M_bargraphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Individual gene (V and J) usage frequencies for CD4 and CD8 types for each subject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Define lists for each gene strings.\n",
    "TRAV=['TRAV1-1*00','TRAV1-2*00','TRAV2*00','TRAV3*00','TRAV4*00','TRAV5*00',\n",
    "      'TRAV6*00','TRAV7*00','TRAV8-1*00','TRAV9-1*00','TRAV10*00','TRAV11*00',\n",
    "      'TRAV12-1*00','TRAV8-2*00','TRAV8-3*00','TRAV13-1*00','TRAV12-2*00',\n",
    "      'TRAV8-4*00','TRAV8-5*00','TRAV13-2*00','TRAV14DV4*00','TRAV9-2*00',\n",
    "      'TRAV15*00','TRAV12-3*00','TRAV8-6*00','TRAV16*00','TRAV17*00','TRAV18*00',\n",
    "      'TRAV19*00','TRAV20*00','TRAV21*00','TRAV22*00','TRAV23DV6*00','TRAV24*00',\n",
    "      'TRAV25*00','TRAV26-1*00','TRAV8-7*00','TRAV27*00','TRAV28*00','TRAV29DV5*00',\n",
    "      'TRAV30*00','TRAV31*00','TRAV32*00','TRAV33*00','TRAV26-2*00','TRAV34*00',\n",
    "      'TRAV35*00','TRAV36DV7*00','TRAV37*00','TRAV38-1*00','TRAV38-2DV8*00',\n",
    "      'TRAV39*00','TRAV40*00','TRAV41*00']\n",
    "\n",
    "TRBV=['TRBV1*00','TRBV2*00','TRBV3-1*00','TRBV4-1*00','TRBV5-1*00','TRBV6-1*00',\n",
    "      'TRBV7-1*00','TRBV4-2*00','TRBV6-2*00','TRBV3-2*00','TRBV4-3*00',\n",
    "      'TRBV6-3*00','TRBV7-2*00','TRBV8-1*00','TRBV5-2*00','TRBV6-4*00',\n",
    "      'TRBV7-3*00','TRBV8-2*00','TRBV5-3*00','TRBV9*00','TRBV10-1*00',\n",
    "      'TRBV11-1*00','TRBV12-1*00','TRBV10-2*00','TRBV11-2*00','TRBV12-2*00',\n",
    "      'TRBV6-5*00','TRBV7-4*00','TRBV5-4*00','TRBV6-6*00','TRBV7-5*00',\n",
    "      'TRBV5-5*00','TRBV6-7*00','TRBV7-6*00','TRBV5-6*00','TRBV6-8*00',\n",
    "      'TRBV7-7*00','TRBV5-7*00','TRBV6-9*00','TRBV7-8*00','TRBV5-8*00',\n",
    "      'TRBV7-9*00','TRBV13*00','TRBV10-3*00','TRBV11-3*00','TRBV12-3*00',\n",
    "      'TRBV12-4*00','TRBV12-5*00','TRBV14*00','TRBV15*00','TRBV16*00',\n",
    "      'TRBV17*00','TRBV18*00','TRBV19*00','TRBV20-1*00','TRBV21-1*00',\n",
    "      'TRBV22-1*00','TRBV23-1*00','TRBV24-1*00','TRBV25-1*00','TRBV26*00',\n",
    "      'TRBV27*00','TRBV28*00','TRBV29-1*00','TRBV30*00']\n",
    "\n",
    "TRBJ=['TRBJ1-1*00','TRBJ1-2*00','TRBJ1-3*00','TRBJ1-4*00','TRBJ1-5*00',\n",
    "    'TRBJ1-6*00','TRBJ2-1*00','TRBJ2-2*00','TRBJ2-3*00','TRBJ2-4*00',\n",
    "    'TRBJ2-5*00','TRBJ2-6*00','TRBJ2-7*00']\n",
    "\n",
    "TRAJ=['TRAJ61*00','TRAJ60*00','TRAJ59*00','TRAJ58*00','TRAJ57*00','TRAJ56*00',\n",
    "      'TRAJ55*00','TRAJ54*00','TRAJ53*00','TRAJ52*00','TRAJ51*00','TRAJ50*00',\n",
    "      'TRAJ49*00','TRAJ48*00','TRAJ47*00','TRAJ46*00','TRAJ45*00','TRAJ44*00',\n",
    "      'TRAJ43*00','TRAJ42*00','TRAJ41*00','TRAJ40*00','TRAJ39*00','TRAJ38*00',\n",
    "      'TRAJ37*00','TRAJ36*00','TRAJ35*00','TRAJ34*00','TRAJ33*00','TRAJ32*00',\n",
    "      'TRAJ31*00','TRAJ30*00','TRAJ29*00','TRAJ28*00','TRAJ27*00','TRAJ26*00',\n",
    "      'TRAJ25*00','TRAJ24*00','TRAJ23*00','TRAJ22*00','TRAJ21*00','TRAJ20*00',\n",
    "      'TRAJ19*00','TRAJ18*00','TRAJ17*00','TRAJ16*00','TRAJ15*00','TRAJ14*00',\n",
    "      'TRAJ13*00','TRAJ12*00','TRAJ11*00','TRAJ10*00','TRAJ9*00','TRAJ8*00',\n",
    "      'TRAJ7*00','TRAJ6*00','TRAJ5*00','TRAJ4*00','TRAJ3*00','TRAJ2*00','TRAJ1*00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gene_usage(df, gene, gene_list):\n",
    "    \"\"\"This function calculates gene usage frequencies for a given gene.\n",
    "    Inputs:\n",
    "    df - pandas dataframe of single sequences,\n",
    "    gene - gene of interest (bestVHit, bestJHit, bestDHit),\n",
    "    gene_list - gene of interest list (TRAV, TRAJ, TRBV, TRBJ).\n",
    "    Returns:\n",
    "    gene_frequency - numpy array of gene of interest frequencies.\"\"\"\n",
    "    germline=pd.DataFrame(index=gene_list,columns=['count'])\n",
    "    for i in gene_list: \n",
    "        germline.ix[i]=sum(df[gene]==i)\n",
    "    gene_frequency=np.divide(np.array(germline['count']).astype(float),len(df))\n",
    "    return gene_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bargraph(ar1,ar2,ar3,ar4,ar5, gene_list, xlabel='', ylabel='',fig_title=''):\n",
    "    \"\"\"This function takes 5 frequency numpy arrays and plots bargraphs on\n",
    "    top of each other.\n",
    "    Inputs:\n",
    "    ar1,2,3,4,5 - three frequency numpy arrays,\n",
    "    gene_list - gene of interest list (TRAV, TRAJ, TRBV, TRBJ)\n",
    "    xlabel='string', ylabel='string', fig_title='string'.\n",
    "    Returns: saved bargraph figure.\"\"\"\n",
    "    f, ax1 = plt.subplots(1, figsize=(10,5))\n",
    "    bar_width = 1\n",
    "    bar_l = [i+1 for i in range(len(ar1))]\n",
    "    tick_pos = [i+(bar_width/2) for i in bar_l]\n",
    "    ax1.bar(bar_l, ar1, width=bar_width, label='S1', color='red', edgecolor='black')\n",
    "    ax1.bar(bar_l, ar2, bottom=ar1, width=bar_width, label='S2', color='orange', edgecolor='black')\n",
    "    ax1.bar(bar_l, ar3, bottom=[i+j for i,j in zip(ar1,ar2)],\n",
    "            width=bar_width, label='S3', color='black', edgecolor='black')\n",
    "    ax1.bar(bar_l, ar4, bottom=[i+j+k for i,j,k in zip(ar1,ar2,ar3)], width=bar_width,\n",
    "            label='S4', color='green', edgecolor='black')\n",
    "    ax1.bar(bar_l, ar5, bottom=[i+j+k+l for i,j,k,l in zip(ar1,ar2,ar3,ar4)], width=bar_width,\n",
    "            label='S5', color='blue', edgecolor='black')\n",
    "    plt.xticks(np.array(tick_pos)-0.5, gene_list, rotation=90, size=8)\n",
    "    ax1.set_ylabel(ylabel)\n",
    "    ax1.set_xlabel(xlabel)\n",
    "    plt.legend(loc=0)\n",
    "    plt.xlim([min(tick_pos)-bar_width, max(tick_pos)])\n",
    "    title=fig_title.split('_')\n",
    "    plt.title(title[0]+' '+title[1])\n",
    "    plt.tight_layout()\n",
    "\n",
    "    return plt.savefig(fig_title+'.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/kgrigait/Desktop/new_data\n"
     ]
    }
   ],
   "source": [
    "cd /Users/kgrigait/Desktop/new_data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Load data\n",
    "TRAcd4_prod_1=pd.read_csv('S1/TRAcd4_productive.txt', sep='\\t')\n",
    "TRAcd8_prod_1=pd.read_csv('S1/TRAcd8_productive.txt', sep='\\t')\n",
    "TRBcd4_prod_1=pd.read_csv('S1/TRBcd4_productive.txt', sep='\\t')\n",
    "TRBcd8_prod_1=pd.read_csv('S1/TRBcd8_productive.txt', sep='\\t')\n",
    "\n",
    "TRAcd4_prod_2=pd.read_csv('S2/TRAcd4_productive.txt', sep='\\t')\n",
    "TRAcd8_prod_2=pd.read_csv('S2/TRAcd8_productive.txt', sep='\\t')\n",
    "TRBcd4_prod_2=pd.read_csv('S2/TRBcd4_productive.txt', sep='\\t')\n",
    "TRBcd8_prod_2=pd.read_csv('S2/TRBcd8_productive.txt', sep='\\t')\n",
    "\n",
    "TRAcd4_prod_3=pd.read_csv('S3/TRAcd4_productive.txt', sep='\\t')\n",
    "TRAcd8_prod_3=pd.read_csv('S3/TRAcd8_productive.txt', sep='\\t')\n",
    "TRBcd4_prod_3=pd.read_csv('S3/TRBcd4_productive.txt', sep='\\t')\n",
    "TRBcd8_prod_3=pd.read_csv('S3/TRBcd8_productive.txt', sep='\\t')\n",
    "\n",
    "TRAcd4_prod_4=pd.read_csv('S4/TRAcd4_productive.txt', sep='\\t')\n",
    "TRAcd8_prod_4=pd.read_csv('S4/TRAcd8_productive.txt', sep='\\t')\n",
    "TRBcd4_prod_4=pd.read_csv('S4/TRBcd4_productive.txt', sep='\\t')\n",
    "TRBcd8_prod_4=pd.read_csv('S4/TRBcd8_productive.txt', sep='\\t')\n",
    "\n",
    "TRAcd4_prod_5=pd.read_csv('S5/TRAcd4_productive.txt', sep='\\t')\n",
    "TRAcd8_prod_5=pd.read_csv('S5/TRAcd8_productive.txt', sep='\\t')\n",
    "TRBcd4_prod_5=pd.read_csv('S5/TRBcd4_productive.txt', sep='\\t')\n",
    "TRBcd8_prod_5=pd.read_csv('S5/TRBcd8_productive.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mzp3L29/+drZv397xdauBLCIiIiLJmT59OitXrmTJkiXst99+LFy4kLPPPpsPfvCD/OY3\nv+G6667jjjvu4MMf/nDH160GsoiIiIgkqb+/nwULFnDaaadxxhlncMEFF7D//vtjZgwMDHDSSSfx\n05/+tOPrVQNZRESiCr37vezd7SLS29asWcNNN910/9+rV69m1qxZrF+/HgB351vf+hZPeMITOr5u\n3aQnIiJRhd79XubudhGp14wZszr62ZwxY1bbmK1bt3LKKaewZcsWpkyZwuzZs1myZAkvf/nL2bRp\nE+7OvHnzOPPMMzuW16ioDWQzOwb4BNAPnOXuHxrz+ouBDwAjwC7gbe7+45g5iYiIiEiYDRtuqX2d\nRx11FNdcc81Dnv/hD38Yfd3RGshm1g98GngesA64zswudvcbC2E/AC52dzezucDXgcfFyklERERE\npJ2YfZCfAtzs7r919x3A+cCLiwHuvtXdR6+7PYxOjkAtIiIiIlJBzAbygcDawt/r8ucexMyONbNf\nA98FXjveG5nZG81shZmt2LRpU5RkRUREROQBD5zDbJ7dzb3ro1i4+0Xu/jjgJWT9kceLWeLu8919\n/n777VdvgiIiIiKTzNSpU7n99tsb2Uh2d26//XamTp1a+T1i3qR3G3BQ4e+Z+XPjcvcfmdljzWzQ\n3TdHzEtEREREJjBz5kzWrVtHU6/cT506lZkzZ1ZePmYD+TrgEDM7mKxh/ArgVcUAM5sN/E9+k96R\nwABwe8ScRERERKSNPfbYg4MPPrjbaXRNtAayu+8ys78FLiUb5u0L7v5LM3tz/vqZwF8DrzazncA9\nwEJv4rl8EREREekZUcdBdvdLgEvGPHdm4fcPA52fQFtEREREpKKu36QnIiIiIpISNZBFRERERArU\nQBYRERERKVADWURERESkQA1kEREReZChoWHMLOgxNDTc7bRFOqbUKBZmdoS73xA7GZHJrG/PPkYW\njwTFi4jEsHHjrUDYqKsbN1qcZES6oOx/2M+Y2U/NbJGZPTxqRiKT1MiO8o3jKvEiIiJSTqkGsrs/\nHTiebOrolWb2VTN7XtTMRERERES6oPQ1Wne/CXgP8C7gmcAnzezXZvbSWMmJxKL+dSIiItJK2T7I\nc4GTgL8ELgNe6O6rzOwAYDlwYbwURTpP/etERESklbJTTX8KOAv4J3e/Z/RJd/+9mb0nSmYiImP0\n9U1jZKT8F5W+vmkRsxERkV5VtoH8l8A97n4fgJn1AVPdfZu7nxstOxGRgpGRbbA4IH7xtmi5SHn6\nYiMiTVO2gXw58Fxga/73NOD7wJ/HSEpERHqHvtiISNOUvUlvqruPNo7Jf9dXfBERERHpOWUbyH80\nsyNH/zCzo4B7JogXEREREWmksl0s3gZ8w8x+DxgwBCyMlpWIiIiISJeUaiC7+3Vm9jjgsPypNe6+\nM15aIiIiIiLdUfYMMsCTgeF8mSPNDHf/UpSsRERERES6pOxEIecCfwKsBu7Ln3ZADWQRERER6Sll\nzyDPBw5397Cpx0REZLcMDQ3nMz+WM2PGLDZsuCVeQiIik0DZBvIvyG7MWx8xFxERGSN0WnRNiS4i\nsvvKNpAHgRvN7KfA9tEn3f1FUbISEREREemSsg3kxTGTEBERERFJRdlh3paZ2SzgEHe/3MymAf1x\nUxMRERERqV+pmfTM7A3AN4HP5U8dCHwrVlIiIiIiIt1Sdqrpk4G/AO4GcPebgEfHSkpEREREpFvK\nNpC3u/uO0T/MbAoht1WLiIiIiDRE2QbyMjP7J2AvM3se8A3gO/HSEhERERHpjrIN5HcDm4AbgDcB\nlwDviZWUSGx9fdMAC3pky4iIiEivKzuKxQjwX/lDpPFGRrYFD144snhblFxEREQkLaUayGb2O8bp\nc+zuj+14RiIiIiIiXVR2opD5hd+nAi8DHtX5dEREREREuqtUH2R3v73wuM3dPw78ZeTcRERERERq\nV7aLxZGFP/vIziiXPfssIiIiDdLXN42REQteRqRXlG3kfrTw+y7gFuDlHc9GREREuk43MstkV3YU\ni2fFTkREREREJAVlu1i8Y6LX3f0/O5OOiIiIiEh3hYxi8WTg4vzvFwI/BW6KkZSIiIiISLeUbSDP\nBI509z8AmNli4Lvu/jexEhMRERGRdAwNDbNx461By8yYMYsNG26Jk1BEZRvIM4Adhb935M+JiIiI\nyCSQNY4fMm9cm2XCRkNJRdkG8peAn5rZRfnfLwHOiZOSiIj0kr49+xhZPBIULyLSTWVHsTjdzL4H\nPD1/6iR3/1m75czsGOATQD9wlrt/aMzrxwPvAgz4A/AWd/95QP4iMomoodVMIzvK77Mq8SIinRby\n32MacLe7fwJYZ2YHTxRsZv3Ap4HnA4cDrzSzw8eE/Q54prsfAXwAWBKQj4hMMmpoiYhIHUo1kM3s\n/WRnev8xf2oP4MttFnsKcLO7/9bddwDnAy8uBrj7Ne5+Z/7nT8huBhQRERER6ZqyZ5CPBV4E/BHA\n3X8P7N1mmQOBtYW/1+XPtfI64HvjvWBmbzSzFWa2YtOmTSVTFhFpvmz6Xiv90HS/IiK7r+xNejvc\n3c3MAczsYZ1MwsyeRdZAftp4r7v7EvLuF/Pnzw+7fVJEpMFCp/zVdL8iIruv7Bnkr5vZ54BHmNkb\ngMuB/2qzzG3AQYW/Z+bPPYiZzQXOAl7s7reXzEdEREREJIqyo1j8h5k9D7gbOAx4n7tf1max64BD\n8pv5bgNeAbyqGGBmjwEuBE5w99+EJi8iIiIi0mltG8j5aBSXu/uzgHaN4vu5+y4z+1vgUrJh3r7g\n7r80szfnr58JvA/YF/iMmQHscvf54cUQEREREemMtg1kd7/PzEbM7OHuflfIm7v7JcAlY547s/D7\n64HXh7yniIiIiEhMZW/S2wrcYGaXkY9kAeDup0bJSkRERESS0tc3jZGRsKmjmzqyTtkG8oX5Q0Sk\npaGhYTZuvLV0/IwZs9iw4ZZ4CYmISMeEjqoDzR1ZZ8IGspk9xt3/193PqSuhFOiffO8LnbJ4dJmY\n+vr6GBkJmEa5L71plLPPTfmRGDduDDsTISIiUod2Z5C/BRwJYGYXuPtfx0+p+/RPvvdVmYI49rTF\nIyMj+FfKx9vxmkZZREQkhnanoIotv8fGTEREREREJAXtGsje4ncRERERkZ7UrovFE83sbrIzyXvl\nv5P/7e6+T9TsRERERERqNmED2d3760pERERERCQF6d0GLyIiIiLSRWXHQRYR6XmhQzyChnmU3pTi\nUJgidVIDWUQkFzrEY7aMhnmU3pPiUJgiddLXPRERERGRAjWQRUREREQK1EAWERERESlQA1lERERE\npEANZBERERGRAo1iISIijRY6PJ+G5hOpZjIN/6cGsoiIRNXX18fISPl/qn19Yf9QQ4fn09B8ItVM\npuH/1EAWaawBzMr/o9dZM+mWkZER/Cvl4+34Zv5DFZHeoQbyOPr6pjEyUr7h0dc3LWI2Iq1sR2fN\nREREOk8N5HGMjGyDxQHxi7dFy0VERERE6tXMntMiIiIiIpGogSwiIiIiUqAuFiLSMeq/33mhwyo1\ndUglEZGUqIEsIh2j/vudFzpEUlOHVOo1GptZpNnUQBYREekwjc0s0my6FicijRE6gURovJQzNDSM\nmZV+wEC3UxYRCaIzyCLSGLEnnAjtQz26zGQTenYUdHZURJpFDWQRkVxoH2pQP2oRkV6k648iIiIi\nIgVqIIuIiIiIFKiLhYh00UB+E1f5eNgeKxkRERFADWQR6art6GYvERFJjbpYiIiIiIgUqIEsIiIi\nIlKgBrKIiIiISIEayCIiEiSbHMUCHppJT6SK0Fkrh4aGu51yz9BNeiKNNUDITWuTccY3iSN4QpXF\nGnlEpIrQWSs3btSNzJ2iBvI4+vbsY2Rx+Slq+/bUiXjphu1BjRTN+Ca9KnSKcH1ZFJF21EAex8iO\n8o3jKvEiItI5oWe09WWxvb6+PkZGwv639fXpZFHTDA0N52epy5oK3Bu0jqbWCzWQZVLSwT8VYd1E\nNFGISD1GRkbwr4QtY8frZFHThHbhAJs09SJqA9nMjgE+AfQDZ7n7h8a8/jjgi8CRwD+7+3/EzEdk\nlA7+qQjrJqK+rCIymaj7UPdEayCbWT/waeB5wDrgOjO72N1vLITdAZwKvCRWHiIiIrsj/DK0SGeo\n+1D3xDyD/BTgZnf/LYCZnQ+8GLi/gezu/wf8n5n9Zawkqh3YdBlXZDIKvUF3dBnpbeGXoUHToos0\nW8wj+4HA2sLf6/LngpnZG81shZmt2LRpU9CyDxzYQh5qHItMRlVuuNVNuiIivacRN+m5+xJgCcD8\n+fNDv8aLNMK0gT71cxYREUlAzAbybcBBhb9n5s+JyDi2bR8JvJdYJoPQEVc02oqIyO6L2UC+DjjE\nzA4maxi/AnhVxPWJSJdV6cMrEwsdcWUyXoVIc3InzXQp0mTRGsjuvsvM/ha4lGyYty+4+y/N7M35\n62ea2RCwAtgHGDGztwGHu/vdsfKaDEJvTJwxYxYbNtwSLyGZNNQfV7ohzcmdNNNl0+h/pxRF7YPs\n7pcAl4x57szC7xvIul5MKrE/hJq7XURiCr5S0B8vF5FOqeN/Z/jIWnFH1QodZ3kyjfLViJv0eo0a\nsNIR/QSdoUpyOLLAMqihlYbgM7D3xclDpGmqzFwXU+g4y5NpsqYE/2OKSCmBjY4kuz+ENpx6oKE1\nNDSMmZV+ZGdsRESkTj1/Bjn88gGkdwlhIP9HKSJNl9oZJGmvN/6PiEiInm8gB18+gAQvIWxH/1BF\nRLojxf8juqFMJC51sZAkhV6GHhoa7nbKIiK1CZ0lNuzGMBHp+TPI0ky6kVFasePLx04b0DkA6ZJe\nuIlWZBJTA7kDQmeuqtafTUQgsLPR9gRvTJTJoRduohWZxNRAbiHmzFXhw6oEvb2IiIiI7AY1kEVE\nREQmIU3605o6PfWkAbKRLMo9+vqmdSlPkbSEdpequkxKQm+I1djM0quy/4WT63+nJv1pTWeQG2G0\nwRsQv7h89MjibYH5iPSmkZGRoO5VEN7FKrWpXcPHZYbmDyUZOra8xjSeDEK7P1b535na519aUwO5\nEbZrKkiRHqGpXcuI3YDV2PLSHfr8N4cayCIikhg1YNsJPRPZC90BROqkBnIXBHeKn4R08O+8OvrK\n9vX1MTKiui0PFTJ+tbRXR3cAkcms5xvIlRqjke/S1HiX7engX07M4QirCO3Dq0bT5KHzwZK60PaC\nJnfpbT3fQK7UGJ1Ed2mKiFShL0LSa0LbCzrZ1dsa10D++c9v0N3HstumDfQFn1HVtMUivaLCyED6\nPyIyqTSugbxr1w50sU5217btI+EDW2na4igm25nI1AbmDx92CuI3GMMbsP6V8vnY8RoZSLpD9yA1\nR+MayCIiTZbawPzBw05BDQ1GNWDbid1fVlfZ4uiFbhmhJypi14uhoeF8PPfOUgNZRESkYWL3l9VV\nNmkltXoRPtlRuatTaiD3on6Czr7oTlwRke5q+pTlIr2mgQ3kPnRzRRuBl2R74ZKPiEhqUhuGMb6w\nGRBnzJjFhg23xEtHZDc0sIE8Mvn6pgWeEaYfDVUn0kNC+vypH6h0T9gMiBs36ib6btPkTq01sIEc\nLuRbPCR4p3xoY7cHGscasF3kAUG969QPtPNCT1KMLiOSuF6Y3KnaSDztNa+BbISfTZXG0YDtIpNY\nlatmMVU56dADJypEmiB4JJ6Ssc1rIIfePlnhIDWwR9xLmrqkIZImDW2ViApXzYLObKnbmkiNmtk3\nvXkN5Bps3xn3kmYvXNIQ6UUa2qq5go7Zk7DbmrQXevKqysgjk/MEWTP7pjeygazGZWdpeCERkWap\nozEXLmwGxL6+afFSqSD85FV4Qzd4Ha9Gw7Z2SSMbyNLe5BteSKQZUjuDVGnqW93b0XV1NObChc2A\nOLJ4W7RMeoaGbW0r1vTdaiCLSMdU6cM72aTWxarSP9TAPr8DewA7w1cjItJOrC8FaiA3REr/UOuQ\n5uXDtEyNfDNpFaF9eNPoaSZVBO1nNY5FJrFmdr1RA1mSlOblw7TcG/lmUpFeFnrSIXQ8/aknpvcF\nNrrA0UHUX3ayaGbXGzWQRUQkOU2/ahZ7NKQkNby/bGgXsZ74UiMtqYEslQwNDbNx462l41MZ11DS\nEtpNRH3K3v5VAAAgAElEQVRZRSSW4C5iCX6pCe1uGPqloNIxuKFXFtRAlkqyxnHzxjWUtAR3E1Hj\nuONSG1VDpDnCJsAYXSYbF7i8mN0Ng78UVDkGR76yEOsY1vMN5NBZ8UaXiXmWKvad/rFnAhSR6mo5\nYxMgtL8/pNmlQZqn+TdXh02AkTF9fjos1shAPd9ADu0HBvHPUsW+039S9n1LUGg3lCpnFqR5ajlj\nIz0v1f6yurm6PY300ww930CWZkr14B8itBuKDoVNFXqZVV+EmibFK5G90F+2+cKGL3tgGX3+J5LK\nlQU1kBsgxRuZ+vqmMTISb1xDHfylOUIvs6b1RahKl6/Yx5jYx7wq75/alUhJQP/24P61ocukON59\nFU28sqAGcgOkeCPTyMi2Ro5rOLmF31Ci0Ud6X+iXUYh/jIl9zEvxmNoLeqUxV1po47jCMpNzvPvQ\n/1VTsePv7XgW5h56aOyu/j7zkYCUB/bIKlgII/x8UMh2NLPw948dH1gP+gf6g+407duzj/u2lz8y\nVNpGEfdB1XWE7onY+7nKDSWTqW5XrRep7edaPguK7/w6Yn/WUqwXCbVBqmzTKpr8+a/yWdgruB0W\n2g1lAA+IN1jp7vPbxUU9g2xmxwCfIBsF7yx3/9CY1y1//QXANuBEd1810XuOuL75pyB0GJbUBoSv\nQ2g3lPh908L7y6Uy5WfaQrer+iCK9AqNANNe8Fnw0K4rVbq6lBCtgWxm/cCngecB64DrzOxid7+x\nEPZ84JD88afAZ/OfDTP5btIJHXcwlU73uyf8sk+QSB/y3Xr/KZ2/bNVzEjmYi0i9QruUgCY7KiX0\n+BjpeBqz1fIU4GZ3/6277wDOB148JubFwJc88xPgEWa2f8ScpEOm7hE3vjcEXoiK3Wiq8P4+Cc/8\nB0vkYC4i9Ro9Mxry2K7GcVuh7YWBSO2LmF0sDgTWFv5ex0PPDo8XcyCwvhhkZm8E3pj/udVgzTjr\nGwQ2j5dIi3N+LePDl2l5NrhlfGhO0eNbnxmdcDuVjd+2faTVOmKXoYP1Inw/h8XH38+h+Tjxt2uX\n6nbo+0+4TGh8Q8rcwWNkz8Z3dh2NP0bGz6mF1D6bdayje/UoMD54mdZfIsaN374zOKdZLddQ5O5R\nHsBxZP2OR/8+AThjTMxS4GmFv38AzK+4vhUx4+tYR2rxKeaUWnyKOanMKrO2kcqcSnyKOanM3Y9P\nNafiI2YXi9uAgwp/z8yfC40REREREalNzAbydcAhZnawme0JvAK4eEzMxcCrLfNnwF3uvn7sG4mI\niIiI1CVaH2R332VmfwtcSjbM2xfc/Zdm9ub89TOBS8iGeLuZbJi3k3ZjlUsix9exjtTi61hH0+Pr\nWEdq8XWsI7X4OtbR9Pg61pFafB3raHp8HetILb6OdTQ9vo51VMnpfo2bKEREREREJKZeGJxWRERE\nRKRj1EAWERERESlQA1lEREREpEANZEmemU03s5lmNj2F+Niq5NMLZYgttXpRRz6pfXZS2wd15JRa\nPUoxJ30W4kgtp6Zt00bepGdmTwD+FXg42QQqDtwFvM/dr++1+BRzqqnMzwbeC9ydP/YB9gb+zd0v\n70J87G0UlE8vlMHMDgD+CZhD9oX9PuBG4EPuvq5FmWPnFHubRs2npjInFZ8vE1SXUitDoseLRm/T\nFHOq6RiZVE6pbdPSdmeWkW49gKuA/cc8dwBwVS/Gp5hTTWX+MTBtzHMPA67uUnzsbRSUTy+UgWz2\nzCePee4pwA8mKHPsnGJv06j51FTmpOKr1KXUyhC7HlXMqdHbNMWcYu+DFHNKbZuWfTS5i8XYqbdt\nnOd6KT7FnGLHbwfmjnnuCODeLsVD3DJXyafpZdgL+OWY536ZPz+RmDnF3qZ15JPaZ6eOMofWpRTL\nkNrxohe2aWo51XGMTC2n1LZpKU3tYjEH+ADwSLLT+w7cDix29xt6LT7FnGoq8/7Au8kqej8wAvwc\n+Ii7P2RK8hriY2+joHx6oQxm9iyyS2PbgD+QXRqbSnZp7Actyhw7p9jbNGo+NZU5qfh8maC6lFoZ\nEj1eNHqbpphTTcfIpHJKbZuW1cgGsoj0FjPbi6zv5d3uvq3b+UhzqS51nrZp96W4D1LMqZMa2UAu\ndBA/nOzbQrsO4o2OTzGnmso89qaVEbIO+GVvKOt0fOxtFJRPL5TBsruN3wQ8FXgEsAX4CfA5d/9D\nizLHzin2No2aT01lTio+XyaoLqVWhkSPF43epinmVNMxMqmcUtumpYV2Wk7hQXgH8UbHp5hTTWW+\nCjhgzHPtbiiLGR97GwXl0wtlAC4GXg48iqxR8EjgZcB3OvhZSK1eRM2npjInFV+lLqVWhtj1qGJO\njd6mKeYUex+kmFNq27Tso6k36YV2EG96fIo51VHm8ZS5mTFWfDfKHJp/u2VSK8O+wDfd/Q53v8/d\n7wQuIDvwttKN7drJbRo7n26to9vxVepS7JxC4lM8XjR9m3ZrHZ2M78Q+SDGn1PbBQ0zZnYW76J+B\npWa2jew0+sPJOoi/t0fjU8ypjjK/GTjDzB7BA5Pa3A68pUvxscscmk8vlOHTwJVmdn3h/ecAn2kR\nX0dOsbdp7HzqWEdq8RBel1IrQ4rHi6Zv0xRzquMYmVpOqW3TUhrZBxnAzPYkO3hMB7Z4mw7iTY9P\nMaca4gd44OzJPe6+vZvx+TLRylwxn14owxTgELK+bHcBv3H3Xd3KKfY2rSmfpD47dZQ5X650XUqx\nDIkeL5q+TZPKqY5jZGo5pbhNy2jkGWQz25vsG4OTdQpv9w++0fEp5lRHmfPYk/Of/9Ht+BrKHJp/\n8DKplcHM9gFencef6+53d7oMoTmFxqeWT03rSC2+Sl1KqgyJHi8avU0TzSn6MTLBnJLapmU1soEM\nvAP4c7KNMQ34lx6PTzGnOsr8j4VlppRYJnZ87DKH5lNlmdTK8Hbg6Xn8viXi68gp9jaNnU8d60gt\nHsLrUmplSPF40fRtmmJOdRwjU8sptW1aSiO7WJhZH/Ce/M9/dfeRXo5PMaeayjwA/D1Zpf9oycss\nMeNjb6OgfHqoDO8FcPcPtIutI6catmnUfOpYR2rxheVK16XUypDi8SJfprHbNMWcajpGJpVTitu0\njKY2kKeTdQqHrJ/WH0vGO3BXxPjQfErF58s8jKyvT9l1xI6vo8x78OB+RTvbxO9ZiN8WIT52vQjK\nJ18mdBuF5hRaL0Lz2QeYSbZNbyvZxSJ0P+xBdkYOyu3n0DJU2Uah+ZSOLywTUobY8aGftaD3z5fZ\nGziIknUp9n6oUOao9aiwTLTPZ6L1oo7PQsx6FFSvK64jdD+M1osR4PcR6kXUelRWI4d5c/et7n5b\n/rj/IGJmr2+1CPAS4EVkY/aNxr+pQ/FTgVeQTXP4CDP7uJn9m5k9ukX8NmB9/rjHzPrMbEl+BqGV\ngzybMnET8FozO9PM3pkfVMfzjDx+B/BvZnZpvsysFvEfAA4D1pdpvIbugwr7DGA/4N+AbwOXm9kP\nzewMM5vZYh073P2u/HH/B8TM3tOJeCLXiwr5QLaN3kc2wcD928XM3t8iPrTuhda7oHzc/W53v9Hd\nfzV6kDWzdpfHQvfDX7j7XYABHzKz/zazT1s2Pel4hoHTgYXA48zsW2b2VTM7rEV86Gftb/J89gO+\nbmbLzOw7ZnZki/ivAX8NTAk48B8JnE3WH+8wM/uumV1hZi9oEf9IYDFwKXC9mX3bzP4+PzMznqif\nTcLrNcBsd78RuAV4nZl9zszebdmd7eM5iOzy8L7AQWb2J2b2fTN7bIv4oHpUocxBx2DqqRd9o59P\n4Bn59nyZmbUaPivFejFM2Oc5dB1R6xFwEnBv8RhZQlDdqLAfpgDPJatPO8zsvWb2ETM7uEV8aseX\ncnw3BlHu1gN49jiP5wArWsRfDLwOeA1wNfDM/Pkfdij+MuAEspllrgeOJas8l7SIHwGuA36YP64g\nG5Jk3Pcvrhv4LNmc44/Lc7yoRfwP8p8XAscAewJ/ClzRIv5G4OPAKuCLwAuBgQ7ug6D40TIQNnD+\na8d5vA74RYfiY9eLoHwK63gO8Mz89+Pb5BRU9yrUu9B8/hf4MfCj/HEVcAfwownKHLofRsvwVbIv\nLFPz/XBpi/gfkfWvewFwM/Aksru1L+/QZ200/nvA4fnvBwJXt4hfBbwt31+XA6cAM1ttn3yZq8kG\nyp8DrM1/3xv4SYv4S/Jy9gPPA/4z34ef79JnM6gejdnP55Dd7HYo2WQGrT5v2/Jt+oX88UVgHfCF\nDtWj0DKHHoPrqBejZf4g2THgGLKG/BcbVC9CP8+hx7DY9ei3wEXAcuD9wBET7eMqdaPCfvg+8Cpg\nEdn/tpfm2+vKFvFJHV/KPhp5Bhn4JvA0sko/+ngaD1yeGmsfd/+8u58D/D9gkZmdMsH7h8bv4e7n\nuvvngK3ufpG7Xw60+nY0H1gNLAOOc/dnAT9192dPsI5Rj3f3D7n7r93982TfzMZzn5lNzXO40t13\nkDWM+lvEb3D3t7n7kcAZZP/grzKzr7eID90HofEQPnD+h4BdZFO0jj52kZ1x7ER87HoRmg9k/0B/\n4O7L8pzmm9lHab2fq9a9svUuNJ+3AbeS/WN5trs/HbjW3Z8xQS6h+8HyM+T7u/v57n5vvh+mtogf\ncfer3P0SsrN5P3P3m6DloPOhn7VtZrYv2T/WDflzd5Dt7/FscfeP5/volcA9wJlmdtUEZe53998D\n/5O/70bPpoBt1a/1EXk57yNrRD0p34fDLeJjfzZD6xGA52c2h8hGgfiNu38daHW140+AnwL3Ah9x\n95OAG9z9tS3iQ+tRaJlDj8F11ItRf+7ub3H3/3b39wKtzhSmWC9CP8+h64hdj25192PJGu2/AN5l\nZivM7N8nKHNo3aiyH77q7p8hO0t9Yb69WsWndnwppamjWPyI7AC4sfikmbWaxWWXmc1w943uvhVY\naGYfILvrsRPxZmb9+c5fWHh+3A+Uu68C3mBmh5NdYrmL9rMlHZBX7keZ2SPcfYtl/W72bhH/D8BX\n8t+vN7PrgEeTHXjHLUMhv5XASuA9ZnZoi/jQfRAaD+ED53+D7Fv42HUc2KH4qPWiQj4AW8xslrvf\n6tlNPW83s9eRDXnzEBXq3gFm9iNg35L1LjSfC4ELzez5wLlmthzYY4J8IHw/QHZGYqRQhr15oI/e\nWBsL++3ZAJaN+dnqhELoZ+1ksjOF+wH/Y2Y3Alt54AatsYqfzU3AWcBZeRla+aqZrQJ+B3wUuMbM\n/gh8p0X8Z8zsGrKzigfzwF3gY/9JjYr92QyqR7kPAl8HtpBNYvBj4PFkZ98ewt3XA+/Ov6y8zcwO\nAvaZ4P0hrB6Fljn0GFxHvTgy/7/z+EKZ+2j9+U+xXoR+nkOPYbHr0eh6tpHNVndBnv9EJzVC60bo\nfvhfMzuL7H/ZDWb2KbIv+ZtbxI8eX9aRNYq7fXwppZE36YWy7KaenT7mzkYz2z+v3GPjH5bH7ygZ\nPzDOe08h+4a4tl28mc0mu2wy7oF8gnXsATzS3f+vVbxlfYJmkA3kfZO3Hsh7xtjKlQoz24t8MHIv\nN35yrDyC6lGL92hZL7qhTN0bZ5mW9a4D+TwLeIK7f2qCmN3eDxVze8hncMzrpT5rhfgpwCBZvb6n\no8k+dF17k51Ja9m31cz6yfpRbvYSo8zkyyTx2SzkMxV4Kg/sh+vcvdU/7bHLPhw4zN1/GjHFidZf\n+zG4TL0YZ5lpwCHu/vMJYpKqF+Np93nejfftaD0ys7nufn0n3quTzOxJwG1kjeKjyRrl38+/iIwX\n37zji+9G/4xUHsBXOxlP1ifyG2R9bPYp8X5R41PNKeY+aLPs62PEM0E/tm6UOTSfJpaB7NLkR8gu\n4VatD5XqUir1IjSfqvGdXAdZt4WTyfr67lN4/k3diK9Sl+rIKWY9qriNQsscuk0Hgb8j66t8IPAx\nsputHl0y/uOdjK+6TMh+i12G0H1QU5l3t160K3PfOI8l5Cd1dze+7KOpfZDHanX3Z6V4d38a8E6y\nsxAXmdklZvYmMxvqRnyqOY3R0X0AYGbPHufxHLKD+27Hj/cWAflDhDKPEZpPlWW6XYY/A75L1k3i\np5aN/vD/8rOrHckptXpRR72uYR3nkfW5vAf4npk9M39+YZfiIbwu1ZFTUac/m1XyCV2myjbdBMwi\nuwn1R2Q3ip09Qfz/FeKXdTi+6jJF7fZblTJsCoivcoyMXebdrRft8tkFXEt2g+HlZF1S/jr/2Yn4\ncnandZ3Kg+ySbMz4Q8nu9t+ZQnyKOcXYB2R9mt5Hdudu8XFzJ+LHWX4wsXq3b0h8L5SB7E7lK4A7\nO5VTavWijnodex0U7lYHppMNK3UKre/0jxpfpS7VnVOFz0K7ehScTwfKELJNryn83mr0gajxVZcJ\n2W91lCFkH9RR5hrqxZHAf+XHoEflz31vgvUHxZcu1+6+QUoPOniJkmz4m0Vk33a+D5zKxMOkRI1P\nLSeyO3f/k2ysyBnA58m6aMxvEb9X/n7n5+9/NvCyNuX9FjBjnOc/0aH4A8jGA/0QcHDh+fd3qMyh\n8YcAnwLeQDZ28rfIhgI6rM0+a3oZ9iQbgmkJ8JN8fU+bID6oLiVYL6LW65o+O5ePjScb/uvebsRX\nqUs1lOGkwmfiIrKzZt8BjuxQPQreRhWXCdmmy8hGyoBsDPXR56/sRnzFdYTutzrKEHqMDM3pT8mG\nqVwCPIHs7PAVwAu6US8Krx+ev/9H2sVWiW/7frv7Bt14EHkMXrL+uN8hG0ev7dmj2PEp5pRX+D8H\n/pJsyKAjye5ObTWW6zfIxgF+LPB6ssbHa8imhWy1jpZjgHYoPnS8y9Ayh8YHjdfZC2UgG3VgOdld\nzU8qud+C6lKC9SJqva7ps/MwYM9xnt+/G/FV6lINZQgd7zq0HlXZRqFlCN2mD6lHZKNlHdSN+Irr\nCN1vsctc5RgZuo7Q8bFrrRfAbODYsu/fLr7sY7cW7taD+JcP9x7nuTnAe7sRn2JOwI/zn31kd+yP\nPj/uBA/AVYXf+3lgsPSJGn+xb0z8UeH3PrIbSj4KLOtQmUPjr2yxvSa6fNjoMgCH5j+NbNiztjdV\nhNalFOtFzHpdxzpSi69Sl2oow3fI7tq/gAcu++7Vwc9aHfUitW1aR5mD91vkMlc5Roau4yf5z6lk\nM1GOnu29phM5pVgvyjwmyzjIQfGeDZyOmf0Z2QwxzyGbInFZN+ITzelqM/sB2ZijF5vZt/Pfb2gR\n/10z+w7ZbFFPAb6cP39Hi3jc/WlmNkx2tvAiM9tONvXkt919w+7GEz6mZmiZQ+NDx+vshTJsMbML\nyMYN3QQ82szuAE72bPzO8QTVpdTqRQ31Ovo6UovPBdWlGnIKHe86aj2quExS27SmMgftt5r+TwUd\nIyus4zwLGx+78fWilE61tOt8EP/y4WfJpkb8J7Kb1Sbs7B07PsWcyGYNeyTZbHGQTUH8+DbxQ2QN\nmqCbngrvEf3mxwhlLh3fqfrbpDKQTQv8rDHPPRP4Uqy61O16sbv5VMk/9jpSiK9Sl+ooA9ml5CFg\nr5TqUZllUt2mMcscut9il2F390HFnPYGHjbB6z1XL8Z7NPUM8g/MbD1ZB/ql7n53h+OnkE29uT1/\neJfjU8zpB8B6sm9rS93912XjgXax9zOzA4CXAC8ku5x+EXBCp+JbvMfr3f2scV6qXOaS8a2cQDYT\nUmkNKsMB7n5F8Ql3X2Zm/1wmJ0rWpcTqRS31OvY6UounQl2KmZNlE06dSDbt7Vc9nwzGzN7k2fTz\nY0WvRxWWSWqbVokPXabCfotdhirHyNAyD5Ldx/FLsisWfw/cY2Yf9/EnhOqJetFOY2fSK5xO/yuy\nBt2Ep9MrxPcBz8iXeTbZHZ5Xjq0UdcWnmFMN++DHwJ1kd9h/29vMiFUhfrypOg34sLvP71AZSsdX\nzKfRZTCzH5J157Ex8Ze5e8upVANzSqpexK7Xdawjtfh8maC6VEMZLiarB7uANwL/lDciftiqbses\nRxXLkNo2raPMQfuthjIHHyMrrOMy4FyyvtYnk92j9QfgHe7+gt3NKcV6UUZjG8hFls1V/zmyIUb2\n6HR8vsyTgZe4+4Tf2uqKTy2nGPvAzPb2vG904bk5wEvd/QMdiL+DrK/Z2EHRT3D32Z0oQ0h8lXya\nXgYzu4LsasVo/OjvPlEDOTCnpOpF7HpdxzpSi89fH1uXRo1bl2oow5XuviD/fTrZsG0/Jru7vm3d\n7nQ9qliG1LZpHWUO2m81lLnVPsDdnzVOkXe3zNe4+5/nv//A3Z9TIqf7fx8vpxTrRRlN7WKBxT9d\nfziAu99oZnuSDbnzzG7Fp5hT7H3g8W80DL3ZM3aZg/NpehlaHeDbCckptXpRQ72Ovo7U4vNlgupS\nDTntMrMZ7r7R3beSzTr2AbKh3MYVuV5XKXNS27SOMhO432ooc/AxskKZzR64ubo4q2J/J3JKsV6U\n0cgzyBb/dP0Ssm9DDwd2ks0j/k3gvPwDU2t8ijnF3gf5Mp8lm5ryx3kun3D353cwfsDdt7fLo2oZ\nKsQH5VNlmdTKYGaPHefpM4E3ufvvOpRTavUiar2uYx2pxefLHEI2gcz1ZBMXfADYBpzm7mu6UIaH\nkd0ktGPM8/u7+/px4qPWo4pleDTwbrKphR8J/Aa4CvjUeJ+RROtF7P0Wu8wnufsXzWw22QQYjwLu\nBt7n7j/r0DoecsyzbPSh/d197TjxBwBvJ+unfaa735I//353P60D+USvF2U09Qzy8wNPp4fGHzL6\nDcnM1gDzPO+o30Ls+BRzir0PoIYbDS3s5s3YZQ7NpxfK8AuyxswtcP/lusOB9wKv7VAZUqsXk/Gm\n3jrK/Hngn8nuwL8IeBnZ8FyfBZ7bhZwuBdab2YPq0XiNrFzselRlmbPJhjd7J9l9Kc8HrgM+Qzap\n1O6+f4qfhdD9FrsMfwN8kWyG0r/z7ArvgWSTdfxFh9bxkGOeu+8imzRkPOeQTdC0C/gvMzvb3b9C\n6yvOKdaLthp5BnnUeKfT3f3S3Y03s9Vk3476yMYEfMfoa+7+w7rjU80pXy7KPijEJ3WjYewyV8yn\nsWUws/2Bt5KNp/kpd/+VmX2vzLf/wJySqhex63Ud60gwvtiP8ip3f3r++7j9KGvKaZiE6lHoMvbg\n/qjTgO+6+7O6vE3r+CwMk8hN/ZaN+X4i2RTKb3D3O8xsL+BSd39GN8psZj8aXXe+no+SNVDnu/u4\njeQU60U7jWwg13C6/gvAreO85O7+L3XHp5hTty6BWBdvNKy7zO3y6aUymNm+wNuAg8iuZrQ6M9KR\nutTNerG7+VSJr2Md3Y43s68Br3L3+8xsD3ffadll4su8ZJ/JJh1fOpF/u2XM7G+ARcA6smmv/8Xd\nl5rZJ9391Bg5pfZZqHgc7lgZzOwxwOlkdeMIsgmStgKnu/uPyrx/aE4l6urFwCnufmvhudcBn3b3\nqZ3Op4748TS1i0Xs0+/D7t7q8m434lPMqZZLIJbWjYbRyxyYT8+Uwd1vB95rZg8HDouQU0r1Inq9\nrmMdqcW7+8LC7zvzn7vM7JguliGpehS6jLt/2czOI5t6ebNnM0syUeM4tXpRcR3J3NTv7v8LnJB/\n2RsE7vL2XTKjltndXzTOc58n6+bUqXyi14t2GnkGGaJf0thCdqPHg54mO5v6kEsaseMTzin2JZOk\nbjSMXeaK+TS+DC3W2WpSjio5JVUvYtfrOtaRWny+zNgJHu7Onx93goemH19qqhepbdM6ypzaTf3j\nTuIBtJrEo5a6Omb5lmN7V8wner0oxXdjGr6UHsCTyS457HY8cEXguqPGp5pTzH0wNidgDe2nag2N\n33uc5+YA7+1Gmavk0/QykDUCxj6eA6zoVN1LrV7Ertd1rCO1+DzuYrIbx14DXA08M3/+h10qQ1L1\nqGIZUtumdZQ59BgWu8yXAa8G3kR2EutYsptOL+lWmSdaX1PqRZlHU7tYJHH6fbKrYR880syeRXbj\n4D3AU82ygQ58/BsHg+K9wtiJkS+l1THGZ2pl+CbjT8rxiFb5VChDavUiar2uaR2pxQPs49llXszs\nAuDzZja3RWz0nBKsR1WWSWqb1lHmCvstdhn2cPcv5Tm9xt0vyn//xxb51FHmsY5r83qK9aKtRnax\nqOF0/T5eboitWuJTzKmmS2Op3WgYu97VMcZnUmUws2+RjXk8dlKOT7j7WztUhtTqxWS8qbeOMl8O\nHF+sS5ZN8PBOH+fGoR44vtRRL1LbpnWUObWb+pcBz/bs5tODPB+X2AqjtnRgHaFl/lPgXcBm4JPA\nh4FpwEfc/ZIO5BO9XpTR1DPIUcf4DW28xo6vYx0Vcqpj7OdhT+tGw9hlrmOMz9TKsNDHmXCgVeO4\nYk6p1YvY9bqOdaQWD/Bisobl/dz9vWb2mS7llFo9qrJMatu0jjKndlP/0Z7NcEehcTyFiW98jl3m\n/yQbZ/yRwH8Dfwr8gaw7yEMayBXyqaNetNXUBnISp98nuTr2wZFmNnYYG6P1jYNB8e7+Bnvgpph3\nAAeZ2Wm0vmEt9iXZ0Hx6oQxVJkcJLUNq9SJqva5pHanFQ/gED00/vtRRL5LaphXig5epsN9ilyF0\nEo/gdVQoc7+7/97M7iC7gXOjZ2e4RzqRTw3xpTS1i0USp98ns5oujV3hAXO+h8a3eI+JxqOsvd5N\nlE+VZVIsg5kNEzYof2gZUqsX0et17HWkFl9Ybpjykx00/fgSvV7kywyTyDatq8zjvMdE+62OMgwT\ndoyMXea3kt24+TvgSrLZ/v5INub4B3c3n258NsfT1DPISZx+n+R6Yh9Y2E0x0cscmE+VZZIrg7vf\nAnwM+Jg9MED9GUCrQfmj16XI21QiqVCXouqFepTaNq1DleNwTHXsg5Ayu/sngE8Ulj0bGHH3P3Yq\nnxQ09Qxy9DF+ZWJ17ANL70bD2GNR1zHGZ4plGDtA/VLgQndf1yI+NKfU6sVkvKk3epnz5UrXpR44\nvhGP5ycAAAePSURBVESvF/kyKW3TOj4LSd3Uny8TeoyMXeagsZlTrBel3rehDeQkTr9PZr2wD4o5\nWYmbYuq8TFQmnyrLpFYGqzaZQtS6FHubShxV6lLkfBpfj1LbpnWochyOnE/0fVChrl4GnAvsBZwM\nvJ/sJr13uPsLOp1ftzS1i4VIJ6R282Yd45rGFprP8z0fg3OUmc0BXuruH4idbAupbVMpJ7W61Av1\nKLVtWofU9lsd+6COsZkbp6lnkJM4/T6Z9cI+sPCbYmJfJqpjjM/kypAv95AB6t390k7kFCr2NpW4\nQupS5Dx6ph6lsk3rUPUYFlvMfVChri4jcGzmJmpkA1mkE6zN/PF1q5JP08tgFSZHiS21bSrlpFaX\neqEepbZN65DafqtjH1Q4bg/4mPHrLRubef/RxnIvUBcLmcyijJ1Ycz5NL0OVyVFiS22bSjmp1aVe\nqEepbdM6pLbf6tgHdYzN3Dg6gyyTVmo3xVTJp0fKMDpA/bHAs4ELmWBylNhS26ZSXkp1qVfqUUrb\ntA4p7rfY+6DicXuYgLGZm0hnkEWkq9x9hGyw+SsBLB+gHujJf8ASj+pS52mbdl+K+8AnwfjYaiDL\nZPbibicwRpV8Gl8GS2xQftLbplJSYnWpJ+pRYtu0Dsnttxr2QZXj9tixmS8CTuhgTl2nLhYi0jVW\nYWIRkfGoLnWetmn3pbgPJsv42DqDLCLddEjIAPUiE1Bd6jxt0+5LcR9MivGx1UAWkW5KbVB+aS7V\npc7TNu2+5PbBaON4vLGZu5FPLOpiISJdk+qg/NI8qkudp23afSnug8kyPrbOIItINw27+2u7nYT0\nBNWlztM27b4U98GkGB9bZ5BFpGvMbAtw/dinadZkCpIA1aXO0zbtvlT3wWQYH1tnkEWkm36W2qD8\n0liqS52nbdp9Se6DFMdm7rS+bicgIiIiIs1hZoePjs/cq+Njq4uFiHSNme3j7nd3Ow9pPtWlztM2\n7b4U90GKYzPHoAayiIiIiJRiZlckODZzx6kPsoiIiIiUldzYzDHoDLKIiIiIlJLi2Mwx6AyyiIiI\niJSV4tjMHaczyCIiIiJSSqpjM3eaziCLiIiISFlJjs3caRoHWURERESkQF0sRERERKSUFMdmjkEN\nZBERERGRAnWxEBEREREpUANZRERERKRADWQRmdTMbF8zW50/NpjZbYW/Pf/5CzP7jpk9YsyybzOz\ne83s4fnf08zsdjPbZ0zct8xsYZ3lGrP+rfnPA8zsm93Ko5iLiEjK1AdZRCRnZouBre7+H/nfW919\nev77OcBv3P30Qvy1wA7gC+7+xfy5rwKXuvs5+d8PB/4HeIy7b+tgrv3ufl/J2PvLUSczm+Luu6rm\nYtn8tebuI1ESFBFpQWeQRUTKWQ4cOPqHmf0JMB14D/DKQtx5wCsKfx9L1mB+UOM4P9v8dTO70cwu\nMrNrzWx+/trRZrbczFaZ2TfMbLSRfouZfdjMVgEvM7MrzexjZrbCzH5lZk82swvN7CYz+9exBTCz\nYTP7Rf77iXnsf+fx/16IG3f9Y95rnpn9xMyuz/N/ZP78lWb2cTNbAbzVzA7O3+uGsTmZ2TvN7Lr8\nPU4r5LjGzL4E/AI4qN2OERHpNDWQRUTaMLN+4DnAxYWnXwGcD1wFHGZmM/LnLwWONLN9C3HnjfO2\ni4A73f1w4L3AUfm6Bska3c919yOBFcA7Csvd7u5Huvv5+d873H0+cCbwbeBk4AnAiYUcWpkHLASO\nABaa2UEl1j/qS8C73H0ucAPw/sJre7r7fHf/KPAJ4LPufgSwfjTAzI4GDgGekudxlJmNzsJ1CPAZ\nd5/j7re2KYOISMepgSwi0tpeZrYa2ADMAC4rvPZK4Pz88v8FwMsA3H0HWUP6uLyx+SSyRvNYTyNr\nYOPuv+CBqVv/DDgcuDpf92uAWYXlvjbmfUYb7TcAv3T39e6+Hfgt7c++/sDd73L3e4Eb8/W0W/9o\nt5FHuPuy/KlzgOIUs8Uc/4IHviCcW3j+6PzxM2AV8DiyhjHAre7+kza5i4hEo6mmRURau8fd55nZ\nNLJG7snAJ83sCLLG3GVZN1n2BH4HnJEvdx7ZWWEDvu3uOwPWacBl7v7KFq//cczf2/OfI4XfR/9u\nd4wvxt+Xx7dbfxljcxzvZhcDPujun3vQk2bD4ywvIlIrnUEWEWkj7z98KvB3ZjaF7OzxYncfzh8H\nAAeY2eiZ1ivJGtAnM373CoCrgZcDmNnhZN0cAH4C/IWZzc5fe5iZHRqhWK20Xb+73wXcaWZPz586\nAVjG+K7mgT7ZxxeevxR4baF/9YFm9ugOlUFEZLeogSwiUoK7/4ysG8QryRp8F40JuSh/nrzbxTeB\nfWndcPwMsJ+Z3Qj8K/BL4C533wScCJxnZteT3Rz4uI4WZgIB638N8JE8Zh7wLy3e8q3AyWZ2A4Wb\nHN39+8BXgeX5a98E9u5UOUREdoeGeRMR6YL8xr893P3efESMy4HD8j7MIiLSReqDLCLSHdOAK8xs\nD7L+uIvUOBYRSYPOIIuIiIiIFKgPsoiIiIhIgRrIIiIiIiIFaiCLiIiIiBSogSwiIiIiUqAGsoiI\niIhIwf8HwnYQM7wxOVQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1434c5b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QiEgthN5JsBEHHlZzUw3dQlpE5Nkq/Wf4ft8DM7sh57aIiNRM6IBCDTyU2uqb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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143e38650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XLuWuu+7i9ttvZ9asWZx33nksWLCAO+64I+prR20gm9kJwNeAduByd//8gMdPAj4D9AHb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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143068910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1416a2cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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u8hMSKYAKZInSxJ6uoL6iE3v0I4iICFTXHUmjuEgTNbZA7urqYmAg7M0i5MQe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\nbnF1yDH2uLrkGMLMfgk8h2QMyZVAv7s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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13bef58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3JwDiuG5XIKmOvIIsSZIkVXgFWZI2I69YSlL92SBL0mZUwizbzrAtqXQ2yJIk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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14204e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RFjZy5EiWLVvGXXfdxe23386MGTO48MILWbBgAXfccUfexRMRyYUqyCIiLa69vZ1p06Yx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\nply92ZrKVEE+F7jUzF5G1Lb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13cbdef90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vLl4FTKuwHkmSJGlEVTbIewC3tywva67bnL8HvldhPZIkSdKIajEPckQcRqNB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\npouFuV+TDAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x141462110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Alpha V CD4\n",
    "gf1 = gene_usage(TRAcd4_prod_1, 'bestVHit', TRAV)\n",
    "gf2 = gene_usage(TRAcd4_prod_2, 'bestVHit', TRAV)\n",
    "gf3 = gene_usage(TRAcd4_prod_3, 'bestVHit', TRAV)\n",
    "gf4 = gene_usage(TRAcd4_prod_4, 'bestVHit', TRAV)\n",
    "gf5 = gene_usage(TRAcd4_prod_5, 'bestVHit', TRAV)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRAV, xlabel='TRAV germline order', ylabel='Frequency', fig_title='TRAV_CD4')\n",
    "\n",
    "#Alpha J CD4\n",
    "gf1 = gene_usage(TRAcd4_prod_1, 'bestJHit', TRAJ)\n",
    "gf2 = gene_usage(TRAcd4_prod_2, 'bestJHit', TRAJ)\n",
    "gf3 = gene_usage(TRAcd4_prod_3, 'bestJHit', TRAJ)\n",
    "gf4 = gene_usage(TRAcd4_prod_4, 'bestJHit', TRAJ)\n",
    "gf5 = gene_usage(TRAcd4_prod_5, 'bestJHit', TRAJ)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRAJ, xlabel='TRAJ germline order', ylabel='Frequency', fig_title='TRAJ_CD4')\n",
    "\n",
    "#Alpha V CD8\n",
    "gf1 = gene_usage(TRAcd8_prod_1, 'bestVHit', TRAV)\n",
    "gf2 = gene_usage(TRAcd8_prod_2, 'bestVHit', TRAV)\n",
    "gf3 = gene_usage(TRAcd8_prod_3, 'bestVHit', TRAV)\n",
    "gf4 = gene_usage(TRAcd8_prod_4, 'bestVHit', TRAV)\n",
    "gf5 = gene_usage(TRAcd8_prod_5, 'bestVHit', TRAV)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRAV, xlabel='TRAV germline order', ylabel='Frequency', fig_title='TRAV_CD8')\n",
    "\n",
    "#Alpha J CD8\n",
    "gf1 = gene_usage(TRAcd8_prod_1, 'bestJHit', TRAJ)\n",
    "gf2 = gene_usage(TRAcd8_prod_2, 'bestJHit', TRAJ)\n",
    "gf3 = gene_usage(TRAcd8_prod_3, 'bestJHit', TRAJ)\n",
    "gf4 = gene_usage(TRAcd8_prod_4, 'bestJHit', TRAJ)\n",
    "gf5 = gene_usage(TRAcd8_prod_5, 'bestJHit', TRAJ)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRAJ, xlabel='TRAJ germline order', ylabel='Frequency', fig_title='TRAJ_CD8')\n",
    "\n",
    "#Beta V CD4\n",
    "gf1 = gene_usage(TRBcd4_prod_1, 'bestVHit', TRBV)\n",
    "gf2 = gene_usage(TRBcd4_prod_2, 'bestVHit', TRBV)\n",
    "gf3 = gene_usage(TRBcd4_prod_3, 'bestVHit', TRBV)\n",
    "gf4 = gene_usage(TRBcd4_prod_4, 'bestVHit', TRBV)\n",
    "gf5 = gene_usage(TRBcd4_prod_5, 'bestVHit', TRBV)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRBV, xlabel='TRBV germline order', ylabel='Frequency', fig_title='TRBV_CD4')\n",
    "\n",
    "#Beta J CD4\n",
    "gf1 = gene_usage(TRBcd4_prod_1, 'bestJHit', TRBJ)\n",
    "gf2 = gene_usage(TRBcd4_prod_2, 'bestJHit', TRBJ)\n",
    "gf3 = gene_usage(TRBcd4_prod_3, 'bestJHit', TRBJ)\n",
    "gf4 = gene_usage(TRBcd4_prod_4, 'bestJHit', TRBJ)\n",
    "gf5 = gene_usage(TRBcd4_prod_5, 'bestJHit', TRBJ)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRBJ, xlabel='TRBJ germline order', ylabel='Frequency', fig_title='TRBJ_CD4')\n",
    "\n",
    "#Beta V CD8\n",
    "gf1 = gene_usage(TRBcd8_prod_1, 'bestVHit', TRBV)\n",
    "gf2 = gene_usage(TRBcd8_prod_2, 'bestVHit', TRBV)\n",
    "gf3 = gene_usage(TRBcd8_prod_3, 'bestVHit', TRBV)\n",
    "gf4 = gene_usage(TRBcd8_prod_4, 'bestVHit', TRBV)\n",
    "gf5 = gene_usage(TRBcd8_prod_5, 'bestVHit', TRBV)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRBV, xlabel='TRBV germline order', ylabel='Frequency', fig_title='TRBV_CD8')\n",
    "\n",
    "#Beta J CD8\n",
    "gf1 = gene_usage(TRBcd8_prod_1, 'bestJHit', TRBJ)\n",
    "gf2 = gene_usage(TRBcd8_prod_2, 'bestJHit', TRBJ)\n",
    "gf3 = gene_usage(TRBcd8_prod_3, 'bestJHit', TRBJ)\n",
    "gf4 = gene_usage(TRBcd8_prod_4, 'bestJHit', TRBJ)\n",
    "gf5 = gene_usage(TRBcd8_prod_5, 'bestJHit', TRBJ)\n",
    "bargraph(gf1,gf2,gf3,gf4,gf5,TRBJ, xlabel='TRBJ germline order', ylabel='Frequency', fig_title='TRBJ_CD8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}