Domain_Mapper_Graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'human_nc_dmpr.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/4q/g4jrw97j6_b_h0sl9s6yp95h0000gn/T/ipykernel_61406/4224976104.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# These files must be generated with Domain_Data_Annotator.py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmpr_nc_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"human_nc_dmpr.npy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mmpr_ib_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"human_ib_dmpr.npy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmpr_dec_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"human_dec_nc_dmpr.npy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_pickle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/homebrew/lib/python3.9/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    405\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'human_nc_dmpr.npy'"
     ]
    }
   ],
   "source": [
    "# These files must be generated with Domain_Data_Annotator.py\n",
    "\n",
    "mpr_nc_data = np.load(\"human_nc_dmpr.npy\", allow_pickle=True)\n",
    "mpr_ib_data = np.load(\"human_ib_dmpr.npy\", allow_pickle=True)\n",
    "mpr_dec_data = np.load(\"human_dec_nc_dmpr.npy\", allow_pickle=True)\n",
    "\n",
    "com_nc_data = np.load(\"human_nc_dcom.npy\", allow_pickle=True)\n",
    "com_ib_data = np.load(\"human_ib_dcom.npy\", allow_pickle=True)\n",
    "com_dec_data = np.load(\"human_dec_nc_dcom.npy\", allow_pickle=True)\n",
    "\n",
    "commpr_nc_data = np.load(\"human_nc_dcomdmpr.npy\", allow_pickle=True)\n",
    "commpr_ib_data = np.load(\"human_ib_dcomdmpr.npy\", allow_pickle=True)\n",
    "commpr_dec_data = np.load(\"human_dec_nc_dcomdmpr.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mpr_nc_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/4q/g4jrw97j6_b_h0sl9s6yp95h0000gn/T/ipykernel_61406/2096555374.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpr_nc_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"b\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"stepfilled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpr_ib_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"stepfilled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmpr_dec_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdensity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"k\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"step\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mpr_nc_data' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'arial',\n",
    "        'weight' : 'regular',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "plt.rc('xtick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.minor', width = 1.5, size=2.3)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3,figsize=(7,8))\n",
    "\n",
    "ax1.hist(mpr_nc_data, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,500), histtype=\"stepfilled\")\n",
    "ax1.hist(mpr_ib_data, bins=50, density=True, alpha=0.6, color=\"r\", range=(0,500), histtype=\"stepfilled\")\n",
    "ax1.hist(mpr_dec_data, bins=50, density=True, alpha=1, color=\"k\", range=(0,500), histtype=\"step\", linewidth=2)\n",
    "ax1.set_xlim([0,500]); ax1.set_ylim([0,0.025])\n",
    "ax1.set_xticks([0,100,200,300,400,500]); ax1.set_yticks([0,0.005, 0.01, 0.015, 0.02, 0.025])\n",
    "ax1.set_yticklabels([0,5, 10, 15, 20, 25])\n",
    "ax1.axvline(med_1 := np.median([n for n in mpr_nc_data if n<500]),color=\"b\",alpha=0.6)\n",
    "ax1.axvline(med_2 := np.median([n for n in mpr_ib_data if n<500]),color=\"r\",alpha=0.6)\n",
    "ax1.axvline(med_3 := np.median([n for n in mpr_dec_data if n<500]),color=\"k\",alpha=0.6)\n",
    "\n",
    "ax1.set_xlabel(\"\\u0394MPR / Residues\", family=\"arial\")\n",
    "ax1.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "std_1 = np.std(mpr_nc_data, axis=0)\n",
    "std_2 = np.std(mpr_ib_data, axis=0)\n",
    "std_3 = np.std(mpr_dec_data, axis=0)\n",
    "\n",
    "print(f\"MPR Medians :: \"+ f\"NC: {med_1:4.4f} +/- {std_1:4.4f}, IB: {med_2:4.4f} +/- {std_2:4.4f}, DEC: {med_3:4.4f} +/- {std_3:4.4f}\")\n",
    "\n",
    "ax2.hist(com_nc_data, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,70), histtype=\"stepfilled\")\n",
    "ax2.hist(com_ib_data, bins=50, density=True, alpha=0.6, color=\"r\", range=(0,70), histtype=\"stepfilled\")\n",
    "ax2.hist(com_dec_data, bins=50, density=True, alpha=1, color=\"k\", range=(0,70), histtype=\"step\", linewidth=2)\n",
    "ax2.set_xlim([0,70]); ax2.set_ylim([0,0.1])\n",
    "ax2.set_xticks([0,10,20,30,40,50,60,70]); ax2.set_yticks([0,0.025, 0.05, 0.075, 0.1])\n",
    "ax2.set_yticklabels([0,25,50,75,100])\n",
    "ax2.axvline(med_1 := np.median([n for n in com_nc_data if n<70]),color=\"b\",alpha=0.6)\n",
    "ax2.axvline(med_2 := np.median([n for n in com_ib_data if n<70]),color=\"r\",alpha=0.6)\n",
    "ax2.axvline(med_3 := np.median([n for n in com_dec_data if n<70]),color=\"k\",alpha=0.6)\n",
    "ax2.set_xlabel(\"\\u0394COM / Å\", family=\"arial\")\n",
    "ax2.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "std_1 = np.std(com_nc_data, axis=0)\n",
    "std_2 = np.std(com_ib_data, axis=0)\n",
    "std_3 = np.std(com_dec_data, axis=0)\n",
    "\n",
    "print(f\"COM Medians :: \"+ f\"NC: {med_1:4.4f} +/- {std_1:4.4f}, IB: {med_2:4.4f} +/- {std_2:4.4f}, DEC: {med_3:4.4f} +/- {std_3:4.4f}\")\n",
    "\n",
    "\n",
    "ax3.hist(commpr_nc_data, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax3.hist(commpr_ib_data, bins=50, density=True, alpha=0.6, color=\"r\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax3.hist(commpr_dec_data, bins=50, density=True, alpha=1, color=\"k\", range=(0,1), histtype=\"step\", linewidth=2)\n",
    "ax3.set_xlim([0,1.]); ax3.set_ylim([0,8])\n",
    "ax3.set_xticks([0,.2,.4,.6,.8,1.]);  ax3.set_yticks([0,2,4,6,8])\n",
    "ax3.axvline(med_1 := np.median([n for n in commpr_nc_data if n<1]),color=\"b\",alpha=0.6)\n",
    "ax3.axvline(med_2 := np.median([n for n in commpr_ib_data if n<1]),color=\"r\",alpha=0.6)\n",
    "ax3.axvline(med_3 := np.median([n for n in commpr_dec_data if n<1]),color=\"k\",alpha=0.6)\n",
    "ax3.set_xlabel(\"\\u0394COM/\\u0394MPR / Å per Residue\", family=\"arial\")\n",
    "ax3.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "std_1 = np.std(commpr_nc_data, axis=0)\n",
    "std_2 = np.std(commpr_ib_data, axis=0)\n",
    "std_3 = np.std(commpr_dec_data, axis=0)\n",
    "\n",
    "print(f\"COMMPR Medians :: \"+ f\"NC: {med_1:4.4f} +/- {std_1:4.4f}, IB: {med_2:4.4f} +/- {std_2:4.4f}, DEC: {med_3:4.4f} +/- {std_3:4.4f}\")\n",
    "ax1.set_box_aspect(1/2.5)\n",
    "ax2.set_box_aspect(1/2.5)\n",
    "ax3.set_box_aspect(1/2.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "# fig.savefig(fname=\"COMMPR_HUMAN.svg\")\n",
    "scipy.stats.mannwhitneyu(commpr_nc_data, commpr_dec_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpr_nc_data = np.load(\"yeast_nc_dmpr.npy\", allow_pickle=True)\n",
    "mpr_ib_data = np.load(\"yeast_ib_dmpr.npy\", allow_pickle=True)\n",
    "mpr_dec_data = np.load(\"yeast_dec_nc_dmpr.npy\", allow_pickle=True)\n",
    "\n",
    "com_nc_data = np.load(\"yeast_nc_dcom.npy\", allow_pickle=True)\n",
    "com_ib_data = np.load(\"yeast_ib_dcom.npy\", allow_pickle=True)\n",
    "com_dec_data = np.load(\"yeast_dec_nc_dcom.npy\", allow_pickle=True)\n",
    "\n",
    "commpr_nc_data = np.load(\"yeast_nc_dcomdmpr.npy\", allow_pickle=True)\n",
    "commpr_ib_data = np.load(\"yeast_ib_dcomdmpr.npy\", allow_pickle=True)\n",
    "commpr_dec_data = np.load(\"yeast_dec_nc_dcomdmpr.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPR Medians :: NC: 160.0000, IB: 76.0000, DEC: 60.0000\n",
      "COM Medians :: NC: 16.9614, IB: 26.9177, DEC: 12.6307\n",
      "COMMPR Medians :: NC: 0.1052, IB: 0.3345, DEC: 0.1941\n"
     ]
    },
    {
     "data": {
      "image/png": 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ibkQcEhF/KCx6J/AP0pSU0AfHChARz0XEHyJirqQlJG1ASoQBnEsffbc5RwFLAycVyvvxWCcBy0q6TtLTkv4h6cjs+lNHj7eSd9CO0liHRS6ViLh/kMUrAM9ExLOF8idY9DmswMDPorHOOmOPsHMkHQjsRGrX7Odj/Qzp+gTAZyPiVkl70EfHK+kVZNdiIuJZLT5VaF99t1mFcjPSr7cjgHtIQ7ycTGqqW0gHj7dOyX5EwyJXnBj6OIezTulI2oc09/BM4Oukpqy+PFbgZ8AsYHvgs5KWJl2864vjlbQE8B3gu9H8zvd+/Dt+B3BvRDRaGGZJmkD6dfMFOni8dUr2+WGR/5kr78dhkecD4yUtlV2XaFieRZ/DfAYOEV1cp1QkHU6aieznwD4REZL68lgBIqLRtjtb0vKk6zNH0T/HeyjwcuDtWbt9g7LXffXdRpo976omiy4FPkSq8XfseOvUZl+nYZFvJ9UA1i+Ub0C6sNlYZ3VJywyyTmlIOhGYDpwD7Jn7qdtXxyppdUkHZMk97w+kC7Rz6Z/jfTfpmstcUhPGQmBzYN/c6345ViStKWmqpFULixqxd/S7rVuyr8uwyNeR+qXv3iiQtCKwHYuO9UpSt8Z35tbZGHglJfs8JB1Gaq75KrB/RDyXW9xXxwq8lHRBds9C+S6krqYX0D/HezDw+sLjNlJPk9cDP6J/jhXSyfpbwPsL5e8hHfdP6eTx9rrfaZf7uH6E1K71BdJNVb8gXZTdoNexjfG4jmPgTVVfIvXBP4J0c8ZvSL1XJubW+V/ST7+DKOnNKMAa2X+Am1n8JpTGY1y/HGsu1pmkeycOJt18801SO+0B/fTdtjj2OSx+U1VfHSvwQ9L9BIeRTuDfznLSbp0+3p4ffA8+7GnAvaSbVK4Dtup1TG04pmbJfhzpKv9D2R/XL4FNC+ssB8zIEsu8LMms2evjKcS4f5boWj1W6ZdjzcW6LPBF0k2Az5CacPbst++2xbEXk31fHSupyeZE4G5SJeYPwLu7cbwe4tjMrAbq1GZvZlZbTvZmZjXgZG9mVgNO9mZmNeBkb2ZWA072ZmY14GRvHSXpxmzatTcUytfLTcn2zhbv3Tdb/udc2awmU7r9W9IfJR1SeH+z6d+eyaZ8+6akFQbudUAMK0l6VtLKLZaPeR8jIenvkr4+yPLG51q8A9dqrk4DoVmXZcPXvo40ZseBpCnWigLYA7iwybK9Wmz6WtIdhg0TSDdfnS6JiMgnw9NJdy02LE+a2vATpBuyWu2jYSfgDxHx6CDrjHUfI/Fu0hgqZiPiZG+dtC/wV9JYL8dK+ngMnHThOmA3SeMiN+aNpImk28lvJo0FkjcvIm7IF0i6inRiOYQ09HHDvcV1gcslrQ38l6QJMfiIp7uS7mIczFj3MWwxcBIXs2FxM451RDZW+T7AZcD5pNr33k1W/SmwEjClUL4baViLYSW3iHgB+CNp6snhaDYBRDO7kI5hNAbsQ9LOkn4jaUE2S9Hn8rOkSfoPSZdImifpcUmXSpqUW75YM46kN0q6JmvK+hOwRWF/x0l6slA2OWvqmZIre62kK7PtPCLpdEnL5pavLul/Jf0rW+fXkrYb5ediPeBkb52yPWnmnPMj4h+keTQPbLLevaQpFvcolO8F/HiE+9yYNOZI3hKSxuUeK0t6P7AfcMFgNW5JryTNDFSstRcNax+SdgQuyWJ8N2nO1WnA17LlS5Cas8aRToz/SWoGulhNps2UtB5ppMOnSQNifQ84a4hYmx3nZsDVpCa195LGzN+bNOBWw7mkeZwPAN5FGlvqYkkrjXR/1iO9HhjIj/58AN8H7sy9/n+kZLJZ9nq97PWepLbtB+DFsZpWICWw15CS159z25lFmiR+XPZYijQBxknZ9j6WW7fV4Gn/IrWzLz/EMRwO/GyIdYa9D+B64JrC+z8APJ99Hqtn731fbvmrSANnTcxe/x34evbvU4FHgWULMQfZwGk0HyRvcrbOlOz1eaR5T5fOrfPmbJ1ts9dPA5/MLV+DdLJap9d/a34M7+GavbWdpOVINfXzcsUzSQmjWe3+J6TksXX2ejfgH9G6ffptLJrc4lnSXJ6HAV9h8fZ6SGPgvx54I3B89p6TI+LQiBiqKWdXhteEM+Q+siaRNwAX5X8FkGYpWoL0S+hh0rjm35b0XUnvAf4eEZ+KiGazEG0DzI6If+fKfjKMeIu2By4HXsjFdT1p+O8ds3V+DXxO0nnZr5ZnIuLIiLhvFPuzHnCyt07Yg9RG/2IPlYh4HPg/4ANKc6mSW3YnaWjbRlPOngzehHMNiya7eB2wKanme3gsPrEJpJPGTRHx24g4jlRL/rKk/zfYASjNBLQtw0v2w9nHiqT/byex6ES1kJTgAdaIdN1hJ9I1jt1JJ8iHJZ2aNfEUrUj6BZH30DDiLVqZNHb+wsJjBdJJGFKzzhmkiTTOAR6SdLYGzphkJeVkb52wL/DHiPhrofwcUhv07k3e8xNgD6Xp+HZl8fbiovlZcr0pIn4XEbfG4nN2DuYLpB5Cp0laa5D1tiUl8eI1gNHu4/Hs+fMMnJ3p9WRt7RFxX0R8EFiVVHP/EfBxmnfffBRYrVBWvB8gGPj/fELh9XxSe3+zuE7M4nosIj4WEWuSmte+Sppx6aPNPgArHyd7a6ssue3A4k04DZcBj9C6KWc90vSD9w/ShDMm2UnhY6S+8CcNsupwm3CGtY+sOeePwIa5E9VNpGaok4B1JE2S9KCkLSLihYi4jjQb0XOk6xJFvwK2l/TSXNnbCus8DixTWOfNhXWuIf06+l0urvtIk2i8StIq2U1ie2THMicijiQ1nzWLy0rI/eyt3d5PqkSsIuljTZbfR2oHXqyLZET8TdJfSTdLTe9kgBFxuaRLgPdLOj0ibmyy2q7A0W3ex2eBCyTNB35G+pXzedK0dH8iJf7HgbMlHUeaiWi/bPnFTXZzGjAVuETSF0iTdx9XWOcS0oXc72ZdNieTpufMO4F0v8P/Svoe8BLgM6TeVH+IiH9Juh34anY95j7S3M3rZsdhVdDrK8R+9NcD+DODTyPYeJxNrtdI9t7PZWVb5MrOYmBvnIuGGUsAR7RY9kpSjfnaJsvWJiXeCe3eB2mi6BtJF6sfBn5ArkcLsCEpgT4CLCDNQbpzbvnfyXrj5PZxBakr5G2kbpHFz/WDLJoGbzbpQvKLvXGydbbJPtt/k04yFwKvzC1fjdTD6kHSVIk3k+s15Ef5H56W0MysBtxmb2ZWA072ZmY14GRvZlYDTvZmZjXgZG9mVgNO9mZmNeBkb2ZWA072ZmY14GRvZlYDTvZmZjXgZG9mVgNO9mZmNeBkb2ZWA072ZmY14GRvZlYDTvZmZjXgZG9mVgNO9mZmNeBkb2ZWAz1N9pJ2k/REoUySPi3pXkn/lnS5pE0L64yX9BVJD0l6QtJMSWt2N3ozs+roWbKXtDVwLqDCos8CxwCnAP8JTASulDQxt86ZwL7A0cABwObALyQt2em4zcyqSBHR3R1K44HDgBOAp4ClI2JCtmx54AHg8xHxxaxsReAe4LiIOFXShsBtwH9FxPnZOhsDtwJ7RsRPu3pAZmYV0Iua/VuBTwJHAqcXlm0JTAB+3iiIiLnAbOAtWdEO2fNFuXVuB/6SW8fMzHJ6kexvBNaPiK8BxZ8Vm2TPdxbK78ot2wR4KCKeGmQdMzPLGdftHUbE/YMsXgF4JiKeLZQ/kS1rrPMEAz0BrNNso5JmFYpeC8wH7hgqXjOzHtgIeCQiXtOuDXY92Q9BDKztN7wwgnWGsvTEiRPXmjx58lojCa7M7rvvPgDWWafp+W6E2yLb1pg31VmVCdRsZObMmcP8+fPbus2yJfv5wHhJS0XEwlz58tmyxjrLN3lvfp3FRMSU/GtJsyZPnrzdrFmzxhxwWUyfPh2AadOmtWFbZNsa86Y6qzKBmo3MlClTmD17dltbHsp2U9XtpJr7+oXyDUi9bRrrrC5pmUHWMTOznLIl++uAp4HdGwVZ18vtgCuzoiuBJYF35tbZGHhlbh0zM8spVTNORDwp6XTgBEkvkPrTfxp4HPhOts6dkn4MfDu70WoucBJwM3BBTwI3Myu5UiX7zKdIF1qPIPW5vw7YLyLy7fEHAF8Bvkj6dXIF8NGIeL7LsZqZVUJPm3Ei4rjG3bO5suci4uiIWD0iJkTELhFxS2GdpyJiakSsFBEvjYg9I+KB7kZvZlYdZWuzNzOzDnCyNzOrASd7M7MacLI3M6sBJ3szsxpwsjczqwEnezOzGnCyNzOrASd7M7MacLI3M6sBJ3szsxpwsjczqwEnezOzGnCyNzOrASd7M7MacLI3M6sBJ3szsxooXbKXNEVSDPJYV9JrWyw7pdfxm5mVURnnoP09sFWh7CXATOB3wH3AjsBTwE6F9Tw1oZlZE6VL9hHxOHBDvkzSaUAA74+IFyRNAv4cETc02YSZmRWULtkXSdoMOAT474h4JCueBNzcu6g6T9KgyyOiS5GYWT8ofbIHvgDcBnw7V/Zq4BlJc4DNgHuBEyLi+802IGlWoWhy26M0MyuxUid7SRsAuwFTI+KFrGxNYBVgY+CTwFzgfcBZkiIizu5VvJ1QrMEPVeM3M2um1MkeOJCUzM/Nlc0FdgX+FBEPZmVXZCeBY4EByT4ipuRfZzX97ToQr5lZKZWu62XB7sAFEfFMoyAiFkTEL3OJvuFSYANJE7oZoJlZFZQ22Ut6OfAK4KeF8k0kfVjS+MJblgEWkLpkmplZTmmTPfCG7LnYvXIt4BvA2xoFSg3ZewC/jgp2U5E04GFm1k5lbrN/FfCviHisUH41cA1wpqQVgQeBqaTumNt0N0Qzs2ooc7JfDZhXLIyI5yW9CzgR+BywMumu250j4nddjbDNKvijxMwqorTJPiI+Msiyx4APdTEcM7NKK3ObvZmZtYmTvZlZDTjZm5nVgJO9mVkNlPYCrQ2uVV/8adOmdTkSM6sCJ/s+0+ok4G6dZvXmZF8xrZK277o1s8E42feJU05J0+8Wm3F8EjAz8AVaM7NacM2+JtyWb1Zvg9bsJe0iafluBdPvmo1u6WYWM+uGoZpxLiGNKf8iSYdLWrVzIVk7RUTTh5nVy1DJfrFqp6QlgS8D63QsohpwAjazbhvNBVq3O5iZVYx745iZ1YCTvZlZDQyn6+UKklYqrJ8ve1GTKQTNzKwEhlOzvwx4JHs8mJVdmSvLP9pC0sqSosljZrZckj4t6V5J/5Z0uaRN27V/M7N+M1TN/oCuRDHQ5tnzLsATufJHs+fPAkcDRwF/B44BrpS0WUTM71aQZmZVMWiyj4jvdyuQgknAPyPi8uKC7CavI4DjIuJrWdmvgXuADwKndjNQM7MqGPZwCZI2ArYGXpYV3Q9cGxH3dCCuScDNLZZtCUwAft4oiIi5kmYDb8HJ3sxsgCGTvaT/AM4EtmVgH/uQdDlwSETc0ca4JgFPS7oO2AL4F/BV4BRgk2ydOwvvuQt4V7ONSZpVKJrcrkCr4OCDex2BmfXaoMle0gbAdcBC4POkC7MPZ4vXAnYADgSulfS6iLhvrAFld+luBjxFaq65B3g7cDKwTBbLMxHxbOGtTwArjHX/Zmb9aKia/bHAPGDriPhnYdktpIuipwHXki6StqsO+Q7g3tyvhVmSJpAuyH4BaDW2wAvNCiNiSv51VtPfri2RmplVwFDJfgfg2CaJ/kUR8YikU0mJeMwi4nngqiaLLgU+RKrxj5e0VEQszC1fHuirnjitml++9a327aP5qJuHc8op09u3EzPruaGS/WoMbBtv5m/AmmMPByStSarZ/ywi8n33l8me55KuHawP3JZbvgFwaztiMJg5E267bfGydp5kzKy7hkr2SwELhrGdp4exreEaD3wLWA74Sq78PaTk/tNs+e7AlwAkrUhqljm+TTH0valTo2nyrtT4+jNnpmeflcyGVLqZqiLibknnASdIeoH0q2EvUrLfPSKelHR6bvltwKeBx4Hv9CpuK5FutH+ZVcxwkv37JL1piHVe3o5gcj4IfAb4GLAGKeG/JyIafes/RboYewSpz/11wH5VuXu2WS7qxzzknGtWHsNJ9ocNc1ttm30jIhaQEvqnWix/jjRcwtHt2qeNQcuO/M7qZmUx1HAJHgLZzKwPDOcO2o8AhwDrku5SnQGcERFN+7Rb7zTrQdNL+1ztGr9ZWQx1B+1/A6eTujReCGwMnEZqoz+y08FVVad7tDRrNbm51UhCZmYMXbM/CDiXdPEzACSdBBwq6ejsBiiz9vAVXbOOGSrZbwwc3kj0mW+Q7pbdALi9U4H1g8U/tsSDkuEPwawHhroAuwzwZKGsMVvVhPaHY2ZmnTCam6oa1dUK3WpZfr2o7LqCbVYfw+la2ar/fNv61ZuZWWcNp2Y/XdK83OtGjf40Sfk7ViMimk4eYv3t6qs7vIN2/QTxBWCrsaGS/dWkGvzyhfLZ2XOx3PpYq1y5zwi30+rksO22I9yQmQ3bUHfQTulSHGZm1kEeDsHMrAZKN8SxlcMNN0zjhhtOHVA+daqvy5tVkWv2ZmY14Jq9LWbq1HhxnJ1JkxbNQztjhm+rMKsyJ3sbkdYjWVaYu2RaDbgZx8ysBkpZs5e0JGmGrINIwynfQxqA7YyICEmvBW5q8tbpEXFE9yKtn+1umdG0fPamU7sciZmNRCmTPWn+2aOBE4AbgDeTxtFfFvgSsDnwFLBT4X0PdC9EM7PqKF2yz2r1hwNfjogvZMVXSlqVNMH4l4BJwJ8j4oYehVk7U6dG0/b6VjX9vuY2fqug0iV7YAXgbOCnhfJbgVUlLUdK9j2fm6nTM1JZj3lYUOsjpUv2ETGXNOdt0TuBf0TEU5JeDTwjaQ6wGXAvcEJEfL/ZNiXNKhRNblvAfWiLO38MwN7zrujqfj1mjlnnlC7ZNyPpQFL7/EclrQmsQppF65PAXOB9wFmSIiLO7nZ8zWakMjMrk9Ine0n7AGcCM4GvAy8BdgX+FBGNWbOuyE4Cx5KagBZTHNAtq+lv17moq9EC0KrP/PkDrnsPzb10zMqt1P3sJR0OnANcBOwTyYKI+GUu0TdcCmwgydMlmpkVlLZmL+lEUjPN2cAHI+K5rHwTYEfgexHxTO4tywALSF0yrUta1dzdSyfjHjpWEqWs2Us6jJTovwrs30j0mbVIN1i9Lbe+gD2AX4cb0M3MBihdzV7SGsAXgT8BPwLeWOjieB1wDXCmpBWBB4GppO6Y23Q3WjOzaihdsiddfB0PvBq4vsnyVYF3AScCnwNWBn4P7BwRv+tWkFXSl4OXmdmIlC7ZR8RZwFnDWPVDnY3E2sG9dMzKoZRt9mZm1l6lq9lbfxiql45r/Gbd5WRv1kkeNM1Kwsm+Dapwt2xZdKNfvsfYMRvIyd6sTvxLo7ac7IfBQxn3Vqua+lqPpuf753UtlM5zMrYOcW8cM7MacM1+BJqNxFCm9vot7pzJ3vNu63UYY1KbXjoj/cNxjd/GyMnebIR8AdiqyMneSsGjZ45SmX5aWqk52VdQs7Fuxj3a8yl5zazEnOytNtz8YnXmZG+V0Kw55/Ds+dRHbhiwbCQXdH0SsDpwsjez1twLqG842ZeYx6EfvIa+Vnad4l0rT3qxrNUF3aEu9DbbT6safyf17FeGL/T2PSf7HN8p2z960YunWaJ2U5CVRaWTvaSDgE8AawNzgMMjotnsVm3RycqPa/HdUazBj3TI5ZH+Qijb9YC2xTPS5h03B/VcZZO9pP2AM0lTE94IHApcJmnziLh7LNsu+52y1lq777R1P/8R8n+U0qpksldqbzkemBERx2dllwO3Ah8HPtrD8AblGny5jPZmrpH+QhjgluGtll8/pnZ/yIiR/hLo2S8HG1Ilkz2wEbAu8PNGQUQslHQx8JaxbtyVEyvjWDya0cFfGaM4+YyoiavJyUozZsAgxzSik1ubTg7NNtMv55eqJvtNsuc7CuV3ARtKWjIinh9sA7Nnz27LBVnX1A06e3Ioa1PSSOMa6cmq6fojPeG14QTZznNs0xNel6hZ+3TZSXof8ENgjYh4KFd+IPBtYGJEPJ4rn1XYxFbA0q22v8Ya2w07lpeVZJTJh597EoDVxk1ow7ZWzLY1d8zb6qSls2N+tg3HbMM3Z8GDgy6fvMwaHV2/Ru6PiLXbtbGq1uwbVfJWZ6oXhnj/ksCzQNOeOw8+OHvYgXT4z3Jy9jxnuG94YOETbdjtg9m2RvSmydnznDYEMDIjO+bJ2fOctsfRfpOz5zk9jGE4JmfPc2DkybrLyX1yY7fd3OkobAVMbOcGq5rs52fPywP/zJUvDzwfEU/mV46IKfnXjZp+sbxsqhInVCfWqsQJ1Ym1KnFCdWJt0hoxZlWdqer27HmDQvkGQDnaVczMSqTKyf4+YPdGgaSlgLcDV/YoJjOz0qpkM05EhKSTga9LmgtcCxwCrAJ8pafBmZmVUCWTPUBEfEPSMsBhpBup5gC7RsRdPQ3MzKyEKtn10szMRqaqbfZmZjYCTvZmZjVQu2Qv6SBJt0taIOl6SVv1OqY8SbtJeqJQJkmflnSvpH9LulzSpj2IbUlJh0v6m6SnJP1V0iHZwHSliTOLZWlJn5d0TxbrVZK2yC0vTay5mMZnn+1ZubLSxClpZUnR5DGzhLHuKOk32f/zeyQdL2nJMsUpaUqLz7PxWLetsUZEbR7AfsDzwLHA24BLgMeB9XsdWxbf1lk8TxbKjwUWkEbz3A34LXA/aViIbsZ3HPA08Glgx+z1c8AnyhRnFssZ2Wf5YWBn4CLSzXjrli3WXMwnku4KP6ts330Wyw5ZfDsDW+YeG5cpVmAb0h3yZ2UxH5n93R5bsjhXKHyOWwJTgH8Bl5Eq422LtSd/1L14kIZY+DvwzVzZUqTB077W49jGkyZheQZ4LJ/sSXcFPwEclStbMUtkh3cxxiWzfZ5QKD8DeLgscWb7nZj9Zz88V7YM8G/gmDLFmtv/a4AngUcayb5scQIfAx5qsaw0sQK/Bi4qlJ0MzCpTnC1iPy37G1i13bHWqRmn6bDIQFuGRR6jtwKfJNVATi8s2xKYwOJxzwVm0924VwDOBn5aKL+V9Ie5A+WIE+Ap4I3A/+TKFpJqpeMpz2cKgKRxwPeAL5NqbQ2lihOYBNzcYlkpYpW0Kqlmv9hwkhFxdKQhEkoRZzOSNiPdL3RMRDxCm2OtU7IfcljkLseTdyOpKelrDBzcrRH3nYXyu3LLOi4i5kbEIRHxh8KidwL/IE0NCT2OEyAinouIP0TEXElLSNqAlEwDOJeSfKY5R5FGYT2pUF62OCcBy0q6TtLTkv4h6cjsmk1ZYn016Vf8U5IuzOJ8WNJxkpYoUZzNfIE03Mu3s9dtjbWyN1WNwgrZc3GIxCdIJ73lSD+Pui4i7h9k8QrAMxHxbKH8CRYdU08oDSm9E6k9saxxfoZ0bQHgsxFxq6Q9KEmskl5Bdg0kIp7V4nMslOYzzSpDm5F+NR0B3EManuRkUhPZQsoR66rZ89mkYdBPBbYjNd8tIP1fL0Oci8kqJLsBUyOiMWpvW7//OiX7sQ6L3CuihDFL2oc0B/BM4OukZqjSxQn8jNRWuz3wWUlLk/7T9zzWrKb5HeC7EdFsuO2yfffvAO6NiMav41mSJpB+mXyBcsS6VPZ8WUQcmf37V5JWISX8kylHnEUHAnNJvzwb2vr916kZJz8scl7TYZFLZD4wXmmgt7zlWXRMXSXpcOAcUg+XfSJdOSpdnAARcXNEzI6I44Cvka6LPEU5Yj0UeDnwGUnjsrZ7SL0Dx1GizzQino+Iq3KJvuFSYFnK85k2/h9fWii/nNT+PY9yxFm0O3BBRDyTK2vr91+nZF/VYZFvJ53h1y+Ub0C6ONpVkk4EppOS/Z65n5iliVPS6pIOkFQ8sf+BdIF2LuWI9d2kax1zSc0gC4HNgX1zr8sQJ5LWlDQ1uwCat0z2XJbPtHEyKs5E10iYpflMGyS9HHgFAzs/tPX/VN2SfRWHRb6O1Ed490aBpBVJ7ZBdjVvSYaTmmq8C+0fEc7nFpYkTeCnpguyehfJdSN1EL6AcsR4MvL7wuI30i+n1wI9KEiekk+S3gPcXyt9DivmnlCPWv5J6NO1VKH878ADl+kwb3pA931Aob+//qV73K+1yH9aPkNq6vkC6qeoXpIuyG/Q6tlyMxzHwpqovkfrgH0G6iPMbUg+YiV2Ma43sD+9mBt4IsiXp+k/P48zFO5N0z8LBpJuAvklq/zygLJ9pi7jnsPhNVaWJk3TB80nSSLO7kHqNvADsVqZYSb+MIvvOdyT1cnoBOLhMcebiPQ54pMWytsXasz/qXj2AacC9pBtsrgO26nVMTb74YrIfR7qw9FD2n+2XwKZdjmv/7D9Qq8cqZYgzF++ywBdJN9I9Q2rC2bNMn2mLuIvJvjRxkppsTgTuJp34/wC8u6Sxvg/4Uxbn7aReLqWLM4vnG8DtLZa1LVYPcWxmVgN1arM3M6stJ3szsxpwsjczqwEne7OKkrRRNj7Neb2OxcrPyd6sug4i9d56Sa8nXrHyc7I3q66XAL8njYq4bI9jsZJzsre+IenGbDq3N7RYvpSkj2brPS7pEaXpCt/WYv11JH1D0t2SnpF0n6TzJL2uybqNqeQObbGtbbPlQ47BJGlzSY9mA6UN5g/A70j3jvxxqO1avTnZW1/Ihgp+HWnMkAObLF8BuBo4gTQd5V7AAaRb6C+W9LHC+luREuiOpJuzdiGN7rgmcH02vHNRAHu0CLF4+/5gdgWuiEVD3baSH9N8wAnILM83VVlfkHQS6Xby75Hm7VwjIp7KLf8esDewTUTMKbz3m6T2740j4u7sxHAL6U7RXQrbWSLbxz7AGyKbzEVSANeSho5YPSL+VXjPP0jTzW0YEROGOJYrgR9GxHcHWWcJ0t3BvyEN/fGjiPjgYNu1enPN3iovS3z7kCZpPp80lO3eueWrkcZL+U4x0WdOIM2l22j3PoA0FtCh+UQPkNW2P0oabuOownYuozBwVWYb0rC0lwzjWJbL1r9siFW3B9YhjT56PrB3k1E+zV7kZG/9oJH4zo+If5Dm6Mw3s+xImjD9F83eHBEPRMRhEfGXrGgX4J8R8fsW6z8OXEEaSTFvQbaPYlPOXsCFpBPBUKYAd2bHMZj9SCN4XkoaW2U54L+GsX2rKSd76wf7AndFxG+y1+cAW2UTOMOi+XHvGeb21hvGuncDEyStVCifCewoaSKkWUhIwwD/eJj73pU02FVL2exQe5B+qTwbETcBvwWmDnMfVkNO9lZpWbPHHkD+xqKZpFp0o3b/fPY83L93Ac8NsU6r5b8gDaf7juz1NqT5QodswsnsytBNOO8hjUD53dwMV98EtpC0xTD3YzXjZG9Vtwepjf6HjYKsmeX/gA9kc842aukvb7URSWvnXv59sHUz6wFPRcRj+cJI01teyqKmnD2BiyJiyCYcSetl+509xKr7kf7v3smiGa3+J1t20FD7sXpysreq2xf4Y0T8tVB+DmmM/d2BX5Fq4m9ptgFJqwN/l/SZrOgiYO1m/emz9ZclTYjS9BoA8BPgLdmvjvcA/zvMY9kVuCYiFrRaIZvCbgppxrDiLFfnAv+V7ddsMU72VlmS1gJ2YPEmnIbLSF0dD8xq3+cAUyW9qsm6nyc13fwoe3026dfAN1v0cPkKqWnmlBahXUiadOLTpCkS29mE8wFSTf6bEXFT/gGcmcW196BbsFoaN/QqZqX1flKFZZXiTVGZ+0gXS9cldZN8I/BrSaeR+sRPJDWJvBM4JCJuh9QUI2kv4GLgJknTgb8Bq5OaSbYndcv8bbOgImK+pCtIU8n9ZJhNOONIvYaOHWLVfYFfRsT8JsuuIzVBHUS6F8DsRU72VmUfyJ6PGGK9D0bEZyW9CTgceG/2nqdJd8nuEhGX598QETdKeg1pKIIjST16HiO1p2+V1aQH8xPSzU7D7YWzJekawJ9arZDd1bsJaQ7lASIiJP0Q+JSkVw+2Lasf30FrZlYDbrM3M6sBJ3szsxpwsjczqwEnezOzGnCyNzOrASd7M7MacLI3M6sBJ3szsxpwsjczqwEnezOzGnCyNzOrASd7M7MacLI3M6sBJ3szsxpwsjczqwEnezOzGnCyNzOrASd7M7MacLI3M6sBJ3szsxqobLKXtKSkT0i6Q9KTkn4jaYdex2VmVkaVTfbAkcCJwPeA3YE7gUslvaaXQZmZlZEiotcxjIqkvwE3RsS+2eslgbuBn0fEIT0NzsysZKpcsx8PPN54ERHPA/OBlXoWkZlZSY3rdQBjcAbwWUk/A24C9gdeCXy6uKKkWYWi15JODHd0NkQzs1HZCHgkItrWLF3lZP9NYAfgilzZMRHx82G8d+mJEyeuNXny5LU6E1pr992XntdZpxv7ui/bVxd2Nlrd/EDMKmLOnDnMnz+/rdusZLKXJOAyYDPgI8DfgJ2AYyXNi4gz8utHxJTC+2dNnjx5u1mzZnUn4Jzp09PztGnd2Nf0bF9d2NlodfMDMauIKVOmMHv27La2PFQy2QPbAG8C3hsRP87KZkkaB3xJ0vcj4snehWdmVi5VvUDb+M1/Q6H8GmBZYL2uRmNmVnJVTfa3Zc/bFMrfCDwH/KO74ZiZlVslm3Ei4neSLga+IWklUpv9FOAo4KsRMa+H4ZmZlU4lk31mL+DzpK6WKwG3Ax8FvtXLoMzMyqiyyT4iFgDTsoeZmQ2iqm32ZmY2Ak72ZmY14GRvZlYDlW2zL7uDD25evskm3Y3DzAxcszczqwXX7Lts5sz0fNtti5d/yx1GzayDnOzHqFVzjZlZmbgZx8ysBpzszcxqwMnezKwGnOzNzGrAyd7MrAac7M3MasDJ3sysBpzszcxqwMnezKwGKp3sJe0o6TeSFki6R9LxkpbsdVxmZmVT2WQvaRvgEtL8s28Hvk6ag/aYXsZlZlZGVR4b52TglxGxf/b6KkkrA9sDx/csKjOzEqpkspe0KrANsHu+PCKO7klAZmYlV9VmnFcDAp6SdKGkpyU9LOk4SVU9JjOzjqlkzR5YNXs+G/ghcCqwHam9fgHwxfzKkmYV3j+5s+GZmZVLVZP9UtnzZRFxZPbvX0laBThG0ikR8XyPYjMzK52qNnk8mT1fWii/HJgArJcvjIgp+Qcwp9MBmpmVSVWT/R3Z89KF8kaNP7oYi5lZ6VU12f8VuB/Yq1D+duAB4O/dDsjMrMwq2WYfES9I+hTwfUnfBGYCOwH7AR+OiBd6GqCZWclUMtkDRMTZkhYCnwIOAO4DPhQRM3obmZlZ+VQ22QNExHnAeb2Ow8ys7Cqd7PvJwQcPLPvWt7ofh5n1Jyf7YWqWjM3MqqKqvXHMzGwEnOzNzGrAyd7MrAac7M3MasDJ3sysBpzszcxqwMnezKwGnOzNzGqgK8le0i6Slu/GvszMbKBu1ewvAV6RL5B0eDZxuDUxY4aQWj/MzEaiW8l+sewkaUngy8A6Xdq/mVmt9XJsHFdPhyFi8Um3XKs3s9HwBVozsxrwqJcV1aqGX/wlYGYG3U32K0haqbDffNmLIuKx7oVlZtb/upnsL2tSdmWLdZcc7kYljQfmAL+JiP1HHlbvzZgx/Hb4VjV3t+Wb2WC6lewP6OC2jwU2BX7TwX2YmVVaV5J9RHy/E9uV9Brgo8C/OrH9bps6dfFau6clNLN26foFWkkbAVsDL8uK7geujYh7RridccD3SP31393WIM3M+kzXkr2k/wDOBLZlYB/7kHQ5cEhE3DHMTR4FLA2cxBDJXtKsQtHkYe7DzKwvdCXZS9oAuA5YCHyedGH24WzxWsAOwIHAtZJeFxH3DbG9VwCfBnaMiGd9cdLMbHDdqtkfC8wDto6IfxaW3QJcKek04FrgGODgVhuStATwHeC7EXH9cHYeEVMK25gFbDe80M3Mqq9bd9DuAHyhSaJ/UUQ8ApwK7DLEtg4FXg58RtK4rO0eQLl/m5lZTreS/WrAncNY72/AmkOs825gbWAuqVloIbA5sC+wUNJ6ow/TzKw/dasmvBSwYBjrPc3QMR0MFMfG/wFwG3A88MCIozMz63OVa/aIiFuLZZIWAI9GxE09CKlUml2sPuKIIzxmjlnNdTPZv0/Sm4ZY5+VdicTMrGa6mewPG+Z6I66CRsTkkb6n3zSruU+fPp0jjjiiB9GYWdl0a7gEj5tPY8CzwwG44YZTexuMmdVKN++g/QhwCLAucBcwAzgjIl7oVgxVc3CLuw08Zo6ZjVS37qD9b+B04FbgQmBj4DRSG/2R3YihTLbccjqTJk3vdRhmViPdal45CDgX2Cwi/jMiXgt8CfhwNvm4mZl1ULeS/cbAWbH4VcRvAMsCG3QpBjOz2upWsl8GeLJQ9mD2PKFLMZiZ1VYve8k0avkestLMrMO62c++Vf9539rZr9ydyKw0upnsp0ual3vdqNGfJml+rjwi4l3dC6seWo3572EUzOqhW8n+alINvjiA2ezsuVhuZmZt1K07aKd0Yz820CmnnMK0adMGlLd1dq9WzTVmVhqVG/Wy0+qWt9y8Y1YPTvYdkMbAMTMrDyf7moqIpj9jNGNGD6Ixs05zsu+gqVMHNoXcfHMPAhkFN++Y9RcPPWxmVgOu2dtiYurUpuVu3jGrtkom+2ykzMNIo2m+HLiHNLDaGVGDdgbfmGpmI1XJZA98BjgaOAG4AXgzaXz8ZUlDJ9dT8SzQuEDQpJ992/ZhZpVQuWSf1eoPB74cEV/Iiq+UtCpwBHVO9v3KP2XMxqxyyR5YATgb+Gmh/FZgVUnLRcRT3Q+rxKpSG69KnGYVVLlkHxFzSXPZFr0T+EezRC9pVqFocvsj6559rm6RFLft/L5bXahtdWHXzMqhcsm+GUkHAjsBH+11LGZmZVT5ZC9pH+BMYCbw9WbrFAdiy2r623U6tn7S1i6Z+eaaxkXk224bRVRmNlyVvqlK0uHAOcBFwD516HZpZjYala3ZSzoR+CTpYu0HI+K5HofUc1dfvfjrux7N/jGp8/t2W75ZuVUy2Us6jJTovwp83DX6mnKXTLNhq1yyl7QG8EXgT8CPgDcWBu26ybX87inl8Ao+CZgNULlkD+wKjAdeDVzfZPmqwL+6GlGHtOxiWSFu3jErh8ol+4g4Czirx2EAnqSk7/kXgvWRyiV7q4ZSNu+Y1ZiTfRs0m6SkTIq9dBq27cIdt7XnXwdWEk72Vh9OvFZjlb6pyszMhsc1e+uJRtv94dnrU2+4AXAvHbNOcbK3UulJV00PrWw14GRvA3Tygu6ApJ0NhNao2VeaTxpWYk72w+D+9J3nrppmneULtGZmNeCa/QiUvT+9dUknm2vcPdQ6xMm+JHoxDk6rtvkyGmlzTul79Yz0hDHS9X1ysAIn+y7b4s4fA7D3vCt6HEm9DHWyKP3JwWyMapvsb7vNnSeqYKRJuJHUa39h181BVlDbZG/tM9Kumvn118pm07p/XnfG6imePGp3UvBJoLac7HPcxbK9enFNYLTNMe1K+m4OsrJysjfrEF8nGAP/Amk7J/sm3MWyuSr13hmudiXc0VwrKMuvCc2YAYPEMqIpnnt1Icy9lYZU6WQv6SDgE8DawBzg8IhoNlVh17XqSnk+O3U5Euu1slwnaOt+q5LU27GdPjkxVDbZS9oPOBP4HHAjcChwmaTNI+LungZntTOS2nUvf00MptVJSRrZtayRntzGun7PVKypqZLJXumv73hgRkQcn5VdDtwKfBz46FDbePDB2W25INsPk4LXXbsGfmu2nTLOBtbpZDnSk0+n129l2CeZke6vXU1ibVbJZA9sBKwL/LxREBELJV0MvKVnUdmY1Gn6xHYca0yd2t0RSofQ6Rp8u5u/etGcNtJfSW3ddy/PNKMl6e3ARcAmEXF7rvzjwCnA0hHxfK58VmETWwFLr7HGdgO2/bJ5t3Ui5Bc9/NyKAKw2bm5H95P29WS2rwkd39doLZ3F+GwJY5z40pGtP3/e8LfRbN12akfso9nOSLffSrv228zsBx8cdPnkZdZoeyxD7bOF+yNi7bHvPalqzX6F7PmJQvkTpJE8lwMeH+T9SwLPPvjg7AEXc0f1lYxI2sMDCzu+o4bJDyx8AtIF7PJaWPwqR21y9jxnzFtaMOYttGcbrU3Onud0bL+djX+k+52cPc/p5K7nLChkgd58BlsBE9u5waom+8ZvoVY/S17Iv4iIKYu9OavpF8v7UZ2OFep1vHU6VqjX8TZpjRizqo5nPz97Xr5QvjzwfEQ82eV4zMxKrarJvtFOv0GhfAOgs43uZmYVVOVkfx+we6NA0lLA24ErexSTmVlpVbLNPiJC0snA1yXNBa4FDgFWAb7S0+DMzEqokl0vGyRNAw4jJfk5wLSyDJdgZlYmlU72ZmY2PFVtszczsxFwsjczq4G+TPaSDpJ0u6QFkq6XtNUQ679K0pWSnpR0r6Sj1MtBLEZoFMe7taRfSZon6QFJZ0t6WbfiHYuRHmvhvcdKqlS75Si+21Wz7/Ox7Pv9uaQNuxXvWIzy7/gaSU9Iuiv7fpfqVrztImk3SUPeQj7mPBURffUA9gOeB44F3gZcQho6Yf0W668GPARcka1/DPAccESvj6VDx/sK0g3gPwfeCvwXcCfpAvdSvT6edh5r4b2vAp5Jf/K9P5YOfbdLZd/jLcB7SF2T/0IaDXbpXh9Pm491Q+BJ4FJgF9IQ5/8GTun1sYzwuLfOjvPJIdYbc57q+cG2+YMT8Hfgm7mypYC7gK+1eM/xwL+AZXNlJwCPViD5jeZ4z8iWL5Urez1p6Im39fqY2nmsufWWBH4L/KMqyX6U3+0Hs4T38lzZZOAB4LW9PqY2H+tRpErLcrmyE7PEqV4f0zCOeTxp4qVngMeGkezHnKf6rRmn6dDHwGBDH+8EXBkR/86VXQCsREqCZTaa4/0LMD1br+HW7Hn9TgTZJqM51oaPk4bSOL1j0bXfaI733cClEXFv7j1zImLNiPhdJ4Mdo9Ec63hgIYsPU/YoMCFbVnZvBT4JHMnw/i7HnKf6Ldlvkj3fUSi/C9hQ0pIt3tNs/fz2ymrExxsR34iIMwrF78yeb2lzfO00mu8WSRuRakUHkWpRVTGa450E3JK1XT8k6RlJF0t6eUcjHbvRHOsPSM0+J0laSdLrgY8BP4uIpzsWafvcSGqi+hqtB3TMG3Oe6rdkP5yhj5u9p9n6+e2V1WiOdzGS1iHNAXATcFVbo2uvER9rdvHqO8DZEXFNZ8Nru9F8t6sCB5Bqwx8EPgBsBlwsqcx3y4/4WCPiTuCI7PEoqZnuYdLxl15E3B8R80bwljHnqTL/AYzGiIY+zr1nJOuXyWiOd9GbU6K/kvQf6j8jawgsqdEc68GkJoLdOhJRZ43meJcClgbe2kgkku4i1SL3AP63zTG2y4iPVdKBwLeBGcD5wJqk+agvlrRTRFTpV9xwjDlP9VvNfjRDH89vsX5+e2U16qGeJb0KuI5UK9g5qymV2YiONTuRfYk0nMa/s5rtEtmycZLK/rc/mu/2SeA3+RpjRNwEzANe3YEY22U0x3o08IuIODgiroqIc0m9VN4E7NO5UHtmzHmq7H/wIzWaoY9vb7E+LLpwWVajGupZ0huBX5PaPN8cETd3Jry2Gumx7kj6zzCTdCFvITA9W7YQ+GwHYmyn0Xy3d5Bq9kXjGF67cK+M5ljXAW7IF0TELaQmnc3aGl05jDlP9WOyH+nQx1cCO0nKtwvuTvqjmdOJINtoxMcraX1SH+aHgK0jN4dvyY30WC8k9VLIP07Nlr2e9PO/zEbzt/xLYBtJa+besx2ph8p1HYt07EZzrLeR+qi/KLsYvzJwd0ei7K2x56le9zftQP/Vj5DasL5A+ln3C1Lf2w2y5RsCW+bWX4P0M3c28A7g01TrpqqRHu//Zcf3PmDLwmONXh9PO4+1yfs/RkX62Y/yu12VdBL/Y5YI/gu4nzQE+BK9Pp42H+tepF8r3yH9ituHdNK4G1i+18czwmM/jkI/+07kqZ4faIc+vGnAvaQbTK4DtsotO6v4Hx54XfYf4mngHuCoXh9DJ46XdAFvYfafpNmj9Ce4kX63hfdWKtmP5nizJHEBqafGY9k6L+31cXToWPcAfk/qUnsv8F1gtV4fxyiOu1myb3ue8hDHZmY10G9t9mZm1oSTvZlZDTjZm5nVgJO9mVkNONmbmdWAk72ZWQ042Zt1kaQ9JM2XdEivY7F6cbI3666DgfeS7mA26xone7Puep40d0DLEUnNOsHJvoIk3SgpJL2hxfKlJH00W+9xSY9IukrS21qsv46kb0i6O5vd6D5J50l6XZN1I3sc2mJb22bLByQzST+T9LFC2Suz9f/SYnvHZcv/2WpY4uzYQtIR2ev1cnE2Hs9LejSbuWmL3Hv3b7JuSHpC0u8l7ddsn01iOEzSBcNYdWPSXKK7DGe73Zb7vPOPF7Kmp+skvb3N+2t8/qsMss5Zkv7czv3WkZN9xUh6BWmMjFuBA5ssXwG4mjQZ8SWkAaMOIE06fXGTZLsVaeCsHYEvkpLQUaTJIK7PJokoCtK4JM3s1SLuccAOwGWFRftmx7KZpK0HvHHR/lYD3txku6sB27Z436eArbLHm4EPkaZw+1V+ZMjMW3Lrbg3sT6p9nzXMBLcrA4+tmUey58clvWQY6/fCAhZ9Fo3P7sOk0TMvkLR5G/d1cbaPeW3cpjXT60GA/BjxoEknkSYNn0YaFXC5wvLvAU8Bk5u895ukkfLWz16vQDoJXNtkO0uQBmNaCLwmVx7ANdl2VmnyngdIJ4/iwE5vBu5tsv4/SJMu/wn4XpOYjyMl3TnAV5ss/1C2vxcHcgPWy17v2WT9bbJlR2ev989er9Jk3QmkQaf+d4jvZHz2mW84xHobZ/s6K3v+QK//nlp93i2WbZTF/aUux3QW8OdefzZVf7hmXyFZM8Y+pBrk+aRktHdu+WqkmvJ3ImJOk02cAJwBLJu9PoA0dOqhEfFUfsWIeAH4KGkEwqMK27mMlAR3L5RvQ5ow5JIm+96VNN563o7AWtn2zgPeK6k4G0/DT4B3Z/PK5u3FyKbb+332vO4w1n2W4U1S/mbgwRh6tq99SW32RwB3kiZBH1TWzPGkpF0k3SLpKUmzJU0urLeRpAuy5qd5ks7JN41kTSEXSPphto3/G8ZxFRXnQEXScpJOz5rZFkiaJek1hXWOlHSHpKcl3SnpM40muWIzjtIsYicrTZj+uKRTgSUL23uxyS5XdoGkWbnX4yR9TtK92X5vkrTjKI65bzjZV8v2pBl6zo+If5DGts43s+xI+o/xi2ZvjogHIuKwiGi0j+8C/DMift9i/ceBK0iTSOQtyPZRbMrZizRpyNNNNtesmWNf4JZIM2X9kHQSatVL5SekY399o0DSqsB2wI9bvKeZjbPn4gQXS2YJYpyk8ZI2AL5F+vVz7hDbHLIJJztJfQC4KCL+BZwJvFnSpsOIeTzwA+AbwH8Cy5CaolbLtv0y0q+tdUmf6YdITSO/lJSfuertpP/zuwGnDRHvuNzjJZI2A/6H9IvuR7lj+nkW0zGk7/9pYJakDbN13k+qZJxK+py+AxxP6xPdaaRKxsmkv4XNyVVoRuDbpF+/XyVVSm4BLhmkqbD/9fqnhR/DfwDfB+7Mvf5/pJ/Vm2Wvj8xebzrM7f2FNGfpYOuckm1zpex1kGqm7yXVeidm5SJNlPFuCk0BwCqk5qAVc2UTSM0zn82VXQ38trD/F7cF/BU4ObfsYOCP+biyf6+XvX4vaUq+ccBypBPFb7L9rpWtuz+tx/f/C2ki9qE+x5uBdw2xzvbZNnfOXq9E+tU0fYj3NeL7UK5sVVKz0bHZ65NIbd6r5NZZn5SY981en5VtZ8Uh9ndci8/iedLE5Tvl1t01W5YvG0e6BvO97PWZpESr3DpHAO8sHN8q2WfyHLlx2oGXAP8k14yT/65zZRcAs7J/b5qtc2BhnSuBq3r9/7hXD9fsK0JpOrI9SM0dDTNJNalG7f757Hm436tI/7kG02r5L0gzC70je70NqRbcrAlnZ+B3ETE3V/YeUgL+Ya7sHOD1klpNjv0TFv81sReD1+rPZ9H8s08CvyUllb0j4v7CujuRTgY7Ar8iTYaxT0T8aJDtk13o3RS4arD1SDXuO0i13nGk6y3nA/sWat+tvBhHRDwCXM+iC9bbZ6/nNWrjpGn+/podT8Mjhe+glQUsmsrx7aRrIn8hXQO5Irfe9qQT1uzcfiE11zX2+2vgP4AbJR0t6VURcUpEXNhkv28k/TJ98W8oIp6mxS/VQUzJnn+R/4WSbedNw/y8+46TfXXsQaoNv5gcIzWz/B/wgewP+J5s0ctbbUTS2rmXfx9s3cx6wFMR8Vi+MCKeBC5lUfLdk9REMZImnBsj4o5c2Y9JvxZa/cSfCWws6dVZG+9QTThHsShpvQZYOyI2jIiLm6z7x4i4KSKuIiW4p4BLJa0xyPYhNYVdHxED2rMbshP1nqQLnM+y6AS0P+nk8+4h9vF0RMwrlD1CqglDmnf1LbntNh6vJl2TaXh4iP00vJB9FjdFxC9Ix7g66fPIz4G6MqnpLX9MC4FDGvuNiB9kx/kCcCLwJ0l/VJNuvcCK2fO/CuUPDTPufFyQfmnm4zqFNFtby26e/czJvjr2JSWkvxbKzyH98e5OqpE+R/qPP4Ck1YG/S/pMVnQRsHaL/3hIWpZUK29Vs/oJ8JYsAbyH1hdKdyF3cVbSOqTaV75WT5bQLgT2kTS+uJGI+CPpwuYepOP9a0Tc2mKfAHflktacJrX5piJiATAVeBnw9SFWb3bhuahxot6VgZOg/y3b12Bekn0XeauxKHnPJ9WGi9t+PdD0foiRiIiHgY+TfsF8LrdofhZDs/1unXv/9yPiDaQTxoGkpH5Ok109mju2vJWLKzIwd00oxBWk6xbNYiueTGrByb4CJK1F6qN+XpPFl5FqeQdmte9zgKmSXtVk3c+Tmm4aTQJnk34NfLNFL5ivkJpmTmkR2oWkNtpPAy+lSROOpEmk2t9vcsXvz57Pb7LNc0k11ve02OdPSIl+D0Z2YXZEIuIa0sloD0nbN1sn61GyM0P3r9+XdC3il7mTz00RcRPpwuv2jQuag2g0lzV6XW1FOrlDuji7KfCn3Hb/TGp/f9MQ2x2WrIZ+LXCopP/I7XdV0jWV/DHtQ/YdS/qOpJnZNh6OiO+S5opt9ovyetIvuxeb6rLml50L6z1Oug+ksc5ypF9uDdeQ/s5XKMS1E+mkNVTTZV8aN/QqVgLvJ52YV1HhpqjMfcCOktYlNV28Efi1pNNI/0EnAvsB7wQOiYjbITXFSNqLdGPLTZKmk2qaq5OaUrYndcv8bbOgImK+pCtIF9x+MkgTzpURkf8P9gHST/O9B/akZCnST/4DKdT8MzOBTwCvJP3H7aRPkRLPVyRtEak7at5rSTXIpr2Z4MVmsx1IMTfzQ9JJ+EDgk4PEckZ2Qn4E+CxpMvEzs2Wnkk4ol0j6KqnJYhrphHDMINscqWnADcB00snnQtJF219IOp50nWNP4COkHkGQeoydLelE4HJSj6oPAz8tbjwiHpf0ZeBoSQuAP2Trrk76RddwCXCApN+Tfll8gvQ9NLYzR9JPgHMlHUf6m55C+iy+1OR7rIdeXyH2Y+gHqZbWqsdI/vG5bP0VSd3d/kLqG/0IqQvlzi22vxYpYdxOujh3PykJva7Juov1hGBRj6A9cmXHsagHzRXAwbllrx/msbwAbEiTm3xIv0ZubhUXg9xU1eR49qfFTVXZ8pOy5VObLDsGOG+I7X8ye/+6g6xzLfAgsNQg8X0AuIt0ofn/gPUK621GapZ7glTz/RWwdW75WQzjxqRmn3dh+XlZPLtkr19K6qL6z+xv52Zg/8J7DiEl3AWkk/zXgQnNPn9SjfxTpJvtniL9+vwqi/fGWYPU++bf2fY+QzoBzcqtM550R/h9pF8Lt5F6q2moz6BfH8o+GDMrIUn7k/q3rxqpf77ZqLjN3sysBpzszcxqwM04ZmY14Jq9mVkNONmbmdWAk72ZWQ042ZuZ1YCTvZlZDfx/deRQCXMKrjgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 504x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'arial',\n",
    "        'weight' : 'regular',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "plt.rc('xtick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.minor', width = 1.5, size=2.3)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3,figsize=(7,8))\n",
    "\n",
    "ax1.hist(mpr_nc_data, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,500), histtype=\"stepfilled\")\n",
    "ax1.hist(mpr_ib_data, bins=50, density=True, alpha=0.6, color=\"r\", range=(0,500), histtype=\"stepfilled\")\n",
    "ax1.hist(mpr_dec_data, bins=50, density=True, alpha=1, color=\"k\", range=(0,500), histtype=\"step\", linewidth=2)\n",
    "ax1.set_xlim([0,500]); ax1.set_ylim([0,0.015])\n",
    "ax1.set_xticks([0,100,200,300,400,500]); ax1.set_yticks([0,0.005, 0.01, 0.015])\n",
    "ax1.set_yticklabels([0,5, 10, 15])\n",
    "ax1.axvline(med_1 := np.median([n for n in mpr_nc_data if n<500]),color=\"b\",alpha=0.6)\n",
    "ax1.axvline(med_2 := np.median([n for n in mpr_ib_data if n<500]),color=\"r\",alpha=0.6)\n",
    "ax1.axvline(med_3 := np.median([n for n in mpr_dec_data if n<500]),color=\"k\",alpha=0.6)\n",
    "ax1.set_xlabel(\"\\u0394MPR / Residues\", family=\"arial\")\n",
    "ax1.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "print(f\"MPR Medians :: \"+ f\"NC: {med_1:4.4f}, IB: {med_2:4.4f}, DEC: {med_3:4.4f}\")\n",
    "\n",
    "\n",
    "ax2.hist(com_nc_data, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,70), histtype=\"stepfilled\")\n",
    "ax2.hist(com_ib_data, bins=50, density=True, alpha=0.6, color=\"r\", range=(0,70), histtype=\"stepfilled\")\n",
    "ax2.hist(com_dec_data, bins=50, density=True, alpha=1, color=\"k\", range=(0,70), histtype=\"step\", linewidth=2)\n",
    "ax2.set_xlim([0,70]); ax2.set_ylim([0,0.1])\n",
    "ax2.set_xticks([0,10,20,30,40,50,60,70]); ax2.set_yticks([0,0.025, 0.05, 0.075, 0.1])\n",
    "ax2.set_yticklabels([0,25,50,75,100])\n",
    "ax2.axvline(med_1 := np.median([n for n in com_nc_data if n<70]),color=\"b\",alpha=0.6)\n",
    "ax2.axvline(med_2 := np.median([n for n in com_ib_data if n<70]),color=\"r\",alpha=0.6)\n",
    "ax2.axvline(med_3 := np.median([n for n in com_dec_data if n<70]),color=\"k\",alpha=0.6)\n",
    "ax2.set_xlabel(\"\\u0394COM / Å\", family=\"arial\")\n",
    "ax2.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "print(f\"COM Medians :: \"+ f\"NC: {med_1:4.4f}, IB: {med_2:4.4f}, DEC: {med_3:4.4f}\")\n",
    "\n",
    "ax3.hist(commpr_nc_data, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax3.hist(commpr_ib_data, bins=50, density=True, alpha=0.6, color=\"r\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax3.hist(commpr_dec_data, bins=50, density=True, alpha=1, color=\"k\", range=(0,1), histtype=\"step\", linewidth=2)\n",
    "ax3.set_xlim([0,1.]); ax3.set_ylim([0,8])\n",
    "ax3.set_xticks([0,.2,.4,.6,.8,1.]);  ax3.set_yticks([0,2,4,6,8])\n",
    "ax3.axvline(med_1 := np.median([n for n in commpr_nc_data if n<1]),color=\"b\",alpha=0.6)\n",
    "ax3.axvline(med_2 := np.median([n for n in commpr_ib_data if n<1]),color=\"r\",alpha=0.6)\n",
    "ax3.axvline(med_3 := np.median([n for n in commpr_dec_data if n<1]),color=\"k\",alpha=0.6)\n",
    "ax3.set_xlabel(\"\\u0394COM/\\u0394MPR / Å per Residue\", family=\"arial\")\n",
    "ax3.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "print(f\"COMMPR Medians :: \"+ f\"NC: {med_1:4.4f}, IB: {med_2:4.4f}, DEC: {med_3:4.4f}\")\n",
    "\n",
    "ax1.set_box_aspect(1/2.5)\n",
    "ax2.set_box_aspect(1/2.5)\n",
    "ax3.set_box_aspect(1/2.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "# fig.savefig(fname=\"COMMPR_YEAST.svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "human_con_rg = np.load(\"human_con_Rg.npy\", allow_pickle=True)\n",
    "human_nc_rg = np.load(\"human_nc_Rg.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09666880405197524 0.16006302360801186\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAFSCAYAAABG/JyrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdbElEQVR4nO3debRcVZn38e9jmENAZFCIUS8grS6FdGuroDZBmRRF0ZYFrlZbIcQRYwenaAuK0rYILw6oNPqKtsP7qt3a4IxIQERto281REVE5lEIJASSMO73j30uHCt1q+rWvbuGm+9nrVpFnXPqnOcebn731K599o6UEpKk6fWIQRcgSTOR4SpJBRiuklSA4SpJBRiuklSA4SpJBQw0XCPi0IhY07Ts6RGRWjw+Nqg6JWmyNhnUgSNiH+DLQDSt2gu4G9i/afmN/ahLkqZD38M1IjYH3gacSA7RzZo22RNYkVL6Rb9rk6TpMohmgRcC7wHeAXyyxfo9gUv6WpEkTbNBhOuvgLGU0ieAVvfePg2YFxGNiLg3Iq6IiNf2t0RJmpq+NwuklG6YaF1E7ALsADyRfHV7B3AkcFZEpJTSl1q8Z1nToqcDq4ErpqtmSRuN3YFbU0p/PdUdDewLrQncARwEXJpSuqla9uMqdI8HNgjXFjbbdttt586fP39uqSKn03XX5ed58/p1vOuq4/XpgNIIaTQarF69elr2NVThmlJaB/yoxaofAAdHxNYppbua3rOg/joils2fP3/fZcuWFatzOp1ySn5esqRfxzulOl6fDiiNkAULFnDBBRdMy6feobqJICL2iIg3Vj0K6rYE1pF7F0jS0BuqcAXmAp8GXjS+ICICeDnw0+Tgs5JGxFA1CwAXAhcBn42I7YCbgGPI3bOeM8jCJGkyhurKNaX0APBS4FvAB4H/BHYCDkgp/XqQtUnSZAz0yjWldAJwQtOy24E3DKIeSZouQ3XlKkkzheEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUwCaDLmDULVrUeZszzihfh6Th4pWrJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAQMN14g4NCLWNC2LiHhvRFwbEWsj4tyIeNKgapSkXgwsXCNiH+DLQDStej/wPuBjwBHAtsB5EbFtfyuUpN71PVwjYvOIeCdwPnB/07o5wHHACSmlT6SUzgYOAuYAR/W7Vknq1SCuXF8IvAd4B/DJpnXPBrYGzh5fkFK6A7gAOLhfBUrSVA0iXH8FjKWUPgGkpnV7VM9/alp+ZW2dJA29Tfp9wJTSDW1WbwPck1K6t2n5mmrdBiJiWdOi+T0XJ0nTZNi6YgUbXs2Oe7CfhUjSVPT9yrWD1cDmEbFpSum+2vI51boNpJQW1F9XV7L7lipQkroxbFeufyRfvY41Ld8V+EP/y5Gk3gxbuF4MrAdeNr4gIrYjX4meN6CaJGnShqpZIKV0V0R8EjgxIh4ELgfeC9wJfG6gxUnSJAxVuFaWkr+8Oo7c5/Vi4LUppZZtrpI0jAYarimlE4ATmpbdD7y7eswIixZNvO6SS/LzkiX9qUVSfwxbm6skzQiGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGGqyQVYLhKUgGbDLoAZYsWdd7mjDPK1yFpenjlKkkFtA3XiDgwIub0qxhJmik6Xbl+H3hyfUFE/FNE7FiuJEkafZ3CNf7iRcQs4GRgXrGKJGkG6KXNNTpvIkkbN7/QkqQCDFdJKqCbfq7bRMSjmravL3tISun2aatMkkZYN+H6wxbLzptg21lTqEWSZoxO4fq6vlQhSTNM23BNKX2xX4VI0kzS9dgCEbE7sA/w6GrRDcDPUkrXlChMkkZZx3CNiL8CPgv8HRv2cU0RcS7wlpTSFQXqk6SR1DZcI2JX4GLgPuBD5C+y/lytngs8Hzga+FlEPCOldF3BWiVpZHS6cj0eWAXsk1K6pWndZcB5EXEa8DPgfUAXA+dJ0szX6SaC5wMfbhGsD0kp3QqcChw4nYVJ0ijrdOW6E/CnLvbze2CXqZcjlbV48WIajUbH7ebPn89pp51WvB7NXJ3CdVNgXRf7Wd/FvqSBazQaLF++nLGxsQm3ueqqq/pYkWYqA1EbnbGxMU466aQJ1y9durSP1Wim6iZcj4yI53bY5nHTUYwkzRTdhOvbutxXmkohkjSTdLr91SEJJakHHcMzIt4UEb+LiLsj4tKIeGtEGLqS1Ean2V/fDHyKfNvrOcC9wGnAvxavTJJGWKcr0IXAl4GnpJSOSCk9Hfgo8MZqskJJUgudwvWJwFkppfqXVZ8GtgJ2LVaVJI24TuG6JXBX07Kbquetp78cSZoZevliavwq1im2JWkC3YTrRP1X7dcqSRPo5iaCUyJiVe31+BXraRGxurY8pZReOm2VSdII6xSuF5KvUOc0Lb+gem5eLkmi8x1aC/pUhyTNKN5pJUkFGK6SVIDhKkkFGK6SVIDhKkkFGK6SVIDhKkkFOEHhDNLNtNHXXXcdc+fOZcmSJf0pStpIGa4zSDfTRl9//fV9rEjaeBmuM0ynaaOPOeaYPlYjbbxsc5WkArxyHSGLFrVff/nlsP32/alFUnteuUpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAYarJBVguEpSAUMZrhGxfUSkFo9vDro2SerGsN7+ulf1fCCwprZ85QBqkaRJG9Zw3RO4JaV07qALGRYXX7yYlSsbbbdZubLB9tvP609Bktoa5nC9ZNBFDJOVKxvceuty5syZeKzW2bPntR3LVVL/DHO4ro+Ii4G/AW4DPg58LKWUBlrZAM2ZM8YznznxWK0ACxf2qRhJbQ1duEbELOApwN3AccA1wCHAR4AtgQ82bb+saRfzixcpSR0MXbhWXgxcm1K6onq9LCK2Bt4VER9NKa0fYG2S1NHQhWtK6QHgJy1W/QB4A7A7sKK2/YL6RtWV7L7lKpSkzoaun2tE7BIRx0TEjk2rtqyeb+t3TZI0WUMXrsDmwBnAPzQtfwVweUrp5v6XJEmTM4zNAldFxNeAEyPiQeD3wCvJ4fqyQdZWytVXf5trrjmt7TYrVzaYPds+rNKoGLpwrRwF/DOwGNiZHLCvSCmdPciiSrn77htZt65zH9Z26yUNl6EM15TSOmBp9dgodNOHVdLoGMY2V0kaeYarJBVguEpSAYarJBVguEpSAYarJBUwlF2xZpJOg1zfddfhrF17I9ts07+aJJVnuBbWaZDr+++/m80229YbBKQZxnDtg3Y3CFx99a4APOEJT+pnSZIKs81VkgrwynUKFi9ezDnnNNpu0+8BV04/vf361athq636U4u0MTNcp6DR6G7SQNtTpY2P4TpFDrgiqRXbXCWpAMNVkgowXCWpAMNVkgowXCWpAHsLbIRuvPFGFixY0Hab+fPnc9ppp/WlHmkmMlw3Mptv/ii22AJWrlw54TZXXXVVHyuSZibDdSPz6Ec/i332gcMOO2zCbZYu3WjmhZSKsc1VkgowXCWpAJsF2li0qP36yy/vTx2SRo9XrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgOEqSQUYrpJUgGMLqKVGo9FxQG1wUG1pIoarNjA2Nga0H1AbHFRbasdw1QYWLlzY1XYOqi1NzDZXSSrAcJWkAgxXSSrAcJWkAgxXSSrAcJWkAgxXSSrAcJWkAgxXSSrAcJWkAgxXSSrAsQU0Jd2MntWvkbMWL15Mo9Fou02j0WDevHnFa5EMV/Wsm9Gz+jlyVqPRYPny5Q/V1cq8efParpemi+GqnnUzela/R84aGxvjpJNO6usxpVZsc5WkAgxXSSrAcJWkAmxz3QhdfDHceGP7bd785v7UIs1UhqsGrpsuVN2wm5WGieGqgeumC1U37GalYWK4aijYhUozjeEqtTBMd55pNBmuUpNhu/NMo8lwVXGdrgKH7YuoYbzzTKPHcFVR3VwF+kWUZiLDVUV1cxUozUTeoSVJBRiuklSA4SpJBdjmqpZOP7277RyDQGrNcJ3A4sWLOeecRtttVq5sMHv28HQhkjQ8DNcJNBoNbr11OXPmTNxFaPbseW3XS9p4Ga5tzJkzxjOf6f3ukibPL7QkqQDDVZIKMFwlqQDbXDUl3XTZmqndtRyWUO0YrhoKoxbSDkuoTgxXqQcOS6hODFdpBuh2kkebKfpnaMM1IhYC7wQeCzSAf0op/XygRUmT1K922W4meVyxYgUXXHBBxxA2gKfHUIZrRLwW+CzwQeBXwFuBH0bEXiklG7I2UtPVLtuv/dx55xjz5vWvXbbTJI9nnnkmV111le3EfTJ04RoRAXwA+LeU0geqZecCfwDeDhw7wPKkrj35yQs7hvTSpUu7urrtpJupcrptJ7YXxPQYunAFdgceD5w9viCldF9EfBc4eGBVqWfdjrA1TMfqV83d9DroxnRNlWMviOkzjOG6R/V8RdPyK4HdImJWSumBqRxg8eLFfP3rjbbbOOKVpkPnkF5Im0/yk9gPTMeMOtN5ddtvl1/eeZs99mi/vpsvBbsVKaVp29l0iIgjga8CO6eUbq4tPxo4E9g2pXRnbfmypl3sPWvWrM223nrrCY+xevVqADbZZKu2tcyatSVbbbXzJH+CyVm/fgsAtthifdHjPHy826rj7dCX46l/5s7tvM0NN0z9OGvX3sQDD6yb+o4qm23WeZt77+3PsdavX8+DDz54Q0rpsVM91jBeuUb1PFHqP9jh/bMeeOCBe1evXt2xZ8H996/tuP6ee6b2ca1L89fl39VGPw4GsG7dzZ036mx+9dyYjp31yfzquTHAGiZrfvXcaLfRHXcUr2My5lfPjU4b3n9/0Tome6y9gW2n41jDGK6rq+c5wC215XOAB1JKd9U3TiktqL8ev5JtXj7MRrFmGM26rbk/RrFmaPlJuGfDOHDLH6vnXZuW7wp00aoiSYM3rOF6HfCy8QURsSlwCHDegGqSpEkZumaBlFKKiI8An4qIO4CfAW8BdgD+10CLk6QuDV24AqSUPh0RWwJvI9840AAOSildOdDCJKlLQ9cVS5JmgmFsc5WkkWe4SlIBIxeuEbEwIv4YEesi4ucRsXeH7Z8aEedFxF0RcW1EvKsaHKavJlt37X1zIuKaiPj70jW2OPZkz/U+EXF+RKyKiBsj4ksR8eh+1VvVMNmaD46IX0XE3dX73jpKvx/Ve4+PiL637/Vwrs+JiNTiMfHtlIOvecfq9/j26vf67IjYrauDpZRG5gG8FngAOB54EfB94E5gbILtdwJuBn5cbf8+4H7guGGuu/a+OcBPyHer/f0w1ww8GVhHHnDnhcCrgD+Rv4zcdEhr3hu4D/gCsD95/OD7gLcP87lueu9TgXvyP+Xhrhm4FjgNeHbT4xHDWDOwafX7exnwCnL30N+SR+jbrOPx+vk/ZIonJoCrgc80/fBXAp+Y4D0fAG4DtqotOxFY2cd/8JOuu9pmX+D3wO39Dtcez/Xp1fpNa8v+tqr9RUNa89erfzxRW/YF4IphPte17WYB/w1c389w7fFcP7L6XTi4X3VOQ81HAWuBx9WWzQduBJ7e6Zij1CzQcihCoN1QhPsD56WU6oMIfBt4FPkffj/0UjfkOi/tsE0pvdT8W+CUartxf6iepz4WXme91LwEODJV/2oq9wKblyqyhV5/PyB3U5wDfLJYda31UvOe1fMlZUubUC81Hwb8IKV0be09jZTSLimlX3c64CiFa8ehCCd4T6vt6/srrZe6AZ6XUjoc+HOxyiY26ZpTSp9OKTUPjPeS6vmyaa6vlV5qvi6l9HuAiHhkRLwGeA15Fox+6en3IyJ2J38yW0huFuinXmrek1znhyJiZUSsjYhvRMRjShZa02vNl1Vt2jdHxD0R8d2IeFw3BxylcN2mel7TtHwN+eeYPcF7Wm1f319pvdRNSmlFyaI66KnmuoiYB3wMWE5uNy6t55oj4vHAHcAXgRXAZ0oUOIFJ11194fY54EsppYvKltdSL+d6T/IngjXkK8I3kdu8fxIR/fik0EvNOwKvI1/ZHgW8GngK8N2I6HgD1lDeoTWBXoYijEluX8JUh1AchCnVXAXreeRf2iOaPnaXMpWa7wSeDzyG3Cb/84j466bmpFJ6qXsR+WPuoUUq6qyXmk8FvpZSOr96fWFE/B74BXA48O/TW+IGeql5U2Az4IUppVUAEXEleV6/l5Pb7Cc0Sleu9aEI61oORVh7T6vt6/srrZe6B63nmiPiqcDF5CuFA1JKfypT4gZ6rjmldEdK6fyU0tfIV1V7AP3q+japuqs/XB8l3xq+trqCekS1bpOI6Me/6Umf65TSZbVgHV/2S2AVsFeJIpv08vtxF/DL8WAFSCktJ9f8tE4HHKVw7WUowj9OsD08/GVLaaM4hGJPNUfEs4Cfkru7PC+l1M8vLyZdc0S8LCKav9hcQe6Otcv0ljehydb9AnIgfJNc533AKdW6+4D3F6ixWS/n+oiI+LumZUFuKrht2ivcUC+/01eQr1ybbcLEV8APGbVwnexQhOcB+0dEvT3lZeSuWI0SRbYwikMoTrrmiBgj9xu8GdgnpfTHVtsV1Mt5fjdwctOy/cgfBy+d/hJbmmzd55B7utQfp1br/hb4t4K1juvlXL8R+HjTlfWLgC2BC8uU+Rd6qflHwHMiYpfae/YFtiZ/OmtvEH3OptBX7U3ktpEPk//HfI/cXrZrtX434Nm17XcmX8JfALwYeC+DuYlgUnU3vfcJDOYmgsme6/+qzu2RbNhJfOchrfkl1bk9g3xF+EbgVuB8an1fh63uFu9fTP9vIpjsuT6o2v6rwAHAm8l9uL85xDXvSL5Y+B9yKL8KuIE8DGrHGx/69j9jGk/QEvKdHmvJfz32rq07q/mXDHhGdTLWA9cA7xqFumvrBhKuk6mZfKV3X1Vnq0ff/pj18PtxKPkLirXkzuGnUrvpZFjrbnpv38O1x3P9YvJND3dX5/pjwJZDXvNu5D7na8h/DM4CHtnNsRxyUJIKGKU2V0kaGYarJBVguEpSAYarJBVguEpSAYarJBVguGpoRcTJ1fB0zxp0LdJkGa4aStW8SnuTh3o7dsDlSJNmuGpYbUq+FfVXPDxcnDQyDNcZIiKWtZhV8/6IuDUi/isintTjfl8aEY3aMdZFxBNbbDe/OuaCLvb5rYhY3G6blNId5Pu5ryfPFTV0Sp3zNsc7KyLaDqIeEVdHxKem87jqzSgNlq3OfgYcV3u9OXmszOOBH0XEHiml9ZPc50HAD2uvtyCPvLRfLwVW448+H1jaYbttyYNsPIL+zRrRixLnfCIn0sUsEBoOhuvMsiql9IumZRdExFrgTHKofW+S+zwIOKb2ejWwICKOTil9roca9wZWp2ruqjYOJwfrF4FXR8TjU0rX9HC80kqc85ZS/wYe1zSwWWDj0DxvEBGxX0T8d/Ux/7cRcVD1kfYfa9vsTp76pD5P00XAd4CTe5xc7iDyOJmdvAb4f8AJ1eujOr2h+tj8nYhYEhG3RMSqiPhqRDyqabsDIuKX1c9+fUR8sD5BXfXR+iMR8Ytqm3d0/+M9pNU53z0ivh0Ra6ra/j0idqitnx0Rn4uIm6rj/iYiXt70862ovX5M5En+VkfEDZEnWKwfb0HVVPGMpuWrIuKE2uudIuJLEXF7RNwVEWdX4/NqCgzXmSUiT/Ux/ti6agP9MHmYtQurjZ5GHtj6FvJcQGeR5wNqbts8CFiWUmqeXfTN5E89vUzp3NzM0OqH2A14LnkCvqurWl8fE8+UW/dc8ridbyH3Mtif2nTKEfGCan9Xkad0OZk8DN0nmvazhDxG7Svr729dblfn/NHkP0yPJ//heAP5Kv5HETE+2v3HyVe6x5LHG/0d8I2IeHKLg84in8dnkD9ZLCHPBju37dnZcD9bksevfS7wVvIkfI8hz3G13WT2pSb9HgPSR7FxKpfReizVteRw2L227VfIU1hsUlu2pNr+H2vLzgbe1nSM71T/fWy1/aHV6/nV6wVtatyBPO7rdh1+lg+Qx/x8ZPX6hdW+X9LhfWeR22mfUlt2SPXefavXPwcuanrfq8lT0zyhen018D/TfM7/hTxw+w61ZWPkAcZfU72+DDijtn4z8hQuT6v9fCuq/z60OlZ9cOe/qZZ9qnq9oHr9jKa6VwEnVP+9qKrhSbX125Bnw33/oH+vR/nhlevMchEPT/3xOvLgvucAr0wp1edrX0AOyftry75R31HkKTD2Y+KrzE+RBz4+PSK6/cLpAODXKfcEaCkighx2/we4q/oC7MfAn4CFXRzjkpTS72qvv0cO9OdFxFbAM4Hv1K82gR+QP8XVv6Trdo61bs/5fuRgX1U77nXkq9MXVNv8FFhYfSw/hhzES1JKraaceQ5wR6q196aUfkP+wzAZ+5GnQLmiVtfaqpYXtH2n2vILrZlldcqzUwIsj4hrycF0D/mj6LgdyH1I625pev0c4PaU0mWtDpRSejAijgZ+Q74qO7OL+jo2CQDPI1/RjQGvb1r3hIiYm1K6oc37b2qqM0XESuBRwHbkEP2X6tFs59p//7lDneO6PefbA88iB32zm6vnY8kj9L+aPAXNgxHxPeB1KaXmSfy2o/XEfje3WNbO9sCTJqir3/OgzSiG6wyWUvpJRHweODoivpFSOqdadQN5fqC65tcdv3hKKV0aESeTJ/r7bRclHUhuw2znteT20MOblm9NDq3Xk7skTWT7+ovIE+LtQA7LO6vFHyK3pza7sUNtHbU556vJbb2tZmddU713HbkL1/ER8Vfk6b3/mfzzvrHpPSuBnVrsq/7zj08z8tAn1OqTQb0712ryHFFHt9hXc1u7JsFmgZnvPeR/QKfWvjj5KXBI/OVMnC9tel83V5kAHyR/ZG91JfiQiNgT2Ar4ZZtttiQHyjdSSsubHsvIX7wc1VR3s70i4rG114eQLyLOTymtIQfJbvV9A/dW9c/r9MN2qdU5v4h8hXhp7bgryL0hnhsRsyJiRVQ3V6SU/pBS+jC5KeFxLY5xPrBtRDx/fEEVyLvVthn/Y1KfJvzZ/OVF1UXkTwlX1+r6NfB28pxX6pHhOsNVHydPAnbn4Xv0P0L+B/sfEXFwRBxbbQP5o+hOwNPIV4qd9r+e/G11p3bXg4Dzmtp5mx1W7efrE6z/Cvnb9gPa7GNT4JzId5a9Dvg88N2U0niovx84IiI+ExEHRsSryBPQjTFN02lPcM5PBR4JfL+q7UXAd8m9A36dUnqA/Ifn+Ih4Q9WN6t3kZpL/bHGYc8k9Eb4SEa+uumx9i/yHYtwl5E8pJ0bEIRFxJPAlcvCP+9/kq+BzI+LwiNgf+L/AEeQ/ROrVoL9R8zE9D2rf5LdYtzn5o/YqYMdq2YFAg/zRbwX543Yid836B+DnkzzG52nTW4Ac1Is6/Aw/BK5ss34bYB3wHxOsP6v6Wd5D/mLpNnIXqy2btnsJecyC9eTmgq8A82rrr6b6xn2az/lTyH2E15CvKs8H9qm9Z3ZV73XV/5fL+cveGmdR9RaoXm8LfIH8zf6fgXcCv6jXDuxT/az3kHsjHAYsp+otUG3zWOBr1Tm7mxzyLx707/SoP5z9dSNUXZ2sSQ9fzRERB5LDba+U0iUDK24KIuIscrejpw66FskvtDZOzwbeERHHkbscPZ7cdnrhqAarNGwM143TR8gfW99NvqPndnJ73XsGWZQ0k9gsIEkF2FtAkgowXCWpAMNVkgowXCWpAMNVkgowXCWpgP8PV1JGFTcSpsQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'arial',\n",
    "        'weight' : 'regular',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "plt.rc('xtick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.minor', width = 1.5, size=2.3)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,figsize=(5,5))\n",
    "\n",
    "ax1.hist(human_nc_rg, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax1.hist(human_con_rg, bins=50, density=True, alpha=0.3, color=\"k\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax1.hist(human_con_rg, bins=50, density=True, alpha=1, color=\"k\", range=(0,1), histtype=\"step\", linewidth = 1.5)\n",
    "ax1.set_xlim([0,0.6]); ax1.set_ylim([0,15])\n",
    "ax1.set_xticks([0,.1,.2,.3,.4,.5,.6])\n",
    "ax1.set_yticks([0,5,10,15])\n",
    "ax1.axvline(med_1 := np.median([n for n in human_nc_rg if n<1]),color=\"b\",alpha=0.6)\n",
    "ax1.axvline(med_3 := np.median([n for n in human_con_rg if n<1]),color=\"k\",alpha=0.6)\n",
    "\n",
    "ax1.set_xlabel(\"Rg/N / Å per Residue\", family=\"arial\")\n",
    "ax1.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "ax1.set_box_aspect(1)\n",
    "\n",
    "# fig.savefig(fname=\"RG_HUMAN.svg\")\n",
    "print(f\"Rg/N Medians :: \"+ f\"NC: {med_1:4.4f}, CON: {med_3:4.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=14804902.0, pvalue=0.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.mannwhitneyu(human_nc_rg, human_con_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeast_con_rg = np.load(\"yeast_con_Rg.npy\", allow_pickle=True)\n",
    "yeast_nc_rg = np.load(\"yeast_nc_Rg.npy\", allow_pickle=True)\n",
    "\n",
    "yeast_cg3d_con_rg = np.load(\"yeast_cg3d_con_Rg.npy\", allow_pickle=True)\n",
    "yeast_cg3d_nc_rg = np.load(\"yeast_cg3d_nc_Rg.npy\", allow_pickle=True)\n",
    "\n",
    "yeast_pfam_con_rg = np.load(\"yeast_pfam_con_Rg.npy\", allow_pickle=True)\n",
    "yeast_pfam_nc_rg = np.load(\"yeast_pfam_nc_Rg.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08961734334361948 0.12791888738092105\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'arial',\n",
    "        'weight' : 'regular',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "plt.rc('xtick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.minor', width = 1.5, size=2.3)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,figsize=(5,5))\n",
    "\n",
    "ax1.hist(yeast_nc_rg, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax1.hist(yeast_con_rg, bins=50, density=True, alpha=0.3, color=\"k\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax1.hist(yeast_con_rg, bins=50, density=True, alpha=1, color=\"k\", range=(0,1), histtype=\"step\", linewidth = 1.5)\n",
    "ax1.set_xlim([0,0.6]); ax1.set_ylim([0,16])\n",
    "ax1.set_yticks([0,5,10,15])\n",
    "ax1.set_xticks([0,.1,.2,.3,.4,.5,.6])\n",
    "\n",
    "ax1.axvline(med_1 := np.median([n for n in yeast_nc_rg if n<1]),color=\"b\",alpha=0.6)\n",
    "ax1.axvline(med_3 := np.median([n for n in yeast_con_rg if n<1]),color=\"k\",alpha=0.6)\n",
    "\n",
    "ax1.set_xlabel(\"Rg/N / Å per Residue\", family=\"arial\")\n",
    "ax1.set_ylabel(\"PDF\", family=\"arial\")\n",
    "\n",
    "ax1.set_box_aspect(1)\n",
    "\n",
    "# fig.savefig(fname=\"RG_YEAST.svg\")\n",
    "print(f\"Rg/N Medians :: \"+ f\"NC: {med_1:4.4f}, CON: {med_3:4.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeast_commpr_nc = np.load(\"yeast_nc_dcomdmpr.npy\", allow_pickle=True)\n",
    "yeast_cg3d_commpr_nc = np.load(\"yeast_cg3d_nc_dcomdmpr.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10517498523513706 0.07988221515980802\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import FancyArrow\n",
    "\n",
    "\n",
    "font = {'family' : 'arial',\n",
    "        'weight' : 'regular',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "plt.rc('xtick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.minor', width = 1.5, size=2.3)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,figsize=(5,5))\n",
    "\n",
    "ax1.hist(yeast_commpr_nc, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax1.hist(yeast_cg3d_commpr_nc, bins=50, density=True, alpha=1, color=\"orange\", range=(0,1), histtype=\"step\", linewidth=3)\n",
    "ax1.set_xlim([0,0.6]); ax1.set_ylim([0,11])\n",
    "ax1.axvline(med_1 := np.median([n for n in yeast_commpr_nc if n<1]),color=\"b\",alpha=0.6)\n",
    "ax1.axvline(med_3 := np.median([n for n in yeast_cg3d_commpr_nc if n<1]),color=\"orange\",alpha=1)\n",
    "ax1.set_xlabel(\"\\u0394COM/\\u0394MPR / Å/Residues\")\n",
    "ax1.set_ylabel(\"PDF\")\n",
    "\n",
    "ax1.set_box_aspect(1)\n",
    "print(med_1, med_3)\n",
    "# fig.savefig(fname=\"YEAST_DMvCATH_COMMPR.svg\")\n",
    "print(f\"COMMPR Medians :: \"+ f\"DomainMapper: {med_1:4.4f}, CATH: {med_3:4.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeast_rg_nc = np.load(\"yeast_nc_Rg.npy\", allow_pickle=True)\n",
    "yeast_cg3d_rg_nc = np.load(\"yeast_cg3d_nc_Rg.npy\", allow_pickle=True)\n",
    "\n",
    "yeast_rg_con = np.load(\"yeast_con_Rg.npy\", allow_pickle=True)\n",
    "yeast_cg3d_rg_con = np.load(\"yeast_cg3d_con_Rg.npy\", allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08961734334361948 0.096921696291973\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font = {'family' : 'arial',\n",
    "        'weight' : 'regular',\n",
    "        'size'   : 16}\n",
    "\n",
    "plt.rc('font', **font)\n",
    "plt.rc('axes', linewidth=1.5)\n",
    "plt.rc('xtick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.major', width = 1.5, size=5.5)\n",
    "plt.rc('ytick.minor', width = 1.5, size=2.3)\n",
    "\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "fig, (ax1) = plt.subplots(1,figsize=(5,5))\n",
    "\n",
    "ax1.hist(yeast_rg_nc, bins=50, density=True, alpha=0.6, color=\"b\", range=(0,1), histtype=\"stepfilled\")\n",
    "ax1.hist(yeast_cg3d_rg_nc, bins=50, density=True, alpha=1, color=\"orange\", range=(0,1), histtype=\"step\", linewidth=3)\n",
    "ax1.set_xlim([0,0.5]); ax1.set_ylim([0,17])\n",
    "ax1.axvline(med_1 := np.median([n for n in yeast_rg_nc if n<1]),color=\"b\",alpha=0.6)\n",
    "ax1.axvline(med_3 := np.median([n for n in yeast_cg3d_rg_nc if n<1]),color=\"orange\",alpha=1)\n",
    "ax1.set_xlabel(\"Rg/N / Å per Residue\", family=\"arial\")\n",
    "ax1.set_ylabel(\"PDF\")\n",
    "ax1.set_yticks([0,5,10,15])\n",
    "\n",
    "ax1.set_box_aspect(1)\n",
    "print(med_1, med_3)\n",
    "\n",
    "# fig.savefig(fname=\"YEAST_DMvCATH_Rg_NC.svg\")\n",
    "print(f\"COMMPR Medians :: \"+ f\"DomainMapper: {med_1:4.4f}, CATH: {med_3:4.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Domain Mapper: 1481\n",
      "CathGene3D: 376\n",
      "Both: 531\n",
      "\n",
      "Tot DM: 2012\n",
      "Tot CATH: 907\n"
     ]
    }
   ],
   "source": [
    "def Print_Dom_Param(domain_data, key1, key2, find):\n",
    "    for i,val in enumerate(domain_data[key1]):\n",
    "        if find in val:\n",
    "            pstr = \"\"\n",
    "            for rng in domain_data[key2][i]:\n",
    "                n,c = rng\n",
    "                pstr += f\"{n}-{c}+\"\n",
    "            print(val, pstr)\n",
    "\n",
    "dom_data = np.load(\"yeast101040_dommap_annote.npy\", allow_pickle=True).item()\n",
    "\n",
    "DOM_Only = 0\n",
    "CATH_Only = 0\n",
    "Both = 0\n",
    "\n",
    "for uniprot, domains in dom_data.items():\n",
    "    if \"ip_topology\" not in domains.keys():\n",
    "\n",
    "        # if domains[\"topology\"].count(\"NC\"):\n",
    "        #     print(uniprot, \": DM Only\")\n",
    "        #     Print_Dom_Param(domains, \"topology\", \"range\", find=\"NC\")\n",
    "\n",
    "        for topo in domains[\"topology\"]:\n",
    "            if \"NC\" in topo:\n",
    "                DOM_Only += 1\n",
    "    else:\n",
    "        if \"NC\" not in domains[\"ip_topology\"]:\n",
    "            # if domains[\"topology\"].count(\"NC\"):\n",
    "            #     print(uniprot, \": DM Only\")\n",
    "            #     Print_Dom_Param(domains, \"topology\", \"range\", find=\"NC\")\n",
    "            for topo in domains[\"topology\"]:\n",
    "                if \"NC\" in topo:\n",
    "                    DOM_Only += 1\n",
    "        \n",
    "        elif not (\"NC\" in domains[\"topology\"] or \"NC IS\" in domains[\"topology\"] or \"NC CP\" in domains[\"topology\"]):\n",
    "            # if domains[\"ip_topology\"].count(\"NC\"):\n",
    "            #     print(uniprot, \": CATH Only\")\n",
    "            #     Print_Dom_Param(domains, \"ip_topology\", \"ip_range\", find=\"NC\")\n",
    "            CATH_Only += domains[\"ip_topology\"].count(\"NC\")\n",
    "\n",
    "    if \"ip_range\" in domains.keys():\n",
    "        if (\"NC\" in domains[\"topology\"] or \"NC IS\" in domains[\"topology\"] or \"NC CP\" in domains[\"topology\"]) and \"NC\" in domains[\"ip_topology\"]:\n",
    "            DOM_NCs = 0\n",
    "            for topo in domains[\"topology\"]:\n",
    "                if \"NC\" in topo:\n",
    "                    DOM_NCs += 1\n",
    "            CATH_NCs = domains[\"ip_topology\"].count(\"NC\")\n",
    "\n",
    "            if CATH_NCs != DOM_NCs:\n",
    "                # print(uniprot, \": Both\")\n",
    "                # print(\"CATH\")\n",
    "                # Print_Dom_Param(domains, \"ip_topology\", \"ip_range\", find=\"NC\")\n",
    "                # print(\"DOMMAP\")\n",
    "                # Print_Dom_Param(domains, \"topology\", \"range\", find=\"NC\")\n",
    "\n",
    "                Both += min([DOM_NCs, CATH_NCs])\n",
    "\n",
    "                if CATH_NCs > DOM_NCs:\n",
    "                    CATH_Only += max([DOM_NCs, CATH_NCs])-min([DOM_NCs, CATH_NCs])\n",
    "                elif CATH_NCs < DOM_NCs:\n",
    "                    DOM_Only += max([DOM_NCs, CATH_NCs])-min([DOM_NCs, CATH_NCs])\n",
    "                    \n",
    "            elif CATH_NCs == DOM_NCs:\n",
    "                # print(uniprot, \": Both\")\n",
    "                # print(\"CATH\")\n",
    "                # Print_Dom_Param(domains, \"ip_topology\", \"ip_range\", find=\"NC\")\n",
    "                # print(\"DOMMAP\")\n",
    "                # Print_Dom_Param(domains, \"topology\", \"range\", find=\"NC\")\n",
    "                \n",
    "                Both += min([DOM_NCs, CATH_NCs])\n",
    "\n",
    "print(f\"Domain Mapper: {DOM_Only}\\nCathGene3D: {CATH_Only}\\nBoth: {Both}\\n\")\n",
    "\n",
    "print(f\"Tot DM: {Both+DOM_Only}\\nTot CATH: {Both+CATH_Only}\")"
   ]
  }
 ],
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