ZDHHC9_connectome_additional_analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import bct\n",
    "from medpy.metric.image import mutual_information\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "from scipy.stats import wilcoxon, ttest_rel, sem\n",
    "import seaborn as sns\n",
    "from statsmodels import robust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import rcParams  \n",
    "rcParams['font.family'] = 'serif'  \n",
    "rcParams['font.serif'] = ['Computer Modern Unicode']  \n",
    "rcParams['text.usetex'] = True  \n",
    "rcParams['axes.labelsize'] = 9  \n",
    "rcParams['xtick.labelsize'] = 9  \n",
    "rcParams['ytick.labelsize'] = 9  \n",
    "rcParams['legend.fontsize'] = 9  \n",
    "rcParams['figure.autolayout'] = True\n",
    "mm2inches = 0.039371\n",
    "single_column = 86*mm2inches\n",
    "double_column = 180*mm2inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_style({'axes.grid': False,\n",
    "               'font.family': [u'serif'],\n",
    "               'font.sans-serif': [u'Computer Modern Unicode'],})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outfolder = '/imaging/jb07/ZDHHC9/connectome_revisions/figures/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plot_adjacency_matrix(network, filename, ticks, title):\n",
    "    import matplotlib.pyplot as plt\n",
    "    from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "    rcParams['xtick.labelsize'] = 6 \n",
    "    rcParams['ytick.labelsize'] = 6  \n",
    "\n",
    "    ## Loading labels\n",
    "    labels = pd.read_csv('/home/jb07/joe_python/GitHub/ZDHHC9_connectome/DK_labels.txt', header=None)[0].values\n",
    "\n",
    "    plt.figure(figsize=(double_column/2, double_column/2), dpi=600)\n",
    "    im = plt.imshow(network, \n",
    "               cmap='afmhot',\n",
    "               interpolation='none',\n",
    "               vmin=0,\n",
    "               vmax=1)\n",
    "    \n",
    "    if ticks == True:\n",
    "        ax = plt.gca()\n",
    "        ax.set_yticks(np.arange(0,network.shape[0]))\n",
    "        ax.set_yticklabels(labels)\n",
    "        ax.yaxis.tick_right()\n",
    "        ax.set_xticks(np.arange(0,network.shape[0]))\n",
    "        ax.set_xticklabels(labels, rotation=90)\n",
    "        ax.grid('off')\n",
    "        rcParams['xtick.labelsize'] = 9\n",
    "        rcParams['ytick.labelsize'] = 9  \n",
    "    else: \n",
    "        plt.tick_params(axis='both',          \n",
    "                        which='both',      \n",
    "                        bottom='off', \n",
    "                        left='off',\n",
    "                        right='off',\n",
    "                        top='off',  \n",
    "                        labelleft='off',\n",
    "                        labelbottom='off')\n",
    "        \n",
    "    #divider = make_axes_locatable(ax)\n",
    "    #cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "    #cb = plt.colorbar(im, cax=cax)\n",
    "    #cb.ax.yaxis.set_label_position('right')\n",
    "    if title:\n",
    "        plt.title(title)\n",
    "    plt.tight_layout(pad=0, w_pad=1, h_pad=0)\n",
    "    plt.savefig(filename, dpi=600)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(folder, model, threshold, weight, subject): \n",
    "    import numpy as np\n",
    "    \n",
    "    matrix = folder + '_subject_id_' + subject + '/_model_' + model + '/_threshold_' + threshold + '/calc_matrix/mapflow/_calc_matrix0/' + subject + '_dwi_' + model + '_' + threshold + '_matrix' + weight + '.txt'\n",
    "    matrix = np.loadtxt(matrix)\n",
    "    \n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "folder = '/imaging/jb07/ZDHHC9/connectome_revisions/connectome/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subject_list = sorted([subject.split('_')[-1] for subject in os.listdir(folder) if re.search('subject', subject)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group differences using different defintions of edge weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def calculate_results(subject_list, threshold_range, model, weight, threshold_type, annotation, figure_name):\n",
    "    \n",
    "    d = []\n",
    "\n",
    "    for subject in subject_list:\n",
    "        for threshold in threshold_range:\n",
    "\n",
    "            # Loading the data\n",
    "            network = load_data(folder, model, str(0), weight, subject)\n",
    "\n",
    "            # thresholding\n",
    "            if threshold_type == 'proportional':\n",
    "                network = bct.threshold_proportional(network, threshold)\n",
    "            elif threshold_type == 'absolute':\n",
    "                network = bct.threshold_absolute(network, threshold)\n",
    "\n",
    "            # binarizing\n",
    "            network = bct.binarize(network)\n",
    "\n",
    "            # calculate average clustering\n",
    "            clustering_coefficient = np.mean(bct.clustering_coef_bu(network))\n",
    "            global_efficiency = bct.charpath(bct.distance_bin(network))[1]\n",
    "\n",
    "            # Setting group variable\n",
    "            group = 'ZDHHC9' if re.search('z', subject) else 'control'\n",
    "\n",
    "            d.append({'ID': subject,\n",
    "                      'group': group,\n",
    "                      'threshold': threshold,\n",
    "                      'clustering_coefficient': clustering_coefficient,\n",
    "                      'global_efficiency': global_efficiency})\n",
    "\n",
    "    df = pd.DataFrame(d)\n",
    "    \n",
    "    for measure in ['clustering_coefficient', 'global_efficiency']:\n",
    "        control_area = [np.cumsum(df[df['ID'] == subject][measure].values)[-1] for subject in subject_list if re.search('c', subject)]\n",
    "        ZDHHC9_area = [np.cumsum(df[df['ID'] == subject][measure].values)[-1] for subject in subject_list if re.search('z', subject)]\n",
    "        print measure + '\\n'\n",
    "        print('ZDHHC9 AUC: mean=' + str(round(np.mean(ZDHHC9_area), 2)) +\n",
    "             '; SE=' + str(round(sem(ZDHHC9_area), 3)))\n",
    "        print('ZDHHC9 AUC: median=' + str(round(np.median(ZDHHC9_area), 2)) +\n",
    "             '; mad=' + str(round(robust.mad(ZDHHC9_area), 3)))\n",
    "        print('control AUC: mean=' + str(round(np.mean(control_area), 2)) +\n",
    "             '; SE=' + str(round(sem(control_area), 3)))\n",
    "        print('control AUC: median=' + str(round(np.median(control_area), 2)) +\n",
    "             '; mad=' + str(round(robust.mad(control_area), 3)))\n",
    "        print('Wilcoxon: W=' + \n",
    "        str(ttest_rel(control_area, ZDHHC9_area)[0]) + '; p=' +\n",
    "        str(round(wilcoxon(control_area, ZDHHC9_area)[1], 3)))\n",
    "        print('Paired t-test: t(' + str(len(control_area) -1) + ')=' + \n",
    "        str(round(ttest_rel(control_area, ZDHHC9_area)[0], 2)) + '; p=' +\n",
    "        str(round(ttest_rel(control_area, ZDHHC9_area)[1], 3)) + '\\n')\n",
    "    \n",
    "\n",
    "        table_df = pd.DataFrame([{'ZDHHC9_mean': round(np.mean(ZDHHC9_area), 2),\n",
    "                              'ZDHHC9_SE': round(sem(ZDHHC9_area), 3), \n",
    "                              'control_mean': round(np.mean(control_area), 2),\n",
    "                              'control_SE': round(sem(control_area), 3),\n",
    "                              't': round(ttest_rel(control_area, ZDHHC9_area)[0], 2),\n",
    "                              'p': round(ttest_rel(control_area, ZDHHC9_area)[1], 3)}])\n",
    "        print(table_df[['ZDHHC9_mean', 'ZDHHC9_SE', 'control_mean', 'control_SE', 't', 'p']].to_latex(index=False))\n",
    "        \n",
    "    # Plotting\n",
    "    fig = plt.figure(figsize=(double_column/3, double_column/3), dpi=600)\n",
    "\n",
    "    for measure in ['clustering_coefficient', 'global_efficiency']:\n",
    "        for group in ['control', 'ZDHHC9']:\n",
    "\n",
    "            copy_df = df.copy()\n",
    "            copy_df = copy_df[df['group'] == group]\n",
    "            median = copy_df.groupby('threshold').median()[measure].values\n",
    "            mad = copy_df.groupby('threshold').agg(robust.mad)[measure].values\n",
    "            colour = '#56B4E9' if group == 'control' else '#D55E00'\n",
    "            marker = 'o' if measure == 'clustering_coefficient' else 'D'\n",
    "            linestyle = 'solid' if measure == 'clustering_coefficient' else 'dashed'\n",
    "\n",
    "            plt.errorbar(x = threshold_range,\n",
    "                         y = median,\n",
    "                         yerr = mad,\n",
    "                         marker = marker,\n",
    "                         markersize = 2,\n",
    "                         color=colour,\n",
    "                         linestyle=linestyle,\n",
    "                         linewidth=1)\n",
    "\n",
    "\n",
    "    plt.ylim(ymax = 1, ymin = 0)\n",
    "    #plt.tight_layout(w_pad=4., h_pad=4.)\n",
    "    \n",
    "    if annotation:\n",
    "        plt.legend(['control', r'\\textit{ZDHHC9}'], loc='best')\n",
    "        plt.ylabel(r'$C_{G}$/$L_{G}^-1$')\n",
    "        plt.xlabel(r'thresholded density')\n",
    "        \n",
    "    plt.savefig(figure_name, dpi=300)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Streamline density"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Streamline density with proportional thresholding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Streamline densities are absolute counts. These need to be thresholded to create binary graphs so that the graph metrics can be calculated. The binarization threshold may significantly influence the results. Therefore, a range of thresholds is tested. Further, thresholding may be proportional or to an absolute value. The proportional value conserves the overall density of the graph, but may level differences between cases and controls. In order to establish how robust differences are, both strategies are implemented and compared. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=11.41; SE=0.051\n",
      "ZDHHC9 AUC: median=11.37; mad=0.152\n",
      "control AUC: mean=11.81; SE=0.03\n",
      "control AUC: median=11.82; mad=0.072\n",
      "Wilcoxon: W=7.34731885805; p=0.018\n",
      "Paired t-test: t(6)=7.35; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "       11.41 &      0.051 &         11.81 &        0.03 &  7.35 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=11.27; SE=0.052\n",
      "ZDHHC9 AUC: median=11.26; mad=0.102\n",
      "control AUC: mean=11.64; SE=0.03\n",
      "control AUC: median=11.65; mad=0.092\n",
      "Wilcoxon: W=8.24644717602; p=0.018\n",
      "Paired t-test: t(6)=8.25; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "       11.27 &      0.052 &         11.64 &        0.03 &  8.25 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADkVJREFUeJzt3X10VPWdx/H3zGTyTJ4IpMhjEuFK1IUQXIuyRR60oK5r\ntcrZ9WG3Liyu655dPVu1ulI8tYUetds/di0qu1u7nh6lBSo96GkXlSogbQmgRxZ/QiCCPORxkgkz\nyUxm7t0/5oIJmYdkMhN+ZL6vczgJ9ze/+/tN5pN779w79xuHZVkIcbE5L/YEhAAJotCEBFFoQYIo\ntJCV6AGGYSwGGpRSjX2WVQJVQJVS6pX0TU9kirhbRMMwioFVUZpWKaXeAdoNw5idlpmJjBI3iEqp\nTuC3UZqK7a8dQFmqJyUyz2COER1RlnXYX0uA9tRNR2SqhMeIRI4F5wCN9rFhLfCSfexYqpQ6EK9z\nfX29nDHPYHV1ddE2ZAM40n1lpb6+3qqrq0vrGEJP9fX1gw6inL4RWpAgCi1IEIUWJIhCCxJEoQUJ\notCCBFFoQYIotCBBFFqQIAotSBCFFiSIQgsSRKEFCaLQggRRaEGCKLQgQRRakCAKLUgQhRYkiEIL\nEkShBQmi0ELc+5pj1bixl5cAdUqpDemdosgEibaIsWrczCESUKnykCRvwMQbMEdt+1AlqvQQq8bN\nduAVwAI2p2w2o4g3YLK1wce9NWMA8PeanPGHyXU5KHQ7eWaPB2/Q5IWvjaUox8muUz28/0UPACHT\n4khHiJBpcV9NAUunFQxY9+M72/EGTK4oc5Pl/PIe9usuy6F2XM6Ir3+4EgWxb42bhj7L71ZK3W0Y\nxmLDMGYnKjuSabwBk3/d3U6T3+STtiCt3SZBEyryXdw0NY9rv5IzoE91cRb5WXnAudD68PdCVZE7\n6hjZTgfZLgeLJueS7/4yCBX5rqiPT/f6hytuyRHDMKYB1UClUmqDfWw4B/AQKc5UrJSKu0XMxJIj\nkSB6aPKHeWxuEdUl2RRnO3A4HP0eA8Tcmlzq7TC0kiNxt4h2cc7GPv8/BhwbzIpHu3gvRFGOk2ev\nK2Xl9lbqKnKj9v/h3g7ausO8uLg8avu6P3bQ3qN7u8n6JdHbh0qKMCXBGzB5Zo+Hth6TyYUuHq4t\n7rfL6gyYrN7dTmu3yeIpeZztNfH3WvhCFj77+46AiUX0mn8QOfjmEmh/45bxMR6Rwi2iGOhs0OTt\nRj+nfWHCFiyYlINpWrz/RTeftvdyqL0XT8DEAYQsmFDgotDtJt/toMDtoMDtpMDtID/LyV//poXX\n47yQy7c1a9+eKhLEOH7x2VnumlFId8ikI2Cy61QPWxv8TCrMorS3FX8INh9xsvFwN1eUuplZ5ubG\nqXlMLcrC7Gpla4OPZZXRX8iQt4Xll/lijn2ptw+VBPEClmXxeVeIH/3uCF1Bi18fHYdlWZTmOinI\nggW+XYxv+4KKD74PZpjyRasoGjOG8rrHz68j5G3h83ULudLbTPPCB3HmFvYbw+w5S1f9Zq70NhP6\nwUGyisadH7vtrecwe87i2fEyMwNnaf76ozhzCym/5THt1z8cEkRba3eYXad6+OBkDwUn/8Adux8B\nYMZT7zGh4is4HA4sy+L4cy+CO49uRxAz4CEv2EH4bDDqOsNdLYR97Vihnn7LzYD/fPuAPmdbI+1m\nGCvgi9pf5/UnzbKstP7bu3evpauzwbC1/XO/tWZ3u/W3b5+2Xt/0c2vX6kXWH1aWWbserrJ2/MPl\nVnvLmah9ezubrYZn5sVcd6a3W5Zl2a/9oHJyyb9rPnccF8vpLc/guOkpTvvCnPGFI1/9Yc74QnS1\nN1PY28H95nu497xMm3scrXV/R+1XF+F/6U66uzqZ9tVbsEJBrHBv5F8oCOFefJ/uwPR3UnDVTVHH\n7W7Yg9ntHfXtNa/Gzk9GvWv+5WH/+SCGulppbjzIiSMH6ThxiPKPXyPszOLjEyZZY6eSO76KCROq\nmD1lPAXBNs5suYOcoJfj5dfQde0aZvn3M/mPz2J9+DS54V6yfR5yLqvB4XLjyMrG4XKDy43D5SZw\n6hBBfydjlz4adV5nXvsngt3eUd+eKpfkFtGyLIKnDvH5C8vwdbTinDQLmhRmOERb8XTMcQYFE6/A\n9fv/xtHrY+oN99HbeozelmMEW46BGSIwroYuT0vkXF5ZJVXTppE/fT55M+aTXXE5DoeD5i1rGP+N\nNTHnIe3x24eyRdQ+iOd2vWbAh/fgu5zZu43gJ2/Ta5qYwW6CrnwOL/sJE6tqMKZMYvKYrPOX0kLe\nFj77x/EY6zvp3PkzShaswJmdS9jXQW9rI2r9A5wMZHPzj/ak6umKPkbNrnnnI1cxsaeTPRUGeV/8\nnlOls2iauoSs2zcy8fKreauxG0/TCTbcNvCvsIW8LTQ+ez04s/js0Sn4qm/EXX0bs6qm4CoowVUw\nm1lr97F/67aL8MzEhbQLYtDn5ZN3fo5/908p9ih63EV46h4gf8XrLKgo6/dJkIVT8lm+LTxgHYHT\niqaNTxBsOUovWfxqyZtkTfoT7i0fM+Cxf3PbLWl9PmJwLnoQm7esYdxfrOb4gfc4/s5/Uqi20Tbx\negoXPcr0BbfzXz9+iof+/K+GtM5g+wlOjZ3D/9z8JEHLxQPX1TBvQvQPHwg9XNQg7nzkaty+Jhrf\nfZUeRy6BuvuovOc5ai+beP4x477x3Zj9Y11mypu5mN/552J5Q4Q6mriyLDst8xepM+JB9J/8lMPv\n/wL//i0Uew7Rk12C8/7XmDdnHm7XwI9UxTpHGPK20PDMtUy13ISqd/a7zJTldPD4NSV4AyYrt5sp\n+QSxSK8RCaL/WD3Hdv6S7n1bMLs7OV11K8VLv8fMa5dw6qFcaq65fkjrM4M9nNz4JMG2E/TkTaAr\naFIa5XFFOU6+OT0/NU9CpNWIBPHAC3dxovJWSm7/CXPmzudPC778ePqBP/sONUNYV8/xjzmx/h5O\n5lezcen7hBwunh8T+6J7vKsuQh8jEsSpaxXXjYl+b8QNK34wqHVYlkXbb/6NM1vXsn3Wdymcdz9P\nV+fzzzvaZdc7CoxIECfGCOFQOBwO6s8E+HDZ2/zl9bMwyiLrlF3v6HDRT9/E42ltAqC0vAKAuuWP\nsTjH2e/2Rtn1jg7a7tM8rU383/du4JM1888HsjzP1S+EYvRIquSI3baSyO2kz6d6UmcPvkN3IHLF\nJHW1BITOkio5YhjGt4E3Uh1CM+DjxKsP89l/3M/+tjA1T+/gpwvfPL9rFqNXoiDGKjlSDcy1A5kS\n/sO7UU/Npr6xmY++9SE3LVpKaXkFN189JVVDCI0lCmLfkiN9Cy55lFLvAkcvKM40JCFvCyFvCy1b\nv0/Dj+/gzZlPMnbFz/jWNVNwuyLHgvJmJDMketf8kmEYi4FSpdQB+5ix1l5+J5FSJJuSGTjkbeHo\n2oUAtC1czdZbd/DQ/MuZPEbrN/IiTYZTcqRxYI/B6wqatPjDmOEQM2Z/jdVl48nL0vZNvEizi7b5\ncY0Zx1vLfoWnvZl1xRLCTDeir37P8Y/w7HoNiHwg4fGF02krNuQSnRiZIFqWRft7L3N47RI2NfRw\n7j4Z+XSMOGdEds0NL95Ly+F9fHDrr7lnwdx+dQLlXbGAEQriR01+cv/+A/5lxlicDrlEJwYakSBe\nteLfMaakpqCjGJ1G5BhxQsWEkRhGXMJGJIjyrlgkIgkRWpAgCi1IEIUWJIhCCxJEoQUJotCCBFFo\nQYIotCBBFFqQIAotSBCFFiSIQgtxg2gYRqX9V+pXxmhfl55piUyTVKUHAPs20/T+bQyRMZKq9GDf\n37yX2H9XWoghSbbSQyUwF6gyDKMoHRMTmSWpSg9Kqc3298UJ+gsxKElXerC//3oa5yYyiJy+EVqQ\nIAotSBCFFiSIQgsSRKEFCaLQggRRaEGCKLQgQRRakCAKLUgQhRYkiEILEkShBQmi0IIEUWhBgii0\nIEEUWpAgCi1IEIUWJIhCCxJEoYW4d/HZt4xWAVVKqVf6LK8lcsP9jUqpJ9I7RZEJki05Umcvb7iw\nFIkQyUiq5IhSaoP97Ryl1IF0TExklmRLjmAYxp3AD9MxKZF5kik5MsduWwV4DMNYqZTypnWWYtRL\nuuQIsCltsxIZR07fCC1IEIUWJIhCCxJEoQUJotCCBFFoQYIotCBBFFqQIAotSBCFFiSIQgsSRKEF\nCaLQggRRaEGCKLQgQRRakCAKLUgQhRYkiEILEkShBQmi0EKyJUeiLhciWcmWHIm1XIikJLrBPmrJ\nkTjLo6qvrx/6zERGSRTEviVHGgaxfIC6ujpHclMTmcRhWVbMRsMwpgHVQKVSaoN9bFgL7Ou7fCQm\nKka3uEEUYqTI6RuhBQmi0IIEUWgh0btmIQbNrqXZYJczPLdsUBc/UhrE4RZ/T1f/Pu3rhjH+KmBv\nvLME8cY3DGMlUKyUen6IY98J3Ejk+a9XSr07xP6VRE6z1SUzd/u5VwFl8YJkGEYxkZ/RYxc0rVJK\nPWEYRolhGLNjlbpO9a55uMXfY/W3gL3212TGP/fbmmx/Syn14CBOVUXtbxjGt4E3YoUwwdj/q5R6\nEHg9VggT9J9DJEjt0bsl7L9KKbXJfh7TYnVWSnUCv43SNKiLH6kO4nCLv8fqf4DIDzOp8e3f9r1A\nopPrsX5oxwzDqLUDlUz/amBugv6xnrvXnv++JMfeDiy3/yXT/0if7xO9BtF+vjHrsPeV6iAOt/h7\n1P6GYdQqpfYDRxNsUWONXwnMBaoMwyhKov/d9vgdSY7vsbdm8eYf7wX7Zt/jriTmfjfwcpJzf8Xe\nm1QR+WWOpwq7xrphGJWGYdzBBXXYY3VM6QntGFdi+hV/B2IWf4/T3wI6gSVKqe8McfxapdRm+/v1\nwF1JjN8AjLXXFXP3muBKVJ29PNYxYry5r433vBPM3UNkS1WslNqcRH+LSEhL4vUfLrmyIrQg5xGF\nFiSIQgsSRKEFCaLQggRRaEGCKLTw/1AGly+Dm2yuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e442c510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0.2, 1., 0.05)\n",
    "calculate_results(subject_list, threshold_range, 'CSD', '', 'proportional', False, outfolder + 'streamline_density_proportional.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph shows the group median of the average clustering coefficient at each density threshold for the *ZDHHC9* group (orange) and the control group (blue). The error bars show the bootstrapped 95% confidence interval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. Streamline density differences with absolute thresholding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=2.85; SE=0.12\n",
      "ZDHHC9 AUC: median=2.83; mad=0.257\n",
      "control AUC: mean=3.4; SE=0.13\n",
      "control AUC: median=3.48; mad=0.178\n",
      "Wilcoxon: W=3.07225812938; p=0.018\n",
      "Paired t-test: t(6)=3.07; p=0.022\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=1.78; SE=0.072\n",
      "ZDHHC9 AUC: median=1.73; mad=0.124\n",
      "control AUC: mean=2.07; SE=0.066\n",
      "control AUC: median=2.08; mad=0.184\n",
      "Wilcoxon: W=4.11486012569; p=0.018\n",
      "Paired t-test: t(6)=4.11; p=0.006\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e3eced10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0, 500, 25)\n",
    "df = calculate_results(subject_list, threshold_range, 'CSD', '', 'absolute', False, outfolder + 'streamline_density_absolute.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Streamline density normalized by ROI size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=11.3; SE=0.037\n",
      "ZDHHC9 AUC: median=11.31; mad=0.144\n",
      "control AUC: mean=11.61; SE=0.031\n",
      "control AUC: median=11.58; mad=0.044\n",
      "Wilcoxon: W=5.88661149806; p=0.018\n",
      "Paired t-test: t(6)=5.89; p=0.001\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &      p \\\\\n",
      "\\midrule\n",
      "        11.3 &      0.037 &         11.61 &       0.031 &  5.89 &  0.001 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=11.36; SE=0.05\n",
      "ZDHHC9 AUC: median=11.36; mad=0.143\n",
      "control AUC: mean=11.78; SE=0.03\n",
      "control AUC: median=11.79; mad=0.068\n",
      "Wilcoxon: W=8.41394481985; p=0.018\n",
      "Paired t-test: t(6)=8.41; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "       11.36 &       0.05 &         11.78 &        0.03 &  8.41 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVjC6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e4421790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0.2, 1., 0.05)\n",
    "df = calculate_results(subject_list, threshold_range, 'CSD', '_ROI_normalized', 'proportional', False, outfolder + 'ROI_normalized.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Streamline density normalized by streamline length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=9.47; SE=0.086\n",
      "ZDHHC9 AUC: median=9.41; mad=0.219\n",
      "control AUC: mean=10.27; SE=0.102\n",
      "control AUC: median=10.25; mad=0.29\n",
      "Wilcoxon: W=9.14413576348; p=0.018\n",
      "Paired t-test: t(6)=9.14; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "        9.47 &      0.086 &         10.27 &       0.102 &  9.14 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=11.52; SE=0.039\n",
      "ZDHHC9 AUC: median=11.54; mad=0.122\n",
      "control AUC: mean=11.91; SE=0.037\n",
      "control AUC: median=11.93; mad=0.14\n",
      "Wilcoxon: W=16.0379654273; p=0.018\n",
      "Paired t-test: t(6)=16.04; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &      t &  p \\\\\n",
      "\\midrule\n",
      "       11.52 &      0.039 &         11.91 &       0.037 &  16.04 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e43f0050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0.2, 1., 0.05)\n",
    "df = calculate_results(subject_list, threshold_range, 'CSD', '_length_normalized', 'proportional', True, outfolder + 'length_normalized.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Influence of crossing fibres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(folder, model, threshold, weight, subject): \n",
    "    import numpy as np\n",
    "    \n",
    "    if weight == '_FA':\n",
    "        weight_switch = str(0)\n",
    "    elif weight == '_RD':\n",
    "        weight_switch = str(1)\n",
    "    elif weight == '_GFA':\n",
    "        weight_switch = str(2)\n",
    "        \n",
    "    matrix = folder + '_subject_id_' + subject + '/_model_' + model + '/_threshold_' + threshold + '/calc_matrix/mapflow/_calc_matrix' + weight_switch + '/' + subject + weight + '_matrix.txt'\n",
    "    matrix = np.loadtxt(matrix)\n",
    "    \n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def calculate_results(subject_list, threshold_range, model, weight, annotation, figure_name):\n",
    "    d = []\n",
    "    threshold_range = np.arange(0,100,10)\n",
    "\n",
    "    for subject in subject_list:\n",
    "        for threshold in threshold_range:\n",
    "\n",
    "            # Loading the data\n",
    "            network = load_data(folder, model, str(threshold), '_FA', subject)\n",
    "            network[np.isnan(network)] = 0 # removing NaN\n",
    "\n",
    "            # Calculating graph metrics\n",
    "            clustering_coefficient = np.mean(bct.clustering_coef_wu(network))\n",
    "            global_efficiency = bct.charpath(bct.distance_wei(bct.weight_conversion(network, 'lengths'))[0])[1]\n",
    "\n",
    "            # Setting group variable\n",
    "            group = 'ZDHHC9' if re.search('z', subject) else 'control'\n",
    "\n",
    "            # Collecting the data\n",
    "            d.append({'ID':subject, \n",
    "                      'threshold': threshold,\n",
    "                      'group': group,\n",
    "                      'clustering_coefficient': clustering_coefficient,\n",
    "                      'global_efficiency': global_efficiency})\n",
    "\n",
    "    df = pd.DataFrame(d)\n",
    "\n",
    "    for measure in ['clustering_coefficient', 'global_efficiency']:\n",
    "        control_area = [np.cumsum(df[df['ID'] == subject][measure].values)[-1] for subject in subject_list if re.search('c', subject)]\n",
    "        ZDHHC9_area = [np.cumsum(df[df['ID'] == subject][measure].values)[-1] for subject in subject_list if re.search('z', subject)]\n",
    "        print measure + '\\n'\n",
    "        print('ZDHHC9 AUC: mean=' + str(round(np.mean(ZDHHC9_area), 2)) +\n",
    "             '; SE=' + str(round(sem(ZDHHC9_area), 3)))\n",
    "        print('ZDHHC9 AUC: median=' + str(round(np.median(ZDHHC9_area), 2)) +\n",
    "             '; mad=' + str(round(robust.mad(ZDHHC9_area), 3)))\n",
    "        print('control AUC: mean=' + str(round(np.mean(control_area), 2)) +\n",
    "             '; SE=' + str(round(sem(control_area), 3)))\n",
    "        print('control AUC: median=' + str(round(np.median(control_area), 2)) +\n",
    "             '; mad=' + str(round(robust.mad(control_area), 3)))\n",
    "        print('Wilcoxon: W=' + \n",
    "        str(wilcoxon(control_area, ZDHHC9_area)[0]) + '; p=' +\n",
    "        str(round(wilcoxon(control_area, ZDHHC9_area)[1], 3)))\n",
    "        print('Paired t-test: t(' + str(len(control_area) -1) + ')=' + \n",
    "        str(round(ttest_rel(control_area, ZDHHC9_area)[0], 2)) + '; p=' +\n",
    "        str(round(ttest_rel(control_area, ZDHHC9_area)[1], 3)) + '\\n')\n",
    "        \n",
    "        table_df = pd.DataFrame([{'ZDHHC9_mean': round(np.mean(ZDHHC9_area), 2),\n",
    "                              'ZDHHC9_SE': round(sem(ZDHHC9_area), 3), \n",
    "                              'control_mean': round(np.mean(control_area), 2),\n",
    "                              'control_SE': round(sem(control_area), 3),\n",
    "                              't': round(ttest_rel(control_area, ZDHHC9_area)[0], 2),\n",
    "                              'p': round(ttest_rel(control_area, ZDHHC9_area)[1], 3)}])\n",
    "        print(table_df[['ZDHHC9_mean', 'ZDHHC9_SE', 'control_mean', 'control_SE', 't', 'p']].to_latex(index=False))\n",
    "\n",
    "    # Plotting\n",
    "    fig = plt.figure(figsize=(double_column/3, double_column/3), dpi=600)\n",
    "\n",
    "    for measure in ['clustering_coefficient', 'global_efficiency']:\n",
    "        for group in ['control', 'ZDHHC9']:\n",
    "\n",
    "            copy_df = df.copy()\n",
    "            copy_df = copy_df[df['group'] == group]\n",
    "            median = copy_df.groupby('threshold').median()[measure].values\n",
    "            mad = copy_df.groupby('threshold').agg(robust.mad)[measure].values\n",
    "            colour = '#56B4E9' if group == 'control' else '#D55E00'\n",
    "            marker = 'o' if measure == 'clustering_coefficient' else 'D'\n",
    "            linestyle = 'solid' if measure == 'clustering_coefficient' else 'dashed'\n",
    "\n",
    "            plt.errorbar(x = threshold_range,\n",
    "                         y = median,\n",
    "                         yerr = mad,\n",
    "                         marker = marker,\n",
    "                         markersize = 2,\n",
    "                         color=colour,\n",
    "                         linestyle=linestyle,\n",
    "                         linewidth=1)\n",
    "\n",
    "\n",
    "    plt.ylim(ymax = 0.5, ymin = 0)\n",
    "\n",
    "    if annotation:\n",
    "        plt.legend(['control', r'\\textit{ZDHHC9}'], loc='best')\n",
    "\n",
    "    plt.savefig(figure_name, dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=0.81; SE=0.044\n",
      "ZDHHC9 AUC: median=0.81; mad=0.098\n",
      "control AUC: mean=1.13; SE=0.041\n",
      "control AUC: median=1.13; mad=0.137\n",
      "Wilcoxon: W=0.0; p=0.018\n",
      "Paired t-test: t(6)=10.81; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &      t &  p \\\\\n",
      "\\midrule\n",
      "        0.81 &      0.044 &          1.13 &       0.041 &  10.81 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=0.52; SE=0.023\n",
      "ZDHHC9 AUC: median=0.52; mad=0.03\n",
      "control AUC: mean=0.8; SE=0.028\n",
      "control AUC: median=0.8; mad=0.094\n",
      "Wilcoxon: W=0.0; p=0.018\n",
      "Paired t-test: t(6)=8.47; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "        0.52 &      0.023 &           0.8 &       0.028 &  8.47 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTSCEKNAEQogCTSCEKNAE/w+WIqD6C6chDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e3f9f650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0,100,10)\n",
    "calculate_results(subject_list, threshold_range, 'CSD', '_FA', False, outfolder + 'FA_weighted.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=0.81; SE=0.044\n",
      "ZDHHC9 AUC: median=0.81; mad=0.098\n",
      "control AUC: mean=1.13; SE=0.041\n",
      "control AUC: median=1.13; mad=0.137\n",
      "Wilcoxon: W=0.0; p=0.018\n",
      "Paired t-test: t(6)=10.81; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &      t &  p \\\\\n",
      "\\midrule\n",
      "        0.81 &      0.044 &          1.13 &       0.041 &  10.81 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=0.52; SE=0.023\n",
      "ZDHHC9 AUC: median=0.52; mad=0.03\n",
      "control AUC: mean=0.8; SE=0.028\n",
      "control AUC: median=0.8; mad=0.094\n",
      "Wilcoxon: W=0.0; p=0.018\n",
      "Paired t-test: t(6)=8.47; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "        0.52 &      0.023 &           0.8 &       0.028 &  8.47 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTSCEKNAEQogCTSCEKNAE/w+WIqD6C6chDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88da763ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0,100,10)\n",
    "calculate_results(subject_list, threshold_range, 'CSD', '_GFA', False, outfolder + 'GFA_weighted.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Influence of diffusion model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adjacency matrices for both models thresholded at the same cut-off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(folder, model, threshold, weight, subject): \n",
    "    import numpy as np\n",
    "    \n",
    "    if weight == '_FA':\n",
    "        weight_switch = str(0)\n",
    "    elif weight == '_RD':\n",
    "        weight_switch = str(1)\n",
    "    elif weight == '_GFA':\n",
    "        weight_switch = str(2)\n",
    "        \n",
    "    matrix = folder + '_subject_id_' + subject + '/_model_' + model + '/_threshold_' + threshold + '/calc_matrix/mapflow/_calc_matrix' + weight_switch + '/' + subject + weight + '_matrix.txt'\n",
    "    matrix = np.loadtxt(matrix)\n",
    "    \n",
    "    # Exclude label entries \n",
    "    entries_to_exclude = [1, 2, 3, 4, 10, 11, 15, 17, 18, 19, 20, 21, 22, 23, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]\n",
    "    matrix = np.delete(matrix,entries_to_exclude, axis=0)\n",
    "    matrix = np.delete(matrix,entries_to_exclude, axis=1)\n",
    "    \n",
    "    # Removing NaN values\n",
    "    matrix[np.isnan(matrix)] = 0\n",
    "    \n",
    "    return matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOoAAAD3CAYAAAAT3MgLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e4613e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOoAAAD3CAYAAAAT3MgLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAFsxJREFUeJztnXuUFdWVxnfrqGuNJi2gMCpKN+IciBqBvji+RQHfCCo+\niNH4GDRrjGNWJsRXZjIunWgkmQyZmIUxxlcc4wOQIUTAmLREjdHbikEdjraAUUfFOKCjUUfxzh/n\nnK7d3VV1qupW3Vu7+/ut1WvvPnVu1enHvt++p07t01Kr1QgAUG62avYAAAB+EKgACACBCoAAEKgA\nCACBCoAAEKgACACBCoAA/qLZAxgoKKW6iWgBEa0jop2I6FoiaieiFiL6jvVvtN9PJaIfa62ftq9t\nZX0u1VqvVkrNJaILiegCrfWvk/RhYznF9ltnm1qIqKa1XmSPbSaiiUR0o9b63Yifh59jsz3HNK31\nZUqpOUT0Njv3UK31TUqpdiK6lIhG299Fi/U3a61vyvSLBUSEQM0FpdQEIpqltV5tv19JREe6IFBK\nbSYTFIvsSxYqpapEVCEi0lq/Y/tc585BJjjudQGYpI+91gIiWqm1/i5ru4eIvmGDb4jWeqFSiojo\ndCLqF0A2EFv7nKOdiKYopcaTDXrWfoEd43ql1INENJX9rKSUmqOUmqu1npfuNwscSH3zocaCdAER\nLdBaP8OOTyWiX/V5zY59vp+qtf4N+34aET2Ypo9S6gIi2sSDxLJSa72BjCLfw67f2vcHccrNg5TI\nBCER3U1EQ8m+wbD2vuPshVXTy+P6gHigqDnAgnQuhQdKa0iK2e4cGxytSqmTbVMLEU3RWp+Wpg8R\nXUdEo0LG9xPr1tg4plEQtJwKEb0U8XN+147lMqXUi2TefO7TWj8U1r8P/6OUGs+yAZACKGpOKKVm\nEVFFa315n/Yp1EdNlVITiegp1lQh8w+/yAb5Ogo+XybqY1PQt7XW/xszzKds31YiOpWnzIy+St8P\nrfVRZAL9KSK6zr5BgQJBoOaADbw5WuvT7fensMNhKewFZCabHKf26VOh/qmyr8+O1D+4SSk1RSn1\nWfvttUqpI8mk4mFqSvaco8MO2HONJyLSWm/QWt+ktZ5E5rOujyFQ0+wgUOtEKbUjEf2YTCA52pnf\n6/OpVdi+6XGFiKrs+7Dgju1jZ5CH9hlbK9m0Wyk1QWu92qpohczsbD+01u+QCehrw44T0Z528ozz\nZERfN4451PuNCaQEn1Hrp0pGnaYqpYaRUctLiXr+QdvtsRYygVTj6THrM5qIVls1nkJEP0/Tx3Kq\nUuo6InqCiN4hNjtLRKfb9HgoEV0bdVuGyHwWVUqdrJT6OhmVbiHz5vKQvXaHPVeLHZf7edvJvIG0\n28/S7vbMpr6TUyAdLXgeFYDyg9QXAAEgUAEQAAIVAAHETiZ1dXXhAywADaSjo6MlrN0761upmNVi\nerb5/oMPgmMt7JTd3cY+syZo++dvGXvZVUHbztsH/tz3jb19z6DtCrsmhi+vedQ3SAu/U/8Deydw\nxIig7YUXA3/UHsbutBMb287GbtwYtL35ZuBPnWbsY2xABx1s7Jo/BG0r2I2VE08wdk/2M6551tg/\nvRW0jRxp7LbbBm2f79i6x1/95BYiIhrP/o5vbzTvo0uXBq9pawv8ffc19pNPgjb3s120OGgbF7hk\n/4w0dZugbdHHxnaxfiOZb39t9DTF424uL2Rt7M9D7FfdD76iImzB8A07B/577xl7Kftf5Te2H7P2\nddZ2rLXn78Pajg38Gfai/H/s68cYuw37XXWwv0VaqtVq5LHYWd+urq7aDt8zgTruLtO2pCM4PoP9\n5Y62dgV7/fU7GDv3vfgB/jXzX4jvmhvumnuwts9Z+4OI17RZuyHFdcZYy+KPnk/xeod7U9zy9Ek9\nbVuNX9y/H/O33GTC6TsXv9rTdvmHGS4OGkK1Wo1UVHxGBUAACFQABOD9jPrBn42936a8PN1duF/g\nf80+1MUTaV/K62hUuht2TX7tvotr+zLW2g0pruM+813M2rKkvu4Tyoks3d3d2ldYP7528Zg5JuVd\nmeF6oFxAUQEQgFdRt7KhPNMqKVfRk9mj0WzSbcCyPOYYX6UeNvu5hPlOCfkMIpssj2Ut818JOX4U\n8zclPCcoP1BUAASAQAVAAN7U1y1kcCnV1yLSXXbvflDiu9n/x5C2sNTVxxbP8d3ZzffnPs5wAVBK\noKgACACBCoAAvKnvajsduSK+G2gQ6z3Hr0S6OyCBogIgAK+iXmWfgPmMrXjzjfeLHE5zmWxtZxPH\n4GPt7MAfe1d83zOt3ZW1oVS9TKCoAAgAgQqAALypr3vo+/qiR9JgdrGWPzzc2YRxpMWX7nLuLG4Y\noMFAUQEQgFdRe0qnDLBJpNf9XQAoDVBUAASAQAVAAN7Ud+4AS3kBkAgUFQABeBX1NluP9hpbb/fF\n6K650HmEsZN/U/CFQEM4kPmuOMiCAq6zO/NdadbQbdNTsGXVlB5/68P6b6p+gLWPdZ/d01Zb9bDp\nf97LdV69N1BUAASAQAVAAN7U90qbP7wa360uXLpLRHSETXl/yva0yDmLAA3kdxF+HMczf1nC1/iq\nZezF/KQf39pYunu/rV43k5XycJtGtI25vactbCMCvj1L1n9lKCoAAkCgAiAAb+rrZLvI1PcINsN7\ns70g0t3Biy/d/fZ2gX/FR8nO+W6GcezP/Jkh1etcHblzWdtV/bv1Snf3tva5lGOBogIgAK+iJt2b\ntB74fjVQUuAjqYpy4vZejWKh5/gXpxs7K2RPVH5fl090pVVSBxQVAAEgUAEQgDf1BWCwwuasKCzb\nroSkvK5ySJZdEOKAogIgACgqABFkmLMqrHIIFBUAASBQARAAAhUAASBQARAAAhUAAXhnfVutfafg\ngQCQlGbudD+C+VmWJWYFigqAALyKOr/d2HN8O+iCQnn5fGNH3dz/2NHMf5b57l0471UyzabRKspp\npIpyoKgACACBCoAAvKnvLn9lHaS+TaXNprzdZwVtY+4wdgXr18L8V+YYO/KmIkcGGgEUFQABeBVV\nF10av0TMtPb+Os9zJfP/pc5zOVwVDKeivn5EUNKBBBQVAAEgUAEQgL9cqK3StNefjM2SCe/C/LLt\n9H0K833FrJKSV7oLgAOKCoAAEKgACMCb+u60k7F72O+zpL5lS3eJgpSXp7vzhxh7z6agjdc1Pjik\nLW/mMD+vSdtTmb+rtfMj+tpfAW2KOA6aAxQVAAF4FXX4cGM/Z7/vv+9yNJOt7UzxmiKZyXynpP82\nJGi7JERGdmO+U1J+nrB7rucw/9bEozP4VHQu8+clPOffHR74b7xh7Hwd3tf9Cg5lbb9NeB1QHFBU\nAASAQAVAAN7Ud+NGY/89w8k7M7ymSMLS1Ps8syavJTwPZxIrQXBrHQ9P8rTbjSPLZN65Dwf+hoSv\nQbpbLqCoAAjAq6hu8mGg8gjznYKFqWgaLsqpBEEWNecoa9tY24asgwFNBYoKgAAQqAAIwJv6Tj3K\n2LY1xo5lx5YXMKBGczDzs6w4+rW9R3nkw/H9krIv89d4+nZY2xVx3N0qPYK1HW/tspTjAs0FigqA\nABCoAAjAm/o+ZvPBDdTbDhR4upulFMsUm/I+Mi1oO+TB9OOYP8zYS96O77c786NSXof7eRakHw4o\nGVBUAATgVdSD3GzL4wWPpAT4lDRs4sgVE8uiohyfkjp41Xu3cD5qFVG9RdpAeYCiAiAABCoAAvCm\nvs/+IfrYBOY/ncNgyk5eE0dhuEmiNBs6+RbOL9rP2JOfie/HN5laEdkLNBMoKgAC8Crq8hjFkKai\nvgr251hbYY+pfYUtsM9r4iiMpErKb8/4XuNTUgdUtPxAUQEQAAIVAAF4U98T7Sru/7CruF8qcjQF\n46tgf6uzTdzS2rerwEDbPRwkA4oKgAC8ijpmjLHb2e8vZsf4yhe80+cDV1H+CN4ka3nBcN/v/KvW\nbmZtt2YbFmgyUFQABIBABUAA3tR3zbPGPk+9LVHve3ogfx6N8OO4ny0Xmxlzo1sxP6JoPigRUFQA\nBIBABUAA3tT3rY3Rx3ZkPmZ9y8HH/5esH9JdWUBRARCAV1FHjrROSO1KXznLgYqrb0QUVGZIs1i+\nSE57LvBXn2Ts+MXNGQvIDygqAAJAoAIgAG/qu+22jRhG85jDfN9u366KfVghsqh01y2yD1tgXwQ1\n5k+wKe+dewVtQ4Yae9zvGzQgkAtQVAAE4FXUz1e2Ns6SLUWPpSmEqWjYBsJE2SbPnJLWu8eNY8O5\ngd92S3zfw6w9M8vux6BUQFEBEAACFQABeFPf1U/ITHlfPt/YUTenf229O46HMYn59aS+vnS3wvxl\nc43dYV4dFwSlAIoKgAAQqAAIwJv6TphkYnnL1TOIiOhEth5tLev3qbXrWdva2caOvaueIWajzaa8\n3WcFbWPuiH+NzRSJT5KGbbTkWy4YdnxR/KVDSTPD6/hL5n9q/yjLWN6983Bj90+x5bibBS/iI0GZ\nSboTxAzmLyloLFBUAATQUqvVIg92dXXVRt1ipieG32DauFr8scCBlZEOa/kGwr6tD6XSzvz1kb0G\nDxdae2OB16hWq9TR0dESdgyKCoAAEKgACMA7mfSfS411CbKUdNdtJZjnBkhdIW1lS3nz2goT6W5v\nwlJeuwE9PRxyLG+gqAAIwKuobaOsY6V0NDt2FPNHbmPsNz/OZVx185y/S9NYYmXvI1bf6DTPgN1E\nFr/94tQ8SkXdhsv1bhM5WG/P+HAPPUBRAQBEhEAFQAT+51H3M3bLWabK2TEXvNpzbBPr93xIynum\ntXdmHV0duHegV1kJh5G+Eg4hhFWUn8na3MqlRfsFbWE7fX+V+TNsfspvmIUVIpvMjrsF9p98GrS1\nfs/YsHSXiOhQm/Kez45neEZh0Ka8jx8b+Ac80P+4m6isTg/aKkuLGQsUFQABIFABEIA39f3EprTX\n/71JedNMIO6aZUQ54e73+tLd05j/ZXtj7Dw2jceX0h1h7YKQ84Slu5zNIW1hhch+xgqRfZE9HeCe\nKf3F/vHXOZT9gc6z9qfsuHtIwfeAAghPdzlPWFtUusuBogIgAK+ibrR7z1z+YfqTSygswFX/zTeM\n3cDauH98Hde51XPcqeuwofHHh++c7DxEgZK+mOJRv32svWFK0Hb4Q/GvAcUDRQVAAAhUACRQq9Ui\nv6rVau0QotohJqPCV05fyn41exz4KtdXtVqtRcUiFBUAAXgnk8ZZ+0jBA5HO0cz3PVqHTYRBWqCo\nAAgAgQqAALyp70DfX2gI8zdF9vKTZyWJItnb2jI/rwv6A0UFQAAIVAAE4E19p9kSK50lKbGSNzzd\nlVaj1z0wsD7Fa35klwZiWaAsoKgACMCrqIsHqJKGIUFJ+W7oYUoatVu6A0oqEygqAAJAoAIgAG/q\nW23EKEBifIXGJBcia7X2HU+/i5h/Q0FjSQN/RPgta/dkbS/lcA0oKgAC8CoqqqSDIvFNfoVRhIqO\nsvZl1jaG+W6ngrtDXvtWSFseKsqBogIgAAQqAALwpr7DrUXqC4qgiP+rYda+neI1LuVdOztoG3tX\n4Hdb+4/sNcutfTLN4DICRQVAAF5FrWczXACagU9Jr4nZInQcU1G3HxBRsCfQ1ayv2zto3ZeCttG3\nJRxkSqCoAAgAgQqAALypLwB5szvzX6nzXG5XzTQ7aoalvI4a8/kWmHF9i0p3OVBUAASAQAVAAKVI\nfQ9k/u+aNgpQNG739jzrGmfYRF4kUFQABOBV1FOsXVjgIPZjPhR1YKGY75T02VODtgvvDfxHrc1z\nsimOqE2j6+H79lm9D9g2pVd8VP95oagACACBCoAAvKlvkSmvY0EDrgEai0tf+cSRS3n3ubdv797w\ndNdVSoh6vtMtkr864ngcvnT3vn0Df9aaZOc86CBjf/lAhgHFAEUFQABeRR1h7ZsFDwTIwleZIWwS\n6MseJQ3DVynh4fSnTExSFeXMtkq6Lt+hQFEBkAACFQABeFNfpLzA0cr8LJUZiti1flUB50wLux1L\n7xV0DSgqAAJAoAIggFIsyuccb+2ypo4ChOGrYO/D3VvNsizwJ2xd4d/mtK4wrMJ9Fvjt2J+PM/aM\n/6rjhCFAUQEQQOkUFUo6cHFCmGVfljlMRX/5N4F/3O/Tj2O+rSd6SZp6ojHwe8pOSfkEUx7r/aGo\nAAgAgQqAALyp71xr5xU8EDB48KW7YRNHvOhYlnSXkyXlPdraFSHH+D1ll9bn9HhrD1BUAATgVdRm\nKem3twv8pE/I5zXdDsJp1AbCYRNH9apovYQpqWPlwYF/1KPR/YiIplu7NOX1oagACACBCoAASncf\n1ZGlIBTS3WJJk+7GVbDnWxd2Mv+31uY5cdQIfOkuJ23K64CiAiAABCoAAvCmvj+0U6lfKTCvbFQd\nV9A44irYZylENtiBogIgAK+ivv9+8YPYtvhLgAKxa9y9O31L4FvM72R+XBG1edsH/iobL1knjaKA\nogIgAAQqAALwpr6X/rn4QSR9JhGUk4GQ8jquStF37Wxjx94V328y8zvTDacHKCoAAijFtosc92T8\nu6wNJUtBGXnhhWT9OnO4FhQVAAEgUAEQgDf1bfQO4Hk/GQ8axzXbBP43P87nnHkXIkvDMcxfHnJ8\nRpexv5gUtJ3wZDFjgaICIAAEKgAC8Ka+/92IUYABgS/d/Zmd0vft9M1L6jQj5XWEpbsc99zsdJbu\nulIr3axfHkXzoagACMCrqO4Dte/dBQwuRjH/5YSv8SmpoyyVOrrPCvwxd0T3+yfmp1nZlAYoKgAC\nQKACIABv6nve3sYuf67ooQBJ8HQ36eL0RuGeKa03DY1LdzmzZwf+VQX9DqCoAAjAq6jHHWedAhV1\ny6opPf6owx4iIiK2s17PAwGseD5lqCY6IJjA/Kc9fR8/1tgDHsh/HHzrxHFWRVafFLSNXxz92vv2\nDfxZa3IdFhHFV2MgCp8gdRNHSVWUiOhsa1s/G7S9cKaxx94ZtOXxGCcUFQABIFABEEBLrVaLPNjV\n1VW7rFIhIqJfFTkI5i+2ud1MX14H6EJrb2zCtU9n/t0NuN7RzI/bsKmMJC2HW61WqaOjoyXsGBQV\nAAF4J5NaGzAIrun1KOkI5g+GqhDNUFIHV1G3l0yRhbXLqKJJy6TmUVQeigqAABCoAAjAm/r+g/0U\nv7DA3IPfM61Yywd2ln12qOIpPz4Y0t0wDmf+Ycx39wkLKjrQQxnTUqJgxRQRkWaFyFxlBh9sTT65\n26vDWJtLeR+ZFrQd8mDy8aUBigqAABCoAAjAm/pu14AdnB7rPrvHbx9zOxERncuOz8p7xx3BzGD+\nEmv5krlVzH8y4UeGpPDKC/yZUZdar/tS0Db6tvTndzWd+WOru1n7GmtbeXDgh+32fb3dtIk/JMBv\nTrpiZLwQmVvI/wWWLn+GLQ28w06x8xlel/IWle5yoKgACMC7MqlSqUQez4tPb2nr8Uecu4GIwp/y\n34X5rxc5IFAI39/R2AMPDNpmswcG3IMW/zouaDvDFhziDwH4Frm7ukVRiYRT1xNY29I+x4iCBfZE\nRHuxRfZJeOL4wN9/WbLXYGUSAMJBoAIgAO9kUiPYyqa7UbhFzXksxQLZSZN+hvHRh8Y+wNLd9SH9\nzmD1NcMmmHz45s7ch73umGNEvZ8pTYpLeX3p7kjmz1P+80JRARBAKSaTOK4MZdISlABEMZn5nXWe\nqxFZHSaTABAOAhUAAZRiMonjUt69WRsqlQLOdObHTRx15njNZk9kQlEBEAACFQABlC71dSDdBVFI\neEZjN+a/FtkrOVBUAARQWkUFQDJ8tdEXdP3ng6ICIACvok6cOLER4wBgQDGE7fQ8cfv6z+ddQlj/\nJQAASYlaQhgbqACAcoDPqAAIAIEKgAAQqAAIAIEKgAAQqAAI4P8B2e4hGuUqJocAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e448d990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOoAAAD3CAYAAAAT3MgLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHXtJREFUeJztnXmcFcW1x88wGlEJQcGIgguoXIwmigP6jCZqkBCjRNTg\nhvmYfLKwxYBBkBfNB83nQwIuT1EWiUswRo1EIokKRI3C07jEGcGN2Ag4Gn2iIuCSxG2c90efon8z\nt7qrb0/fO7dmft9/zpnTdbtr7tyac27VqVM1zc3NQgipbrq0dwcIIW44UAnxAA5UQjyAA5UQD+BA\nJcQDOFAJ8YDt2rsDpHwUCoVBInJ9EASDY66fJiL9RGSDiGwVkRoRGRYEwbRCofBDEXlbm9aIyK5B\nEFxvuceiIAhOL8svQLZBj1rl6GDKRBAEq0Tk7UKh0N1y3x+KSL8gCK4IguCPQRA8KOGAHVooFA4V\nkWa1/1FEnhKR/pZ7DNX2Rfcn+cKBWsUUCoXPiciYNt6mJua+s4IguALtQRC8JCJ3iMiuIjK4lf1+\ny717iMiiHPpIHDD0LSOFQmGKiDRIOFg2B0GwSj3ZerX1CIJgsYaofxCRH4nIFgk/+FMl9GL9C4XC\nqSKyIQiC1erFFmjbC0VkTBAEjbb7JnRtsLYtwgzeQqEwrVAovCgiD4jInUEQ/LXV7/Y57euvte+X\nl/j2kBKgRy0TGrI2Q0j5Ix1MzUEQPKgf/CGFQuFQDVHXSzgYjT5Y9S0agq4WEdHXrReR+iAIhsMg\nLbpvQvd6uPofBMHXRWSYhGHvzEKhcEGrJqfr81bp75v0PNJGOFDLxxAJB6gEQfBSEATjRKTO2JT1\nInK86jUishmu9QB7a3YNguBd+DnpvjYeEMt3TpHwe6cZdEEQNAZBcH0QBENE5MxWTfcrFAqn6j+k\nBmH4W1Y4UMvHkwKDQUPFemk5QPaT0GMl0ayvHwq21oO3pPsGQfCOiPyqUCj8KqbJfhqOI08aRa/9\nXj39YgnD8DMSfwvSJmq4e6Z8aLi4QSLPulptT0k42PoFQXCDfvAXicgsCb/vXS/h0siFImKWPjYE\nQfAgtL1QZ2TxWbH3DYLgBkv/TpVwgG/Q123RZ5wmIrtI6OFrJFzC+XUQBO/qPWfqPR/U+5jn3Cki\nC4IgaMzh7SMAByohHsDQlxAP4EAlxAM4UAnxAA5UQjwgMTOpoaGBM02EVJC6ujrburk7hXDw4DDl\nc+3Z4c+994iuvfkG3EjvtPfe8OLPdhMRkfc3vr/NdP990eW/PBvKE2HF7n9W6b3hNh+pxJy3A0B/\nUeVhYJukq4oDBoiVrl1D2R3SyXv2DOXLL0e2HpDD06eP9gM60q9fKDdtimwffxzp3brpffaJHvTq\nmjBX4S34JT/8MJRHnrVPZGz6NNK3qw1lTfR3bN4Ubm555uEo96EW/qIHH75z2K62dptt/VNh20cf\njdq9+FKkDzsulGc8FNl+c0QoT3hCEpm/e6T/Tj8bf0t+iXwVdPM3fQ1svVXeND2ydbm0+D6vQ7rF\n1QtCidkj60Aff1AoL3g+spk/+ThoN3d+7216l3EbRURk6eHR9b59Q3nwkd222SZOCT/r18J9poF+\nl8oAbF9WeU19vcSRuDzT0NDQ3O3KcKAeeHtoe/yE6PoRyyw3hP8HTZeEkfXQ6dEH7iFsq3LL+ZGt\nx1Wx3cnEF0D/B+g/UTm7jfc343gr2PBfovnw7QS21Zb7mH7+G2yNeE+9qXlPRUS6wPtqe3aTftC+\npB8yEZHnLM8mdg4GPel96w36xthWburr62M9Kr+jEuIBHKiEeIAz9B1wexj6rlkT2o6EcHfVKZE+\nWYPvFnuhLPxyh0j/mX4vQ1+/v8oXpfrYTyV+VzYJrnc4XotflfVrrawFG3xNTOTroN9nuf6L6Ouo\nvNQUygfh+stSjO37Pqk8DH0J8RznrO8b+u3YeFL0ooPuivRzVe4Gr33Lcj/jRUVERqpcAter+T+6\n8aQwL7vNk169S2SbtKX4tR+A/p5K9I73quwDNtsk64cWW1/QNzdFupkAsXlRpBfo1fz+d2boUQnx\nAA5UQjzAGfqaRAYz5XSBJdwVEVmoEiJAJ8+U0LaasIWStnAXeQV0239HY1s2MbLtalnk7Qf6SpWv\ngg3LPMw6JpRfWymJPJZ8mVQB9KiEeAAHKiEe4Ax999q75c8PgP550E3IixFgQSXmNSIbYuwdne1V\nPmq5Zgt3kYWOe/8ZdUfIS/yBHpUQD3B61C7dw50BTZf8S0REai+JMpnetLQvgG486R8PiWznPR3p\nuEuiEtj6ZuNA0P9hub4f6LYq1nuB/k/LdbNWiRlBe6p0OcGVUIvwGEca2GSVVzruCRui5HVHW9I+\n0KMS4gEcqIR4gDP0NZu+T3bFTwqGlCbkPfVpa9OKsz/oSaEvhruYLmjWT62HtgC2cNfGizF6EuNc\nux6AtE0Z7lY/9KiEeIDTo96vh+2Zygy2bWpx/KRKPKnhXtBty0k2XAntlWZNCW1tlSSIn9CjEuIB\nHKiEeIAz9F3eKnPetp9URMREuVipAJPFqw1XyFtt2KpLkM4DPSohHuD0qCdpsdzLdcsUlvNcUty8\nzWzWbV5TIOfVVDr4NlSTuaSTlQY3nvQ7YLulPTpSIqeBvlzlv9p4T1uNJ9syGnIQ6M9brttoWhIV\nnK4duaro+kCVy78X2fb9Tcqblwg9KiEewIFKiAc4Q9+r9ID5kSrLUVpyM1Q16Kkh72NQkf9+Laz2\n65hwV6PyFtvm0mYH+UYp4a4J99KGeuVgcQltzWfL9rmqA73Bct213v1F0NO+HwVLuIuMUHlxmcJd\nhB6VEA/gQCXEA9x1fVWaqLMcdV+nwgyvCXmxIv85KuPCPrOHs1vM9c5K8RFS5aUn6G9neL35bOG+\nYXNOHc4Uz4NT48bDiYJJvAD6iSrvtTUEcN/xOst109/joKLfzrpAvyDmnkepdJ1y1xp6VEI8wOlR\nP3I1yIHHQb9PPek5YEs7gXI06MtjW3UebNUpsmAqXrjul8WL2rBtQcSteGm9KJb7Ggj671VetnNk\nm2pZ3HVlgQ1Xz74O+hPnSQ1ZN3nQoxLiARyohHiAM/StRBL4KEgNvE5nrbKkxzHcLQ95hdA2XIXg\n2sIrMbrBFu4il0FRPluVklka8uKJ40lrwSLZN6rQoxLiAU6P6voPkQfTO1mCfWfAeEqXl6zmDDJX\nra9GlZ+ArVxbO+lRCfEADlRCPMAZ+lb6BGqTYM9jU/ymLSHtkaD7cCRkJSqZ0KMS4gEcqIR4gDP0\nNcUoknfm5YcpjoYJ9iY1kOuknQNXuOsqu1JOvgL6wxV8Lj0qIR7g9KgT+4Xyuy8lt8sLWwaJD550\nCOhPluH+m84LZa9r46/FXe9otOfpBZX0ogg9KiEewIFKiAc4Q9/CgFAeqKFvORO0q4VvgJ427G5r\nuGsmSOLCut00pF07OrINuLXlNRGRvvCaFVoEeBjscKjQN5hOxQjQ7y7TM+hRCfEAp0c1ZPGkpvYN\nHiDsqlNTDcR50bTnv2CtnbTbBF0TJGbfgvGitmsiLbNk9vehlH4HoFxeFKFHJcQDOFAJ8QBn6Nu1\nayhNMfvZsS2LCVpJkeikb5HqOPrwHlgAPckyI3Qy6GZ7IsznyN9V4uYFHo1I8oYelRAP4EAlxAOc\noW/37qEsJeRNohrCXZEo5B0B4e46XXfE2dI/WV7baLHN2S3Sf/xW9n6dCLpthrwP6K+lvOfNMA19\nscblcftFK1F6h5QOPSohHuD0qD17VaIblcd40hfhCG/XuqPZ4rQVbM+qRC/6fbh+Y4n9slWJR84E\n/cqU9/zog0g3Hvu6mLb0pNUJPSohHsCBSogHOEPflxuz3zzt4ULlJC7B3qTdlZJmZ/YiTgLbs5Z2\np8Da7I0lZuvj8X62Q4x+fC60vTmUtgkvEZHhKn+YdtaJVC30qIR4gNOj9uiR/ebGk7ZnjZu4BHuT\ncRTnjZK4GvQ9VO4ENluGUxbwbBRzSHC/m4vbDQIda1stnRuuGd1zUzTTdXJDPn0jlYUelRAP4EAl\nxAOcoW+fvi1/Lsdey0qBfbed/2PWSV0FrO6FyaJPPg1llpByxXGRvuyhUM6KaZt0mjeGuweCvt2E\nMOT9+4lSBL4XXUF/PuE5pP2gRyXEAzhQCfEA94nj61r9DLprNjdt6ZJKgf0we0obwfaOSlwnxRle\nk8iPs7om4X3GZyLbDjtE+gXvxfdn3kORftzuqrwR3z4NOEn/ZZVDLNn9+F5gWmK1hb6VPqmhWqFH\nJcQDnB61X/9Qmv/Uw+HaHY7XVosntWFb6nymlRQR2RP0T3XiaCHMxJyrv+RUaDfro3R9WAR6UwZP\nep0u4o59PbKBM5cVKoeC7a8qC2B7pPRHVwzjSePWijsL9KiEeAAHKiEe4Ax9394USrMHE8Pdq6FS\n2aRqKd2gmKXOuGy+tSpdlRl2BN2sleLEkQl549Y/07JY5QFgw72ht2msejZsWDUhL/wKLZL6Z2wf\nypUfR7YxKhdAOwwrK3F6dhY6Y7iL0KMS4gFOj/pRwsSIy4vupTKuPk85sXlSW1aVrTLDKY4Sorj8\nknbiKC1xFRZGqyd9AE7SPV5TqDAQ2Bf0h9WT3ud4Zmf3Vj5Aj0qIB3CgEuIBztC3W7dQZikj2R4h\nbxKudV1TiMxVlWFyQrZRWxkJ+hLQTUWK4x07BhpjdOI39KiEeIDTo+6yb1iBeyd5V0RaLh9AFcqq\n856+siTGPk9zgcdnyGCaAvrlpb/cO/qp7EiHNtOjEuIBHKiEeIAz9H11TRjymooImJT/LuhmxEPS\nDquuO+gPep3KP8S0TRvyHgX6QJWdIdxFOlLIa6BHJcQDOFAJ8QBn6PvWmy1/xnB2mKX9Y6B3tCP8\nTDGyuVCZIS5UTcLMSm4A2xdU4gnna0BP+x5+DfRlKtuzrnJHxnwN/EsFnkWPSogHOD3qB7pYajtH\nBkvx1Fhea6ojtKdHzdObLFdP+rXdI9unOsmzuLh5LINVfgFs96R87V6g29aul4F+voY0C+EPQI+a\nH8aT2ipo5A09KiEewIFKiAc4Q9//OjsMHv99f3HQhEX0l04M5a6zI9vKNnUtH1yhHhaRf0FlXPL+\nTKNY1jTjKjOYJHtMDTQTUDhxlBZXqmY96LdoR86G+P8U/SqTJRWxmslS/GwefIVpy/tRrnAXoUcl\nxAOcHrXmkyYRsXumx0FHT+oTltrU1gOEXcRNmMUl2YtkO+D5/8ZE+nAtfGQ7TFkkOnJyeSeYQcpS\npWICeNGLVM7IozNlgB6VEA/gQCXEA5yhr2xXW7aHl/Nsmk3nhbLXtaW/1nbSt0jy0Ye3Qel5LOlp\nsE1crC1u5mTPBcW2UaBjptTSI0L5zScyPKgT0Ay6CXmrNYuLHpUQD+BAJcQD3LO+XcKx3HRJmCT4\njUuigAFL2ppE84VgW6m5VWNhoQlnOk3I+x2w3eLqUEp205B37ejINuDW4na4FnyGygnnRrb+N6d7\n3mgId221d13rdLYayDjDawt5DRjufhX0QYeGcg5k/9tOAyAiv9RazRd9GNmafhetjteeE58I2zQ9\n0msvzbtnIfSohHhATXNzc+zFhoaG5sPULXaZ/E5su45GXMlOG7ajD/PiS6A/E9uKlAs8mvJbKrFa\nhpl4ymvSqb6+Xurq6mz7W+hRCfEBDlRCPMA5mfT0/8aHvH1Afy2P3uRIW9ZRMdx1JXvbjj7Ma74G\nw12zVpqlogTJBi6H2wrEVXKdlR6VEA9wetTaVi1+AYlKm5si3Sy13J1Dp/LALM+01eujFzVVLnqA\nzZzAiJP3ruyWo1UeB7alKhti+mE8Kaz8iOMYGhmr8jpHO5KNSmZ+0aMS4gEcqIR4gDP0PfjwcHNm\n09DPiojI98dv3HYNy1nOPCaUd0NZh8kqH4B2T0syB6n8FGxZ9m2a1eGVkPa0f8q0p2+Afu+caJqo\nVtN6sBr9CpUzto9s5qRvEXvoa9bnsBDZTzUJ5rcQQ2MZymUaZh1yaGSzZSuNBd1cng8bAla/0fKa\nSMuyr/db+usrcVU30rLooEg//fni6+M05C0lMylrCV16VEI8gAOVEA9IkUIY1l7/0uTwSKj34Xpj\nGTuWF3gQ0wbL9d/uF+mmhvGPYHr47sGRvruGkIdD/RZT0xX/49nCR9sZpfuCbYDKs2DK+HsQN5u8\nsjmfj2wTWp1igO1EROZqf7HkyH/+O5Rdf2XppERhP559u8LelOQMUwgJ8RznZNL6p0JP+lzZu1Ie\nbF4UuRjKS3zTcn1EvcUImB18YxJb2TNbGi36KR8UtxOJJsdsXtTWTkTkGfWkxouKxHtSg5nAegLq\nqB5uqwBHKgo9KiEewIFKiAc4J5OePz2cTTnXFUN2AmzF2MyaqKWeGSElwckkQjzHOZm0tpUnxWyP\nXqA/JsVo8QMpQ/GDdmFHlWeC7RGVWc4+KTcm46gjZRt1VuhRCfEADlRCPMAZ+g7TTZMz9LRtTCZ2\nJRb7EPKWkrj9XCuJvJpPd3LFhLy4yWC5rSGpeuhRCfEADlRCPMAZ+p75UCW60X5k2afoG1D8Xf6u\nqYFMC/QLelRCPMDpUW+qgqP7TFGxLJUeiAgGRfSk2UjKQIOdkmU5QlSEHpUQL+BAJcQDnKFvNZxW\nzZC3Y/BdlQvbeJ+bIdY8t1yxZiuSNl1AeWvRAxqkz46Rbdp/2v58elRCPMDpUQnJi4UWW5aJmEp5\nUeybObT7n5Z2jaBvO+ooBy+K0KMS4gEcqIR4AENf0q5kiWJdGymyVqMXEdk4LtJ7zy++vs5y8kI/\nuP5ShmemgR6VEA9wetR5WsR5/BvJ7fKmJ+hvV/bRpMzsBTp+ANN6I5enNNfjjr88UqW1Kgl40RfO\nivSBt4fyADi/aKLKcdDuBG2Xt2elRyXEAzhQCfEAZ+h7W4VDXoMr3MXwyba2RaoX198LTokU18cv\nqYCb7chLEXvIa8DiuSbcjbs+W+UyaMfJJEI6MRyohHiAM/R9xNUgB04DfXHK1zDc9Y87vxjKbz9r\nv27b82m+4sT9vauhZvFa0I9RuTLnZ9CjEuIBVZGZ9Bd3E1IFfBf0hSlfY7yoiMgo9aSPDItsR4NL\nHKgSParNk94zJNJPejJlRyx8H/Qbs9+mRTnWn+vvtmVrZGtLHw30qIR4AAcqIR5QFaHv++3dAZKK\nhY7rtrXtUTBx9LCGhUfHzAD9SeU0sC1T+TTYRkAoeWltKKdjmQUlLoXQ4Ap3Xa83rAG9l56c9njO\nlVHoUQnxAKdH/YrKh8vckTQwG6l6sFVmsH2YMJMnzpO2ZhboV+8Symu2FD9PROTkb4Vy+l3F90ny\ngq0xW9Uws+gnn430Oe8VX9da5i08asGSzZQH9KiEeAAHKiEe4Ax9B6isVOibtDuf4W71YKvMYEtI\nHwi6yTz6k6UdguHyRA15+4PtQNAPtYS8WbD1ffJ7xbaRoJvf45Mb9t5mu/y8V0QknxKhCD0qIR7A\ngUqIBzhD30qfpG1C3gLYkqqUiySX1iDtg9lT+gLYjI7rpDMd95misu8ukQ1ngJPAsHvSHpF+1euh\n3AzX30p3S1lisW33g1e26cHoUE671f56Ezrb7pMEPSohHuD0qL0r0QsL75bQttKedBDoqyr8bF8w\nlRlsa9/LwXY1eMpJ6imnwPXLVU4DL3oX7IscqG7zMzOK+7AJ9IWvR/quKl2Rmg0cD2YdFTOcjo3x\npIbVGZ4pQo9KiBdwoBLiAe4Tx6eH8uZLy92VkDqVmKj/uq1hO4Lh7iCLjUTY1r4x/JsDIa1ZK8WJ\nowv1Ok46rYEyIH9OePamGD0LJkSfBP09RvMob4RF5VEqZ4sdsz23scTn06MS4gFOj1pbAU+KZ4k0\nqJwHNSPTVulPuy0pTzqTJ407QDjtWS+mMgNWPFgH103GEXrZxTpxFOdFTZWGtlRoQM4A/Q7Qz9c+\nXQGJ+uY9mLNbZDtP13m2nh/ZelwV6Rsz9oselRAP4EAlxANqmpubYy82NDQ0Dx48uILdIR0FWwX7\nGpWXwheub42IdJNgXxOZ5MOLQonrpHkVJXMxBvQFCe1OAP1TlXEF+5LKn9bX10tdXV2N5RI9KiE+\nwIFKiAc4Q989rw9D3z2TfH8baY/Z2mqF6Yn+cRDoz7fhPgx9CfEc5zrq7DJ6UkNn8KL9QE86ms9H\nL5p2HdUHVp8S6WmrR7TFi6aFHpUQD+BAJcQDnKHvZleDCuA6es8HynUSdRKVCknN/dNOCrraYXUP\nk0zvOoG+FA5R+bTlGoa7rn6aDSRdwfY3Szs4EyvzMZH0qIR4gNOjVmKC4GCL/g+wmWTt31egLx2J\nSk/upJ0UdLX7KdQ3ukn3OObpUc3E3r/BZnuvXP2cpcdI/NxSSxe9cR6HLdOjEuIBHKiEeIAz9B2v\naRcryrhY9JxFPxFsDHn94EjQ21Jw7ioo6bFLfDMnh4CO69hpS3UeA/pKy/XRGvJO+1xkG/lRKOeV\nUCl/H3cTelRCfIADlRAPcIa+UyuRH2XhXtAv2zmUU//VLl2pKFlK0FQLrnA3bdkUnOF9IbaVG1wn\nzXJmky3cRcyf57F3ItubKi8s4e+YZracHpUQD3B61MYKdMJFZ/Ckhgnw3/cisJsCB7/cIbL97MNK\n9Cg/bJ7Utlkh7TkwpbA2w2vWfSfSB90SSjyJ0dR6W2R57YqcoyF6VEI8gAOVEA9whr5jVV5X5o6Q\nEKy3gecembW2agx3N44LZe/5pb82y2aFuNq7BrOntK2nke9/S7FtAuhzVS6CEg+nl2nylR6VEA9w\netS588KD5h4eH9b4Lsc/jKYlUaWgwsiwxgEUZRdTlP0ySDU51bZHqQNjjspt+l10rsDAc8JU8rjk\ne/Ofvhz/5fHvs4d60hfOimwDb49/7b6g/xgqz1/wXuuWdtCLmpKeWIjEeNK4bWom4wiXX8zEkc2L\nIvNAn69nMA6EE5PH6nuddwRKj0qIB3CgEuIBrJTvGVhL8gKVl9sa5oipuIAndNtOEs9Cf9A3JLSz\nnUyeJ91Vuk66/yLoJrS+J6c+sFwoIZ7jnEyyHZVXrXwFdMum+w4Bxj/l9qSGwGJDL5p2IsYGetGR\nKnEbmkmZPR+86BUZJqBcGE9qW34RiTzps2Azjz4KbLaaSYiZhFuf2KoYelRCPIADlRAPcIa+e/VV\npYyhLyxDiTmFD5Ooh2v8MwsSnW1bg3wLd4eDHndMXxJmMgPfi6VHRPqYJ0JZ7jKrB2jIOxFss1O+\nFit52CovnKTyqzGnnSeB06Az4XvRWfpBweR/c/sW66SwVe0e/ezhZFOjytvgDzla/5BxGVelhrwG\nelRCPIADlRAPcK6jDnroWBER2XPK+yIS7WrPk8bvRfrFvwnlEFg3e0Vn/B6B17wK+msJ94YDreXu\nbN2rCENV/tXRrml6pNdeWnwdF+E+mR7frhRMWPgJ2DDcNmuhSyGFcISmEGJ6o4kQ14DtM6CvvWFv\nERGp/cEr0hr8vebsFul3a/yK4b3JmMTZWPyUj9JiZI9CZYY/GAkJ9gPgO9khi0N5NNznMv2FjoLv\nLcu0wts4KHeBYbApWYzfOU3fuY5KiOc4PepvNTPpmkr1SBkDukm4PgBsHeGIP984D/RrLddtWUbH\ngm2GHsLSs1dkw+T9y3YK5Ug4+vDYW0M5Cu6DE1XGS5cyGTdrx1AuhUJKJkEf3dlY0G07+EykMRfq\npJ6gnvS9KZHtQFjwNt7T9l7SoxLiORyohHiAcx210iGvwXbQOcPd9qXvjvCDpf6mLal+Behbt4by\n8Sfs95+qpzZNvbX4Gn4Ot54f6T2ust+rNbg3db72HUt6rtRZUvwi6CpYYdZEJ8DEkQl5uznyO6FG\nnSw7IrbZNuhRCfEA52TSnTqZNLNSPSIkJ/I4QLhU9lZZvMAUz74q7+RkEiF+w4FKiAc4J5PSHlGX\nN5hV0qgyKQOJFGPWnX2bhBsJ+mqVWFlho0W3bTwoR7hrK7oHB6SXFPIaGlO0oUclxAM4UAnxAGfo\n25Zj79qCq6QFceNbyGuwfd1qrHQnYrDtJ90edJMaiJsNrszhufSohHiA06OafGPXIbWE+IKtMkZb\nwAmkOSqx0sadkImV9Zn0qIR4gNOjFg47TEREqvAQMUIyYZZTepbh3iatqHshsh38caRnfaYzhTDj\nfQkhGYhLIUwcqISQ6oDfUQnxAA5UQjyAA5UQD+BAJcQDOFAJ8YD/B9s37zZ1rhhbAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e43db8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOoAAAD3CAYAAAAT3MgLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88e3f19790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model in ['CSD', 'CSA']:\n",
    "    control_networks = np.rollaxis(np.asarray([load_data(folder, model, str(10), '_FA', subject) for subject in subject_list if re.search('c', subject)]), 0, 3)\n",
    "    ZDHHC9_networks = np.rollaxis(np.asarray([load_data(folder, model, str(10), '_FA', subject) for subject in subject_list if re.search('z', subject)]), 0, 3)\n",
    "    plot_adjacency_matrix(np.mean(control_networks, axis=2), outfolder + 'control_' + model + '.png', False, 'control ' + model)\n",
    "    plot_adjacency_matrix(np.mean(ZDHHC9_networks, axis=2), outfolder + 'ZDHHC9_' + model + '.png', False, r'\\textit{ZDHHC9} ' + model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantifying the similarity of those matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 0.65\n",
      "SE: 0.011\n",
      "median: 0.63\n",
      "mad: 0.041\n"
     ]
    }
   ],
   "source": [
    "correlations = list()\n",
    "\n",
    "for subject in subject_list:\n",
    "    network1 = bct.binarize(bct.threshold_proportional(load_data(folder, 'CSD', str(10), '_FA', subject), 0.1))\n",
    "    network2 = bct.binarize(bct.threshold_proportional(load_data(folder, 'CSA', str(10), '_FA', subject), 0.1))\n",
    "    correlations.append(pearsonr(network1.flatten(), network2.flatten())[0])\n",
    "    \n",
    "print('mean: ' + str(round(np.mean(correlations), 2)))\n",
    "print('SE: ' + str(round(sem(correlations), 3)))\n",
    "print('median: ' + str(round(np.median(correlations),2)))\n",
    "print('mad: ' + str(round(robust.mad(correlations), 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clustering_coefficient\n",
      "\n",
      "ZDHHC9 AUC: mean=1.07; SE=0.054\n",
      "ZDHHC9 AUC: median=1.06; mad=0.145\n",
      "control AUC: mean=1.4; SE=0.107\n",
      "control AUC: median=1.31; mad=0.368\n",
      "Wilcoxon: W=0.0; p=0.018\n",
      "Paired t-test: t(6)=3.28; p=0.017\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &      p \\\\\n",
      "\\midrule\n",
      "        1.07 &      0.054 &           1.4 &       0.107 &  3.28 &  0.017 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "global_efficiency\n",
      "\n",
      "ZDHHC9 AUC: mean=0.76; SE=0.035\n",
      "ZDHHC9 AUC: median=0.79; mad=0.077\n",
      "control AUC: mean=1.21; SE=0.066\n",
      "control AUC: median=1.21; mad=0.285\n",
      "Wilcoxon: W=0.0; p=0.018\n",
      "Paired t-test: t(6)=7.83; p=0.0\n",
      "\n",
      "\\begin{tabular}{rrrrrr}\n",
      "\\toprule\n",
      " ZDHHC9\\_mean &  ZDHHC9\\_SE &  control\\_mean &  control\\_SE &     t &  p \\\\\n",
      "\\midrule\n",
      "        0.76 &      0.035 &          1.21 &       0.066 &  7.83 &  0 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKIAAACiCAYAAADC8hYbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBf8LbGngG/2uiPAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f88ddc3b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_range = np.arange(0,100,10)\n",
    "calculate_results(subject_list, threshold_range, 'CSA', '_FA', False, outfolder + 'CSA_FA_weighted.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}