HuProteomePeptideSpace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install biopython\n",
    "import sys, os, copy, pprint, glob\n",
    "#import pathlib\n",
    "from importlib import reload\n",
    "import pathlib, importlib\n",
    "import csv, fnmatch, random\n",
    "import Bio.Entrez #used by accessions_to_gb\n",
    "from ftplib import FTP\n",
    "from io import StringIO\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import pyfastx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils.BitOperations as B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import UniProt:  \n",
    "Download One protein per gene\n",
    "https://www.uniprot.org/proteomes/UP000005640"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Fasta> data/UP000005640_9606.fasta contains 20605 sequences"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huprot_path=\"data/UP000005640_9606.fasta\"\n",
    "fa_file = pyfastx.Fasta(huprot_path)\n",
    "fa_file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N-mer Generation/Characterization:                         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload(B)\n",
    "B.DEBUG=True\n",
    "class BitMap:\n",
    "    \"\"\"Stores chosen probes and provides universal indexing for peptides mapping to protein bitfields\"\"\"\n",
    "    def __init__(self):\n",
    "        self.d={}\n",
    "        self.n=0\n",
    "        self.LOCK=False\n",
    "    def process_peptide(self, peptide):\n",
    "        for p in peptide.Lst:\n",
    "            if not(p in self.d):self.d[p]=0\n",
    "            self.d[p]+=1\n",
    "    def all_peptide_counts(self):\n",
    "        return list(self.d.items())\n",
    "    def order(self, count_limit=None):\n",
    "        if self.LOCK:\n",
    "            print('BitMap Has been Locked')\n",
    "            return None\n",
    "        lst=list(self.d.items())\n",
    "        lst.sort(key=lambda x:-x[1])\n",
    "        self.Lst=[]\n",
    "        for i in range(len(lst)):\n",
    "            p,n=lst[i]\n",
    "            if count_limit and n<count_limit:break\n",
    "            self.Lst.append(p)\n",
    "        self.n=len(self.Lst)\n",
    "        self.LOCK=True\n",
    "    def write_token_to_bitfield(self, p, bitfield):\n",
    "        #bitfield[self.Lst.index(p)]\n",
    "        if not(p in self.Lst):return -1\n",
    "        try:\n",
    "            x= self.Lst.index(p)\n",
    "            bitfield[self.Lst.index(p)]=1\n",
    "        except:\n",
    "            print('BFOverLimit:self.Lst.index(%s) bitfield sz: %i, idx: %i'%(p, bitfield.max, self.Lst.index(p)))\n",
    "            return self.n\n",
    "    def display(self, limit=10):\n",
    "        print('BitMap: %i '%len(self.Lst))\n",
    "        for i in range(min(limit, self.n)):\n",
    "            p=self.Lst[i]\n",
    "            print('%s\\t%i'%(p, self.d[p]))\n",
    "            \n",
    "class Peptide:\n",
    "    def __init__(self, seq, label, nmer_sz=6):\n",
    "        self.nmer_sz=nmer_sz\n",
    "        self.seq=seq\n",
    "        self.label=label\n",
    "        self.acc=label.split('|')[1]\n",
    "        self.Lst=[]\n",
    "        self.bf=None\n",
    "        self.nmer_tile()\n",
    "    def nmer_tile(self, nmer_sz=None):\n",
    "        nmer_sz= self.nmer_sz if (nmer_sz==None) else nmer_sz\n",
    "        for i in range(0,len(self.seq)-nmer_sz,1):\n",
    "            self.Lst.append(self.seq[i:i+nmer_sz])\n",
    "        self.Lst.sort()\n",
    "    def make_bitfield(self, bit_map):\n",
    "        self.bf=B.BitArray(bit_map.n+1)\n",
    "        for p in self.Lst:\n",
    "            bit_map.write_token_to_bitfield(p, self.bf)\n",
    "    def display(self):\n",
    "        print('acc: %s seq_len: %i, bf_occupancy: %i'%(self.acc, len(self.seq), self.bf.count()))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BitMap: 101 \n",
      "LLL\t637\n",
      "SSS\t521\n",
      "PPP\t484\n",
      "AAA\t454\n",
      "EEE\t443\n",
      "SSL\t387\n",
      "LPP\t335\n",
      "SLL\t334\n",
      "LEE\t330\n",
      "LLA\t329\n"
     ]
    }
   ],
   "source": [
    "NMER_SZ=3\n",
    "\n",
    "bitmap=BitMap()\n",
    "pLst=[]\n",
    "max_r=1000;i=0\n",
    "for seq in fa_file:\n",
    "    if len(seq.seq)<100:continue\n",
    "    i+=1\n",
    "    name=seq.name;\n",
    "    prefix, acc=name.split('|')[0:2]\n",
    "    if prefix=='tr':continue\n",
    "    pLst.append(Peptide(seq.seq, name, nmer_sz=NMER_SZ))\n",
    "    bitmap.process_peptide(pLst[-1])\n",
    "    if i >max_r:break\n",
    "all_peptide_counts=[p[1] for p in list(bitmap.d.items())]\n",
    "\n",
    "bitmap.order(count_limit=200)\n",
    "bitmap.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc: A6NJT0 seq_len: 531, bf_occupancy: 43\n",
      "725\n"
     ]
    }
   ],
   "source": [
    "#select proteins by size; Compute n-mer embedding\n",
    "protLst=[]\n",
    "for p in pLst:\n",
    "    if 100<len(p.seq)<800:\n",
    "        p.make_bitfield(bitmap)\n",
    "        protLst.append(p)\n",
    "        if random.random()<0.001:p.display()\n",
    "print(len(protLst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 3))\n",
    "\n",
    "axs=plt.subplot(131)\n",
    "plt.title(\"Histogram of Peptide(Probe) Prevalence\")\n",
    "axs.hist(all_peptide_counts, bins=100, range=(0,1000))\n",
    "\n",
    "axs=plt.subplot(132)\n",
    "plt.title(\"Histogram of Protein Sequence Length\")\n",
    "axs.hist([len(p.seq) for p in pLst], bins=100, range=(0,1500))\n",
    "\n",
    "axs=plt.subplot(133)\n",
    "plt.title(\"Histogram of peptide hits for each Protein\")\n",
    "axs.hist([p.bf.count() for p in protLst], bins=20, range=(0,500))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sp|A8K5M9|CO062_HUMAN 2 102 128 17 17\n",
      "725\n"
     ]
    }
   ],
   "source": [
    "iLst=[];plotLst=[]\n",
    "protLst.sort(key=lambda x:-x.bf.count())\n",
    "for p in protLst:\n",
    "    row=[int(x) for x in list(p.bf.bin_str())]\n",
    "    if random.random()<0.001:print(p.label, p.bf.array_sz, p.bf.max, len(row), p.bf.count(), len(p.bf.export_bit_idxLst()))\n",
    "    iLst.append([p.label] + row)\n",
    "    plotLst.append(row)\n",
    "bf_img=np.array(iLst)\n",
    "print(len(iLst))\n",
    "img = Image.fromarray(bf_img, mode='1')\n",
    "img.save('bf_img.jpg', format='JPEG', subsampling=0, quality=100)\n",
    "LL=[N for N in bitmap.Lst]\n",
    "extra_spacers=len(row)-len(LL)\n",
    "LL=['seq_name']+LL+list(range(extra_spacers))\n",
    "df = pd.DataFrame(iLst, columns=LL)\n",
    "df.to_csv('hitmap.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(30.5,0.5,'Protein_subset'), Text(0.5,12.5,'Peptide n-mers')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7WnwxacdlrS9rbZMqHxqzB6m9W0qFJQY5fRV8sNSlHZ8UfiaNCtjhCkAVYaByUTEMlGaWzjKTpl1mC/Vhra61srTlacclRkjTXKulPqyVIUWMQJPSVCLl6sx8bLSrDKnG4CiwhoE+uXiIuGzVNr2Z+pIKh1cANMJO6LwYIU3r2FRiBmitrvm4hMpZg9V8RCP4xbbJorhoEKtEWPoWOxkRM05HgFUB/HUhibhstXb9mPqSCpubgOSGZhYpxolH2r9QXyQCTQrBKYRYs5IlYcN3jbSKgUX48Y3XWnmxtnPztioeF/sM0Shz1iAGS8+ms68KqNB8AOqDJppGClu01JEWSs24xfouzMcnhUnIvG8x4xcSduZtWmqbVRFJKTxpZuukK1ba31ZDADv0AdhcAdy9e1cw509iYiLWrl2Lffv2oXnz5sRtl2RYTwZHC4nQ4CtrKTLEWjkx4+FDSjMTqZAhmVHTCiwS5HDMkvYhha+AdFVB4n8yh3Slqwl+kdhhMjibK4CGDRtiz549xr8NOX8M/Pbbb7hw4QLR583M8Qy0vOuXVJCzOMuETD9iXkZSQS4m+oPWPCBm9ki6yqA5L6RU+NqgUTa0glxKkxVNPVLTIOnzrJl9KNFWAGyY5/wpLS1FfHw8Vq9ejcjISOr2LNkyLZ2nFc589WnKkpqZWEwpcthvSYW4JYVrfo523DRmD9rZrLUZtdC4rLUhBMuMn7QtofFpgl8iNB8AG+Y5f9asWYMBAwagUaNGotpjnbmxCFSxqwySmShrWyyORBpY27JWX+geCLVJ2p6lcbD4iEj7IG2r4iqXtg/NwSsx2gdh2KiY8+f8+fO4dOkSZs2aJbo9UvMK6d8kKwUxL7ClsdIoGb62aAQWrW+CZHwsph1L5yuORay9m0b4iZ0x09wrsavNin/TPmsk49IQgR2uAFSzDyArKwu9e/fGTz/9hDp16mDjxo3YunUrPDw8AAD379+Hr68vVqxYgS5duhC1yfdNYAMswoEP2lkWi+CXw+QjZoZM2qZY04+1scm5GmKFxP9EetwAiY+F9DmQwmntSLDuAyg5+gVxWc9ubzH1JRWqWQGY5/yZMGECJkyYYDwfERGBDRs2SBIFRDu7FfMS8pUlnYXROGppVx3WoG1DSTMCiZmGtA0pIl3kMP2QPickzzXfeXOsPe/OrhSosMMVgKoUgNicP7TQ2vpZnH58Lx2tuclSWyymB1rERh5VPCbWSUljciN13NIocb77ynIv+P7mK0+yQhBrciNZJWuKgAA7jAJSjQlIDkg/CUkr3GheGKnMH2IQ4/STw3FL64sRGguNKUhMW7RtC0FjrqG9/2Jm66QTBWdbCTCbgA5vJC7r2X2CcCEFUM0KQG6sPcy0DzeJEKF16tEgp+9C7Hlrs2K++yV2RSBmpsqyiiPtg6S8kPAlNUsKHScdj7W+nEHoS4oWBaReWGymYpfWYtpiiU7hK8cy+2V1alury2euMa9nrQ8p7idfm0JmOVrzjaU6Ugpf2sgo1omQhhmaCcgyCQkJOHToEO7du2eSzoHvuNA5Usoe3pDkoaZ1GkvRh7Uy5v2SrghIxk17bWJWHWJXKmKigMzL0dwDsTN9a33YwqZuSzOkPcFsAjrwEXFZz1enMvUlFYqsALp3747Ro0djxIgRRMeFzpHCOrPna8f8RbI2e+RrQ0wED6kZgGXWa805StO2lHVp/ArmCM1yLbXN6guy9nzwjV8ORcuHEsraKdGigCwTGhpKdVzoHA0kLzipfdtam6R1hGBxMPP1LTQ2sWWsjUWMo5ZmLHIpd5KyQsqRpT8W8wxpXZLfiqZfjf/HDk1ADu8DEPOCG6AV2hXbEDPDpyknF7TOVJYIKdI+WcxMpOO2do50pk/jF+Hri9RHIAbN9i8zmhNYfUghPIReUhK7vRz2V1oBJMbmT6sQaFZFJHWEkErRkip7krb4xkYzDtJZfMVzYs2MmplHIjQTkPogmeGRvmxizBykgp9vjCSw2KxphQarwiAtY60Pkv6F6lobg9DzQOrnEWO2IW1DSmGtCX6J0ExA9gHtA0/6wtP0yRKlYg5pHSls/6TnrYU+il250Kxk+NqgOa9EtAzpveDD2iSExVynIQI7XAEoEga6dOlSpKSk4OHDh6hTpw68vb2xf/9+3uPW6tBA+lF4UlgUAGtoIUkbctrSxTgvpRYw1pSKWGXC156YtknaIm2TtTzNuKT0M9gzzGGg38UTl/X8eyxTX1Lh8KkgpLB/myOleUMKxPoZSASq+XlahUBSlgUlBClpWySrOprfROw4tZm+OJgVwLeLict6Dotj6ksqHF4BGGCZocppClByVi5luB+N4GI1b5A4QMVibYUopyCVY9ZNq/g1p7ApzArgq4XEZT1HLGHqSyocXgGIMQEp8QLIYZKQMxpIaNxKm8OkalsKaE1E1srStk1SlnY1J6XZ1J5gVgBfkmcz9hy5jLjszZs3ERMTg0ePHsHb2xsJCQlo3LixSZmcnBzMmzcPmZmZKCsrQ+fOnfHee+/B3d26m1cxBSCU2iExMRFr1641Off9999jy5YtcHV1hZubG+bPn0+1QYwvFQRtRI4BGoHLumqQQ3GxCEda4cLSrxzj5CtnQCkBJ/VqSMoxGXAGYW8JZgWwdR5xWc/RK4jLjh49GkOGDMHAgQOxZ88e7Ny5E1u3bjUps2zZMri7u2Pu3LkoKyvD8OHD8fbbb6Nv375W21YsCshaaofffvsNFy5cQGBgoPFYXl4eli9fjpSUFNStWxeHDx9GbGwskpOTifv0DOxq8cUhFcpCs12SmbSQUrE2dr62haJ8+MrRzNqFrpVGIIldLYgRTKRtm4/JkllJKqErhcmKZgVgXof0udFghGIunZ+fj/z8/ErHvby84OXlZfw7JycHv//+OzZv3gwA6NevH5YsWYLc3Fz4+PgYy7m4uKCoqAh6vR6lpaUoKyuDv7+/4DgUUwB8M/fS0lLEx8dj9erViIyMNB43LEyKiopQt25dFBQUoH79+tT9kghSVpspzThoFQJNeaFxixHapOdJViy2cIAKnbfWh1SCUWkfEa2JR04fi1NBEQa6ZcsWJCYmVjoeFRWF6Oho49+ZmZnw9/eHm5sbAMDNzQ1+fn7IzMw0UQCTJ09GdHQ0unTpgpKSEowYMQIdOnQQHIfN9wGsWbMGAwYMQKNGjUyO+/j4YNGiRRg0aBBq164NvV6Pbdu2UbVdkmH5k5BiZ8piooJYXyAxs3ShccphThDjsxD6W4pxiIn0kmqFIuXEQajvimVZZ/ya0BcJhQKIjIzE4MGDKx2vOPun4eDBgwgKCsKWLVtQVFSE8ePH4+DBg+jTp4/VejZVAOfPn8elS5cwa9asSucKCwvx9ddfY+fOnWjSpAmSk5MRFRWFvXv3wsXFhah9PhNQxfOWYHmBbPHy0K5oxAhrvvNi/Sk0WFPEtL8hzb0QaksOgS9FW1KsADXo4XQ64rLmph4+AgICkJWVBZ1OBzc3N+h0OmRnZyMgIMCk3Jdffonly5fD1dUVtWrVQkREBE6fPq1uBXD27FncuHED3bt3BwDcv38fY8eOxYoVK1BYWIhatWqhSZMmAIC+ffti3rx5yMvLM1n6WINPCNHO5FkiMUgFDstSnwXW8VlTklILIBo7Pe39twatX8EaUilYOa9DQyQy7AT29fVFcHAwkpKSMHDgQCQlJSE4OLiSDGzYsCGOHj2KNm3aoLS0FCdPnkTPnj0F27epApgwYQImTPjftzEjIiKwYcMGNG/eHJcvX8aVK1eQk5MDX19fnDp1CjVr1kSdOnWo+qCZ+ZNibekv9kUlmU1K5eSrWF4qoUDibBfqi8bJzRrxpERUjZhnj2X2LmQSJF21aYpCJDLlAlq0aBFiYmKwfv16eHl5ISEhAQAwfvx4TJ06Fa1bt8b8+fMRFxeH/v37Q6fTISwsDH//+98F21YsDJQktUNFBQAAmzdvxnfffYcqVarAw8MDMTExVGGgQjuBDcixdDdAKvRIy7NAYkohVTKkAlZMHdp7xtKmmN9eSdOKFCGxBjTBbh3WMNDidVHEZatPqewAtgVOsxGMRAGYwzpjJS1D0gdLGyTt22KmTIq1MZBGEklxHWIFqrXfUEplQuvwlsJk5UgwK4C1k4nLVo9ez9SXVDi8AjAgpeNQDFLZfpUaLylCApdkHFIKPdI+SfomFZhqC6vUZv7iYFYAH75DXLb6u58w9SUVNg8DVQpL9mPzc2KR4wUnme0K1WVxXrOWJ/Fd8P1NA+vKhaRv1jYs3QtSezxtX2LLWupbUxyU2GE6aMUUAF8qiJ9++glr1qwBx3HQ6/WIjo5Gr169ADz1CXh4eKBq1aoAgFmzZqFrV/WZUpQyzUglnC1Bew1CJheacah1BUBahmUFQOq4F4PY+6oJfpHo7c+YYtNUEBzHYc6cOfjqq6/QvHlzXL16FW+++SZ69OgBV1dXAMBHH31UKW8QK7Rx3Xz1aF4wKWaifOMQOk56XmxZa2MRU5dmTGJNajTjE0KoTZbVHMt4NBRG+yIYP3zRO66urigoKAAAFBQUwM/Pzyj8WWGd9QsJeiUii2giXliibcTGy7PM2mkVmBSmIimVtgExykTsasK8nJTCXjP9MKKtAOhwcXHBhx9+iMmTJ6N69eooKirCJ5+YOkdmzZoFjuPQoUMHzJgxg2qrtGdgV4svJ+nsUA67LC1ymAIMfytlFiNVLlJG7JCMS642xay0+NoiqS+VrV8T/Gxwmg+AjvLycnzyySdYv349OnTogF9//RXTp0/H/v37UaNGDXz11VcICAhAaWkpli1bZkwaR4Olh1poJi+FIGKdPUphEuJDzlmjmLBbtQl6VgUkhQ+Abyw0947U+a7kvgaHhiIVhFqwqQK4cuUKsrOzjVnrOnToAE9PT6SlpaFNmzbGfBceHh4YPnw4Jk2aJGn/JCGMrG0LzQ7FCE6xUTRyCF6SsYi1wzuKIKKJAmJ5Pqz1awnW50nDDM0EREf9+vVx//593LhxA02aNEFaWhoePnyIZ555BsXFxdDpdKhVqxY4jkNycjKCg4Op2hdaHpM6ZqUURLT2bZpNRHzlSK6DdqbJIpBofQAkPgtzlFAectwDKfwfYkNgHUXh2gw7NAHZPBXE3r17sWnTJmOGz6lTp6JHjx5IT09HdHQ0dDod9Ho9mjZtivfeew9+fn7EfUqdCkJMW2JNPNacv/b+orKYNaTq0/y4pf6l3htBA4tpzV6fC1vDuhGsKPYfxGVrxH/D1JdUOPxOYDnivM1R6oWjdVZLOWtUUrjIEa6qhJLhG5OcznaaKDGxCo2mjj3DrAAWCidfM1BjyXdMfUmFw+8EltJsI6cQJBHurLZ/awKJZGZMixIbkaRYtUlVV87QTAOWngHWfpVUWA6NHfoAtBUAAXIKETkiMOQ0sYidcdPUoVnRyLHfQk6lImdbfG0bUMPqTs2wrgAK575OXLZmwg9MfUmFIgogLy8Pc+bMwZ07d+Dh4YFnn30W8fHxePz4MWJjY/HgwQO4u7ujdevWiIuLQ7Vq1XDu3DmsWrXK+OHk8PBwzJ49m2qTWMVkcNaQc6bHaqaxJJhI/QpKrljkbJvFpGVAzlWcLdsUo2hpxyBlm2qGWQHMrvyJRz5q/nMXU19SoYgCePToEa5du4awsDAAT/MCPX78GJMnT0Z+fj5atmwJvV6PGTNmoFmzZpgyZQquX78ODw8PNG7cGKWlpYiMjMSwYcMwaNAg4n75FACpQ1CMQJVq1iWn00+K2S7JPbSl0CD9LfnOyzUWOVYX2gxeGpgVwKyBxGVrrt7D1JdUKOID8Pb2Ngp/AGjXrh22b9+Ohg0bGo+5urqiTZs2SEtLAwCT/D8eHh5o2bIlMjIyJBkPSyim2LZpX1ISeyzpeFlmv3La74VgUTK0IZA046BV3lKEiVrrU+y4hM6rRZnbDXboA1DcCazX67F9+3ZERESYHP/rr7+wc+dOzJgxo1KdnJwcHDp0CBs3bqTqq+I+ABI7sgFaQUvycoh14FYsRyoUhISiFDNGltUR6/20tTBSon8+c5+1Z5X0vgk9N9qKQhycHSoAKhPQgQMH8Oqrr1Y6fvDgQcGvzxtYvHgxsrKykJhOjEdcAAAgAElEQVSYaLTnl5eXIzo6GoGBgVi4cKFJ+cLCQowZMwZ9+vTBmDFjSIcKACh7eEORpb0lIW1ADWYQOXwVLLNzWzpRpfh95HCyKxmiqQl4y7CagAqi+hKXrZWYzNSXVFApgPbt2+PcuXOVjnfq1AlnzpwRrJ+QkIBr165hw4YN8PDwAADodDrMnDkT1apVw4oVK4wbwgCgpKQE48ePR8eOHTFt2jTSYRqpqACsvTC0DjcWgUpansW5J6UDkdThbK1tTdDwwxokwNKHHH3ZM8wKYHLlyTEftdYfYOpLKohMQOnp6QCe5u83/H/FcwZhbo0PPvgAly9fxsaNG43l9Xo9YmJi4ObmhmXLlpkI/ydPnmDixIlo27atKOHPhzUbKm1dKV8coeW4NcTanFls00LXTmMKUruAklNIy+EUlqJuxfosbTgVjmoCatGiBVxcXGCpaN26dREdHY1hw4bx1v/jjz/Qr18/NG7cGNWqVQMANGzYEEOHDsU777yD5s2bG81B7du3R1xcHL766issXbrUxBncp08fqoRwfCYg0iggA1K+fFKsNuwlDp12DAaUNNvZ+vqVWAHQjkHO0FI1w7oCyH+nN3FZr08OMfUlFVQmoJEjR+LLL7+UczySwuoDEBsuaq2uFLBG+Yh5wdUU909jzlODspQ7DFRDGpgVwPhexGW9NqUw9SUVovYBZGZmIisrC+3atZNjTJIhlRNYyhmqHEt6UqeklKsKJU0TcvhelBC0torpV9sqx15gVgBjexKX9frs30x9SQWVAsjMzMT06dNx9epVuLi44Pz58zh48CCOHTuGZcuWyTlOUdBuBLMlcrz4UkJiJjCH1KYvZuXCGvmipENfSpR4TpzV9s+qAB5HdicuW3vLYaa+pIJKAYwbNw6hoaGYMGECwsLCcPbsWRQUFGDAgAH46aefeOvxpYLw8fFBUFCQiQ9g1apVCAoKAgB88skn2LdvH8rLy9G2bVssWbKEyOFsgDUbqJThfSxtkPYhp7lLDrOHFH2qQZnLOWs3oOT1qeGe2gJmBTCKQgFsU4cCoNoIdunSJWzcuBGurq7GiJ1atWoZP+rOh4uLC8aNG2eSCmL16tVYvnw5AOCbb75BjRo1TOocP34cSUlJ+O677+Dp6YmFCxfiiy++wIQJE2iGbETMQy3lC6BkBJEt2lDieiz1oYQJR6gvKUw8pFFgcjpsnVXwS4U9bgSjUgC+vr64ffs2nnvuOeOxP//80/jpRj74UkFY4+rVqwgNDUX16tUBAN26dcPatWupFYAS4YjWXkK+tmnNI9b6FRs+STJLFxtSKmbFRduHlG2L7U8pSBVHxbLWylhCTddrlzi6AhgzZgwmTpyICRMmoLy8HElJSfjkk08wfvx44jYspYIYNWoUdDodunXrhujoaHh4eKBVq1bYsWMHcnNz4eXlhQMHDuDePbolWklG5VQQFVHygRfroLU2RqHxs56viJTOVVaFaw1bhlOSIJUfwdp5qa7VWX0BorG/L0LSRwH9+OOP+Pbbb5GRkYH69evjzTffRI8ePYjrm6eCyMzMREBAAAoLCzF79mw0b94c06dPBwBs2bIFu3fvRtWqVdG5c2ds374dp0+fJu7LkhNYbSGQUvRFupJRam+BVMjpuBUzS5YiusYWvhYNMlh9AHlDXyYuW2fHz0x9SYWiH4SxlAqiIqmpqdi8eTO2bdtW6VxycjK2bdsmaDqqSEUncEUc9UURG60itk5F5Ax1tFReCqUn13j56ltqQ00bwQw46vshBKsCyB0cTlzWZ9cRpr6kgsoElJSUhODgYDRt2hQ3b97EwoUL4erqiri4ODRt2tRqXUupIB4/foyqVauiWrVqKC8vx6FDhxAcHGys8+DBA9SrVw+PHz/Gpk2bMGXKFBGX+BQ5bNLW2uLrV8oYbVK/glA9S+fMoe2Dpl+he2DNzCGmLi2sqwgWMx4fjurrsGsc3QTUo0cPfPPNN6hbty4mTpyI5557DtWrV8fZs2exdetW3np8qSDGjRuH2NhYuLi4oLy8HCEhIZg/f74xIqh///7Q6/UoLy/HiBEjMHr0aKqLk+qLYDQhekpswrHlRh8lYvbF9KkGE5YSv7WYOnL4WhwR1hVATn/yFYDvPnWsAERlA33y5Am6dOmCEydOwN3dHZ07dybKBqo0Uu8EVvIFkcJ0RetwVgraVZIUfYkRoAakjr5iHZ/UqGEMaoBZAbxGoQD2q0MBUJmAfHx8cPv2bVy/fh2tW7eGh4cHSkpKLCaJUwNiTSJy2ERpXzIpl/Ys1yPnZjgl/Am0NneScUgZXSWHcrHFs6YBcDKZgG7evImYmBg8evQI3t7eSEhIQOPGjSuVS05OxscffwyO4+Di4oLNmzejbt26VtumUgCTJ0/G66+/Djc3N3zwwQcAgJMnT6JFixY0zdgUS6YJvtBLPgFFY0O3xcvFEuFiXkdOGzPfikRKvwjtWKy1SVKWFSGFQNK3JtBtA1cuT7txcXEYPnw4Bg4ciD179iA2NraSyf3SpUtITEzEli1bUK9ePRQUFBBlTaCOAiopKQEAeHp6Anj6uUa9Xo969erx1rGWCmLnzp344osvoNfr0ahRI6xcuRLe3t4Ann5MPj4+Hr/99hvc3d3x6quvIioqinisfFFABtQQeSGHvdiAvQgCFr8CqWNZzpBSGqTcTyF1n84Oqwkouzu5CcjvMJkJKCcnB71798bp06fh5uYGnU6HsLAwpKSkwMfHx1hu5syZeOGFF/DGG29QjZn6m8BlZWX4+eefkZ2dDT8/P4SHh6N27dpW6/Clghg7diw+/PBD7NmzBz4+Pli/fj3ef/99xMfHAwBiYmLQuXNnvP/++wCAhw8f0g7XiJjZupwhhHI45KRsU4nwRDlNFUL3m8YEJOU9YBX8YpS8JviVgcYElJ+fj/z8/ErHvby84OXlZfw7MzMT/v7+cHNzAwC4ubnBz88PmZmZJgogLS0NDRs2xIgRI1BcXIyePXti0qRJJh/ZsgSVAjh58iSio6Px3HPPITAwEJmZmYiPj8fatWvxwgsv8NbjSwVx/fp1BAcHGy8kPDwcI0eORHx8PG7duoXr16/j448/NtYTsmeZU5JxzOrDTyswWWaNpHVpbLukTl45beosqCmEUYxyUdKZzdcHC7aMJnNIOOvCtiJbtmxBYmJipeNRUVGIjo6m7lqn0+HatWvYvHkzSktLMW7cOAQGBmLQoEFW61EpgCVLliA+Ph59+/7v48cHDhzA4sWLcfDgQaI2KqaCaNGiBS5fvoz09HQ0bNgQSUlJKC4uxqNHj/Dnn3/C398fCxYswJUrV1C3bl3MmTMHzZo1Ix6v2BmxFMKPdMZp6zaF+uBrW0gZ0YxDjtBSKU1AfCi5ipOibaE2LP2WmjIgh2YFEBkZicGDB1c6XnH2DwABAQHIysqCTqczmoCys7Mr5V8LDAxEnz594OHhAQ8PD3Tv3h0XL16UVgFkZ2ejd2/Tz5717NkTCxcuJG5jyZIlqF69OkaOHAlXV1csWLAA06dPh4uLizGlhLu7O3Q6Hf773/9i5syZCA0NRUpKCiZNmoQff/yRZsgWH3aphYKYnZ4kJgjasUghFPjaIv3b0vWyrios/U60bdLcf1YhrKSTWI42NKEvDk5PvgIwN/Xw4evri+DgYCQlJWHgwIHGzbgVzT8A0K9fPxw5cgQDBw5EeXk5Tp06VUlWW4LKCbx06VI888wzJhuytm3bhtu3b+O9994TrC+UCuLixYuIiorC0aNHcenSJbz77rs4fPiw8Xzbtm3x008/Vbp4Pkg3ggkhRVSNObSzY5a6jvJC29oxagsHrpTQPsfOthJgdQLfeyFCuND/0+BkKnHZtLQ0xMTEID8/H15eXkhISECTJk0wfvx4TJ06Fa1bt4Zer0dCQgKOHj0KV1dXdOnSBXPnzjV+Z4UPQQUwfPhwoyNBr9fj4sWL8PX1hb+/P7KyspCTk4O2bdvi66+/ttrRBx98gHPnzmHjxo3GCCLgf+kenjx5gilTpqBLly546623wHEcBgwYgPfffx/NmjXD2bNnMXPmTBw5ckTQsWFASAHQOtwMiHkZ5Ng8RDseMRFRQn1YMwU5g9BQI2pVQGqHVQHcDSNXAA1PkysAORFUALt27SJqyJI9ywBfKoh169Zh3LhxyMjIQFlZGfr27Ytp06YZtdalS5ewePFilJaWwtPTEwsWLECbNm1Ir010KgglImDU0CaJ6YrvvC2hGbdQORqnu71Dez3OpsxZFUB6x+7EZRudPSxcSAEUzQaqNGUPbxj/X84HmGZmLWf/UkYtSV1ebmw5HtJoLJa2lXh+1fJb2gpWBXAnlFwBPPOLHSqA77//nvcc7QYEJRCbC0hsuKWlNmjqsI5T6vJSY+8b7WzZl72sPu0ZVgVwuz35d1GePUcXzCIXVApg1KhRJn8/fPgQ6enpCAkJsZjD39aQ+gAM2FJIq2WnqhwoEW+uRNgnX1+k5cXUUetv6oiwKoCbbXsSl33uv/9m6ksqmE1A33//PdLS0jB37lyr5SZPnoy7d+/C1dUV1atXx8KFCxEcHMx73FodUtSwApAzxJS0rpJKRBNc8iCHAjNAEgThDL8nqwK40boXcdkml1KY+pIKZgWg1+uJ0kEXFBSgVq1aAJ5+VnLdunXYtWsX73FrdUjhWwGIjXCREikdtba0eytp9pBSIJG07ShmJJa2nUmhsyqAtL8Jx90baHr5EFNfUkG1EUyvN93qVlJSgr179xqFtDUqliksLDSGcvIdFzpHQkmG5VQQpA8zywrA0lhoxkDStpxmJSGUEAhCm7esQesYZ3lOhNomQY22faG9LBqmyJUOWk6oFEDLli0rCWF/f38sWbKEqP6CBQtw4sQJcByHTz/9VPC40DkhKr4AUphDWAQSnzIRKs8CzXiVCH2VQzDxtSXldYjdL6I0UobAkpzXMEVPkQtILVCZgO7dM10ieXp6Eu/Krcju3buxf/9+bNq0iei40Dk+SH0ASuzwlCMkUI5YfWda8rMidP/l3G0sBUo45+0JVhPQtRavEpcNunqAqS+pYPIBpKenw9XVFQ0a0KdcaNOmDY4cOYI6deoQHRc6ZwlLPgAaO7IcLzhfH0oICZZVkJqEA0tUDW09e0KJiYwjw6oArjTrK1zo/wn+I5mpL6mgMgHNmDEDI0eORPv27bFz504sXrzYmNBt6NChvPWKioqQn59vzGCXmpqK2rVro0qVKsjMzKx03Nvbm7eO4WMxJFgSFGLMNubtiWmLxvZMOi5aoUZjAuIbpxri0aWwsSvpYJarjjmkv5Em+OWBJhmcWqBaAbzwwgs4cuQIPDw80L9/fyxatAheXl6YMmUKUlL4w5oePnyIyZMno6SkBK6urqhduzbmzp0Lf39/i8dbtWrFW6dVq1bEFydVMjghLNle1RxZJAck90BOfwLpikXouNzjVhOOel1iYV0BXG7Sj7js324kMfUlFVQKIDQ0FL/88guysrLwxhtv4Nixpw9Q+/btce7cOdkGKZaKn4QU4wAVg5pMJ2o3e4gJhRUqL6dZTg0CU+nn2ZlgVQCXnutPXLb1zX1MfUkFlQkoODgYn3zyCe7du4eXX34ZAJCVlYWaNWvKMTZmKoaBWnoplBDWSoYSsvbNMg45Np2JuRdihTbJPgA1CFSSMahRcTkD9phVjWoFcOfOHaxZswbu7u6YM2cOfH19cfDgQVy6dAmzZ8+Wc5yiEJsOWiju3xxbv1hKzPRJZ8XOtntUbpTwJ2gK4imsK4ALzw4gLtvu9l6mvqRC8mygixYtwqJFiyodt5TWoVatWpgyZYqxTEFBAQoLC427im/evImYmBg8evQI3t7eSEhIQOPGjYnHIvX3ANTiCJUqCojG7i3FaslR/R9isLfxOgOsCuBco4HEZdun72HqSyqoTEAk7N2716ICSEhIMEnrMH/+fOzatQt79vzvRixbtgw6nc74d1xcHIYPH46BAwdiz549iI2NxdatW6UesoaGhgYz9rgRTHIFwLegEErrUFpain379uGzzz4DAOTk5OD333/H5s2bATz95uWSJUuQm5tLvPmMLxWEAdowT1unVpB61khzb+QIX7UXXwvreOUwi8kRuGBAW5WIg9MUAKzm67GW1iE1NRX+/v7GMM/MzEz4+/vDzc0NAODm5gY/Pz9kZmYSKwDaB5nUFETTvpy7dWnHI0eUk1A5S2XVIGBYHMukWLtXrJFPtlSeGpbRVgACLFu2DMDTtA6rVq0ySeuwc+dODBkyRNL+SGdeUm6coV1F0CBkj1cCOTdrqcUuroZxSBEhpe0MVhY7DAJSzgRUkUGDBiE2NhZ5eXmoU6cOsrKycPbsWaxatcpYJiAgAFlZWdDpdHBzc4NOp0N2drZxZzAp1hyeUr0gNEt8KRQC6fjVtCeBBLWMk/R+im1HSqRYaan1ebA3dHpXWw+BGskVwIABlUOhhNI67Nq1C+Hh4SY5fnx9fREcHIykpCQMHDgQSUlJCA4OFpV8DhC3DCctx2JHphE2tKsLW5heWIS3EoJfjhWYLbDUN63Cl3JiowHYYTZo+jDQ48eP48qVKyguLjY5Pm3aNN46QmkdevfujQULFqBbt24m9dLS0hATE4P8/Hx4eXkhISEBTZo0IR6r2C+CKYHYXbCW6thipk+zW1ZqP4jaBZMaFERF1DYetcIaBnq0Pn8+NHO63d/B1JdUUCmA+Ph4HDhwAGFhYfD09DQ5t2LFCskHx4pcCkDKzU9yOonV+sILXbPao1JYZ9Zqw97GKxesCuBnf3IF8HKWHSqAsLAw7N69m9oObytYk8HJmVdGSdSyAUuOlYotN+XJMX7a3eka0sGqAFL9/05cNiLrO6a+pILKB+Dt7U30+Ue1ILQPwFJ5gN+GLlS+4jEDUkYB2QIhAUUjzOXwSdgiRFNKSO+JLZ4XtZva1AYH+wsDpVoBfPPNN/j555/xzjvvoG7duibnGjVqRNRGYmIi1q5di3379qF58+a4cOECYmNj8eTJEzRo0AD//Oc/4evri3PnzmHx4sXGejk5OahXrx71R+Hl2DBjLfzS3gU+K2q/J7ZO42HLNpVo255hXQGk+P+DuGyvrG+Y+pIKqhWAIcXDzz//bHLcxcUFV65cEaz/22+/4cKFCwgMDATwNGR09uzZWLFiBUJDQ7F+/XqsXr0aK1asQPv27U3SREyePBkdOnSgGa5opAj3sxdzh9SQjM2WQk2OiYC9CH5b9uUMOEUUkFhKS0sxatQorF69GpGRkdiwYQP++usvzJ8/H0lJTz+OkJubi+7du+P8+fMmdXNychAREYHDhw9XWnlYQ2onsJRC3V5nePYiNGiilOwBlo1fjnYv5IJ1BZBMsQLoa48rABbWrFmDAQMGmJiKMjMzjasBAPDx8YFerzdm/zSwe/duvPTSS1TCH5D+wZZyNi+nYpLCd2GA1DmptlmwkC1d7cJPzCpDyE/DV15DGuzRByCoAMaOHWtM0DZ8+HDeXD9fffUVbxvnz5/HpUuXMGvWLFGD/OGHHzBjxgzqerROYGvtAGQvo9SCksSmTtsmjaOWpS1SWBzLYlGr8FPT5EJzAtNhh58EFlYAgwYNMv6/tQ+/W+Ps2bO4ceMGunfvDgC4f/8+xo4di1GjRiEjI8NYLjc3Fy4uLiaz/wsXLuDRo0cIDw8X1beUkJiApJ5tKfXiKWE7p4XFtMbXhi1m/tYEqZz+BL5xkCpYTejTobfDFYBiPoCKREREYMOGDXj++efRq1cvrFy50ugETk9PN9lUZvhwzJw5c6j70fYBqGcMlpAi3l4JE5QaHLNCJjoNdlh9AD/UH05c9vX7XzP1JRVUPgCO47Bjxw4kJSUhLy8P+/btw9mzZ/HgwQP07duXunNXV1esWrUKcXFxJmGgBv766y8cOHAA3377LXXbALsJSArnrhqEhlqFBEuUlfk5Ka+V1sQm5f1X8jfUlIq06K2kwlcrVCuADz/8EP/5z38QGRmJuLg4/PLLL0hPT8e0adPwww8/yDlOUZCuAJTc+SkFrONg2b3LgpIpFNSqFG3dv1rGoEZYVwA7AkYQlx2aye8zVRIqBRAeHo5du3bBx8cHHTt2xNmzZ8FxHDp16oSzZ8/KOU5R0IaBSrndXioBZMl+zOpQVtuLb0uTi4Z1nOm3YFUA31IogGEUCoDm2+g3btzA4MGDMXz4cMydO1ewbSoTkE6nQ40aNQD878tfRUVFqF69Ok0zimEpzNHaMdYZNUlbtKsMS+Vt4VDmU45SCG/WqCZHQE25fir+hs74W4hFrigg0m+j63Q6xMXFoUePHsRtUymA8PBwrFixAvPnzwfw1CewZs0avPLKKzTNKEZFH4AUglSJmaqYEFM5oY0UkcJvIie2ntHy9S/l5EPsGDTY0FFEAeXn5yM/P7/ScS8vL3h5eRn/pvk2+saNG/Hyyy+juLi4Urp+Pqg+YTNv3jxkZ2ejQ4cOKCgoQEhICDIyMqji+xMTExEUFITr169XajsoKAhFRUUmxzmOQ2RkJMLCwmiGKgvmMyKlZ0j2OiMryTjG63AE2K5LqG2+vszrGf6maYuvDZL+pcLQnvnKVsz9NtSreJ72njgzehfyf1u2bEH37t0r/duyZYtJm9a+jV6Rq1ev4vjx43jrrbeoxky1AqhZsybWr1+PnJwc3Lt3DwEBAahXrx5xffNcQAZSU1N5N5h9+eWXaNCgAa5evUozVADaDEeOTUViTBVSr5ik2KAkh9lJznBhkvBQsaZMaytbZ3+HaKDJBRQZGYnBgwdXOl5x9k9KWVkZFi5ciBUrVhgVBSlUCmDQoEHYvXs3fH194evrazz++uuvC0YBlZaWIj4+3pgLyEBeXh4SExOxZcsW7Ny506TOrVu3sH//fqxcuRKHDx+mGSoA4TBQ2ugfA2LalDPCRcklvRJhtTRmJ1uaM5Qy/Vk7b4uwVw3L0GyoMjf18EHybfQHDx7gzp07mDBhAoCn5iWO41BYWIglS5ZYbZ9KAdy+fbvSMY7jcPfuXcG6lnIBAU+/MhYdHV3pOwN6vR4LFy5EXFwc3N3FpSwyLPUN/y8WFoemmDbE9iFnn3zIsUlLTsVma/u3GiKebH0PHBU5nMAk30YPDAzE6dOnjX+vXbsWxcXF0kUBGXbhlpWVVdqRe+/ePTz//PNW6/PlAjpw4ACqVKli0Yn82WefITQ0FMHBwUQKxhJCKwAxoZg09Sy1IXZ5bq2uWLOBFNchxf1lqS9WuciJtfsrdrYt50Y3oT6l6tfRkSsd9KJFixATE4P169cbv40OAOPHj8fUqVPRunVr0W0T7QNITEwEAGzYsAETJ040OVe3bl306dPHJH+PORs3bsTWrVvh4eEB4GkuIF9fX9SqVQuFhYXGGf69e/cQGBiITZs24Z///CeuXbsGV1dXlJeX48GDBwgICMDevXtRs2ZNoouztBFMyrh6S8hpAnKWmZsSG9Xs7V6SjFfJDYKOCOs+gA2NRhKXnZj+JVNfUkG1EezYsWPo2pX94TDkAmrevLnJ8aCgIJw7d86418DA3bt3MWTIEJNlDgmkG8GkmJ3z4awvk5TIoaANyKn0NewLVgWwnkIBTFaJAqAyrnft2hWnTp3Cnj17kJ2dDT8/PwwYMAAvvPCCXONjhuTllCqShKR/R7Rry40t/CcVEfoNadq0h9/K0sRGzeNVC/b4RTCqfQA7duzA9OnTUa9ePfTs2RN+fn6YNWsWvvuO7gv3qamplWb/AHDt2rVKs38AaNiwIfXs34AWyqZhKyzF0JM+j2L2GEiF9s6Ig6P4pxaoTEC9e/fGmjVr0KJFC+Oxq1evYurUqUhJSZFlgCzI9UlIA7Z+SVhjx5Uev7PY6+VAznug3d+nsJqA1jxDbgKadscOTUCPHj1C06ZNTY41adIEjx8/lnRQUmKL6Alr/csxHtr0ArZ60eU0tcmJlL8ha2isHAht9pO7f0fBHk1AVAqgffv2WLlyJWbNmgVPT08UFxfj/fffR0hICHEbiYmJWLt2Lfbt24fmzZvj+++/x5YtW+Dq6go3NzfMnz8foaGhAICZM2fi9OnTePDggUXnsBBS7TgVekFo/AtyvEikG9lssVNV7nHJidR5eyzVVfM9UOOY1IzO1gMQAZUCWLx4MWbOnInQ0FDUrl0bjx8/RkhICP71r38R1TdPBZGXl4fly5cjJSUFdevWxeHDhxEbG4vk5GQAwBtvvIH58+fjxRdfpLwsMuSYjdGaXZQQAEqsfsT0waK47OW+qRkhBaftA6DDIb8JbIDjODx58gSbN2/Gw4cPjVFA9evXJ6pvKRWEwf1QVFSEunXroqCgwKQ9KaKLrM3WlRRqcppj1DiLZBEeNJFbtNcup/lGjb+DNeR4/p0ZhzYBubi4oH///jh37hzq169PLPgNWEoF4ePjg0WLFmHQoEGoXbs29Ho9tm3bRtWuNZSKmpBicxnL5idbhJTS5OuhhaauVKs3lh3XYscito6GOlFTdA8pVCag4OBg3Lx5s5IjWAi+VBCFhYX4+uuvsXPnTjRp0gTJycmIiorC3r17ebOD0iDXy0XiA5DTySdnFA1p22pbubA6apW6HiVXLLTY2wpGbejtUAVQKYBOnTph/PjxGDx4MOrXr28ipN944w3eemfPnsWNGzfQvXt3AE9TQYwdOxbz5s1DrVq10KRJEwBA3759MW/ePOTl5VX62IEY5LJhWnMK2+LlUVu4Z8W+LfUvx7iUcLbTjkFsGUAT/PaIwzuBz507hwYNGuDMmTMmx11cXKwqgAkTJhhTlQL/SwVRWlqKK1euICcnB76+vjh16hRq1qyJOnXqUF6GZaR6oOU0d0iBWsfFB0tUlRC2nGHbOuTYHE2wK4vD+gBKSkrw8ccfo0aNGmjZsiUmTpxoTOzGwt/+9jeMHTsWI0eORJUqVeDh4YE1a9YYV7VHSbYAACAASURBVBZRUVG4ePEiAKBPnz5o3rw5PvvsM+Z+zSEV8GoJdVQy6ZeUkVJyhFWS9GttDEr2rTSkzmq1jdtesccoIKKdwPPmzcPly5fRtWtXHD16FGFhYVi4cKES42Oi7OEN4/9LaZdVG2rMKS9n1lVbm9xIsffnytlg3Qn8XuPhxGWX3vqaqS+pIFIAXbp0wQ8//GD8FuWIESOQmpqqxPiYsJQOmgW1hRAqsbpQoxCzFwWgVhwtnFUqWBXAAgoFsEwlCoDIBFRcXAw/Pz8ATz9RVlhYKOugpERKoS1naKMYR6nYF9jaeTULfgO2HpO95eWhfY5tfX/tFYf1Aeh0Opw6dcq4cau8vNzkb0B401ZERAQ8PDxQtWpVAMCsWbPQtWtXq+kerKWJIEWO2TpLG6RtS6FsWGzucgoBe0kKp4TPwhw1RXJp0KGzwzBQIhNQRESE9UZcXAQ/2s73EZiTJ0+iefPmePHFF00UQF5eHrp3726SJuJf//qXMU0ECUImIDW/XLYem637twaNf0HN1yEFJPfC0e+BVLCagGY1fpO47Opb25n6kgqiFYCc9n6+lYNQmggpcLYXQo4NYVJCGg5KY7qypZ1bjo1h5pBsQnQ2pWgrHH4jGCuzZs0Cx3Ho0KEDZsyYAS8vL96yUqSJsDY7qnhMjdCEUbK0qSYcbdOTnIqWJVEebaisBhn2J/4pPwjDQmZmJgICAlBaWoply5ahqKgIq1evNp43/x5wYWEhxo0bh+XLlxvTRHz88cdUaSKkigJSS5il1GGUajEv2Xo8tkSqPR0kbTjzfSaB1QQ0rfE/iMuuufUNU19SodgKICAgAADg4eGB4cOHY9KkSVbLHz9+XJI0EVIsw20RmkmytJeybQNCwlmK6+MzUci5QU2twk+O50SuvjSsY49OYEUUQHFxMXQ6HWrVqgWO45CcnIzg4GCrdRo2bMicJqIk45iky3CW0Ex7Qch+rGQEjAFL9512HLb0adCs4pRYHalVGdo7mg+Ah5ycHERHR0On00Gv16Np06aIi4sDwJ/uQShNBAlSPeBq2aRl7zhbpI4Uqzg5FIFmepMH+xP/CvoAbAGfD0DqWZacjmW1Oq3l3BVtL2gTA8eC1QfwTuOhxGU/ubWDqS+pUDQKSC1IHSYnh81aqbbE9iWlD0AOSO8Ry71Uqg4tmpKxDQ67E9jZkCN23F5CN5UQnEogpfJWEiny9Nj7b2evcHZoBFJMAfClgggKCkLz5s3h6uoKAFi1ahWCgoJw9+5d9OrVC82aNTO28cUXX0j2rQAxsLwwtnzp5NgA5ijCQ4pwWyW/A6BEH5qCEIcWBSTARx99VCkVBAB88803JjmADNSqVQt79uyRbTy23BSk5GqCxUSlBiEg5+9kbxsEzZFjH4A9Xb+a0ExAdoaQUJbTyae2l0zJyByhaCDzMVkrp8TeCDnrsqLtA1APejuMp1FFKohRo0ZBp9OhW7duiI6ONn5trKioCK+//jqApxvBxo4dSxUGKrQPQMp0zwbUsLyWY7OWlH2QbrhT26pEM41oWMP+xL8KUkEYjhcWFmL27Nlo3rw5pk+fjtLSUhQUFMDX1xc5OTmYNGkShg4diqFDyUOtyh7ecOgNNGobj1Q46nWxoOSKzFnvP2sY6PBnBxOX/fr2Lqa+pMLmqSAMx2vWrImhQ4di8+bNxnK+vr4AAF9fX/Tv3x/nzp2jUgBSP8BSOIGlaEvKNsxRw8vvyIJHjfmcHPl+K4kWBcQDXyqIx48fo2rVqqhWrRrKy8tx6NAhY4qInJwceHl5oUqVKigpKUFqaipefvll6r7VINAq9m+uCFiQ49rUHqUkZV1boAafBR/2di/VRrmmACzDlwrixo0biI2NhYuLC8rLyxESEoJp06YBAH799Vd89NFHcHV1RXl5OV5++WWMHDlSlvGRLoGVsK3bqi1bImfMuxqwlwgjNY/NHpBrBXDz5k3ExMTg0aNH8Pb2RkJCAho3bmxSZt26dUhOToabmxvc3d0xffp0dO0q/Hs6ZSoIc7SZj22x5eYzKdp01JQQ9qK4pILVB/D6swOIy/5wey9x2dGjR2PIkCEYOHAg9uzZg507d2Lr1q0mZY4dO4bQ0FB4enri6tWrGDlyJI4fP45q1apZbVtTABawh5dTKbR7wY6UsfpiPgSj/YZksCqAwc/0Jy67684+onI5OTno3bs3Tp8+DTc3N+h0OoSFhSElJYU3LT7HcQgNDcX+/fsFv6Lo1PsA+FDyRZFj9kg7o6btX4MOpcx+pOG1pGjPBx006aDz8/ORn59f6biXl5fJlxIzMzPh7+8PNzc3AICbmxv8/PyQmZnJqwB2796NZ555hugTujZPBbFz50588cUX0Ov1aNSoEVauXAlvb2/cvHkTsbGxePDgAdzd3dG6dWvExcUJLmnMsdUSnxRbbjITkxrC3meTckRjmbetFh8RK2oaiz1Akwpiy5YtSExMrHQ8KioK0dHRosdw5swZrFmzBp9//jlRecVMQBEREdiwYYNJKoi0tDS89dZb2LNnD3x8fLB+/Xrcv38f8fHxuHv3LvLz89GyZUvo9XrMmDEDzZo1w5QpU4j7ZP0kpNqUhxqygMpVzx6xl/xOGmSwmoD6PtOXuOw3l78hWgHQmIDOnz+Pd999F+vXr0erVq2IxmFTE9D169cRHBxsvJDw8HCMHDkS8fHxaNiwobGcq6sr2rRpg7S0NOo+WGZ8csZYi3mBlXzZSfuSc0YtJ3LO1pUQzvZyn50Jmrm0uaDnw9fXF8HBwUhKSsLAgQORlJRkIjMNXLx4EdOnT8dHH31ELPwBhVcANWvWNEkFkZOTgxEjRuDbb79Fw4YNsWrVKnz++ec4ffo0vL29jXX/+usvDBkyBDNmzED37t2J+5R6JzALmoOOH1vkHXIGnPnaxcC6Aujd6FXisofSDxCXTUtLQ0xMDPLz8+Hl5YWEhAQ0adIE48ePx9SpU9G6dWsMGTIE9+7dg7+/v7GeIbOyNWyeCmL//v3YvHkzXFxc0KNHD7z//vv49ddfUbNmTQBAeXk5oqOjERgYiIULF1L1yWoCkhO1vZxypkB2lG8M2Bva/aSDVQH0atSHuGxK+kGmvqTCJmGg165dw6RJk5Cammpy/OLFi4iKisLRo0cBADqdDjNnzkS1atWwYsUKqkRwgLoVgKMjRwSJFLn7SdpUe10NeWBVAD0a9SYu+2P6Iaa+pMKmqSAA4MGDB6hXrx6ePHmCjz76CGPGjAEA6PV6xMTEwM3NDcuWLaMW/nxYEkyaCcI+sHQPWe+r2iN2tOfHftBx9vdFAEVWAOnp6ZVSQbz33nvw8/PDuHHjkJGRgbKyMvTt2xfTpk2Dq6srfv75Z7zzzjsmXwtr37494uLiiPuVagWgNkcn6z4AS/mIbH1NGhq2hnUF8HLDHsRlf777I1NfUuHQO4HFOoHlcNiqTYloqBNtxm87WBVAtwbkASpH7x1m6ksqHHonsNQx7EraetWa90ZDXlgc5drva1vscSbt0CsAIROQ9sKoE5LfxdF+O7ErRC1dg3SwrgBeahBBXPbEvVThQgqgmAJ48uQJli9fjpMnT6Jq1apo164dZsyYgTlz5uDOnTvw8PDAs88+i/j4eOMmB740EaQ4SxSQtsfA8VBDhlFnUy6sCuCFBq8Qlz157yemvqRCMQWwdOlSuLq6Yt68eXBxccHDhw/h7u6Oa9euISwsDACQkJCAx48fY/ny5VbTRJBiSQEoHQWk4fioQVhLgRrHpCSsCqBTYDhx2TMZR5j6kgpFfABFRUXYvXs3jhw5YgznrFu3LgAYhT8AtGvXDtu3bwdgPU0EC470cDvbC6vWGamYlZcafELmqOme2iPaJyF5SE9Ph7e3NxITE3H69GnUqFED06ZNQ2hoqLGMXq/H9u3bERHx1I7WokULXL58Genp6WjYsCGSkpJQXFxs/CqOlKj5wbcm9NQ8bjlRi2Ocr66Uu6Np2tSwLfboTlVEAZSXlyM9PR0tW7bE3Llz8d///hcTJ07Ev//9b2PKhyVLlqB69erGzz4+99xzWLBgAaZPn25MEwEA7u4OHbhUCe3F17CEs63+7AGa7wGoBUWkaWBgINzd3dGvXz8AQNu2bVGnTh3cvHkTrVu3RkJCAm7fvo0NGzYYN30BwGuvvYbXXnsNwNM0Ef7+/kaFwYKjvjxKXJct01vLERVkq53AttzBrCEP2gqABx8fH4SFheHEiRPo0qULbt68iZycHDz77LP44IMPcPnyZWzcuBEeHh4m9fjSRLCihP3VFukllBAKcnzdytE+pOIoqNXnolZ00FJB8JKeno758+fj0aNHcHd3x7vvvovAwED069cPjRs3Nn7pq2HDhli3bh0A8KaJIMWWYaCOusrgQ8qP5xhwhnvnbM+JmmGNAvqbf2fispezTjH1JRUOvRFMru8B2PtuTLWOUa3j0nAOWBVAK/8w4UL/z29Zp5n6kgqHVgAVVwBaAjR5UbvwVvv4+LDXcdsjrAog2K8Tcdkr2WeY+pIKp1EA1lDSsemML7T5NTvjPdBQP6wKoIVfR+KyV7PPMvUlFTZNBbFkyRLe49bqkCK1D8AZBZejX7OjX58lnPGaSWBVAM3rhQoX+n+uP/iFqS+pUCyo/p///CeqVq2KQ4cOGVNBWDsudM4W2OsLw/LC2+s1kyKnH0etGV0d/Te1FdoHYXgoKipCeHg4jhw5gho1aggeFzpHii0+Cq/NrhwP7YtxzgHrCqBJ3RDisjcenmfqSypsmgqiZs2avCkiSNJHCGGLl0qNL7ImZOgwv19yfm9YjTmBNMTB2eEKwKapINasWcObIoIkfYQYHHFzixwfsnFmpLpfatu5LISmXNjQUkHwwJcKwtPTkzdFRIMGDaymjyDBkrB3xIfbFtekCQt+5Jj5K4Eax2RP2GNAJfm2WgYqpoIAYJIKgu+4tTo0eAZ21R5slVOSccziPg3S82pDe+acEz044n9qwaapIMLDw3mPW6tDitROYG3W67goZRrUniH1wuoEDvBuSVw289HvTH1JhbYRTMOpkeN7AHLX1ZAHVgVQ3zuYuOz9R1eY+pIK50qu7+BoQoUe893JFY+R1tXQAOzTB+B0CsARo4AMONr1KIl277QJBCtqsu2ToogJ6O7du5gyZYrx74KCAhQWFuLMmTO4efMmYmJijJ96TEhIQOPGjQEAkydPxt27d+Hq6orq1atj4cKFCA4mX2bRmoC0PD62QbuvGmqA1QRU16s5cdmH+deZ+pIKm/gAli1bBp1Oh9jYWIwePRpDhgzBwIEDsWfPHuzcuRNbt24F8FRR1KpVCwDw448/Yt26ddi1axdxP872PQBnEqTOdK0aysCqAOrUfJ64bF7hn0x9SYUiYaAVKS0txb59+zBkyBDk5OTg999/N8b69+vXD7///jtyc3MBwCj8AaCwsBAuLi7M/RtCCuUOK7RFKCBfn/YWRkkC7f11xHugoS7sMQxUcR9Aamoq/P390apVK1y+fBn+/v5wc3MDALi5ucHPzw+ZmZnw8fEBACxYsAAnTpwAx3H49NNPmft3xhmjM16zOUKpqKXwDWmrEudGcwITsHPnTgwZMoS4/LJlywAAu3fvxqpVq7Bp0ya5hqZhR5AKW9K8PlIIbU3wOzd6O1QAipqAsrKycPbsWfTv3x8AEBAQgKysLOh0OgCATqdDdnY2AgICKtUdNGgQTp8+jby8PNnHSWsuUMqspPE/SE1A2q5cDaXgKP5TC4quAHbt2oXw8HDUqVMHAODr64vg4GAkJSVh4MCBSEpKQnBwMHx8fFBUVIT8/HyjMkhNTUXt2rXh7e0t+zjlTNClmQmkRbufGmrBHlcAikYB9e7dGwsWLEC3bt2Mx9LS0hATE4P8/Hx4eXkhISEBTZo0wcOHDzF58mSUlJTA1dUVtWvXxty5c9GqVSvi/vi+CawJCw0NDXNYo4CqVmtEXPbJX+nEZa2FyhvQ6XRYunQpjh07BhcXF0yYMAFDhw4VbFtLBaFhN2izfQ05YVUAHlUbEpctfXKXuKy1UHkDu3fvxr59+7Bp0yY8evQIgwYNwtdff42GDa2PSfEwUA0NsWj2/P+h+ZzUB8dxxP/y8/Nx9+7dSv/y8/NN2hQKlTeQnJyMoUOHwtXVFT4+PujRowcOHjwoOGaHTgXBqtE1NNSO9oyrhzKK32Lt2rVITEysdDwqKgrR0dHGvzMzMwVD5Q3lAgMDjX8HBATg/v37guNwaAWgoaGhoUYiIyMxePDgSse9vLwUHYemADQ0NDQUxsvLi0jYVwyVd3Nz4w2VDwgIQEZGBtq0aQOg8oqAD80HoKGhoaFSKobKAzAJla9Inz59sGPHDuj1euTm5uLHH39E7969Bdt36CggDQ0NDXuHL1R+/PjxmDp1Klq3bg2dTof4+HjjJ3THjx+PYcOGCbatKQANDQ0NJ0UzAWloaGg4KZoC0NDQ0HBSNAWgoaGh4aRoCkBDQ0PDSdEUgIaGhoaT4nAbwfLy8oxboOvXr29MPU1CUVERbt26hWeffRY1a9aUa4gaGhoaqsBhVgB37txBZGQkevXqhVmzZmHWrFno1asXIiMjcevWLYt1YmNjjUmVfv31V/Ts2RNz5sxBz549cfz4cQVHL0xaWppgmby8PFy5cgVXrlyh/nBOUVERfvvtNxQWFoodIhFiPujjCNdeUlKCy5cvV0r2ZY3//Oc/ROXEXrtSv7mGiuEchGHDhnF79uzhdDqd8ZhOp+N2797N/f3vf7dYp3///sb/HzVqFPff//6X4ziOu3HjBjd48GDZxlpcXMxdunSJe/z4Me9583+vvPIKV1JSwhUXF1cqf/v2bW706NFcaGgo17dvX65v375caGgoN3r0aO7mzZsW+1i4cCGXk5PDcRzH/fLLL9wLL7zA9e3bl+vcuTN37NixSuWfPHnCrV+/nnvvvfe4n376yeRcfHy8xT7Onj3L9e3blxszZgx3584drl+/flzbtm25l156iTt37pzdXHtubi43f/587u233+a+/PJLk3NRUVEW+0hJSeFCQkK43r17cxcuXOBefvll7tVXX+U6derEHT58uFL5P/74o9K/bt26cX/++Sf3xx9/WOyD9tppr1sqhJ53S5w4cYK47OPHj6narkheXp6oeo6CwyiA3r17U5/r1auX8f9ff/11k3P9+vWzWEcJYRAUFMS1aNGCCwoKqvSvRYsWlcorofzmzZvHvfvuu9znn3/Ovfbaa9zSpUuN5wYNGmSxjyFDhnCHDx/mdu3axYWHh3PJyckcx3HcyZMnuaFDh1qso8Zrj46O5hISErhDhw5xb731FjdlyhSurKyM4ziOGzhwoMU+BgwYwF29epU7c+YM16lTJ+7XX3/lOI7j/vzzT4t1goKCuFdeecXkX8uWLblXXnmFi4iIsNgH7bWLmfCoVfnl5ORw8+bN49q1a8e1a9eOa9u2LdeuXTtu3rx5RiVnzrp164zn/vjjD65Hjx5c27ZtufDwcO7SpUsW6zg6DqMAhg0bxu3bt4/T6/XGY3q9ntuzZw+vsImLi+NWrFjBFRcXcwkJCdz+/fs5juO448ePcyNHjrRYRwlhEBMTw82fP58rKCgwHnvllVd4r10J5VfxWElJCTdp0iRu3rx5nF6v573uisfNx89XR43XPmDAAOP/6/V6btGiRdyYMWO4v/76S7JrX7t2LTdu3Dju7t27vPXMob12MRMetSq/MWPGcOvXr+dyc3ONx3Jycrh169ZxY8aMsVin4jVOmDCBS0lJ4TiO486cOcMNGzbMYh1Hx2F8ACtXrsSOHTsQFhaG/v37o3///ggLC8P333+PlStXWqwzf/58lJeXo1u3bvj3v/+NGTNm4G9/+xs+//xzLF++3GKd27dvY86cOejVqxc+//xz1KtXD++88w6ePHnCOzYXFxcEBQWhY8eOqFGjBtq3bw8AaNq0qcXyK1asQI8ePfDWW2/hyJEjxjb48Pb2RlJSErgKWT04jsPevXt5Mw6+8MILWLlyJUpKShAWFobk5GQAwIkTJyx+d1mn0xn/v1q1ali7di1KSkowe/Zs6PV6i33odDrk5ubizp07ePToEW7fvg0AyM3NRWlpqd1ce8Wxuri4IC4uDs2bN8eECRN4f3cXFxekpaXh/PnzKC4uxoULFwA8/bxfxXtpICoqCtOnT8fMmTOxfft2wesWc+201w0o87xHRUWhadOm2LZtG1JTU5Gamgp/f3+kpqbi8OHDFuvcu3cPkyZNMgny8PHxweTJk3H3ruWvbVX8HR88eICePXsCADp27Ii//vqL93ocGltqHznIycnhLl++zF2+fJl3KWhOUVERd+XKFe7y5csmMwpL9OnTp9KxlStXcqNHj7Z4juOemkj+/PNP7ty5c1xYWBh3/vx5juOeLr35Zl4c93T5PX36dG7u3LlceHg4b7mbN29yo0eP5jp27Mj169ePe+2117jQ0FBu1KhRXFpamsU6T5484ZYsWcKFhoZyPXr04IKCgrhWrVoZ7fXmjBo1irty5YrJMZ1Ox82aNcuiaYbjOG7Hjh1c+/btuQ4dOnB79+7lhg0bxk2YMIHr0qULt3nz5v9r7/xjqir/OP4GLiJaM4lCxFhaE9rdiF8XTX6KoYRGCbRQ8Uc1isohRQEj4xLYVCwhViPdHBst6SdETJFmqZRaSKNdCBKhgCRUGFyEMO4FPt8/2D1fftwDnHMBJ3xeG9u953ne5/lxD+dzznnez3NE2zO87QkJCZLbrlKpprTt0dHRVF5ePmb74cOHRdv+ww8/kEqlolWrVtGFCxdo165dtHHjRvLw8KDi4mLR9vT19dGhQ4dox44d5OvrK5pveNs9PT0n9btLbTfRzB3vv//+Oz377LN0/PhxIiLRK38DoaGhRseRfv31V9EyXn/9dcrLyyMiouTkZOHO5MqVKxQRETFuebMVXgxOIi+++CKio6OhUqlGbM/MzMSRI0fwxx9/jNGcOXMGiYmJMDc3R2ZmJo4ePYq2tjZcu3YNarUaTz755LhllpSUoLy8HGq1etx8HR0daG1tBTC0PvjoJWON0dvbi+bmZgwMDIyraWxshKWlJRwc/v+e5QsXLuCxxx5DWVkZ/P39jeq0Wi2ICIsXL0ZPTw9+/PFHODo6QqlUTlg3ADh16hTKy8uRkpIybj5D23t7e2FlZYUVK1ZMaOU1tL27uxvW1tZ48MEHjWq6urpgZmZm9Kq6vr4eDz/88ITtGBgYQG1tLZYsWQJbW9sJ81dUVODSpUvYtm3bhOvGD2/7smXLxqwVP5rr16+jubkZACbMP97xfvToUdTW1o7RjHe8p6amCq83HI1Op0N2djaqqqrw119/oaysTLRev/32GxISEmBlZSUcky0tLejr60NGRgZcXV3HaLq6upCUlIS6ujrY2dlBo9HA3t4e1tbW2L9//6SPyVnFbQ5AdxydnZ2k1WqNpokNWI2mv7+fqqqqqK2tzWi61IG32tpa2rx5M4WHh1N9fT1FR0eTi4sL+fn5UU1NjdEyDJqIiAhB8+ijj4pq5AzUydH89NNPwufu7m564403KDAwkHbv3m20v4w5WzZu3Dius0WqxsvLi9LT00X70hgGzei7JjGkDpwSEV29epVeeOEFcnZ2JmdnZ/Ly8iIXFxfav38/9fX1mZyfaGaO99FUVlbSkSNHJpVXo9FQaWkplZaWkkajGTEGKEZjYyOdPn2aSktL5+zgrwEOAFPIeI9zpGikDrxt27aNTp8+TYWFhRQQEEBFRUVERPT999/Tzp07jZYrVSNnoE6OZrij6J133iG1Wk2XL1+mw4cP0549e8bkl+NskapZu3Ytvfvuu7R69Wp6+umn6ZNPPhE9KcrVSB04JSKKioqioqIi0mq1lJeXRx988AG1t7dTcnIypaammpx/OB0dHVRTU0M1NTUTPiY1RcPMLBwAJGLsqtbw5+3tPSUaqa6T4SfNgICAEWliJw+pGjkuFTma4WWHhoaSTqcTvhsLlnKcLVI1hr7S6XRUUlJC0dHR5OrqSnFxcSPuWEzRyHFMDQ9kREO2W6KhsZmgoCCT8xPJm2chRyPGVNqx5WhmO7NuKYjpZtOmTXBwcBjhvDCg1WqnRGPMdXLw4EFR18nw/Xp7e49IE3PoSNXs3r0bNTU1iI+Px1NPPYUtW7ZM6FKRo9HpdGhoaAARwczMDJaWlkKauflY05rB2bJnzx7B2RISEjKus0WOBgAsLS0RHByM4OBg3LhxAwUFBUhPT8epU6dM1hhcQzdv3hRcQ66urqKuIQBQKBRobm6Go6MjqqurMW/ePKGfFIqx/9pS8wNAQkICtm7ditzcXKH/BwcHUVxcjMTERHz++ecma+rr60X7T2xms1qtxrJly+Dv74/8/HxcvHgRWVlZUCgU+Pvvv6dMM+u5reHnDiQwMJCuXbtmNM3Pz29KNFJdJ6+88soI37yB1tZW0clQcjRE0lwqcjSGx0OGR0aGfuvu7jY64UyOs0WqRuwKfDykauS4hs6cOUOrVq2iTZs2CToiora2NnrrrbdMzk8kb56FVI2Tk9OI33z4n1KpNLofOXMz5GhmOxwAJHLgwAHh+exo0tPTp0QzFQNvREP21vb29knnl6KRMlBnisZAb2+v6AmdSJqVV6pm+COsySJHM5zJDpx2dXWRRqMxGsynIr+cCZZSNXIuquTYU+VoZjtsA2UYRpTGxkao1WrU1tbCzs4OwJCN1NnZGampqVixYoXJmoMHDyIoKEiYMDacffv2Ye/evWO2y7Fjy9HMdjgAMAwzIXLmmMjRTBatVgszMzMsWrRoTJrY3Aw5mtnOrFkKgmGY6cPGxgZKpRJKpVI4kU80gVGOZjRi+e+55x6jJ3IAeO2116ZMM9thFxDDMKKIOXSISNShI1UjxwU0U5rZDgcAhmFEmQnb80yUIVcz2+EA7P9c4gAABdBJREFUwDCMKA4ODjh+/LgwmDscsfWfpGpmogy5mtkOjwEwDCPK+vXr0dLSYjTNsJyyqZqZKEOuZrbDLiCGYZg5Ct8BMAzDzFE4ADAMw8xROAAwdwxubm6ii3YVFBRgy5YtM1wjhrmzYRcQYxKBgYFob2+HhYUFrK2t4e/vj71792LhwoUm7Xf79u0IDQ3FM888I2yrrKw0tboMwwyD7wAYk/n4449RWVmJwsJCVFVVIScn53ZX6Y5FbOlnhpkOOAAwU4adnR18fX1x5coVAEB3dzeSk5Ph4+MDX19fZGZmCie4goICREZGIj09HR4eHggODsbFixcBDC3OVVFRgbS0NLi5uSEtLQ0A4OTkhKamJgBDMzdjYmLg7u6OiIgI4R23BhoaGvDcc8/By8sLGzZswMmTJ0XrvX37dmRlZSEyMhJubm54/vnn0dHRMW7+zMxMIX9MTAw6OzsRHx8Pd3d3hIeH4+rVq5OqS1JSEtRqNaKjo+Hq6opffvkF586dQ0hICNzc3ODr64tjx45J+RkYZvLcljVImVnD2rVr6fz580RE9M8//1BISAhlZmYSEdHLL79Mb7/9trDEdHh4OOXn5xMR0ddff02PPPII5ebmkk6noxMnTpC7uzt1dnYS0dDrC7/44osRZa1cuZIaGxuJiCguLo5iY2Pp33//pcuXL5OPjw9FRkYS0dAyz35+fvTVV1+RXq+n6upq8vLyorq6OqNtiIqKonXr1tGff/5Jt27doqioKDp06JBom6Oioujxxx+npqYmunnzJj3xxBO0fv16On/+POn1enrzzTcpKSlpUnVJTEwkd3d3qqiooIGBAfrvv//I29ubLl26REREWq2Wqqurpf8wDDMJ+A6AMZlXX30Vnp6e2Lp1K1QqFWJiYtDe3o6ysjIkJydjwYIFuPfee7Fr1y6cOHFC0NnY2GDnzp2wtLRESEgIli9fjrNnz05Y3sDAAL777jvExsZiwYIFWLlyJTZv3iyknz17Fg4ODggPD4dCoYBSqcSGDRtQWloqus+wsDAsX74c8+fPR3BwMGpra8etQ1hYGBwdHXH33XfDz88PDzzwANasWQOFQoHg4GDU1NRMui7r1q2Dh4cHzM3NYWVlBYVCgfr6evT09GDRokVQKpUT9gnDyIEHgRmT+eijj7BmzZoR2+rq6tDf3w8fHx9h2+DgIOzt7YXvdnZ2I14RuXTpUty4cWPC8jo6OtDf3z9iX0uXLhU+t7S0QKPRwNPTU9g2MDCA0NBQ0X3ed999wmdra2v09vYCAFJSUlBcXAwAeOmllxATEwMAsLW1FfJbWVmN+D5//nxBP5m6DG8HAGRnZyMnJwfvv/8+nJycEB8fDzc3t/G6hGFkwQGAmRaWLFmCefPm4eeffxZ93+z169eFd/8CQGtrKwIDAyfct42NDRQKBVpbW/HQQw8JWgP29vZQqVTIzc01uR1paWnCGIQc5NTFxcUFOTk50Ov1+PTTTxEXF4dz587JrgPDiMGPgJhp4f7774e3tzcOHDiAnp4eDA4Oorm5GeXl5UKejo4O5OXlQa/Xo6SkBA0NDcKiXLa2tqKefwsLCwQFBeHDDz/ErVu3UF9fj8LCQiE9ICAAjY2N+Oabb6DX66HX66HRaNDQ0DC9jTaC1LrodDp8++236O7uhqWlJRYuXAgLC4sZrjUzV+AAwEwbGRkZ0Ov1CAkJgUqlQmxsLNra2oR0FxcXNDU1YfXq1cjKykJ2djYWL14MANixYwdKS0uhUqmwb9++MftOSUlBb28vvL29kZSUhLCwMCHtrrvuwrFjx3Dy5En4+vrCx8cH7733HnQ63fQ3ehRy6lJUVITAwEC4u7vjs88+Q0ZGxgzWmJlL8GJwzG2hoKAAX375JfLz8293VRhmzsJ3AAzDMHMUDgAMwzBzFH4ExDAMM0fhOwCGYZg5CgcAhmGYOQoHAIZhmDkKBwCGYZg5CgcAhmGYOQoHAIZhmDnK/wApbvUmISGW7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns; sns.set()\n",
    "\n",
    "plt.show()\n",
    "ax = sns.heatmap([p[:] for p in plotLst])\n",
    "plt.title(\"Peptide Hit Bitfields\")\n",
    "ax.set(xlabel='Peptide n-mers', ylabel='Protein_subset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(30.5,0.5,'Protein_idx'), Text(0.5,12.5,'Protein_idx')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dissimilarityMatrix\n",
    "NumSeqs=90;pairLst=[]\n",
    "DM=[[0 for x in range(NumSeqs)] for x in range(NumSeqs)]\n",
    "for i in range(NumSeqs):\n",
    "    a=protLst[i]\n",
    "    for j in range(i, NumSeqs, 1):\n",
    "        b=protLst[j]\n",
    "        d=(a.bf)^(b.bf)\n",
    "        DM[i][j]=d.count()\n",
    "        pairLst.append([i,j,DM[i][j]])\n",
    "pairLst.sort(key=lambda x:x[-1])\n",
    "plt.show()\n",
    "ax = sns.heatmap(DM)\n",
    "plt.title(\"Protein Dissimilarity Matrix\")\n",
    "ax.set(xlabel='Protein_idx', ylabel='Protein_idx') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/wRvdu3evYmJi5PF4JEnTpk3Ttm3b2h0DAESO6I5uIDc3V8YYjR49WvPnz1e/fv1UXV2tQYMG+e4TGxsrr9erkydP+h1zu90djQMA6CQdKoiSkhLFx8erqalJTzzxhAoKCkJ2uSgurk/QcwcM6NuJSToPuQJDrsCQKzDk6mBBnLvs5HK5NH36dN1zzz2+26uqqnz3q62tlcPhkNvt9jsWiJqaenm9JuDMAwb01eef1wU8r6t1Za6OLqjudrw6glyBIVdgOpLL6XQE/It10G9zPX36tOrqvg5qjNHWrVuVnJwsSRoxYoQaGhpUUVEhSVq7dq0yMjLaHQMARI4LegZRWFio7du364svvtDtt98ut9utNWvWaO7cuWppaZHX69WwYcOUn58vSXI6nVq2bJny8/PV2NiohIQELV++vN0xAEDkcBhjAr9OEwG4xBTYtrMeLA1qbtlT2d3ueHUEuQJDrsBcNJeYAADfbRQEAMCKggAAWFEQAAArCgIAYEVBAACsKAgAgBUFAQCwoiAAAFYUBADAioIAAFhREAAAKwoCAGBFQQAArCgIAIAVBQEAsKIgAABWFAQAwIqCAABYURAAACsKAgBgRUEAAKwoCACAFQUBALCiIAAAVhQEAMCKggAAWFEQAAArCgIAYBUd7gCIbE1nWzRgQN+g5jY0NqvuqzOdnAhAqFAQ8MvVI0pZD5YGNbfsqWzVdXIeAKHDJSYAgBUFAQCwoiAAAFYUBADAioIAAFi1WxBFRUVKS0tTUlKSDhw44Lv94MGDysnJUXp6unJycnTo0KEOjwEAIke7BTF58mSVlJQoISGh1e35+fmaPn26ysvLNX36dOXl5XV4DAAQOdotCI/Ho/j4+Fa31dTUaP/+/crMzJQkZWZmav/+/aqtrQ16DAAQWYL6oFx1dbUuv/xyRUVFSZKioqI0cOBAVVdXyxgT1FhsbGwnPSQAQGe4aD9JHRfXJ+i5wX51RFeL1Fwd0ZWPKVKPF7kCQ67AhDJXUAURHx+vY8eOqaWlRVFRUWppadHx48cVHx8vY0xQY4GqqamX12sCnjdgQF99/nnkfQFEV+YK50LvysfU3c5jR5ArMN/FXE6nI+BfrIN6m2tcXJySk5O1efNmSdLmzZuVnJys2NjYoMcAAJGl3WcQhYWF2r59u7744gvdfvvtcrvd2rJlix577DEtXLhQq1evVr9+/VRUVOSbE+wYACBytFsQixYt0qJFi867fdiwYVq3bp11TrBjAIDIwSepAQBWFAQAwIqCAABYURAAACsKAgBgRUEAAKwoCACAFQUBALCiIAAAVhQEAMCKggAAWFEQAAArCgIAYEVBAACsKAgAgBUFAQCwoiAAAFYUBADAioIAAFhREAAAKwoCAGBFQQAArCgIAIAVBQEAsKIgAABWFAQAwIqCAABYURAAACsKAgBgRUEAAKwoCACAFQUBALCiIAAAVhQEAMCKggAAWFEQAACr6I5uIC0tTS6XSzExMZKk3Nxcpaamas+ePcrLy1NjY6MSEhK0fPlyxcXFSZLfMQBAZOiUZxCrVq1SaWmpSktLlZqaKmOMFixYoLy8PJWXl8vj8WjFihWS5HcMABA5uuQS0969exUTEyOPxyNJmjZtmrZt29buGAAgcnT4EpP09WUlY4xGjx6t+fPnq7q6WoMGDfKNx8bGyuv16uTJk37H3G73Be8zLq5P0HkHDOgb9NyuFKm5OqIrH1OkHi9yBYZcgQllrg4XRElJieLj49XU1KQnnnhCBQUF+uEPf9gZ2fyqqamX12sCnjdgQF99/nldFyTqmK7MFc6F3pWPqbudx44gV2C+i7mcTkfAv1h3+BJTfHy8JMnlcmn69OnatWuX4uPjVVVV5btPbW2tHA6H3G633zEAQOTo0DOI06dPq6WlRX379pUxRlu3blVycrJGjBihhoYGVVRUyOPxaO3atcrIyJAkv2NoW99+vdQzplOuCALABenQ3zg1NTWaO3euWlpa5PV6NWzYMOXn58vpdGrZsmXKz89v9VZWSX7H0LaeMdHKerA0qLllT2V3choA3UGHCmLIkCHatGmTdWzUqFEqKysLeAwAEBn4JDUAwIqCAABYURAAACsKAgBgRUEAAKwoCACAFQUBALCiIAAAVhQEAMCKggAAWFEQAAArCgIAYEVBAACsKAgAgBUFAQCwoiAAAFYUBADAioIAAFhREAAAKwoCAGBFQQAArCgIAIAVBQEAsKIgAABWFAQAwIqCAABYURAAACsKAgBgFR3uAKHWdLZFAwb0DWpuQ2Oz6r4608mJACAydbuCcPWIUtaDpUHNLXsqW3WdnAcAIhWXmAAAVhQEAMCKggAAWFEQAACrbvciNUKHd4wBFzcKAl2Gd4wBF7ewXWI6ePCgcnJylJ6erpycHB06dChcUQAAFmF7BpGfn6/p06crOztbpaWlysvL05/+9Kdwxelyffv1Us8Y/4c72MsxANAVwlIQNTU12r9/v1544QVJUmZmph5//HHV1tYqNjb2grbhdDqC3v/A/r2CnhvsfnvGROuXhduD3u8fFv2oQ7kvxrntHeuOrIGuRK7AkCswweYKZp7DGGOC2lsH7Nu3Tw8//LC2bNniu23q1Klavny5rr766lDHAQBY8DZXAIBVWAoiPj5ex44dU0tLiySppaVFx48fV3x8fDjiAAAswlIQcXFxSk5O1ubNmyVJmzdvVnJy8gW//gAA6HpheQ1Ckj755BMtXLhQX331lfr166eioiJdeeWV4YgCALAIW0EAACIbL1IDAKwoCACAFQUBALCiIAAAVt3m21wPHjyohQsX6uTJk3K73SoqKtLQoUNDnuPEiRN66KGHdPjwYblcLl1xxRUqKChQbGyskpKSlJiYKKfz695etmyZkpKSQpYtLS1NLpdLMTExkqTc3FylpqZqz549ysvLU2NjoxISErR8+XLFxcWFJNPRo0d17733+n6uq6tTfX29/vWvf7WZt6sUFRWpvLxcn332mcrKypSYmCjJ/9oKxbqz5fK3ziSFZK21dbz8nbdQrDVbLn/rrL3MncXfOfN3XLr0mJluYubMmWbTpk3GGGM2bdpkZs6cGZYcJ06cMDt27PD9vHTpUvPII48YY4xJTEw09fX1YclljDGTJk0yH374YavbvF6vmTJlitm5c6cxxpji4mKzcOHCcMQzxhhTWFholixZYoyx5+1KO3fuNFVVVeft19/aCsW6s+Xyt86MCc1aa+t4tXXeQrXW2sr1Td9cZ/4yd6a2zpm/49LVx6xbXGI69+WAmZmZkr7+csD9+/ertrY25FncbrfGjh3r+3nkyJGqqqoKeY4LtXfvXsXExMjj8UiSpk2bpm3btoUlS1NTk8rKynTLLbeEZf8ej+e8T/v7W1uhWne2XJGwzmy5/AnVWmsvV7jWWVvnzN9x6epj1i0uMVVXV+vyyy9XVFSUJCkqKkoDBw5UdXV1WD+97fV69eKLLyotLc1328yZM9XS0qKJEydq7ty5crlcIc2Um5srY4xGjx6t+fPnq7q6WoMGDfKNx8bGyuv1+i6ZhNLrr7+uyy+/vNUXOn47b79+/UKayd/aMsZExLqzrTMpvGvNdt4iZa3Z1llbmbvKN8+Zv+PS1cesWzyDiFSPP/64LrnkEs2YMUOS9Oabb2rDhg0qKSnRxx9/rOLi4pDmKSkp0csvv6z169fLGKOCgoKQ7r8969evb/VbXaTnjRTfXmdSeNdapJ+3b68zKfSZbecsHLpFQUTilwMWFRWpsrJSzzzzjO+FwnN5+vTpo5/+9KfatWtXSDOd27/L5dL06dO1a9cuxcfHt7o0UVtbK4fDEfJnD8eOHdPOnTuVlZXlN2+o+VtbkbDubOvsXG4pPGutrfMWCWvNts78Ze4K3z5n/o5LVx+zblEQkfblgCtXrtS+fftUXFzse1r/5ZdfqqGhQZLU3Nys8vJyJScnhyzT6dOnVVf39b8CbYzR1q1blZycrBEjRqihoUEVFRWSpLVr1yojIyNkuc7ZuHGjrr/+evXv399v3lDzt7bCve5s60wK71rzd94iYa19e521l7mz2c6Zv+PS1ces23wXU6R8OeBHH32kzMxMDR06VD179pQkDR48WHfccYfy8vLkcDjU3NyslJQU/frXv1bv3r1DkuvIkSOaO3euWlpa5PV6NWzYMC1atEgDBw7Url27lJ+f3+ptdJdddllIcp2Tnp6uRx99VBMnTmw3b1cpLCzU9u3b9cUXX6h///5yu93asmWL37UVinVny/XMM89Y11lxcbF2794dkrVmy7VmzRq/5y0Ua62t8yidv86k0K21tv5uKC4u9ntcuvKYdZuCAAAEpltcYgIABI6CAABYURAAACsKAgBgRUEAAKwoCACAFQUBALCiIAAAVv8PN2RraLIAt6cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(tight_layout=False)\n",
    "plt.title(\"Histogram of Dissimilarity\")\n",
    "yo=axs.hist([p[-1] for p in pairLst], bins=20, range=(0,200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probe Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import multiprocessing as mp\n",
    "from contextlib import closing\n",
    " \n",
    "def chooseMostPrevalentProbe(probeLst, probe_bitmap, randomize=True):\n",
    "    number_Lst=[probe_bitmap.d[probe] for probe in probeLst]\n",
    "    if randomize:\n",
    "        n_sum=sum(number_Lst)\n",
    "        r=int(random.random()*n_sum)\n",
    "        for i in range(len(number_Lst)):\n",
    "            r-=number_Lst[i]\n",
    "            if r<0:break\n",
    "        return probeLst[i]\n",
    "    else:\n",
    "        return probeLst[np.argmax(number_Lst)]\n",
    "    \n",
    "    \n",
    "def GenerateProbeSetBF(probeLst, probe_bitmap):\n",
    "    probeset_bf=B.BitArray(probe_bitmap.n+1)\n",
    "    for probe in probeLst:\n",
    "        probe_idx=probe_bitmap.Lst.index(probe)\n",
    "        probeset_bf[probe_idx]=1\n",
    "    return probeset_bf\n",
    "\n",
    "def ComputeProteinClusters(proteinLst, probeset_bf):\n",
    "    \"\"\"This function computes the number of clusters \n",
    "    given a set of proteins and choice of probes\"\"\"\n",
    "    proteinprobehit_bfLst=[]\n",
    "    dedup_dict={}\n",
    "    for protein in proteinLst:\n",
    "        pap=protein.bf&probeset_bf\n",
    "        bstr=pap.bin_str()\n",
    "        if not bstr in dedup_dict:dedup_dict[bstr]=0\n",
    "        dedup_dict[bstr]+=1\n",
    "        proteinprobehit_bfLst.append(pap)\n",
    "    return len(dedup_dict)\n",
    "\n",
    "    \n",
    "def ComputeProteinClusters_DoesNotWork(proteinLst, probeset_bf):\n",
    "    #ToDo: fin efficient/correct way to sort bitfields \n",
    "    \"\"\"This function computes the number of clusters \n",
    "    given a set of proteins and choice of probes\"\"\"\n",
    "    proteinprobehit_bfLst=[]\n",
    "    for protein in proteinLst:\n",
    "        pap=protein.bf&probeset_bf\n",
    "        proteinprobehit_bfLst.append(pap)\n",
    "    #dedupe rec count\n",
    "    proteinprobehit_bfLst.sort()\n",
    "    current_rec=proteinprobehit_bfLst[0]\n",
    "    cluster_count=1\n",
    "    for rec in proteinprobehit_bfLst:\n",
    "        if rec!=current_rec:\n",
    "            cluster_count+=1\n",
    "            current_rec=rec\n",
    "    return cluster_count\n",
    "\n",
    "def ChoiceCycle(proteinLst, probe_bitmap, probeLst, choice_window=0):\n",
    "    #Computes info gain for each remaining probe\n",
    "    probeset_bf=GenerateProbeSetBF(probeLst, probe_bitmap)\n",
    "    probe_choiceLst=[]\n",
    "    for probe in probe_bitmap.Lst:\n",
    "        if (probe in probeLst):continue\n",
    "        probe_idx=probe_bitmap.Lst.index(probe)\n",
    "        if probeset_bf[probe_idx]:continue\n",
    "        probeset_bf[probe_idx]=1\n",
    "        cluster_count, proteinprobehit_bfLst= ComputeProteinClusters(proteinLst, probeset_bf)\n",
    "        probe_choiceLst.append([cluster_count, probe])\n",
    "        probeset_bf[probe_idx]=0\n",
    "    probe_choiceLst.sort(key=lambda x:-x[0])\n",
    "    top_num_clusters=probe_choiceLst[0][0]\n",
    "    choiceLst=[]\n",
    "    for i in range(len(probe_choiceLst)):\n",
    "        c=probe_choiceLst[i]\n",
    "        if top_num_clusters-c[0]>=choice_window:\n",
    "            choiceLst.append(c)\n",
    "        else:\n",
    "            break\n",
    "    print('ChoiceCycle:probe_choiceLst:%i choiceLst:%i top_num_clusters:%i'%(len(probe_choiceLst),len(choiceLst), top_num_clusters))\n",
    "    return choiceLst\n",
    "\n",
    "def compute_protein_clusters_Runner(t):\n",
    "    #For multiprocessing\n",
    "    proteinLst, probeset_bf, probe_idx, probe = t\n",
    "    probeset_bf[probe_idx]=1\n",
    "    cluster_count= ComputeProteinClusters(proteinLst, probeset_bf)\n",
    "    return [cluster_count, probe]\n",
    "    \n",
    "def ChoiceCycle_mp(proteinLst, probe_bitmap, probeLst, choice_window=0, num_ranks=12):\n",
    "    #Computes info gain for each remaining probe\n",
    "    #Multiprocessing version of ChoiceCycle\n",
    "    probeset_bf=GenerateProbeSetBF(probeLst, probe_bitmap)\n",
    "    probe_choiceLst=[]\n",
    "    processingLst=[]\n",
    "    for probe in probe_bitmap.Lst:\n",
    "        if (probe in probeLst):continue\n",
    "        probe_idx=probe_bitmap.Lst.index(probe)\n",
    "        if probeset_bf[probe_idx]:continue\n",
    "        processingLst.append([proteinLst, probeset_bf.copy(), probe_idx, probe])\n",
    "    with closing( mp.Pool(num_ranks) ) as p:\n",
    "        probe_choiceLst=p.map(compute_protein_clusters_Runner, processingLst)\n",
    "    probe_choiceLst.sort(key=lambda x:-x[0])\n",
    "    top_num_clusters=probe_choiceLst[0][0]\n",
    "    choiceLst=[]\n",
    "    for i in range(len(probe_choiceLst)):\n",
    "        c=probe_choiceLst[i]\n",
    "        if top_num_clusters-c[0]>=choice_window:\n",
    "            choiceLst.append(c)\n",
    "        else:\n",
    "            break\n",
    "    print('ChoiceCycle:probe_choiceLst:%i choiceLst:%i top_num_clusters:%i'%(len(probe_choiceLst),len(choiceLst), top_num_clusters))\n",
    "    #print(choiceLst)\n",
    "    return choiceLst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choice Routine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChoiceCycle:probe_choiceLst:100 choiceLst:100 top_num_clusters:4\n",
      "1 4 100 SSS\n",
      "ChoiceCycle:probe_choiceLst:99 choiceLst:99 top_num_clusters:8\n",
      "2 8 99 PPP\n",
      "ChoiceCycle:probe_choiceLst:98 choiceLst:98 top_num_clusters:16\n",
      "3 16 98 AAA\n",
      "ChoiceCycle:probe_choiceLst:97 choiceLst:97 top_num_clusters:32\n",
      "4 31 97 EEE\n",
      "ChoiceCycle:probe_choiceLst:96 choiceLst:96 top_num_clusters:59\n",
      "5 57 96 SSL\n",
      "ChoiceCycle:probe_choiceLst:95 choiceLst:95 top_num_clusters:96\n",
      "6 92 95 LPP\n",
      "ChoiceCycle:probe_choiceLst:94 choiceLst:94 top_num_clusters:141\n",
      "7 140 94 SLL\n",
      "ChoiceCycle:probe_choiceLst:93 choiceLst:93 top_num_clusters:194\n",
      "8 186 93 LEE\n",
      "ChoiceCycle:probe_choiceLst:92 choiceLst:92 top_num_clusters:239\n",
      "9 238 92 LLA\n",
      "ChoiceCycle:probe_choiceLst:91 choiceLst:91 top_num_clusters:289\n",
      "10 283 91 LLG\n",
      "ChoiceCycle:probe_choiceLst:90 choiceLst:90 top_num_clusters:334\n",
      "11 316 90 LLE\n",
      "ChoiceCycle:probe_choiceLst:89 choiceLst:89 top_num_clusters:359\n",
      "12 352 89 ALL\n",
      "ChoiceCycle:probe_choiceLst:88 choiceLst:88 top_num_clusters:389\n",
      "13 378 88 PPS\n",
      "ChoiceCycle:probe_choiceLst:87 choiceLst:87 top_num_clusters:415\n",
      "14 409 87 SLS\n",
      "ChoiceCycle:probe_choiceLst:86 choiceLst:86 top_num_clusters:446\n",
      "15 436 86 LLS\n",
      "ChoiceCycle:probe_choiceLst:85 choiceLst:85 top_num_clusters:470\n",
      "16 470 85 LSS\n",
      "ChoiceCycle:probe_choiceLst:84 choiceLst:84 top_num_clusters:498\n",
      "17 487 84 LGL\n",
      "ChoiceCycle:probe_choiceLst:83 choiceLst:83 top_num_clusters:513\n",
      "18 506 83 LSL\n",
      "ChoiceCycle:probe_choiceLst:82 choiceLst:82 top_num_clusters:530\n",
      "19 516 82 PLP\n",
      "ChoiceCycle:probe_choiceLst:81 choiceLst:81 top_num_clusters:538\n",
      "20 527 81 ERL\n",
      "ChoiceCycle:probe_choiceLst:80 choiceLst:80 top_num_clusters:547\n",
      "21 536 80 PSP\n",
      "ChoiceCycle:probe_choiceLst:79 choiceLst:79 top_num_clusters:555\n",
      "22 539 79 QEE\n",
      "ChoiceCycle:probe_choiceLst:78 choiceLst:78 top_num_clusters:558\n",
      "23 551 78 AAL\n",
      "ChoiceCycle:probe_choiceLst:77 choiceLst:77 top_num_clusters:569\n",
      "24 560 77 LAA\n",
      "ChoiceCycle:probe_choiceLst:76 choiceLst:76 top_num_clusters:577\n",
      "25 567 76 RLL\n",
      "ChoiceCycle:probe_choiceLst:75 choiceLst:75 top_num_clusters:584\n",
      "26 573 75 GLL\n",
      "ChoiceCycle:probe_choiceLst:74 choiceLst:74 top_num_clusters:589\n",
      "27 581 74 QLL\n",
      "ChoiceCycle:probe_choiceLst:73 choiceLst:73 top_num_clusters:597\n",
      "28 591 73 SGS\n",
      "ChoiceCycle:probe_choiceLst:72 choiceLst:72 top_num_clusters:605\n",
      "29 598 72 ELL\n",
      "ChoiceCycle:probe_choiceLst:71 choiceLst:71 top_num_clusters:612\n",
      "30 602 71 PAP\n",
      "ChoiceCycle:probe_choiceLst:70 choiceLst:70 top_num_clusters:616\n",
      "31 608 70 PLL\n",
      "ChoiceCycle:probe_choiceLst:69 choiceLst:69 top_num_clusters:622\n",
      "32 612 69 GEK\n",
      "ChoiceCycle:probe_choiceLst:68 choiceLst:68 top_num_clusters:626\n",
      "33 615 68 LAL\n",
      "ChoiceCycle:probe_choiceLst:67 choiceLst:67 top_num_clusters:629\n",
      "34 619 67 LEL\n",
      "ChoiceCycle:probe_choiceLst:66 choiceLst:66 top_num_clusters:634\n",
      "35 622 66 PSS\n",
      "ChoiceCycle:probe_choiceLst:65 choiceLst:65 top_num_clusters:637\n",
      "36 624 65 EEL\n",
      "ChoiceCycle:probe_choiceLst:64 choiceLst:64 top_num_clusters:638\n",
      "37 628 64 SPS\n",
      "ChoiceCycle:probe_choiceLst:63 choiceLst:63 top_num_clusters:641\n",
      "No more optimization is possible\n",
      "['LLL', 'SSS', 'PPP', 'AAA', 'EEE', 'SSL', 'LPP', 'SLL', 'LEE', 'LLA', 'LLG', 'LLE', 'ALL', 'PPS', 'SLS', 'LLS', 'LSS', 'LGL', 'LSL', 'PLP', 'ERL', 'PSP', 'QEE', 'AAL', 'LAA', 'RLL', 'GLL', 'QLL', 'SGS', 'ELL', 'PAP', 'PLL', 'GEK', 'LAL', 'LEL', 'PSS', 'EEL', 'SPS']\n"
     ]
    }
   ],
   "source": [
    "probe_bitmap=bitmap\n",
    "RND_CHOICE=False;NUM_RANKS=12\n",
    "probeLst=[chooseMostPrevalentProbe(probe_bitmap.Lst, probe_bitmap, randomize=RND_CHOICE)]\n",
    "cycleLst=[];cycleLL=['cycle', 'num_clusters', 'num_choice_possibilities', 'probeSet']\n",
    "discernible_clusters=0;clusters_to_discern=len(protLst)\n",
    "cycle=0;previous_discernible_clusters=0\n",
    "while discernible_clusters < clusters_to_discern:\n",
    "    cycle+=1\n",
    "    choiceLst=ChoiceCycle_mp(protLst, bitmap, probeLst, choice_window=0, num_ranks=NUM_RANKS)\n",
    "    prLst=[c[1] for c in choiceLst]\n",
    "    probe=chooseMostPrevalentProbe(prLst, probe_bitmap, randomize=RND_CHOICE)\n",
    "    choice_idx=prLst.index(probe)\n",
    "    #Choose first of equals You can also choose randomly or choose from within choice_window range\n",
    "    num_clusters,probe =choiceLst[choice_idx]\n",
    "    if num_clusters>discernible_clusters:\n",
    "        probeLst.append(probe)\n",
    "        cycleLst.append([cycle, num_clusters, len(choiceLst), '|'.join(probeLst)])\n",
    "        discernible_clusters=num_clusters\n",
    "        print(cycle, num_clusters, len(choiceLst), probe)\n",
    "    else:\n",
    "        print('No more optimization is possible')\n",
    "        break\n",
    "    \n",
    "print(probeLst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_dir=\"./\"\n",
    "clst=[[r[0], r[1], len(r[3].split('|'))] for r in cycleLst]\n",
    "df=pd.DataFrame(clst, columns=['cycle',\n",
    "                                'num_clusters', \n",
    "                                'num_probes'])\n",
    "df.to_csv(out_dir+'choice_cycle_run.csv')\n",
    "\n",
    "plt.plot(list(range(len(clst))), [c[1] for c in clst])\n",
    "plt.title(\"Resolution by Choice Cycle\")\n",
    "\n",
    "plt.xlabel('Cycle/Probes Chosen')\n",
    "plt.ylabel('Resolution (clusters)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protein Digestion:  \n",
    "\n",
    "https://pyopenms.readthedocs.io/en/latest/installation.html  \n",
    "\n",
    "https://readthedocs.org/projects/pyopenms/downloads/pdf/release_2.4.0/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyopenms import *\n",
    "\n",
    "p=ProteaseDB()\n",
    "n=[]\n",
    "p.getAllNames(n)\n",
    "\n",
    "def digest(enz, fa_file, rngLst=[0, 50, 100, 150, 200, 250, 300, 400, 500, 1000, 2000]):\n",
    "    dig.setEnzyme(enz)\n",
    "    size_dict={}\n",
    "    fa = pyfastx.Fasta(huprot_path)\n",
    "    oLst=[]\n",
    "    for seq in fa_file:\n",
    "        result = []\n",
    "        s=seq.seq;name=seq.name;\n",
    "        prefix, acc=name.split('|')[0:2]\n",
    "        if prefix=='tr':continue\n",
    "        dig.digest(AASequence.fromString(s), result)\n",
    "        result=[r.toString() for r in result]\n",
    "        print(name, len(result), end='\\r')\n",
    "        for i in range(len(rngLst)-1):\n",
    "            min_sz=rngLst[i];max_sz=rngLst[i+1]\n",
    "            mami='%i_%i'%(min_sz, max_sz)\n",
    "            if not(mami in size_dict):size_dict[mami]={}\n",
    "            l=list(filter(lambda x:min_sz<=len(x)<max_sz, result))\n",
    "            if l:\n",
    "                size_dict[mami][acc]=l\n",
    "    return size_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Asp-N/B'\n",
      "sp|W6CW81|PYDC5_HUMAN 73011538 317754747935492321199 83258 613 27 32 1213815 372636 4751 2646 512 1523 411799 539 1240 1517 89\r"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>enz</th>\n",
       "      <th>size_range</th>\n",
       "      <th>num_proteins</th>\n",
       "      <th>avg_frags</th>\n",
       "      <th>stdev_frags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'Asp-N/B'</td>\n",
       "      <td>50_200</td>\n",
       "      <td>16922</td>\n",
       "      <td>2.847358</td>\n",
       "      <td>2.723181</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          enz size_range  num_proteins  avg_frags  stdev_frags\n",
       "0  b'Asp-N/B'     50_200         16922   2.847358     2.723181"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huprot_path=\"/tdata2/human_proteome/UP000005640_9606.fasta\"\n",
    "\n",
    "dig = ProteaseDigestion()\n",
    "oLst=[];LL=['enz', 'size_range', 'num_proteins', 'avg_frags', 'stdev_frags']\n",
    "\n",
    "enz_names=[\n",
    "    b'Trypsin',\n",
    "         b'Clostripain/P',\n",
    "         b'Glu-C+P',\n",
    "         b'TrypChymo',\n",
    "         b'Arg-C/P',\n",
    "         b'V8-E',\n",
    "         b'Chymotrypsin',\n",
    "         b'elastase-trypsin-chymotrypsin',\n",
    "         b'staphylococcal protease/D',\n",
    "         b'Trypsin/P',\n",
    "         #b'unspecific cleavage',\n",
    "         b'Chymotrypsin/P',\n",
    "         b'proline-endopeptidase/HKR',\n",
    "         b'PepsinA + P',\n",
    "         b'cyanogen-bromide',\n",
    "         #b'CNBr',\n",
    "         b'V8-DE',\n",
    "         b'Lys-C/P',\n",
    "         b'leukocyte elastase',\n",
    "         b'proline endopeptidase',\n",
    "         b'glutamyl endopeptidase',\n",
    "         b'Alpha-lytic protease',\n",
    "         b'2-iodobenzoate',\n",
    "         b'iodosobenzoate',\n",
    "         b'Arg-C',\n",
    "         b'Asp-N/B',\n",
    "         b'Lys-C',\n",
    "         b'PepsinA',\n",
    "         b'Asp-N_ambic',\n",
    "         b'Lys-N',\n",
    "         b'Formic_acid',\n",
    "         b'Asp-N',\n",
    "         #b'no cleavage'\n",
    "          ]\n",
    "\n",
    "enz_names=[\n",
    "    b'Asp-N/B'#,b'CNBr',\n",
    "]\n",
    "#p.getAllNames(enz_names)\n",
    "rngLst=[50, 200]#[0, 50, 100, 150, 200, 250, 300, 400, 500, 1000, 2000])\n",
    "for enz in enz_names:\n",
    "    print(enz)\n",
    "    dig.setEnzyme(enz)\n",
    "    size_dict=digest(enz, fa_file, rngLst=rngLst)\n",
    "    for i in range(len(rngLst)-1):\n",
    "        min_sz=rngLst[i];max_sz=rngLst[i+1]\n",
    "        mami='%i_%i'%(min_sz, max_sz)\n",
    "        clst = np.array([len(size_dict[mami][k]) for k in size_dict[mami].keys()])\n",
    "        oLst.append([enz, mami, len(size_dict[mami]), clst.mean(), clst.std()])\n",
    "df = pd.DataFrame(oLst, columns=LL)\n",
    "enz_str=str(enz).replace(' ','').replace('/', '-').replace('+','plus').replace(\"'\", '')[1:]\n",
    "df.to_csv('all_digestions50-200.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fragLst=[]\n",
    "for k in size_dict[mami].keys():\n",
    "    fragLst.extend([len(x) for x in size_dict[mami][k]])\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.violinplot(x=fragLst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}