZ/4\nxCea1v/5YFkRuUvSzQLxfB3kXtPMH9PMZKIk4gZ9uWTjlq8k4n5uVnclaZblrj93SV4zlCkxuj55\nX9Q0y9F7q7CiflwVJjSDHlM0qjVAiY6fNUDLdYFQyHDN/byvT7N2nxcnf9cK4QpgQPMNHEjC9JfT\nPE+iVC1e76P+arZH6+wKRTqOqtWqdRto+eo/52/uKgYeazWNvFQqIZvN4uTJk/a90mfCttCUrMlN\ngAUy5hrvfD5v3RjAgqsqlUoltsikwMDrs9ksTp061eDqKZfL1iowMjJitwlVMq1UKmhra8PIyIhd\niaHHuT788OHD9rgSZLVatdnEuKOf9gHHycjICObn55tGbdOa4+tD7l0wPj5u0wyzjaVSyQakfvvb\n38aFF16IsbGxhCaZz+cxODiIZ599dtGI7ImJCdx7773o7OxMJO7hSpQTJ05gfHwcmUwm4R4gsT/0\n0EMwxmDr1q2JvbzHxsbQ09ODe+65B6lUCuvXr28Y57lczpq/fbE42WwWt99+O5555pkG/tixYwc2\nbdqETZs24Z577km413h/Jh364he/iFtvvRVHjx61AsfIyAj6+/sxMDCA7373uzhy5EhDHVKpFDZs\n2IDdu3c3JfG1a9fi4MGDuOiii7zHny+WpY+8GQmr+dX1XwNJDd71O1JjVPMy4dPiCPUnu3D9mPys\nloJmGiKQDHpzNTGeR5Ki+bdZAIgP7jF1HZD4XDM/SYPR2/Tz6qBnndSErhHW6nvWdrKNriaq2rL+\nTqFAJyVXQ3U1Whd82fQ5UPt2l5uRYDVznQbF6Z/6432Bd6p5u8vK1Peu/e8LkmM5za5Z7uC41efX\nTMDl89fnyAmZf+xDzQ2glhSuwaa5XSd0FbBcN5ZaZtx14no9x40e5zNiO6rVaiKYzPX7q68WWFhW\np1YmnxIzOTmJqakpFIvFxO9cm5zJZOye5dpG+tynp6dRq9WsOZjBWjRRM6mNb7MPYMH3Pj4+jomJ\nicR5FOb5Dmnecd9zePrpp7F582Z7nBYblrV///6Ej5nCzb59+xrWqGvdbrjhhoZ1+sDCkrQDBw4k\n6sG5mjnmKZw8+eST+OEPf2g3Oenv78fJkyeRy+Vw+PBhW0/XjXrllVfi4Ycf9vYfEGv73/nOdxI8\ncS6w7DRyoDk56YvIl97VUFmOluH62gnVin3Q63gPzermao88T/17PmFjsTSjbl20T+iHJZGqNgg0\nRnlqO9yy2Y80BwPJCYnns3xtk2rgKhxwIiKh0+zNstVPDixM6m473Lqrdt1sbOjkqLnz9RlzzPB8\nRsWTqCm8KPHqNWotcV9CjRHQerovOvtH/eKuoKKCiRvV3mpBbyQ+/e6OcX2HOCbUGuWOARIGj+v5\nHGfUyKMoSmiYavkBFghIx6mWD/jXHet3VyCu1WoJjdoVvHRM8Z4a4Ebfv5bh6wOWQasH28h3Qa0d\nLC+dTtvAN2rk5XIZg4ODdn06A98WWye9detW7Ny50353rQTuenb3mc/OzuLpp5+23317uutzZT56\nmuLd49VqFbfddhvuvvtuez/ts927d2PDhg3Ys2ePt48mJiYS5VHzp0Z+8uRJrFixIpEJztXIh4eH\nGwLoXNDFca6xrNaRd3V1RUoOCpK3fufk75I2y2jmhyVcv7D6jd2AJpewXaHDNXe75mH33vq7zyXg\ntsG39lzLYYCV5gCnNUEDy5jvnDmN1UdIbVStBCyLZedyuUTCF+ZT137XYyQ43ZqRdVK/s7su3G2n\nz3yuLyI1JK6PpxakS7zoT+VyMv5nVir+TUxMJILSOGnzvpofnvVhwJ8mtfFJ7KwL+1qJm1mrVAPn\nOnhaArgkrlqthnXkAQEvEkRnuY58WWnkwMJErlqKa5ZSuOSrvwELJOkzR/rM2fzskrardbtBb1pX\n9/60HOg91Pyu5fK/1tf1x7v30HMorRMkIxK9mqVVYFFt3A0a4z3V9KmCDsnTfS7af7lcDuVy2Wo/\njAR3NXK2TSNE1TTp1kc1Nx5XjVyJ0zVTz83NWfKkeZ0Er+ZtNwiSOb3ZpyRunqP972sbn4uWzyV2\nmkmO6S553N2optWggpcPHMMcK/q7at7uu5TL5awfmr70ZrERFMBUo3YFZgAN7x+PUyhupq0u1cZm\nWcWowXZ0dNgERNpGaqOrV6/G6OhoQ5BasVi0/cdxrNfT96/mZK3Hpk2bUKvVsHfvXnR2dmJqaipx\nfXd3NyYmJhBFkY1D0HYu1W4F589mSqS6FnzncFOXcrnsXeJ1+eWX44tf/CI2b97s7WtjDNatW4e2\ntrZEBrilsuq5ZaxZs8ZG1fuOf+c738FNN93U9Lgv2PD5Yln5yDnR+SZd/vE8nuv6Wd0yVOt2CZlQ\n7dlHDK6v3RUyaPLUwUnTqDu4tXyflUGvVTeCtkvb4kadA803h+FkuBhISrqJibovgMZc83NzC7mu\nXYsA4RKqXqvtV2HC1dRdc7bWQcmZ3yuVSqKfXQFFTd3unyaGcRPI8Hf1l3MM6P1dc7iew35UP7om\npGHeam6QQYGDfvJWgrp8mrl/iPb29oaocFcAdid3mqJZfjMSB/xEquTmW0euxwEsanIG/OvEffn7\nFWpa9rVRI9GbXc913L7rS6WSjRvw9UGz3OvE+Pi4XWYH+BWqpZLh+Lb2VDTLKunD+vXrF12n7QYk\nurjooosa0rhqbMXpoBmJE81InOByt3OBZUXkrlmbQVnuxh78rKZkVyOi+Za/6R8DcNwMZloHHZR8\ncfS/z/TP/0uZ/ZWU1ITsI0D3ftoOt86Ea7LW69lmtpH1ptmc20ZyN7NsNpvY3YwkzzowS5Ku1feZ\nwVXr9a2nVglffdKqDbtxDipA6XPQ4DM30M4NfNJrSOLqI69UKtbkzoAhEq6ryWuwHAMGXc1e/6vJ\nvFwuY2JiAuPj45iensbExAQmJyftfZiXfTm5wk4HbtARfdaEu3rBFVKabSrB8cyyXLJ1N8whSPI8\n18205h53oe6VxY5rvTs7OxOuM7euvb29ts4+zbavr8+W2d7e3qA1MqhsYmIi4VtnHZjkhFYLEhz7\njm3Zu3cvNmzY4N3RC1gQpnm+m32uWV8AwOrVqxPxRu441gA+H4lrWVu2bMGPf/zjpvd9/PHHG7K2\nKS688EIrBFB4eetb37poW1xw/LkZ8Aj20SWXXOI9zgBF7hN/tlhWpnWSgU9LdUlJf3eJzPXv6oTt\nBlwBSVO8jyxoetb78Ry+lDzHJRze0/XX+8zvWh9fewjej2ZnF5SOeY0GgWn9aSJ226wJXHTicTPI\naR+oz5h15oRComYdAFjhgJo/n63W0Z1k3e1fVYPXvuR4YD+w/6j1UuNVgmYqVkYGaxS6mx6WQqL+\nMUqXAo8rOOmzpAAwOTlpNf3p6WlMTk5a8lfhotUC3BRcR97b24v5+flEsBCwQFIXXHABjh8/jlOn\nTiXIqLOzE52dnejo6LDPhyiXy4iiyMYjrFy5EocOHUrMD8YspGTt6+uzW2cqsbKOfX19OHLkSMNc\nQMLq7u5GpVJJLLsCFoiuu7sbMzMzGB8fbwhmy2az6O3tRXt7u416pll1YmIC6XQaQ0NDuOCCC/D4\n448DWMj2durUKQDAwMAANm/enNAEK5WK3XoTAK677jrce++9iePlctlmdbv66qvtemkgXlbFd3P3\n7t1YsWJFA4lGUYTOzk7UajVs27YNq1evtuuwGaDH8tevX4+3vOUtNpDtQx/6EIBY281kMti6dSte\n8YpX4KmnngIAu6adCsLGjRuRy+UsUbPt1Obf+c534v7778fDDz/c1BS+ffv2Bm1cs9L19vbi6aef\nxsDAgF0f//nPf94mtfnDP/zDRFDaxz/+8YR2zWQwHEtuFryxsTFcd9112LlzZyJ5D8fV29/+dtx7\n77144IEHGur+fLGsgt36+/sjoHlaSl9AmU7cvmsI18/Jz7yfG01O8vKZZ9RHC/g1dPf8perntg9o\n3IFN78X/JF1Kyi6BaMBbJpOxOZFd4nXvQ7O6gkSqmjl3PaOFRH3u2u8aZWyMsdq/mvHdALL29vZE\nkhzdcIX+b4VaBHhP1XBmZ2cT2y1S4x0ZGbHa9tTUFMbGxiyZUtNWglVtj/Xt7OxEoVCwe7JTkKKW\nRbJh8Fy5XMbo6KhNMFKpVCyx03y+2Fg52+CYnwYY7MYJN52ON8ZxJ1/6X+vXNFhZ9L1glrf/v71z\niY3zqqP4cdzYY2fcOIkztolb0lIgaVBKFVBLaRUJFiygCHUDSCAhWKBKlIeExJIt7EBdQZB4CKTu\nKtgAFRUUCiISUdWoSBGUKm1UJU1tHI89jh0/hsXoXJ/vP/cbp03SzBfOT7I8M9/7ee7/da9mdKv3\nbefOnan6QPYDQ0NDhdKuGCNnNQrXr+59JjG2Wq2uGm0AySPFYwS6Xc88hqGhIezataur4xneR/V6\nHbt378bi4mLXPCMjI5icnES73ekRjSLHfdixYwf27NmDzc1NLC4uFuq4OXDR5uYmGo0GLl++3DWK\n2o4dnT7eDxw4gP/85z+Fc7Rr167UYOUASNqg0fcb0Mngzrm+eZzvec97ClnrQMe6ffPNNzEwMIDd\nu3djbW2tEHI4evQorly5gtnZ2dTpTtSvnTt34t5778WLL77Yte177rkH733ve/G73/1uW8/WN7/5\nTbRaLZw4caKwf2xo5WL8hw4dwl/+8hdMT08nb2Hkq1/9Kn7xi1+kRii51ue5r1zranHrZ7W6+T9a\nckC3az66oVXc1MWeE3cg37Ocfo/ri9Nz3oKy9Wm8WF3vMXYfGxGcpi/9eB51mzFmq/FkrVWOMWdg\nK3Eu5h1w3WVVArFhtrGxkSxg/tF9TJFTgVO3tVqqsRMcPR+cn8fFMjO1sLW8i79pDgRd4GoVUwS4\nTrrFl5eXsbS0VPiv/cLr0Kh6XPzM+TW2XlUrXIllTNqnN9AR8YWFha6cFKLWNYB0HWNMXHMTdBua\nKMewR64Onc8Gr5l6g3iPAN3ePt0nEuPF6iqmCGu8WBNy19fX0Ww2sz3MMbyyvLyM//73vwWXO/NB\n2PGK1nHzGrBhOj8/X+hXnNTrdSwuLuLOO+8snCOWxmmHSbGOPArb2bNncTAMGqKNtPPnz+Nd73pX\nmrZ//37Mzs6mddAzRo4ePYrTp08nC7csWa6s/It15L3i5sqPf/xjnDhxIrncG40GLl68iNnZmSiw\nigAAFuZJREFU2dLn8syZM/jXv/5VmqPxwAMP4MUXX7xuCW5KX7nWgeKDW2blxphx2fIkl9kO5F8a\nigqQxm3jg6zfc9vPPfzxYqvIaS9puh+6rdzNFMU8ogle/B4/05KM3bNubGyk3t2Y3MYHnbW7eg54\nfLR0NFzA9fK7hhm4P7ELWP1NP8e4Os8PBZGNj5hYFsccp3CqJ0EbPTkLWe9T9u4FdFy+7FJUs/zp\nHaCILy0tZTuXuZWIiWHxvl9YWOg5fjUbtSqEAwMDSTi1p7OhoaGuGmx6csrqwBmeUqEeGCh2Bcv1\nkhjLz8W19XsUnPX19UKIgNvkfOz0pWxc8WazibGxsWSRx65oFxcXC8vH2Pjy8jJGRkYKFrkuz8Q3\nJoO1Wq2u3ujYACtjcHCwZzLa4uJi4RzQEtdzpImLp0+fLrXySW6gFcJj+ve//126vMLEN1rkFy9e\nxMzMTM8Et+eee65ngtvJkydTCOR601dCHl3LGoOOCSI5tHWdI7rpdRtR0HOlWCosfNFTkPiyjslu\n3AbFLWbg6nZIjPtyeb50ooVLFy63r9YGgPQQcrm1tbXkxo5dYmrcPZdrQNcis0LLPARx32Pug1q6\nAwMDyS1JL4a+eHR/1BKPJW78Ta3aKOiacc6ktJjkpsuoNR6PU6/X6upq13Wnxyb2vU7vQ6vVKozW\ndquJ+NVSJuIkF7NVYkgsdm+6nRWWy1JXYmMkTr8e1y0+G2UiDnR7ALj86uoqhoaGsoOaaI6KVgDp\n8lwuZ9XGY+wl4rn5uY1e9LquwPajia2treGuu+4qzQZnw2Q7HSExBHStWeoAbog1DvSZkOfcyCRa\n6hqToSBxWZ0vWs76otUkNY3FErWMczemWufaSuc6cqITGxMx0Yy/q0Wo+xpF8rbbbksZ1mxM8MXD\nDHSeG8bxdNhSuhC1saDHG5PmdGAWxhMZl+R6arVawRUNbCW5aakWxZIWllr0al3xOtJToHF1LeWK\n535tbS2NNEXBbjabBauctdp0W2p4Q610tc71eun1U5c/j0d7fGNjgW70q61ZrTrblRLFeaP1F6ez\nXjz+rp4eXT4X11bq9XryzOT2l8mqbLiXxT97HS9/61Xfvr6+nuLaZeeMiXIR5iHkRBzoJORx+7w/\nlYcffhiXL1/GqVOnsh4MJhvq+1aPhe+njY0N1Go17Nu3D6+//nr2HPAaljWwtgspLS4u4oMf/CDa\n7XZXLHx0dBTHjx/H4cOH8dRTT2WXn56exujoKC5evFiI88dj6rWP7XY7O9ysTu/labra5+Fq6asY\nOS23WOJEoczFxFVsKUZMvNIyK948w8PDheQqbo/rZuKM/qb7pOvVBoEeg5KL5WicmcvrscbldP3a\nUKFw6Hdahxr/jd/VAlWR4vq1PEqtRS2f4j7GhgX3g/sZY/16jrh8rDFXVzM9CYxvas21xtR1X2MN\ntx5P7vhUvOPx6Z+KuMbS4/XQunPNjF9YWMDCwkJyqf+/oO7EWI4W2a4OnJ2/vJXl19a6+95WtP/y\nnIDSzUu2ewnnauW3swKjKL7VF/3y8nLPcrAY0448//zzePjhh0unt1qtQolYLjSo774o4gB6bv+t\ncv/992cT2q4WTbAkep17iTgpE3HyTj7jfSXkWjNelqAGbNWR63SNn6roxz8muEWBZiY2uzBlfXTs\nbpTLqtBHEQaKwhWFPj7UueSxKBqKzsv5tCab61RRU3FhjFaTwVTk1b2cc1Nr46Hdbne5qHW7nDcX\n7lDhVKGm5cB94T7TJU1xZKKYdiDEbapQU7zV26Cuc54HJgTFjnz0XHM9uj02RjQxTztzYfJSs9lM\n7vTtrI5bBQ5hy+eu3W4Xaqz1vsgJmIqieuX4u9aRa6yaonbgwAEA3YO10BOlY4mX1YZzeX3H9Mrf\nideW4St97vUz68jLKBNArmNmZgZAdwOD0zk4yuLiYiF/gMfA7Z86dQrHjh3rEjA2xHgM9DDFwVto\n7TORTxtwDz30UJc1rudgu46ClPvvv79rqFWyvLyMz3/+86XWONDpDIaxcm63rL+C7Rqex44d6zld\nO9G5kfSVaz260oGtQQ14E+SSuMoSz3SajuUdLUiuU2OxhHFsvrC5PN3YOXJlXVy2VxKMzhv/c1l1\nsRO+pDROz/3UDFptbaro83d2b6kvKy4bwxeaIxDDADF5RxPj4nGpC57HltsPHicTWjShLpcAp/uo\nYq7Zzerm5x+XiaEQ7mNsMOn55/7zRRhzGf4f2dzcTHXOo6OjyWNEMdFSvcnJyfTiZHZyrVZLVvbe\nvXuTNb62toa5uTmMjY2llzCHoQQ6VjatyI9//OMAOgNf0Eq6fPkyXn31VayurmJ6ehpAx0pjtjjz\nKQAUMuQ1eTO+P+hWXllZKXgG2HiJHir1BB08eDDtM+H277333pRcxtplcvbsWXz4wx/GY489BgD4\n2c9+Vni/NZtNjI+Pp45POBQrp8/NzaHZbGJiYgJnzpzBF7/4xa6xumu1WhL7ffv2oV6v48qVK6lc\nE0AhA/79738/duzYUQgdLSws4LOf/Sz++c9/phpyzWd45JFHksBvbGyk7f36179G5MEHH8QLL7yA\n48eP47nnnuuavh3j4+NYWVnB0NBQelbr9XqqI+d7AkBqnLNBSCYmJnDo0KGuc0V4z+eqD24EfSXk\nPHlaD03xjslT+h9AQQjU9R4FNSfy+j2XSKVJXRSKGK9WYjyO+3YtL3ON+6uAAlsNB/5GMYmZ35yf\nLiQeq7rJtCYX2Cpl0450VJgolMBWb0ZxvxgrVlFUS5/nmMl0eozMA9Drqg0W7pMmHHIb6vrWh1O9\nHNHazuUyaINLGys6n54PswUt40ajgY2NDbz22muF6Xxh79+/H2+++WaXUFFEJyYmsLS01JUEFt3J\nsTZ6ZWUlJTndc889KWFJy8suXboEoCNSZcmMQMc6GxkZ6XKp8r4cGxsrhGp0+sbGRsr3iLkRKysr\nOH/+PBqNBiYmJnD+/HkAW0J+4cIFXLhwAePj4zh8+HBBHM6ePYtnn302fX/iiSeSUALA008/jfn5\n+bTMo48+iueffz5Nn5ubw8bGRhoJ7Ve/+lXBWGLjhOdlamoKQ0NDhZHPgK3EsAcffBBvvPFGl4fj\n5Zdfxssvv4yPfexjePzxx/Gb3/wGANJ62LCemprCbbfd1nUdeR/86Ec/wk9/+tOeAp4Tf7X+X3jh\nha7wjDYuJyYmUq7B8PBwGhqWPPLII5ibmyucR93G9PR0KhF8p+irDmEajUYa/YwvdBURtQ57xdFy\nvbBFVLBUEDiN52VlZaUQG9a4MoVCM+q1lZk7tzFWfL0sNZ6rKDAUc53G39jFrTacGLbguaHFxLDH\n0NAQxsfHMTw8nMIRtVqtUNuv+6Td69LaZtek0bInvOZ82bKBMjo6mjw0agHrZz3PdM0DSK52re/m\n5/n5+UImu17zGKLICf3NoF2RDmGGh4dT+IUdwmxubibLWMukylzr+jyrF2l9fWv4T42dt9tbQ5fG\nhCN+P3DgQCGOy23v2bMH8/PzGB8fTwIPbN2DdE3HZ5fWJOeLDW31TmmSbe64c4lkuXcJx9CO03lO\nWC7FfWVDYu/evXj99dcxMTGRBFyP4Rvf+AZ++MMfpnPBa8RjZ/ei/E8OHTqEM2fOpHOscWY+0zxH\n9XodR44cwd/+9jcASNsq44EHHsDJkycBoBAeiHzuc59Do9HAT37yk0KDief02LFjOH/+fFfSIO8X\n7jPn57aOHTuGU6dO9UxwA9Azwa0X1/o895VFrglr6i7lf7W06YLLWeb6e65jBt5UmqmuosCHkBYi\nhYwv8rIHK7rayjLS42/Xg1xIQWO6eg7Uso1ZuGpxq1dEhx7lfwoo56VrOpcroGVcjCfzHFGcuV/R\nK8B56BbXa6uxS3Wpc5ua1R6t9JhcGPdZv0d3qrk64gv38uXLhXO9tLTUM8GN9eFl9eitViu7POeP\nL1W+rHMizv2p1+sFEY/etLi9XJZ9rxBazF6PoahYQ11mbDFZK05vtVoYHR1N3od4zzLxTUVcYeIb\nLc7cNYqCFkcCYxVB2TloNptJxAFs64I+efIk7rvvvred4La+vo7bb7+9tGwu1trzGvD+vZEifj3o\nKyFXMdEHgSMSMclMyVncfCi0RazzxkZC/D1mYMYY7erqalettW47irW2xnXeGyHmvX6n2Kp7mHG7\nspi+QuFTd7Suj67tdrtdqE/nMpxPuzuN21NBJ7HEjNsZGhrqyihWKxoo1gCrmOs+A8XrpnF7DQNY\nxK+dnCj18q4B2482VmbZ5uA9WbZPZTXWvfb3rYZTcvu43TbeKrnSRlrTZSVqPO5c3DfuTxS0mKWe\nO8Zewn41XI2IP/XUU/j617+ePf5ms4lms1noUa5s/4DtjzlyMytR+sq1zr7Wo8tUS8ai6zZHbO3q\nOhlzBTruFBUmuoCBrWQUFQ9mN2sClfbopXHSXGs6xmZvFjyvg4ODqT9zzcDX/zq6GV3p9XodIyMj\nyT3Pa6Mu7thI4vmgiLJLSQDpGqvVr9UAmkWujZKYSwFsPUxRyLWOV0cni32eX7lyBQsLC0nwWYve\nbyJeFdf6zd4HY6rALeVajzGlWK8cy9A0ESt26ELLjLTbWwMhaNIT/6vLXVFXbW5/yyz36FWI67yZ\naNJe7AAF2OqRLGaO0yuytraG5eXlJNiMo3Ne9Zpo6IL0ytbnNtjznJaMRctYh67VHIXoXtfrq7kN\nFHSuQ6+l1oTf7OtVda6mJvdqYEO7l4W+Xd5JjOsS3mtMuovQtbyd+1Tvw3ea7bpNjTHxyLVeJ56j\n7TwjV5MbtF1/ApqDoNTrddx9993Y3NzESy+91DV9ZmYmlYi+Uxnl7wR9JeQ5dy6tZcZYtaex6FbV\ni0vhiYlxKgQ51y7jxtpTEZeL+5Xbf6Lu5FwS2s0mZuADxTGddd95njWJh8lLGqeOLvpYKhhzFjQW\nT3e5ijgFWsvIuG8aG++V7EZ3uqKx9Fgqpvtmrp2pqak0UlWOsbGxUlcv0J0oxs933HEHzp07Vyqs\nFOQoGlw+JrPxXuf9sHfv3kLWMafz+Sjb7rVWp7wdKOBlhsORI0e6ssyVstjvdoIcG0VxXibjla1L\n4/DXi6WlJUxOTiY3fOwffXZ2FpOTk6nBcz0Tjm8mfSXkq6urybrWix4759AXP61KZsNyfu2AgsvR\nuopZ6poJy5ZgLuNds9UZG45x4LjOGO/ttxtHGz6xIaVZ7nRLaxIdsNVhQq5DBxXt6HrXGLROp5hr\nj31av81ryg4lNBSg+875o2eGDUFeQ01+0/uC89EDYd4eO3fuxH333Yf5+flk6bbbW/16HzlyBLt3\n78bS0hLm5uZSpQLH5GajbnBwEI8++miq9T137hzOnTuHffv2pdKgu+66K01/44038Mc//hF33313\noaSU/XBfunQJzz77bJf3aO/evV0irnkf4+PjGBkZKbXKYx7KO8Hm5mbqFCYOj7mxsYGRkRF86Utf\nAoCujlJWVlbw3e9+N3XucuXKFfz2t78F0LlOzzzzDD7wgQ8Uljlx4kTqEvYjH/kIgM6wpeTJJ5/E\nrl270Gw28ZnPfAZAp+HEZ/eXv/wlPvrRj+LPf/5zEvKHHnooLf/EE0+k9XH9U1NTafp3vvMdfOpT\nnwLQ6dwl8slPfjIdz7lz5wpCzpHttKT4VqCvhJxCOTw8XChJohjGFid/ZwcnnD8mWPE/B7boJV78\nrp3PaGOArtr48tf/URT0cz/VGetNrBYuyy9iYhAFjiVksTSIaEOMDSr2+64JgHrO1IWfy4OIIQ5N\njtNl9Jqr90XDMeqN0H7qY9KiuXYuXbqES5cuYXp6GkePHsWrr74KAEnIOb3RaODw4cOphpp84hOf\nANB5Yf/+97/vGr3qfe97X/rcarUKNdQA8O53vzt9npmZwV//+tfCdK1DL+vgg42LPXv2YH19vbQ+\n+GaIOIDU0yEAHD58OJ1joHOeX3rppeRm/trXvoY//elPafo//vEP/OEPf0jfx8bGUgc6APDMM88U\nxuQeHR3FV77yla6GzA9+8AMAnVr8b33rW139Afz85z8H0BlY5Mtf/jK+8IUvFKY//vjjADrX4Mkn\nn0y1/+Tb3/42AOCxxx7D9773PXz/+98vTP/Qhz6UPv/973/H008/jTLGxsZuGQEnfZXspskx7A6V\nn6Po0uqmJUahAPKlRJqgFsuxNLYbLUeugy9+7SVMrbmc8EV3esxw7xc0YSy6tnNJbGo5M1FNr4US\n54kZ8+rR0P2JVQEaRuH+6Db5G69PLFWLGfHcD429syyOfbhrt6v9RpWS3fS633nnnV2dwqibevfu\n3djc3Cy42++4445UD81Qiz7D+/fvx+DgIC5cuICRkZFCPgzQsQZrtRouXLjQ5TEDOm708fFxvPba\na9m4bK1WS6OKcV/je1NzQW7mO3VwcBBTU1NYWloqjZcfP34cs7OzBXe7Phd79uxJPbcRej3KRu+6\n/fbbMTAwgIWFhfSsRU8WXellDR7WoZfx6U9/OnUkk2NmZgaTk5Oo1WpdDTag896o1Wo4ePAgXnnl\nlb4a7+Ban+e+FfJeRJdIdNkCxXgvv+dENDYQdJmc66XfYt3XGz2XTP5T65fzEJ1Xreoo7NqHvlrL\nsVGlyxO6wLVPAT6U/M4XKPteZ3UB18dl6UrNddzB8Atj62ystVqtvrveVRLyt7Fc4b5gEhbvnZjo\ntJ2LdLsEtfHxcSwtLZUmarFb4Ha7ndyyZZ61my3kAFI1Sq+hUK934lvMZejlfXy7Lu2yBDfSaDQA\ndNzwr7zyStfxs0OcsbGx9Fz3i5j/Xwq5ubGokOZEW+fROLo2iqJrWhsB2thSYiNK/+f6VWcjQzPm\nWdamA7vE7eeOkXkXXFYz+tnTWz9xKwt5Gf0Q07yarPV+eqfeDK5H1vq1cPToUZw+fbp0eqPRwMWL\nF2/Y9t8Ot5SQG2OMMeat0VfDmBpjjDHmrWEhN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0xxpgKYyE3\nxhhjKoyF3BhjjKkwFnJjjDGmwljIjTHGmApjITfGGGMqjIXcGGOMqTAWcmOMMabCWMiNMcaYCmMh\nN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0xxpgKYyE3xhhjKoyF3BhjjKkwFnJjjDGmwljIjTHGmApj\nITfGGGMqjIXcGGOMqTAWcmOMMabCWMiNMcaYCmMhN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0xxpgK\nYyE3xhhjKoyF3BhjjKkwFnJjjDGmwljIjTHGmApjITfGGGMqjIXcGGOMqTAWcmOMMabCWMiNMcaY\nCmMhN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0xxpgKYyE3xhhjKoyF3BhjjKkwFnJjjDGmwljIjTHG\nmApjITfGGGMqjIXcGGOMqTAWcmOMMabCWMiNMcaYCmMhN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0x\nxpgKYyE3xhhjKoyF3BhjjKkwFnJjjDGmwljIjTHGmApjITfGGGMqjIXcGGOMqTAWcmOMMabCWMiN\nMcaYCmMhN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0xxpgKYyE3xhhjKoyF3BhjjKkwFnJjjDGmwljI\njTHGmApjITfGGGMqjIXcGGOMqTAWcmOMMabCWMiNMcaYCmMhN8YYYyqMhdwYY4ypMBZyY4wxpsJY\nyI0xxpgKYyE3xhhjKoyF3BhjjKkwFnJjjDGmwljIjTHGmApjITfGGGMqjIXcGGOMqTAWcmOMMabC\nWMiNMcaYCmMhN8YYYyqMhdwYY4ypMBZyY4wxpsJYyI0xxpgKYyE3xhhjKoyF3BhjjKkw/wPbxeBF\n/tP1egAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1faf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fd, hog_image = hog(img, orientations=9, pixels_per_cell=(10, 10),\n",
    "                    cells_per_block=(1, 1), visualise=True)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)\n",
    "\n",
    "ax1.axis('off')\n",
    "ax1.imshow(img, cmap=plt.cm.gray)\n",
    "ax1.set_title('Input image')\n",
    "\n",
    "# Rescale histogram for better display\n",
    "hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))\n",
    "\n",
    "ax2.axis('off')\n",
    "ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)\n",
    "ax2.set_title('Histogram of Oriented Gradients')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2250,)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(fd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    ">>> X, y = make_classification(n_samples=1000, n_features=4,\n",
    "...                            n_informative=2, n_redundant=0,\n",
    "...                            random_state=0, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.reshape(y,[1000,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1000)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(y.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.zeros([54000,1250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54000\n"
     ]
    }
   ],
   "source": [
    "n = np.shape(train_data)[1]\n",
    "print n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,n):\n",
    "    temp_image = train_data[:,i]\n",
    "    \n",
    "    temp_image = np.reshape(temp_image,[250,100])\n",
    "    temp_image = temp_image.T\n",
    "    temp_fd = hog(temp_image, orientations=5, pixels_per_cell=(10, 10),\n",
    "                    cells_per_block=(1, 1))\n",
    "    \n",
    "    X[i,:] = temp_fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 438.,   75.,   53.,   38.,   38.,   48.,   35.,   42.,   44.,\n",
       "          43.,   32.,   33.,   22.,   25.,   24.,   26.,   22.,   21.,\n",
       "          16.,   10.,   12.,   12.,   16.,   14.,    9.,   11.,    9.,\n",
       "          14.,    8.,    8.,    7.,    9.,    2.,    4.,    3.,    2.,\n",
       "           4.,    3.,    1.,    3.,    0.,    1.,    4.,    3.,    1.,\n",
       "           1.,    1.,    0.,    0.,    3.]),\n",
       " array([ 0.        ,  0.01999895,  0.03999789,  0.05999684,  0.07999579,\n",
       "         0.09999474,  0.11999368,  0.13999263,  0.15999158,  0.17999053,\n",
       "         0.19998947,  0.21998842,  0.23998737,  0.25998632,  0.27998526,\n",
       "         0.29998421,  0.31998316,  0.33998211,  0.35998105,  0.37998   ,\n",
       "         0.39997895,  0.4199779 ,  0.43997684,  0.45997579,  0.47997474,\n",
       "         0.49997369,  0.51997263,  0.53997158,  0.55997053,  0.57996948,\n",
       "         0.59996842,  0.61996737,  0.63996632,  0.65996526,  0.67996421,\n",
       "         0.69996316,  0.71996211,  0.73996105,  0.75996   ,  0.77995895,\n",
       "         0.7999579 ,  0.81995684,  0.83995579,  0.85995474,  0.87995369,\n",
       "         0.89995263,  0.91995158,  0.93995053,  0.95994948,  0.97994842,\n",
       "         0.99994737]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SpAHmZepGkrRBBr0kdc6gl6TOGfSS1DmDXpI6Z9BLUucMeknqnEEvSZ37XzrPlq9trTRi\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc0ef810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(X[1,:], 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_final = np.zeros([18000,1250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,18000):\n",
    "    temp_image = X_test[:,i]\n",
    "    \n",
    "    temp_image = np.reshape(temp_image,[250,100])\n",
    "    temp_image = temp_image.T\n",
    "    temp_fd = hog(temp_image, orientations=5, pixels_per_cell=(10, 10),\n",
    "                    cells_per_block=(1, 1))\n",
    "    \n",
    "    X_test_final[i,:] = temp_fd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 1)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators=2,max_depth=2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/viraj/venvs/tensorflow/lib/python2.7/site-packages/ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=2, n_jobs=1,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test_predict=clf.predict(X_test_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88127777777777783"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}