tools/analysis.ipynb

   1 {
   2  "metadata": {
   3   "name": ""
   4  },
   5  "nbformat": 3,
   6  "nbformat_minor": 0,
   7  "worksheets": [
   8   {
   9    "cells": [
  10     {
  11      "cell_type": "code",
  12      "collapsed": false,
  13      "input": [
  14       "%pylab inline"
  15      ],
  16      "language": "python",
  17      "metadata": {},
  18      "outputs": [
  19       {
  20        "output_type": "stream",
  21        "stream": "stdout",
  22        "text": [
  23         "Populating the interactive namespace from numpy and matplotlib\n"
  24        ]
  25       }
  26      ],
  27      "prompt_number": 1
  28     },
  29     {
  30      "cell_type": "code",
  31      "collapsed": false,
  32      "input": [
  33       "!pkill -9 ipdf"
  34      ],
  35      "language": "python",
  36      "metadata": {},
  37      "outputs": [],
  38      "prompt_number": 2
  39     },
  40     {
  41      "cell_type": "code",
  42      "collapsed": false,
  43      "input": [
  44       "from common import *\n",
  45       "import build\n",
  46       "import scaling\n",
  47       "import saveload"
  48      ],
  49      "language": "python",
  50      "metadata": {},
  51      "outputs": [],
  52      "prompt_number": 23
  53     },
  54     {
  55      "cell_type": "code",
  56      "collapsed": false,
  57      "input": [
  58       "# If things are changed run this instead of restarting kernel\n",
  59       "scaling = reload(scaling)\n",
  60       "build = reload(build)\n",
  61       "saveload = reload(saveload)"
  62      ],
  63      "language": "python",
  64      "metadata": {},
  65      "outputs": [],
  66      "prompt_number": 24
  67     },
  68     {
  69      "cell_type": "markdown",
  70      "metadata": {},
  71      "source": [
  72       "# Compile programs\n",
  73       "\n",
  74       "Build for each type of real, then save a local copy of the executable."
  75      ]
  76     },
  77     {
  78      "cell_type": "code",
  79      "collapsed": false,
  80      "input": [
  81       "try:\n",
  82       "    options[\"built\"] = saveload.load_obj(\"built\")\n",
  83       "except:\n",
  84       "    options[\"built\"] = []\n",
  85       "options[\"tobuild\"] = [\"float\", \"double\", \"mpfr-16\",\"mpfr-32\", \"mpfr-64\", \"mpfr-256\", \"mpfr-512\", \"mpfr-1024\", \"Gmprat\", \"cumul-float\", \"path-float\", \"path-Gmprat\", \"path-mpfr-1024\"]\n",
  86       "options[\"tobuild\"] = [b for b in options[\"tobuild\"] if b not in options[\"built\"]]\n",
  87       "options[\"tobuild\"] = [\"path-Gmprat\",\"path-mpfr-1024\"]\n",
  88       "    "
  89      ],
  90      "language": "python",
  91      "metadata": {},
  92      "outputs": [],
  93      "prompt_number": 14
  94     },
  95     {
  96      "cell_type": "code",
  97      "collapsed": false,
  98      "input": [
  99       "build.BuildAll()"
 100      ],
 101      "language": "python",
 102      "metadata": {},
 103      "outputs": [
 104       {
 105        "output_type": "stream",
 106        "stream": "stdout",
 107        "text": [
 108         "Building: ['path-Gmprat', 'path-mpfr-1024']\n",
 109         "\r",
 110         "[                  0%                  ]"
 111        ]
 112       },
 113       {
 114        "output_type": "stream",
 115        "stream": "stdout",
 116        "text": [
 117         " \r",
 118         "[*****************50%                  ]  1 of 2 complete"
 119        ]
 120       },
 121       {
 122        "output_type": "stream",
 123        "stream": "stdout",
 124        "text": [
 125         " \r",
 126         "[****************100%******************]  2 of 2 complete"
 127        ]
 128       },
 129       {
 130        "output_type": "stream",
 131        "stream": "stdout",
 132        "text": [
 133         "\n"
 134        ]
 135       }
 136      ],
 137      "prompt_number": 15
 138     },
 139     {
 140      "cell_type": "code",
 141      "collapsed": false,
 142      "input": [
 143       "saveload.save_obj(options[\"built\"], \"built\")\n",
 144       "options[\"built\"]\n"
 145      ],
 146      "language": "python",
 147      "metadata": {},
 148      "outputs": [
 149       {
 150        "metadata": {},
 151        "output_type": "pyout",
 152        "prompt_number": 16,
 153        "text": [
 154         "['float',\n",
 155         " 'double',\n",
 156         " 'mpfr-16',\n",
 157         " 'mpfr-32',\n",
 158         " 'mpfr-64',\n",
 159         " 'mpfr-256',\n",
 160         " 'mpfr-512',\n",
 161         " 'mpfr-1024',\n",
 162         " 'Gmprat',\n",
 163         " 'cumul-float',\n",
 164         " 'path-float',\n",
 165         " 'path-Gmprat',\n",
 166         " 'path-mpfr-1024',\n",
 167         " 'path-Gmprat',\n",
 168         " 'path-mpfr-1024']"
 169        ]
 170       }
 171      ],
 172      "prompt_number": 16
 173     },
 174     {
 175      "cell_type": "markdown",
 176      "metadata": {},
 177      "source": [
 178       "# Accuracy of Rendering VS Scaling"
 179      ]
 180     },
 181     {
 182      "cell_type": "code",
 183      "collapsed": false,
 184      "input": [
 185       "scaling_data = saveload.load_dict(\"scaling_data\")\n",
 186       "scaling_data.keys()"
 187      ],
 188      "language": "python",
 189      "metadata": {},
 190      "outputs": [
 191       {
 192        "metadata": {},
 193        "output_type": "pyout",
 194        "prompt_number": 20,
 195        "text": [
 196         "['mpfr-32',\n",
 197         " 'mpfr-256',\n",
 198         " 'double',\n",
 199         " 'float',\n",
 200         " 'mpfr-512',\n",
 201         " 'path-mpfr-1024',\n",
 202         " 'mpfr-1024',\n",
 203         " 'cumul-float',\n",
 204         " 'mpfr-64',\n",
 205         " 'path-float',\n",
 206         " 'path-Gmprat',\n",
 207         " 'mpfr-16',\n",
 208         " 'Gmprat']"
 209        ]
 210       }
 211      ],
 212      "prompt_number": 20
 213     },
 214     {
 215      "cell_type": "code",
 216      "collapsed": false,
 217      "input": [
 218       "del scaling_data[\"path-Gmprat\"]\n",
 219       "del scaling_data[\"path-mpfr-1024\"]"
 220      ],
 221      "language": "python",
 222      "metadata": {},
 223      "outputs": [],
 224      "prompt_number": 26
 225     },
 226     {
 227      "cell_type": "code",
 228      "collapsed": false,
 229      "input": [
 230       "#scaling_data = {}\n",
 231       "p = ProgressBar(len(options[\"built\"]))\n",
 232       "p.animate(0)\n",
 233       "for i,b in enumerate(options[\"built\"]):\n",
 234       "    if b in scaling_data.keys():\n",
 235       "        #print \"Skip %s\" % b\n",
 236       "        continue\n",
 237       "    print b\n",
 238       "    p.animate(i)\n",
 239       "    scaling_data[b] = scaling.FixedScales(\"./\"+b, steps=2000, fps=10, xz=0.5, yz=0.5)\n",
 240       "saveload.save_obj(scaling_data, \"scaling_data\")"
 241      ],
 242      "language": "python",
 243      "metadata": {},
 244      "outputs": [
 245       {
 246        "output_type": "stream",
 247        "stream": "stdout",
 248        "text": [
 249         "\r",
 250         "[                  0%                  ]"
 251        ]
 252       },
 253       {
 254        "output_type": "stream",
 255        "stream": "stdout",
 256        "text": [
 257         " path-Gmprat\n",
 258         "\r",
 259         "[***               7%                  ]  1 of 15 complete"
 260        ]
 261       },
 262       {
 263        "output_type": "stream",
 264        "stream": "stdout",
 265        "text": [
 266         " ./path-Gmprat - Quit early after 149 steps - Exception [Errno 32] Broken pipe\n",
 267         "./path-Gmprat - Couldn't exit - [Errno 32] Broken pipe\n",
 268         "path-mpfr-1024\n",
 269         "\r",
 270         "[*****************80%**********        ]  12 of 15 complete"
 271        ]
 272       },
 273       {
 274        "output_type": "stream",
 275        "stream": "stdout",
 276        "text": [
 277         " ./path-mpfr-1024 - Quit early after 149 steps - Exception [Errno 32] Broken pipe\n",
 278         "./path-mpfr-1024 - Couldn't exit - [Errno 32] Broken pipe\n"
 279        ]
 280       }
 281      ],
 282      "prompt_number": 27
 283     },
 284     {
 285      "cell_type": "markdown",
 286      "metadata": {},
 287      "source": [
 288       "<table>\n",
 289       "    <tr><td><b>Original</b></td><td><b>Scaled</b></td></tr>\n",
 290       "    <tr><td><img src=\"original.bmp\" width=\"50%\"/></td><td><img src=\"final.bmp\" width=\"50%\"/></td></tr>\n",
 291       "</table>"
 292      ]
 293     },
 294     {
 295      "cell_type": "code",
 296      "collapsed": false,
 297      "input": [
 298       "fig = figure(figsize=(6,4))\n",
 299       "l = []\n",
 300       "for b in [\"mpfr-16\", \"mpfr-32\", \"mpfr-64\", \"mpfr-256\", \"mpfr-512\", \"mpfr-1024\",\"path-Gmprat\"]: \n",
 301       "    plot(-1.0*scaling_data[b][\"accuracy\"][:,4], scaling_data[b][\"accuracy\"][:,-1])\n",
 302       "    l += [b]\n",
 303       "#xscale(\"log\")\n",
 304       "legend(l, loc=\"best\")\n",
 305       "title(\"Loss of Precision for a 1x1 pixel grid\\nView Top Left: (0.5,0.5)\")\n",
 306       "xlabel(\"Log10(Magnification)\")\n",
 307       "ylabel(\"Representable Lines\")\n",
 308       "\n",
 309       "fig.savefig('../../sam/figures/loss_of_precision_grid_0.5.pdf', format='PDF')"
 310      ],
 311      "language": "python",
 312      "metadata": {},
 313      "outputs": [
 314       {
 315        "metadata": {},
 316        "output_type": "display_data",
 317 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V2hFFYTRSksRSJajVcQSmER3aEA0iTRILoiSmEJRFejEx2hHkgTRT0QRZTCEoZ6IERXaBpvE6XV\nhYTKXqhHaBqvMDQGQnSFeiBNlMIsLCEFpZS9UI9QD0QgmoVFdEZES5k0RTXHQAQVlFL2Qj1CV6IL\nQyksoiuUwmqiKIWliFJYwlAKi+gKpbB0YNasWbC0tISrqyu/benSpejRowd69+6NyZMn4+nTp/xj\nISEh6Nq1K5ycnHDy5El++59//glXV1d07doVixcvrvWYNIiuiFJYwlAPhOgOBZAX5u/vj+joaLlt\nI0eOxM2bN3Ht2jV069YNISEhAID4+HgcOnQI8fHxiI6ORkBAAL8y6oIFCxAeHo7ExEQkJiYq7LM6\n6oEoomm8AtE03hdmbm4OjuOa/a1jx4Xo3j2swdtR82Zubq7z33mdBZAhQ4bAzMxMbpunpydEospD\nvvzyy0hPTwcAREZGwsfHB0ZGRrCzs4OjoyPi4uKQlZWF/Px8uLu7AwB8fX1x9OhRlcesWdpDUD0Q\nZS/UI3QhoTCciGqCvKjc3FwwxujWSG+5ubk6/5032BjIzp074eXlBQDIzMyEVCrlH5NKpcjIyFDY\nbmtri4yMDJX7FInky3pw4ITVeOA4va0HIgLVuRBERO8TIZoybIiDrl27Fi1atMD06dN1ut8bN4JQ\nWgrcvg3IZDL09+gPBgEfCjUjjx7hqAciCMdRD4Q0DzExMYiJidHJvuo9gERERODEiRM4ffo0v83W\n1hZpaWn8/fT0dEilUtja2vJprqrttra2Kvfdq1cQxowBZsyovP+89LmwFJYeF5TiACEhlNAbRZoJ\nmUwGmUzG31+1apXW+6rXFFZ0dDQ2bNiAyMhItGrVit8+fvx4HDx4ECUlJUhOTkZiYiLc3d1hZWUF\niUSCuLg4MMawd+9eTJw4UeX+a2aiKIVFKSzBOHqfCNFUnQUQHx8feHh44M6dO+jYsSN27tyJhQsX\noqCgAJ6ennBzc0NAQAAAwNnZGd7e3nB2dsbo0aMRFlY5gwEAwsLCMGfOHHTt2hWOjo4YNWqUymMq\nBBCOE5bC0uMAQiksYTgRRz0Qopa/vz/Mzc0xYMCAhm5Ko1BnKawDBw4obJs1a5bK5wcGBiIwMFBh\ne9++fXH9+nVBx6QeiCLKzAjEgcZASK3Onz+PU6dOITMzUy6DUpuzZ89i9erVuHr1KszMzJCcnKzw\nnC+++AJffPEFHj58iE6dOiEyMhJdu3bVdfPrhF5diV5zLFzEiWgQHZSaEYRSWESN1NRU2NnZqQwe\nZWVlCtuSLwCEAAAgAElEQVTatm2LOXPmYMOGDUpfs2PHDuzcuRMnTpxAQUEBjh8/jvbt2+u03XVJ\nrwJIzbFwjuOa/SC6iOOoByIApbD0l52dHTZu3IhevXpBLBZj9uzZyM7OxujRo2FiYgJPT0/k5eUh\nJSUFIpEI33zzDWxtbWFjY4PPPvsMABAeHo65c+fit99+g1gsxqpVqxATEwOpVIr169fD2toas2fP\nVjh2//79MWPGDNjb2ys8VlFRgVWrVmHz5s1wcnICANjb2ytcP9eYqU1hJSUlQSqVolWrVjh79iyu\nX78OX19fmJqa1kf7NEIpLEWUmRGI3ii9xXEcjhw5gtOnT6O0tBRubm64evUqdu3aBScnJ3h5eWHL\nli3w8/MDUDnNNSkpCXfv3sXw4cPRp08fzJ49G4aGhtixYwfOnz/PPy87Oxu5ubm4f/8+KjT8Epqe\nno6MjAxcv34dfn5+MDQ0hK+vL1auXMmPATd2ansgU6ZMgaGhIZKSkvDWW28hLS1N59dv6AoNoiui\nFJZAlMKqcxynm5s2Fi5ciA4dOsDGxgZDhgzBwIED0bt3b7Rs2RKTJk3C1atX+eeuXLkSrVu3Rs+e\nPeHv78+P5yr7+xCJRFi1ahWMjIzQsmVLjdpUdYnCL7/8ghs3buDs2bM4cOAAwsPDtTvJBqA2gIhE\nIhgaGuLIkSNYuHAhNmzYgKysrPpom8aU9UC0eqEeoUF0geiNqnOM6eamDUtLS/7n1q1by91v1aoV\nCgoK+PsdO3bkf+7UqRMyMzNV7rdDhw5o0aIFACA4OBhisRhisZifYVqb1q1bAwA++OADSCQSdO7c\nGW+99RZOnDgh/MQamNoA0qJFC3z77bfYs2cPxo4dCwAoLS2t84ZpQ2Epk/9+XVH7zVKPAwiNgQhD\nYyDNS22fCffv35f7ubaLl6unmgIDA5Gfn4/8/HyEhYWpbUP37t354KNqn42d2gCyc+dOXLp0CcuX\nL4e9vT2Sk5Mxc+bM+mibxpSNhXMQkMbS81lYVFBKAI4qEpJKa9asQWFhIW7evImIiAhMnTpV630x\nxlBUVITS0lIwxlBcXIySkhIAgLGxMaZOnYr169ejoKAA6enp+Oabb/gv6k2B2kF0FxcXhIaG8lHZ\n3t4eH374YZ03TBvKOhJVM7FEXC2xUo9nYVFmRiB6o5qV6t/yq5Y7rzJs2DA4OjqioqICS5cuxb/+\n9S+lz6u5H2ViY2MxfPhw/rmtW7eGTCbDmTNnAADbtm3DvHnzYGNjA1NTU8ybNw/+/v46Ocf6wDE1\n+Z2oqCgsXboUxcXFSElJwdWrV7Fy5UpERUXVVxsF4TgOb73F0Ls3sGDB/7YbrjZE4fJCGBkYqX7x\noEHAunXA4MF139B6FpyaioLycgQ7ODR0Uxq1q0Ouwn6tPUyHNr7ZhU0Fxwmc9dhIpaSkwMHBAWVl\nZXzZCX2i6vfzIr83te9SUFAQ4uLi+LnJbm5uuHfvnlYHq2uqeiBqU1h6PAZCKSyBRKAeCCEaUhtA\njIyMFK75aKzRWdlQhqBrQfQ8gOjnmekYjYEQNK0B7MZA0BjI/v37UVZWhsTERGzZsgUeHh710TaN\nKRvKELSciT4PotMsLEE4jmZhNXd2dnYo19PS1nVFbVdi69atuHnzJlq2bAkfHx9IJBJs3ry5Ptqm\nsdoG0dW+UE8H0SkzIxC9UYRoTG0PpE2bNggODkZwcHB9tOeFKA0glMKiMRAhKIVFiMbUBpA7d+5g\n48aNSElJ4Veb5DiOn4bWmNAguiJKYQlDKSxCNKc2gLz++utYsGAB5syZAwMDAwCNd6CJBtEVUWZG\nIJptQIjG1AYQIyMjLKh+YUUjpvRK9ObeAwGlsAQR0WKKhGhK7SD6uHHj8OWXXyIrKws5OTn8rTFS\nFgdEnEj9BwPNwmr2OI6j5dyJWlTSVp7aABIREYGNGzfCw8MDffv25W+NkapB9OY8C4syMwLRG0XU\nqF7S9tKlS4Je8/nnn6NLly6QSCSwtLSEv78/8vPzAQCPHj2Cj48PbG1tYWpqisGDB+P333+vy1PQ\nObUBJCUlBcnJyQo3dWbNmgVLS0u4urry23JycuDp6Ylu3bph5MiRyMvL4x8LCQlB165d4eTkhJMn\nT/Lb//zzT7i6uqJr165YvHhxrcekQXRFIlBqRhCqB0LU0Kak7YQJE3D58mU8e/YMt2/fxv3797F2\n7VoAQEFBAV5++WVcuXIFubm58PPzw5gxY/DPP//U6XnoksoAcvr0aQDA4cOHceTIEYWbOv7+/oiO\njpbbFhoaCk9PTyQkJGDEiBEIDQ0FAMTHx+PQoUOIj49HdHQ0AgIC+P/MCxYsQHh4OBITE5GYmKiw\nT7mToUF0BRzHUWZGAFrOXX81ZElbBwcHfhmoiooKiEQiWFtbA6hcmPadd96BpaUlOI7D3LlzUVJS\ngoSEhPp7c16QykH0c+fOYcSIETh27JjSWVeTJ0+udcdDhgxBSkqK3LaoqCjExsYCAPz8/CCTyRAa\nGorIyEj4+PjAyMgIdnZ2cHR0RFxcHDp37oz8/Hy4u7sDAHx9fXH06FGMGjVK6TFpEF0RZWYEopK2\nequhS9p+++23WLBgAfLz8zFt2jSVmZS//voLJSUlcHR0rJs3og6oDCCrVq0CUDkGUtMPP/yg1cGy\ns7P5SmCWlpbIzs4GAGRmZsoNSkmlUmRkZMDIyAhSqZTfbmtri4yMDJX7pwsJFVEKSyBKYdU5bpVu\npv+zlZr/nqpK2gKVX24tLS3Ru3dvAMCkSZNw+vRpPoAoK2k7YsQItSVtVZk+fTqmT5+OpKQkvP76\n6/j888/x7rvvyj3n2bNnmDlzJoKCgiAWizU+v4aidhqvMu+++y5ee+21FzqwsrX1X9SvvwbByAgo\nKQFkMhlkMhmthUUpLEEohVX3tPng15UXKWl7/fp1lfutWdI2JCQEADBz5kyFqoSOjo746KOPEBoa\nKhdACgsLMW7cOHh4eNRLraWYmBjExMToZF9aBRBtWVpa4sGDB7CyskJWVhYsLCwAVPYs0tLS+Oel\np6dDKpXC1taWLzxftb228pJDhwahdWvg44//t625r4VFKSyBKIXVrKgradu9e3f+Z01K2gYGBtZ6\n3NLSUhgbG/P3i4uLMXHiRHTq1Anbt28X2vwXUvXlukpVtkkb9bou+/jx47F7924AwO7duzFx4kR+\n+8GDB1FSUoLk5GQkJibC3d0dVlZWkEgkiIuLA2MMe/fu5V+jDA2iK+JAqRlBKIVF/kuXJW137NiB\nR48eAaicLBQaGoopU6YAqAwmr732GoyNjZUOFTQFKnsg1aff1lQ1dlEbHx8fxMbG4vHjx+jYsSNW\nr16Njz76CN7e3ggPD4ednR2+++47AICzszO8vb3h7OwMQ0NDhIWF8ZE9LCwMb775JgoLC+Hl5aVy\nAB2gQXRlRHQhoTDUVWtW6quk7cWLF/Hxxx/jn3/+gY2NDWbPns2nry5evIjjx4/D2NhYruZSdHQ0\nBg0a9MLnWB9UlrStOYOqJjs7uzpojvY4jsOKFQwcBwQF/W+7zWc2+GPuH7CVqO6GYtIkYOZMQM3M\nsqZoZ1YWzj99il1OTg3dlEbtpvdNdHitAyy8LRq6KU0WlbRt3OqipK3KHkhjCxBCqFzKpDkPooNS\nM4LQcu6EaEyvwiwVlFJEKSyBKIVF0HhXGm+s9CqA0CC6IppcJAzVAyFVJW31MX1VVwS9U8+fP8ed\nO3fqui0vjAbRFVEKSyAqnEKIxtQGkKioKLi5ueHVV18FAFy9ehXjx4+v84Zpg65EV0SZGYFoDIQQ\njakNIEFBQYiLi+MXBHNzc8O9e/fqvGHaoNV4FdEYiDCUwiJEc2oDiJGRkdwcZQCNNkdIBaUUUUVC\ngSiFRYjG1EYCFxcX7N+/H2VlZUhMTMTChQvh4eFRH23TmKpB9OY8C4tSWAJRCosQjakNIFu3bsXN\nmzfRsmVL+Pj4QCKRYPPmzfXRNo3RILoiKmkrDKWwiBBU0lae2gDSpk0bBAcH4/Lly7h8+TLWrl2r\nsiJXQ6NBdEWUmRGIumpEDW1K2gLAlStXMHToUIjFYlhZWWHLli0Kz4mNjYVIJMInn3yiyybXOZVX\noo8bN07liziOQ1RUVJ006EXQILoiGgMRSETTnUnthJS0NTSU/0h9/PgxRo8ejc2bN+O1115DSUmJ\n3MrjQOWiiosXL8aAAQOa3IWMKgPIe++9p/JFjfUkaRBdEaWwhOE4jq641FN2dnZ4++23sWfPHiQn\nJ8Pb2xvBwcF48803cfHiRbi7u+P7779HXl4eHBwcsH37dgQFBYExhvfeew/vvfcewsPD8fbbb6O0\ntBRisRjvv/8+hg0bhjfeeAOLFi3C559/jpEjR/KrjVfZtGkTRo0aBR8fHwCVk5KcaqxL99lnn2HU\nqFHIzs5ucl9iVAaQ6uvFFxcX4/bt2xCJROjevTtfQKWxUZXCas6D6JTCEohSWHqrIUvaxsXFwdXV\nFYMGDUJSUhJefvllfPnll3zRqtTUVOzatQtXrlzBv//97/p7U3RE7RjI8ePH4ejoiEWLFuHtt99G\nly5dcOLEifpom8aUzsKiFBalsISgeiB1j+N0c9NCVUlbGxsbDBkyBAMHDkTv3r3RsmVLTJo0CVev\nXuWfq6ykLaD876N6SduWLVsqPJ6Wlobdu3djy5YtuH//Puzt7fneCAAsWrQIa9asQZs2beqkSmtd\nUxtAlixZgrNnzyI2NhaxsbGIiYlRqOfbWCidhdXMB9EphSUMlbStB4zp5qaFFylpm5mZqXK/NUva\nisViiMViBAQEAACMjY0xefJk9O3bFy1btsTKlStx8eJF5Ofn49ixYygoKMDrr7/+37eHNbkvMWpL\n2kokEjg6OvL3HRwcIJFI6rRR2qJBdEWUmRGIVp1sVuqrpG2vXr1UHv/MmTO4fPkyrK2tAQBPnz6F\ngYEBbty4gf/85z+Cz6UhqQwghw8fBgD069cPXl5e8Pb2BgB8//336NevX/20TkM0jVeRCJSaEYRS\nWOS/1qxZg6+//hr37t1DREQE9u/fr/W+/P39MWXKFCxatAjOzs749NNPMWTIEEgkEnz66adYtmwZ\ngMq/vcWLF8PW1rZJTeVVGUCOHTvGR1cLCwvExsYCqOyyFRUV1U/rNEQFpRRxHEdfrAWgFFbzUl8l\nbV955RUEBwdjzJgxeP78OYYMGYJvv/0WANC2bVu0bduWf27r1q3Rpk0bhaWjGjOVJW2bGo7j8OWX\nDDduAGFh/9vutt0N4ePD8ZL1S6pfPH8+0Ls3sGBB3Te0np148gRbMzLwk4quNKmUuCgRrbu0hnSx\ntKGb0mRRSdvGrV5L2lYpLCxEeHg44uPjUVhYyEfcnTt3anVAAAgJCcG+ffsgEong6uqKXbt24Z9/\n/sHUqVP5i3W+++47PhKHhIRg586dMDAwwJYtWzBy5Eil+6VBdEWUwhKIUliEaExtmJ05cyays7MR\nHR0NmUyGtLQ0uW6XplJSUvDNN9/gypUruH79OsrLy3Hw4EGEhobC09MTCQkJGDFiBEJDQwEA8fHx\nOHToEOLj4xEdHY2AgACl860BGkRXhlJYAtFsA4LGe5F0Y6U2gCQlJeHTTz9F27Zt4efnhxMnTiAu\nLk7rA0okEhgZGeH58+coKyvD8+fPYWNjg6ioKP5CHj8/Pxw9ehQAEBkZCR8fHxgZGcHOzg6Ojo74\n/fffle6bBtEV0eeiMDQGQqikrebUvlNVc5xNTExw/fp15OXl4dGjR1of0NzcHO+99x46deoEGxsb\nmJqawtPTE9nZ2fzcbEtLS2RnZwMAMjMzIZX+Ly8tlUqRkZGhdN/UA1FEJW0FouXcCdGY2jGQuXPn\nIicnB2vWrMH48eNRUFCATz/9VOsD3r17F5s3b0ZKSgpMTEzw+uuvY9++fXLPUXdFpqrHfvwxCOnp\nQFBQ5VIsMpkMIk6kfikTkUh/lzKhCwmFoa4aaSZiYmIQExOjk32pDSAjRoyAubk5hg0bhuTkZAB4\noZK2ly9fhoeHB9q1awcAmDx5Mn777TdYWVnhwYMHsLKyQlZWFiwsLAAAtra2cqtXpqenq7ywZ8KE\nIFy4UBlAqlAKi66PE4LqgZDmourLdZVVq1ZpvS+1KazXXntNYVvVpffacHJywqVLl1BYWAjGGE6d\nOgVnZ2eMGzeOX8ly9+7dmDhxIgBg/PjxOHjwIEpKSpCcnIzExES4u7sr3TelsBRRCksgWnWSEI2p\n7IHcunUL8fHxyMvLw5EjR8AYA8dxePbs2QtdSNi7d2/4+vqiX79+EIlEeOmllzBv3jzk5+fD29sb\n4eHh/DReAHB2doa3tzecnZ1haGiIsLAwlSksGkRXRCksgWgMhBCNqQwgCQkJOHbsGJ4+fYpjx47x\n28ViMb755psXOugHH3yADz74QG6bubk5Tp06pfT5ytaYUUbZUEZzHwOhFJYwlMIiQvj7+yMyMhLd\nunXTqCqhvlIZQCZMmIAJEybg4sWL8PDwqM82aY1SWIoohSUQpbCIGtVL2got671hwwbs2bMHqamp\naN++PQICAvD+++/zj9vZ2eHhw4cwMDAAAAwaNAjR0dH8448ePcLixYtx4sQJiEQieHl5KUw6akhq\nB9EdHR2xdu1apKSkoKysDEDlh/KLXIleVyiFpYgmFwlEKSyihjYlbQFg79696NWrF5KSkjBy5Eh0\n7NgRU6dOBVD5Wfrjjz9i+PDhSvc5efJkvPzyy0hLS4OxsTFu3LihuxPSAbWD6BMmTMCzZ8/g6emJ\nMWPG8LfGSNmaiM19MUUaAxGGUlj6y87ODhs3bkSvXr0gFosxe/ZsZGdnY/To0TAxMYGnpyfy8vKQ\nkpICkUiEb775Bra2trCxscFnn30GAAgPD8fcuXPx22+/QSwWY9WqVYiJiYFUKsX69ethbW2N2bNn\nKxx76dKl6NOnD0QiEbp164YJEybgwoULcs9R9QX35MmTSE9Px/r16yEWi2FgYIDevXvr/g16AYLW\nwlq3bl19tOWFKV0Li2veJW2pIqFA1FXTWw1Z0rY6xhjOnTuHBTUWbZ0xYwYqKirg5uaGDRs28DVE\nLl26hO7du8PPzw8//fQTHBwcsHHjRgwdOrQO3iXtqA0gY8eOxfHjxxttr6M6SmEpos9FgUQ0VlTX\nOB1dvMaqXcMgVFVJWwAYMmQILC0t+W/zkyZNwunTp/kAoqyk7YgRI9SWtFUn6L8XqPn7+/Pbvv32\nW7z00kuoqKjAF198gVdffRV37tyBRCJBeno6Tp48ifDwcEREROCHH37AhAkTkJSUxF9H19DUBpDN\nmzcjODgYLVq04N+kqum8jQ2lsBRRCksYjuPAyumdqkvafPDryouUtL1+/brK/dYsaRsSEgKgchHa\nsGp1JbZt24Z9+/bh/PnzcsFm4MCB/M8fffQRdu/ejfPnz2PMmDFo3bo17O3t+YAzdepUrF27Fhcu\nXMD48eM1fg/qgtoxkIKCAlRUVKCoqAj5+fnIz89vlMEDoBSWMpTCEoi6as2KupK21X/WpKRt1Wdk\n9eCxc+dOrF+/HqdPn4aNjU2t7apem0PZeIe6ZZ7qm9oAUlFRgb1792L16tUAKt9QVavhNjSVPRB1\nH6B63AOhz0WBqB4I+a81a9agsLAQN2/eREREBD9jShv79+/H8uXLcfLkSdjZ2ck9lpaWhgsXLqCk\npARFRUXYsGEDnjx5gkGDBgGoTK3l5uZiz549KC8vxw8//ICMjAz+8cZAbQAJCAjAb7/9JleGMSAg\noM4bpg1VBaWadQ+EUliC0HLuzYuQkrb/+te/Xrik7SeffIKcnBz0798fYrEYYrGY//zMz89HQEAA\nzM3NIZVKcfLkSfz0008wMzMDAJiZmSEqKgobN26Eqakp1q9fj8jISJibm+vkPdAFtSVtq2YsVP0L\nVHatrl27Vi8NFIrjOBw+zLB3L/Cf//xv++j9o7HQfSG8unqpfnFQUGUP5AUWFWusrubnY9adO7ja\nr19DN6VRSw1ORXl+ORxCHBq6KU0WlbRt3OqipK2geiDl5eX8/UePHjXaN5dSWIpoDEQgSmERojG1\nkWDhwoWYNGkSHj58iMDAQAwaNAjLli2rj7ZpjFJYiiiFJQylsAhAJW01pXYa7xtvvIG+ffvi9OnT\nACpLzPbo0aPOG6YNWgtLEQ2iC0SrTjZ7VSVtiXBqeyB3796Fvb093n77bbi4uOCXX35BXl5efbRN\nY7QaryIRKDUjCKWwCNGY2gAyefJkGBoaIikpCW+99RbS0tIwffr0+mibxuhKdEUcx9EXayGoq0aI\nxtQGEJFIBENDQxw5cgQLFy7Ehg0bkJWVVR9t0xhdia6IPheFoTEQQjQnaBbWt99+iz179mDs2LEA\ngNLS0jpvmDboSnRFlMISiJZzJ0RjagPIzp07cenSJSxfvhz29vZITk7GzJkz66NtGqMUliJKYQlE\nXTVCNKY2gLi4uCA0NBRubm4AAHt7e3z44Yd13jBtUApLEX0uCkP1QIgQ/v7+MDc3x4ABAxq6KY2C\n2gASFRUFNzc3jBo1CgBw9erVF14JMi8vD6+99hp69OgBZ2dnxMXFIScnB56enujWrRtGjhwpN9Mr\nJCQEXbt2hZOTE06ePKlyv5TCUkQlbQWikrZEjeolbYXWQw8KCoKRkRG/jIlEIkFKSgr/+CeffAJX\nV1cYGRlhVY2VMI4fP47BgwfDzMwM1tbWmDt3rtyqwY2B2gASFBSEuLg4fn0WNzc33Lt374UOunjx\nYnh5eeHWrVv4+++/4eTkhNDQUHh6eiIhIQEjRoxAaGgoACA+Ph6HDh1CfHw8oqOjERAQoLJwC12J\nroiWcxeIxkCIGkJK2tbEcRx8fHzkVjKvvqhi165dsWHDBowZM0bhIsZnz55hxYoVyMrKwq1bt5CR\nkYGlS5fq9JxelNoAYmRkBFNTU/kXvcBSJk+fPsX58+cxa9YsAIChoSFMTEwQFRXFF3Tx8/PD0aNH\nAVReuOjj4wMjIyPY2dnB0dFR5WrAdCW6Iro+ThhKYemvhixpyxir9Qusr68vRo0aBbFYrPA8Hx8f\njBw5Eq1atYKpqSnmzp2rUA63oam9Et3FxQX79+9HWVkZEhMTsWXLFnh4eGh9wOTkZHTo0AH+/v64\ndu0a+vbti82bNyM7O5sv8mJpaYns7GwAQGZmply+USqVIiMjQ+m+6Up0RZTCEohSWHqrIUvachyH\nY8eOoV27drC2tsbbb7+N+fPna3UesbGx6Nmzp/ZvRB1QG0C2bduGNWvWoGXLlvDx8cGrr76KTz75\nROsDlpWV4cqVK9i2bRv69++Pd955h09XVVFXNEXVY7t3ByElpXJxXZlMBplMRiksSmEJQymsOhfD\nxehkPzIm0/g1DVXS1tvbG2+99RYsLS1x6dIlTJkyBaamppg2bZpG7f/ll1+wZ88endRiiomJQYyO\nygvXGkDKysowZswYnD17FsHBwTo5oFQqhVQqRf/+/QEAr732GkJCQmBlZYUHDx7AysoKWVlZsLCw\nAADY2toiLS2Nf316errKCmGzZgUhMbEygFShFBalsISgFFbd0+aDX1caqqRt9XUDBw4ciMWLF+OH\nH37QKIBcunQJM2bMwOHDh+Ho6Cj4dapUfbmuUnPwXhO1DmYYGhpCJBLpdO0rKysrdOzYEQkJCQCA\nU6dOwcXFBePGjcPu3bsBALt378bEiRMBAOPHj8fBgwdRUlKC5ORkJCYmwt3dXem+KYWliFJYAtF8\n52alPkvaCqUss3L16lVMmDABEREReOWVVzTeZ11Tm8Jq06YNXF1d4enpiTZt2gCoPNEtW7ZofdCt\nW7dixowZKCkpQZcuXbBr1y6Ul5fD29sb4eHhsLOzw3fffQcAcHZ2hre3N5ydnWFoaIiwsDCVKSya\nhaWIPhcFElGgJZXWrFmDr7/+Gvfu3UNERAT279+v9b4iIyMxdOhQmJqa4o8//sCWLVvkUvZlZWUo\nKytDeXk5SktLUVRUhBYtWkAkEuHGjRsYNWoUtm3bBi+vWgriNSC1AWTy5MmYPHky/6HNGHvhNfN7\n9+6NP/74Q2H7qVOnlD4/MDAQgYGBavdLs7AU0RiIMBzHUa6vGRFS0raiouKFS9oeOnQIs2fPRnFx\nMaRSKZYtWya3ksecOXOwZ88e/v7atWsREREBX19ffPbZZ3jy5AlmzZrFz1q1s7OrNaVW39SWtAWA\n4uJi3L59GxzHwcnJic/5NSYcx+HSJYZFi4C4uP9tnxM1BwOkAzDnpTmqX/zNN8Dvv1f+q2eyiovh\ndvkyHgwa1NBNadSydmbh6fmncNrl1NBNabKopG3jVhclbdX2QI4fP4758+fDwaGyVvS9e/ewffv2\nRtmloh6IIkphCUT1QAjRmNoAsmTJEpw9e5Yf/b979y68vLwabQBROojejBdTpBSWMLScOwGopK2m\n1AYQiUQiN3XMwcEBEomkThulLVpMUREHoEJPz02naL5zs0clbTWnNoD07dsXXl5e8Pb2BgB8//33\n6NevH44cOQKgcpC9saAUliJKYQlEKSxCNKY2gBQVFcHCwgKxsbEAKi+cKSoqwrFjxwA0rgBC03gV\ncZTCEoRSWIRoTm0AiYiIqIdm6AYt566IlngSiFJYhGhM7Vy1O3fuYMSIEXBxcQEA/P3331izZk2d\nN0wbKisSNvMr0WkMRABKYRGiMbUBZO7cuQgODuav/XB1dcWBAwfqvGHaoBSWIkphCUSDRYRoTG0A\nef78OV5++WX+PsdxKleebGiUwlJEKSxhaAyECEElbeWpDSAdOnRAUlISf/+HH36AtbV1nTZKW5TC\nUkQpLIFoOXeihjYlbc+ePYtXXnkFpqamsLe3V3g8JSUFr7zyCtq0aYMePXrg9OnT/GNCS9rm5OSg\nQ4cOGDJkiPYnpyW1AWTbtm146623cOfOHdjY2ODzzz/HV199VR9t0xilsBRRCksgSmERNbQpadu2\nbTroJswAABsGSURBVFvMmTMHGzZsUPoaHx8f9O3bFzk5OVi7di1ee+01PH78GIDwkrYffvghnJ2d\nG+QiSLUBpEuXLjh9+jQePnyIO3fu4Ndff0Vc9cWmGhFKYSmiz0VhqB6I/mrIkrb9+/fHjBkzlPY+\nEhIScPXqVaxatQotW7bE5MmT0atXLxw+fBiAsJK2Fy9exM2bN+Hv798gk0BUTuMtKCjA9u3bcffu\nXfTs2RPz589HZGQkli9fDkdHR0ydOrU+2ykIXYmuSASaXSQIDRbprYYsaVubmzdvwsHBgS+TAVSu\nVH7z5k2lz69Z0ra8vBwLFy7Ejh07cO3aNU3fFp1QGUB8fX0hkUgwcOBAnDx5EhEREWjVqhW+/fZb\n9OnTpz7bKBhdia6I4zi6vEEIGgOpczExukmxyGSa/54aqqRtbQoKCmBiYiK3TSKRICMjQ+G5ykra\nbtmyBQMGDICbm1vjCyBJSUn4+++/AVSuWW9tbY3U1FS0bt263hqnKVpMURGlsIShFFbd0+aDX1ca\nqqRtbdq2bYtnz57JbcvLy1NYa1BZSdvMzExs3boVf/75Z63HqGsqA4iBgYHcz7a2to06eACUwlKG\nStoKRCmsZkVdSdvu3bvzP2tS0lZI4bsqLi4uuHfvHgoKCtC2bVsAwLVr1+QKTqkqafv7778jKysL\nzs7OAIDCwkIUFhbCxsYGGRkZ9TagrnIQ/e+//4ZYLOZv169f539urKvxUgpLES3nLhClsMh/rVmz\nBoWFhbh58yYiIiJeaLyXMYaioiKUlpaCMYbi4mKUlJQAALp164Y+ffpg1apVKCoqwpEjR3Djxg1M\nmTIFAGotaevl5YXU1FRcu3YN165dw+rVq+Hm5oa//vqrXmdjqeyBNMVljSmFpYiWeBKGUljNS32V\ntI2NjcXw4cP557Zu3RoymQxnzpwBABw8eBBvvvkmzM3N0blzZxw+fBjt2rUDAGzatEllSdsWLVrA\nwsKCP46JiYnCtvogqKRtXSgvL0e/fv0glUpx7Ngx5OTkYOrUqfxc6++++w6mpqYAgJCQEOzcuRMG\nBgbYsmULRo4cqbA/juNw/z6DhweQlva/7SvOroChyBArhq1Q3Zjjx4GwsMp/9QxjDKLYWDCZrKGb\n0qg9OfEEGVsz0OunXg3dlCaLSto2bnVR0rbB3qUvvvhC7uKX0NBQeHp6IiEhASNGjEBoaCgAID4+\nHocOHUJ8fDyio6MREBCgcrocpbAUVb2/Tfk/dr0Q0XtEiKYaJICkp6fjxIkTmDNnDv+fNioqip9G\n5+fnh6NHjwIAIiMj4ePjAyMjI9jZ2cHR0VFuKlt1dCW6cpTGUo/jOHqTCJW01VCDBJB3330XGzZs\nkOsmZmdn81PrLC0tkZ2dDaByuppUKuWfJ5VKlc6TBuhKdFVoJpYANN+52asqaauP6au6oraglK79\n+OOPsLCwgJubG2JiYpQ+R9lgVc3HlfnssyDk5wNBQYBMJoNMJmv2iykC9NkoCNUDIc1ETEyMys9e\nTdV7ALl48SKioqJw4sQJFBUV4dmzZ5g5cyYsLS3x4MEDWFlZISsri59NYGtri7Rqo+Lp6ekq52V/\n8EEQdu+uDCBVRJxIfQ9EJNLrHoiIrkZXixNRCos0D1VfrqusWrVK633Ve18tODgYaWlpSE5OxsGD\nBzF8+HDs3bsX48ePx+7duwEAu3fvxsSJEwEA48ePx8GDB1FSUoLk5GQkJibC3d1d6b6VxQEKIJW/\nZFrSXQ0RXQdCiKbqvQdSU1U66qOPPoK3tzfCw8P5abwA4OzsDG9vbzg7O8PQ0BBhYWEqU1gGBopx\nwEBkgPIKNde0GBgATfC6F6EMqAeiFmfAAfr7J1AvzMzMaBC6ETMzM9P5Phs0gAwbNgzDhg0DAJib\nm+PUqVNKnyd0iQCRSDEOGHAG6nsgyiKPHhFxHMqpB1IrzoCjHsgLysnJaegmNBqXL/dDt27/B4mk\nX0M3pU7p1XQDZR0JESdCOVPz1VJZ5NEjBgAFEHVEACun94joBseJ0By6tHofQCiFVZnCogBSO0ph\nEV3iOAMwdV9c9YDeBRCFMRDOQH0PpBkEEP1N0OkGZ8BRD4ToEAWQJkfpGIiIxkBEoBSWWgY0C4vo\nDscZoDnMC9erAFIVB6p/Voo4kfoUlr6PgVAKSy1ORCksojscJ6IeSFPDcYqrklAK678BpKEb0chR\nCovoFqWwmqSa2SgDEQUQA46jCwnVMaBZWER3aBC9iaqZjaLrQGgMRAjOgJYyIbpDYyBNVM3OBI2B\nUApLCE5EKSyiSzQG0iRRCksRpbAEoBQW0SFKYTVRylJYzf1CQkphqUeD6ESXKlNY+vuZUkXvAkjN\nWEDXgVAKSwgaAyG6VNkD0f8/KL0PILQWFl0HIgithUV0isZAmiSFMRBKYcEAVA9EHUphEV2iFFYT\npTAGQoPolcu5N3QjGjlaTJHoEg2iN1EKYyDUA6EUlgDUAyG6RQGkSVI2BtLcS9pSPRABqKQt0aHK\neiD6+5lSRS8DCF0HIo+Wc1ePUlhElyiF1UTRdSCKqKStepTCIrpFAaROpKWl4ZVXXoGLiwt69uyJ\nLVu2AKisp+zp6Ylu3bph5MiRyMvL418TEhKCrl27wsnJCSdPnqx1/8quA2n2PRBQCkstuhKd6BD1\nQOqIkZERPv/8c9y8eROXLl3Cl19+iVu3biE0NBSenp5ISEjAiBEjEBoaCgCIj4/HoUOHEB8fj+jo\naAQEBKCilvEKZdN46UJCSmGpQxcSEl2ixRTriJWVFfr06QMAaNu2LXr06IGMjAxERUXBz88PAODn\n54ejR48CACIjI+Hj4wMjIyPY2dnB0dERv//+u8r910xhCVpMkeMqq1Dp6bd0SmGpx3EcABpIJ7pC\nFxLWuZSUFFy9ehUvv/wysrOzYWlpCQCwtLREdnY2ACAzMxNSqZR/jVQqRUZGhsp9apXC4ji9vhqd\nUlgCURqL6EhzSWEZNtSBCwoKMGXKFHzxxRcQi8Vyj3Ecx38jVEbVY0FBQcjOBr76Cpg+XQaZTCZs\nEB34X+QxbLC3pM7QdSDC8APpRg3dEtLUNeYr0WNiYhATE6OTfTXIp2VpaSmmTJmCmTNnYuLEiQAq\nex0PHjyAlZUVsrKyYGFhAfx/e/ceE8XV/gH8OzvMSl0o0gqIYCVduYgsu1sENIKWtF5aW7xVxaYo\nFW1ao9W8xtSSJmLearVN47XG/Iy2qH3BJqbaixCVSAWqoAi2qVZNii0g0BZvsAh7O78/CKMLu7DC\n4u4MzyfZxLmc4dlDmMfnnLkACAkJQU1Njdi2trYWISEhdo+bnZ2NggLg7beBiRM71gm8AJPV1HtQ\nggCYTMCQIf37ch5I4DiYKIH0ihM4MBMDvN0dCZE6jhNgdea84wYvvtjxn+tOGzdu7POxnvgQFmMM\nmZmZiI6Oxpo1a8T1qampyMnJAQDk5OSIiSU1NRV5eXkwGo2orq7GjRs3kJCQ4PD4SmVHHhCXeSVM\nFid+kV0byohSoaAE4gSFUtGRQAjpJ4VCCcbkeT551BOvQEpLS3H48GHExsZCr9cD6LhMd/369Viw\nYAH279+PsLAwfPPNNwCA6OhoLFiwANHR0fDy8sKePXt6HN4SBMBofGRZIcBoMTrc32FDGRE4DkYZ\nX2XmKpzAwWqkfiL911GBNLs7jAH3xBNIUlKSw8twT58+bXd9VlYWsrKynDq+3QrEmVKSKpBBjyoQ\n4iocp/TYISxXkt2d6N0qEJ4qEIHjYKQE0itO4MCM1E+k/xQKAYzJ83zyKNklEJoD6U7JcTDREFav\nOCUHq4n6ifQfxw2OORDZJRB7cyCPdRWWDAkKBVUgTlAICqpAiEt0zIFQBSI59ioQp4awlErZDmEp\n6TJep3BKjuZAiEsMlquwZJdAulYgXgovmK1msN5OoHKuQOgqLKfQVVjEVagCkaiuhQTHcc4NY8m5\nAqEhLKcolDSERVyjowKR5/nkUbJLIPYKCYEXep9Il3kFQpPoveMEmkQnrsFxAg1hSZG9QsKpeRCq\nQAY9qkCIq3TcByLP88mjZJdA7N3O4dTd6HK/D4QqkF7RHAhxFboPRKK8vYG2ti7rvLzRZm6z36Cn\nhjLhrVCgjRJIrxTeCljbqJ9I/ykU3rBa5Xk+eZTsEohKBRgMXdYpVTCYDPYb9NRQJlQ8DwMlkF7x\nKh5WA/UT6T+FQgWLRZ7nk0fJLoH4+HTPAz5KHxiMgzeB+PA8DDJ9WZYr8T48LAbqJ9J/PO9DCUSK\n7FYgwiCvQBQKSiBOUKgUlECIS/A8VSCS5HAIaxBXICqqQJzCq3hYW2kIi/Qfz6tgtba6O4wBNzgS\nyGCvQGgOxCm8ioawiGt0VCCtvT8BQ+JkmUBaWrqsU6rQYmyx36CnhjKh4nm0UAXSK17Fw9JC/UT6\nj+N4cJyX7K/Ekl0CGT4caGrqsu6p4WhqbbLfoKeGMjFcENBkMsn+f0P9JQwXYGqS/93D5MkQhOEw\nmeR5TukkuwQSEAD8/XeXdaoA/G34236DToGB3RvKhIrnwQE0D9ILIUCA6W9KIMQ1lMoAmEzyPKd0\nkkwCKSgoQFRUFMLDw7F161aH+w0b1jGV0d7+cF2gKhCNhsaef0BgINDYyz4SFqhUolGmz/pyFSFQ\ngLFR/ncPkydDEAJhNMr3nAJIJIFYLBasXLkSBQUFuHLlCnJzc3H16lW7+yoUwHPPAdXVD9eN9huN\n6rvVdvcXjRoF1NQAbvpfelFR0YAef/SQIah+8GBAjj3QsQ+0zvi9R3mjvbYdVrO0LjiQS/9LlaP4\nvb1Ho62tl/OOxEkigZSXl2PMmDEICwuDIAhIS0vD8ePHHe6v0QBVVQ+XYwJj8GvjrzBbzY5/iI8P\nEBwMXLvmwsidN9B/RDEqFS4N0EUCcjkB8CoeypFKtF6V1uWXcul/qXIUv0qlQXNzxZMN5gmTRAKp\nq6vDqFGjxOXQ0FDU1dU53P/VV4H//e/hcoAqABHPRiD/Rn7PP6hrQxl5IyAAOQ0NNJHei4B5AWj4\nqsHdYRAZGD58Nv7991uYzfK8uhOQSALhOO6x9k9PBy5dsq1C1ietx3/P/rfnhv/5D7B3L3DvXh+i\n9GxThg2DwHE4cfu2u0PxaCErQtDwVQNMd2m+iPSPt/co+PtPRX39/7k7lIHDJODcuXNs+vTp4vLm\nzZvZli1bbPZRq9UMAH3oQx/60OcxPmq1us/nZo4xzx/TMJvNiIyMRGFhIUaOHImEhATk5uZi7Nix\n7g6NEEIGLS93B+AMLy8v7N69G9OnT4fFYkFmZiYlD0IIcTNJVCCEEEI8jyQm0Xvi7A2GniQsLAyx\nsbHQ6/VISEgAANy+fRtTp05FREQEpk2bhrt377o5yoeWLl2KoKAgaDQacV1P8X7yyScIDw9HVFQU\nTp486Y6QbdiLPzs7G6GhodDr9dDr9cjPf3iFnifFX1NTg5SUFIwbNw4xMTHYuXMnAOn0v6P4pdL/\nbW1tSExMhE6nQ3R0ND788EMA0ul/R/G7rP/7PHviAcxmM1Or1ay6upoZjUam1WrZlStX3B1Wr8LC\nwlhTU5PNunXr1rGtW7cyxhjbsmUL++CDD9wRml1nz55lly5dYjExMeI6R/H+9ttvTKvVMqPRyKqr\nq5larWYWi8UtcXeyF392djb7/PPPu+3rafHX19ezyspKxhhjzc3NLCIigl25ckUy/e8ofqn0P2OM\nGQwGxhhjJpOJJSYmsuLiYsn0P2P243dV/0u6AnncGww9Cesycvjdd99hyZIlAIAlS5bg2LFj7gjL\nruTkZPj7+9uscxTv8ePHsWjRIgiCgLCwMIwZMwbl5eVPPOZH2Ysf6P47ADwv/hEjRkCn0wEAfHx8\nMHbsWNTV1Umm/x3FD0ij/wFg6NChAACj0QiLxQJ/f3/J9D9gP37ANf0v6QTyuDcYegqO4/Dyyy9j\n/Pjx2LdvHwCgsbERQUFBAICgoCA0evhzuRzFe+vWLYSGhor7efLvZNeuXdBqtcjMzBSHIDw5/ps3\nb6KyshKJiYmS7P/O+CdMmABAOv1vtVqh0+kQFBQkDsdJqf/txQ+4pv8lnUAe9wZDT1FaWorKykrk\n5+fjiy++QHFxsc12juMk9d16i9cTv8t7772H6upqVFVVITg4GGvXrnW4ryfE39LSgnnz5mHHjh3w\n9fW12SaF/m9pacEbb7yBHTt2wMfHR1L9r1AoUFVVhdraWpw9exZnzpyx2e7p/d81/qKiIpf1v6QT\nSEhICGpqasTlmpoam+zpqYKDgwEAAQEBmDNnDsrLyxEUFISGho5HaNTX1yMwMNCdIfbKUbxdfye1\ntbUICQlxS4w9CQwMFP/wly1bJpbpnhi/yWTCvHnzkJ6ejtmzZwOQVv93xv/WW2+J8Uup/zv5+flh\n5syZqKiokFT/d+qM/+LFiy7rf0knkPHjx+PGjRu4efMmjEYjjhw5gtTUVHeH1aPW1lY0NzcDAAwG\nA06ePAmNRoPU1FTk5OQAAHJycsQ/NE/lKN7U1FTk5eXBaDSiuroaN27cEK808yT19fXiv7/99lvx\nCi1Pi58xhszMTERHR2PNmjXieqn0v6P4pdL///77rzi88+DBA5w6dQp6vV4y/e8o/s7kB/Sz/wdg\n0v+JOnHiBIuIiGBqtZpt3rzZ3eH06o8//mBarZZptVo2btw4Meampib20ksvsfDwcDZ16lR2584d\nN0f6UFpaGgsODmaCILDQ0FB24MCBHuPdtGkTU6vVLDIykhUUFLgx8g5d49+/fz9LT09nGo2GxcbG\nslmzZrGGhgZxf0+Kv7i4mHEcx7RaLdPpdEyn07H8/HzJ9L+9+E+cOCGZ/v/ll1+YXq9nWq2WaTQa\n9umnnzLGev57lUL8rup/upGQEEJIn0h6CIsQQoj7UAIhhBDSJ5RACCGE9AklEEIIIX1CCYQQQkif\nUAIhhBDSJ5RAiMfx8fHp9zFu376NlJQU+Pr6YtWqVTbbKioqoNFoEB4ejtWrV9ts2717N7766isA\nQEZGBlQqFVpaWsTta9asgUKhwG0Xv1v+1q1bmD9/vri8aNEiaLVabN++HRs2bEBhYeFjH/PPP/9E\nbm6uuFxRUdHt+zqrvb0dkydPhtVq7VN7IlMDeA8LIX3i4+PT72MYDAZWUlLC9u7dy1auXGmzLT4+\nnpWVlTHGGHvllVdYfn4+Y4wxq9XKdDodM5lMjDHGMjIymFarZYcPH2aMMWaxWJhGo2GjRo3q9jh+\nV6qvr2djxozp93HOnDnDXnvtNRdE1CErK4sdPXrUZccj0kcVCJGEqqoqTJgwAVqtFnPnzhUfz3Dh\nwgXx5Vzr1q0TH8kwdOhQTJo0CUOGDLE5Tn19PZqbm8XHMyxevFh8FHdpaSmioqLg5fXwTc8LFy7E\nkSNHAABFRUVISkoCz/Pi9jlz5mD8+PGIiYkRn6wMAPv370dkZCQSExOxfPlysQrKyMjA6tWrMWnS\nJKjVahw9ehRAx5NqO2OfNm0a6urqoNfrUVJSgoyMDHG/CxcuYNKkSdDpdEhMTERLSwtu3ryJyZMn\nIy4uDnFxcTh37hwAYP369SguLoZer8f27dtRVFSE119/HUBHhTZ79mxotVpMnDgRv/76K4COFw0t\nXboUKSkpUKvV2LVrl/idUlNTbSoaQiiBEElYvHgxPvvsM1y+fBkajQYbN24EALz99tvYt28fKisr\n4eXl1e3JoV2X6+rqbB64GRISIj6uuqSkBPHx8Tb7R0RE4J9//sHdu3eRl5eHtLQ0m+0HDhzAxYsX\nceHCBezcuRN37tzBrVu38PHHH6OsrAylpaW4du2aTRwNDQ0oLS3FDz/8gPXr13f7rt9//z3UajUq\nKyuRlJQkPvTOaDQiLS0NO3fuRFVVFQoLC/HUU08hKCgIp06dQkVFBfLy8vD+++8DALZu3Yrk5GRU\nVlbaPIcKADZs2IC4uDhcvnwZmzdvxuLFi8Vt169fx8mTJ1FeXo6NGzfCYrEAAHQ6HX7++ecefktk\nsKEEQjzevXv3cO/ePSQnJwPoeIHP2bNnce/ePbS0tCAxMREA8Oabb9p9SY6z/vrrL4wYMaLb+rlz\n5yI3NxdlZWViDJ127NgBnU6HiRMnora2FtevX0d5eTmmTJmCYcOGwcvLC/Pnzxfj4jhOfPDe2LFj\n7b73xd53YIzh2rVrCA4ORlxcHICOuSKe52E0GrFs2TLExsZiwYIFuHr1qsPjdCotLUV6ejoAICUl\nBU1NTWhubgbHcZg5cyYEQcCzzz6LwMBAMcYhQ4bAarWira2t174kg4NX77sQ4lkcnRidSR4hISGo\nra0Vl2tra20qkq7H4DgOCxcuRFxcHDIyMmwqiaKiIhQWFuL8+fPw9vZGSkoK2traulU9XY+pVCof\nK+ZHY7Fn27ZtCA4OxqFDh2CxWODt7e3U8Rz97Efj43keZrPZpo27329BPAdVIMTj+fn5wd/fHyUl\nJQCAQ4cO4cUXX4Sfnx98fX3Fdxnk5eV1a9v1JBkcHIynn34aZWVlYIzh0KFDmDVrFgBg9OjRNo+5\n7mz/3HPPYdOmTVixYoXNtvv378Pf3x/e3t74/fffcf78eXAch/j4ePz000+4e/cuzGYzjh492u+T\nLsdxiIyMRH19PS5evAgAaG5uhsViwf3798XK6eDBg+KQk6+vr/jqgK6Sk5Px9ddfA+hIhAEBAfD1\n9e0xobW3t4Pn+W7zSmTwogqEeJzW1labVxWvXbsWOTk5ePfdd9Ha2gq1Wo0vv/wSQMdk9fLly6FQ\nKDBlyhT4+fmJ7cLCwtDc3Ayj0Yhjx47h1KlTiIqKwp49e5CRkYEHDx7g1VdfxYwZMwAASUlJ2L17\nt00snSf+d955p9u6GTNmYO/evYiOjkZkZCQmTpwIABg5ciSysrKQkJCAZ555BlFRUTZxPZpMnPl3\nJ0EQcOTIEaxatQoPHjzA0KFDcfr0aaxYsQLz5s3DwYMHMWPGDPEyaK1WC57nodPpkJGRAb1eLx63\nc7Jcq9VCpVKJ77bo6e16lZWV4nckBADoce5E0gwGA1QqFQBgy5YtaGxsxLZt2/p0LMYYXnjhBZSV\nldkM4/QnLrPZjLlz5yIzM1OsdKQqKysL8fHxmDNnjrtDIR6ChrCIpP3444/Q6/XQaDQoLS3FRx99\n1OdjcRyH5cuXi0M7/ZGdnS3G9fzzz0s+ebS3t6OkpMTj35RJniyqQAghhPQJVSCEEEL6hBIIIYSQ\nPqEEQgghpE8ogRBCCOkTSiCEEEL6hBIIIYSQPvl/8Fo74NacQ9sAAAAASUVORK5CYII=\n",
 318        "text": [
 319         "<matplotlib.figure.Figure at 0x7f1de96875d0>"
 320        ]
 321       }
 322      ],
 323      "prompt_number": 10
 324     },
 325     {
 326      "cell_type": "code",
 327      "collapsed": false,
 328      "input": [
 329       "fig = figure(figsize=(6,4))\n",
 330       "l = []\n",
 331       "for b in [\"mpfr-1024\", \"Gmprat\"]:\n",
 332       "    plot(-1.0*scaling_data[b][\"accuracy\"][:,4], scaling_data[b][\"performance\"][:,3])\n",
 333       "    l += [b]\n",
 334       "#xscale(\"log\")\n",
 335       "\n",
 336       "legend(l, loc=\"best\")\n",
 337       "xlabel(\"Magnification\")\n",
 338       "ylabel(\"Average FPS (100 frames)\")"
 339      ],
 340      "language": "python",
 341      "metadata": {},
 342      "outputs": [
 343       {
 344        "metadata": {},
 345        "output_type": "pyout",
 346        "prompt_number": 42,
 347        "text": [
 348         "<matplotlib.text.Text at 0x7f37e035ca10>"
 349        ]
 350       },
 351       {
 352        "metadata": {},
 353        "output_type": "display_data",
 354 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WHVqlVEREQYpImIiGD58uUAJCQk4OPjQ9u2bc3mzc6uqCzWrl1L165dHXULEifDmGLRCI0Dr+ew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\nrkJggg==\n",
 355        "text": [
 356         "<matplotlib.figure.Figure at 0x7f37e04233d0>"
 357        ]
 358       }
 359      ],
 360      "prompt_number": 42
 361     },
 362     {
 363      "cell_type": "markdown",
 364      "metadata": {},
 365      "source": [
 366       "# Invariance after scaling"
 367      ]
 368     },
 369     {
 370      "cell_type": "code",
 371      "collapsed": false,
 372      "input": [
 373       "invariance_data = saveload.load_dict(\"invariance_data\")\n",
 374       "invariance_data.keys()"
 375      ],
 376      "language": "python",
 377      "metadata": {},
 378      "outputs": []
 379     },
 380     {
 381      "cell_type": "code",
 382      "collapsed": false,
 383      "input": [
 384       "invariance_data = {}\n",
 385       "p = ProgressBar(len(options[\"built\"]))\n",
 386       "p.animate(0)\n",
 387       "for i,b in enumerate(options[\"built\"]):\n",
 388       "    if b in invariance_data.keys():\n",
 389       "        continue\n",
 390       "    invariance_data[b] = scaling.TestInvariance(\"./\"+b, fps=10, x0 = 0.5, y0 = 0.5, w0 = 1e-6, h0 = 1e-6, xz=400, yz=300)\n",
 391       "    p.animate(i)\n",
 392       "   \n",
 393       "#saveload.save_obj(invariance_data, \"invariance_data\")"
 394      ],
 395      "language": "python",
 396      "metadata": {},
 397      "outputs": [
 398       {
 399        "output_type": "stream",
 400        "stream": "stdout",
 401        "text": [
 402         "\r",
 403         "[                  0%                  ]"
 404        ]
 405       },
 406       {
 407        "output_type": "stream",
 408        "stream": "stdout",
 409        "text": [
 410         " \r",
 411         "[***               7%                  ]  1 of 15 complete"
 412        ]
 413       },
 414       {
 415        "output_type": "stream",
 416        "stream": "stdout",
 417        "text": [
 418         " \r",
 419         "[***               7%                  ]  1 of 15 complete"
 420        ]
 421       },
 422       {
 423        "output_type": "stream",
 424        "stream": "stdout",
 425        "text": [
 426         " ./mpfr-16 - Quit early after 1 steps - No precision left\n",
 427         "\r",
 428         "[*****            13%                  ]  2 of 15 complete"
 429        ]
 430       },
 431       {
 432        "output_type": "stream",
 433        "stream": "stdout",
 434        "text": [
 435         " ./mpfr-32 - Quit early after 801 steps - Took too long to render frames\n",
 436         "\r",
 437         "[********         20%                  ]  3 of 15 complete"
 438        ]
 439       },
 440       {
 441        "output_type": "stream",
 442        "stream": "stdout",
 443        "text": [
 444         " \r",
 445         "[**********       27%                  ]  4 of 15 complete"
 446        ]
 447       },
 448       {
 449        "output_type": "stream",
 450        "stream": "stdout",
 451        "text": [
 452         " \r",
 453         "[*************    33%                  ]  5 of 15 complete"
 454        ]
 455       },
 456       {
 457        "ename": "KeyboardInterrupt",
 458        "evalue": "",
 459        "output_type": "pyerr",
 460        "traceback": [
 461         "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
 462         "\u001b[1;32m<ipython-input-28-471152fea0f5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minvariance_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0minvariance_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTestInvariance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"./\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0myz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0manimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
 463         "\u001b[1;32m/home/sam/Documents/University/2014/ipdf/code/tools/scaling.py\u001b[0m in \u001b[0;36mTestInvariance\u001b[1;34m(binname, x0, y0, w0, h0, s, steps, xz, yz, testsvg, renderer, fps)\u001b[0m\n\u001b[0;32m    102\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loop %d pxzoom %s %s %s\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0myz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    103\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"querygpubounds step%d.dat\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m                         \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"step%d.dat\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    105\u001b[0m                                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m                         \u001b[0mp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdin\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"loop %d printspf\\n\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mfps\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Print an FPS count to signal it is safe to read the file\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
 464         "\u001b[1;32m/usr/lib/python2.7/genericpath.pyc\u001b[0m in \u001b[0;36misfile\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m     27\u001b[0m     \u001b[1;34m\"\"\"Test whether a path is a regular file\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 29\u001b[1;33m         \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     30\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
 465         "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
 466        ]
 467       }
 468      ],
 469      "prompt_number": 28
 470     },
 471     {
 472      "cell_type": "code",
 473      "collapsed": false,
 474      "input": [
 475       "fig = figure(figsize=(6,4))\n",
 476       "for b in invariance_data.keys():\n",
 477       "    plot(invariance_data[b][\"accuracy\"][:,4], invariance_data[b][\"accuracy\"][:,-2])\n",
 478       "#xscale(\"log\")\n",
 479       "yscale(\"log\")\n",
 480       "legend(scaling_data.keys(), loc=\"best\")\n",
 481       "xlabel(\"Total scaling factor\")\n",
 482       "ylabel(\"Accumulated Error\")\n",
 483       "#fig.savefig(\"../../sam/figures/cumulative_error_grid.pdf\", format=\"PDF\")"
 484      ],
 485      "language": "python",
 486      "metadata": {},
 487      "outputs": [
 488       {
 489        "metadata": {},
 490        "output_type": "display_data",
 491 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7qI4z9fR+whTVHNIZCeeoABG5qe2mEI00O60eBQVynlln640/7RFNX62FlsJHvS6qlU09QbHOLj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\nyOQgSZIkNSCTgyRJktTA/wOgbRbeVj3ezAAAAABJRU5ErkJggg==\n",
 492        "text": [
 493         "<matplotlib.figure.Figure at 0x7f3051f4b3d0>"
 494        ]
 495       }
 496      ],
 497      "prompt_number": 195
 498     },
 499     {
 500      "cell_type": "code",
 501      "collapsed": false,
 502      "input": [
 503       "for b in invariance_data:\n",
 504       "    plot(1.0/invariance_data[b][\"accuracy\"][:,2], invariance_data[b][\"performance\"][:,3])\n",
 505       "xscale(\"log\")\n",
 506       "legend(invariance_data.keys())\n",
 507       "xlabel(\"Magnification\")\n",
 508       "ylabel(\"Average FPS (100 frames)\")"
 509      ],
 510      "language": "python",
 511      "metadata": {},
 512      "outputs": [
 513       {
 514        "metadata": {},
 515        "output_type": "pyout",
 516        "prompt_number": 196,
 517        "text": [
 518         "<matplotlib.text.Text at 0x7f3051dd37d0>"
 519        ]
 520       },
 521       {
 522        "metadata": {},
 523        "output_type": "display_data",
 524 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DDVAWzXju0Su3ULWL0auHFDt6QQE4NmuxofB2bOBJ56imDG1grwf86BjwcNWwb7YIqDQ6OdyBq5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\n4EBRFEVJ+H8hbiqovByBAQAAAABJRU5ErkJggg==\n",
 525        "text": [
 526         "<matplotlib.figure.Figure at 0x7f30521fc8d0>"
 527        ]
 528       }
 529      ],
 530      "prompt_number": 196
 531     },
 532     {
 533      "cell_type": "markdown",
 534      "metadata": {},
 535      "source": [
 536       "## Time to render frames\n",
 537       "\n",
 538       "Compare CPU and GPU frame rates for test image and fixed bounds"
 539      ]
 540     },
 541     {
 542      "cell_type": "code",
 543      "collapsed": false,
 544      "input": [
 545       "def time_vs_frames(exec_name, bounds = [0.,0.,1.,1.], min_frames=0, \\\n",
 546       "                    max_frames=100, step=10,test_image=\"svg-tests/rabbit_simple.svg\"):\n",
 547       "    binname = options[\"local_bin\"]+exec_name\n",
 548       "    data = []\n",
 549       "    p = ProgressBar(sum(xrange(min_frames, max_frames, step)))\n",
 550       "    display(\"Time VS Frames for %s\" % exec_name)\n",
 551       "    p.animate(0)\n",
 552       "    i = 0\n",
 553       "    for frames in xrange(min_frames, max_frames+step, step):\n",
 554       "        pt = [frames]\n",
 555       "        # -l means to turn off lazy rendering\n",
 556       "        # -Q means don't show the window\n",
 557       "        cmd = binname + \" -l -Q -b %s %s %s %s -f %d %s\" % tuple(map(str, bounds) + [frames, test_image])\n",
 558       "        \n",
 559       "        # Everything on GPU\n",
 560       "        start = time.time()\n",
 561       "        os.system(cmd + \" -r gpu -T gpu\")\n",
 562       "        end = time.time()\n",
 563       "        pt += [(end - start)]\n",
 564       "                \n",
 565       "        # Everything on CPU\n",
 566       "        start = time.time()\n",
 567       "        os.system(cmd + \" -r cpu -T cpu\")\n",
 568       "        end = time.time()\n",
 569       "        pt += [(end - start)]\n",
 570       "        \n",
 571       "        data += [pt]\n",
 572       "        i += frames\n",
 573       "        p.animate(i)\n",
 574       "        \n",
 575       "    return asarray(data)"
 576      ],
 577      "language": "python",
 578      "metadata": {},
 579      "outputs": [],
 580      "prompt_number": 7
 581     },
 582     {
 583      "cell_type": "code",
 584      "collapsed": false,
 585      "input": [
 586       "def plot_time_vs_frames(tf, new_figure=True):\n",
 587       "    if new_figure:\n",
 588       "        figure(figsize=(9,7))\n",
 589       "        yscale('linear')\n",
 590       "        xscale('linear')\n",
 591       "        legend([\"GPU\", \"CPU\"])\n",
 592       "        xlabel(\"Frames\")\n",
 593       "        ylabel(\"Time To Render\")\n",
 594       "        title(\"Quick, to the TARDIS of Infinite Precision!\")    \n",
 595       "        \n",
 596       "    plot(tf[:,0], tf[:,1], 'o-')\n",
 597       "    plot(tf[:,0], tf[:,2], 'x-')\n",
 598       "    "
 599      ],
 600      "language": "python",
 601      "metadata": {},
 602      "outputs": [],
 603      "prompt_number": 8
 604     },
 605     {
 606      "cell_type": "code",
 607      "collapsed": false,
 608      "input": [
 609       "for p in options[\"built\"]:\n",
 610       "    plot_time_vs_frames(time_vs_frames(p, bounds=[0.5,0.5,1e-14,1e-14]), False)"
 611      ],
 612      "language": "python",
 613      "metadata": {},
 614      "outputs": [
 615       {
 616        "metadata": {},
 617        "output_type": "display_data",
 618        "text": [
 619         "'Time VS Frames for single'"
 620        ]
 621       },
 622       {
 623        "output_type": "stream",
 624        "stream": "stdout",
 625        "text": [
 626         "\r",
 627         "[                  0%                  ]"
 628        ]
 629       },
 630       {
 631        "output_type": "stream",
 632        "stream": "stdout",
 633        "text": [
 634         " \r",
 635         "[                  0%                  ]  1 of 450 complete"
 636        ]
 637       },
 638       {
 639        "output_type": "stream",
 640        "stream": "stdout",
 641        "text": [
 642         " \r",
 643         "[                  0%                  ]  1 of 450 complete"
 644        ]
 645       },
 646       {
 647        "output_type": "stream",
 648        "stream": "stdout",
 649        "text": [
 650         " \r",
 651         "[*                 2%                  ]  11 of 450 complete"
 652        ]
 653       },
 654       {
 655        "output_type": "stream",
 656        "stream": "stdout",
 657        "text": [
 658         " \r",
 659         "[***               7%                  ]  31 of 450 complete"
 660        ]
 661       },
 662       {
 663        "output_type": "stream",
 664        "stream": "stdout",
 665        "text": [
 666         " \r",
 667         "[*****            14%                  ]  61 of 450 complete"
 668        ]
 669       },
 670       {
 671        "output_type": "stream",
 672        "stream": "stdout",
 673        "text": [
 674         " \r",
 675         "[********         22%                  ]  101 of 450 complete"
 676        ]
 677       },
 678       {
 679        "output_type": "stream",
 680        "stream": "stdout",
 681        "text": [
 682         " \r",
 683         "[*************    34%                  ]  151 of 450 complete"
 684        ]
 685       },
 686       {
 687        "output_type": "stream",
 688        "stream": "stdout",
 689        "text": [
 690         " \r",
 691         "[*****************47%                  ]  211 of 450 complete"
 692        ]
 693       },
 694       {
 695        "output_type": "stream",
 696        "stream": "stdout",
 697        "text": [
 698         " \r",
 699         "[*****************62%****              ]  281 of 450 complete"
 700        ]
 701       },
 702       {
 703        "output_type": "stream",
 704        "stream": "stdout",
 705        "text": [
 706         " \r",
 707         "[*****************80%**********        ]  361 of 450 complete"
 708        ]
 709       },
 710       {
 711        "output_type": "stream",
 712        "stream": "stdout",
 713        "text": [
 714         " \r",
 715         "[****************100%******************]  451 of 450 complete"
 716        ]
 717       },
 718       {
 719        "metadata": {},
 720        "output_type": "display_data",
 721        "text": [
 722         "'Time VS Frames for double'"
 723        ]
 724       },
 725       {
 726        "output_type": "stream",
 727        "stream": "stdout",
 728        "text": [
 729         " \r",
 730         "[                  0%                  ]"
 731        ]
 732       },
 733       {
 734        "output_type": "stream",
 735        "stream": "stdout",
 736        "text": [
 737         " \r",
 738         "[                  0%                  ]  1 of 450 complete"
 739        ]
 740       },
 741       {
 742        "output_type": "stream",
 743        "stream": "stdout",
 744        "text": [
 745         " \r",
 746         "[                  0%                  ]  1 of 450 complete"
 747        ]
 748       },
 749       {
 750        "output_type": "stream",
 751        "stream": "stdout",
 752        "text": [
 753         " \r",
 754         "[*                 2%                  ]  11 of 450 complete"
 755        ]
 756       },
 757       {
 758        "output_type": "stream",
 759        "stream": "stdout",
 760        "text": [
 761         " \r",
 762         "[***               7%                  ]  31 of 450 complete"
 763        ]
 764       },
 765       {
 766        "output_type": "stream",
 767        "stream": "stdout",
 768        "text": [
 769         " \r",
 770         "[*****            14%                  ]  61 of 450 complete"
 771        ]
 772       },
 773       {
 774        "output_type": "stream",
 775        "stream": "stdout",
 776        "text": [
 777         " \r",
 778         "[********         22%                  ]  101 of 450 complete"
 779        ]
 780       },
 781       {
 782        "output_type": "stream",
 783        "stream": "stdout",
 784        "text": [
 785         " \r",
 786         "[*************    34%                  ]  151 of 450 complete"
 787        ]
 788       },
 789       {
 790        "output_type": "stream",
 791        "stream": "stdout",
 792        "text": [
 793         " \r",
 794         "[*****************47%                  ]  211 of 450 complete"
 795        ]
 796       },
 797       {
 798        "output_type": "stream",
 799        "stream": "stdout",
 800        "text": [
 801         " \r",
 802         "[*****************62%****              ]  281 of 450 complete"
 803        ]
 804       },
 805       {
 806        "output_type": "stream",
 807        "stream": "stdout",
 808        "text": [
 809         " \r",
 810         "[*****************80%**********        ]  361 of 450 complete"
 811        ]
 812       },
 813       {
 814        "output_type": "stream",
 815        "stream": "stdout",
 816        "text": [
 817         " \r",
 818         "[****************100%******************]  451 of 450 complete"
 819        ]
 820       },
 821       {
 822        "metadata": {},
 823        "output_type": "display_data",
 824        "text": [
 825         "'Time VS Frames for GMPrat'"
 826        ]
 827       },
 828       {
 829        "output_type": "stream",
 830        "stream": "stdout",
 831        "text": [
 832         " \r",
 833         "[                  0%                  ]"
 834        ]
 835       },
 836       {
 837        "output_type": "stream",
 838        "stream": "stdout",
 839        "text": [
 840         " \r",
 841         "[                  0%                  ]  1 of 450 complete"
 842        ]
 843       },
 844       {
 845        "output_type": "stream",
 846        "stream": "stdout",
 847        "text": [
 848         " \r",
 849         "[                  0%                  ]  1 of 450 complete"
 850        ]
 851       },
 852       {
 853        "output_type": "stream",
 854        "stream": "stdout",
 855        "text": [
 856         " \r",
 857         "[*                 2%                  ]  11 of 450 complete"
 858        ]
 859       },
 860       {
 861        "output_type": "stream",
 862        "stream": "stdout",
 863        "text": [
 864         " \r",
 865         "[***               7%                  ]  31 of 450 complete"
 866        ]
 867       },
 868       {
 869        "output_type": "stream",
 870        "stream": "stdout",
 871        "text": [
 872         " \r",
 873         "[*****            14%                  ]  61 of 450 complete"
 874        ]
 875       },
 876       {
 877        "output_type": "stream",
 878        "stream": "stdout",
 879        "text": [
 880         " \r",
 881         "[********         22%                  ]  101 of 450 complete"
 882        ]
 883       },
 884       {
 885        "output_type": "stream",
 886        "stream": "stdout",
 887        "text": [
 888         " \r",
 889         "[*************    34%                  ]  151 of 450 complete"
 890        ]
 891       },
 892       {
 893        "output_type": "stream",
 894        "stream": "stdout",
 895        "text": [
 896         " \r",
 897         "[*****************47%                  ]  211 of 450 complete"
 898        ]
 899       },
 900       {
 901        "output_type": "stream",
 902        "stream": "stdout",
 903        "text": [
 904         " \r",
 905         "[*****************62%****              ]  281 of 450 complete"
 906        ]
 907       },
 908       {
 909        "output_type": "stream",
 910        "stream": "stdout",
 911        "text": [
 912         " \r",
 913         "[*****************80%**********        ]  361 of 450 complete"
 914        ]
 915       },
 916       {
 917        "output_type": "stream",
 918        "stream": "stdout",
 919        "text": [
 920         " \r",
 921         "[****************100%******************]  451 of 450 complete"
 922        ]
 923       },
 924       {
 925        "output_type": "stream",
 926        "stream": "stdout",
 927        "text": [
 928         "\n"
 929        ]
 930       },
 931       {
 932        "metadata": {},
 933        "output_type": "display_data",
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KX4+99kth9wrbaCnJwnUas3MnTom/j6Xtkr4VFZmXxs7vSBPnNBSGft2TOekJD/olBc3uY2aXV1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\nfBjbLZuwfLKVfEU3svpPIF+7D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 935        "text": [
 936         "<matplotlib.figure.Figure at 0x7f252628efd0>"
 937        ]
 938       }
 939      ],
 940      "prompt_number": 9
 941     },
 942     {
 943      "cell_type": "markdown",
 944      "metadata": {},
 945      "source": [
 946       "## Precision of rendering\n",
 947       "\n",
 948       "Compare images"
 949      ]
 950     },
 951     {
 952      "cell_type": "code",
 953      "collapsed": false,
 954      "input": [
 955       "def rgb2gray(rgb):\n",
 956       "    return np.dot(rgb[...,:3], [0.299/256., 0.587/256., 0.144/256.])"
 957      ],
 958      "language": "python",
 959      "metadata": {},
 960      "outputs": [],
 961      "prompt_number": 10
 962     },
 963     {
 964      "cell_type": "code",
 965      "collapsed": false,
 966      "input": [
 967       "def bmpdiff(img1, img2):\n",
 968       "    p = subprocess.Popen(\"./bmpdiff %s %s\" % (img1, img2), bufsize=0, stdin=subprocess.PIPE, \\\n",
 969       "                         stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n",
 970       "    out = p.stdout.readlines()\n",
 971       "    if len(out) != 1:\n",
 972       "        raise Exception(\"bmpdiff %s %s failed: %s \" % (img1, img2, str(map(lambda e : e.strip(\"\\n\"),p.stderr.readlines()))))\n",
 973       "    result = map(float, out[0].strip(\" \\r\\n\").split(\"\\t\"))\n",
 974       "    for i in xrange(len(result)):\n",
 975       "        if isnan(result[i]):\n",
 976       "            result[i] = 0\n",
 977       "    return result"
 978      ],
 979      "language": "python",
 980      "metadata": {},
 981      "outputs": [],
 982      "prompt_number": 11
 983     },
 984     {
 985      "cell_type": "code",
 986      "collapsed": false,
 987      "input": [
 988       "def precision_vs_zoom(exec_name, xy, start_dims, end_dims, steps, test_image=\"svg-tests/rabbit_simple.svg\", show_outputs=False):\n",
 989       "    binname = options[\"local_bin\"]+exec_name\n",
 990       "    data = []\n",
 991       "    bounds = [xy[0], xy[1], start_dims[0], start_dims[1]]\n",
 992       "    dw = float(end_dims[0] - start_dims[0])/float(steps)\n",
 993       "    dh = float(end_dims[1] - start_dims[1])/float(steps)\n",
 994       "    p = ProgressBar(steps)\n",
 995       "    display(\"Precision vs Zoom of \\\"%s\\\" using %s\" % (test_image, exec_name))\n",
 996       "    p.animate(0)\n",
 997       "    im = None\n",
 998       "    figure(figsize=(10,10))\n",
 999       "    for i in xrange(steps+1):\n",
1000       "       \n",
1001       "        cmd = binname + \" -l -b %s %s %s %s %s\" % tuple(map(str, bounds) + [test_image])\n",
1002       "        \n",
1003       "        # Don't show the windows\n",
1004       "        os.system(cmd + \" -Q -r gpu -T gpu -o gpu%d.bmp\" % i)\n",
1005       "        os.system(cmd + \" -Q -r cpu -T cpu -o cpu%d.bmp\" % i)\n",
1006       "        \n",
1007       "        pt = [i]\n",
1008       "        pt += bounds\n",
1009       "        pt += [bmpdiff(\"gpu\"+str(i)+\".bmp\", \"gpu\"+str(max(i-1,0))+\".bmp\")[3]]\n",
1010       "        pt += [bmpdiff(\"gpu\"+str(i)+\".bmp\", \"gpu0.bmp\")[3]]\n",
1011       "        pt += [bmpdiff(\"cpu\"+str(i)+\".bmp\", \"cpu\"+str(max(i-1,0))+\".bmp\")[3]]\n",
1012       "        pt += [bmpdiff(\"cpu\"+str(i)+\".bmp\", \"cpu0.bmp\")[3]]\n",
1013       "        pt += [bmpdiff(\"cpu\"+str(i)+\".bmp\", \"gpu\"+str(i)+\".bmp\")[3]]\n",
1014       "\n",
1015       "        if show_outputs != False:\n",
1016       "            clear_output()\n",
1017       "            if i == 0:\n",
1018       "                im = imshow(imread(\"cpu0.bmp\"), cmap=get_cmap(\"gray\"))\n",
1019       "            else:\n",
1020       "                im.set_data(imread(\"cpu\"+str(i)+\".bmp\"))\n",
1021       "                draw()\n",
1022       "            title(\"Bounds: %s\" % str(bounds))\n",
1023       "            \n",
1024       "        display(\"Precision vs Zoom of \\\"%s\\\" using %s\" % (test_image, exec_name))\n",
1025       "        p.animate(i)\n",
1026       "        \n",
1027       "        if show_outputs != False:\n",
1028       "            display(gcf())        \n",
1029       "            \n",
1030       "        \n",
1031       "        # [step, x, y, w, h, gpu_delta, gpu_total_delta, cpu_delta, cpu_total_delta, gpu_vs_cpu\n",
1032       "        data += [pt]\n",
1033       "        bounds[2] += dw\n",
1034       "        bounds[3] += dh\n",
1035       "\n",
1036       "        \n",
1037       "    close(gcf())\n",
1038       "    return asarray(data)"
1039      ],
1040      "language": "python",
1041      "metadata": {},
1042      "outputs": [],
1043      "prompt_number": 12
1044     },
1045     {
1046      "cell_type": "code",
1047      "collapsed": false,
1048      "input": [
1049       "pz = precision_vs_zoom(\"single\", [0.5,0.5],[1e-5,1e-5],[1e-6,1e-6], 100, \"svg-tests/fox-vector.svg\", show_outputs=True)"
1050      ],
1051      "language": "python",
1052      "metadata": {},
1053      "outputs": [
1054       {
1055        "ename": "NameError",
1056        "evalue": "name 'precision_vs_zoom' is not defined",
1057        "output_type": "pyerr",
1058        "traceback": [
1059         "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
1060         "\u001b[1;32m<ipython-input-1-177c6c5e9c41>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprecision_vs_zoom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1e-5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1e-5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1e-6\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1e-6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"svg-tests/fox-vector.svg\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshow_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
1061         "\u001b[1;31mNameError\u001b[0m: name 'precision_vs_zoom' is not defined"
1062        ]
1063       }
1064      ],
1065      "prompt_number": 1
1066     },
1067     {
1068      "cell_type": "code",
1069      "collapsed": false,
1070      "input": [
1071       "pzd = precision_vs_zoom(\"double\", [0.5,0.5],[1e-13,1e-13],[1e-15,1e-15], 1, \"svg-tests/fox-vector.svg\", show_outputs=True)"
1072      ],
1073      "language": "python",
1074      "metadata": {},
1075      "outputs": [
1076       {
1077        "metadata": {},
1078        "output_type": "display_data",
1079        "text": [
1080         "'Precision vs Zoom of \"svg-tests/fox-vector.svg\" using double'"
1081        ]
1082       },
1083       {
1084        "output_type": "stream",
1085        "stream": "stdout",
1086        "text": [
1087         " \r",
1088         "[****************100%******************]  1 of 1 complete"
1089        ]
1090       },
1091       {
1092        "metadata": {},
1093        "output_type": "display_data",
1094 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QAA4BBhCwAAwCHCFgAAgEOELQAAAIcIWwAAAA55zMzO+k49HgVgt0CDu+CCC07aVllZqdatW5/F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\nQ4QtAAAAh/4XJX37efghybUAAAAASUVORK5CYII=\n",
1095        "text": [
1096         "<matplotlib.figure.Figure at 0x7f2525984e50>"
1097        ]
1098       },
1099       {
1100        "output_type": "stream",
1101        "stream": "stdout",
1102        "text": [
1103         "\n"
1104        ]
1105       }
1106      ],
1107      "prompt_number": 16
1108     },
1109     {
1110      "cell_type": "code",
1111      "collapsed": false,
1112      "input": [
1113       "pzgmp = precision_vs_zoom(\"GMPrat\", [0.5,0.5],[1e-13,1e-13],[1e-15,1e-15], 1, \"svg-tests/fox-vector.svg\", show_outputs=True)\n",
1114       "# Need to adapt mpfrc++ for required precision. Probably not worth it, just go back to mpfr?"
1115      ],
1116      "language": "python",
1117      "metadata": {},
1118      "outputs": [
1119       {
1120        "metadata": {},
1121        "output_type": "display_data",
1122        "text": [
1123         "'Precision vs Zoom of \"svg-tests/fox-vector.svg\" using GMPrat'"
1124        ]
1125       },
1126       {
1127        "output_type": "stream",
1128        "stream": "stdout",
1129        "text": [
1130         " \r",
1131         "[****************100%******************]  1 of 1 complete"
1132        ]
1133       },
1134       {
1135        "metadata": {},
1136        "output_type": "display_data",
1137 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4XonaWv6wYQEZF2PHjwAKampq9Vxv79+5Gbm4vt27djxYoV6NatWxG1juj9wZ4tIqJi6MyZM5g2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\n",
1138        "text": [
1139         "<matplotlib.figure.Figure at 0x7f25259b5450>"
1140        ]
1141       },
1142       {
1143        "output_type": "stream",
1144        "stream": "stdout",
1145        "text": [
1146         "\n"
1147        ]
1148       }
1149      ],
1150      "prompt_number": 17
1151     },
1152     {
1153      "cell_type": "code",
1154      "collapsed": false,
1155      "input": [
1156       "def plot_precision_vs_zoom(pz, gpu=True):\n",
1157       "    figure(figsize=(9,7))\n",
1158       "    if gpu:\n",
1159       "        plot(pz[:,3], pz[:,6], 'o-')\n",
1160       "        legend([\"GPU\", \"CPU\"])\n",
1161       "        \n",
1162       "    yscale('log')\n",
1163       "    xscale('log')\n",
1164       "    plot(pz[:,3], pz[:,8], 'o-')\n",
1165       "    xlabel(\"Width\")\n",
1166       "    ylabel(\"Average distance to closest pixel in original\")\n",
1167       "    title(\"Arr Captain We Be Sinking Into The Sea of Precision\")"
1168      ],
1169      "language": "python",
1170      "metadata": {},
1171      "outputs": [],
1172      "prompt_number": 454
1173     },
1174     {
1175      "cell_type": "code",
1176      "collapsed": false,
1177      "input": [
1178       "plot_precision_vs_zoom(pz)"
1179      ],
1180      "language": "python",
1181      "metadata": {},
1182      "outputs": [
1183       {
1184        "metadata": {},
1185        "output_type": "display_data",
1186 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Cl4+vTpmDlzJszMzJCUlISpU6fi8ccf10dsRERERFrROOQ0aNAgHD16FDKZDKmpqSplhsIhJyIi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\n",
1187        "text": [
1188         "<matplotlib.figure.Figure at 0x7f94baa28290>"
1189        ]
1190       }
1191      ],
1192      "prompt_number": 384
1193     },
1194     {
1195      "cell_type": "code",
1196      "collapsed": false,
1197      "input": [
1198       "plot_precision_vs_zoom(pzd, False)"
1199      ],
1200      "language": "python",
1201      "metadata": {},
1202      "outputs": [
1203       {
1204        "metadata": {},
1205        "output_type": "display_data",
1206 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tWr8PHxMVmHpiRHkgMAhYXSbsk//CCdfRXC71uT2rQJGDcOWL4ccHBIQmzsTuTnO0KlUuHEiR44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\n+yAnvW50tPjVvpIjiY1NQUREhsOBg8BCVFaKthRLl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1207        "text": [
1208         "<matplotlib.figure.Figure at 0x7f94bae39bd0>"
1209        ]
1210       }
1211      ],
1212      "prompt_number": 455
1213     },
1214     {
1215      "cell_type": "code",
1216      "collapsed": false,
1217      "input": [],
1218      "language": "python",
1219      "metadata": {},
1220      "outputs": []
1221     }
1222    ],
1223    "metadata": {}
1224   }
1225  ]
1226 }