mutations_distribution_genes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the mutations density in the genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there specific genes in which a significant portion of the mutations fall?\n",
    "We want to answer this by finding the distribution of the number of mutations per gene.\n",
    "That is, for each integer, we want to know how many genes have that number of mutations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy import optimize\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first map genes to the number of mutations they harbor (read from a random sample of 100,000 mutations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "data": {
      "text/plain": [
       "[('PCDH15', 1014985),\n",
       " ('RBFOX1', 817112),\n",
       " ('CSMD1', 743708),\n",
       " ('DLG2', 592735),\n",
       " ('SPOCK3', 525639)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from ICGC_data_parser import SSM_Reader\n",
    "\n",
    "\n",
    "\n",
    "mutations_per_gene = Counter()\n",
    "\n",
    "mutations = SSM_Reader(filename='/home/ad115/Downloads/simple_somatic_mutation.aggregated.vcf.gz')\n",
    "\n",
    "# Fix weird bug due to malformed description headers\n",
    "mutations.infos['studies'] = mutations.infos['studies']._replace(type='String')\n",
    "\n",
    "consequences = mutations.subfield_parser('CONSEQUENCE')\n",
    "\n",
    "\n",
    "\n",
    "for i, record in enumerate(mutations):\n",
    "    if i % 100000 == 0:\n",
    "        print(i)\n",
    "    affected_genes = [c.gene_symbol for c in consequences(record) if c.gene_affected]\n",
    "    mutations_per_gene.update(affected_genes)\n",
    "    \n",
    "mutations_per_gene.most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55754"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mutations_per_gene)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to group by number of mutations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(188, 145),\n",
       " (190, 143),\n",
       " (213, 140),\n",
       " (179, 139),\n",
       " (177, 139),\n",
       " (198, 138),\n",
       " (206, 137),\n",
       " (199, 134),\n",
       " (187, 134),\n",
       " (192, 134)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution = Counter(mutations_per_gene.values())\n",
    "distribution.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6XQY0IiLfMaAREQDgUrPe7e2//dFEzJqU43Cb0D7/LD0l3utcNCIi8h0DGhEBAPRGs8f7\nFIKAdatmYtKILABATlaidB/P3iQi8j/PGxmFgS+++ALbtm1Dc3MzlixZgunTp4e6JKKoZTK7Bq3r\npw+SPtaolbj72pGYOjIbucMz7Z7H1ZpERP4W9A7a73//e+Tn52PBggUOt+/YsQNz587FnDlz8Mor\nrwAAZs+ejcceewyPPPIINm/eHOxSiWKK2U3Qus4uoAGANk6FyZf1hFLR8avDXbAjIqLuCXpAu/HG\nG/Hqq6863GY2m1FQUIBXX30VmzZtwscff4zTpzuOmnnppZdw2223BbtUopjS9aDFgEZE5G9BD2hT\npkxBamqqw22FhYUYMGAA+vXrB41Gg/nz5+PLL7+E1WrFU089hRkzZmD06NHBLpUopjgPVS65aois\n540amI7LR7rfaoOIiLomLOagVVZWolevXtLn2dnZKCwsxJtvvondu3ejqakJpaWluPXWWzu9Tlpa\nAlQqZaDLRVZWcsBfI9bwPfWvrryfCWWXHD6/Y+EY2c/94/J8LFz1gcf7vzpyATUNbfjlkvE+1xUu\n+DPqX3w//Y/vqX+F+v0Mi4Dmye23347bb79d9uPr61sDWI1NVlYyqqubAv46sYTvqX919f2sb3D8\n8+PP78k/P7EdwH7zzMF+u2Yw8WfUv/h++h/fU/8K1vvZWQgMi202srOzcfHiRenzyspKZGd3vvkl\nEflXYbG8Y548WX3nZK+POXy6pluvQUQUK8IioI0dOxYlJSUoKyuDwWDApk2bMGvWrFCXRRRT9p+s\n6tbzB/ZK8fqY5zcUdus1iIhiRdAD2gMPPIBbbrkFZ8+exYwZM/Duu+9CpVJh9erVWL58Oa699lpc\nc801GDZsmPeLEVG3tbYZ8eW35UF7vUvNeuw+dhFWbnBLRORR0OegPfvss25vnzlzJmbOnBnkaojo\n033n8PGu0qC93uNvHURlvQ6JWjXGDckI2usSEUWSsBjiJKLQqb3k/gzOQKms1wEA6pvagvq6RESR\nhAGNKMaplKE57FzgIetERB4xoBHFOJWKvwaIiMINfzMTxTilwrWTNSC7axs0Pv2LafjzXVO6WxIR\nUcwL641qiSjwnBdT/u3+GYhTd+1EjvSUeKSnxMt6LAc4iYg8Y0AjinFmpzM4tXFB+rXAhEZE5BGH\nOIlinMkSmv3IBCY0IiKPGNCIYpxzBy1YuIiTiMgzBjSiGGcyc0d/IqJww4BGFONMAeig/d9vr8K8\nqf07fcyR0zX4n39+izaDye+vT0QU6RjQiGJcY6vB79dUKhS4Nm9Ap4858F01TpdfwtP/Puz31yci\ninQMaEQxbPexiyg+3xiQaydp1chM9b7lxpkLgXl9IqJIxoBGFMMq6loBAPGaru175o2vCwEsVivK\nq5phcd6cjYgoxjCgEcUwS/sWGz+c0g8A0Cs9wa/X92UrjfKqZvz7i++x+rV9+PJAuV/rICKKNNyo\nliiGiQFt3JBMZKclYOTANP++gMx8duR0Df53Q6H0+dEztZjTHhqJiGIRAxpRDDO3BzSlQkD+mF5+\nv77c/pl9OPPpiUREUYpDnEQxzGIX0AKii7vR8pQBIop1DGhEMczcPhlfEaCA1tWr8pQBIop1DGhE\nMcxisW1SG6gOWleDFvMZEcU6BjSiGGY2B7aD1lUCW2hEFOMY0IhimDjEGagO2qQRWV16Xn2THoXF\ntX6uhogocjCgEcWwQC8SuH76YPz5rin42/0z0DcrUfbzSiub8Ny7R7DrWEVA6iIiCncMaEQxqKK2\nBfVNemmbjUANcSoUAvpnJ0Mbp0LPHlqfn//qx0Woqm8NQGVEROGN+6ARxaCH/28vACB3WCaAAG6z\n4QeNLUb09PP+uURE4Y4dNKIYptObAARnkUCXj9cM3+xIRBQwDGhEMezkuQYA4d1BC9/KiIgChwGN\nKMZY3bSygtNB62ILjQmNiGIQAxpRjLG4C2hB2Hes6yOcTGhEFHsY0IhijLi1hr1gbAxrdvO6cnDP\nWiKKRQxoRDHGOSjdNmd4UF7XXTAkIiL3GNCIYoxzUArW+oCuBrRgDL8SEYUbBjSiGOPcQROClNDc\nzX0jIiL3GNCIYoxrBy28AxobaEQUixjQiGKMSwctSAFIrbT9uhmek+rT84KxBQgRUbhhQCOKMc4B\nLVgdtDuvuQwTh2fhZ9eN9u2JVuDDb85i/8mqwBRGRBSGGNCIYkyohjh7piVgxY1jkZ4Sj75ZiQAA\nbZzS6/MOfFeF978+i5fePxboEomIwgYPSyeKMaEa4rRXcPfl7a8t4O7Hv+r0sR9+UxKEioiIwgsD\nGlGMce6gBWOTWmeheE0iokjCIU6iGOMyB42T8ImIwg4DGlGMcd7ugvGMiCj8MKARxRh20IiIwh8D\nGlGMcZ2DFqJCiIjIIwY0H1ysa8VL7x1BQ7M+1KUQdZnZbHH4nGddEhGFHwY0H5RXNWPzrhI88fYh\nhjSKWOYIPhOz9lIbzlxoREVtC6wR/HUQEXnDgOaDSSOysPjqoaisa2VIo4jlPMQZSTHnwZd24bE3\nDuDh/9uLXccuhrocIqKAYUDzgSAIuGP+KFyT158hjSLW5/vLHD6P1E7U0TO1oS6BiChgGNB8JAgC\nlswc4hDS6psY0igyWCxWHC+pd7gtQvMZN7sloqjGgNYFziHtyX8xpFFk2LSn1OW2SO2gERFFMwa0\nLhJD2rV5AxjSKGJ8+12Vy22hzmfiuZxERNSBAa0bBEHA4pmDGdIoYpjMrmks1P2znJ5JGNInxefn\ncYCTiKIZA1o3MaRRJDGZLC63hcMQ57QxvUJdAhFRWGFA8wOXkPb2QYY0CktGs2tAcz6bk4iIQo8B\nzU8cQlq9jiGNwpLzOZxA6OegAaEfZiUiCjcMaH4khrT5+eEf0j7ZU4o/vroXTa2GUJdCQeRuODNi\nj3qK0LKJiORgQPMzQRBw44zwDmmNLQZ88M1ZnK9pwYc7S0JdDgWRfT6bNCILl4/siYnDs0JXUDcY\njRacrWhE8YVLYTGPjojInxjQAiDcQ9qn+87BYLRAqRCw7fB5VNS2hLokChJtnFL6eFhOD9y7aAzU\nqsj8NfDtqWo8+o8D+Msb32JvUWWoyyEi8qvI/M0cAcI1pDW2GPDVwXKkJcfhJwtGwmyxYsO24lCX\nRUGSlhzf8UkUdZ2+L78U6hKIiPyKAS2AwjGkid2za/MGYOrIbAzPScWh72vw3bl670+miGd/ULqb\n9QJERBQmGNACLJxCmn33bMb43hAEAUt/MAwA8O8vT3O7hRhgtnRss2GNorWTWw+eD3UJRER+xYAW\nBOES0uy7Z2qVbS7SoN4pyBuVjdLKJuw9znk80U7cZqNnmhZXjOkd4mo6uPu3QXZ6go/XiJ7ASUTE\ngBYkziHtiSCHNOfumb0bZw6GSqnAezuKYTCag1YTBZ/FYkVCnAqP35OPlERNqMuRaNwsVBiek+rT\nNX7yxFa3Z40SEUUiBrQgsg9pVUEOae66Z6LMVC3mTMlBXaMeW/aXBaUeCg2zxQqFIvw2EMsbnY0r\nxvbCH++YLN3m7txQb/7z1Wl/lkVEFDIMaEEWipDWWfdMND9vIJK0amzaU4pLLdy8NlpZLFYoleEX\n0NQqJX4yfxQG9e44ND01KXw6fEREwcaAFgLuQlpdY1vAXq+z7pkoIV6FRdMHQW8w44OdZwNWC4WW\n2WKFMgw7aPYevn0Sbp87AvPzB4S6FCKikGFACxExpC2YZgtpT/7rUEBCWmOr9+6ZaOaEPuiVnoDt\nh8/jfA03r41GFqs17I92GtInFVfl9oVayV9PRBS7+BswhARBwA1XBjakfbbXe/dMpFIqcPPVQ2G1\nAu9udT+Xx2q1oqK2BVX1rX6tkwKvoVmP+kZ92HfQREKYB0kiokBShbqAWCeGNAD4eFcpnvzXIfz2\n1lykp8R7eaZ3ja0GfCmzeyYaPzQDl/XvgcLiWpwoqcOogekwmS34vvwSjpyuweHTNaiq1yFOo8RT\nP5+GJK2623VScDzwwjcAEJaLBNxhPiOiWMaAFgZcQtrbh/CbWyYgs4e2W9cVu2c3XeW9e2Zfy9JZ\nw1Dw9/146/NT6J+djKPFtWjVmwAAcRol+mcn4VxlM3YcuYBr8zhPKNLwBAEiovDHgBYmnEPa71/Z\ngykje2LO5H4OK9vkErtnPZI0srtnogG9kpE/phd2HbuIitpWZKTEIX90L4wfloER/dJgNJmx6m+7\n8NXBcsy9vB+UCo6Uhzv7/e3qA7ggxZ/YQSOiWMaAFkbEkNazRwI+2VuKPccrsed4JYb0TcHsSf0w\naUQWVDInTnd0zwbK7p7Z+9Hs4Riak4ohfVKRk5XoMB9IrVJg2the2HrwPA6dqsHky3r6fH0Krmad\nUfrYYLJ08sjwwTloRBTLGNDCjCAImD6uN64Y2wsnSurx+YEyFBbXovj8cfRI0uDqiTmYOaEPUhI8\n7xHVne6ZKCFehasm9PV4/+xJOdh68Dw+P1DGgBYBWtpMoS7BZ4xnRBTLGNDClCAIGD0oHaMHpaOy\nrhVffluOnUcrsHHHGXz0TQnyRmVj9uQc9M9Odnlud7tncvTOSMSYwek4dqYOpRebMKCXax0UPkzm\nyOia2RMEAXdecxlOltZjzwmeE0tEsYWThyJAdnoCfjRnOJ755RW4dfYwpKfEYefRCvz59f14/K2D\n+Pa7Kpgttr+A/dE9k2vO5H4AgM8P8HiocGe2WxnQy8dDyENpxvg++Nl1o0NdBhFR0LGDFkG0cSrM\nmdwPP5iUg6PFtfji23IcP1uHU2UNyEiJw6xJOai51Bbw7plo9KB0ZKcnYF9RJW66eihSw+jwbXJk\nsQtoq5ZOCGElREQkBwNaBFIIAsYPzcT4oZk4X9OCr74txzfHKvDu1mIACEr3TKxj9qQcvPX5KWw/\ndB7XTR8U8NekrhE7aIumD0JGavf32AtXNZciY4UqEZE3HOKMcH0zE7Fs7gg888srcPPVQzGgVzJu\nnT084N0z0RVje0Ebp8TWQ+cjcp5TrBA7aJGySS0RUaxjQIsSifFqzJvaH3+6cwqmBHFVZbxGhSvH\n9cGlFgP2F1UF7XXJN+IcxUg55smTtOQ4r48xGM34aFcJ6pv0QaiIiCgwGNCo22ZNyoEA22IBq5Xb\n1IcjcYgz0gOaHJ8fKMPGHWfw4sajoS6FiKjLuhTQDAYDqqur/V0LRaiePbSYMCwTJRebUHyhMdTl\nkBuxNMTZ0GQAAFysaw1xJUREXSc7oN1///1oampCW1sbFi5ciPnz52P9+vWBrI0iyOxJOQCAbYfO\nh7gScieWOmhERNFAdkA7e/YskpOTsW3bNkydOhXbt2/H+++/H8jaKIJcNiANPZI0OHK6RprvROEj\nljpoVnCYnYgin+yAZjLZjorZv38/Zs6cCa1WCwUPyaZ2giBgwrAstLSZcLr8UqjLISdSBy0Gzrf8\n6iC7uEQU+WQnrCFDhmD58uXYunUr8vPz0dbG/YbI0YShmQCAw6drQlwJOYuWDtpd11wW6hKIiIJC\n9ka1TzzxBHbu3IkRI0YgISEBlZWVWLVqVSBrowgzckAPxKmVOHy6FktnDQt1OWTH3L66VqmM7IA2\nZnBGqEsgIgoK2R20+Ph4zJ49G9nZ2dDpdEhJScGUKVMCWRtFGLVKiTHth7tX1LaEuhyyY5EWCXBa\nAhFRJJDdQduyZQsee+wxaXsNq9UKQRBQVFQUsOIo8owfmolvT1Xj8Oka9M5IDHU51E6cg6aIgTlo\nRETRQHZAe+qpp/Dcc89hwoQJXBxAHo0bmgEBwOHva3DN1AGhLofaWbjNBhFRRJGdtFJTUzFx4kSG\nM+pUSoIGQ3JScfr8JTS2GkJdDrUzR8kiAV8I7BYSUQSTnbbmzJmDt99+Gw0NDdDpdNJ/RM5yh2bC\nagWOFteGuhRqx41qiYgii+whzjVr1gAACgoKpNs4B43cmTAsE+9uK8bh0zW4Ymzvbl/vwMkqfLSr\nBPcuGs15bT4yWyx47I1vUXqxCUBsddDsWaxWPPnWQUwYloV5U/uHuhwiIq9kB7STJ08CAFpabKvz\nEhP5FyW51ys9AT3TtDh2pg5GkxlqlbLL19p1rALrNxXBagXOVjQyoPmoorZVCmcA0D87KYTVdN3i\nmYPR1Grs8vMbWww4VX4Jp8ovMaARUUSQPcRZVlaGpUuXIj8/H/n5+bjllltQVlYWyNooQgmCgAlD\nM6E3mnHyXEOXr7P98Hms/9gkmvmoAAAgAElEQVQWzgDAYOQRUr5SKzv+iN989VAkxqtDWE3Xzc8f\niFt+4NveeuKwLhFRJJId0FavXo2bbroJR44cweHDh3HTTTdh9erVgayNIljusPZTBb7v2qkCJ0vr\n8Y9Pv0OiVo1F0wcBAAxGs9/qixX2G9OmJceFsJLg0+lN0Bv4M0NEkUl2QKurq8OSJUsgCAIUCgUW\nL16Murq6QNZGEWxoTioS41U4fLoGVqvvnYyvCysAAL+4fgyG9k0FAOhN7KD5zO6tT0qIzO6Zs4G9\nkmU/tuaSbSFTbM68I6JIJjugKRQKnDlzRvr87NmzUCq7PreIoptSocC4IRmob9LjXGWzT881GM04\n9H01MlLiMaJ/D6hVth9To4ndEF/ZR9p4TXT8eR3er4fsx+qNFmkPOCKiSCJ7kcD999+P2267DSNH\njoTVasV3332HJ598MpC1UYSbMCwLu49XYmdhBQb40PU4eqYWbQYzrp7YF4IgIE5tCxacg+Y7q104\n0Wpk/3GPGo+9cQDZaVo8dNvEUJdCROQT2b+xZ8yYgU2bNuHIkSMAgPHjxyM9PT1ghVHkGz8kAz3T\ntPjqYDkmX5aFEf3TZD1vb1EVAGDqyGwAgEZt66BxDprvLHbDy9q42AtoAFBZrwO4aS0RRRivQ5wG\ng203eJ1OB61Wi7y8POTl5UGr1XKjWuqURq3E8gWjAAFYv6kIOr3J63N0ehMKT9egV3oC+vW0bQmh\nad+mQ88Oms/sp/9FyxBnl7JWF+ZBEhGFktd/Ui9duhQbN25Ebm6uw9EpPCyd5BjaNxXXTB2AzXtK\n8e7W07h93mWdPv7I6RoYTBZcPrKn9PMmddA4B81n9h20uCgJaF3x4Eu7Ql0CEZFPvAa0jRs3AujY\nqJbIV4umD0JhcS22Hb6A3OFZGDs4w+Nj97UPb17ePrwJdHTQjFzF6TP7xpEihof5TGZ20Igosshe\nxdnc7LoSz91tRM7UKgWWLxgJpULA65uL0NLmfkf4ljYjjp6pRb+eSeiT2XFigJpz0LpM3OJk9uSc\nEFfifxq1Apf1l7+ik4goksieNbxs2TKpm9bZbUTu9M9OxqLpg/DfHWfwyOv7kaTt2JNLpVbCZDRD\nZzDDbLHi8pE9HZ6rEASoVQrOQesCcYgzmrpnQjd3NftkTymuyRvgp2qIiALDawfNZDJBp9PBYrGg\nra0NOp0OOp0OVVVVXCRAPrkmrz8mjchCY6sBF2pbpP/KKptwobYF9U1tyEiJQ/7oXi7P1agUnIPW\nBeIQZzQFtKsm9kVivAr3LBzdpbn/724r9n9RRER+5rWDtm7dOrzwwgu28xUnTJBuT0pKwp133hnI\n2ijKKBUK/PKGsS63Z2Ulo7q6yc0zOmjUSg5xdoHYQYuifIaePbRYu3IGAGDLfp4HTETRyWtAW7Fi\nBVasWIGCggKevUkho1Ep0MaA5rOiknoAgEIRRQnND9oMJpw814BxQzKiqrtIRNFD9hy0lStX4tln\nn0VRURH0er10+xtvvBGQwojsadRKNLa6X1xA7l1q1uO/O2zHs0VrBunq2sz/++gEDn1fg5/MH4kr\nxvb2a01ERP4gexXnww8/DIVCgZKSEtx8881QKpUYN25cIGsjkmjUCg5x+qilrWNj4O5OrI82x0vq\nAADnq1tCXAkRkXuyA1ppaSlWrlyJ+Ph4LFiwAC+//DIOHDgQyNqIJBqVEmaLFSYzV3LKZd81i9oh\nTp4QQERRSnZA02g0AAC1Wo2Ghgao1WrU1dUFrDAie+KB6dystms4xOmvJxIRBYfsOWgDBw5EQ0MD\nFi5ciKVLlyI5ORmjR48OZG1EErVKPO7JAm1ciIuJEDxFQAa+LUQUpmQHtKeffhoAcNddd2Hs2LFo\namrClVdeGbDCiOxpeJqAzyyWjoQWrfmMjTAiilayA1pzczOSkpIAAJMnT3a5jSiQNO1DnHIDmtli\ngVLhfQTfYrECQnR2mAx2w8HR+PV1h1kMr0x4RBSmZM9BW7ZsmazbiAIhrv3AdIOMOWjffleFXz67\nAxW13lfoPfH2Qby08Vi36ws3Or0Jj73RsYhHiNaA1sWAZbYwmRFRePPaQTOZTDAajdJRT+Lhy01N\nTTzqiYLGlyHOsqpmGEwWXKxtRe+MxE4fe66qGT0SNX6pMZxU1Tv+2YzWRZzdxveFiMJUt456uuuu\nuwJaHJHIfpGAN+JKT2+PtVqtMBjNMEbh1h3OHaJo7aBZOUZJRFGKRz1RROhsDprFaoWAjhBiMIoB\nrfNum9lihdUafVt3GE1mhwUCQDTvg9a9p7fpTTCaLNI/AIiIwoXs30oMZxRKcVJAcw1Tr358AgV/\n75hvJQYzb8FLDHvRFND+/eX3uPF3H6OqodXh9ihtoKFHUvf2XNl2+ALueXob3tte7KeKiIj8Q/Yq\nzry8PLfDJLt37/ZrQUTuaNo7HHqnrpjVakXh6Vro9CZYrVYIgiANbboLc/b07fdHU0Dbsr8MAPDd\nuQaH26N1iHPZ3BHI6qHFp/vOdes6m3aXYvHMIX6qioio+2QHtPfee0/6WK/X46OPPoJKJfvpRN2i\n8dBBa2g2oFVvO3PSaLJAo1badcY6H+IUO21mixUWizWqhgFNZqchzuj50hykJGpw86yh3Q5oRETh\nRvYQZ9++faX/Bg8ejF//+tfYvn17IGsjkogdNOfQVV7dLH2sbw9mBpmLBIx2YS/aFgo4n1karR00\nIqJo1eWZsWVlZaitrfVnLUQeiR00vVMHzV1AM8qcW2Y/XGo0WbDug2PYtLvED9WGnmtAC1EhEWTN\nO0fwefsQ8ZHTNSj4+360tplCXBURxaouzUGzWCwwmUz4wx/+ELDCiOx52getvKpjM1oxvOlldtDs\nh0sNRjP2FVXhYl0r5ucP9EfJIeX8tZuiaJ5doBw9U4ujZ2oxZ0o//O+GQgDA3hMXcfXEnBBXRkSx\nqEtz0FQqFTIzM6FUKgNSFJEzjYeTBM7bddCcV2UavWxqax/2xHlsLbro6Jg4f+3i10dERJFBdkDT\n6/VYt24dzp07B5Op45f9hg0bAlIYkT13HTSzxYILtR3bSegNZofHeJ2DZne/OJTVqjf6p+AQc/7a\nWzhUR0QUUWQHtF//+tdYtGgRbrjhBnbOKOjcddAq63QOc63EVZniY7zOQbMLey06WzDT6c0wmS1Q\nKUOzcanRZMGxM7UYMzjDYfPU4yV1yMlKQqrdsVQnSurQNzMRqW72Aiu52OTweWtbdATPUCoqrUev\n9ASkJXdv7zUiIjlkBzSVSoXly5cHshYij9x10MQFAlk94lHd0CbNQevooHnbZqMjwNl3mFr1JqQk\nhOZ8zo92ncXHu0pxTV5/3HTVUABARW0Lnvn3YaQmarDmvukAgOoGHZ7+92EkxquwduUMr9cdkJ0c\n0LqjlbhZSe0lHZ761yGolAJeefDqkNZERLFBdpvgyiuv5LYaFDJSB80hoNkWCAzpkwqgY4hT7lmc\nDnPQ7DpMoVy5d7bC1vk6Vdax0Wxdox4AcKnFIN0mfixn6HLBtAGYOaGvP8sMO3/56VQ8cW9+wK7f\n1Gr7+XDeX46IKFBkd9Dy8/Pxi1/8AgqFAhqNRtq1nScJUDAoFAJUSoVD6BIXCAzuk4I9JyqhN9qG\nJ8WDwo1eThLw1EEThztDQd0+tGoydQQB54PPAdsJCnKNHJAeVZvwutM7IzHUJRAR+ZXsgLZ69Wr8\n9a9/xejRo6FQ8GBhCr44tcJliDNJq0ZmDy0AW0fMft6Z1yFOhw6aXUAL4XwtldIWpOzn1lnchDHn\nw9A7o4zycEZEFI1kJ63U1FTMmzcP/fr1czhVgChY1KqODpreYEZ1QxtyshKlg9T1RrND6BLDWllV\nMz7eVeLSdbLfB80+lHnaasNiseKjXSWoa2yTbvu68ALOXGjs5lfWQVycYB/QrG47aPKvyYDmm7sf\n/0r6+P2vzzosJiEiChbZAW327Nn417/+hYaGBuh0Ouk/omCxP2ezqsH2s9c7wymgOXTQbB9/caAM\n/91xBhfrWh2uZ99ha5HRQdt3shIbd5zBX//5LQDbvLXXN5/EY28c6O6XJlFKHTT/DXGK14wFowem\n+fV6zTqjdAA9EVEwyR7ifO655wAAjzzyCARBkOagFRUVBaw4InsalRLN7ZO1m1ptk+RTEjWIa1/h\nqTdanDpoto917Zu0Ok/+t++g2S8S8DTxvq19EUJt+6T9QGz+6q6D5m6I011o80QZQ1MSHlg6AT9/\nZrvXBSK+aLJbnEFEFCyyA9rJkycDWQeRV3FqhdT1amwPaMkJaqmDZnDuoLUHsDajY1CT7vfUQfOw\nSEBcyQfYwp9O7/+hL7cBzU0Y8+Vw91ga4hQEAXEapV8DGhFRKMgOaEShplErYTJbYbZYpLCUnKCB\nRuN+DprZYoXFYpW233DuePnaQWto0ksfn6tsduhi+WtzW2kVp90Qp7sOmi/bPcTSECfg2/w8IqJw\nFTtjHxTxNCpxs1q7gKZVO8xBcz49wGAyS0OTLh00o29z0OrtAtqZC40OQ6b23TVnzToj9hy/6NIJ\ns1qt2HWsAruOVcBqtaK1zYgjxTUAnDtortd0Pvy8tc2Erw6Wu339WOqgBcO331Vh9/GLaDPw+Cwi\nChx20ChiqNuDmNFkQbPdEKdGpYAAwGAwS6cJiAwmi9RBcx6StB/itO+GeQxozR0B7XyNbYsPUWOL\nweMRQC/896i08Wze6F7S7WVVzXj1Y9sczr6ZSfj7pydR0X62qH097jpozkOc2w+fx7vbit2+fizN\nQQsIp3z7t43HAAAzxvfGndeMDEFBRBQL+JubIkacquO4J/shTkEQoFEroTdapIUBIqPRInU6XIY4\nPcxT8rTNRkOTXhrGNJgsDtcT58S5I4Yz+4AHAFX1Haugqxt0KHU6P1PkbkGAySmgNXWyuS47aIFR\nUuH++0VE5A+yA5rFYsGpU6dQWVkZyHqIPNKIQ5kmC5paDRAAqYulUSscttmQhkNNZs+LBDzsb+Xu\nYHGT2YLGFgN6ptk2xTUaHQPapWbvK/3iNY4Na/v91Oqa9M4Pl7hbJOA8xNnZxrWxNgeNiCgayB7i\n/PnPf47S0lKYTCa0tbVh1KhRGDVqFFauXBnI+ogk9gemN+mMSNSqpSOM4tRKh0UCiVo1DE16GIwW\naTGAa0CzQKUUHCbcpySo0dJmkraRETW2GGAFkJUajws1LTCaLQ7X66yDJtK2L2YQidt1AI5hzZmc\nVZydTYxnBy0wuBaBiAJJdkArKSnBp59+CkEQ0NjYiOPHj+P48eOBrI3Igf2B6U2tRiQndMwBi1Mr\n0dCslzpoifFq1Dfp0aTrCE7uttlIjFc7HELeIykOja1GtBnM0MZ1/PEQFwhkiR00k1NAk7FXllrl\nGNDqmuR10NwPcbouOPCEc9C6R3CehEZEFASyf3MPHjwYZrOtO5GSkoL8/HwsX748YIURORM7aG0G\nM1p0RiQnaOzuUzpsVJuktYWrppaO4Up3HbREu4n+AKSJ/s4LBcSAlpkSD6VCcAloVfU6/OXNAzh0\nqtpj/VarFYe+r8Zf3jgAnd6EusY2KBUCFIIgHfzujtnNMk7nOWiddtBibIjTl1MW5Pj8QBnue3qr\ny+1lVc1YvX6fNO+x+Pwl/Om1fdh1rAJ/fm0fHnxxF+5+/CvUNPDEFSLyneyA1rNnTzz44IOcg0Yh\nI3bQ6pr0sAJOHTQFTGaLtKWGGLyaWu07aB1zzkxmC8wWKxLjHZvIGanxAIDGFseAJs43S0pQQ6VS\nwGiyOGyzcfh0DYrPN2Ltf496rN9ssWLte0dRfKERh7+vQYvOhOQENRLiVQ5beLg8z82eZ21OK1Ld\nrfQEgEG9k6EQYiugBVN5dTNOlV0CAKz74Ji0MvdcVTNq24etP9xVEsIKiShSyR7iTEhIQElJCa67\n7jokJSVhzJgxGD16NH72s58Fsj4iidhBq71k+4vPvoMm7oXW3L6aMTHeFtAaW9130MT90uyHMQUA\nWT1sQ5jOgUk8MDtOrYRaqXCZgyaHfYjSqG3X0KiUsFjNMBk8d31MbjpozU4dPk9do58uHO1TjdQd\nDMJE5D+yA9rvfvc76ePy8nIUFRXhxIkTASmKyB1xFafYmUi2G56M0zgGtCQ3HTT7VZcG+8DV3hFT\nqxTSEGeD05YY4uM10uPNsFqtSNKqpdd0x36lqP1k/7j2g98T4lQwmi0wd3J0k7tTA+yPo7JarR47\naFwfEDyeGpX8FhBRV3Rpo9qcnBzk5ORgzpw5/q6HyCNxiFMKaHZDnGJ4EwNZojgHzamDJq7OlLbj\nUCtsHTGTBRq1Ej2SbAGt0w6aSiGd+5kQr4JOb/J4eLn9CQX2AU2tsnXQ1CoFlEah0xWB4nwz+wBg\nHwptR1q5f67A4c3A8/IW81tARF3B5V0UMTod4lSJAa29gxbv2kEzW6zS0KZzRwyAQwfNOaCJW3Vo\n1Aqp46bTm6CNU3V6Bqf9nmpmuy6XxWKF0WgLaAovbS5xDpr9XDL7RQxmi9XjEGcszj8L1VmcMfhW\nE1EAMaBRxBA3nxWHHx06aBrbfeKO+h2LBByHH8V5Y/Yb2ooBTaNSSB005yFO5zlobQYzDEYLEuJU\nUDmtknz634dQe6kNVqsVz79XKN1u30EzmCywwhYKPe1TJoYusYNmtlix/fB5PPn2QVQ3dGzR0dRq\nwDfHLrq9BkND4DW3Gl2+J474TSAi3/kc0AwGA3Q6nfQfUbCIw5hih8TdIgG9wQwBQEL75P9GuzM7\ngY55aFIHTWXfQbN9nKRVu+mgOQ5xikOa8RqlSwftREk91m86gWad0eEvbfuAJq421aiUHjto4tdp\nv6XGPz79DifPNTg8buuh826fD8Brdy6aDc9JDcrrbNl/zuV7Yo8hmYi6QvYctM8//xyPPvooqqtt\n+zyJc3mKiooCVhyRPTGgiZw3qrV/nNpuzzQASEuKQ1OrUdpqw3kOmvgxYNsLrcpp7yq9NMTZEegA\n9wENAOoa9dJwqnjKgX1AEztyKpUCSg9/g1usViggSMOrnlg7uTsW56CJ73KfrCScKr8U8Nfr5JQt\nIqIukx3QnnzySTz33HOYMGECFNyZnEIgTuX4c5ek9RTQFNKCAlGP5Dicq2ruGOJ0MwdNHEJNS45D\nWftjxW04OjpoHYEOAOI0rkOcgK1zJ4bAeI0toIlhEbDvoHmeg2axWAGl50PdJZ1ksBhuoIUNfguI\nqCtkJ63U1FRMnDgxaOGsrKwMf/jDH/CrX/0qKK9H4U9tF8ISnCbnOwQ0lUIKW6L09sn/HQHNzRy0\n9mu4W8mpN9qGTlVKhWMHTa2ESuX6Z8I2R80WwuKdtgABAL3BVkdnc9DErTPEneo96SwAxGIHLdi8\nvsP8HhBRF3hNW+Jcszlz5uDtt99GQ0NDl+eg/f73v0d+fj4WLFjgcPuOHTswd+5czJkzB6+88goA\noF+/fvif//kfn65P0c0+dCUnahzvcx7idApNPdoDmjgHTW/q6KCp7FZxAh3HPdU3OwY0jUYJQRAc\nrh2nUULl4R8tYudL3KPNfuVlW3t462wVp7h1Rvc6aLEbDmL3KyeiaOB1iDM3NxeCIEgrygoKCqTP\nfZ2DduONN+LHP/6xw6a3ZrMZBQUFeP3115GdnY0lS5Zg1qxZGDp0aBe+HIpm4hwxwHH+GQDEaTru\nU6sULvPV0pIcO2hGqYOm7JiD5hTQGuw6aAajRRpidQhoaiVUKvdRQOzAaTW2P2b2e6JV1+uka3XW\nQau5pMPF2la394vsj5xyFpv5rH1SWJC+9ppLnlZvOpbR2GJAnEYpdXtb20yoamhFSoIG6SnxqL3U\nhvSUOHY9iQiAjIB28uRJv73YlClTUF5e7nBbYWEhBgwYgH79+gEA5s+fjy+//JIBjVwoFQqolAJM\nZqvDKQIAHOacueugpTkNcYr/j9O4DnGKj61r7PiLV280S/erlR2vFa9ROsxJs3ehpkV6DOC4+/+B\n76qluj0FtKZWAx7+v71u77O3/fAFj/fF8ipORZASWmcnSQCQEtrKtTuRGK/C2pUzAAArntshPeSX\nN4zB3zYew7V5A7DkqiGBKpWIIojsRQLNzc1ISkryepuvKisr0atXL+nz7OxsFBYWor6+HmvWrMGJ\nEyfw8ssv45577vF6rbS0BKicJocHQlZWcsBfI9bIfU/jNCqYdEb0zEh0eE6j3eHhSVoN+vTq2GJB\npVSgX98etk+UCmRlJaOivYM1bkQ2jpypAwCkJscjKysZo9qHLGubDdJrmMwWpCTa7k9Jie+oOyMR\n2njHsChqax+aTE22Pd7g5jinHqlaxLcHOWeCuksHfTjomZXs0k2MdmIHKtFpGDxUErQa6eeopc3k\n9me9qP3A9V3HLuLnN00Ian2hwt+j/sf31L9C/X7K/htg2bJl2Lhxo9fb/CUtLQ0FBQU+Pae+vvOh\nIH/IykpGdXVTwF8nlvjynoorJlUCHJ7T2mK3b5nVitraZqiUCpjMFsSpFWhrtd1fV69DVVUjTpyt\nRXpKHCwGE8ztQcpkNKO6ugmC1Yo4jRLF5Q3Sa+j0ZigEAdXVTTAa7M70bDPC6mGfharaFqkewDbE\n5cygN0qv76ymplnOW9Ip8X2IJeJ2JjqdEaMGpuFESX1I69HpDA4/q+5+1vXt8xPNFktM/H7h71H/\n43vqX8F6PzsLgV4DmslkgtFohMViQVtbmzQXrampyS8b1WZnZ+PixY5d0CsrK5Gdnd3t61J0Eo90\nst+kFnDdZgOwzSkzmS2I1yil7TJ0ehOqG3RoajViymU9AcBlDppCEJCTmYiSi00wmS1QCIIU9AC4\nLBJQutlmA+gIZOIQp07vOless0UCRm+LA2SI6UUCQuiOfXKoA4LHo7jsHkRE5MBrQFu3bh1eeOEF\nCIKACRM6Wu9JSUm46667ul3A2LFjUVJSgrKyMmRnZ2PTpk145plnun1dik5i+HJZJGC3gECcj6ZW\nKwA9EK9RSScLtOpNKD7fCAAY0tc2DOo8Bw0A+mYlofhCIy7WtiIjNd7hfsd90DzPQbvkFNDcbTjb\n2T5oRjdDor6K4XwWPnz4HoRDoCSi8OA1oK1YsQIrVqxAQUEBVq9e3a0Xe+CBB7Bv3z7U19djxowZ\nuO+++3DTTTdh9erVWL58OcxmMxYvXoxhw4Z163UoeokhyTmg2YcrtdqxIxbXvtu/WqWATm/C6Qu2\n+T5D+qbYHu9mdWZOViIAoLy6WXotsUvnvA+a9w6a5z9mAgSPiwT80UHjisDIILCFRkROvAY0g8EA\njUaDBx980O2Qplarlf1izz77rNvbZ86ciZkzZ8q+DsUuMXQlax2HOFVK23YVZotVGgYVO2lisNLG\nqaDTm1B8/hJUSgUGZCe3P1dwuDYA5GTZFr+UV7dgcHunzV1A66yDJp0kEOd5kr7OYPI4DOl1/zNy\nK9yaUALCryYiCn9eA9rSpUuxceNGl/3QAPAsTgo6Tx008T6d3uTSEROHGLVxKlTVt8JqBYb2TZUm\nz6vth0Tb5fS0BbTPD5Rh74mL7dd37MzZrq2C0sskfPH13TEYLQHtoMUiMfCGS/Pwi2/LcbykTvp8\n7XuFmD62t+OD7GrdceQCvjlagQdvzXW7wMNqtWLNO0cwamA65k3t7/F1K+ta8beNR3HLD4bh3a3F\nWHjFQEwcntXtr4eIgsPr8i5xlebJkydRVFSEkydPSv8xnFGw5Y3OxrQxvaSTAeyJ89Ccg5QYkK4Y\n0ws9e2jRKz0BV0/sKz1v5IA0jOjXA0P7dmzNkaRVY9qYXlApBdQ26tuv76aDpvbcQRN1NsQ5c0Kf\nThYJdH7Ekzd5o2Jzsc0DS8fjsv49MG/qgFCXIqmw22z40Pc1WPvfox4f+/dPTuL78ks4X+1++xWd\n3oxjZ+vwztbTnb7mxq/PoLy6BU//+zBKK5vwQievSUThR/Y2G++++y7y8/ORk5MTyHqIOpU3qhfy\nRvVye58YoDoWCbQPcbYHpAXTBmLBtIEuz+vXMwm/u22iy+3LF4zCe9uLsWl3qe26bgKaSil4nIMm\n0nrooC2bOwLaOJXXDtqPZg/D21983+lruHPnNZf5/JxoMKRPKn77I9fvZyTwutqTiGKG7IB24sQJ\nrF+/HkajEXl5ecjPz0deXh4yMzMDWR+RbFJA89BB64rstASX69t3zARBcNtBs5935KmDJgYzTwFN\nnIPmbQjVEy4QiBz8ThGRM9kB7U9/+hMA4OLFi9i2bRueffZZVFRUcJiTwoZGIwY0x05XfDd20u+V\n3hHQNGrHOWsi5w6aQhAQp1FA1366QZyHgCgGM2/7oHkKcN54OMM9prAjRUSRSnZAO3bsGHbv3o3d\nu3ejuroa06dPR35+fiBrI/JJxxCn2EGzfd6tDlp6xypld3PQALh00ATB1jUTA5qn1xeDnbchTpWX\nIVRP2EGLTvy2EsUG2QFtyZIlyM3NxQMPPIApU6YEsiaiLukY4nRclempgyVHkt2h7OL1VU4BzXkI\nUhAEaONUqG/SQxAcV33aU7W3uLwtElB2sRXGv8cjDxt+RCSSHdDeeecd7NmzBy+++CJqa2sxceJE\nTJs2DT/84Q8DWR+RbHFOc8/sN6rtKvsulLjlgcopUDl31BRCx8IAjUrpMYB5m4PW3SFOdtAih7tv\nlZW7pxHFNNkBbdy4cRg3bhwWLVqErVu34pVXXsF//vMfzkGjsKFx7qBJQ5yyf8w71aSznQxgcWpz\nOAcoQRAQ3360lCA43h+vUaLN0N4ZU3Y+B+3Ad9UAEHOHnceiHUcqANiOIhP945PvkJkaj29PVWPM\n4HTkDs3E1RNz8I9PT3b5dS7WteLTvaW4+ephSIj3z58LIgoM2b/5CwoKcO211+LWW29FYWEhVq5c\nie3btweyNiKfDOqdguQENbJ62OaNDchORkKcCn0yE7t13dvnjoBKqcC4wRkAgIwU29mcs9r3UnPu\noP34h8MxpI/tGKnh/SSCu2oAABpESURBVHo4BDDx0HagI5h565B52sZj5IA0X76MmHRtXvjshear\n0somfHvKFtKPnanDm1tOAQD2FVV1+Zr/++4R7DhSgU/2lvqlRiIKHNn/hBoxYgTuvPNO9O/veedq\nolCaMb4PrhzXWxramzQiCxOHZ3Z7qO+q3L64KrdjY1uNWon1v7tauq44R6x3RgIeWz5Vuv3avAEu\n4c1+wYDSyxy0jsc53i++zgc7z6KotL6LX1VsGDM4A//7q+n49fM7Q11KWGhqNQIA9IbubYJMRIEn\nO6AtXbo0kHUQ+YVzGAvUPCz766pVto9VSoXD7Ro323vYD7dKc9C81Og8xKlUCJxf5gO+V0QUiTi5\nhaibxFWccrbDcOigeZmDJj3O6X6x88YVf0RE0YsBjaib1FJA8/7HyT6gidtseDspwPm6YqBjPpOH\nDTQiikRdCmgGgwHV1dX+roUoIomdMDkBzX6RgLdtNpwfJ+rYFo0RjYgoWskOaPfffz+amprQ1taG\nhQsXYv78+Vi/fn0gayOKCGIHzduh6YCHIU4vLR7n6wrtW9ByiJM6w7l3RJFNdkA7e/YskpOTsW3b\nNkydOhXbt2/H+++/H8jaiCKCOETp7tB0Z24XCfjYQSPf8N0jokgkexWnyWTbQHH//v2YOXMmtFot\nFDyNmQhq8UxNH+egyd1mw2xxapUxcVC75949goVXDERGSjxKLzbBZLaizWBCs84Ik9ki+zpHz9Si\nb2Yi0lPi0dhiwJkLjZgwLNMvNZZebAIADOiV7JfrEcUK2QFtyJAhWL58Oc6cOYNVq1ahra0tkHUR\nRYyEeDUUgoDkBLXXxzrMQfNyWLr99XumaVFVrwMAWNvHNgf2SulqyTFFPFEiGhUW16KwuBaJ8Sq0\ntJm8Pt7+pAJRdYMOa945ApVSgVcevAqP/uMAahvb8PDtkzCkT2q3a3zk7/sBAK89NKvb1yKKJbID\n2hNPPIGdO3dixIgRSEhIQGVlJVatWhXI2ogiQpJWjdV3TkZmqtbrY+PU9h001202brhyECAI2Ljj\nDADgz3dNQWqiBqvvmII/rt+L+iY9TGZbQJs4PBNDc1JxuvySw2v85LoxGN6H3QqRWqXAoz+5HF98\nW47thy+EupyAkBPOPGnW2TavFTtutY22f3zXN+qBPt2vjYi6RvYYZXx8PPLy8tDY2AgAyM7OxowZ\nMwJWGFEk6Z+dLOtswzgvQ5y9MhKRmqhxuC4AJMSrMLi3rWMm/kUqCAKGuulwXDYgTTruimz6ZiUh\nPTku1GUQEckmO6Bt374d8+fPx3333QcAOHr0KO69996AFUYUjeyPfnI3xCnAzZyzdqr255pMHXOL\nBDd/grl4j7yx/wnjamCi8CQ7oD3//PPYsGEDUlJs/4ofO3Yszp07F7DCiKJRnH1AczPEKQgCzB4m\nd6vaH2c/+dvdFh3cXsE95hAiiiQ+LcPMyspy+Fyj0Xh4JBG5o3YzB01lF9AUAmDx0kEzmq12j3cN\nY972VYtZTGhEFEFkB7TExETU1NRI/zrfu3cvkpM5EZnIFxq7Dpr4Z8mlg+ZhzEk8qcB+iNPdFh3M\nZ+4xn7ln5TtDFJZkr+L8zW9+g5/+9KcoLy/HsmXLUFJSgpdeeimQtRFFHY3adcsHx4DmuYMmboTr\nOMTp+jhv+6rFKisnWxFRBJEd0MaNG4c33ngDBw8eBADk5uZK89GISB77DppI6dxB8zjEaXuc0WsH\njQGNiCjSyQ5oAJCcnIz8/HyYzWYAgE6ng1bL5fxEcrntoAky56C1b8thf6/7RQLdqzFasYHmAd8X\norAkO6Bt2bIFjz32GKqrqwHYhgsEQUBRUVHAiiOKNmofOmjO4Uvl5rnucJGAe9GQQ+5+/Cu/XOfL\nb8vR1GrAFWN7I8HudAvnYeCaSzp8+E0Jllw1BCkJXBRGFEyyFwk89dRTeO6553D8+HEUFRXh5MmT\nDGdEMuWNyoZGpYBapcDAXsnok5ko3ec8By1vdC8AwO3zRjhcY8plPQEAt80ZLt3WLzsJSoWAeVP7\nO1yDXOWNyg51CWFlX1EV1rxzxOG2yvbjxADbz9GrHxdhZ2EF3t16OtjlEcU82R201NRUTJw4MZC1\nEEWtn103Wuo6//GOyQ73OXfQ+mYmYv3vrnaZS5bVQ+ty+5hBGXhp1UyolAp8ute2L6GtgxYN/SL/\n6tP+vv7kia2hLiWs2P+k2O/BZ7UCLW22Y6Da9OYgV0VEsjtoc+bMwdtvv42GhgbodDrpPyKSRwxW\ngiA4hCyF0z5o9o/1dA174vYbnT2GbPjeEFGkkN1BW7NmDQCgoKAAgiBwDhqRn4hncgL+CRBsoJEv\n5PzE8ceJKPhkB7STJ08Gsg6imKV0moNGFExWDx8D8sIbEQWGT0c9NTc34/jx44GqhSgmOZ8kQERE\nJDugbd++HfPnz8eKFSsAAEePHsW9994bsMKIYgU7aBQuPP348RQGouCTHdCef/55bNiwAampqQCA\nsWPH4ty5cwErjChWOC4S8MccNKY88hf+LBGFik9DnFlZWQ6fazTcuJCou/zdQeNfqeQTq9sPPd5C\nRMEhO6AlJiaipqZG+tf53r17kZycHLDCiGKFwxw0xisKE2zEEoWW7FWcq1atwk9/+lOUl5dj2bJl\nKCkpwYsvvhjI2ohigkKwxTIr/PSXIv9iJb/hDxNRqMgOaMOHD8e6deukVZyjR49GdjaPTiHyB4XC\ndgYnz9GkYLNYPY9xiosDxIdYLFZAsP2jwv5j++vY/wxzcQFR18kOaLm5udIGtYBtIrJKpcK4cePw\n6KOPYvDgwQErkijaKdsDmj/ymVIhwOL9YUQAgMffOih9vPq1fdLHL71/XApdh0/X4L3txdi0u9Tl\n+b9aMg690xPw+1f2AACWLxiJaWN6AwCe+tehQJZOFNVkB7T7778fcXFxWLJkCaxWKzZu3Ij6+nr0\n69cPf/rTn/Dmm28Gsk6iqKZUCoCpeysw7795PE6VNSAjVYvq6iY/Vhddbp09DCazBe9uLQ51KWHN\n4tT9chfOAGDTrhLkDu9YQPbu1mIpoJ081xC4AominOxFAp999hnuvPNOJCUlITk5Gbfffju2b9+O\nG2+8EQ0N/ENI1B0K6ZzOrl9j7OAMLJ45xE8VRa85k/vhivYAIRo3JAOvPTQrRBUREbmSHdB0Oh3K\nysqkz8vKytDa2goAUCqV/q+MKIaIW21wDhpFIs41I/I/2UOcK1euxJIlSzBmzBgAwPHjx/HII4+g\npaUF8+bNC1iBRLFA3GqD+YyIiAAfAtrcuXMxefJkHDlyBAAwfvx4ZGRkAACPfCLqJqUU0JjQKLKw\nd0YUGLIDGgBkZGRg1izO0yDyN3bQgozvMxGFOZ+OeiKiwFAobH8UOQeNIh07akT+wYBGFAY4xEmR\njGsEiPyPAY0oDPhjmw3yAQOF3zCcEQUGAxpRGGAHjYiI7DGgEYUBpZIdNIpcbKIR+R8DGlEYUHCj\n2uDi20xEYY4BjSgMKDkHjSKWU/+Mk9KI/IIBjSgMSPugsbVDkYihjMjvfNqologCQ8mNailCna1o\nwtmKJunzxlYjjpyugTbO8a8Xk9mCbYfOQ6NW4rLBmaiobES8RokR/dM6vb5Ob8LOoxWYPra3wzXr\nm/Q4eKoaV+f2lf6BQxRNGNCIwkCPpDho45RQq9jUDgatRhnqEqLa/24odLlt26HzePuL711uf+2h\nzk+n+c9X32PHkQpU1Lbi9rkjpNuf+c9hXKhpgTZOiWljene/aKIww78NiMLAsrkj8D8/y4dKyT+S\nwaBWKbFmxRWhLiOm1DXqu/S8itpWAEBlXavD7RdqWgAADc2G7hVGFKb4twFRGFCrFEhN1IS6jJiS\nmhSHgb2SQ10GEZFbDGhEFLM4tZ2IwhUDGhERRSwrV5BSlGJAIyIiIgozDGhERBT22CmjWMOARkQx\ni7tnhT9+jyhWMaARERERhRkGNCIiIqIww4BGREREFGYY0IgoZnHaeeTj2gGKVgxoRERERGGGAY2I\niIgozDCgEVHM4hYOEUDgd4liEwMaERFFLE5Bo2jFgEZEREQUZhjQiIiIiMIMAxoRERFRmFGFugAi\nIop+n+475/b2ux//CqMGpqGyTofJl2UhNTEOJrMFpf/f3v3HRlXmexz/DC0VSmmppUxFWlRWCdbu\nxl0Ky25uG4rQaNE/QK4xSnNzNVfdbDfZbqJLYrrSVtC4VxuTXQVd3L3eGxMTFLEo1rYB5C6XWfHq\nbHdk1WppC7YUHErpIP119g/o1MIM9HTnzDkz8379U+b0TM+X7zR9PvOcc57p6lNWxjR91nFKknS4\n/ZS27vybDEm3/Whe8PmtR3sndPwTvWflbT2p5bdeKxc3HiAGENAAJKwf5+eoratPP7wpW9L5uzq5\n6Dz6fG1+SdJ7no7L7vd/vm5J0sELXyXJ23pyQseo+dOH6gsManbGNH1/wexJVgpED6c4ASSslYvn\n6bc/+4mKfjBXkvT7ymKbK4JV+gKD474CTkdAA5CwXC6Xrk6fFnx8VUqSjdUgGvhoKMQKAhoAAIDD\nENAAAAmD+wMQKwhoAAAADkNAAwAAcBgCGgAAgMMQ0AAAAByGgAYAAOAwBDQAQMJgHTTECgIaAACA\nwxDQAAAJg3XQECsIaAAAAA5DQAMAAHAYAhoAAIDDENAAAAAchoAGAADgMAQ0AEDCYB00xAoCGgAA\ngMMQ0AAACYN10BArCGgAgITBKU7ECgIaAACAwxDQAAAJg1OciBUENAAAAIchoAEAADgMAQ0AkDC4\nSQCxItnuAgAA+Gf86nf/qxHD0OKFc5R/3dXavrdVI4ahuVkzdM3s1HH7vveXdh070a+CBVnq7T+n\n1s7T+un3czRnVqqOnejX9+ZlqOfUWQ0Nj+iarBnjnvtFZ6+uzZ6h6Vcla3BoRK1He3VT7ixNmWL9\nhW3HT53VcIiaEL8IaACAmObvOydJajrUqaZDncHtX58MSJ+N3/doT7+O9vRrt6c9uK3po07NnT1D\nx070a+O/L9FvtnkkSdt+XRLc56uvT2vTfx/S/JyZ+s2/Feq1ps+15/+Pan3pQi2/9VoL/3fn/frF\nA5fUhPhGQAOAEO748XxlZUzTDdeky0iaovZjvZp+VbL+a/dh9X87pJmpU/XLf/2Bqv/44bjnVawp\nkMvl0vPbvTZVjsk4dqJfknTi1NmQ3+/2ByRJR7r6JEl/bT0hSfryaG9UAhoSDwENAELInHlVcODN\nzp6p67LPn1pq8LSr9dhp5bln6rqc9Eued+tN2ZKktcU3aPveL6NXMCJiopeouS6s18ElbbAKNwkA\ngAVcLLiVELjpAFYhoAGACYzHCSrsC89vBKxBQAMA4IKJzoiNTpASz2AVAhoAmMCJS0jfOYVNQoNF\nCGgAYAGDi5Ni1MRet9GgzqsMqxDQAAC4gnBBjCAOqxDQACAEBt7ENOGXnbt0YTECGgBYgGU24lvw\nFCc5HhYhoAHAZDAyJ5SL4zb3CMBqBDQAMIOJsYQUfhk0IhqsQUADAMAkTmHDagQ0AABMYpkNWI2A\nBgCAWaPXoJHQYBECGgBMwpXGZU6AxZmLXnBeX1iNgAYAIYS7xsjF0BzXJj4hdv73gPXyYBUCGgAA\nJnGPAKxGQAMA4IKJzoixUC2sRkADAMAsZtBgMQIaAEwCMyeQuAYN1iGgAUAIYQdeZk6gsZtFiGew\nCgENAKxAkItr3CQAqxHQAAC4AuOiuTIXC9XCYgQ0AAAm6eLgBkQKAQ0AzGA8jmsTnxEbnUKzqhIk\nOgIaAFiBgTuuuchnsBgBDQDM4OLwuBbulOXFM2uucN8AIoSABgCAWQR1WIyABgBWYACPKxcvq8E6\naLAaAQ0AgFETTVwsswGLEdAAALiCsNegARYhoAFACK4wS8UzMMc3kxNofBYnLENAAwDALD7rCRYj\noAFACMyM4HLGZtBsLQNxjIAGABZwcTI0rrFQLaxGQAOASWCGDZKiPoXG713iIKABgAnMi8U5k/kn\n2nGJeJY4CGgAAJg0epcvgQlWIaABAHBBuM/ivFjwJk6m0GARAhoAACaN5bMoX4NGQksYBDQAAACH\nIaABAHBBuJskL/moJ5vW2eAmzsRBQAMAM1hBHlLwHOcIgQkWSba7gHACgYA2btyoqVOnasmSJbrr\nrrvsLgkAACAqojqDtmHDBi1btkyrV68et33fvn0qLS3VypUrtXXrVklSQ0ODSktLVVtbq+bm5miW\nCQDAZblkz22cnOJMHFGdQVuzZo3uv/9+PfbYY8Ftw8PDqq6u1iuvvCK32627775bJSUl6u7u1sKF\nCyVJSUlJ0SwTAJCgjvvPBv/d2XNmbPup8dv7AgOSpNP9g+P2s9rRE2eUnHTp3EpgyNA3/v6o1RHv\nzhlSioyxaw1tENWAVlhYqM7OznHbvF6v5s+fr9zcXElSWVmZmpqa5Ha71dXVpUWLFmlkZCSaZQJI\nYBlpKeo9M6C01Kkhv5+dMU2fdUhZGdMu+3PSZ4R+Ppzt7T+3Bf9d9QdPyH2+u/3k6W/D7meF6j9+\nGLVjJbr7V92kkh/Os+34tl+D1t3drZycnOBjt9str9er9evXq6amRnv27NHy5csn9LMyM1OVnGz9\nbFt29kzLj5Fo6Glk0c/J++0virT/k2O641++p6QpY++eR3v683tu1Q25bbrjp9crbfpUVf/HMvX2\nD+g//+eQShbnBvdbXTRDfztySgf++nXI44wGQThD4c1u/cXXrTt+cp1avjypwLdDWpo/NjYZhqF3\n/tymxYvccl+dqpERQ+8dPKKVS/JCzmhF2t/b/RocHNYtC2ZbfixIyUlTVLw4T9lZM2yrwWVE+ZNX\nOzs79fDDD6u+vl6StHv3bn3wwQd68sknJUk7duyQ1+tVVVWV6Z/d09MX0VpDyc6eGZXjJBJ6Gln0\nM/LoaWTRz8ijp5EVrX5e7s207ctsjJ7KHNXd3S23221jRQAAAPayPaAVFBSora1NHR0dGhgY0K5d\nu1RSUmJ3WQAAALaJ6jVolZWV8ng88vv9KioqUkVFhdatW6eqqio9+OCDGh4e1tq1a3XjjTdGsywA\nAABHiWpAe/bZZ0NuLy4uVnFxcTRLAQAAcCzbT3ECAABgPAIaAACAwxDQAAAAHIaABgAA4DAENAAA\nAIchoAEAADgMAQ0AAMBhCGgAAAAOQ0ADAABwGAIaAACAwxDQAAAAHMZlGIZhdxEAAAAYwwwaAACA\nwxDQAAAAHIaABgAA4DAENAAAAIchoAEAADgMAQ0AAMBhCGgAAAAOQ0ADAABwGAIaAACAwyQ98cQT\nT9hdRCwLBAJ6/PHHtXfvXp05c0YLFy60u6SY19HRoaefflpvvfWWbr/9drvLiXmNjY3atm2bdu7c\nqbS0NOXl5dldUkxrbW1VXV2d3nzzTfn9fhUUFNhdUlwIBAK65557NGfOHF1//fV2lxPTDh48qEcf\nfVSffPKJUlNTNW/ePLtLinkjIyOqq6tTY2OjvvnmGy1atMjyYzKDFsKGDRu0bNkyrV69etz2ffv2\nqbS0VCtXrtTWrVslSQ0NDSotLVVtba2am5vtKDcmmOlpbm6uNm3aZEeZMcNMP2+77TbV1tZq48aN\neuedd+wo1/HM9HPBggWqrq5WXV2dPvroIzvKjQlmeipJL730Em/ILsNMP10ul1JTUzUwMKCcnBw7\nyo0JZnra1NSkrq4uJScnR6+nBi7h8XiMlpYWo6ysLLhtaGjIWLFihdHe3m6cO3fOuPPOO43PP//c\nePHFFw2fz2cYhmFUVlbaVbLjmenpqIqKCjtKjQmT6efmzZuNlpYWO8p1PLP9bGxsNB544AFj586d\ndpXseGZ6un//fqO+vt7Yvn270dzcbGPVzmWmn8PDw4ZhGEZPTw/j0mWY6emWLVuM1157zTCM6I1N\nzKCFUFhYqIyMjHHbvF6v5s+fr9zcXKWkpKisrExNTU1yu93q6uqSdH4KFKGZ6SmuzEw/DcPQM888\no6KiIuXn59tUsbOZ/f1csWKFXn75Zb399tt2lBsTzPTU4/Ho448/Vn19vV5//XX+loZgpp9Tppwf\n2tPT0zU4OGhHuTHB7Fifnp4uScH+Wi05KkeJA93d3eOmNd1ut7xer9avX6+amhrt2bNHy5cvt7HC\n2BOup36/X88995x8Pp+2bNmihx56yMYqY0e4fr766qs6cOCA+vr6dOTIEd177702Vhk7wvXz4MGD\nev/99zUwMKDi4mIbK4w94XpaVVUlSXrjjTeUmZkZtQEw1oXrZ0NDg/bv36/Tp0/rvvvus7HC2BOu\np+Xl5aqpqdGhQ4dUWFgYlVoIaP+k1NRUbd682e4y4kpmZqaqq6vtLiNulJeXq7y83O4y4sbSpUu1\ndOlSu8uIS2vWrLG7hLiwatUqrVq1yu4y4sr06dOjfm00b1Mm6LunMqXzKdvtdttYUeyjp5FFPyOL\nfkYePY0s+hl5TuopAW2CCgoK1NbWpo6ODg0MDGjXrl0qKSmxu6yYRk8ji35GFv2MPHoaWfQz8pzU\nU5dhGIYtR3awyspKeTwe+f1+ZWVlqaKiQuvWrdPevXu1adMmDQ8Pa+3atXrkkUfsLjVm0NPIop+R\nRT8jj55GFv2MPKf3lIAGAADgMJziBAAAcBgCGgAAgMMQ0AAAAByGgAYAAOAwBDQAAACHIaABAAA4\nDAENAADAYQhoAAAADsOHpQNAGE899ZQCgYCOHz+ur776Snl5eXrhhReUnMyfTgDWYgYNAMLw+Xzq\n7e3V888/r3fffVft7e364osv7C4LQALgbSAAhHH48GHt2LFDKSkpkqShoSHNmjXL5qoAJAJm0AAg\nhI6ODqWlpWnu3LmSpN7eXp09e1Y5OTk2VwYgERDQACCETz/9VLfcckvwsc/n080332xjRQASCQEN\nAELw+XzKz88PPm5paRn3GACs5DIMw7C7CAAAAIxhBg0AAMBhCGgAAAAOQ0ADAABwGAIaAACAwxDQ\nAAAAHIaABgAA4DAENAAAAIf5B+jnrGV7Rk5/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sorted(distribution.keys())\n",
    "y = [distribution[i] for i in x]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.title('Mutation distribution by gene')\n",
    "plt.xlabel('$n$')\n",
    "plt.ylabel('genes with $n$ mutations')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the data resembles a power law but does not quite fit. It looks like it has a bump in the middle, this may be because the genes have wildly varying lengths. In order to correct this we have to normalize the mutations per gene by the length of the gene. This is done as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n",
      "9000\n",
      "10000\n",
      "11000\n",
      "12000\n",
      "13000\n",
      "14000\n",
      "15000\n",
      "16000\n",
      "17000\n",
      "18000\n",
      "19000\n",
      "20000\n",
      "21000\n",
      "22000\n",
      "23000\n",
      "24000\n",
      "25000\n",
      "26000\n",
      "27000\n",
      "28000\n",
      "29000\n",
      "30000\n",
      "31000\n",
      "32000\n",
      "33000\n",
      "34000\n",
      "35000\n",
      "36000\n",
      "37000\n",
      "38000\n",
      "39000\n",
      "40000\n",
      "41000\n",
      "42000\n",
      "43000\n",
      "44000\n",
      "45000\n",
      "46000\n",
      "47000\n",
      "48000\n",
      "49000\n",
      "50000\n",
      "51000\n",
      "52000\n",
      "53000\n",
      "54000\n",
      "55000\n",
      "56000\n"
     ]
    }
   ],
   "source": [
    "# In order to find out the length of the \n",
    "# genes, we will use the Ensembl REST API.\n",
    "import ensembl_rest\n",
    "from itertools import islice\n",
    "\n",
    "def chunks_of(iterable, size=10):\n",
    "    \"\"\"A generator that yields chunks of fixed size from the iterable.\"\"\"\n",
    "    iterator = iter(iterable)\n",
    "    while True:\n",
    "        next_ = list(islice(iterator, size))\n",
    "        if next_:\n",
    "            yield next_\n",
    "        else:\n",
    "            break\n",
    "# ---\n",
    "        \n",
    "# Instantiate a client for communication with\n",
    "# the Ensembl REST API.\n",
    "client = ensembl_rest.EnsemblClient()\n",
    "\n",
    "\n",
    "normalized_counts = dict()\n",
    "lengths_distribution = Counter()\n",
    "for i, gene_batch in enumerate(chunks_of(mutations_per_gene, size=1000)):\n",
    "    # Get information of the genes\n",
    "    gene_data = client.symbol_post('human',\n",
    "                                   params={'symbols': gene_batch})\n",
    "    gene_lengths = {gene: data['end'] - data['start'] + 1\n",
    "                        for gene, data in gene_data.items()}\n",
    "    lengths_distribution.update(gene_lengths.values())\n",
    "    \n",
    "    # Get the normalization\n",
    "    normalized_counts.update({\n",
    "        gene: mutations_per_gene[gene] / gene_lengths[gene]\n",
    "            for gene in gene_data\n",
    "    })\n",
    "    \n",
    "    print((i+1)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('IGHD7-27', 639.0),\n",
       " ('MIR663A', 456.5483870967742),\n",
       " ('IGKJ4', 192.54054054054055),\n",
       " ('IGKJ1', 190.89473684210526),\n",
       " ('IGKJ3', 189.60526315789474),\n",
       " ('IGKJ2', 185.25641025641025),\n",
       " ('IGHJ4', 179.93478260869566),\n",
       " ('IGKJ5', 178.31578947368422),\n",
       " ('IGHJ3P', 174.84),\n",
       " ('IGHJ5', 174.0)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Counter()\n",
    "c.update(normalized_counts)\n",
    "c.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2.0, 18),\n",
       " (2.5, 12),\n",
       " (3.0, 11),\n",
       " (0.5, 11),\n",
       " (0.25, 9),\n",
       " (0.3333333333333333, 9),\n",
       " (0.6666666666666666, 9),\n",
       " (1.5, 8),\n",
       " (1.4299065420560748, 7),\n",
       " (1.0, 7)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_distribution = Counter(normalized_counts.values())\n",
    "normalized_distribution.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAEDlmB4GffnllyVJL730UrHp8+bN44HuAAAALmI6rPEgdwAAgOpnehgUAAAA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sorted(normalized_distribution.keys())\n",
    "y = [normalized_distribution[i] for i in x]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xscale('log')\n",
    "plt.title('Mutations per base distribution by gene (normalized)')\n",
    "plt.xlabel('$x$')\n",
    "plt.ylabel('genes with $x$ mutations per base pair')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2473537"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(lengths_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(lengths_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(107, 530), (104, 185), (106, 112), (62, 94), (105, 87)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths_distribution.most_common(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DBsycORMzZszASy+9BAC49tpr8eijj2Lx4sX48MMPM91U0inWz4iISO8yHtBuvvlmLF++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\nYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sorted(lengths_distribution.keys())\n",
    "y = [lengths_distribution[i] for i in x]\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.title('Gene lengths distribution')\n",
    "plt.xlabel('$L$')\n",
    "plt.ylabel('genes with length $L$')\n",
    "\n",
    "plt.savefig('gene-lengths.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({2963: 2,\n",
       "         1308: 4,\n",
       "         1020: 8,\n",
       "         34493: 1,\n",
       "         86: 44,\n",
       "         36466: 1,\n",
       "         1596: 2,\n",
       "         660: 5,\n",
       "         282: 20,\n",
       "         943: 7,\n",
       "         11207: 1,\n",
       "         8176: 1,\n",
       "         146: 6,\n",
       "         57459: 1,\n",
       "         104: 185,\n",
       "         20910: 1,\n",
       "         174474: 1,\n",
       "         978: 3,\n",
       "         231363: 1,\n",
       "         59196: 1,\n",
       "         979: 7,\n",
       "         164: 37,\n",
       "         27606: 1,\n",
       "         16895: 1,\n",
       "         9326: 1,\n",
       "         6718: 1,\n",
       "         258991: 1,\n",
       "         61: 27,\n",
       "         16005: 1,\n",
       "         118384: 1,\n",
       "         6299: 1,\n",
       "         381: 10,\n",
       "         69711: 1,\n",
       "         5101: 1,\n",
       "         580: 14,\n",
       "         416: 5,\n",
       "         555: 6,\n",
       "         399: 3,\n",
       "         8285: 1,\n",
       "         3325: 2,\n",
       "         848: 7,\n",
       "         52319: 1,\n",
       "         93357: 1,\n",
       "         524: 6,\n",
       "         9265: 1,\n",
       "         35919: 1,\n",
       "         7405: 5,\n",
       "         17740: 1,\n",
       "         156: 12,\n",
       "         87575: 1,\n",
       "         5484: 1,\n",
       "         95: 45,\n",
       "         52447: 2,\n",
       "         4087: 1,\n",
       "         1122: 5,\n",
       "         42074: 3,\n",
       "         2541: 4,\n",
       "         994: 8,\n",
       "         47151: 1,\n",
       "         23486: 1,\n",
       "         534: 7,\n",
       "         57949: 1,\n",
       "         391: 11,\n",
       "         293: 32,\n",
       "         97308: 1,\n",
       "         1098: 2,\n",
       "         15167: 1,\n",
       "         66146: 1,\n",
       "         33950: 2,\n",
       "         21226: 1,\n",
       "         7920: 3,\n",
       "         275: 12,\n",
       "         298: 70,\n",
       "         175278: 1,\n",
       "         1314: 1,\n",
       "         1656: 4,\n",
       "         9089: 3,\n",
       "         108: 74,\n",
       "         6155: 1,\n",
       "         2184: 4,\n",
       "         37049: 1,\n",
       "         19818: 2,\n",
       "         152: 7,\n",
       "         5280: 2,\n",
       "         64629: 1,\n",
       "         13517: 2,\n",
       "         228716: 1,\n",
       "         21198: 1,\n",
       "         5080: 2,\n",
       "         54965: 1,\n",
       "         129: 21,\n",
       "         124882: 1,\n",
       "         78: 43,\n",
       "         150: 5,\n",
       "         107: 530,\n",
       "         17643: 1,\n",
       "         6329: 2,\n",
       "         17485: 2,\n",
       "         15124: 2,\n",
       "         3342: 2,\n",
       "         20741: 1,\n",
       "         96017: 1,\n",
       "         3111: 1,\n",
       "         49393: 1,\n",
       "         12645: 2,\n",
       "         337: 8,\n",
       "         15538: 2,\n",
       "         1468: 3,\n",
       "         158471: 1,\n",
       "         7109: 1,\n",
       "         29480: 1,\n",
       "         33197: 1,\n",
       "         19307: 1,\n",
       "         111638: 1,\n",
       "         90: 30,\n",
       "         4631: 1,\n",
       "         118: 40,\n",
       "         13237: 2,\n",
       "         7018: 1,\n",
       "         42670: 2,\n",
       "         80106: 1,\n",
       "         39072: 1,\n",
       "         11315: 2,\n",
       "         582: 6,\n",
       "         7010: 2,\n",
       "         14980: 1,\n",
       "         3481: 1,\n",
       "         44603: 1,\n",
       "         9266: 1,\n",
       "         20470: 2,\n",
       "         67064: 1,\n",
       "         366928: 1,\n",
       "         58000: 2,\n",
       "         783: 3,\n",
       "         208264: 1,\n",
       "         93557: 1,\n",
       "         34129: 1,\n",
       "         2569: 2,\n",
       "         383: 3,\n",
       "         20246: 1,\n",
       "         7166: 1,\n",
       "         20751: 1,\n",
       "         4182: 1,\n",
       "         88384: 1,\n",
       "         103: 86,\n",
       "         250: 14,\n",
       "         38482: 1,\n",
       "         157: 4,\n",
       "         20711: 1,\n",
       "         28762: 1,\n",
       "         22540: 1,\n",
       "         73940: 1,\n",
       "         120: 28,\n",
       "         3993: 1,\n",
       "         66: 40,\n",
       "         7057: 2,\n",
       "         17894: 1,\n",
       "         48599: 2,\n",
       "         61391: 1,\n",
       "         910: 12,\n",
       "         84812: 1,\n",
       "         93103: 1,\n",
       "         1563: 2,\n",
       "         8105: 2,\n",
       "         41764: 1,\n",
       "         9487: 1,\n",
       "         21095: 1,\n",
       "         3873: 3,\n",
       "         280: 20,\n",
       "         4247: 2,\n",
       "         6500: 1,\n",
       "         1157: 4,\n",
       "         238: 8,\n",
       "         495: 8,\n",
       "         6641: 4,\n",
       "         18756: 1,\n",
       "         132: 20,\n",
       "         126: 38,\n",
       "         84805: 1,\n",
       "         83: 45,\n",
       "         27799: 1,\n",
       "         295175: 1,\n",
       "         56742: 1,\n",
       "         73190: 1,\n",
       "         26563: 1,\n",
       "         79: 38,\n",
       "         15510: 1,\n",
       "         33334: 1,\n",
       "         52781: 1,\n",
       "         357: 5,\n",
       "         67700: 1,\n",
       "         31387: 2,\n",
       "         4332: 1,\n",
       "         42226: 1,\n",
       "         318: 22,\n",
       "         161: 8,\n",
       "         299: 57,\n",
       "         3102: 1,\n",
       "         8960: 2,\n",
       "         28235: 1,\n",
       "         10563: 1,\n",
       "         24252: 3,\n",
       "         26080: 1,\n",
       "         2998: 1,\n",
       "         1302: 3,\n",
       "         2411: 3,\n",
       "         33264: 1,\n",
       "         13464: 2,\n",
       "         63625: 1,\n",
       "         160047: 1,\n",
       "         50645: 1,\n",
       "         26775: 1,\n",
       "         135: 30,\n",
       "         67374: 1,\n",
       "         148408: 1,\n",
       "         42331: 1,\n",
       "         23627: 1,\n",
       "         549: 3,\n",
       "         165: 15,\n",
       "         8946: 3,\n",
       "         50277: 1,\n",
       "         140568: 1,\n",
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       "         186: 6,\n",
       "         27088: 1,\n",
       "         934: 10,\n",
       "         ...})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths_distribution"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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