Example System - Marshall et al. 2017.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook to calculate the spectrum of $\\Phi$ values for Marshall et al. 2017 (3 Node Fission Yeast Cell Cycle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jakehanson/.brew/lib/python3.7/site-packages/ipykernel_launcher.py:8: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n",
      "  \n",
      "\r",
      "Computing concepts:   0%|          | 0/7 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading data\n",
      "\n",
      "system partitioned\n",
      "\n",
      "Network =  NodeLabels(('Z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                 \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PyPhi Value =  0.090278\n",
      "\tNumber of Non-unique Constellations = 729\n",
      "\n",
      "Evaluating Cut  Cut [A] ━━/ /━━➤ [B, C]\n",
      "\tNumber of Non-unique Constellations = 9\n",
      "\n",
      "Evaluating Cut  Cut [B] ━━/ /━━➤ [A, C]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, B] ━━/ /━━➤ [C]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [C] ━━/ /━━➤ [A, B]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [A, C] ━━/ /━━➤ [B]\n",
      "\tNumber of Non-unique Constellations = 81\n",
      "\n",
      "Evaluating Cut  Cut [B, C] ━━/ /━━➤ [A]\n",
      "\tNumber of Non-unique Constellations = 9\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 =  [[0.569445, 0.694445, 0.631945, 0.555556, 0.680556, 0.618056, 0.576389, 0.701389, 0.638889, 0.618055, 0.319445, 0.444445, 0.381944, 0.305556, 0.430556, 0.368056, 0.326389, 0.451389, 0.388889, 0.381945, 0.506945, 0.493056, 0.513889, 0.569444, 0.506944, 0.444444, 0.493055, 0.430555, 0.763889, 0.263889, 0.51389, 0.756944, 0.694444, 0.631944, 0.069445, 0.194445, 0.131944, 0.256945, 0.131945, 0.194444, 0.256944, 0.319444, 0.611111, 0.736111, 0.673611, 0.361111, 0.486111, 0.423611, 0.548611, 0.652778, 0.597222, 0.722222, 0.659722, 0.715278, 0.680555, 0.347222, 0.472222, 0.409722, 0.611112, 0.548612, 0.534722, 0.555555, 0.805556, 0.743055, 0.743056, 0.368055, 0.673612, 0.423612, 0.486112, 0.361112, 0.402778, 0.527778, 0.465278, 0.111111, 0.236111, 0.173612, 0.236112, 0.298611, 0.173611, 0.298612, 0.590278, 0.590277, 0.666667, 0.583334, 0.708334, 0.645834, 0.604167, 0.729167, 0.645833, 0.604166, 0.666666, 0.583333, 0.333334, 0.458334, 0.395834, 0.354167, 0.479167, 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     ]
    }
   ],
   "source": [
    "import pyphi\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pyphi import phi_spectrum\n",
    "\n",
    "# Read in data\n",
    "print(\"reading data\\n\")\n",
    "df = pd.read_table('s_pombe_st_wild_encode.dat',header=None,names=['s1','s2'])\n",
    "N_states = np.size(df.index)\n",
    "\n",
    "# Get unique end states\n",
    "unique_ends = np.unique(df['s2'])\n",
    "\n",
    "# Partition the system based on invariant microstates\n",
    "sym_groups = {}\n",
    "for s in unique_ends:\n",
    "    group_list = df[df['s2']==s]['s1'].values\n",
    "    sym_groups[s] = group_list\n",
    "    \n",
    "    \n",
    "# Check to make sure system is partitioned\n",
    "counter = 0\n",
    "for group in sym_groups.keys():\n",
    "    counter = counter + np.size(sym_groups[group])\n",
    "if counter != N_states:\n",
    "    print(\"error - system not properly partitioned\")\n",
    "else:\n",
    "    print(\"system partitioned\\n\")\n",
    "    \n",
    "# Generate Microscopic TPM\n",
    "TPM = np.zeros((N_states,N_states))\n",
    "for s2 in sym_groups.keys():\n",
    "    for s1 in sym_groups[s2]:\n",
    "        TPM[s1][s2] = 1.\n",
    "        \n",
    "        \n",
    "# Set up network object\n",
    "network = pyphi.Network(TPM, node_labels=['Z','A','B','C','D','E','F','G','H'])\n",
    "print(\"Network = \",network.node_labels)\n",
    "\n",
    "# Put the system into a given state\n",
    "state = (0,0,1,1,0,0,1,0,0)\n",
    "nodes = ['A','B','C']\n",
    "\n",
    "## Get the requisite Subsystem\n",
    "subsystem = pyphi.Subsystem(network, state, nodes)\n",
    "\n",
    "## What does PyPhi Give? Should be 0.09\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi MIP =  [0.       0.027778 0.041667 0.055556 0.0625   0.069445 0.083333 0.090278\n",
      " 0.097222 0.104166 0.104167 0.111111 0.118055 0.118056 0.125    0.131944\n",
      " 0.131945 0.138889 0.145833 0.145834 0.152777 0.152778 0.159722 0.159723\n",
      " 0.166666 0.166667 0.173611 0.173612 0.180555 0.180556 0.1875   0.194444\n",
      " 0.194445 0.201389 0.208333 0.208334 0.215277 0.215278 0.222222 0.222223\n",
      " 0.229166 0.229167 0.236111 0.236112 0.243055 0.243056 0.25     0.250001\n",
      " 0.256944 0.256945 0.263889 0.26389  0.270833 0.270834 0.277777 0.277778\n",
      " 0.277779 0.284722 0.284723 0.291666 0.291667 0.298611 0.298612 0.305555\n",
      " 0.305556 0.305557 0.3125   0.319444 0.319445 0.326389 0.32639  0.333333\n",
      " 0.333334 0.340277 0.340278 0.340279 0.347222 0.347223 0.354166 0.354167\n",
      " 0.36111  0.361111 0.361112 0.368055 0.368056 0.374999 0.375    0.375001\n",
      " 0.381944 0.381945 0.388888 0.388889 0.38889  0.395833 0.395834 0.402777\n",
      " 0.402778 0.402779 0.409722 0.409723 0.416666 0.416667 0.42361  0.423611\n",
      " 0.423612 0.430555 0.430556 0.437499 0.4375   0.437501 0.444444 0.444445\n",
      " 0.451388 0.451389 0.45139  0.458333 0.458334 0.465277 0.465278 0.465279\n",
      " 0.472221 0.472222 0.472223 0.479166 0.479167 0.48611  0.486111 0.486112\n",
      " 0.493055 0.493056 0.499999 0.5      0.500001 0.506944 0.506945 0.513888\n",
      " 0.513889 0.51389  0.520833 0.520834 0.527777 0.527778 0.527779 0.534721\n",
      " 0.534722 0.534723 0.541666 0.541667 0.541668 0.54861  0.548611 0.548612\n",
      " 0.555555 0.555556 0.555557 0.562499 0.5625   0.562501 0.569444 0.569445\n",
      " 0.576388 0.576389 0.57639  0.583332 0.583333 0.583334 0.590277 0.590278\n",
      " 0.590279 0.597221 0.597222 0.597223 0.604166 0.604167 0.61111  0.611111\n",
      " 0.611112 0.618055 0.618056 0.618057 0.624999 0.625    0.625001 0.631944\n",
      " 0.631945 0.638888 0.638889 0.63889  0.645832 0.645833 0.645834 0.652777\n",
      " 0.652778 0.652779 0.659721 0.659722 0.659723 0.666666 0.666667 0.67361\n",
      " 0.673611 0.673612 0.680555 0.680556 0.687499 0.6875   0.694444 0.694445\n",
      " 0.701388 0.701389 0.70139  0.708333 0.708334 0.715277 0.715278 0.722221\n",
      " 0.722222 0.722223 0.729166 0.729167 0.73611  0.736111 0.736112 0.743055\n",
      " 0.743056 0.75     0.756944 0.756945 0.763889 0.76389  0.770833 0.770834\n",
      " 0.777777 0.777778 0.784722 0.784723 0.791667 0.798611 0.805555 0.805556\n",
      " 0.819444 0.819445 0.826389 0.833333]\n",
      "244\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_marshall_2017.csv\", Phi_MIP, delimiter=\",\")\n",
    "# print(\"Results Saved\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 11,
   "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 $\\Phi^{MIP}$')\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('Fission Yeast Cell Cycle (3 Node)')\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(ncol=2)\n",
    "# plt.legend(ncol=1,bbox_to_anchor = [1.25,1.0],prop=font,fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"FISSION_YEAST_CUTS.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
    "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 = '/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",
    "## 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",
    "    sorted_values = sorted(set(Phi_Spectrum[1][i]),reverse=True)\n",
    "    if pyphi_val in sorted_values:\n",
    "        pyphi_index = i\n",
    "        pyphi_x = sorted_values.index(pyphi_val)\n",
    "    \n",
    "    plt.scatter(range(len(sorted_values)),sorted_values,alpha=0.25,label=cut_label)\n",
    "    \n",
    "# Plot the PyPhi Value\n",
    "plt.scatter(pyphi_x,pyphi_val,marker='x',color='black',label='Published Value')\n",
    "\n",
    "## Plot the theoretical max\n",
    "# plt.axhline(1.5,linestyle='--',color='black',alpha=0.5,label='Upper Bound')\n",
    "\n",
    "## Finish Plotting\n",
    "plt.title('Fission Yeast Cell Cycle (3 Node)')\n",
    "plt.ylabel(r'$\\Phi$ Value(s)',fontsize=12)\n",
    "plt.xlabel('Rank Order')\n",
    "# plt.ylim((0.0,.85))\n",
    "plt.legend(prop=font,ncol=1)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"FISSION_YEAST_RANK_ORDER.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
}