checking_comparison_timeconstants.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key text.latex.preview in file C:\\Users\\PeterDell\\anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 123 ('text.latex.preview : False')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key mathtext.fallback_to_cm in file C:\\Users\\PeterDell\\anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 155 ('mathtext.fallback_to_cm : True  # When True, use symbols from the Computer Modern')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key savefig.jpeg_quality in file C:\\Users\\PeterDell\\anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 418 ('savefig.jpeg_quality: 95       # when a jpeg is saved, the default quality parameter.')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key keymap.all_axes in file C:\\Users\\PeterDell\\anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 466 ('keymap.all_axes : a                 # enable all axes')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key animation.avconv_path in file C:\\Users\\PeterDell\\anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 477 ('animation.avconv_path: avconv     # Path to avconv binary. Without full path')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n",
      "\n",
      "Bad key animation.avconv_args in file C:\\Users\\PeterDell\\anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle, line 479 ('animation.avconv_args:            # Additional arguments to pass to avconv')\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import pylab\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import csv\n",
    "\n",
    "import scipy\n",
    "from scipy import stats\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "slowbegin = 300\n",
    "fastbegin = 600\n",
    "numNode=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "string1='TrueTrueassociative/all_TC_mod_in_True_mod_topTrue_Direct_associative_all_39.npy'\n",
    "#string1='TrueFalseassociative/all_TC_mod_in_True_mod_topFalse_Direct_associative_all_39.npy'\n",
    "time_constants_n = np.load(string1) \n",
    "foo = np.mean(time_constants_n.T,axis=1)\n",
    "data_ass=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "\n",
    "string1='TrueTrueFalse/all_TC_mod_in_True_mod_topTrue_Direct_False_all_39.npy'\n",
    "#string1='TrueFalseFalse/all_TC_mod_in_True_mod_topFalse_Direct_False_all_39.npy'\n",
    "time_constants_n = np.load(string1) \n",
    "foo = np.mean(time_constants_n.T,axis=1)\n",
    "data_false=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "\n",
    "string1='TrueTrueinput_driven/all_TC_mod_in_True_mod_topTrue_Direct_input_driven_all_39.npy'\n",
    "#string1='TrueFalseinput_driven/all_TC_mod_in_True_mod_topFalse_Direct_input_driven_all_39.npy'\n",
    "time_constants_n = np.load(string1) \n",
    "foo = np.mean(time_constants_n.T,axis=1)\n",
    "data_indrive=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Narrative Integration Time Constant')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(5,5))   \n",
    "plt.title('Associative, Input-Driven, Control')\n",
    "#plt.ylim(0,75)\n",
    "#plt.boxplot(dataTFF,showmeans=True)\n",
    "SE_bool=False\n",
    "plt.boxplot(data_ass,showmeans=True,showfliers=SE_bool)\n",
    "plt.boxplot(data_indrive,showmeans=True,showfliers=SE_bool)\n",
    "#plt.boxplot(data_false,showmeans=True,showfliers=SE_bool)\n",
    "\n",
    "plt.xlabel(\"100 element neuron bin\")\n",
    "plt.ylabel(\"Narrative Integration Time Constant\")\n",
    "\n",
    "\n",
    "#plt.savefig('checking2/integration_time_constants_400_600_800.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(9,4))   \n",
    "plt.subplot(121)\n",
    "plt.title('Associative, Control')\n",
    "plt.ylim(-5,55)\n",
    "#plt.violinplot(dataTFF,showmeans=True)\n",
    "SE_bool=False\n",
    "plt.violinplot(data_false,showmeans=True,showextrema=SE_bool)\n",
    "\n",
    "#plt.violinplot(data_indrive,showmeans=True,showextrema=SE_bool)\n",
    "plt.violinplot(data_ass,showmeans=True,showextrema=SE_bool)\n",
    "#plt.violinplot(dataTFF,showmeans=True,showextrema=SE_bool)\n",
    "plt.xlabel(\"100 element neuron bin\")\n",
    "plt.ylabel(\"Narrative Integration Time Constant\")\n",
    "\n",
    "pval = 0.000001\n",
    "for index in range(0,10):\n",
    "    t,p = stats.ttest_rel(data_false[index],data_ass[index])\n",
    "    #t,p = stats.ttest_rel(gaussian_filter1d(group_lesioned[:,i],sigma=filter),gaussian_filter1d(group_standard[:,i],sigma=filter))\n",
    "    if p<pval:\n",
    "        if t > 0:\n",
    "            plt.plot(index+1,-0.5,'b*')\n",
    "        else:\n",
    "            plt.plot(index+1,-0.5,'r*')\n",
    "for i, lists in enumerate(data_false):\n",
    "    plt.plot(i+1, np.mean(lists),'b_')\n",
    "for i, lists in enumerate(data_ass):\n",
    "    plt.plot(i+1, np.mean(lists),'r_')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('Input Driven, Control')\n",
    "plt.ylim(-5,55)\n",
    "#plt.violinplot(dataTFF,showmeans=True)\n",
    "\n",
    "plt.violinplot(data_false,showmeans=True,showextrema=SE_bool)\n",
    "\n",
    "plt.violinplot(data_indrive,showmeans=True,showextrema=SE_bool)\n",
    "#plt.violinplot(data_ass,showmeans=True,showextrema=SE_bool)\n",
    "#plt.violinplot(dataTFF,showmeans=True,showextrema=SE_bool)\n",
    "plt.xlabel(\"100 element neuron bin\")\n",
    "plt.ylabel(\"Narrative Integration Time Constant\")\n",
    "\n",
    "\n",
    "for index in range(0,10):\n",
    "    t,p = stats.ttest_rel(data_false[index],data_indrive[index])\n",
    "    #t,p = stats.ttest_rel(gaussian_filter1d(group_lesioned[:,i],sigma=filter),gaussian_filter1d(group_standard[:,i],sigma=filter))\n",
    "    if p<pval:\n",
    "        if t > 0:\n",
    "            plt.plot(index+1,-0.5,'b*')\n",
    "        else:\n",
    "            plt.plot(index+1,-0.5,'r*')\n",
    "for i, lists in enumerate(data_false):\n",
    "    plt.plot(i+1, np.mean(lists),'b_')\n",
    "for i, lists in enumerate(data_indrive):\n",
    "    plt.plot(i+1, np.mean(lists),'r_')\n",
    "plt.show()\n",
    "#plt.savefig('checking2/integration_time_constants_400_600_800.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, lists in enumerate(data_ass):\n",
    "    plt.plot(i, np.mean(lists),'ro')\n",
    "    plt.plot(i, np.std(lists),'bo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_range=np.zeros(10)\n",
    "ass_means =np.zeros(10)\n",
    "ass_stds =np.zeros(10)\n",
    "for i, lists in enumerate(data_ass):\n",
    "    #print(i, np.mean(lists))\n",
    "    ass_means[i] = np.mean(lists)\n",
    "    ass_stds[i] = np.std(lists)\n",
    "    x_range[i] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "false_means =np.zeros(10)\n",
    "false_stds =np.zeros(10)\n",
    "for i, lists in enumerate(data_false):\n",
    "    #print(i, np.mean(lists))\n",
    "    false_means[i] = np.mean(lists)\n",
    "    false_stds[i] = np.std(lists)\n",
    "    x_range[i] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "indrive_means =np.zeros(10)\n",
    "indrive_stds =np.zeros(10)\n",
    "for i, lists in enumerate(data_indrive):\n",
    "    #print(i, np.mean(lists))\n",
    "    indrive_means[i] = np.mean(lists)\n",
    "    indrive_stds[i] = np.std(lists)\n",
    "    x_range[i] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_range,ass_means)\n",
    "plt.fill_between(x_range, ass_means+ass_stds, ass_means-ass_stds,alpha=0.3)\n",
    "plt.plot(x_range,false_means)\n",
    "plt.fill_between(x_range, false_means+false_stds, false_means-false_stds,alpha=0.4)\n",
    "pval = 0.000001\n",
    "for index in range(0,10):\n",
    "    t,p = stats.ttest_rel(data_false[index],data_ass[index])\n",
    "    #t,p = stats.ttest_rel(gaussian_filter1d(group_lesioned[:,i],sigma=filter),gaussian_filter1d(group_standard[:,i],sigma=filter))\n",
    "    if p<pval:\n",
    "        if t > 0:\n",
    "            plt.plot(index+1,-0.5,'b*')\n",
    "        else:\n",
    "            plt.plot(index+1,-0.5,'r*')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2233eedbec8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure(figsize=(9,4))   \n",
    "plt.subplot(121)\n",
    "plt.title('A. Associative Path Lesion: Increase TCs')\n",
    "plt.ylim(-5,55)\n",
    "\n",
    "plt.plot(x_range,false_means,linewidth=2.5)\n",
    "plt.fill_between(x_range, false_means+false_stds, false_means-false_stds,alpha=0.4)\n",
    "plt.plot(x_range,ass_means,linewidth=2.5)\n",
    "plt.fill_between(x_range, ass_means+ass_stds, ass_means-ass_stds,alpha=0.3)\n",
    "\n",
    "\n",
    "\n",
    "plt.xlabel(\"100 element neuron bin\")\n",
    "plt.ylabel(\"Narrative Integration Time Constant\")\n",
    "\n",
    "pval = 0.01\n",
    "for index in range(0,10):\n",
    "    t,p = stats.ttest_rel(data_false[index],data_ass[index])\n",
    "    #t,p = stats.ttest_rel(gaussian_filter1d(group_lesioned[:,i],sigma=filter),gaussian_filter1d(group_standard[:,i],sigma=filter))\n",
    "    if p<pval:\n",
    "        if t > 0:\n",
    "            plt.plot(index,-0.5,'b*')\n",
    "        else:\n",
    "            plt.plot(index,-0.5,'r*')\n",
    "#for i, lists in enumerate(data_false):\n",
    "#    plt.plot(i, np.mean(lists),'b_')\n",
    "#for i, lists in enumerate(data_ass):\n",
    "#    plt.plot(i, np.mean(lists),'r_')\n",
    "\n",
    "#mark the spot\n",
    "index=4\n",
    "plt.plot(index, np.mean(data_ass[index]),'ro')\n",
    "index=8\n",
    "plt.plot(index, np.mean(data_ass[index]),'ro')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.title('B. Input Driven Lesion: Decrease TCs')\n",
    "plt.ylim(-5,55)\n",
    "#plt.violinplot(dataTFF,showmeans=True)\n",
    "\n",
    "plt.plot(x_range,false_means,linewidth=2.5)\n",
    "plt.fill_between(x_range, false_means+false_stds, false_means-false_stds,alpha=0.4)\n",
    "plt.plot(x_range,indrive_means,linewidth=2.5)\n",
    "plt.fill_between(x_range, indrive_means+ass_stds, indrive_means-ass_stds,alpha=0.3)\n",
    "\n",
    "\n",
    "plt.xlabel(\"100 element neuron bin\")\n",
    "plt.ylabel(\"Narrative Integration Time Constant\")\n",
    "\n",
    "\n",
    "for index in range(0,10):\n",
    "    t,p = stats.ttest_rel(data_false[index],data_indrive[index])\n",
    "    #t,p = stats.ttest_rel(gaussian_filter1d(group_lesioned[:,i],sigma=filter),gaussian_filter1d(group_standard[:,i],sigma=filter))\n",
    "    if p<pval:\n",
    "        if t > 0:\n",
    "            plt.plot(index,-0.5,'b*')\n",
    "        else:\n",
    "            plt.plot(index,-0.5,'r*')\n",
    "#for i, lists in enumerate(data_false):\n",
    "#    plt.plot(i, np.mean(lists),'b_')\n",
    "#for i, lists in enumerate(data_indrive):\n",
    "#    plt.plot(i, np.mean(lists),'r_')\n",
    "\n",
    "#mark the spot\n",
    "index=8\n",
    "plt.plot(index, np.mean(data_indrive[index]),'ro')\n",
    "index=1\n",
    "plt.plot(index, np.mean(data_indrive[index]),'ro')\n",
    "\n",
    "#plt.savefig('indrive_assoc_path_lesions_simulation.png',dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Ttest_relResult(statistic=-13.507296253951987, pvalue=4.184637543025488e-24)\n",
      "1 Ttest_relResult(statistic=-12.435849158181039, pvalue=7.172917146697688e-22)\n",
      "2 Ttest_relResult(statistic=-14.367694854737708, pvalue=7.356730111056729e-26)\n",
      "3 Ttest_relResult(statistic=-16.2708714556901, pvalue=1.3238548752014385e-29)\n",
      "4 Ttest_relResult(statistic=-16.54321311755606, pvalue=3.999893043654559e-30)\n",
      "5 Ttest_relResult(statistic=-13.439602240064097, pvalue=5.7712343767084746e-24)\n",
      "6 Ttest_relResult(statistic=-13.31193922452018, pvalue=1.0596080198720053e-23)\n",
      "7 Ttest_relResult(statistic=-13.396410901834598, pvalue=7.08694021344936e-24)\n",
      "8 Ttest_relResult(statistic=-20.88288368374064, pvalue=7.328997155075253e-38)\n",
      "9 Ttest_relResult(statistic=-13.287380401150273, pvalue=1.1912280772580679e-23)\n"
     ]
    }
   ],
   "source": [
    "for index in range(0,10):\n",
    "    low=index*100\n",
    "    high = low+99\n",
    "    print(index, stats.ttest_rel(data_indrive[index],data_ass[index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "boxplot() got an unexpected keyword argument 'nofliers'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-b493b52b108f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m99\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m199\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m299\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m399\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m499\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m599\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m699\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m700\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m799\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m800\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m899\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfoo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m900\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m999\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      4\u001b[0m \u001b[1;31m#plt.boxplot(data, nofliers=False,whis=[20, 50],nomeans=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnofliers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnomeans\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\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      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m#plt.ylim(0,75)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: boxplot() got an unexpected keyword argument 'nofliers'"
     ]
    }
   ],
   "source": [
    "foo = np.mean(time_constants_n.T,axis=1)\n",
    "\n",
    "data=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "#plt.boxplot(data, nofliers=False,whis=[20, 50],nomeans=True)\n",
    "plt.boxplot(data, nofliers=False,nomeans=True)\n",
    "plt.title(string1)\n",
    "#plt.ylim(0,75)\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('checking2/integration_time_constants_classic_sorted.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(check[:800])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparison/FalseFalseFalse/all_TC_mod_in_False_mod_topFalse_Direct_False_all_39.npy\"\n",
    "\n",
    "check_FFF = np.load(string1) \n",
    "foo = np.mean(check_FFF.T,axis=1)\n",
    "\n",
    "dataFFF=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899]]#,foo[901:999]]\n",
    "dataFFF=[foo[0:143],foo[143:286],foo[286:429],foo[429:572],foo[572:715],foo[715:858],foo[858:1000]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "plt.boxplot(dataFFF, showfliers=False,showmeans=True)\n",
    "plt.title(string1)\n",
    "plt.ylim(0,70)\n",
    "plt.show()\n",
    "print(check_FFF.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "string1=\"comparison/FalseFalseFalse/all_TC_mod_in_False_mod_topFalse_Direct_False_all_39.npy\"\n",
    "\n",
    "check_FFF = np.load(string1) \n",
    "for i in range(40):\n",
    "    check_FFF[i] = np.sort(check_FFF[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo = np.mean(check_FFF.T,axis=1)\n",
    "\n",
    "data_FFFsorted=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "suffix = ' sorted'\n",
    "string1 += suffix\n",
    "plt.title(string1)\n",
    "plt.ylim(-2,55)\n",
    "plt.boxplot(data_FFFsorted, showfliers=False,showmeans=True)\n",
    "plt.savefig('checking2/integration_time_constants_FFF_sorted.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1,8):\n",
    "    print(143*i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparison/TrueTrueFalse/all_TC_mod_in_True_mod_topTrue_Direct_False_all_39.npy\"\n",
    "\n",
    "check_TTF = np.load(string1) \n",
    "foo = np.mean(check_TTF.T,axis=1)\n",
    "\n",
    "dataTTF=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "dataTTF=[foo[0:143],foo[143:286],foo[286:429],foo[429:572],foo[572:715],foo[715:858],foo[858:1000]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "plt.title(string1)\n",
    "plt.ylim(0,70)\n",
    "plt.boxplot(dataTTF, showfliers=False,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparisonEDR/TrueTrue400/all_TC_mod_in_True_mod_topTrue_breadth_400_39.npy\"\n",
    "\n",
    "check_TTF = np.load(string1) \n",
    "foo = np.mean(check_TTF.T,axis=1)\n",
    "\n",
    "dataTT400=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "dataTT400=[foo[0:143],foo[143:286],foo[286:429],foo[429:572],foo[572:715],foo[715:858],foo[858:1000]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "plt.title(string1)\n",
    "#plt.ylim(0,75)\n",
    "plt.violinplot(dataTT400, showmeans=True,showextrema=False)\n",
    "#plt.boxplot(dataTT400, showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparisonEDR/TrueTrue800/all_TC_mod_in_True_mod_topTrue_breadth_800_39.npy\"\n",
    "\n",
    "check_TTF = np.load(string1) \n",
    "foo = np.mean(check_TTF.T,axis=1)\n",
    "\n",
    "dataTT800=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "dataTT800=[foo[0:143],foo[143:286],foo[286:429],foo[429:572],foo[572:715],foo[715:858],foo[858:1000]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "#fig= plt.figure(figsize=(8,4))    \n",
    "plt.title(string1)\n",
    "plt.ylim(0,75)\n",
    "plt.boxplot(dataTT800, showfliers=False,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparison/TrueFalseFalse/all_TC_mod_in_True_mod_topFalse_Direct_False_all_39.npy\"\n",
    "\n",
    "check_TTF = np.load(string1) \n",
    "foo = np.mean(check_TTF.T,axis=1)\n",
    "\n",
    "dataTFF=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "dataTFF=[foo[0:143],foo[143:286],foo[286:429],foo[429:572],foo[572:715],foo[715:858],foo[858:1000]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "plt.title(string1)\n",
    "plt.ylim(0,75)\n",
    "plt.boxplot(dataTFF, showfliers=False,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparisonEDR/TrueTrue800/all_TC_mod_in_True_mod_topTrue_breadth_800_39.npy\"\n",
    "\n",
    "check_TTF = np.load(string1) \n",
    "foo = np.mean(check_TTF.T,axis=1)\n",
    "fig= plt.figure(figsize=(8,4))\n",
    "data=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899]]#,foo[901:999]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "#fig= plt.figure(figsize=(8,4))    \n",
    "plt.title(string1)\n",
    "#plt.ylim(0,75)\n",
    "plt.boxplot(dataTFF,showmeans=True, showfliers=False)\n",
    "plt.boxplot(dataTTF,showmeans=True, showfliers=False)\n",
    "\n",
    "plt.boxplot(dataTT400,showmeans=True, showfliers=False)\n",
    "plt.boxplot(dataTT800,showmeans=True, showfliers=False)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "string1=\"comparison/FalseTrueFalse/all_TC_mod_in_False_mod_topTrue_Direct_False_all_39.npy\"\n",
    "\n",
    "check = np.load(string1) \n",
    "foo = np.mean(check.T,axis=1)\n",
    "\n",
    "data=[foo[0:99],foo[100:199],foo[200:299],foo[300:399],foo[400:499],foo[500:599],foo[600:699],foo[700:799],foo[800:899],foo[900:999]]\n",
    "#plt.boxplot(data, showfliers=False,whis=[20, 50],showmeans=True)\n",
    "plt.title(string1)\n",
    "#plt.ylim(0,20)\n",
    "plt.boxplot(data, showfliers=False,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTTF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.boxplot(dataTTF[3:10], showfliers=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed=99\n",
    "fig= plt.figure(figsize=(15,12))    \n",
    "fig.suptitle(string1, fontsize=16)\n",
    "\n",
    "plt.subplot(221)\n",
    "timebegin=1500\n",
    "timeend=2200\n",
    "resbegin = 200\n",
    "resend = 299\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(222)\n",
    "resbegin = 700\n",
    "resend = 799\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(223)\n",
    "timebegin=1500\n",
    "timeend=2200\n",
    "resbegin = 200\n",
    "resend = 205\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check_raw[timebegin:timeend,resbegin:resend])\n",
    "\n",
    "plt.subplot(224)\n",
    "resbegin = 700\n",
    "resend = 705\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check_raw[timebegin:timeend,resbegin:resend])\n",
    "plt.savefig('checking2/checking_sorted_%s.png' % seed)\n",
    "\n",
    "fig= plt.figure(figsize=(15,12))    \n",
    "fig.suptitle(string2, fontsize=16)\n",
    "\n",
    "plt.subplot(221)\n",
    "timebegin=1500\n",
    "timeend=2200\n",
    "resbegin = 200\n",
    "resend = 299\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check2[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(222)\n",
    "resbegin = 700\n",
    "resend = 799\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check2[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(223)\n",
    "timebegin=1500\n",
    "timeend=2200\n",
    "resbegin = 200\n",
    "resend = 205\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check2_raw[timebegin:timeend,resbegin:resend])\n",
    "\n",
    "plt.subplot(224)\n",
    "resbegin = 700\n",
    "resend = 705\n",
    "begin=0\n",
    "end=1000\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check2_raw[timebegin:timeend,resbegin:resend])\n",
    "\n",
    "\n",
    "plt.savefig('checking2/checking_EDR_%s.png' % seed)\n",
    "plt.close('all')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "foo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "post_norm = False\n",
    "if post_norm:\n",
    "    print(\"doing post norm\")\n",
    "    for j in range(check_sum.shape[1]):\n",
    "        mean = check_sum[:,j].mean()\n",
    "        if abs(mean) > 0.1:\n",
    "            check_sum[:,j] = check_sum[:,j]/mean\n",
    "        else:\n",
    "            check_sum[:,j] = check_sum[:,j]     \n",
    "    for j in range(check2_sum.shape[1]):\n",
    "        mean = check2_sum[:,j].mean()\n",
    "        if abs(mean) > 0.1:\n",
    "            check2_sum[:,j] = check2_sum[:,j]/mean\n",
    "        else:\n",
    "            check2_sum[:,j] = check2_sum[:,j]      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#just get the sum of all the states\n",
    "for seed in range(40):\n",
    "  \n",
    "    string1='results_w_stops_check/data/sherlock-Sorted_states_seed%s.npy' % seed\n",
    "    string2='comparison/TrueTrueFalse/mod_in_True_mod_topTrue_Direct_False_%s.npy' % seed\n",
    "    check = np.load(string1) \n",
    "    check2 = np.load(string2) \n",
    "    check_raw = np.load(string1) \n",
    "    check2_raw = np.load(string2) \n",
    "\n",
    "    normalize_en_route = False\n",
    "    if normalize_en_route:    \n",
    "        for j in range(check.shape[1]):\n",
    "            mean = check[:,j].mean()\n",
    "            if abs(mean) > 0.01:\n",
    "                check[:,j] = check[:,j]/mean\n",
    "            else:\n",
    "                check[:,j] = check[:,j]     \n",
    "        for j in range(check2.shape[1]):\n",
    "            mean = check2[:,j].mean()\n",
    "            if abs(mean) > 0.01:\n",
    "                check2[:,j] = check2[:,j]/mean\n",
    "            else:\n",
    "                check2[:,j] = check2[:,j]         \n",
    "    \n",
    "    \n",
    "    if seed == 0:\n",
    "        check_sum = check\n",
    "        check2_sum = check2\n",
    "        check_raw_sum =  check_raw\n",
    "        check2_raw_sum =  check2_raw\n",
    "    else:\n",
    "        check_sum = check_sum + check\n",
    "        check2_sum = check2 + check2_sum\n",
    "        check_raw_sum =  check_raw + check_raw_sum\n",
    "        check2_raw_sum =  check2_raw + check2_raw_sum\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig= plt.figure(figsize=(15,12))    \n",
    "fig.suptitle(\"Normalized Average of 40 Sorted States\", fontsize=16)\n",
    "\n",
    "plt.subplot(221)\n",
    "timebegin=0\n",
    "timeend=2682\n",
    "resbegin = slowbegin\n",
    "resend = slowbegin+99\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check_sum[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(222)\n",
    "resbegin = fastbegin\n",
    "resend = fastbegin+99\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check_sum[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(223)\n",
    "resbegin = slowbegin\n",
    "resend = slowbegin+5\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check_raw_sum[timebegin:timeend,resbegin:resend])\n",
    "\n",
    "plt.subplot(224)\n",
    "resbegin = fastbegin\n",
    "resend = fastbegin+5\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check_raw_sum[timebegin:timeend,resbegin:resend])\n",
    "plt.savefig('checking2/checking_sorted_sum_%s.png' % seed)\n",
    "\n",
    "fig= plt.figure(figsize=(15,12))    \n",
    "fig.suptitle(\"Normalized Average of 40 EDR States\", fontsize=16)\n",
    "\n",
    "plt.subplot(221)\n",
    "resbegin = slowbegin\n",
    "resend = slowbegin+99\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check2_sum[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(222)\n",
    "resbegin = fastbegin\n",
    "resend = fastbegin+99\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "sns.heatmap(np.corrcoef(check2_sum[timebegin:timeend,resbegin:resend]),  cmap='viridis')\n",
    "\n",
    "plt.subplot(223)\n",
    "resbegin = slowbegin\n",
    "resend = slowbegin+5\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check2_raw_sum[timebegin:timeend,resbegin:resend])\n",
    "\n",
    "plt.subplot(224)\n",
    "resbegin = fastbegin\n",
    "resend = fastbegin+5\n",
    "plt.title('%s' % resbegin, fontsize=16)\n",
    "plt.plot(check2_raw_sum[timebegin:timeend,resbegin:resend])\n",
    "\n",
    "#plt.savefig('checking2/checking_EDR_sum_%s.png' % seed)\n"
   ]
  }
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