Example System - Hoel 2016 Micro Macro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook to calculate the spectrum of $\\Phi$ values for Hoel et al. 2016 (AND+AND+AND+AND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Computing concepts:   0%|          | 0/15 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing State =  (0, 0, 0, 0)\n",
      "Analyzing State =  (1, 0, 0, 0)\n",
      "Analyzing State =  (0, 1, 0, 0)\n",
      "Analyzing State =  (1, 1, 0, 0)\n",
      "Analyzing State =  (0, 0, 1, 0)\n",
      "Analyzing State =  (1, 0, 1, 0)\n",
      "Analyzing State =  (0, 1, 1, 0)\n",
      "Analyzing State =  (1, 1, 1, 0)\n",
      "Analyzing State =  (0, 0, 0, 1)\n",
      "Analyzing State =  (1, 0, 0, 1)\n",
      "Analyzing State =  (0, 1, 0, 1)\n",
      "Analyzing State =  (1, 1, 0, 1)\n",
      "Analyzing State =  (0, 0, 1, 1)\n",
      "Analyzing State =  (1, 0, 1, 1)\n",
      "Analyzing State =  (0, 1, 1, 1)\n",
      "Analyzing State =  (1, 1, 1, 1)\n",
      "Network =  NodeLabels(('A', 'B', 'C', 'D'))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyPhi Value =  0.0\n",
      "\tNumber of Non-unique Constellations = 6561\n",
      "\n",
      "Evaluating Cut  Cut [A] ━━/ /━━➤ [B, C, D]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [B] ━━/ /━━➤ [A, C, D]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, B] ━━/ /━━➤ [C, D]\n",
      "\tNumber of Non-unique Constellations = 1\n",
      "\n",
      "Evaluating Cut  Cut [C] ━━/ /━━➤ [A, B, D]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, C] ━━/ /━━➤ [B, D]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [B, C] ━━/ /━━➤ [A, D]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, B, C] ━━/ /━━➤ [D]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [D] ━━/ /━━➤ [A, B, C]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, D] ━━/ /━━➤ [B, C]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [B, D] ━━/ /━━➤ [A, C]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, B, D] ━━/ /━━➤ [C]\n",
      "\tNumber of Non-unique Constellations = 81\n"
     ]
    }
   ],
   "source": [
    "import pyphi\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pyphi import phi_spectrum\n",
    "import caffeine\n",
    "\n",
    "caffeine.on(display=True)\n",
    "\n",
    "n_nodes = 4\n",
    "n_states = 2**n_nodes\n",
    "\n",
    "## Create TPM\n",
    "reverse_states = itertools.product([0,1],repeat=n_nodes)\n",
    "\n",
    "## Reverse the order so that the states are ordered in little-endian\n",
    "all_states = []\n",
    "for state in reverse_states:\n",
    "    all_states.append(state[::-1])\n",
    "    \n",
    "## Create a map between indices and states\n",
    "state_index_map = {}\n",
    "index = 0\n",
    "for state in all_states:\n",
    "    state_index_map[index]=state\n",
    "    index = index+1\n",
    "    \n",
    "\n",
    "## For each state, get the probability of transitioning\n",
    "TPM = np.zeros((n_states,n_states))\n",
    "for i in range(n_states):\n",
    "    current_state = state_index_map[i]\n",
    "    print(\"Analyzing State = \",current_state)\n",
    "    \n",
    "    # Get probality of each node transitioning\n",
    "    node_probs = []\n",
    "    for j in range(n_nodes):\n",
    "        if j == 0 or j == 1:\n",
    "            input_to_node = current_state[2:4]\n",
    "        else:\n",
    "            input_to_node = current_state[0:2]\n",
    "        if input_to_node == (1,1):\n",
    "            p_on = 1.0\n",
    "            p_off = 0.0\n",
    "        else:\n",
    "            p_off = 0.7\n",
    "            p_on = 0.3\n",
    "        node_probs.append([p_off,p_on])        \n",
    "        \n",
    "    # Get probability of global state transitions using node transitions\n",
    "    for n in range(n_states):\n",
    "        next_state = state_index_map[n]\n",
    "#         print(\"\\t Next State = \",next_state)\n",
    "        transition_prob = 1.0\n",
    "        for m in range(n_nodes):\n",
    "            ## get node_probs at the value of that state\n",
    "            index = next_state[m]\n",
    "            transition_prob = transition_prob*node_probs[m][index]\n",
    "            \n",
    "#         print(\"\\t Transition Prob = \",transition_prob)\n",
    "        TPM[i][n] = transition_prob\n",
    "        \n",
    "        \n",
    "## Make sure transitions sum to unity\n",
    "for k in range(n_states):\n",
    "    if not np.isclose(np.sum(TPM[k][:]),1.0):\n",
    "        print(\"ERROR TPM NOT SUMMING TO UNITY\")\n",
    "        \n",
    "# Set up network object\n",
    "network = pyphi.Network(TPM, node_labels=['A','B','C','D'])\n",
    "print(\"Network = \",network.node_labels)\n",
    "\n",
    "# Put the system into a given state\n",
    "state = (0,0,0,0)\n",
    "nodes = ['A','B','C','D']\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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi MIP =  [0.       0.027778 0.029167 0.055555 0.055556 0.056944 0.056945 0.058333\n",
      " 0.058334 0.083333 0.083334 0.084722 0.084723 0.086111 0.086112 0.0875\n",
      " 0.087501 0.111111 0.111112 0.1125   0.112501 0.113889 0.11389  0.115278\n",
      " 0.115279 0.116666 0.116667 0.116668 0.138889 0.13889  0.140277 0.140278\n",
      " 0.140279 0.141666 0.141667 0.141668 0.143055 0.143056 0.143057 0.144444\n",
      " 0.144445 0.145833 0.145834 0.166667 0.166668 0.168055 0.168056 0.168057\n",
      " 0.169444 0.169445 0.169446 0.170833 0.170834 0.172222 0.172223 0.173611\n",
      " 0.173612 0.175    0.175001 0.194445 0.194446 0.195833 0.195834 0.195835\n",
      " 0.197222 0.197223 0.197224 0.198611 0.198612 0.2      0.200001 0.201389\n",
      " 0.20139  0.202778 0.202779 0.204167 0.222223 0.223612 0.225    0.225001\n",
      " 0.226389 0.22639  0.227777 0.227778 0.227779 0.229166 0.229167 0.229168\n",
      " 0.230555 0.230556 0.230557 0.231945 0.233333 0.233334 0.251389 0.25139\n",
      " 0.252778 0.252779 0.254166 0.254167 0.254168 0.255555 0.255556 0.255557\n",
      " 0.256944 0.256945 0.256946 0.258333 0.258334 0.259722 0.259723 0.261111\n",
      " 0.261112 0.280556 0.280557 0.281945 0.281946 0.283333 0.283334 0.283335\n",
      " 0.284721 0.284722 0.284723 0.284724 0.28611  0.286111 0.286112 0.2875\n",
      " 0.287501 0.288889 0.28889  0.309722 0.309723 0.309724 0.311111 0.311112\n",
      " 0.311113 0.312499 0.3125   0.312501 0.312502 0.313888 0.313889 0.31389\n",
      " 0.315277 0.315278 0.315279 0.316667 0.316668 0.338889 0.33889  0.340277\n",
      " 0.340278 0.340279 0.341666 0.341667 0.341668 0.343055 0.343056 0.343057\n",
      " 0.344445 0.344446 0.368055 0.368056 0.368057 0.369444 0.369445 0.369446\n",
      " 0.370833 0.370834 0.370835 0.372223 0.397222 0.397223 0.397224 0.398611\n",
      " 0.398612]\n",
      "177\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_hoel_2016.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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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([0],pyphi_val,'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",
    "# ax.axhspan(np.min(Phi_MIP), np.max(Phi_MIP), alpha=0.15,color='black',label='Possible $\\Phi^{Max}$ 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('Hoel et al. 2016 (0000)')\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((-0.0,0.6))\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(\"Hoel_2016_OFF.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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