SOMInactivation_TuningCurves_diffGaussFits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=blue size=\"10\">Analyze changes in tuning curves under inactivations of SOM</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the functional derived from a difference of gaussians (Mexican hat) to fit the bandwidth tuning curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'tuning_curve_feature_plotting' from '/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/tuning_curve_feature_plotting.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import tqdm\n",
    "from importlib import reload\n",
    "#from tuning_curve_fit_funcs import carandini_fit_wStats\n",
    "import tuning_curve_fit_funcs as tcfits\n",
    "import tuning_curve_feature_plotting as tcfplot\n",
    "reload(tcfits)\n",
    "reload(tcfplot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SOMBaselineSpikeRates\n",
      "laser\n",
      "noSOMsustainedResponses\n",
      "PVBaselineSpikeRates\n",
      "bandwidths\n",
      "noPVsustainedResponses\n"
     ]
    }
   ],
   "source": [
    "DATA_DIR = '/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/Lakunina-Jaramillo-Data/'\n",
    "filename = 'inactivated_cells_responses.npz'\n",
    "data = np.load(DATA_DIR + filename)\n",
    "print(\"\\n\".join(data.keys())) #:gives the list of arrays in the file\n",
    "data.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BLisZeroBW = True #whether to replace pure tone evoked rates with baseline (spontaneous) rates\n",
    "WNoctave = 6 #what BW (in octaves) to attribute to white noise stimulus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SOM inactivation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/57 [00:00<?, ?it/s]/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/tuning_curve_fit_funcs.py:221: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  Stats['SuppInd'] = 1 - Stats['wnResp']/Stats['maxResp']\n",
      "/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/tuning_curve_fit_funcs.py:230: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  RawStats['SuppInd'] = 1 - RawStats['wnResp']/RawStats['maxResp']\n",
      "100%|██████████| 57/57 [00:04<00:00, 12.59it/s]\n",
      "  4%|▎         | 2/57 [00:00<00:03, 14.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Elapsed time was 4.52789 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 57/57 [00:18<00:00,  3.07it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Elapsed time was 18.5615 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#fit all cell TC's with and without SOM inactivations \n",
    "Celltype = 'SOM'\n",
    "CelltypeLabel = 'SOM-inactivation'\n",
    "Response_type = 'sustained'\n",
    "mExp = 1\n",
    "\n",
    "TCfeatures_SOM_Laser_on = tcfits.FitAllCells(DATA_DIR + filename, Celltype, Response_type, BLisZeroBW,\\\n",
    "                             WNoctave,  mFixed=mExp, function_class_fit= \"diff_of_gauss_fit\",\\\n",
    "                                INACTIVATION=True,laser=1)\n",
    "\n",
    "TCfeatures_SOM_Laser_off = tcfits.FitAllCells(DATA_DIR + filename, Celltype, Response_type, BLisZeroBW,\\\n",
    "                             WNoctave,  mFixed=mExp, function_class_fit= \"diff_of_gauss_fit\",\\\n",
    "                                INACTIVATION=True,laser=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goodness of fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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Zs2a8+uqrHD58mK1bt5KcnBx4f2lFoKpMnTqViRMnBtb95S9/IScnh7/97W889dRTAKSn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\nQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/B171KWtP4CJgdRnL++Fcphecm3c+C+f+yzwjEJFzROQzYIx7IFsBj4rIMhGp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\nPXjMH2ltBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "for ii,laser in enumerate(['on','off']):\n",
    "    TCfeatures = eval('TCfeatures_'+Celltype+'_Laser_'+laser)\n",
    "    R2 = TCfeatures['GoF']['R2']\n",
    "    R2Label = TCfeatures['GoFLabels']['R2']\n",
    "    plt.subplot(2,1,ii+1)\n",
    "    #fig = plt.figure(figsize=(10,5))\n",
    "    n, bins, patches = plt.hist(R2, 15, facecolor='g',alpha=0.7)\n",
    "    plt.axvline(x=np.median(R2),c='r',linestyle='--')\n",
    "    plt.axvline(x=np.mean(R2),c='g',linestyle='--')\n",
    "    plt.xlabel(R2Label)\n",
    "    plt.ylabel(CelltypeLabel+ ' Cell Count')\n",
    "    plt.title('laser '+laser)\n",
    "    plt.text(np.median(R2)*.7, 7, 'median('+R2Label+') = %1.3f' % np.median(R2))\n",
    "    plt.text(np.mean(R2)*.7, 6, 'mean('+R2Label+') = %1.3f' % np.mean(R2))\n",
    "    #plt.axis([40, 160, 0, 0.03])\n",
    "    #plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-9ba9a61e7e01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\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     33\u001b[0m     \u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwait\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[0;32m---> 34\u001b[0;31m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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pKSmEhYWRlpZ2wfLlSWGXhJeGrdyEBjl5UvMXDByo5+npmgho8WKRpk01PEjT\nphecFMeSg82bRerWFWnQQOSnn/T7B5FXXvGoWN4SGqRNmzYSHR2drSxn7ojiIiNvxoUwZ84cefnl\nl53nEyZMkNdff11ERH766Sepk5EjJAuTJk2SzZs3X1C/aWlp0rx5c9mzZ48kJydLUFCQ7Ny5M1e9\nHTt2SIcOHeT06dOSmpoqffv2ld9//z3PchGRadOmyeJ8/u+9NjSIxY3MmaP5CzJGF2vXquG7c2d9\n801P1/0FBpSz5KBLF/2uK1TQUcdjj8HQoRoV+LnnPC2dR7n77ruJjo7muuuu46WXXnLmxX7kkUfY\ns2cPISEhPPzww7nuW7RoEUFBQQQHB3Pbbbexb98+OmYJVTNz5kxnWPR9+/bRrl07JkyYQJcuXRg3\nbly20cu0adN48cUXAVi8eDGXXXYZISEhjB8/3jmKyEpERATXX3+983zHjh20cUw3NmvWjEpZFm2K\nCFOmTGHQoEF06dLlAr4p2LRpEy1btqR58+ZUqlSJkSNH8vnnuVcX7Nq1i27duuHn50fFihXp1asX\nn332WZ7loCOhCDem3LU2jNLGyZPwwguaa/vyy7Vs+nR1/3QkqLG4kXbtYP16tR0NHgyffqpTf488\nAg0aqHuzB5k4cSJbt24tuGIRCAkJyTYd4oo33niDb775htWrV1O3bl2eeOIJAJ599lmioqJcyrRz\n506mT5/Ohg0bqFu3LsePH+fkyZP59vPbb7/xzjvv8Nprr/HLL78wceJEJkyYAMCSJUv45ptv2LVr\nFx999BEbNmzA19eXCRMmEBERwe233+5sJyUlhejoaAIDA51lGQpDRJgzZw7Ts0znvvrqq6xcuZKE\nhAT+/PNP7r777lyy9ezZ02WE3ZkzZ9KvXz/n+YEDB2jSpInz3N/fn59++inXfR07dmTq1KkcO3aM\nqlWr8vXXXxMaGsq1117rsjzjnp9//jlXW8WFVRiljblz4e+/M0cXmzbB99/D88/ntmFY3EOzZrBu\nnbrWXncdvP++5h8ZNszTkpUqVq1axbBhw5yhzy+++OICFUbW7HudO3cmLi6OgwcPcvToUWrXrk1A\nQABz5sxh8+bNdO3aFVBbRf0coXDi4+OpVauW83z//v0kJiYyePBgDhw4QFBQkHNkA/DAAw/wQAH5\nZdatW1eozy0uljK4CpnUrl07pkyZQv/+/alevTrBwcFUrFgxz3LQJFGVKlXKlVSquLAKozSRmgqv\nvaZvt5deqmXTp0Pt2uDijcfiRho1gh9/hEGDNIPfggVQvbquDH/jDQ3H4lPyM74FjQS8CXGRYrVi\nxYqkp6c7z3NmrsuZfW/YsGGr7qBVAAAgAElEQVR8/PHHHD58mJEjRzrbHTNmDDNmzMiz76pVq2Zr\ne/v27YSFhbFq1Sr+/vtvOnbsyMaNG53G+8JQ2BGGv78/+/dnxlaNjY2lUaNGLtscN24c48aNA+Cx\nxx7D398/33KA5ORkqrgr3XBhjR2lYSvzRu8lS9TIuny5nkdF6fm0aZ6Vqzxz8qTIVVdlGr/ffluP\nR48WSU0tERG8xejdtGlTOXr0qIiIVKtWTURE4uPjJSAgwGX9qKgoadWqlcTHx4uIyLFjxyQlJUXq\n1Kkj8fHxcvbsWbn88svlqaeeEhHXBvSoqCjp3r27tGrVSg4ePCgiIjt37pSWLVvKkSNHnO3u27cv\nV//+/v5y5swZERGZMWOGTJo0yXlt8uTJ8thjj53vV5Evqamp0qxZM4mOjnYavaOiolzWzfgMMTEx\n0qZNGzl+/Hi+5fHx8dK2bds8+7ZG7/LEq6/qdMjgwXreti0sWaLpVS2eoUYNzZ+eYfyOjdXIwYsX\nw4gR5T48es7sdFnp0KEDU6dOpVevXgQHBzNp0iR8fX158sknufzyyxkyZEiB2es6dOhAYmIijRs3\npmHDhgC0b9+ep59+mgEDBhAUFET//v05dOhQrnsHDBjA+vXrAbVfdO7c2Xnt2muv5euvv77Qj++S\nihUrMmfOHAYOHEi7du24+eab6dChg/P64MGDOXjwIAA33XQT7du359prr2Xu3LnUrl073/LVq1cz\nOOP54A4Kq1lKw1amRxi//KJvri++6GlJLK5ITRUZM0b/Rg8+KDJrlh4PGiSSlOTWrr1lhFHa2LJl\ni4wePdrTYhQrN9xwg+zevTvP63aEUV549VX1xhk7Vs8nT1ZvKYt3ULGi2jEefFCTWG3bBq+/rkmt\njhzxtHQWF3Tu3Jk+ffq4dLktjaSkpDB06FCna7A7sEbv0kB8PEREaBKkWrU0BMjs2XDPPZ6WzJIV\nHx946SWoUweefFK92bZuhbp1NePh6dNqGLd4DWMzXsDKAJUqVcrmOuwO7AijNDB/vmbVu+8+PX/h\nBQ3D7WIhlMXDGKP5NObMgS++0BhUJ07AlCnQs6eGc7FYSilWYXg7aWnqStu3L3TooPGM5s+H22+H\nLIt/LF7GvffCBx/A//4HvXrpOo3du6F3b81fYrGUQqzC8HY+/1yTIWV4Qs2apesxHnnEs3JZCmbE\nCPWg2rNHc4XPn6/h5sPCXEcWtli8HKswvJ1XX9W0q0OG6PlNN+mUVMuWHhXLUkj694fVqzNTvb76\nqsYB69ev3LvcWkofbjN6G2MWAEOAOBHp6Cj7CMgw4dcCTohIiIt79wGJwDkgTQqZDarMsX27riZ+\n4QUNeAfQrZtultJD164af2rgQB0pzpwJDRtCluB2FktpwJ0jjHeBq7MWiMgIEQlxKIlPgE/zub+P\no275VBaQ6Uo7bpwGHXzwQZ2espQ+2rSBDRt0tHj//TriAF14uWWLR0WzWAqL2xSGiKwFjru6ZjSA\nzM3AB+7qv9Rz7JiuFh49WmNFvf46vPKK9ekvzTRurOHRr7hCQ88/84yGSO/TB/773xITIyIigsDA\nQHx8fAgMDCyWcNjVPeQunJKSwsSJE2nRogWtWrXi+uuvJzY21nk9a1Kn5ORk+vXrR0hISL5JkHKG\nWM+rzvvvv19sn6PUUNgVfuezAYFAlIvyMPJZXQjsBbYAm4HwAvoIByKByLxi1pRKnntOVwrv2CFy\n+rRIvXoiAwZ4WipLcXD2rMiIEfr3HTNGpFUrkWrVRH744byaK8pK78WLF4ufn58Azs3Pzy/fpDuF\nISN2lDtxlTTpoYcekrFjx0paWpqIiCxYsEC6du0q6enpIpI9qdPGjRslLCyswH4Kk/Rp9erVcs01\n15zPx/AoF7rS21MK43XgoXzua+TY1we2AWGF6a/MhAZJTxdp2VKkVy89nz1b/1Rr13pULEsxcu6c\nyEMPZYYP6dBBpHJlkS+/LHJTOR8CvXr1yrXNnTtXRESaNGmSTVlkbBnZ5Y4ePZrr3sLgSmEsX75c\nLrvsMgkJCZG+ffvK4cOHRURkzZo1EhwcLMHBwRISEiInT54UEZHnn39eQkNDpVOnTvLkk0+KiD68\n27ZtK/fcc4+EhIRkCyJ4+vRpufjiiyUhISFbvz169JCVK1fK+PHjxdfXVzp27CjPPvustGjRQi66\n6CIJDg6WP//8M9s9kZGREhQUJN26dZPJkyc7FcbevXulR48e0rlzZ+ncubNs2LBBREQuv/xyZ1uz\nZs3Ks563UeoUBmpoPwL4F7KNacDkwtQtMwpj/Xr907z7rkhysoi/v0jPnp6WyuIOXn5ZU+qGhooE\nB4s88USRmyiKwjDGuFQYOtlQvArj+PHjzjf9t956yxkNdsiQIbJ+/XoREUlMTJTU1FT59ttv5a67\n7nKOIq655hr58ccfZe/evWKMkY0bN+Zqf9u2bRISEpKrfOLEiTJ79mwRyR5BN79RQadOnWTNmjUi\nItkUxunTp50RbX///XfJeMbkbCuvet7GhSoMT4QG6QfsFpFYVxeNMdUAHxFJdBwPAP5dkgJ6nIUL\noVo1daE9fVrTgd58s6elsriDBx8Ef3+1aTRpArfdpuUJCVCz5nk1uWbNmjyvBQQEEONiDUjTpk0B\nqFu3br73F4XY2FhGjBjBoUOHSElJoZkjI+SVV17JpEmTGDVqFDfeeCP+/v589913fPfdd86IsadO\nneKPP/4gICAgW9KkrIjkzqeRX3leJCQkcOLECXr16gXAbbfdxooVKwBITU3lvvvuY+vWrVSoUIHf\nf//dZRuFrVfacZvR2xjzAbARaGOMiTXGjHNcGkkOY7cxppExJiOWcANgvTFmG7AJ+EpEvnGXnF7H\nmTPw0Ueava16dTV4v/WW+vNbyiY33QQrV2rsqe7ddYV469aaiKmYmT59On5+ftnK/Pz8sqUjLS7u\nv/9+7rvvPnbs2MGbb77pTFj0yCOPMH/+fM6cOUO3bt3YvXs3IsKjjz7K1q1b2bp1K3/++aczQVDO\npEkZtGzZkpiYmFxJi7Zs2UL79u0LLWd+Cuall16iQYMGbNu2jcjISFLyWDtT2HqlHXd6Sd0iIg1F\nxFdE/EXkbUf5HSLyRo66B0VksOM4WkSCHVsHESn+X7I3s2yZutCOGQNr1oCLXL+WMkiPHhpGpE4d\n/ds3aqTBJWfNKtZuRo0axbx582jatCnGGJo2bcq8efMYNWpUsfYD+ubeuHFjABYuXOgs37NnD506\ndWLKlCmEhoaye/duBg4cyIIFCzjlcDc+cOAAcXFx+bZfrVo1xowZw6RJk5wRZxctWkRSUhJXXXVV\noeWsVasWNWvWdObGyOo1lpCQQMOGDfHx8eG9995z9lOjRo1siiqvemUNG63W21i4EJo21UB1wcEa\nAXX7dg1qZynbtGwJGzfqiGPNGmjfHh56SKclH3+82H4Do0aNKnYFkZSUlC1N6KRJk5g2bRrDhw+n\ncePGdOvWjb179wKaRnb16tVUqFCB9u3bM2jQICpXrsyuXbvo3r07oG66ixcvpkLGgtU8mDFjBpMn\nT6Z169b4+PjQtm1bPvvssyJNSQG88847jB07Fj8/PwYOHOgsnzBhAjfddBNLly6lT58+ztFOUFAQ\nFStWJDg4mDvuuCPPemUNozaPskFoaKhERkZ6Wozz58ABCAjQuEOdO2uk04gIuPVWT0tmKUlSUjRH\n+zvvaIbFvXv1+I47XFbftWsX7dq1K1kZLaUSV78VY8xmKeQCaTvC8CYWL4b0dDV8jhypb5zW2F3+\nqFQJ3n5b7RiPPqpKo08fT0tlsdjgg16DiE5HXXmlRjfdskUj0la0Or1cYoz+/ZcuhcOHdYpy7VrN\nF15G58ct3o99GnkLP/8Mu3apR9SRIxAUlOliaSm/DBsGzZvD9ddrhNvUVIiKgvfeA19fZ7WiupJa\nyh/FYX6wIwxvYeFCqFIFhg9XL5mtW200U4vSpQtERsJll+n5Rx+pfcvhplqlShWOHTtWLA8ES9lE\nRDh27BhVqlS5oHas0dsbSE7WcNdXX62ulFdeqd5RFktWUlI0Te9bb+l5nz7w5Zek+voSGxvrXOdg\nsbiiSpUq+Pv745tlZArW6F36+OKLzEVbYWG6YGv8eE9LZfE2KlWCN99Ud+v779ccG5s349uzp3MV\ntcXiTuxrrDewcKGGvl65Uld2WzdaS14Yo/nCV62CGjU0E+Onn2ooEYvFzViF4WmOHIEVKzQb2/Ll\nGluoRg1PS2Xxdnr3Vk+61q11oV+rVrqOx2JxI1ZheJqICHWTPHxYY0fdf7+nJbKUFpo21Wmpa66B\no0dVaWzd6mmpLGUYqzA8zaJFEBoKv/8OEybAxRd7WiJLaaJyZfjyS13gd+aM/pY+/tjTUlnKKFZh\neJLdu2HbNl1vsWsXPPGEpyWylFaeeUYjBYioa/asWXpssRQjVmF4kqVLdX/NNbqi20N5kS1lhFGj\ndDX45Zdr0MIbboDjxz0tlaUMYRWGJ1m6VNdfXHWVcxGWxXJBXHmlRrydNUunqtq1UzuHxVIMWIXh\nKXbvhh071FjZv7+u8rZYigNj1B7WuLH+vsLCbAwqS7FQoMIwxlQxxgwzxsw2xiw1xiwyxvyfMaZD\nAfctMMbEGWOispRNM8YcMMZsdWyD87j3amPMb8aYP40xjxT9Y5UCMqaj0tM1yJzFUpxUrqwjjdat\nNWrAE0/AgAFw6JCnJbOUYvJVGMaYacAGoDvwE/AmsARIA541xnxvjAnK4/Z3gatdlL8kIiGO7euc\nF40xFYC5wCCgPXCLMabw+RZLCx98oP/IGWHMLZbiplEjWLcOOnVSG9n69RrUctkyT0tmKaUUFBrk\nZxGZlse1WcaY+kCAq4sistYYE3geMl0G/Cki0QDGmA+B64Ffz6Mt72T3bvWKAnWHtFjcRb16uip8\n0CDo1k2N4jfcAGPHwssv20WiliKR7whDRL4CMMb0dLz5OzHGdBGROBEparS/+4wx2x1TVrVdXG8M\n7M9yHusoc4kxJtwYE2mMiTx69GgRRfEQGdNR330HHTt6VhZL2ad2bfjxR1UQ//sfTJwI776rMams\nQdxSBApr9P4WWGWMaZClbP559Pc60AIIAQ4BL7qo4yqof54O5SIyT0RCRSS0Xr165yGSB/joI+jR\nQ43dFktJULmy7vfu1dhl99+vxvGwMB3lpqR4Vj5LqaCwCuM34AVgjTHmCkdZkbO1iMgRETknIunA\nW+j0U05igSZZzv2Bg0Xty2vZuhV27gR/f09LYimPBAZCr14wezbcfjuMGwfPPqtrN2xYEUsBFFZh\niIh8CVwHzDHG3Ec+b/15YYxpmOX0BiDKRbWfgVbGmGbGmErASGB5UfvyWqZO1f3w4Z6Vw1I+qVwZ\nlizRiMjTpkGDBmoEP3QIunaFJ5/U/CwWiwsKqzAMgIj8AfQEwoC8vKP0BmM+ADYCbYwxscaYccDz\nxpgdxpjtQB/gn466jYwxXzv6SAPuQ6fBdgFLRGRnkT+ZN5KSonaLiy7SjGkWiyfw9dUYZv/4B0yf\nrrlYfv1Vlch//qMZ/n76ydNSWryQ8864Z4wJEJG/ilmeC8JrM+5FROjIIiZGz/v21dwXFosnEdHs\nfWPGZNo4vv5ak3cdPAj//Cf8+9/g5+dZOS1upSgZ9/JVGMaYV8nf4PxA0cVzH16pMCIiIDwckpIy\ny6pW1X/UUaM8J5fFkpVjx+Cll3SaKikJ/u//NLtfy5aaAbJvX09LaHETRVEYBU1JRQKbHdt1WY4z\nNktBTJ2aXVmAhqHOsGVYLN7A8uU6PTVihIapeeMNXb8hAv366cvN4cOeltLiYQo9JWWM+UVEOrtZ\nngvCK0cYPj6uw0wbo2FBLBZvYfZsXaMxaBB88omOhM+cUS+qZ5/V8+nT4e67oUKFgtuzlAqKc4SR\nFRtc/3wIcLkQPu9yi8VTPPigTpV+8w0MHgyJiaok/vUvDZTZtSvcd5+uGN9sJxjKIzZarbuZPl29\nUrLi56flFou38Y9/aCKmQ4fg1KnM8tat1cPv/fchNhYuu0wj4sbHe05WS4lTkNE7kcyRhR+QMRlv\n0LUZF7lXvKLhlVNSAE2a6D8ZaB7m6dOtwdvi3aSkQKVKkJYGJ09mTx2ckKDRb197TWNRTZumyiPn\ni5GlVFBsU1IiUkNELnJsFbMc1/A2ZeG1/PlnprKIiYF9+6yysHg/lSrp/r77NIzNwSzBFmrWhFde\n0fTCl12mdo+gIFixwjOyWkqMgsKbF5gztDB1yjUffqj79u2t3cJS+rjlFti/X2NOzZ6toUV8fHS/\ndavaO774QpMzDR6s2+7dnpba4iYKsmF8box50RgTZoypllFojGlujBlnjPkW1zkvLBkYR8gtGwrE\nUhrp1Qu+/z5zIV9MjHr9xcTo+qL334chQyAqCmbOhA0bNAJzeDgcOOBp6S3FTEFTUn2BH4DxwE5j\nTIIx5hiwGLgEGCMiH7tfzFJMY0dk9uuu86wcFsv50q0b1KqV2z08KSlzPVGlSvDQQzoFe++9Gj69\nZUuNhHviRImLbHEPBXpJicjXIjJKRAJFpKaI1BGRK0RkuojYlTz5sXcvfPaZRqbt7NVLWCyW/Mlr\n0V5MDLzzjtrmQBM2zZ4Nv/0Gw4bBc89B8+Y6+jh7tsTEtbgH61brLkQ0nMJXX+nowhQ5GrzF4j3k\nZX/z8dHsfc2a6fbdd1oeGAjvvQe//KIjlIcf1hHHa6/ZaLilGKsw3MXPP+sI49w5Ox1lKf1Mn547\nCKGfnyZj2rFDvaY6d9Y84qBJwlq1grlz4bbb4OOPVYncey+0aGEVRynFKgx38eGH+vZVvTr07u1p\naSyWC2PUKJg3T9cRGaP7efNg9Gg1ct9/P3z6aWbK4Xr1oF27zNwbw4bpIr/PPrOKoxRTlFhSPYBW\nIvKOMaYeUF1E9rpVuiLiNQv3zp3TIfyxYzq6WLLE0xJZLJ7h3Dl1v129Wkci776r5f36aX7xpCRd\nFDhxom41anhU3PJIsceSMsY8BUwBHnUU+aKeUhZXrF+vbojJyXY6ylK+qVABLr0UJk/W6StjdBs+\nXBcEVqkCx49rpr86deDxxyEuToMeWryOQo0wjDFbgc7AloyItcaY7SKSZ9Y9Y8wCYAgQJyIdHWUv\nANcCKcAe4E4RyeVzZ4zZByQC54C0wmo/rxlhpKVpvuSPPoKjR7OHVbBYLJmkpEBkpGYA/N//YPv2\nzFXmLVvC1VdDnz7Qs6dmqrQUO+6IVpsiqlnE0UG1AuoDvEvuRX3fAx0diuZ3MkcsrugjIiGF/SBe\nRcWKOvwOC7PKwmLJj0qV4IorNP/G1q2wa5fm5EhLg507NanTkCFQuzbMmaP3pKZmD4xoKTEKqzCW\nGGPeBGoZY+4CVgLz87tBRNYCx3OUfefI2Q3wP8C/iPJ6P2vWaMTPqCg7HWWxFJU2bXTq6q+/YMoU\njVsFakSPi9O1HOvWqQK54gpdOLhyZe4kZRa3UBSjd39gABqp9lsR+b4Q9wQCX2ZMSeW49gXwkYjk\nsoUYY/YCf6MjmjdFZF4+fYQD4QABAQGXxmTkzfYUt9+uLoRnzuiq1xYtPCuPxVKaSUrSNMevvKIv\nYXXrqsdVhQqak+Pnn9Ww7uuraz46dFBnEz8/zeVhKZBiy+mdpcHnRGRKQWUu7gvEhcIwxkwFQoEb\nxYUAxphGInLQGFMfnca63zFiyReP2zDOnIEGDaBaNTXgRUV5ThaLpSwhoqP32bM1nawxGuhw9Gj9\nf9uwAf79b1UcEyfqFFe3bmr/6NMHLr8cKlf29KfwStxhw+jvomxQ4UXKxBgzBjWGj3KlLABE5KBj\nHwd8Blx2Pn2VOF9/rVnK4uLsdJTFUpwYow/+Zctgzx6NURUZCSNHasrYSpU06RPATTfpOo+TJzVb\nYK9euiYkgz171NhuKTIFhTe/xxizA2hjjNmeZdsLbC9qZ8aYq1H33OtExOWkozGmmjGmRsYxOg1W\nOl7VP/xQPTnS0+H66z0tjcVSNmnWDJ5+Wu0cn30GnTrBf/6jCwIHDdIouU8/DVu26GLBZcvgqacy\n7+/fX20gAwdqrvKfflIju6VACsq4VxOoDcwAHslyKVFEjru+y3nvB0BvoC5wBHgK9YqqDBxzVPuf\niNxtjGkEzBeRwcaY5uioAqAi8L6IFCqfqcenpMLD1SD399+6DsPHLqS3WEqEffvg7bfVYL5/v764\nDR+uNsUePTL/F0V0Rfrq1br9+quW33OPrjpPT1dbSEiI2knKAcVuw8jScH2gSsa5iPxVdPHch8cV\nRnKyenOMGAFvveU5OSyW8kp6Ovz4o67r+Phjdb8NDFRbx8iRahTPypEjWj8wULMH7tih2QNr1lS3\n+AwbSFBQmX0BdIfR+1pgFtAIiAOaArtEpEO+N5YwHlUYf/2lmcYGDlSj3LXXekYOi8WinD6t01GL\nFqnrbXq6Koybb9aXujZtct+TkKARpjNGIHv2aPkXX+h6kAMHdGV6hw5lRoG4Q2FsA64CVopIZ2NM\nH+AWEQm/MFGLF48pjGPH4JJL1CsjMjLTrc9isXgHhw/DJ59o9IX163VqKjhYFceNN7pWHqDTW2vW\nqBNLzZoatffxx9W9t3fvzBFI27alNoWBOxRGpIiEOhRHZxFJN8ZsEhGv8l7ymMKYNw/Gj4cmTfTN\nY8WKkpfBYrEUjgMHdLrqo49g40Yta9MGhg7V7bLL8h49xMZqytqMEUhsrLrrnjihcbE2bVKDesuW\npUaBuENhrASGosbvuui0VFcRueJCBC1uPKYw+vTRKanoaF1gdP/9JS+DxWIpOvv36xTysmU6kkhL\n09mC667TKairrtJ1Hq4Q0SmrXbsyp6Avu0wXEzZunDkCueoq9ezyUtyhMKoBZ1A33FFATSBCRI7l\ne2MJ4xGFcfCgpmAdOBC++Qb++EPfLiwWS+nixAldS7Vsmc4SnDql6zvCwnSR4KBBOhLJb+Tw+++w\napWOPtas0TVZ11+vbYJOi3XtmncGQw/gNi+pLB1UAEaKSESRb3YjHlEYs2frytKePXWe9PffS7Z/\ni8VS/CQnq61jxQpVIrt2aXlgoL4c9u2ro4e6dfNuQ0TddtPS1F5y+DA0bKjXmjfPHIEMGAD167v7\nE+VJsSkMY8xFwL1AY2A5GqbjXuBhYKuIeNXqNI8ojMRE/VGNGaN2jJdfLtn+LRaL+4mJ0f/zFSt0\n9JCYqOUhIao8+vbVl8bq1fNuIz1dI/Bm2D9+/FHXbL39tuZFP3gQ1q5VRXLJJSXysaB4FcbnaBDA\njUBfdBFfJeBBEdlaDLIWKx6zYXz9NVxzjU5JDRxY8v1bLJaSIy1N7RQ//KDbf/+roUYqVIAuXVRx\nhIXpgsE6dfJu59w5zf8REKD15s+Hu+7Sa+3aZY5ArrnGrV6XxakwdohIJ8dxBSAeCBCRxGKRtJgp\ncYXx3nuaICk6GhYsUP/sKlUKvs9isZQdkpI0+OGaNRrpYdOmzDzl7durAuneXQMgtm6dtwdWWlpm\nOtvVq7WtU6fUDlKvnpadOKGxsTLy7EREaIj3v/5SxTN9uuZfLwLFqTC2iEiXvM69jRJXGEFBmoP4\n8GF9I/jyy5Lr22KxeCdnz+p6rHXrdNuwQQMhAtSqpYqjWzfdh4aqMnBFaqpOYYWE6PkNN6jx3Bh9\n9jRsqEokQzmBjkTmzSuS0ihOhXEOOJ1xClQFkhzHIiJelTOxRBXGzp3QsaMu4nn6aZg7FyZMKJm+\nLRZL6SE9HX77TVPQZmxRUVoOun7r0kt1OuvSS3Vr0CB3OykpOnpZs0YVxapVrvtr2lRjaxUSt3tJ\neSslojAyhoAZiZpuvFGDmUVHe7WvtcVi8SISEzUBVNYtq4flJZeoZ1VQUObWtm1mvnPQqS1Xz29j\nMpVRISiKwqhY6FYtqizCw7Ong1y2TIeGVllYLJbCUqOGGrV7984sO3lSbRibN6sxfNs2ddvPyN1R\nsaKuA2nfXrc6dTR8e07cuMbDKoyiMHVq7tzB6ek2Ib3FYrlwLrpIvavCwjLL0tJ05JGhQH79VcOv\nf/KJ61GEn58avt2EVRhF4a88orlbhWGxWNxBxYqZI4qRIzPLz5xRRfLWW7B4sUbZbdr0vLykioK1\nYRSFwMBM20VWAgJcl1ssFouX446c3hZQ7Z1zAU2FCvDMM56Rx2KxWEoQtyoMY8wCY0ycMSYqS9nF\nxpjvjTF/OPa187h3jKPOH8aYMe6Us9CMGgXTpmUvu/12tw4BLRaLxVtw9wjjXeDqHGWPAD+ISCvg\nB7LnCgdUqaA5wC8HLgOeykuxlDi1HWJMmaL7nArEYrFYyihuVRgishY4nqP4emCh43ghmmcjJwOB\n70XkuIj8jQY9zKl4PMP69boy8+efNVmSF4UptlgsFnfiCRtGAxE5BODYu4rr2xjYn+U81lGWC2NM\nuDEm0hgTefTo0WIXNhcbNuiy/nXrNEa+xWKxlBO81ejtKkOJS3cuEZknIqEiElovr5gsxUV6Ojzw\ngK66TE21CsNisZQrPKEwjhhjGgI49nEu6sQCTbKc+wMHS0C2/PHx0fSr8fG6UvMKr8pQa7FYLG7F\nEwpjOZDh9TQG+NxFnW+BAcaY2g5j9wBHmWf55RdN+v7tt5qnN2tcF4vFYinjuNut9gM0+VIbY0ys\nMWYc8CzQ3xjzB9DfcY4xJtQYMx9ARI4D/wF+dmz/dpR5lrvuguHDNRKkTZRksVjKGXald2FJTNRY\n9gMGaGa9P/+EFi3c05fFYrGUEHaltzv46Sc1ev/9tyoKqywsFks5wyqMwrJ+vRq9o6J0lGGxWCzl\nDKswCsv69TqqOH3a2i8sFku5xCqMwjJ/vrrRVqwIffp4WhqLxWIpcazCKCyBgbBjB3TvrolOLBaL\npZxhEygVhm++0YxXW7bA0097WhqLxWLxCFZhFIb582HtWj22Bm+LxVJOsQqjIETU4F2jhrrVduni\naYksFovFI1gbRkHs2X0+qr0AABsVSURBVANHjsCxY9C/v2bYs1gslnKIVRgFsW6d7hMS7HSUxWIp\n11iFURDR0VCtmh5bhWGxWMox1oZREP/5jyZNiouDxi5zOFksFku5wI4wCiIpCf77X7u622KxlHus\nwsiPFSugVy9ITrYKw2KxlHuswsiPlSs1aVLlytCzp6elsVgsFo9ibRj5sX69KosePaBqVU9LY7FY\nLB6lxEcYxpg2xpitWbaTxpiJOer0NsYkZKnzZEnLyenTGgokKcl6R1ksFgseGGGIyG9ACIAxpgJw\nAPjMRdV1IjKkJGXLxqZNkJamx9Z+YbFYLB6fkuoL7BGRGA/L4Zp69XRld4cOnpbEYrFYPI6njd4j\ngQ/yuNbdGLPNGLPCGJPnE9sYE26MiTTGRB49erT4JAsLg3PndHRhTPG1a7FYLKUUjykMY0wl4Dpg\nqYvLW4CmIhIMvAosy6sdEZknIqEiElqvXr3iES49HTZuhOPHrf3CYrFYHHhyhDEI2CIiR3JeEJGT\nInLKcfw14GuMqVtikm3bpusvAPr1K7FuLRaLxZvxpMK4hTymo4wxlxij80DGmMtQOY+VmGTr1+so\no107qF+/xLq1WCwWb8YjRm9jjB/QHxifpexuABF5AxgG3GOMSQPOACNFREpMwB9/1P0115RYlxaL\nxeLteERhiEgSUCdH2RtZjucAc0paLkfnsGaNHvfv7xERLBaLxRvxtJeU97FvnyZLqljRhgOxWCyW\nLHh6HYb3Ua0aXHIJNG9uw4FYLBZLFuwIIyfnzsHhw3D99Z6WxGKxWLwKqzByMmuW7q39wmKxWLJh\nFUZW4uNh5kydlgoO9rQ0FovF4lVYhZGVDRt0f/nl4GO/GovFYsmKfSpm5fPPdX/zzZ6Vw2KxWLwQ\nqzCysnq17gcP9qwcpZGJE3WzWCxllnKvMCIiIggMDMTHx4fAffuIqFYNmjTxtFilj61bdbNYLGWW\ncq0wIiIiCA8PJyYmBhEhBgg/e5aIiAhPi2axWCwFku2FNzDQ7c+ucq0wpk6dSlJSUraypHPnmDp1\nqockslgslsKR64U3Jobw8HC3Ko1yrTD++uuvIpVbLBaLt/Doo4/mfuFNSnLrC2+5Dg0SEBBATEzu\n7LDFlojJYrFYzpP09HQOHjxIdHQ0e/fuZe/evQBMmzYNgP3797u8z50vvOVaYUyfPp3w8PBcWjou\nLo6hQ4eycOFCatas6SHpLBZLWefEiRNOhRAdHU18fDzPPfccACNGjODjjz921jXGEBwczKRJk0hI\nSKBWrVqcOHEiV5sBAQFuk7dcK4xRo0bBhg2Me/11koGmPj7cOmQIr69dy/Lly2nTpg0vvvgit956\nK8bm9bZYLEXk7NmzxMTEOBXCnj17mDx5MmfOnOFf//oXixYtyla/atWqnD59mlOnTrF//35atWrF\nuXPnSE5O5vTp02zfvj3fl1g/Pz+mT5/uts9jSjIvkbsJDQ2VyMjIwt8QEQHh4fR2jDDWAPj5cWbO\nHHZ26sQ999xDZGQkQUFBLFiwgEsvvdQdYpcNevfWfUYuEYulDJKWlkZCQgInTpzgxIkTHD9+nOjo\naKKjo4mJieHgwYO0bNmS1NRUtm7dSlRUVJH7uOiii6hduzY1a9akVq1aufZZjyMjI1m4cCFHjhwh\nICCA6dOn64twETDGbBaR0ELV9ZTCMMbsAxKBc0BaToEdKVpnA4OBJOAOEdmSX5tFVhiBgRATQ2/H\n6ZqM8qZNYd8+UlNTCQwM5ODBgwDceeedzJgxgwYNGhS+j/KCVRiWUkB6ejqJiYmcOHGCv//+2/ng\nz3me9fjYsWPEx8dz8uRJzp49W2Af1atXp169evj6+pKSksLFF1/MJZdcQuPGjWnUqBG1a9d2PvQz\njjPOa9SoQYUKFUrgm8ikKArD01NSfUQkPo9rg4BWju1y4HXHvvjIyzjkKPf19SUqKorx48ezdOlS\n3nnnHSIiIvDz8yMhIeG8NbrFYjl/zp4963ygZ927euDnLEtISKCgl+Tq1atTp04dKleuTHR0NGlp\nadmujx49mv79+3P69GnWrl1LixYtaN26NR06dKB9+/ZULcN5dDytMPLjemCRI5f3/4wxtYwxDUXk\nULH1EBAALrykyGI0ql27NkuWLGHJkiXccccdnDlzhpSUFACn3zOQr9KIiIhg6tSp/PXXX1bJWMo9\nIkJiYmKuh31+51nLk5OT823fz8/P+eZeu3ZtGjVqRLNmzTDGkJaWRpMmTejSpQsAU6ZMIT4+PpsS\nmTJlCo8//jhHjx7lqaeeonnz5jRr1sy5r1WrlrPuPffc454vyUvx5JTUXuBvQIA3RWRejutfAs+K\nyHrH+Q/AFBGJzFEvHAgHCAgIuNSVm2ye5GHDYN48cPFA9/f358CBA7nKL7roItasWUOjRo2oV68e\nPlki3WYsrsnqieXn58e8efPKltIoxikpq2C9n/T0dBISErI93PN78Oe8lp6enmfbPj4+zmma2rVr\nZ3v451Xm6+tLQkIC8fHx1KlTh6uuuor09HSCg4P5888/s00lhYeH8+abb3Lu3DnGjRvH/7d37vFV\nVVce/66QdwwEkQQiNdfQAFYKxIDTGkCoFXFkbKlFGFFAK6USrTIzdGirzKAyjrV2BsXWgi/AIEjV\nESug+AihH+SlKEwKUiG8JgKBADGQQAJr/jjnXm/CTXITktzcZH0/n/PJuWefx9o3yf6dvdbea3s8\nnmqCkJqaWu1/uK0TLjGMVFUtEpFkYDVwn6rm+5W/DTxWQzB+oaof13bPBscwAHJzGfaTn8Dp0+Sl\npcHs2QHFApw/5Pq+r1//+tc8+uijHDt2jIkTJ/Lhhx9SVlZ23nlpaWns2bOnYba2ZppIMNqNwLYC\namv0S0pKam3svVt9rp2oqChfw16zoa/tmPdzYmLieQ32mTNn2LdvH7t37+bcuXOMHDkSgJtvvpl1\n69Zx9OhR37k33ngjK1asAGDKlCkkJiZWEwSPx0NsbGwzfKPhSVjEMFS1yP15WETeAK4G8v1OOQD4\nZwHsARQ1uSHjx8P8+c5+PY1dbRP9OnbsSExMDMXFxSxfvpysrCz69evHnj17AooF2Gzy2giYruXU\nKe6//34iIyOJjY0lLi6OhIQEsrOzAWfeTGVlpa8sNja23bwh+rt3/Bv6mo1+IBE4fvx4nY1+dHR0\ntQa9W7duXHHFFec1/IG2+Pj4Bg1FV1UOHTpEQUEBx44d46abbgLg3nvv5a233uLAgQO+Xknfvn19\ngpGRkUFqairp6enVXEde/vjHPzbmazVqISSCISIJQISqfuXujwAernHacuBeEVmCE+w+0aTxi0YQ\naKJffHw8v//97xkzZgzPP/88jz32GD/60Y/weDzk5OQwZ84cDhw4cN69UlJS2L17d7U/7vZKSUkJ\neXl55OXlBRRkgKNHjzJu3Djf56SkJI4dOwZATk5OtQlOAOnp6ezatQuASZMmsX79ep+YxMXFkZGR\n4WtMfvOb37B///5qguPxeLjtttsAeOeddygvLycuLs5Xfskll/h+d8ePHycqKorY2NhGj3ApLy+v\ntdGv79jZs2drvW/NN/3k5GT69OlT7djFF18csNGPi4tr0vlHX331FYWFhezfv98nCI8//jgLFy6k\nsLCQ8vJyADp16uSbkJacnMzQoUN9YnD55ZfTs2dP3z2ffPLJJrPPqJ9Q9TBSgDfcP8ZIYLGqrhKR\nnwGo6rPACpwhtV/gDKu9M0S2+vC6RGrzr99zzz1MnjyZN998kzlz5jB9+nSioqKIjIysNtIiPj6e\nxMREevfuzZQpU3jwwQfp1q1bSOoUCo4cOUJ+fj7XXnstXbp0YdGiRTzwwAPEx8cTGxsbcOhiamoq\nq1evpqKigvLy8mqN5NSpUxkxYgTl5eW+8vj4eF95nz59KC8vr1bu3/PLy8tj/fr1vjKA7Oxsn2BM\nmzaN7du3V7PnhhtuYNWqVQD069fPl6YhKiqK6Ohohg8fTk5ODseOHWPmzJm+QO25c+c4d+6cr64l\nJSUcPny4zkYfIDExkY4dO/qGYvbt25eUlBTfeP2uXbsGbPwTEhKarNGvL7ZUWVnJvn37KCwsZMiQ\nIcTExLBo0SLmzp3rm8XspbS0lMTEROLj4+nVqxcjR46s5jZSVUSEmTNnNontRtPQvifuuQxz/e95\nTTyHYMuWLTz99NMsXLjQ1yB07tyZWbNmccstt/DII48wf/58YmJimDZtGtOnTw/fVCR1xDDKyspY\ntWoVa9asIS8vzzeZacmSJYwdO5aioiL27NnDwIEDWbZsWUhjGKrKiRMnKC4u5vTp05SUlFBQUMDB\ngwcpKSnxvdmfOXOGiIgISkpK2Lt3L2VlZfWO0Y+IiKBDhw506NCBlJQUsrKySEpKYtGiRVRVVVVz\nD91+++3MmjWLyMhI0tLSzrvXzJkzmTVrFl9++SWpqalERkZW60E99NBD3H333ezfv58JEyZU6z3F\nxcUxadIkhgwZQlFRES+++GK1stjYWLKzs7nssss4ceIEO3bs4IMPPuDhhx+uVsf4+Hjuu+8+NmzY\n4Os5eN1GBQUFfOtb32Lx4sW89NJL1cQgPT2d/v37ExUV1US/NeNCCIugd3PQ2gTDS3FxMS+88AJL\nly5ly5YtgPMGO3bsWDIzM3nmmWdYsmQJzz77LFOmTGkWG5odP8E4fPgw+fn5dO/enezsbAoLC0lP\nTyc+Pp7BgwczbNgwhg0bxsCBAwM2Gk0xSkpVKS0t9TXy/q6cQJt/WV0Nf4cOHXxv8f4/vVttx5OS\nkoiOjq7T3srKSl8vKCYmhqSkJKqqqti4caOv91NRUUFFRQV9+/alf//+nDhxgqeeeqpaWXl5OePG\njeOGG26gsLCQiRMnViurqKjgiSeeYNy4cXz00Udcc80159nz6quvMmbMGFavXs2IESNqtTs5OZme\nPXueN/R00KBBJCQkNOh3ZoQGE4wG0tyC4c/OnTtZtmwZS5cuZdu2bYgIWVlZ9OnTh9GjRzN8+HBW\nrlzJqVOnmDRpEpGRrXmqzNe8duWVfHj8OHmdO1NQUADAhAkTWLBgAarKpk2byMzMbPBbZVVVVbXG\nPNhGvz7fflxcHF26dDmvse/cubPvuH+j791PTExsU3nFVJWqqqrzXHbdu3enU6dOHDlyhI0bNzJq\n1KiAAXIRqXOIrNH6McFoIC0pGP5s376dZcuW8f7777NhwwZOnz6NiJCYmEhpaSndu3dn2rRpTJ06\ntVW9rR06dIg1a9Zw5MgRpk6dCsC3L7qIwvJyBl9/va8HkZWVRVRUFKpKWVlZwKBtXT9LSkooLS2t\n0xbvUMwuXboEfNv3FwX/t38bVtkwPB5PwAEJbW54eDvEBKOBhEow/KmoqGDTpk2sWbOGNWvWkJ+f\n75tRDtCtWzcyMzO54ooryMjIoFu3bqSkpPi25hIUVeXMmTOsWLGC119/nXXr1rF7927Aicc8+uij\nlJaWsu+pp6hSpfTaawOma6iZXsGfyMjIWt/yA7l7/N08LZ13p71i82PaLiYYDaQ1CEZNKisr2b59\nO/PmzePll19mwIABvgBkIB97QkICycnJJCQkVAtgejcRQVXP2yorK33+be9WVlbG8ePHKSsro6qq\nqt4RPACRInSOjKSzmzoh0OSsQMM3u3Tp0qQjeYzmw2bgt01MMBpIaxQMf06fPk10dDQiwowZMygo\nKODOO+8kLi6OQ4cO+bbi4mJOnjzp80P7b95hijW36OhoYmNjOXv2LAcPHvQJBThDRO+44w569uzp\nG8/vzapZc4sdOdJp9Fvpd2gYRmDCYqa3ETwxMTG+/aSkJPLy8li5ciV33XUXM2fOpEePHg26X1FR\nkc/1NWbMGK677jo2bNjA9ddfz9ChQ30xiMzMzOCD7tZDMIw2T/vIn9CGmDFjBrt27SInJ4eXXnqJ\njIwM36pdubm5eDweIiIi8Hg85Obm+q47efIkU6ZMoXfv3lx66aXcdtttvPLKK77Z0IMGDaKkpIS3\n336b6dOnM2jQoLAZoWUYRstgghGGJCcnM2fOHHbu3Mmtt97KVVddRW5uLpMnT2bv3r2oKnv37mXS\npEmMHj0acAKUeXl59OrVi9/+9rds3ryZkpISX3r2iIgIEwjDMOrEWogwxuPxsGDBAgBGjRrlS2vh\npaqqivfeew9wxsvv2LHDgsuGYTQa62G0EWrLfnvy5EnfvomFYRgXgglGG+Eyv1UCgzluGIbRUEww\n2gizZ8+ulqEVnLjF7NmzQ2SRYRhtDROMNsL48eOZN28eaWlpiAhpaWk2C9cwjCbFgt5tiPHjx5tA\nGIbRbFgPwzAMwwiKFhcMEfmGiHwoIttFpEBE7g9wzjAROSEin7qbLbtlGIYRYkLhkqoC/llVPxGR\nROBjEVmtqn+tcd5aVR0VAvsMwzCMALR4D0NVv1TVT9z9r4DtwKUtbYdhGIbRMEIawxARD5AJbAhQ\n/F0R+UxEVorIlS1qmGEYhnEeIRslJSIXAa8BD6hqzWXVPgHSVLVMRP4e+B8go5b7/BT4qfuxTEQ+\nb6RJl4jIkUZe29q4BAhNXZp2Nnno6tH0WF1aH22lHnBhdUkL9sSQrIchIlHAn4F3VPV3QZy/Bxio\nqs32yxWRzcHmhG/ttJW6tJV6gNWlNdJW6gEtV5dQjJIS4Hlge21iISLd3PMQkatx7DzaclYahmEY\nNQmFSyobuAPYJiKfusd+BVwGoKrPAj8G7hGRKqAcGKdtaWlAwzCMMKTFBUNV/wLU6ehW1bnA3Jax\nyMe8Fn5ec9JW6tJW6gFWl9ZIW6kHtFBd2tSa3oZhGEbzYalBDMMwjKAwwTAMwzCCot0LhoiMFJHP\nReQLEZkRansai4i8ICKHReR/Q23LhRJMvrFwQURiRWSjOwm1QERmhdqmC0FEOojIFhH5c6htuRBE\nZI+IbHNz1W0OtT0XgogkicifRGSH+z/z3WZ7VnuOYYhIB2AncD1wANgE/GOAvFatHhEZCpQBC1W1\nb6jtuRBEpDvQ3T/fGPDDMP29CJDgTkKNAv4C3K+q60NsWqMQkX8CBgIdwznXW0vM7WopRGQBTu69\n50QkGohX1ePN8az23sO4GvhCVXer6hlgCfCDENvUKFQ1HygJtR1NQVvKN6YOZe7HKHcLy7c0EekB\n3AQ8F2pbDAcR6QgMxZnbhqqeaS6xABOMS4H9fp8PEKYNU1ulnnxjYYHrxvkUOAysVtVwrct/A78A\nzoXakCZAgXdF5GM3vVC4kg4UAy+6rsLnRCShuR7W3gUj0HyQsHz7a4vUk28sbFDVs6o6AOgBXC0i\nYecyFJFRwGFV/TjUtjQR2ap6FXAjkOO6dMORSOAq4A+qmgmcBJotFtveBeMA8A2/zz2AohDZYvjh\n+vtfA3JV9fVQ29MUuK6CPGBkiE1pDNnAza7vfwnwPRF5ObQmNR5VLXJ/HgbewHFPhyMHgAN+vdY/\n4QhIs9DeBWMTkCEil7vBonHA8hDb1O4JJt9YuCAiXUUkyd2PA74P7AitVQ1HVX+pqj1U1YPzf/KB\nqt4eYrMahYgkuIMpcN03I4CwHF2oqgeB/SLS2z10HdBsg0NClt68NaCqVSJyL/AO0AF4QVULQmxW\noxCRV4BhOGnaDwD/pqrPh9aqRhMw35iqrgihTY2lO7DAHZEXAbyqqmE9JLUNkAK84eY3jQQWq+qq\n0Jp0QdwH5LovvbuBO5vrQe16WK1hGIYRPO3dJWUYhmEEiQmGYRiGERQmGIZhGEZQmGAYhmEYQWGC\nYRiGYQSFCYbR6hGRs25W0c9E5BMRuaaJ7usJNruviKSKyJ9qKcsTkYHu/q8aef8HRGSCuy8i8qCI\n/E1EdrqZe6+s5/phTfW91LjvqHDPsGs0HSYYRjhQrqoDVLU/8EvgsZY2QFWLVPXHQZz6q/pPqY6I\nRAJ3AYvdQznANUB/Ve2FU9/lIhJbx22Gudc0NW/jzPCOb4Z7G2GGCYYRbnQEjoGTa0pE3nd7HdtE\n5AfucY+7LsB8dw2Kd91Z1ohIlttT+QinYcY9vkJE+rn7W0Rkprv/iIjc7d9bEJE4EVkiIltFZCng\nvfd/AnFubyjXvXWHQHbU4HvAJ6pa5X7+V+A+VT0FoKrvAuuA8e5zRrp1/sytvwf4GTDNffYQEfkH\nEdng1uU9EUkRkQhx1oFI8qv3F25ZVxF5TUQ2uVu2+2zFSWcStqnMjabDBMMIB7yN8A6c1NqPuMcr\ngNFuErnhwJNuWhGADOAZVb0SOA7c4h5/Efi5qtZcZCYfGOKmi67CmW0OMBhYW+Pce4BTqtoPmA1k\nAajqDL7uDY2vxw5/snHW/PCmq05Q1V01ztkMXCkiXYH5wC1uj2uMqu4BngX+y332Wpx1N77jJqRb\nAvxCVc8BbwKj3Wf9HbBHVQ8Bc9zrB7k2Plfj2UMC2G20M0wwjHDA2wj3wUnct9AVBgH+Q0S2Au/h\npKZPca8pVFVvWpGPAY+IdAKSVHWNe3yR3zPW4qwrMBjHDXOR64bxqOrnNewZCrwMoKpbga112H6e\nHQHO6Y6TorouBCeT8neAfFUtdJ9f2xooPYB3RGQbMB3wxkCWAmPd/XHuZ3ByXM11U7EsBzp68y3h\npGVPrcc+ox1ggmGEFar6EXAJ0BXHRdMVyHLThx8CvH7+036XncXJGeRtdAOxCWcluSE4vY0twGTc\nN/9ApgRpciA7alKOa7ebxv2kiKTXOOcqnKRyddXBn6eBuar6bWAKX38vHwHfdHsqPwS8mYAjgO+6\nwjxAVS91F6/CvbY8iGcabRwTDCOsEJE+OIkijwKdcNZoqBSR4UBaXde66cVPiMhg99B4v7IzOItp\n3Qqsx+lx/Avnu6PAERRvPKEv0M+vrFKc1OwNYTvwTb/PTwBP+cVdvo/T81mM0+BfKyKXu2UXu9d8\nBST63aMT8H/u/kS/eipOOu/f4WQDPuoWvQvc6z1PRAb43asXYZrN1WhaTDCMcMAbw/gUx4UyUVXP\nArnAQBHZjNOAB5M2/E7gGTfoXfOteS1wyA02r8Vx6wQSjD/guKy24qxAt9GvbB6w1S/oHQwrcdxc\nXp7G6fFsE5HPgYeAH6hquaoWAz8FXheRz/japfQWMNob9Ab+HVgmImuBmutWLwVu97sW4Oc43+VW\nEfkrThDdy3AcN53RzrFstYbRChCRN3AC038LtS3+iEgKTvrv60JtixF6TDAMoxUgzgI4KaqaH2pb\n/BGRQUClX+DeaMeYYBiGYRhBYTEMwzAMIyhMMAzDMIygMMEwDMMwgsIEwzAMwwgKEwzDMAwjKP4f\n2Z2WVkQCxCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TCfeaturesOn = eval('TCfeatures_'+Celltype+'_Laser_on')\n",
    "TCfeaturesOff = eval('TCfeatures_'+Celltype+'_Laser_off')\n",
    "test_BWs = TCfeaturesOn['test_BWs']\n",
    "Bandwidths = TCfeaturesOn['Bandwidths']\n",
    "R2off = TCfeaturesOff['GoF']['R2']\n",
    "R2on = TCfeaturesOn['GoF']['R2']    \n",
    "    \n",
    "#visualize all fits, from worse to best\n",
    "import pylab as pl\n",
    "from IPython import display\n",
    "\n",
    "R2inds = np.argsort(R2off)\n",
    "ii=0\n",
    "for cel in R2inds[:]:\n",
    "    pl.clf()\n",
    "    baselineOff = TCfeaturesOff['Baselines'][cel]\n",
    "    baselineOn = TCfeaturesOn['Baselines'][cel]\n",
    "    pl.plot(test_BWs,baselineOn + TCfeaturesOn['fitTCs'][:,cel],'r-',label='fit curve ('+R2Label+' = %1.2f)' % R2off[cel])\n",
    "    pl.plot(Bandwidths, baselineOn + TCfeaturesOn['rawTCs'][:,cel],'ro--',label='Laser On data')\n",
    "    pl.axvline(TCfeaturesOn['MiscParams']['prefBW'][cel],ymax=.17,c='r')\n",
    "#    pl.axhline(x=baselineOn,c='k',linestyle='--')\n",
    "    pl.plot(test_BWs,baselineOff + TCfeaturesOff['fitTCs'][:,cel],'k-',label='fit curve ('+R2Label+' = %1.2f)' % R2on[cel])\n",
    "    pl.plot(Bandwidths, baselineOff + TCfeaturesOff['rawTCs'][:,cel],'ko--',label='Laser Off data')\n",
    "    pl.axvline(TCfeaturesOff['MiscParams']['prefBW'][cel],ymax=.15,c='k')\n",
    "#    pl.axhline(x=baselineOff,c='k',linestyle='--')\n",
    "\n",
    "    pl.xlabel('Bandwidth (Octave)')\n",
    "    pl.ylabel('Rate (Hz)')\n",
    "    ii+=1\n",
    "    pl.title('Cell %i of %i,   %i%%' % (ii,len(R2),100 * ii//len(R2)))\n",
    "    pl.legend()\n",
    "    display.display(pl.gcf())\n",
    "    display.clear_output(wait=True)\n",
    "    time.sleep(.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Save all fit plots, from worse to best\n",
    "PLOTS_DIR = DATA_DIR + 'Plots/TCplots/inactivate'+Celltype+'/'\n",
    "%matplotlib inline\n",
    "\n",
    "R2off = TCfeaturesOff['GoF']['R2']\n",
    "R2on = TCfeaturesOn['GoF']['R2']    \n",
    "R2inds = np.argsort(R2off)\n",
    "ii = 1\n",
    "for cel in R2inds[:]:\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    baselineOff = TCfeaturesOff['Baselines'][cel]\n",
    "    baselineOn = TCfeaturesOn['Baselines'][cel]\n",
    "    ax.plot(test_BWs,baselineOn + TCfeaturesOn['fitTCs'][:,cel],'r-',label='fit curve ('+R2Label+' = %1.2f)' % R2on[cel])\n",
    "    ax.plot(Bandwidths, baselineOn + TCfeaturesOn['rawTCs'][:,cel],'ro--',label='Laser On data')\n",
    "    ax.axvline(TCfeaturesOn['MiscParams']['prefBW'][cel],ymax=.17,c='r')\n",
    "#    ax.axhline(x=baselineOn,c='k',linestyle='--')\n",
    "    ax.plot(test_BWs,baselineOff + TCfeaturesOff['fitTCs'][:,cel],'k-',label='fit curve ('+R2Label+' = %1.2f)' % R2off[cel])\n",
    "    ax.plot(Bandwidths, baselineOff + TCfeaturesOff['rawTCs'][:,cel],'ko--',label='Laser Off data')\n",
    "    ax.axvline(TCfeaturesOff['MiscParams']['prefBW'][cel],ymax=.15,c='k')\n",
    "#    ax.axhline(x=baselineOff,c='k',linestyle='--')\n",
    "\n",
    "    TCfeatures = TCfeaturesOff\n",
    "    pars = [TCfeatures['MiscParams']['SuppInd_wBL'][cel],TCfeatures['CarandiniParams']['RD'][cel], TCfeatures['CarandiniParams']['RS'][cel],TCfeatures['CarandiniParams']['sigmaD'][cel],TCfeatures['CarandiniParams']['sigmaS'][cel]]\n",
    "    TCfeatures = TCfeaturesOn\n",
    "    pars = pars + [TCfeatures['MiscParams']['SuppInd_wBL'][cel],TCfeatures['CarandiniParams']['RD'][cel], TCfeatures['CarandiniParams']['RS'][cel],TCfeatures['CarandiniParams']['sigmaD'][cel],TCfeatures['CarandiniParams']['sigmaS'][cel]]\n",
    "    ax.set_title(('OFF: $SI$ = %2.1f, $R_D$ = %2.1f, $R_S$ = %2.1f, $\\sigma_D$ = %2.1f, $\\sigma_S$ = %2.1f\\n'+\\\n",
    "                 'ON: $SI$ = %2.1f, $R_D$ = %2.1f, $R_S$ = %2.1f, $\\sigma_D$ = %2.1f, $\\sigma_S$ = %2.1f') % tuple(pars))\n",
    "    ax.set_xlabel('Bandwidth (Octave)')\n",
    "    ax.set_ylabel('Rate (Hz)')\n",
    "    ax.legend()\n",
    "\n",
    "    fig.savefig(PLOTS_DIR +CelltypeLabel+'_cell_TC_gauss_fit_'+str(ii)+'.png')\n",
    "    plt.close(fig)\n",
    "    ii +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['Ncells', 'MiscParamsNames', 'MiscParams', '__version__', 'MiscParamsLabels', 'RawMiscParams', 'CarandiniParamsLabels', 'fitTCs', 'BLisZeroBW', 'GoFNames', '__globals__', 'rawTCs', 'Bandwidths', 'Fit_curve_type', 'GoF', 'Celltype', 'test_BWs', 'Response_type', 'CarandiniParams', 'GoFLabels', '__header__', 'CarandiniParamsNames', 'Baselines'])\n"
     ]
    }
   ],
   "source": [
    "SAVE_DIR = '/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/'\n",
    "from scipy.io import savemat, loadmat, whosmat\n",
    "filename = SAVE_DIR+'TCfeaturesToPlotOn.mat'\n",
    "savemat(filename,TCfeaturesOn, oned_as='column')\n",
    "\n",
    "MYDIC = loadmat(filename)\n",
    "print(MYDIC.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.isinteractive()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'deepdish'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-9777e2fec805>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mSAVE_DIR\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mdeepdish\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSAVE_DIR\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'TCfeaturesToPlotOn.h5'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mTCfeaturesOn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'deepdish'"
     ]
    }
   ],
   "source": [
    "SAVE_DIR = '/Users/yashar/Google-Drive/Documents/Work/Projects/AuditoryContextualModul/Matlab/'\n",
    "import deepdish as dd\n",
    "filename = SAVE_DIR+'TCfeaturesToPlotOn.h5'\n",
    "dd.io.save(filename,TCfeaturesOn)\n",
    "\n",
    "MYDIC = dd.io.load(filename)\n",
    "print(MYDIC.keys())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning curve feature distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AbsPrefBW , Baseline , SuppInd_noBL , SuppInd_wBL , peakAbsResp , peakNegResp , peakRate , peakResp , peakSignedResp , prefBW , wnRate , wnResp\n"
     ]
    }
   ],
   "source": [
    "TCfeaturesToPlotOn = TCfeaturesOn['MiscParams']\n",
    "TCfeaturesToPlotOff = TCfeaturesOff['MiscParams']\n",
    "TCparamNames = TCfeaturesOff['MiscParamsNames']\n",
    "ParamsLabels = TCfeaturesOff['MiscParamsLabels']\n",
    "print(\" , \".join(TCparamNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ncells = 57\n"
     ]
    },
    {
     "data": {
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y8mCxWFBaWoqhQ4dGsNroar8cFYC2z1XbtrM1BcCWnAK/azcCHg/MdrvscnrE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\n",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"white\", color_codes=True,palette='husl')\n",
    "sns.set_palette(\"husl\")\n",
    "sns.reset_orig()\n",
    "print('Ncells = {}'.format(TCfeaturesOn['Ncells']))\n",
    "\n",
    "xs = TCfeaturesToPlotOff['SuppInd_wBL']\n",
    "ys = TCfeaturesToPlotOn['SuppInd_wBL']\n",
    "marg_dict = {'kde':True,'bins':15}\n",
    "ee = .02\n",
    "lims = [-ee,1+ee]\n",
    "jgrd = sns.JointGrid(x=xs, y=ys, xlim=lims, ylim=lims)\n",
    "jgrd.plot_joint(plt.scatter, color=\"r\",alpha=.6)#, edgecolor=\"white\")\n",
    "jgrd.plot_marginals(sns.distplot, kde=True, color=\"r\",bins = 15)\n",
    "# jgrd = sns.jointplot(x=xs,y=ys, kind='scatter', color='r', xlim=lims, ylim=lims, marginal_kws=marg_dict,alpha = .6)\n",
    "fig = jgrd.fig\n",
    "axs = fig.get_axes()\n",
    "axs[0].plot([0,1],[0,1],'k-');\n",
    "#fig.get_children()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tuning_curve_feature_plotting as tcfplot\n",
    "reload(tcfplot)\n",
    "\n",
    "betterOrder = [10,1,4,3,2,7,5,9,11,12,8,6]\n",
    "tcfplot.makeHistograms(TCfeaturesToPlotOff, TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,nbins=15)\n",
    "print(\"Ncells = %1.0f\" % TCfeatures['Ncells'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "betterOrder = [10,1,4,3,2,7,5,9,11,12,8,6]\n",
    "tcfplot.makeHistograms(TCfeatures['RawMiscParams'], TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,nbins=15)\n",
    "print(\"Ncells = %1.0f\" % TCfeatures['Ncells'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_ftr = 12\n",
    "pairs = list(zip(range(n_ftr),range(n_ftr)))\n",
    "betterOrder = [10,1,4,3,2,7,5,9,11,12,8,6]\n",
    "pairs = [[betterOrder[i]-1 for i in p] for p in pairs] \n",
    "tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels,\\\n",
    "                         pairs,figsize=(15,15),cols=4,hspace = .3, wspace = .4,\\\n",
    "                        yTCfeatures = TCfeatures['RawMiscParams'])\n",
    "print(\"\\n\\n Comparing raw (y-axis) vs. fit-curve-extracted TC features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations of preferred BW with other quantities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pairs = [(5,1),(6,1),(8,1),(9,1),(10,1),(3,1),(11,1)]\n",
    "pairs = [[betterOrder[i-1]-1 for i in p] for p in pairs] \n",
    "tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels, pairs,uniformYs=True,figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations of *Suppression Index* with other quantities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pairs = [(5,3),(6,3),(8,3),(9,3),(10,3),(1,3),(11,3)]\n",
    "pairs = [[betterOrder[i-1]-1 for i in p] for p in pairs] \n",
    "tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels, pairs,uniformYs=True,figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlations of rates and responses (not interesting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inds = range(5,13) #for rates\n",
    "pairs = [(j,i) for i in inds for j in inds]\n",
    "pairs = [[betterOrder[i-1]-1 for i in p] for p in pairs] \n",
    "tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels, \\\n",
    "                 pairs,cols=len(inds),figsize=(27/1.5,23/1.5),hspace=.4,wspace=.4,uniformYs=True,symm=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference of Gaussians parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TCfeatures = eval(Celltype+'features')\n",
    "\n",
    "TCfeaturesToPlot = TCfeatures['CarandiniParams']\n",
    "TCparamNames = TCfeatures['CarandiniParamsNames']\n",
    "ParamsLabels = TCfeatures['CarandiniParamsLabels']\n",
    "print(\" , \".join(TCparamNames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "betterOrder = [1,4,2,3,5,6] #list(range(1,7))\n",
    "tcfplot.makeHistograms(TCfeaturesToPlot, TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,figsize=(8,10),nbins=15)\n",
    "print(\"Ncells = %1.0f\" % TCfeatures['Ncells'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean sigma_D is smaller for PV than E and SOM (in general), which may be consistent with \n",
    "another paper's (can't remember which) finding (told to me by Santiago) about the RF width of\n",
    "the three cell types. \n",
    "\n",
    "\n",
    "On the other hand, mean sigma_S of PV and SOM is larger than E's, in line with their weaker surround suppression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "inds = range(6)\n",
    "pairs = [(j,i) for i in inds for j in inds]\n",
    "betterOrder = [1,2,3,5,6,4]\n",
    "pairs = [[betterOrder[i]-1 for i in p] for p in pairs] \n",
    "tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels, \\\n",
    "                 pairs,cols=len(inds),figsize=(14,14),hspace=.4,wspace=.4,symm=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "betterOrder_x = [2,3,5,6]\n",
    "betterOrder_y = [10,1,4,8,6,12]\n",
    "pairs = [[i-1, j-1] for j in betterOrder_y for i in betterOrder_x ] \n",
    "sig_vec = tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels, \\\n",
    "                 pairs,cols=len(betterOrder_x),figsize=(14,18),hspace=.3,wspace=.2,uniformYs=True,uniformXs=True,\\\n",
    "                 yTCfeatures = TCfeatures['MiscParams'],yTCparamsNames = TCfeatures['MiscParamsNames'],\\\n",
    "                         yTCparamsLabels = TCfeatures['MiscParamsLabels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pairs1 = [pairs[i] for i in np.nonzero(sig_vec)[0]]\n",
    "sigs = tcfplot.makeScatterPlots(TCfeaturesToPlot, TCparamNames, ParamsLabels, \\\n",
    "                 pairs1,cols=len(betterOrder_x),figsize=(14,6),wspace=.5,\\\n",
    "                 yTCfeatures = TCfeatures['MiscParams'],yTCparamsNames = TCfeatures['MiscParamsNames'],\\\n",
    "                         yTCparamsLabels = TCfeatures['MiscParamsLabels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limiting to positively responsive cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "cellinds = np.nonzero(TCfeatures['RawMiscParams']['peakResp']>0)[0] #== np.nonzero(TCfeatures['RawMiscParams']['prefBW']>0)[0])\n",
    "n1 = len(cellinds)\n",
    "Ncells = TCfeatures['Ncells']\n",
    "perc = 100*n1//Ncells\n",
    "#print(('\\n\\n %i%% (or %i/%i)   of ' % (perc,len(cellinds),TCfeatures['Ncells']))+CelltypeLabel+' cells have a positive response.')\n",
    "print(('\\n\\n %i/%i  of ' % (len(cellinds),TCfeatures['Ncells']))+CelltypeLabel+' cells have a positive response.')\n",
    "colors  = ['lightcoral', 'lightskyblue']\n",
    "labels = ['positive response','suppression only']\n",
    "plt.pie([n1,Ncells-n1], colors=colors, startangle=90,labels=labels, autopct='%1.0f%%');\n",
    "#plt.legend(patches, labels, loc=\"best\")\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TCfeatures = eval(Celltype+'features')\n",
    "TCfeaturesToPlot = TCfeatures['MiscParams']\n",
    "TCparamNames = TCfeatures['MiscParamsNames']\n",
    "ParamsLabels = TCfeatures['MiscParamsLabels']\n",
    "\n",
    "%matplotlib inline\n",
    "betterOrder = [10,4,2,7,5,9,11,12]\n",
    "tcfplot.makeHistograms(TCfeaturesToPlot, TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,nbins=15,cell_inds=cellinds)\n",
    "print(\"Ncells = %1.0f\" % n1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TCfeatures = eval(Celltype+'features')\n",
    "\n",
    "TCfeaturesToPlot = TCfeatures['CarandiniParams']\n",
    "TCparamNames = TCfeatures['CarandiniParamsNames']\n",
    "ParamsLabels = TCfeatures['CarandiniParamsLabels']\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "betterOrder = [2,3,5,6] #list(range(1,7))\n",
    "tcfplot.makeHistograms(TCfeaturesToPlot, TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,figsize=(8,10),nbins=15,cell_inds=cellinds)\n",
    "print(\"Ncells = %1.0f\" % n1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<style type=\"text/css\">\n",
    "body  {\n",
    "   background-color:#E8E2E8;\n",
    "};\n",
    "</style>\n",
    "<font color=#dd8010 size=\"4\">\n",
    "Summary: It didn't really make a difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limiting to SI>0 cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "cellinds = np.nonzero(TCfeatures['MiscParams']['SuppInd_wBL']>0)[0] #== np.nonzero(TCfeatures['RawMiscParams']['prefBW']>0)[0])\n",
    "n1 = len(cellinds)\n",
    "Ncells = TCfeatures['Ncells']\n",
    "perc = 100*n1//Ncells\n",
    "#print(('\\n\\n %i%% (or %i/%i)   of ' % (perc,len(cellinds),TCfeatures['Ncells']))+CelltypeLabel+' cells have a positive response.')\n",
    "print(('\\n\\n %i/%i  of ' % (len(cellinds),TCfeatures['Ncells']))+CelltypeLabel+' cells have a suppression index > 0.')\n",
    "colors  = ['lightcoral', 'lightskyblue']\n",
    "labels = ['SI > 0','SI = 0']\n",
    "plt.pie([n1,Ncells-n1], colors=colors, startangle=90,labels=labels, autopct='%1.0f%%');\n",
    "#plt.legend(patches, labels, loc=\"best\")\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TCfeatures = eval(Celltype+'features')\n",
    "TCfeaturesToPlot = TCfeatures['MiscParams']\n",
    "TCparamNames = TCfeatures['MiscParamsNames']\n",
    "ParamsLabels = TCfeatures['MiscParamsLabels']\n",
    "\n",
    "%matplotlib inline\n",
    "betterOrder = [10,4,2,7,5,9,11,12]\n",
    "tcfplot.makeHistograms(TCfeaturesToPlot, TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,nbins=15,cell_inds=cellinds)\n",
    "print(\"Ncells = %1.0f\" % n1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TCfeatures = eval(Celltype+'features')\n",
    "\n",
    "TCfeaturesToPlot = TCfeatures['CarandiniParams']\n",
    "TCparamNames = TCfeatures['CarandiniParamsNames']\n",
    "ParamsLabels = TCfeatures['CarandiniParamsLabels']\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "betterOrder = [2,3,5,6] #list(range(1,7))\n",
    "tcfplot.makeHistograms(TCfeaturesToPlot, TCparamNames,ParamsLabels, CelltypeLabel,betterOrder,figsize=(8,10),nbins=15,cell_inds=cellinds)\n",
    "print(\"Ncells = %1.0f\" % n1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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