Example System - Oizumi et al 2014.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook to calculate the spectrum of $\\Phi$ values for Oizumi et al. 2014 (OR+AND+XOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Computing concepts:   0%|          | 0/7 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network =  NodeLabels(('A', 'B', 'C'))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyPhi Value =  1.916665\n",
      "\tNumber of Non-unique Constellations = 27\n",
      "\n",
      "Evaluating Cut  Cut [A] ━━/ /━━➤ [B, C]\n",
      "\tNumber of Non-unique Constellations = 1\n",
      "\n",
      "Evaluating Cut  Cut [B] ━━/ /━━➤ [A, C]\n",
      "\tNumber of Non-unique Constellations = 1\n",
      "\n",
      "Evaluating Cut  Cut [A, B] ━━/ /━━➤ [C]\n",
      "\tNumber of Non-unique Constellations = 9\n",
      "\n",
      "Evaluating Cut  Cut [C] ━━/ /━━➤ [A, B]\n",
      "\tNumber of Non-unique Constellations = 1\n",
      "\n",
      "Evaluating Cut  Cut [A, C] ━━/ /━━➤ [B]\n",
      "\tNumber of Non-unique Constellations = 1\n",
      "\n",
      "Evaluating Cut  Cut [B, C] ━━/ /━━➤ [A]\n",
      "\tNumber of Non-unique Constellations = 1\n",
      "\n",
      "Cuts =  [Cut [A] ━━/ /━━➤ [B, C], Cut [B] ━━/ /━━➤ [A, C], Cut [A, B] ━━/ /━━➤ [C], Cut [C] ━━/ /━━➤ [A, B], Cut [A, C] ━━/ /━━➤ [B], Cut [B, C] ━━/ /━━➤ [A]]\n",
      "\n",
      "Phi Spectrum =  [[1.965274, 2.027774, 1.92361, 1.98611, 1.951385, 2.013885, 1.993052, 2.055552, 1.951387, 2.013887, 1.979163, 2.041663], [1.979164, 2.041664, 2.006941, 2.069441, 1.92361, 1.98611, 1.951387, 2.013887, 1.951386, 2.013886], [1.854165, 1.895831, 1.868053, 1.90972, 1.951386, 1.923608, 1.881942, 1.89583, 1.916665, 1.958331, 1.930553, 1.972221, 2.013886, 1.986108, 1.944442, 1.95833, 1.937497, 1.909719, 1.999997, 1.972219, 1.881943, 1.944443], [1.854165, 1.916665, 1.881942, 1.944442, 1.90972, 1.972221, 1.937497, 1.999997, 1.881943, 1.944443, 1.909719, 1.972219], [1.805554, 1.868052, 1.833332, 1.89583, 1.861109, 1.923607], [1.930553, 1.805554, 1.868052, 1.95833, 1.833332, 1.89583, 1.986108, 1.861109, 1.923607]]\n"
     ]
    }
   ],
   "source": [
    "import pyphi\n",
    "import numpy as np\n",
    "from pyphi import phi_spectrum\n",
    "from pyphi import utils\n",
    "# from pyphi.phi_spectrum import get_all_CES\n",
    "\n",
    "# Transition probability matrix. Saying where each state goes (little-end notation)\n",
    "tpm = np.array([\n",
    "    [0.,0.,0.],\n",
    "    [0.,0.,1.],\n",
    "    [1.,0.,1.],\n",
    "    [1.,0.,0.],\n",
    "    [1.,0.,0.],\n",
    "    [1.,1.,1.],\n",
    "    [1.,0.,1.],\n",
    "    [1.,1.,0.]\n",
    "])\n",
    "\n",
    "# Set up network object\n",
    "network = pyphi.Network(tpm, node_labels=['A','B','C'])\n",
    "print(\"Network = \",network.node_labels)\n",
    "\n",
    "# Put the system into a given state\n",
    "state = (1,0,0)\n",
    "nodes = ['A','B','C']\n",
    "\n",
    "## Get the requisite Subsystem\n",
    "subsystem = pyphi.Subsystem(network, state, nodes)\n",
    "\n",
    "## What does PyPhi Give?\n",
    "pyphi_val = pyphi.compute.phi(subsystem)\n",
    "print(\"PyPhi Value = \",pyphi_val)\n",
    "\n",
    "## Calculate all Phi values\n",
    "display_CES= False  # if True, output will display constellations\n",
    "Phi_Spectrum = phi_spectrum.get_phi_spectrum(subsystem,display_CES)\n",
    "\n",
    "print(\"\\nCuts = \",Phi_Spectrum[0])\n",
    "print(\"\\nPhi Spectrum = \",Phi_Spectrum[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi MIP =  [1.805554 1.833332 1.854165 1.861109 1.868052 1.868053 1.881942 1.881943\n",
      " 1.89583  1.895831 1.909719 1.90972  1.916665 1.923607]\n",
      "14\n",
      "Results Saved\n"
     ]
    }
   ],
   "source": [
    "Phi_MIP = phi_spectrum.get_Phi_MIP(Phi_Spectrum)\n",
    "print(\"Phi MIP = \",Phi_MIP)\n",
    "print(len(Phi_MIP))\n",
    "np.savetxt(\"results/phi_spectrum_oizumi_2014.csv\", Phi_MIP, delimiter=\",\")\n",
    "print(\"Results Saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf DejaVu Sans\n",
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf DejaVu Sans\n",
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf DejaVu Sans Mono\n",
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf DejaVu Sans\n",
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf DejaVu Sans Mono\n",
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf DejaVu Sans\n",
      "/Library/Fonts/AppleGothic.ttf AppleGothic\n",
      "/System/Library/Fonts/LastResort.ttf .LastResort\n",
      "/System/Library/Fonts/ZapfDingbats.ttf Zapf Dingbats\n",
      "/Library/Fonts/AppleMyungjo.ttf AppleMyungjo\n",
      "/Library/Fonts/Arial Unicode.ttf Arial Unicode MS\n"
     ]
    }
   ],
   "source": [
    "from fontTools.ttLib import TTFont\n",
    "import matplotlib.font_manager as mfm\n",
    "\n",
    "## Choose a font that renders the cut symbol properly\n",
    "def char_in_font(Unicode_char, font):\n",
    "    for cmap in font['cmap'].tables:\n",
    "        if cmap.isUnicode():\n",
    "            if ord(Unicode_char) in cmap.cmap:\n",
    "                return True\n",
    "    return False\n",
    "\n",
    "\n",
    "uni_char = \"\\u27A4\"\n",
    "\n",
    "font_info = [(f.fname, f.name) for f in mfm.fontManager.ttflist]\n",
    "for i, font in enumerate(font_info):\n",
    "    if char_in_font(uni_char, TTFont(font[0], fontNumber=0)):\n",
    "        print(font[0], font[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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z1zan69Z1snLlCp544jEAUqnksL6H0TCuhZwxJgDcDRwGhIB/tNY+vMfj5wE3AEXgbmvtXcYYD7AJWDP0tOXW2uvGM/cup3a0cO+KTQAEQ36SuSKpXJH3HDnFiTgiIiJ1pbW1jc7ONXR0zOfFF1cyc+Ys1q7tJJNJ8/Wv/wvd3d1ce+0VGHM41q7mn/7pG+Tzed73vnM488yza95r9eo/cNRRb+Wll15g7tx5u8cbG5uYPHkyt9zyzzQ0NPD0078hEonuHYUzzjibT31qMZMmtbB4cXU1r7NzDU8+uYy77rqHXC7HlVdeWvOaYDBIb28vpVKJTCZDV9cWAGbPPowzzljAGWe8m97enTzyyEN0d3fv83vY8xTvaBjvFblLgR5r7QeNMZOAF4GHYXeR903gWCAN/K8x5mEgDqy01p43zllfZ/7kBi5dOIOlnT109edpCQd4z5FTdH2ciIjIMHz2s5/nm9/8GpVKBZ/Px+c+dz2trW38+7/fya9//Tjlcpkrr7yGlpYWdu7s4SMfuQKv18sHPnDp6wqgP/zhZT72sY/g8Xi47robdq+Oeb1e/u7v/p5Pf/rvqFQqRKMxrr/+ptdlSSQSzJp1GDt39uxucpgxYyaRSIRrr70CgJaWVrq7d+x+TUtLK8ce+w6uuupDTJs2gxkzZgLwoQ9dwS23fJmHH/4vMpk0V1xx9bC+h9Hg2fWNjwdjTAPgsdamjDEtwHPW2rlDj/0Z8DVr7buHvv4m8Fuq97r7LNAPZIFPWGvtmx2nUChV+sZ4v82mpihjfQwZOc2LO2le3Enz4k4HOy9bt25gypTZu78e79uPHIp8Pi+lUnnM3n/vOQNoa4s/Dyzc32vHdUXOWjsAYIyJAw8AX9jj4QTVYm2XFNAIvAr8k7X2fmPMicC9VFft3pDP56Gp6fXLqKPJ5/OO+TFk5DQv7qR5cSfNizsd7Lxs2+bB5/vT/f4XLDhiNGJNeHv+nY42j+fA65Zxb3YwxswEfgbcbq390R4PJameRt0lDvQBK6heM4e19mljzDRjjMda+4ZLiaVSZcx/ytRPsu6keXEnzYs7aV7c6WDnpVKpjOnq0UQ01itylcrr65a2tuHdc2+8mx3agceAxdbaJ/Z6eDUwf+jauQHgZOAbwBeBHuBrxpi3Aq+9WREnIiIiMlGM94rcPwDNwPXGmOuHxu4CYtbaO40xnwR+SfW6uLuttZuNMbcA9xpjzqG6Mnf5OGcWERGpK5VKBY/H43QMGYaD7VUY12aH8TKWzQ7aa9WdfN2rCK1bQji/jVyonfzcsyi1LnA6lgzRKTx30ry408HOS3d3F+FwlFgsoWJulIzVqdVKpUI6nSSXy9DaOrXmMVc2O9Q77bXqTr7uVURfvINyqBES0/AkdxJ98Q4yR1+jYk5EJpzm5jZ6e3cwMLD/PVFleDwez0GvnL0Rvz9Ic3Pbgb9+FLMc8rTXqjuF1i2hHGqkEmoEj5dKqJHy0HhGhZyITDA+n/91qztycNy8ej12vbSHoK5kjoaQr2ZMe606z5vaTCVY291TCcbxpjY7lEhERGR8qJAbgamJMAP5Us2Y9lp1Xjk+Hc9gqmbMM5iiHJ/uUCIREZHxoUJuBE7taCGVK5LMFSlXKrv3Wj21o8XpaBNafu5ZePP9ePL9UCnjyffjzfeTnzs2GxSLiIi4hQq5Edi112oiHKCrP08iHFCjgwuUWheQOfqa6jVyyS1UQo1qdBARkQlBzQ4jNH9yA/MnN7j6wseJqNS6gEzrAoJNUTKaFxERmSC0IiciIiJSp1TIiYiIiNQpFXIiIiIidUqFnIiIiEidUiEnIiIiUqfUtTpCa7YPsLSzh55ckZawn1M7WnT7EREREXGEVuRGYM32Ae5dsYlkrsDUxhDJXIF7V2xizfYBp6OJiIjIBKRCbgSWdvYQD/tJhP14PR4SYT/xsJ+lnT1ORxMREZEJSIXcCHQlczSEfDVjDSEfXcmcQ4lERERkIlMhNwJTE2EG8qWasYF8iamJsEOJREREZCJTITcCp3a0kMoVSeaKlCsVkrkiqVyRUztanI4mIiIiE5AKuRGYP7mBSxfOIBEO0NWfJxEOcOnCGepaFREREUfo9iMjNH9yA/MnN9DUFKVPm7OLiIiIg7QiJyIiIlKnVMiJiIiI1CkVciIiIiJ1SoWciIiISJ1Ss8MIPf7Kdu5/qYueTIGWaICL3zqV098y2elYE15wzc+J/P4H+LLbaYxMJnvk5QzOP9fpWBPe2mQnT21dRm+5m2ZvKydNOYV5iQ6nY4mIHDK0IjcCj7+yndueWk8qV6CtIUAqV+C2p9bz+CvbnY42oQXX/JzYMzfjyfdDbDKefD+xZ24muObnTkeb0NYmO/np+h+RKqRoj7STKqT46fofsTbZ6XQ0EZFDhgq5Ebj/pS6iQR/xsB+v10s87Cca9HH/S11OR5vQIr//AZVAjEqoEbxeKqFGKoEYkd//wOloE9pTW5fR4I8TD8TxerzEA3Ea/HGe2rrM6WgiIocMFXIjsH0gTyxYu9dqLOhj+0DeoUQC4E1vpRKI14xVAnG86a0OJRKAbbmtxPyxmrGYP8a2nOZFRGS0qJAbgckNIdKDtXutpgdLTG4IOZRIAMqxKXgKqZoxTyFFOTbFoUQC0B6eQrqYrhlLF9O0hzUvIiKjRYXcCFz81qlkBkukckXK5TKpXJHMYImL3zrV6WgTWvbIy/EU0tVr5MplPPl+PIU02SMvdzrahHbSlFMYKKZIFVKUK2VShRQDxRQnTTnF6WgiIocMFXIjcPpbJrP4pDnEwwF2DBSIhwMsPmmOulYdNjj/XLILPoQnn4Ttv8eTT5Jd8CF1rTpsXqKD98+5hHggzrbsNuKBOO+fc4m6VkVERpFuPzJCp79lMqe/ZbL2WnURX/cqAr2rGZx7JqHEJAaTOwn0rqbQvYpS6wKn401o8xIdzEt06N+LiMgY0Yqc1L3QuiWUQ43VrlVPtWu1HGoktG6J09FERETGlAo5qXve1GYqwb26VoNxvKnNDiUSEREZHyrkpO6V49PxDO7VtTqYohyf7lAiERGR8aFCTupefu5ZePP91a7VSrVr1ZvvJz/3LKejiYiIjKlxbXYwxgSAu4HDgBDwj9bah/d4/DzgBqAI3G2tvcsYEwHuBSYDKeAya+2O8cwt7lZqXUDm6Guq18Qlt1AJtZM5/ANqdHAB7bXqToXONQw+uZT8zm6Kk1oJnnwqgY75TscSkQMw3itylwI91tqTgHcDt+16YKjI+yZwBvBO4GpjTDtwLfDy0Gt+CHxhnDNLHSi1LiDzjk9RPu82Mu/4lIo4F9Beq+5U6FxD7sf3UU6l8E1pp5xKkfvxfRQ61zgdTUQOwHgXcvcD1w/92UN15W2Xw4FOa22vtXYQeBo4GTgR+J+h5ywBTh+nrCJyELTXqjsNPrkU4nG88TgerxdvPA7xeHVcROrOuJ5atdYOABhj4sAD1K6uJYD+Pb5OAY17je8ae1M+n4empuhoRH6TY3jH/BgycpoX9+gtd9Pe0I7X48Xr9RAOBwiGmtiW3aY5clB+Zze+Ke3VIs5TnZdKsInSVs2LW+hzzH3cPCfjfkNgY8xM4GfA7dbaH+3xUBLY8x4ScaBvr/FdY2+qVKqM+c1HdYNTd9K8uEezt5WegT7igTjhcIBcrkCqkKI50Ko5clBxUiuDPX1443+al3IqhXeS5sUt9DnmPk7MSVtbfP9PYpxPrQ5d8/YY8Flr7d17PbwamG+MmWSMCVI9rboc+F/g7KHnnAU8NV55ReTAaa9VdwqefCqkUpRTKSrlMuVUClKp6riI1B1PpVIZt4MZY/4F+EvglT2G7wJi1to79+ha9VLtWv1XY0wUuAeYCgwCl1hrt77ZcQqFUmWsKuc12wdY2tlDT65IS9jPqR0tzJ/cMCbHkpHTT7Luoq5Vd9rVtepX16or6XPMfRxakXseWLi/541rITdexqqQW7N9gHtXbCIe9tPaGKa7P0cqV+TShTNUzLmEPgDdSfPiTpoXd9K8uI+bCzndEHgElnb2EA/7SYT9eD0eEmE/8bCfpZ09TkcTERGRCUiF3Ah0JXM0hHw1Yw0hH13JnEOJREREZCJTITcCUxNhBvKlmrGBfImpibBDiURERGQiUyE3Aqd2tJDKFUnmipQrFZK5IqlckVM7WpyOJiIiIhPQuN9Hrp7Nn9zASXMncf9LXTyzoY+WaICL3zpVjQ4u4OteRWjdErz5bURD7eTnnqVtulxAXasiUs92bt7Axhefo5DuIxBrYtbRxzJp+mynY9XQitwIrNk+wFPrdjK/Lca5fzaF+W0xnlq3kzXbB5yONqH5ulcRffEOPPl+SEzDk+8n+uId+LpXOR1tQtNeqyJSz3Zu3sCqJx5lMJOmYVILg5k0q554lJ2bNzgdrYYKuRFQ16o7hdYtoRxqpBJqBI+XSqiRcqiR0LolTkeb0LTXqojUs40vPkcwEiMYjeHxeAlGYwQjMTa++JzT0WqokBsBda26kze1mUqwdiuTSjCON7XZoUQCsC23lZg/VjMW88fYlnvT+3mLiLhCurebQKR2f9VAJEq6t9uhRPumQm4E1LXqTuX4dDyDqZoxz2CKcny6Q4kEoD08hXQxXTOWLqZpD09xKJGIyPDFmlspZGtvAlzIZog1tzqUaN9UyI2AulbdKT/3LLz5/uo1cpUynnw/3nw/+blnOR1tQtNeqyJSz2YdfSyD2TSDmTSVSpnBTJrBbJpZRx/rdLQaKuRGYFfX6podaX7+u62s2ZHmpLmT1LXqsFLrAvKz/wJ/9yo8f/gv/N2ryM/+C3WtOmxeooP3z7mEeCDOtuw24oE4759zibpWRaQuTJo+mwWnnUMwGmNgZw/BaIwFp53juq5V3X5kBPbsWj1+aK/Vp9btZPakqIo5B/m6VxHa8CuKrQvwJU6kmNxJaMOvKDXPVTHnsHmJDuYlOrR3pIjUpUnTZzNp+mxXf4ZpRW4E1LXqTupaFRGRiUqF3Aioa9Wd1LUqIiITlQq5EVDXqjupa1VERCYqFXIjoK5Vd1LXqoiITFQq5EZg/uQGLl04g0Q4QFd/nkQ4wKULZ6jRwWGl1gVkjr6meo1ccguVUCOZo69Ro4OIiBzy1LU6QvMnNzB/coOrO1gmolLrAjKtCwg2RcloXlxj2ZZf89CGB+gt9NAcaOGC2RdxyrR3OR1rwssufYL8gz+lb2cPTGoh9L73Ezn1NKdjicgB0IqciIyJZVt+zZ32dgaKA7SEWxkoDnCnvZ1lW37tdLQJLbv0CbLfu43ywADetlbKAwNkv3cb2aVPOB1NRA6ACjkRGRMPbXiAqD9Kg78Br8dLg7+BqD/KQxsecDrahJZ/8KcQieKNN+D1evHGGyASrY6LSN1RISciY6I7303UV7vhdNQXpTvvrg2nJ5py9w6I1c4LsWh1XETqjgo5ERkTraFWMqXa6xUzpQytIXdtOD3ReFvbIL3XdaTpTHVcROqOCjkRGRMXzL6ITDHDQHGAcqXMQHGATDHDBbMvcjrahBZ63/shm6GcGqBcLlNODUA2Ux0Xkbozoq5VY0wbcDZggBLwCvCItTY5Btlcac32AZZ29tCTK9IS9nNqR4tuPyKyD7u6Ux/a8AA9uW6aAy1cOu9yda06bFd3av7Bn1Le0Y13Uguhy65Q16pInfJUKpX9PskYEwL+Efgr4GlgLeAD5gLHAfcBN1prs2MXdfgKhVJlLG4Nsmb7APeu2EQ87Ke1MUx3f45Urqh7ybmIbgvjTpoXd9K8uJPmxX2cmJO2tvjzwML9PW+4K3I/A34EfMFam9/zAWNMEPj/h57z7hHmrCtLO3uIh/0kwn68Hg+JsH/3uAo5ERERGW/DLeQuttam9/WAtXYQuMcY8+DoxXKnrmSO9niwZqwh5KMrmXMokYiIiExkw2p22LOIM8bMHfr9vcaYG4wxjUPPGRibiO4xNRFmIF+qGRvIl5iaCDuUSERERCayEXWtGmPuAD5rjDkcuA2YA3x/LIK50akdLaRyRZK5IuVKhWSuSCpX5NSOFqejiYiIyAQ00r1W3w68A/gccI+19jpjzHOjH8ud5k9u4NKFM1ja2UNXf56WcID3HDlF18eJvAHttSroxzkgAAAgAElEQVQi9Wzn5g1sfPE5Cuk+ArEmZh19LJOmz3Y6Vo2RFnJea23ZGPMXwM1DY7FRzuRq8yc3MH9yg7qKRPZj116rUX+UlnAryXyKO+3tACrmRMT1dm7ewKonHiUYiRGf1EKqN8mqJx5lwWnnuKqYG+kNgTuNMb+getuRZcaY+4CXRj+WiNQ77bUqIvVs44vPEYzECEZjeDxegtEYwUiMjS+660TkSAu5D1O9Dck7rbUF4CngylFPJSJ1T3utikg9S/d2E4jUfoYFIlHSve76DBtWITd0KhVrbdpae6+19o9DX3/PWpsZes4ZY5ZSROqO9loVkXoWa26lkK39DCtkM8Sa3fUZNtwVuXcZYx4yxpxtjNl9XZ0xJmiMOdMY83NAhZyI7Ka9VkWkns06+lgGs2kGM2kqlTKDmTSD2TSzjj7W6Wg1hrVFF4AxZhFwI3Ai0EV1i67JVE+vftlau3yMMo7YWG3RBfD4K9u5/6UuejIFWqIBLn7rVE5/y+QxOZYMX3DNz4n8/gf4s9spRiaTPfJyBuef63SsCe/Hnffx0MYHGCimaPDHuWDWRXyg46+cjiVD1LTlTpoX93Cya3W0t+jCWvsscNbQDYA7gDKwzlrbP5JgQwXhV621p+w1/kHg00A/8ANr7feNMR5gE7Bm6GnLrbXXjeR4o+nxV7Zz21PriQZ9tDUE6M8UuO2p9QAq5hwUXPNzYs/cTCUQg9hkPNl+Ys9Um6pVzDlnbbKT9em1nNB+Mi0NTfQM9LE+vZa1yU7mJTqcjicisl+Tps9m0vTZri6uR3r7EYDBoV+/B0a0pYEx5jPAB4H0XuOtwJeBY4A+4HFjzBND+VZaa887gJyj7v6XuogGfcTDfrxeL/GhvVbvf6lLhZyDIr//AZVAjEqoEbze6u9D4yrknPPU1mU0+OPEA3G8Hi/xQHz3uAo5EZHRMdKdHY4D1gKPAtOBzcaYPx/BW6wF3ruP8bnAS9bandbaMvAccBzVGxBPN8YsNcb8whhjRpJ3tG0fyBML+mrGYkEf2wfyDiUSAG96K5WhImGXSiCON73VoUQCsC23lZi/9jaTMX+MbTnNi4jIaBnpitzXgdOB+6y1m4wxlwL/Agzryj9r7YPGmMP28dAa4AhjTDuQAk4DXh0a/ydr7f3GmBOBe4dzLJ/PQ1NTdH9PG7FpTWGS2SKJYPWvze/3kcwWmNYUHpPjyfB4G6fhzfVDsBEPHvx+L+RS0DhN8+Kg2U0zSQ4mSQQTeL0ewuEAycEks5tmal5cwufzai5cSPPiPm6ek5EWclFr7apdC2PW2l8YY75ysCGstb3GmE8ADwI9wEqgG1gBFIee87QxZpoxxmOtfdMOjVKpMibnsi88Ygq3PbWeUqlMY7R6jVxmsMRlC2e49tz5RBB8y4eq18iVyvgijZSy/XgKadJHf5RBzYtjjm06gZ+u/xH5fHH3NXIDxRSnTT5L/15cws3X/Uxkmhf3cWJO2tri+38SIy/kCsaYZqACMFqnOoduaXIMcBIQBH4F/APwRaqF3deMMW8FXttfETeWdl0Hd/9LXewYqHatXrFolq6Pc9iu6+Aiv/8BpLdTiUwm8/a/0/VxDpuX6GBObF61a3Xzn7pWdX2c87JLnyD/4E/p29kDk1oIve/9RE49zelYInIARlrIfQX4DTDVGPOfVO8dd/WBHtwYcwnQYK29c6gmXAnkgFuttd3GmFuAe40x51Bdmbv8QI81Wk5/y2ROf8tk/cTkMoPzz2Vw/rk0NUXp17y4wrItv+bh135GQyDOtIZpJPMpHn7tZ0yJTtVeqw7KLn2C7Pdug0gUf1srxf5U9WtQMSdSh4Z9H7ldjDEdwF9QbZT4tbV29VgEOxhjeR+5XVTIuZPmxT0+vvxvGCgO0OBvwO/3USyWdn/9reNvdzrehNW3+BrKAwN443+al3JqAG9DA0233eF0PEGfY27k0KnV0b2PHIAx5mWGTqsOucYYg7X2z0aYT0QOcd35biYFJ9WMaa9V55W7d0BLS+1gLFodF5G6M9JTq4v3+HMQuADYMnpxRORQ0Rpq3b0Ct4v2WnWet7WN8sAAxP80L6QzeFvbnAslIgdsRPeRs9b+Zo9fv6Ja2OmKchF5He216k6h970fshnKqQHK5TLl1ABkM9VxEak7Iyrk9qEFmDYaQUTk0HLKtHdx/swLGSikeLXPMlBIcf7MC9Xo4LDIqacRvOB9VAaSFF6xVAaSBC94nxodROrUwVwj5wFmAbo6VkReR3utulOhcw2VtZ0ETzqFSEsT2Z4+Kms7KXSuIdAx3+l4IjJCB3ONXAXY4cauVRFxnvZadafBJ5dCPI43Hsfj9eKNxykPjauQE6k/IyrkrLW/GasgInJo2ZbbSluo9mbZ2mvVeeWtW/FMrp0XTyxGeavmRaQeDauQ28dtR2ro9iMisrf28BRShdTulTiAdDFNe3iKg6nEO2UK5VQKT/xP81JJp/FO0byI1KPhrsgt3v9TRET+5KQpp/DT9T8CIBhqIlVIMVBMcfbM8xxONrEFTz6V3I/vowxUgk2UUylIpQiec77T0UTkAAyrkNMpVREZqXmJDo5vO5GHNjzAip4emgMtXDBbe606LdAxHz7wVww+uZTS1m14J7USPOd8XR8nsg87N29g44vPUUj3EYg1MevoY5k0fbbTsWqMtGv1eOBzQAPVrlUfMMdaO2sMsolIHVub7GT5jqeZl5hPS8Ox9Az0sXzH08xsmKVizmGBjvkEOuZrKyiRN7Fz8wZWPfEowUiM+KQWUr1JVj3xKAtOO8dVxdxI7yP3b8BvgQRwH5AEHhztUCJS//bVtdrgj/PU1mVORxMR2a+NLz5HMBIjGI3h8XgJRmMEIzE2vvic09FqjLSQq1hrvwosA14BLgZOHu1QIlL/tuW2EvPHasbUtSoi9SLd200gEq0ZC0SipHvdtV/0SAu51NDva4EjrbU5qqdXRURqtIenkC6ma8bUtSoi9SLW3EohW3vpQSGbIdbsrv2iR1rIPWuM+Qnwa+DvjTG3AqXRjyUi9e6kKacwUEyRKqQoV8q7u1ZPmnKK09FERPZr1tHHMphNM5hJU6mUGcykGcymmXX0sU5HqzHSnR0+ASyy1r5qjPk4cDpwyejHcq812wdY2tlDT65IS9jPqR0tzJ/c4HSsCc/XvYrQuiV489uIhtrJzz2LUusCp2NNaOpaFRmZvq1pNq3qo5gp4Y/6mLGgiaYpsf2/UMbEpOmzmX7kMdgnf0nXK/2E4o2Yk890VaMDgKdSecP7/O5mjHkI+I619omxj3TwCoVSZSw6sdZsH+DeFZuIh/20Nobp7s+RyhW5dOEMFXMO8nWvIvriHZRDjYQSk8gnd+LN95M5+hoVcw5am+zkp+t/RIM/vnuv1YFiivfPuUTFnEuoa9U9+ramsf+7jUDYR7wpTKovRyFXwpzQrmLOITVdq80JUr1JBrPpcetabWuLPw8s3N/zhntq9SngNmPMamPMR40x8f2+4hC0tLOHeNhPIuzH6/GQCPuJh/0s7exxOtqEFlq3hHKokUqoETxeKqHGalG3bonT0SY0da2KDN+mVX0Ewj6CET8ej4dgxE8g7GPTqj6no01Y9dK1OtwbAt8K3GqMeSdwFXC9MeYB4F+ttX8Yy4AHIpfL8fzzo/8X/eLKjUyKBejHQzDkYzBfokKFdekCz4e2j/rxZHiiL7xAOdICnhShoJ/8YBEqZbzZdWR87voHN5G82LmSpmAzvZ6dBIN+BgeLlCtl1g6u4fmU5sUN4vEIqVTW6RgCrHqhi1BD9b/k3Z9jQH6gSL5xi5PRJqzfvbiScLyxWlgHfQwOlqhUKuRS/ZSmjP2K3Lvf/a5hPW9E18gN7fDwG2PMJOCDwA+NMf3W2uEdbZyUyyWamppG/X1ntGcYyBdpCPkIhfz4AkUG8iVmtPvH5HgyPOHJs/AMpqgEY4RCfvL5YvXr+CyCmhfHbPrtJtKz0/x/f2YIhQL48gVe/Z2ld0MvTQs1L24Qj0fw+UJOxxCgtT1PIV8iEPIRCgUI5gsU8iXi7T79/+KQtinTKGSzBCKR6v8t/iKFbJb4lGmumpORdq3ukgfSVG8I7K4+3DG0aHYT6cFq8VauwEC+RHqwyKLZ7pnQiWhw5sl4B1N4BlNQKeMZTOEdTDE4U7c4dNI7j3kX/3PbEl79naVcKfPq7yz/c9sS3nmMq37uE3GFKR0JivkyhXx11aeQL1HMl5nSkXA62oQ19fCjKOQzFLLZ6pxksxTyGaYefpTT0WoMq9lhF2PMCcBfA+cDvwK+68Z9WPv7k5XVqzvH5L3/2JPh2Q199OVLNIV8LJrdxGEt0f2/UMaUt3ctwdeeJDTYTT7YyuDMkyk3z3M61oT3i6cf5Rtf+icOP/1wVj++mr+/4TrOPvEcp2PJEJ1adZfkjixbO5OUcmV8YS9TOhIk2iJOx5rQ+rZtoWv1yxQzSfzRBFMPP4qm9mnjcuzjjjtmWM0Owzq1aoz5LPBhIAbcBRxhrZ2Qt2c/rCXKYS1RfQC6TLl5HrnmeQTiEXKaF9c48uijOPr0t7H8Z7/l+Av/nCOPdtdPshNVYcMGCs89y2B/L8XGZgLHLiIw2123VJiIEm0REm0R/f/iIk3t02hqn+bqORnuqdUzgc8Dh1lrvzRRizgRGb6NAxu5+1d38sLjKznhohN54fGV3P2rO9k4sNHpaBNaYcMGBh99hEo6jbe1hUo6zeCjj1DYsMHpaCJyAIbbtaqLWkRkRP5r2U/5zXeX8p6PXcj8YzqYYWby39/+GU2BJj5+7t87HW/CKjz3LMRieGIxPF4vnliMytC4VuVE6s+BNjuIiLyptWs6Oe+jFzDriGpxMOuI2Zz30QtYu2Zsrl+V4al074DoXtddRSPVcRGpOyPdoktEZFhOveA00sV0zVjbW9o47MjDnAkkAHha26ik0xDbY7eATBZPa5tzoUTkgA27kDPGHAF8FDgM+CPwL9ba1WMTS0Tq3cK2RSzZ9AgAgWCCdDFNppTmnVN1pYaTAscuql4jB1QC8WpRl04TOEXzIlKPhnVq1RjzNuD/gHnA76gWc/9njFELmojs06yGWcyIzGJl9woeWf8wK7tXMCMyi1kNs5yONqEFZs+GWbMYXPEsqf9+mMEVz8KsWbo+TqRODXdF7mbg69baG3cNGGOuA74GnDUGuUSkzj27/Rme6PoV0UCMybHJDAymeaLrV7RG2lg0+Tin401YuWeWU3j8MTzRBvzt7RRTaQqPP0autY3wccc7HU9ERmi/K3LGmFOAk4DnjTEn7/oFvAScNPRnEZEaj2/+JRF/hKgvitfjIeqLEvFHeHzzL52ONqENPrYEQmG8DVE8Xi/ehiiEwtVxEak7w1mR+w8gAty+j8eiwL2AzpWISI3ewV4aA401Y2FvmN7BXocSCUC5dyc0NdcORiPVcRGpO/tdkbPWzgS2A6dZa2fu+gWcDGyz1qqIE5HXaQ42kyvnasZy5RzNweY3eIWMB2/zJMjsdYf6TLY6LiJ1Z7j3kfsJcLcxZiqAMaYduBv4z7EKJiL17fTpZ5ItZsmUMpQrFTKlDNliltOnn+l0tAkteMZZkM9RHshQKZcpD2Qgn6uOi0jdGW6zw/XAo8AmY0w30AL8L3DDWAUTkfq2q6Hh8c2/pC/fS8LfxPmzLlSjg8N2NTQMPraE8s5evE1NBC98nxodROrUcLfoSgEnG2NOoHo93AZr7W/HNJmI1L1Fk49j0eTjXL3h9EQUPu54wscdr3kROQSMaGcHa+3/Ul2JO2DGmEXAV621p+w1/kHg00A/8ANr7feNMRGqzRSTgRRwmbVW+8iIiIiIMM57rRpjPgP8GxDea7wV+DJwCvBO4K+MMYcB1wIvW2tPAn4IfGE884qIiIi42bgWcsBa4L37GJ8LvGSt3WmtLQPPAccBJwL/M/ScJcDp45JSREREpA6M6NTqwbLWPji00ra3NcARQ92wKeA04FUgQfVUK0Pjjft47et4vR7i8cjBB34TPp93zI8hI6d5cSfNiztpXtxJ8+I+bp6TcS3k3oi1ttcY8wngQaAHWAl0A0kgPvS0ONA3nPcrlytjfgGvLhJ2J82Luzy7/Rke3/xLksU+Ev4mTp9+prpWXUT/XtwluSPL1s4kpVwZX9jLlI4EiTZ3Fg8TRd+2LXStfpliJok/mmDq4UfR1D7N6Vg1xvvU6j4ZY/zAMVS3Ans/8BaqTRX/C5w99LSzgKccCSgiI/bs9mf4ybofkSllaAo1kyll+Mm6H/Hs9mecjibiOskdWdav7KGQLxFJBCnkS6xf2UNyhwptp/Rt28La5csoZLNEm5opZLOsXb6Mvm1bnI5Ww9FCzhhziTHmamttcWhoJbAM+La1thv4LtVTrk8DVwM3OZNUREZKe62KDN/WziT+kJdAyIfH4yEQ8uEPednamXQ62oTVtfplAqEogUikOieRCIFQlK7VLzsdrca4n1q11v6RaiMD1tof7TF+E3sVatbaDHDxeOYTkdGhvVZFhi+XKhCKB2rG/CEfuVTBoUSS6e8lkmiqGfOHw2T63fUZ5opTqyJy6NFeqyLDF44HKOZLNWPFfInwXsWdjJ9oYzPFXO1nWDGXI9rors8wFXIiMia016rI8E3pSFDMlynkS1QqFQr5EsV8mSkdCaejTVhTDz+KQj5DIZutzkk2SyGfYerhRzkdrYYKOREZE4smH8dfzr2EqC9KX76XqC/KX869RF2rIvuQaIsw55gWAiEf2eQggZCPOce0qGvVQU3t05h3/CkEIhEyfb0EIhHmHX+K67pWXXH7ERE5NGmvVZHhS7RFSLRF9O/FRZrap9HUPs3Vc6IVOREREZE6pUJOREREpE6pkBMRERGpUyrkREREROqUmh1EZMxor1V3yj2znMHHlpDs64OmJoJnnEX4uOOdjiUiB0ArciIyJrTXqjvlnllO7sf3UU5n8E5qppzOkPvxfeSeWe50NBE5ACrkRGRMaK9Vdxp8bAmEwngboni8XrwNUQiFq+MiUndUyInImOgd7CXsDdeMaa9V55V7d0J0r5vMRiPVcRGpOyrkRGRMaK9Vd/I2T4LMXjc2zWSr4yJSd1TIiciY0F6r7hQ84yzI5ygPZKiUy5QHMpDPVcdFpO6oa1VExsSu7tTHN/+SvnwvCX8T58+6UF2rDtvVnTr42BLKO3vxNjURvPB96loVqVMq5ERkzGivVXcKH3c84eOO17yIHAJ0alVERESkTqmQExEREalTKuRERERE6pQKOREREZE6pWYHERkzGwc2smLHs/RXemn0NLOwbRGzGmY5HUvElZI7smztTFLKlfGFvUzpSJBoi+z/hTJm+rZtoWv1yxQzSfzRBFMPP4qm9mlOx6qhFTkRGRMbBzayZNMjpItpWkOtpItplmx6hI0DG52OJuI6yR1Z1q/soZAvEUkEKeRLrF/ZQ3KHuoqd0rdtC2uXL6OQzRJtaqaQzbJ2+TL6tm1xOloNFXIiMiZW7HiWqC9GzB/D6/ES88eI+mKs2PGs09FEXGdrZxJ/yEsg5MPj8RAI+fCHvGztTDodbcLqWv0ygVCUQCRSnZNIhEAoStfql52OVkOFnIiMie7BbiK+aM1YxBele7DboUQi7pVLFfCHfDVj/pCPXKrgUCLJ9PfiD9fuF+0Ph8n0u2u/aBVyIjImWoOtZEuZmrFsKUNrsNWhRCLuFY4HKOZLNWPFfIlwPOBQIok2NlPM1e4XXczliDa6a79oFXIiMiYWti0iU0qTLqYpV8qki2kypTQL2xY5HU3EdaZ0JCjmyxTyJSqVCoV8iWK+zJSOhNPRJqyphx9FIZ+hkM1W5ySbpZDPMPXwo5yOVkOFnIiMiVkNszhrxnnE/DG6893E/DHOmnGeulZF9iHRFqFtTozkjhwbf99DckeOtjkxda06qKl9Gu3zj6Bv2ybWv7CCvm2baJ9/hOu6VnX7EREZM7MaZjGrYZb29BTZj+SOLDvWp0m0hYnFQ6RTeXasT9PQHFYx55C+bVvYtuYPNLXPINbYQLp/gG1r/kBDa5urijmtyImIiDhMXavuo65VERERGRZ1rbqPulZFRERkWNS16j7qWhUREZFhUdeq+6hrVURERIYl0RZhzjEtBEI+sslBAiEfc45pUaODg5rapzHv+FMIRCJk+noJRCLMO/4UVzU6gLpWRWQMbRzYyIodz9Jf6aXR08zCtkW6/YgLFDZsoPDcswz291JsbCZw7CICs2c7HWvCS7RFSLRF1OXtIk3t02hqn+bqOdGKnIiMiY0DG1my6RHSxTStoVbSxTRLNj3CxoGNTkeb0AobNjD46CNU0mm8rS1U0mkGH32EwoYNTkcTkQOgQk5ExsSKHc8S9cWI+WN4PV5i/hhRX4wVO551OtqEVnjuWYjF8MRieLxePLEYxGLVcRGpO+N+atUYswj4qrX2lL3G/wr4FFAC7rbWfndofCWw60Y66621Hx7HuCJygLoHu5kUaKkZi/iidA92O5RIACrdO6Cldl6IRqrjIlJ3xrWQM8Z8BvggkN7Hw98AjgAGgFXGmB8DWcCzd9EnIu7XGqyeTo35Y7vHsqUMrcFWB1OJp7WNSjoNsT/NC5ksntY250KJyAEb71Ora4H3vsFjvwMagTDgASrAW4GoMeYxY8yvjTHHjU9METlYC9sWkSmlSRfTlCtl0sU0mVKahW2LnI42oQWOXQTpNJV0mkq5XC3q0unquIjUnXFdkbPWPmiMOewNHv498DzV1br/stb2GWMyVFfq/g2YDywxxhhrbfHNjuP1eojHx7Zl2+fzjvkxZOQ0L+5xRNwQi4V4pms53bkdtMbaOHvqWRyWOMzpaBPbkW8hHwuRXf5byt07CLe2ETnnLEJz5jidTIboc8x93Dwnrrj9iDHmz4BzgDlUT63ea4y5GHgY6LTWVoBXjTE9wFTgtTd7v3K5MuZtwm5uRZ7INC/u0uJp55xpF9TMi+bHBVqn4DvvvTQNzcsgMKh5cQ19jrmPm+fELV2r/VSvh8taa0vAdqAZuAK4FcAYMw1IAF1OhRQRERFxE0dX5IwxlwAN1to7jTF3AE8bYwapXkv3g6Gn/cAY8zTVa+au2N9pVREREZGJYtwLOWvtH4Hjhv78oz3Gvwd8bx8vuWR8komIiIjUF7ecWhURERGREVIhJyIiIlKnVMiJiIiI1CkVciIiIiJ1SoWciIiISJ1SISciIiJSp1TIiYiIiNQpV2zRJSIiMtEld2TZ2pmklCvjC3uZ0pEg0ebO/T0nir5tW+ha/TLFTBJ/NMHUw4+iqX2a07FqaEVORETEYckdWdav7KGQLxFJBCnkS6xf2UNyhzv395wI+rZtYe3yZRSyWaJNzRSyWdYuX0bfti1OR6uhQk5ERMRhWzuT+ENeAiEfHo+HQMiHP+Rla2fS6WgTVtfqlwmEogQikeqcRCIEQlG6Vr/sdLQaKuREREQclksV8Id8NWP+kI9cquBQIsn09+IPh2vG/OEwmf5ehxLtmwo5ERERh4XjAYr5Us1YMV8iHA84lEiijc0Uc7masWIuR7Sx2aFE+6ZCTkRExGFTOhIU82UK+RKVSoVCvkQxX2ZKR8LpaBPW1MOPopDPUMhmq3OSzVLIZ5h6+FFOR6uhQk5ERMRhibYIc45pIRDykU0OEgj5mHNMi7pWHdTUPo15x59CIBIh09dLIBJh3vGnuK5rVbcfERERcYFEW4REW4R4PEIqpW5VN2hqn0ZT+zRXz4lW5ERERETqlAo5ERERkTqlQk5ERESkTqmQExEREalTKuRERERE6pQKOREREZE6pUJOREREpE6pkBMRERGpUyrkREREROqUCjkRERGROqVCTkRERKROqZATERERqVMq5ERERETqlAo5ERERkTqlQk5ERESkTqmQExEREalTfqcDiIiICCR3ZNnamaSUK+MLe5nSkSDRFnE61oTWt20LXatfpphJ4o8mmHr4UTS1T3M6Vg2tyImIiDgsuSPL+pU9FPIlIokghXyJ9St7SO7IOh1twurbtoW1y5dRyGaJNjVTyGZZu3wZfdu2OB2thgo5ERERh23tTOIPeQmEfHg8HgIhH/6Ql62dSaejTVhdq18mEIoSiESqcxKJEAhF6Vr9stPRavy/9u4/xrK6vOP4e3aX3WVXZHcLBd1GVrQ8gD8QwYqyTYFFKmC1Yo2ubTU2GP/wj8ZiWzX+Y4ypxjZpI9qWkv7QJlaN+kfTsiEU21QtqS6gdIXH8tNUXEVwQffXsLPTP85dZpiF3TvD7P0+Z+77lUwy95yzmSf55Ln73HvO95wleWp12bLl7Nq165j+jamp/fzsZ35SqsZcajKXmsyljp/86BFWPWsF+w7A5OMr2D95AIBHdx3gF3etalzdeHpo54OsPuFE9u/Zw+SB5UxOTjE9Pc2unQ/ynGM8Y8zHkhzkVq9ezXnnveKY/o1169awa9eeY/o3NH/mUpO51GQudax69AdM7j3AyuNXsHr1cezb9/gTr1983sbW5Y2l5TsfYHLPblauWTuTyZ7drHzBC3jZMZ4x5mPkg1xEvBL4eGZeNGf7bwPXAFPA32bmX0bEMuDTwDnAfuDqzLx7xCVLknRM/dLZ68iv/wiAVatWMLn3AI/vm+L0805qXNn4et7LXsF3/+1fAFi16tlM7tnN5N7dvPDVF7UtbI6RXiMXEX8EXA+sfordfwpcClwIXBMR64HfBFZn5quA9wN/NqpaJUkalXWnriUuPIWVx69g90/3s/L4FcSFp7Du1LWtSxtbGzaextlbrmTlmrX8/JGHWblmLWdvuZING09rXdqTjPobuXuAq4DPPsW+7wAnAgeACWAa2AxsA8jMWyLi/BHVKUnSSK07dW334ynvMjZsPI0NG08rnclIB7nM/FJEbHqa3f8DbAd2A1/OzF0R8Wzg0VnHTEXEisw8cKS/s5ZHp6EAAA1/SURBVHz5BOvWrVmUmp/+byw75n9D82cuNZlLTeZSk7nUUzmTEosdIuKlwJXA84GfA/8YEW8GHgNOmHXosqMNcQBTU9PHfHKuPJ2PM3OpyVxqMpeazKWeFpmcfPIJRz+IOveRexTYC+zNzCngx8B64OvAFQARcQFQ6+YtkiRJDTX9Ri4i3gY8KzOvi4i/Br4WEZN019L9Pd31cq+JiG/QXTf3zmbFSpIkFTMxPT3duoZF9/jjU9OeWh1P5lKTudRkLjWZSz2NTq1uB466yLPKqVVJkiTNk4OcJElSTznISZIk9ZSDnCRJUk85yEmSJPXUkly1CjwEPNC6CEmSpAU6DTj5aAct1UFOkiRpyfPUqiRJUk85yEmSJPWUg5wkSVJPOchJkiT1lIOcJElSTznISZIk9dSK1gX0QURsGOKwg5m565gXoyeYS03mUpO51GMmNfUtFwe54Tw4+Jk4wjHLgeeNphwNmEtN5lKTudRjJjX1KhcHueHcmZnnHumAiLhtVMXoCeZSk7nUZC71mElNvcrFa+SG86pFOkaLy1xqMpeazKUeM6mpV7n4iK4hRMQrgJMy84Y5268AfpSZ29tUNt7MpSZz6YeIWAOcDTyQmQ+1rkdmUllErAWuAt6amVe2rmc2v5EbzseB7z7F9h3AJ0Zci2aYS03mUlBEvD4i7o+IWwdD9Q7gWuCOiHhH4/LGkpnUFhErI+KNEfFF4IfAJcBfNS7rMA5ywzkhMx+Yu3Gw7aQG9ahjLjWZS00fAS4D3g18AdiSmRcALwXe17KwMWYmBUXEZRHxd8B9wJuAzwCPZOY7M/Of21Z3OAe54aw/wr41I6tCc5lLTeZS08HM/F5mfhO4LzPvBcjMHwMH2pY2tsykpm3A6cDmzPydwfB2sHFNT8tVq8O5KSI+CnwoM6cBImIC+DBwc9PKxpu51GQuNS2LiPV0H+APDn4/dHsFP9S3YSY1vRx4K9172b3AP9HdbqQkB7nhXANcD9wdEbcPtp0DfAu4ullVMpeazKWmE4HtzAwKt87a56q3NsykoMy8HbgdeH9EvBrYChwXETcAX8nM65oWOIerVuchIk4HXjR4uePQ1+Bqy1xqMhdJS0VELAMupVu1+nut65nNQU7SooqIUzNz5zM9Rlrq7JWa+paL5+AlLbZ/XaRjpKXOXqmpV7l4jZykxXZORDx2hP0TwJH2S+PCXqmpV7l4alWSJKmn/EbuGYiIOwe/fiozr21ajJ5gLrVERAD3ZKb3xSrIfqnDXqmpei5eI/cMZOZZwGa6uz+rCHMp5yOAjxsqyn4pxV6pqXQunlpdoIg4CXj40A1PVUdEbAa2ZuZ7Wtcy7iLil4E7gf8dDAwqxn6pwV6pqQ+5eGp1CBFxAfAx4BG6yfyzdM+MXBYRb8/MbS3rE0TEucDbgDfTfbPw5bYVaeAP6b75PyMi3pSZX2pdkOyXouyVmsrn4iA3nGuBD9Ldhftm4PLMvCUizgQ+R/dcNo1YRJxBd8ftrcBPgM8DE5l5cdPCBEBEPAd4++DlBPDHQLk3wXFhv9Rlr9TUl1y8Rm44KzLzxsz8IrAzM28ByMy7Gtc17u4CLgFel5mbM/OTwFTjmjTjvXQfFv8B+AZwfkRc2raksWa/1GWv1NSLXPxGbjgHZ/2+d84+r5Fr5yq6Bxt/NSK20T3YeOLI/0SjEBEnAhuBczJzx2DbG4DXAze1rG2M2S8F2Ss19SkXFzsMISKmgN10b3rHA3sGuyaA1Zl5XKvaBBGxFngD3SmjS4DP0D3Y+MamhY2xiFiWmQeH3a7RsV9qsVdq6lMuDnJaUiJiPd0F3G/JzC2t6xFExL3A3Zl5Weta9GT2Sy32Sk3Vc/HUqpaUzPwpcN3gRzVsAva1LkKHs1/K2YS9UtEmCufiYochRMSti3GMFpe5SMOzX6SlyW/khnNWRHznCPsn6G5NotEyF2l49ou0BDnIDefMIY5xGf/omYs0PPtFWoIc5IaQmQ/Mfh0Rz83MB1vVo465SMOzX6SlyWvkFubDEfFbrYvQYcylpv1AtC5Ch7Ff6rFXaiqdi4PcPEXEc4HfpXtUh4owl9JWAdm6CM2wX8qyV2oqnYuD3Py9F1gJvLziozrGmLlIw7NfpCXCQW4eImId8G5mHsv1/oblaMBcpOHZL9LS4iA3P+8BngX8J3A/cHFEnN+0IoG5SPNhv0hLiIPckCJiNfBrwBWZeRHdUv4/AN7Vsq5xZy7S8OwXaenx9iPDmwB+PTOnATLzceAvBg+gVjvmUkxEXAfcANyUmT+jezj7Y22r0oD9Uoi9UlPfcpmYnp4++lF6koi4GXggM9/ZuhbNMJcaIuKVwOXAFmASuBHYlpnfblqYnsR+ac9eqalvuTjILUBEHATuysyzW9eiGeZST0T8AnAZ3ZviS4Db6N4Qv9C0MNkvxdgrNfUhF0+tSjpmMvNh4HODHyLiPOC1TYuSCrJXaupDLg5ykkYmM7cD21vXIVVnr9RUMRdXrUqSJPWUg5ykRTV4/JOko7BXaupbLp5aXZj9wImti9BhzKWG6yNiA/DvwDbga5l5oG1Jegr2S3v2Sk29ysVVqwvgaq+azKWOwY1nL6Jb6XUh8H26N8Rtmfn9hqVpwH6pwV6pqU+5OMgtgG+ANZlLXRHxfLo3xNcCp2bmrzQuaezZLzXZKzVVzsVTq5KOqYiYohsYXgR8OiJWtq5Jqsheqal6Li52kHSsTQx+AMjMyYa1SJXZKzWVzsVBTpIkqac8tTqEiPgA3QWOtw02fQB4uGFJwlyk+bBfpKXJQW449wK/HxHnAN8GbqB7iK7aMhdpePaLtAS5anWeIuJculUrlwHLgZvoPuX+d9PCxpy51OXqyHrsl5rslZqq5+I3cvM0OC1xG/AnEfFs4DXA1YBvgA2ZS3nRugDNsF9Ks1dqKpuL38hJOqaqf5qVqrBXaqqei6tWJUmSespBbggRcVzrGnQ4c5GGZ79IS5PXyA3nvyLi/5h5ztr9jetRx1yk4dkv0hLkNXJDiohNdKu8XgtsBL5Gt3z/PzJzf8PSxpq51Ff9+pJxYr/UZq/UVD0XB7kFGJyi+FW6N8OLgIcy88qmRclcioiIDXM2nUV3GceOWdsOZuau0VWlueyX9uyVmvqWi6dWFyaBuzPzMoCI2Ni4HnXMpYYHBz+Hnk04Pev3Q5YDzxtlUTqM/dKevVJTr3JxkFuYTcC+Qy8y8wftStEsmzCXCu7MzHOPdEBE3Hak/RqJTdgvrdkrNfUqF1etSlpsr1qkY6Slzl6pqVe5OMhJWlSZuW/264hYExHnR8TJT3eMNKZeEhGXz90YEVdExHlgrzTSq1wc5CQtqoh4fUTcHxG3RsQVdBcIXwvcERHvaFyeVMnHge8+xfYdwCdGXItm9CoXBzlJi+0jdA9jfzfwBWBLZl4AvBR4X8vCpGJOyMwH5m4cbDupQT3q9CoXB7mF2U/hB+iOMXOp4WBmfi8zvwncl5n3AmTmj4EDbUvTLPZLe+uPsG/NyKrQXL3KxVWrC7MKuKt1ETqMudSwLCLW031QPDj4/dDSfT881mG/tHdTRHwU+FBmTgNExATwYeDmppWNt17l4iAnabGdCGxnZni7ddY+70AuzbgGuB64OyJuH2w7B/gWcHWzqtSrXBzkJC2qzNzUugapDzJzN7A1Ik4HXjTYvOPQ5Qhqo2+5OMhJktTQYEAoOSSMs77k4vUqkiRJPeU3cguzFXi0dRE6jLlIw7NfpCVgYnraa4+PJiIem7Np9oN0D73+YWaeMbqqZC7S8OwXaWnyG7nh3NOnB+iOEXPpkYi4c/DrpzLz2qbFjCf7pSfslZqq5uI1csN50yIdo8VlLj2SmWcBm4H7WtcypuyXnrBXaqqai6dWn4GI2Axszcz3tK5FnYg4CXj40E0cVYf90lZEvBA4JTO/Pmf7ZrpTqve0qUxz2Sv1VP6/xVOr8xQR5wJvA95MN5V/uW1F4ysiLgA+BjxC93zPz9I9B29ZRLw9M7e1rE/2SzF/DnzgKbY/Otj3G6MtR7PZK3X07f8WB7khRMQZdCu8tgI/AT4PTGTmxU0L07XAB+meJHAzcHlm3hIRZwKfA0o127iwX8o6JTPvmLsxM++IiE0N6hl79kpZvfq/xWvkhnMXcAnwuszcnJmfBKYa1yRYkZk3ZuYXgZ2ZeQtAZvr8yLbsl5rWHWHf8SOrQrPZKzX16v8WB7nhXAX8EPhqRPxNRGxhZum+2jk46/e9c/aVu45hjNgvNX0rIt41d2NEXE33bFyNnr1SU6/+b3GxwzxExFrgDXRfg18CfAb4Smbe2LSwMRURU8Buuje+44E9g10TwOrMPK5VbbJfqomIU4CvAJPMDG7nAyuBN2bmzla1jTt7pZa+/d/iILdAEbGe7qLUt2Tmltb1SJXZL3VExMXAiwcvd2TmzS3r0ZPZK5ovBzlJkqSe8hq5IUTErYtxjBaXudRkLjWZSz1mUlPfcvH2I8M5KyK+c4T9E3TLlDVa5lKTudRkLvWYSU29ysVBbjhnDnGMS8ZHz1xqMpeazKUeM6mpV7l4jZwkSVJPeY2cJElSTznISZIk9ZSDnCRJUk85yEmSJPWUg5wkSVJP/T80pVjeo9J+wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 633.6x388.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pyphi import models\n",
    "sns.set_style('darkgrid')\n",
    "%matplotlib inline\n",
    "\n",
    "## UTF Characters\n",
    "LINE = \"\\u2501\"\n",
    "CUT_SYMBOL = LINE * 2 + \"/ /\" + LINE * 2 + \"\\u27A4\"\n",
    "\n",
    "## Set Font\n",
    "# font_path = '/usr/local/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n",
    "font_path = '/Users/jakehanson/.brew/lib/python3.7/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf'\n",
    "font = mfm.FontProperties(fname=font_path) # find this font\n",
    "\n",
    "## Initialize fig, ax objects\n",
    "fig = plt.figure(figsize=(8.8,5.4))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "## Plot published value and possible values\n",
    "plt.plot([2],[1.92],'kx',label=r'Published Value')\n",
    "ax.axhspan(np.min(Phi_MIP), np.max(Phi_MIP), alpha=0.15,color='black',label='Possible Values')\n",
    "\n",
    "## Scatterplot Phi Values for each cut\n",
    "x_labels = []\n",
    "for i in range(len(Phi_Spectrum[0])):\n",
    "    cut = Phi_Spectrum[0][i]\n",
    "    \n",
    "    ## Get label for cut\n",
    "    from_nodes = models.fmt.fmt_mechanism(cut.from_nodes, cut.node_labels)\n",
    "    to_nodes = models.fmt.fmt_mechanism(cut.to_nodes, cut.node_labels)\n",
    "    cut_label = from_nodes+CUT_SYMBOL+to_nodes\n",
    "    \n",
    "    ## Plot\n",
    "    plt.scatter([i]*len(Phi_Spectrum[1][i]),Phi_Spectrum[1][i],alpha=0.5)\n",
    "    x_labels.append(cut_label)\n",
    "    \n",
    "## Clean Up\n",
    "plt.title('Oizumi 2014')\n",
    "plt.ylabel(r'$\\Phi$ Value(s)',fontsize=12)\n",
    "plt.xticks(range(len(Phi_Spectrum[0])),x_labels,rotation=90,fontproperties=font)\n",
    "# plt.ylim((0.0,1.0))\n",
    "plt.legend()\n",
    "# plt.legend(ncol=1,bbox_to_anchor = [1.25,1.0],prop=font,fontsize=12)\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"Oizumi_2014.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}