{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#WT line specific mutations\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "##First calculate WT, and all other mean reference frequencies, to see the WT specific mutations\n",
    "- WT specific heterozygote mutations have around 50% reference base count in WT samples, and around 100% in non WT samples. So the average reference base frequency among WT samples is around 50%, and around 100% among non WT samples. To see these mutations I will plot almost all positions on an (average other samples refbase freq, average WT refbase freq ) plane. WT specific mutations are expected to be in the middle right (1,0.5), and are expected to be clearly separated from other positions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Toggle code\n",
    "\n",
    "from IPython.display  import HTML\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%writefile wt_vs_all.py\n",
    "#!/usr/bin/python\n",
    "\n",
    "#First script:\n",
    "    # Input is a filtered pileup-like format\n",
    "    # there are lines in Orsi's format, and i dont use them\n",
    "    # writefile magic writes these files\n",
    "    # they will be later executed by slurm\n",
    "\n",
    "#import modules\n",
    "import subprocess\n",
    "import sys\n",
    "import re\n",
    "import numpy as np\n",
    "import fnmatch\n",
    "import os\n",
    "\n",
    "#input ouput files\n",
    "input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'\n",
    "output_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all'\n",
    "subprocess.call(['mkdir',output_dir])\n",
    "output_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/heatmap/'\n",
    "subprocess.call(['mkdir',output_dir])\n",
    "\n",
    "#which file to run on come in cmdline arg\n",
    "input_fname=sys.argv[1]\n",
    "\n",
    "#filenames for samplenames\n",
    "rm_dup_dir='/nagyvinyok/adat83/sotejedlik/orsi/bam_all_links'\n",
    "#collect filenames\n",
    "fnames=[]\n",
    "for fname in os.listdir(rm_dup_dir):\n",
    "    if (fnmatch.fnmatch(fname, '*.bam') and \n",
    "        not fnmatch.fnmatch(fname,\"*.bai\")): #strange .bai convention!!!\n",
    "        fnames.append(fname)\n",
    "fnames=sorted(fnames)\n",
    "\n",
    "#select the group samples set\n",
    "group=['Sample1_RMdup_picard_realign.bam','Sample4_RMdup_picard_realign.bam'] #R1\n",
    "for i in range(1,8)+[9]: #R2\n",
    "    group.append('DS00'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(10,12): #R2\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(41,51): #R3\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in [73,74]: #R4test\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(81,98): #R4\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "\n",
    "#create array to index into numpy arrays\n",
    "group_bool,else_bool,group=[],[],set(group)\n",
    "for sample in fnames:\n",
    "    group_bool.append(sample in group)\n",
    "    else_bool.append(not (sample in group))\n",
    "group_bool,else_bool=np.array(group_bool),np.array(else_bool)\n",
    "\n",
    "#filter for pileup lines\n",
    "# there is a # in the beggining of Orsis format lines\n",
    "cmd_filt_pup_lines= ' grep -v \\'#\\' '+ input_dir+input_fname\n",
    "\n",
    "#matrix for heatmap\n",
    "resolution=200 #resolution hard coded !!!!!!!!\n",
    "heat_mat=np.zeros((resolution+1,resolution+1),dtype=np.int32)\n",
    "\n",
    "#run the pipeline\n",
    "from subprocess import Popen, PIPE\n",
    "p = Popen(cmd_filt_pup_lines, stdout=PIPE, bufsize=1,shell=True)\n",
    "with p.stdout:\n",
    "    for line in iter(p.stdout.readline, b''):\n",
    "        #parse line\n",
    "        linelist=line.strip().upper().split(' ')\n",
    "        covs=np.array(map(int,linelist[3::2]),dtype=np.int32)\n",
    "        bases=linelist[4::2]\n",
    "        ref_count=[]\n",
    "        for i in xrange(len(bases)):\n",
    "            ref_count.append(len(re.findall('[\\.\\,]',bases[i]))) \n",
    "        ref_freq=np.array(ref_count,dtype=np.double)/covs\n",
    "        #calculate group freqs and save in matrix\n",
    "        group_freq=np.mean(ref_freq[group_bool])\n",
    "        else_freq=np.mean(ref_freq[else_bool])\n",
    "        heat_mat[int(resolution*group_freq),int(resolution*else_freq)]+=1\n",
    "p.wait() # wait for the subprocess to exit\n",
    "\n",
    "#save it\n",
    "np.savetxt(output_dir + input_fname.split('.')[0]+'.mat',heat_mat,fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Run them in slurm\n",
    "\n",
    "import os\n",
    "import subprocess\n",
    "input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'\n",
    "for filename in os.listdir(input_dir):\n",
    "    try:\n",
    "        print subprocess.check_output([ 'sbatch',\n",
    "                                       '--mem',str(1000),'./wt_vs_all.py' ,\n",
    "                                       filename],stderr=subprocess.STDOUT),\n",
    "    except subprocess.CalledProcessError, e:\n",
    "        print e.output,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "###Plot the 'heatmap'\n",
    "- its half heatmap, half scatter because of the high resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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atEmdOnH6AwAAtYmawBjt1Vv069dv05IlS7oeckht1qAA1WDZsmXafffdO/TX\nnDRrj6jtyqjluj+fuqxqjZu4+rF82xZC0uN3ZJy+x3A9fpKYSdraId931Pc1JT3+6PojQsvB3oDR\n/n7B+0RT8+eGmsDqxZ/CHf3whz9885hjjrE333yz3njjDa4KAhVk06ZN+uMf/6gxY8bYCy+88I1S\njwcAAKAUuBIYo72/lp1xxhnvDRw48MPJkyfv+dOf/nS7d955p/PmzZtLMTwAjjp16qTdd9/9k2nT\npr1x2mmnvVfq8bSpxqs5KDziBq6IGbjiKmD1MpXSTLkElpV6AAAAAB3V0TTSQh4j7phxzwumf0bF\ntYEgHTSs3+H7Jdpu5XkLBxV4KAXT3NxsT75lUqmHIUm664wZamhoKIsmxqSDxmhpaakr9RhQeYgb\nuCJm4IO4gauu67oTM3AyoX8TMVOlmAQCAAAAQA1hEhiD3Hn4IG7gipiBD+IGrjb12EDMwMmdLy8g\nZqoUN4YBAJQdn5YJKL00W6eUikvsFeP1xh0j6fF918WNxYVL+4ikdYDU/aUj8Xt1XmHHgeLjSmAM\n6i3gg7iBK2IGPogbuCJm4Or8plOJmSrFJBAAAAAAagjpoDGot4AP4gauKWLEzNZ8U9QqNQXRR0fj\nphDvm+9+Sv0ZJk1zTCtN2Xc/HX5eZ61ZvfaV2G2T7jNfimfcezp3xSPZx2PqjwytCzYuO2nW5TnX\noTjoE1i9uBIIAAAAADWESWAMcufhg7iBK2IGPogbuKJPIFxRE1i9mAQCAAAAQA0x1pJhncOyUg8A\nAMpFqWu2UD6qPRYK0Z4kX81hKdtQuLR9SDrOfM9Leszov1HHz56ec1vaQnRcv8P3y7lu5XkLBxVx\nKKlqbm62J98yqdTDkCTddcYMNTQ0mFKPQ+JKIAAAAADUFCaBMai3gA/iBq6IGfggbuCKmIGrCf2b\niJkqRYsIAEBe1Zj2V+0KkdaY5n7KSVzKpe+6OC77KUT7CJcU0FzPC7Z5kLZu9RA0aclPcm774IqH\nQ+vmL30+535I+Sys9t7f97sNz/z8vBIMCAXFlcAY9O6CD+IGrogZ+CBu4IqYgSv6BFYvJoEAAAAA\nUEOYBMYgdx4+iBu4Imbgg7iBK2IGrugTWL2oCQQAVDSXW9PXklp+7fm41M8lXRe3z3yfRbE/qzTq\nBeNqAKVwzeDo+iNC64J1gPNiagDhZ6tWD4FOG6sWvhS/bcS/fW7nvNugMnElMAa58/BB3MAVMQMf\nxA1cETORGT++AAAgAElEQVRwdefLC4iZKsUkEAAAAABqCOmgMVpaWur4qxlcETdwRcx0TDmnPfq2\nEMi1j+B+2oubNI6X9Pj5ti3F55L0+OUWM4VoQ9FeWmfXdd3rNvXYkMq5JtraISqY5jlv6XOhdTa6\ncQ60hPDj8r7l2/b8plPrrl4wcw0tIqoPVwIBAAAAoIYwCYzBX+bhg7iBK2IGPogbuErrKiBqB30C\nqxeTQAAAAACoIdQExqBOBz6IG7giZqpXGvVzuda1FzeFqHVz2Wepa+3SOL5vy5Fyq4EsRE1gsO1D\ntEVEcJ0kjRo2IPt4fkwbCOr+0pdWS4fRwwZoULeBdcs2Luf3UxXiSiAAAAAA1BAmgTH4yzx8EDdw\nRczAB3EDV9QEwhVXAasXk0AAAAAAqCHUBMagTgc+iBu4qqWYKXUfuXLS0dff0tJSt7rzKwWvCaw1\nxXgPo/V6Q3odnH08aclPQutmNF6Z83nBsSapZWw71yT9Hkb3aQMd/qI1gFEmZh11gB2XVt1f0KqF\n4c9lnqQJ/Zvq7nx5wZqrG1M/HEqMK4EAAAAAUEOYBMaolb/MI13EDVwRM/BB3MAVMQNXd768gJip\nUqSDAkCFi0sRKzfFHlsa6afl/P6W01g6wjc9MUlbhPa29W0DETeeuLHk2+cza5/KPo62XihEDCdd\n52IebSBSl1bKp+/7H3reeakMBWWEK4ExWlpa6ko9BlQe4gauiBn4IG7gquu67sQMnJzfdCoxU6WY\nBAIAAABADWESGIPcefggbuCKmIEP4gau6BMIV1cvmEnMVClqAgGgwlVLXVghpPHe8P6mz7fOMt92\nvp9VXL1gGvtMc9uk+/Ct9Qu2fojWJwbF1QBK0qihB2QfX0tNYEGNHjYgfoPAemttaNX8p1/IuZ8x\n++T+/FH5uBIYg3oL+CBu4IqYgQ/iBq6oCYSrbT/YnpipUkwCAQAAAKCGkA4ag3oL+CBu4IqYgY9K\njptSp9im1QaiEGmkaaRxSu2ncrbVBMalfAaXw4mD+VNAg6697L7E29ayNNpA5PtcTODxqJjU0WBq\nqCSNrT9Km3psWGNCe0C14EogAAAAANQQJoExqLeAD+IGrogZ+CBu4IqaQLgiZqoXk0AAAAAAqCHU\nBMao5HoLlA5xA1fEDHyUQ9ykVQeX1j7j6vfi1iU9XtzYfJ+X7/hJawRH1x8RWm6v7q+tJtBuVe23\nxYMrHs4+nh+pNVtFq4eyEazti35OUU2BbWPrB+3WcUFvyerFlUAAAAAAqCFMAmNQbwEfxA1cETPw\nQdzAFfVdcEXMVC/SQQFkFSK1C0D18k25dNmn73MLcT7zTdX0TWl1Eb2Nf3stItqMrT8q+ziuDcSq\nheH0z34j98u5DrmF3rfHw+/b6JiWDUlF2z5E00ODkRF3vDGBuED140pgjHKot0DlIW7gipiBD+IG\nrqjvgitipnoxCQQAAACAGsIkMAb1FvBB3MAVMQMfxA1cUd8FV8RM9aImEACAMlaoWt006veikrZJ\nKHW9cVrvadLn5avz8201ESfY6qHNoG4Dteyt5aGfxbYXiBQMUgfoKVCUF6wPlMI1mOGqznCtX2xr\nhzyunXZf9vHeI7+QaLs25zWdomsW3K7NC9/wPj7KE1cCY1BvAR/EDVwRM/BB3MDVso3LiRk4uWbB\n7cRMlWISCAAAAAA1xFgbvTkwWi1raWmp4y+tcEXcwBUxAx/ETfXwbQsRNHfFI6Hl0fVHbLVuULeB\ndcs2Ll8TagPxGCmeaeh3+H75N9LW7RwWBD6LWeMvDK2bu3LLZ7pV+4bAv9+D27VnzD5b2oWMnz09\ntC7YMiK4XZttP9i+7qMd1q8Z1vvQQbEHKWPNzc325FsmlXoYkqS7zpihhoaGaOZvSXAlEAAAAABq\nCJPAGPyFFT6IG7giZuCDuIEragLh6qMd1hMzVYpJIAAAAADUEFpExKDeAj6IG7giZrYWVyNV6vYC\n5YK4KT6XuEyrJUbwudHjB+sAgzWA0XVtNYAT+jfV3fnyAmKmyEZH6gCDQm0gVj4aWjc2WgcYZLaU\nlY2pD9fyRetDg/udfdKUyI621BbOWxE+/pj6I9V1Xfe6TT02EDNViCuBAAAAAFBDmATG4C+s8EHc\nwBUxAx/EDVxxFRCuuApYvUgHBaoE6XMolmisFSK+iNniKsZnmjQ9shhjiTtm3PHiUj7zbRv3vLiU\nz7hj2EAq31YpgIHWA1G0hei4uJYQ0fTPYFpn8DOTwp9bdF3Qgysezrlu/tMvhJZHDT0gtBxNFw0a\n2uvQ7GOjsuhcgCLhSmCMlpaWulKPAZWHuIErYgY+iBu4mtC/iZiBk67ruhMzVYpJIAAAAADUECaB\nMai3gA/iBq6IGfggbuCKmkC4oiawelETCFQJaqjKUynqmwqtGl4DworxmSY9hstYfL9faT0v6bp8\n+w8+N672yxc1gOmIqwNMKtqGIa5eMC4W4ur8oq0lgns9afb00LpRQ7ccI662kPN+9eFKYAzqLeCD\nuIErYgY+iBu4Or/pVGIGTgZ1G0jMVCkmgQAAAABQQ0gHjUG9BXwQNwhKkkJDzFSvpK0HvPbZWWt2\n026p7LMYCvFe+O7HpX2Dr7i2D1FJU0CjKYDB9gLRlhDtpYBevWAm55qURNtAjNlny2czd+Uj0c23\nPK/+iNBy8DOc0XhlzudFW4BEl+PMDcRXtH1E0KzxF4aWWxtGrNlLR7WzNSodVwIBAAAAoIYwCYxB\nvQV8EDdwRczAB/274IqaQLjiPFO9mAQCAAAAQA0x1tr8W9WmZaUeAAAkUY1tKFB4hajRK2dJX69L\nqwff+sFoDWDw1vyzxk8OrQvWfs2P1P3F/QuOthDJuLR9iNYBBkXbMgS51IfmEm0fEVcTGDeWuP1G\n21cEa1CH9jpkUOKdlpnm5mZ78i2TSj0MSdJdZ8xQQ0ODKfU4JK4EAgAAAEBNYRIYgzod+CBu4IqY\ngQ/iBq6oCYQragKrFy0iAKDC1UIqn69KTXl0SUn0Vez3o5w/izTGFk3PC6bSxaV/StKsb29JAT1p\n1vTQuqRFO6R/JtOR9E+XNMugNFKRjZJnEMbFYlRwPC5tJ1D5uBIYg95d8EHcwBUxAx/EDVzRJxCu\nNvXYQMxUKSaBAAAAAFBDmATGoN4CPogbuCJm4IO4gStqAuGKmsDqRU0gAKBqlVvtWVKVOu44abym\nfO1Q4mqvkh7f9xb+cXVXceskae7KLbVYTZE6tGBbCOr+/PQbuaUOcNXCl3Kuk8J1gL41gHF84yuu\nzi9fDeCQXgdnH1+wJNyCJKgQrxfliyuBMai3gA/iBq6IGfggbuCKmkC4oiawejEJBAAAAIAawiQw\nBvUW8EHcwBUxAx/EDVxREwhX1ARWL2oCAQA1ybd3l29tWzF6/5WzNF5/vu2C60v9fgfrtEZHarTm\nBer8oqI1a4kbBSK3QIu9uBrAqGh/x2Cvvnx1nj7iavtG1x8Ru21QdNzB5fmR2LOBALt22n1b7eu8\nplN0zYLbtXnhGzEjRyXiSmAM6i3gg7iBK2IGPogbuKImEK6uWXA7MVOlmAQCAAAAQA0hHTRGS0tL\nHX9phSviBnHaS0Fsi5mk6YlIR1opiEFxqaNpp5y2tLTUre78SuhcU4i4cUmrLHUKZpy4VFHfNNLg\nttEUvKCTZk8PLY+KaQNRyPTP85tOravaq4GBlM8fX3JCaNX8p1/IPo5L/5SK3yYh2L4hLoaCqahS\n/DhtJIiCqaPR2AvuZ8zsrVNct/1g+7qPdlhfnTFT47gSCAAAAAA1hElgDK7mwAdxA1fEDHwQN3BV\ntVcBUTBcBaxeTAIBAAAAoIZQExiD2i74IG4Qp726qLaYKXXNFDrO9zP0eV5LS0vd0N0OKfi5xmVs\nLnWPvsdIus9C7GfSkp/kXBdtGRCsw2oaekBoXfQ2/cVSzTWBwdYPxoTr54J1gGP2CX9OJ91zRWi5\n0DWBcbEed2yX+tthvQ4NLUfrCXM+r3f4eU+v/YO6rutet6nHhqqMmVrHlUAAAAAAqCFMAmNwNQc+\niBu4Imbgg7iBq2q9CojC4Spg9SIdFEBNK0Rbhkpt9VCp40Z5Siv9M60WFXHrgrfmj6bOBdM8o7fe\nDwqmf0ZF0z9XPfZSzm0RI/DRnDf1xMRPi0uzHBVJ1U0aC77tUaLbBeMmmlKcdP9xsefCp1UNKhdX\nAmO0tLTUlXoMqDzEDVwRM/BB3MDV+U2nEjNwwnmmejEJBAAAAIAawiQwBvUW8EHcwBUxAx/EDVxR\nEwhXnGeqFzWBAGpaGjUP1VJHkcZt+iv1tZdaIdonuByzEC0a0hp3Wm0n4rZN2hYgrg1E1LXT7ss+\n3nvkFxLtH/GCbSAKJWmbhrjzfnRdXOzF1QEmFd2HS+yjdnElMAZ50PBB3MAVMQMfxA1cURMIV5xn\nCssYM9kY86wx5n1jzFpjzEPGmP3b2e5SY8ybxpgNxphFxpgO/0WESSAAAAAAFN8hkm6SNEzSVyV9\nIukJY8xObRsYYy6Q9CNJZ0oaLGmtpMeNMTt05MDG2ty3Oy42Y0wXSZdJOl7SbpJaJN0r6VJr7aeB\n7S6VdIqknSQ9I+kH1tqXAuuvk/QdSesl/cRae19g3TclTbLWfiXPcJal8ZoAFF45p2OW89hQPio1\nTnzHXaj0tLiUPF/BlgFR8yKtH4JoA9Fx/Q7PfbFj9LABoeVgSmQ0TTfY6sEl/bLYaZWlTuPM8/0d\nVKxxpK25udmefMukUg9DknTXGTPU0NBgcq03xmwv6X1JTdba3xljjKQ1kn5urb2idZtuykwEz7PW\nzvQdS7ldCbxQ0mmSzpK0r6RzJJ0haXLbBvlmw62TvG9JOkzSJEm/MMbs3Lquh6TrlJlAAgAAAEC5\n2FGZ+dk/Wpf7SuotaWHbBtbajZKeknRQRw5UbpPAwZIestb+zlr7urX2N5J+K2mIJLXOhs+VdIW1\ndp619kVlrvj1kHRC6z6+IOm/rbXPWWvvl/QvSZ9rXTdd0mxr7ctJBkMeNHwQN3BFzMAHcQNX1ATC\nFeeZortR0vOSlrYu79r6/7cj260NrPNSbncHfUTSBcaYfa21f20tehyhzORNyjEbNsa0zYZnSnpB\n0inGmJ6S9pG0naSVxpihkg6VFM4fAAAAAFC1Vr63uiTH/WTten2ydn12+dnBz6qhoaHdbVvL2Q6S\nNNwmq9frUE1fWU0CrbW3GGN2l/QXY8wnyozvZ9ba/2zdJG42XNe6j4XGmHskPSvpQ0knSdog6TZl\nUk0nGmPOaf3ZWdbapcqB3ijwUU1xUyl1SuU6LinZ2AoZM8X+DDtyPN+WBUmfV87x7DOWfHFTis/C\nl28tYVwN1ZBeB4fWXbAkW1kSWxfmUgNYaXV/1dwncN6KR7OPo20ebODfytF6wbhYiMZbMDaCdYb5\n9pNU9HjBGA7Gb/R4Lq/J5fiSpM5as3rtK2V1vvRx6NGHlnoIkqTBAwa3+3NjzPWSxkkaYa19LbDq\nrdb/95b0t8DPewfWeSmrdFBjzNmSJihzY5gBykzgfmCM+W6Cp2e/4dbaadbaemvtF621C5SpDVwi\naZ2kacpcXZws6YHWm9EAAAAAQFEZY26UdJykr1prX4msflWZyd7IwPbdJA2X9MeOHLesJoGSpkia\nbq19wFr7orX2HmVu5NL254/gbDgo52zYGPN5Sd+VdIEyk78/WGvfttY+LmlbZW5Ak9Xc3Hxpc3Pz\npVImD7rtv7b1LLOcb3nlypX7prm/Ui53Xde9ruu67mUznmpdbvtZIfYf/Py6rute8NfTkeN1NN7y\nHa/a4nnlypX7Fvr9zPf8Yn/e+Y4XXP9Wy1t1b7W8lV0e1G1g3aBuA3MeP7oc3X5Qt4F1E/o3ZZcn\n9G+qC9bYnd90atkv33fujfumub9CL0ff7+Bye59P3Oe77brt67Zdt713PHRd193peGksu8RvocbT\n9rPgv4+RHmPMzZJOlnSipPeNMbu2/re9JLWmhd6gTLncaGNMg6S7lLmwdV/7e0147DJrEbFW0jRr\n7c2Bn02WNNFau0/rjWHelPQfkdukvq3MbVJvj+zPSFok6QZr7fzWNNBDrbWjW9e9J+kQa+2f2xnO\nspaWlrpqSu1DcRA3cEXMVJZySSstx7hJI4202LfFj4qmg8a1gQhW5KxaWP6poec3nVpXDimhca0f\ngqL30Z81/sLs47krH1Eu0XTQOC5tGYLpmc+sfSrxMXyPF2Qj5V/B9NeOtL3IN5au67rXbeqxYc3Q\nXodUdIuIi56/tdTDkCT9bMD3Qy0ijDGblTmTRMP9UmvtZYHtpipT1raTpKcVaY/no9xSIedL+okx\n5lVJLymTEvpDSXdLmdmwMeYGSRcaY16WtELSRco9G54o6V1r7fzW5cWSLjPGNEo6QNImSX/NNZhy\n++WKykDcwBUxAx/EDVyVwwQQlWVTjw3ETAFZaxNlZVprpylT0paacpsE/lCZlg43K5Pi2aLMHT+z\nM2Fr7QxjzHat27TNhkdaa9cHd2SM6a1MeulBgecuN8ZcIWle63HGW2s/KugrAgAAAIAyUlY1gdba\n9dba86y1fa213a21/ay1F1lrN0W2m2atrbPWbmetHdHe5dDWur++1tqWyM+vtNb2stbuY61dGDee\nYK0BkBRxA1fEDHwQN3BFn0C4CtYJorqU25VAAECRlEttWzlweS8qtQ1FMbjWGyV5Xtx2vvWDLm0g\ngiqtJUQlGj0sdzvnzO0cMqJ1cNE2CUHBuInGzKQlPwktx9XXRdtCFFrwNUXHlVYbiOB7k9b3C5Wh\nrK4ElhvqLeCDuIErYgY+iBu4oiYQrqgJrF5MAgEAAACghpAOGqMcb7+N8kfcwFWpYqbWUhXjpPVe\nFOM9bUvR6rque92B/QYXJG6StnrwTaP1TTNzeV40PTAufS42BTTQ+qHfyP1yrqsExWwRkbQNRFz6\nZ1yrh7RSM69qvCLnfqPxFve9KETqZHBscS0pXGI9Kt93va1FROIdomJwJRAAAAAAagiTwBhczYEP\n4gauiBn44K/zcEVNIFxxnqleTAIBAAAAoIZQExiD2i74IG6qUyFv/V+rMVPr7RSCfGrrWlpa6p5e\n+4c17a0r5NiSHs+lRirpti51WFY2tDx+1uWBdWFJWz9UWg1gVDFrAuME6wBdWj0E181ovDLndi5x\nEq0tTBqLcd/ZuH24PC+uDjBodP0RoWXfGsH2xk1NYPXiSiAAAAAA1BAmgTFq8S/z6DjiBq6IGfgg\nbuCqHK4CorJwFbB6kQ4KAAnUcqpiocSlbNXC+53G63V5nm/6WlotIuL4ptI9uOLhxMeIpoDCXbBF\nxlapsZGODaMCKZ8LIi04gumJ81Y8GloX1xYi+DyXOHHhm+Kc9Jhx6a6+hvU6NLQcTXH1bc8S/H7V\nwjm51nAlMEZLS0tdqceAykPcwBUxAx/EDVyd33QqMQMng7oNJGaqFJNAAAAAAKghTAJjUG8BH8QN\nXBEz8EHcwBU1gXC1bONyYqZKURMIAK1oWVBatfZ+l/r1+tZNxdUyprHPKJe6v3mR2rNckraEQESg\n1Kzf4fvl3k7hOsBgfaBUmLq4OIX4rvnW40bbNfjWILrU+RXiO4vKx5XAGNRbwAdxA1fEDHwQN3BF\nTSBcURNYvZgEAgAAAEANYRIYg3oL+CBu4IqYgQ/iBq6oCYQragKrFzWBANCqEHUj1BlWplL3LCzE\n8QvR78+lnihYBxati0pqfqTmL1prhuIZ7fDex/X+i0oap759Rl1i1rfuryP9O0v5vGitpsvnhsrD\nlcAY1FvAB3EDV8QMfBA3cEVNIFx1XdedmKlSTAIBAAAAoIaQDhqDegv4IG4QlCQtqJAxQzqqn0p4\nn/LFTVq3freyoWXftM64bZO2gbCR5biWELSB2FqaNYH5UnOTphL6pnW6SNpqweV5vutcjl+Ifcbt\nZ3T9EaHl1u/lGr1VGedEuOFKIAAAAADUECaBMai3gA/iBq6IGfggbuCKmkC4ok9g9WISCAAAAAA1\nxFgbzbBHq2WlHgAAAIWq60x6u/2oIb0Ozj5+Zu1TObdLWucXFVfnF0Xdn59+h+/X4X2YyPLsk6Z0\neJ8ufFue5Ps+Ja29831e3H586/4KVS8Y/A5f3XjVoMQHKTPNzc32oudvLfUwJEk/G/B9NTQ0RL8+\nJcGVQAAAAACoIUwCY1BvAR/EDVwRM/BB3MAVNYFwRU1g9aJFBICKV+ttENJ6/YW4NXshdCTVq5TS\natmQ9BhppatFma0SATsumAK6amE4xbPfyC2pi6R/piP4Hgff36jRkbYPpeabAplWO4egSUt+Elq+\nqvGK7OMLlkwOrZvReGWHj+fyfY7bNvq8YFVY4/hvh9fJ6vym7XXNgnt19cKrXIeMMseVwBj0e4MP\n4gauiBn4IG7gKs0+gagNxEz1YhIIAAAAADWESWAM6i3gg7iBK2IGPogbuKImEK6ImepFTSCAoipE\nzVY51X2VQlqvv1Lex7hxlvNrKMbY0qgHje4nui5YCzWm/sjEx5gfqPsbFak1Cy7Pj3Suog4wfb51\ngMHPe+6KR2KPUYgWBnHb+dY0p/U7KdguJfq9SKsOsBBMoMT308deD617eu0ftO0H22v0MQcL1Ycr\ngTGot4AP4gauiBn4IG7givouuPpoh/XETJViEggAAAAANYR00BgtLS11/KUVroibeOWcrlcqbTFT\nKS0akI6Opq91Xde9blOPDaFzjUuqbFyKWvT290FxKaAPrng457qmoQdkH18z7d6c28nmXoXk+h2+\ndcrnhP5NdXe+vCDx76foZ50vBTQoaQuD6HZJUyej8RwX+2m1R0nagiWtsfi+F2mluBpj2j3PoDpw\nJRAAAAAAagiTwBhczYEP4gauiBn44K/zcOVyFRCQOM9UMyaBAAAAAFBDqAmMQW0XfBA3cNUWM+VU\nB1iIVh61KK5OyPc9bXtee+eapDVL0fXRdXF1f8GSvZNmXZ5zXSzq/lLXXg1gVHs1gXEtIYxMaHls\n/VGeowtLo7Yu6f47sh+XY6S1bVCh22xE99ve86gJrF5cCQQAAACAGsIkMAZXc+CDuIErYgY+iBu4\noiYQrrgKWL1IBwWAVqRAblHq15709vLlLq10tkLsI7g+rrVD1Pylz2cfx2V1rnrspcT7RDJJUj6T\niEsBDaYCR1tCzGi8Mvs43/kyrbYMubicI3y3Lcb3N25b3/cwrfNnJZ1r4Y4rgTFaWlrqSj0GVB7i\nBq6IGfjouq47cQMnE/o3ETNwwu+n6sUkEAAAAABqCJPAGNRbwAdxA1fEDHxQqwNX1ATCFb+fqhc1\ngQDQqpLqH9KoWyk3pXxNLvWg1VI7mrQOcF6gBjAf6gCLK1jbF63PXBD43GadNCW0ziiZaKsQl/q1\nuPYGcd+ZpN893+9odJ1vTV6xayBdpHX+qsbfM9iCK4ExyIOGD+IGrogZ+KAmEK62JWbgiPNM9WIS\nCAAAAAA1xFgbd4Pnmras1AMAgHJRDWlBxUjj9D1GWqlkLq0egkj5LF9xbSGCaZ2jYto+jK0/KrQc\nbP0QTflMSxqx7/IdjTtHuXy/0jgvFDuN1Pd9cnzuoOQjKi/Nzc32oudvLfUwJEk/G/B9NTQ0JM3I\nLiiuBAIAAABADWESGIM6HfggbuCKmIEPanXgipiBK34/VS8mgQAAAABQQ2gREYPeKPBB3MBVJcRM\npdYBBhXqNRTi1vBxt7Rvs6nHhjW+NYBSuA6QOr/K5FIHKG3pLZlGHWBHvk++9XtJa+3Sqv9Nq7Yv\nyffZdZ8+27V3jHwq4fcT/HAlEAAAAABqCJPAGORBwwdxA1fEDHxQ3wVXxAxc8fupepEOCgBAB8Sl\nVxUi7S2paNuHVQsjKZ90iCqZuLYPUcGUz/mRzzS4HJca6isuZvPFaBrpmS7fA982Nr4tKuJeU3Rd\n0vNAWu0ygCS4EhiDPGj4IG7gipiBj7b6LiApYgau+P1UvZgEAgAAAEANYRIYgzxo+CBu4IqYgQ/q\nu+CKmIErfj9VL2oCgTLjW9dQ6uMVe9ylVmuvt5yV82eRxq3hc7WBGNRtoK6Zdm/ug1PzVxVM4PHs\nk6aEV9otH/LclY+EVs1dsWU5jZYQUb7tDKLbRvfjWy+YVouINPbTkVrKtMfior2xdd3QXavXvlJ2\n51Z0HFcCY5AHDR/EDVwRM/CxbONy4gZOqAmEK2KmejEJBAAAAIAaYqwlXySHZS0tLXX8hR6uiBsE\nJUnh6WjMFCNNqBjKNa0z3/ubRjuHqGAKaLTVQ5sJ/ZvqLrrxCs41lciEF/uN3NIyYrRDqwcT2NFV\njVeE1j2z9qmttu+6rnvdph4b1ri0PsilI20YfL/fvi1XfBW7JMP3+C7tOlz3E4iZQYl2Uoaam5vt\nRc/fWuphSJJ+NuD7amhoMPm3LDyuBAIAAABADWESGIOrOfBB3MAVMQMfd768gLiBE+q74IqYqV5M\nAgEAAACghtAiIga1XfBB3CAoSS1GR2OmnOrnXBTqFu9py3fspG0ghvQ6OLQ8aclPcm6bqw5QklY9\n9pIk6fymU+uu1kucaypAv8P3Cy271P0Fja0/KrRsA31ALlgyObRuRuOVWz2/7VyTtA7N5TsaJ40a\nwHz7SesYvsf3PZ7ve5prH2lrqwks2AFQMlwJBAAAAIAawiQwBldz4IO4gStiBj6uXjCTuIETzjVw\nxVXA6sUkEAAAAABqCDWBMajtgo9yjJti9F8r1x5v5aa996kcY6YYfOt7Sl0v6Fu3E63ZCorWALbV\n/cU5v+nUOq4Glo9o3Z+vMfVHhpbnrngk57bBPoHR58WdawrRp68Q+0nru+0ybt8aPd8ehkmPUaga\n6nzHpyawenElEAAAAABqCJPAGLX4l3l0HHEDV8QMfHAVEK4418AVVwGrl7HW5t+qNi0r9QAAAIWT\nNH2rI6lVwd+w42ddHlr3fwv/kn2898gvhNYlSQdF6cWlgAY/3yWzZ4fWzV2ZO8Uz2gYiDYVImc6X\ngolnjHMAACAASURBVJj0O1SM9G7fFO44ab3euH0WW56xDSriUFLV3NxsL3r+1lIPQ5L0swHfV0ND\ng8m/ZeFxJTBGS0tLXanHgMpD3MAVMQMf5zedStzACecauOq6rjsxU6WYBAIAAABADWESGIPcefgg\nbuCKmIEPagLhinMNXFETWL1oEQEAKSt2u4xSt0woZ3Gfhe8t1l22nRe4vf/s8ReG1h302Lezj6kB\nrD4/nnpC9vG8lY+G1hWi7i9OKc4RSY/h0k4h6T5d9uFSL5i0nUXc+x13/EK1gfD14IqHi3o8FBdX\nAmOQOw8fxA1cETPwQU0gXFHfBVeDug0kZqoUk0AAAAAAqCGkg8Ygdx4+iBs4pyx11prVa1/xbkWQ\nVnuDalSIlLSouG1toEnESbOnJ95nEtQEVo7R9UeEloNpdpJktOWO8Vc1XhFad8GSydnHY+qP9Dp+\nNr57KW/MpHX+SHqOitOR9iy+28WlYKbBN921FOfyGY1XStKacTq26MdG4XElEAAAAABqCJPAGNTp\nwAdxA1fU6cAHNYFwxe8nuHqr5S1ipkoxCQQAAACAGmKstfm3qk3LSj0AALWl1mv5Svn6XW7NHida\n6zVv6fM5t6UtROXpd/h+oWUTeDzrpCmRrbf8+2reinCLiGht35BeB2cfP7P2qcTrfBW7dU0x6pZ9\naxB9x5ZWa4li8z3XDe11yKCCDarAmpub7UXP31rqYUiSfjbg+2poaDD5tyw8rgQCAAAAQA1hEhiD\n3Hn4IG7gipiBD2oC4Yr6Y7giZqoXLSJQcnGpCWntt5xSMVB4hYopHy5pSHFpOeUUw76pVe2tL8Qx\n09hncN3cFY+E1vnepp/0z8oUTQENGjVsQPbx1vldW34SbBXSnrg0T98U0EK3Oohy+a6nMba486fL\n8ZKeB/IdI3iecDlHpPFvFd/3HrWNK4Ex6PcGH8QNXBEz8EGfQLja1GMDMQMnxEz1YhIIAAAAADWE\nSWAM6nTgg7iBK2IGPqgJhCvqu+CKmKle1ASi5AqVn07ee+0qp/YCvmMp5/gtxXe2EHV/cVzqe4Jt\nIeJaQqB8xdX9BY0O1ABK0tj6oxI9L+l27fGtFU5a65ZW+wbf/ZTbuS5p/WBarymN119u7yEqA1cC\nY1CnAx/EDVwRM/BBTSBcUd8FV8RM9WISCAAAAAA1hHTQGC0tLXX8hR6uiJvqUeh0prb9d13XvW5T\njw1rqjGlpxC3PPdtO+GSOufbBmJUJF1wfgHTQ89vOrWOq4HJJU35jAqmgLqkdfq2DMjXZiXpuvbi\nvb3fT4VqR5P0O1uI73pHWiaklQ7ro1AtdXzPkU+v/UP295PXQFDWuBIIAAAAADWESWAMrubAB3ED\nV/yVFT64CghX/H6CK34/VS8mgQAAAABQQ6gJjEFtF3zy6jsaN771CMitlDUeSfbfFjN89rn5tnqI\n20dczVbcumBLiKhoDeCqx15yHmdS1ATmYcKLwXrNuFrNaBuIoGBcSPG1fnHrXOr+4mrk4rR3Pmnv\n95NLrWwhzku+NXkurXlczq3FrklMup9i/O5qL76oCaxeXAkEAAAAgBrCJDAGVwHhg7iBK2IGPrgK\nCFeca+CKq4DVi3RQIEYpUvJIAywu11usV9rxyk1cepNvGpivuHQ9K5t9HE3/nBeTSljI9E+46Tcy\n3BJiwdMvZB/PHn9haN3cleE0z1zSSvH0VanniLRSGdM4RxSjfYTv5+TyvGKUD1RqvCEZrgTGaGlp\nqSv1GFB5iBu46rquOzEDZ+c3nUrcwAm/n+CKmKleTAIBAAAAoIYwCYxB7jx8EDdwRc0FfFATCFf8\nfoIrYqZ6UROIilKM21Wj+iSt3ejo7dfzbZdv22IodRuKNN5vl3VJxbV9QHUYNfSA7ON5Kx8NrRtb\nf1SH9x/3XY+uczkvJK2jjUqjti6t48UdP24/5VxX6Ts239rR6PNK/bsElY8rgTHIg4YP4gauiBn4\noCYQrjjXwBUxU72YBAIAAABADTHW2vxb1aZlpR4AgOpWqNSqpM8tRqpqGulq+aSRPhZsCSFJc1ds\naRkQbQlBG4jy1e/wLW0hTGTd7JOmFPTYHfn++KYExu3D9zzge/xic0m/TbrO5RhpKXWKvoNBpR6A\nr+bmZnvR87eWehiSpJ8N+L4aGhqip6eS4EogAAAAANQQJoExyIOGD+IGrugTCB/UBMIVv5/gipip\nXkwCAQAAAKCG0CIiBr1R4IO4QVLZ+o9eco6ZSmlREXc79LTGFtxPsJZPksbUH5l97NIGIloHWI5q\nsU9gsOYvn1HDBoSWg5///KdfCK2bPf7CnPspRq1sGrGf6DzQWWtWr30l8fHSOrcUo34vbjvfurty\n+uzTqiF3OcbQXofwb5oqxpVAAAAAAKghTAJjkAcNH8QNXBEz8EFNIFxRfwxX/H6qXkwCAQAAAKCG\n0CcwN/oEoub51nykUd/Skf3E7bfM+zBVBJfaFJd6n6C42j6Xur8x9UdlH580e3po3axvT84+Hh9Z\nF4c+gSUU6a7Vb2S4RnB0oA4wGBdSOG7G7BNeN2/lozmfF+RSa9aR70nctoWW1vkyjffGt+9nnLTO\nUcX4XJK+h4XqKxs5Bn0CU0CfQAAAAABASTAJjEEeNHwQN3BFzMAHNYFwxbkGrqgjrV60iACQU7Fv\npV2o9JpaTgFN63bkweellfbm+7lYJS9jmBtID42WPwTTQ0nxLB/RNhCjhh6QfRxt7TA60gZibCD9\nNypuXVwKaFAh2hlElfp8lcY5Irrsm1ZZjPei1MePOycWYmyuv9dbPm3Rbr128zoWyhtXAmPQGwU+\niBu4Imbgoxb7BKJjONfAFTFTvZgEAgAAAEANYRIYg9x5+CBu4IqYgQ9qAuGKcw1cETPVi5pAoMYU\nqg1DodVC+wiXsSStsUmrbiRp3YrLLd0Lcft3F8EKwWirgVULqREspmgdYNCCQB2gSw1gIRSixjWf\nUrcpSOPYvi0MXOqPkx4v3z58ayJ99hHdtlJ/P6MycSUwBnnQ8EHcwBUxAx/UBMIV5xq4ImaqF5NA\nAAAAAKghJnrLbGQta2lpqeMvIMglV9oGcVN8xUihSevW8O2lOpVLzCRNbfNtCSFJc1c8kn0cvS1/\ncN3oyLpgO4dgy4CoeUufz7kuTiW2iDi/6dS6arkaGEwH/b+Ffwmt+/HUE3I+z8iElpO2enCR5nc9\nibTOZ+19T7uu6163qceGNcVOQfQ9fxZibPn2mTQdtdTtI4ol8PtpUEkGkILm5mZ70fO3lnoYkqSf\nDfi+GhoaTP4tC48rgQAAAABQQ5gExiiHv8yj8hA3cEXMwEe1XAVE8WzqsYGYgRN+P1UvJoEAAAAA\nUGTGmIONMQ8ZY/5mjNlsjPlOO9tcaox50xizwRizyBiT+3bKDsquRYQxZjdJV0o6UlIPSf8n6fvW\n2qcC21wq6RRJO0l6RtIPrLUvBdZfJ+k7ktZL+om19r7Aum9KmmSt/Uq+sZRLnQ4qSzXHTTnUJ7Sn\nGGNJq26kvbEWM2Z86/7SuG26JFnlrkMPrpu74uHwukD9ukvdXyXW+iVVaTWBwTYcqx4Pfy6h1g+R\nNhBB8wPtIiRp9vgL0xlcgbl8n9I6nyU91xTiPBB9ru/rLaffM1Lx2zmUw+uv5n/TlIntJf1Z0t2S\nZincvUjGmAsk/UiZec0rki6R9LgxZl9r7QcdOXBZXQk0xvSUtESZN+AoSf0lnSlpbWCbtjfjTEmD\nW9c9bozZoXX9NyV9S9JhkiZJ+oUxZufWdT0kXafMBBIAAAAASsJa+4i19iJr7YOSNgfXGWOMpHMl\nXWGtnWetfVGZyWAPSbnvmJVQWU0ClZm0vWmtPdlau8xau9pau8ha+7KU+M34gqT/ttY+Z629X9K/\nJH2udd10SbPb9pcPf/mAD+IGrogZ+Kikq4AoD5xr4IqYKam+knpLWtj2A2vtRklPSTqoozsvt3TQ\nUZIeMcbMkXSopDWSfmGtvbl1fbtvhjGm7c2YKekFSae0XlXcR9J2klYaY4a27jN3ngk6pBipEb4K\nMbZyen3FUqmvudApkFHFvsW6i0LEflwK6IORtM75CVM541I+Vy0MpxKG0gyrOP2z4gVuih78zKRw\nXIyKpIOOrT+q3ceSWywmlVZ6YlzqoEublbjnpdHKxXcsLmMrtzYQcZJum9Z5vlzLLKTyHluN2LX1\n/29Hfr5WUl1Hd55oEmiM2axMima0r0X0Z9Za27kD49lb0hnKpGxOV2bC9h/GGLVOBPO+GdbahcaY\neyQ9K+lDSSdJ2iDpNkmnSZpojDmn9WdnWWuX5hoMedDwQdzAFTEDH5VWE4jSa+sTWOpxoHJUS8ys\n3bA2/0YF8P5f1+pfr7yTXX72k0FqaGhIY9cdbvSe9ErgWZIukzRX0tOtPxuqzJW7S7X1pMxXJ0n/\na62d0rr8J2NMvaQfSLo599MkBd4Ma+00SdPalo0xU5SpNVzX+vMvSfqipAeMMX2ttZ+0bdvc3Hyp\npOwH1NLSUidtuRzOcvxy13Xd66Qtt6Eu9XjaltVZofGplwp2vPXr1/dQq3J5/bW+HPz8Wz5tSS1+\n04qnNqX+fkX3n/R40e9X8PmDug3Uso3L10jSoG4D63bqv6fufHlBdllSaH1weUL/pjpJ2e3bli9a\n+NIaKTMJkqS5Hy7Orn+/2/BsmmTb+mpdHtB3/x7BiWCpx5NvOdfnGV3+h14PxYOUO76i3+euG7rn\njN+ky4X6/ZDG8aOvz+V81nVd97rOH3fZ6vdT3Pc3bv+u56dc6wv1+7nYxyv076dSLHfd0F1tAv8+\nvlQV6LghB5fmwEPCi4O7DHZ59lut/+8t6W+Bn/cOrPNmgndcy7mRMb+R9Btr7czIz0+RNMpae3RH\nB9K6v9ckLbTWnhr42XhJt1prdzDG7C1ppaTB1trlgW1+J2mttXZCO/v8vKRHJB0gaYKkRmvtca3r\n1koa0VpbGLUsjddUS2otHRSVoxDpoMVOkylGDKeVohYUlw4aTfsLIh20+vQ7PPddzYMpRXHpoFHF\nTgf15TIu0kELmw5abv8eKOeUy8jYBpVwKB3S3Nxsf//JolIPQ5L0tS4j1NDQEM2slCQZY9Yp0/Fg\nVuuykfSmpP+w1l7R+rNuylx8O89ae3tHxpJ0Erhe0pestSsjP6+X9Cdrbff2n+k4GGPulbSHtfbg\nwM9+Kmm0tbbB9c1o3X6RpBustfNb00APtdaObl33nqRDrLV/bmc4TAJTVG4nXVS+cvrFWW6TR99J\nb5DvP66jk74ol/YOQUzuKk/cpC860Qv+cWB0ZJ0JTBHH1B+Z0ujCfCcJxajtSyqtyVwhuEzKcm2X\n1vFL/V5UMCaBKYhOAo0x20uqb11cokybvN9Ietda+4YxZpKkC5W5kLVC0kWShkva11q7viNjSXp3\n0L9LOradn4+V9E47P/d1vaShxpgLjTH7GGOOVSYV9WYpU3Ao6QZJFxhjRhtjGiTdpUya533t7G+i\nMm/i/NblxZK+aoxpVKb2cJOkv+YaTDRVC0iCuIErYgY+2tItgaQ418AVMVNwgyU91/pfN2XK1p5r\n/b+stTOUmR/drMz9TnpLGtnRCaCUvCbwYkl3GmMOldR2I5Vhkr6uzEQrFdbaZcaYUcrcFOZiSasl\nXWStvTWwzQxjzHbKvBk7KVOjuNWbYYzpLWmKArdQtdYuN8ZcIWmeMq0jxltrP0pr/AAAAACQhLX2\nv5Xnolz0XidpSZQOKknGmCGSzlGmD5+V9BdJP7fWPpP2oMoE6aBAlYhLBfJdVynSqJGKypfyGeea\nS+9NcSQoZ5mqiy1+NPWE7OMFkbTguPrQoLj6QBdpnQeSrnMZj2+qaFpprL778N22EOn0paihLue6\nw5SQDpqCuJrAYkvcJ7B1sndC3g0BAAAAAGUraU2gjDG7GmPON8bcaoz5bOvPhhtj+hZueKVFHjR8\nEDdwRczABzWBcMW5Bq6ImeqVaBJojBmozA1UTpD0PUk7tq46TNLlhRkaAAAAACBtSVtE/Lekp6y1\nl7T2sPiStfb/jDHDJM2x1u5Z4HGWAjWBALLKrcajED3A0moDEbRVS4jAr5xovz9Uvri2ENHWD0HB\n1g9zVzwSWpdWn8BC99RzeV6p69mKrdza6MQp9fHLGDWBKSinmsCk6aAHKtOKIeotZW5VCgAAAACo\nAEkngR9K+kw7P99X0tr0hlNeyIOGD+IGrogZ+KAmEK4418AVMVO9kt4ddIGkqa3N2yVJrTeEmSHp\nwUIMDCiVQtyCupzTe6oh9SXf+1uI11js960QrR6i+wwWB5w0e3po3aihB+Tcz1YpnzFIAa0ucemf\n0XynuJRPs9XWyfh+L0rRQiAp3/NXWq+pEOe2NNpVRLct1HtR6FiopN+zT6/9g/4/e/ceb1dZ3/v+\n+xQaY5TaHiuY1e01RJCuukOJJYgHYXu42WNXEnrzEip7V05bd7ceISpQhdjNRQXr2da24t7FJoo9\n7SEXd8slVEUqAjVcjl1yMQm9mgWx1rYpEVP02X/MdXnGkzWfNX7PGmOOMcf8vF8vXoy5xpxjPHPO\n3xwzzxy/3/gtObhMf7P/60M1bpRT9kzgRvUas39T0jJJX5K0R9I/SfqNeobWvOXLl+9regwYPsQN\nrIgZ5PjQjuuJG5hwrIHVoaMOEjMdVfZM4L9JOl3SaZJOUm/yeJ/3/s9qGhcAAAAAoAYLngl0zh0p\n6V8kvcx7/3nv/Ye89x8YhQkgedDIQdzAiphBDmoCYcWxBlZLDiwjZjpqwTOB3vunnXN/I2nJAMYD\nNK5s3nvTNQZVafPYcg3icuSDft1y2zlYbA1aP0xENYDbg7q/tYlL/cc1fyvO6l8zhu5JtYHYtvvW\n2eW47UMdx934M1JF/WAVdXZWua/NfPucqe9KPS63tUWuOo67bftea9t4AKl8TeBvSrrGOfe8OgfT\nNuTOIwdxAytiBjmoCYQV9V2wIma6q2xN4EWSXiLpG865v5f0ZLDOe+9fUfnIAAAAAACVKzsJTLWB\n8Il1Q21qamqMX+hhRdxUJ5WGVXbdfLdz9m+5nzX1p0zM5F5GPVa2DcT2RNuHazd9uv9GI3tvoyVE\nXTZOXDjWxNnAVFuIFJ8IFMvnObUu9dlLpTmm9l9FeuRC98s91pXd58w2lhxYNnboqIP7cls2DKLV\nQe4+qnic9bF1a0NriZmYaWTnqFXfSaBz7n2SrvPePynpBkl/573//sBGBgAAAACoXKom8HJJz55e\nfkzSj9Y/nHbhbA5yEDewImaQg5pAWHFGB1bETHel0kG/IelnnXN/KslJeoFzbul8d/Te/20dgwMA\nAAAAVMt5P3+OvnPuQkkfk3TEAtvw3vuF7jOMdlHb1Q5tyIm3GFTctLmOIaWOcS+0zTou8V7Fdma2\nMRMzVbV9uClo9RALa/32JNo5UMvXfm2oCYxbQqxfee7s8tbdtxTWxW0h+llMO5Q62sHUIfc5Lbb9\njbX+eDG1dWXHadGm97CL5m0rMldHurqBIVVicnLSf+7pLzQ9DEnSa488Q+Pj467pcUiJM4He++ud\nc38s6UWS7pd0jqR/HNTAAAAAAADVS14d1Hv/bUnfds79R0l3eu+fGsyw2oGzgMhB3MCKmEEOagJh\nxbEGVtQEdlffdFBoV9MDwGgi3WXxBpE+lpv6VFXKZ5hqty5IwZOkDZuvLLUNUj5RRqolRJzTtO6U\nn5xdXh/FZVmLSQctu51BH1uHKX2/qtdpEOmhVex7mN6bQYtigXTQCrQpHTR1ddCRNzU1Ndb0GDB8\niBtYLTmwjJiB2caJC4kbmPD9BCu+n7qLSSAAAAAAjBAmgQnkziMHcQMrai6Qg5pAWPH9BCu+n7or\neWEYAIM36FqJNtU/VFUvF0tdYr1s7UhVY8vdZqrtw6lvfnPhNrXeWKxUHWDcFiJUtg4wbh8RPm4x\n9Vxl11nuW2U7mGFQR5uNOp5/Xd9lZZ9jnW2D2qJt40G1Sk0CnXOXS5rvXxVe0lOS9ki61Xv/nQrH\n1jj6BCIHcQOrmT5MTY8Dw6WpPoEYXnw/wYqY6a6yZwJ/TtILJS2TNBMIY5K+I+kJSS+Q9E3n3Gne\n+8cqHyUAAAAAoBJlawI/KOkvJL3Ye/9C7/0LJb1Y0j2SflPSj0l6VNJv1THIpvDLB3IQN7DiLCBy\ncBYQVnw/wYqY6a5SfQKdc38laa33/v+P/r5K0nbv/Yudc2skfdZ7f3Q9Qx04+gS20CBy8Nucnz8s\nqqrpqaMmL6WqseX2CUzV/W27+4HC7b07gx5/lACiYmVrAs9b+bq+94vj2QVdBS09BMP6wdzeg1L5\n40nTx/2qjidNP4+yBvEc+F4vJ34vws/eB0+9hj6BFRjGPoHHSFo6z9+fMb1Okvarly7aGfTTQQ7i\nBlb0YUIO+gTCiu8nWK1eehIx01FlJ4F/Jun3nHM/5Zz7gen/fkrS70q6ffo+PyGJekAAAAAAaLGy\n6aDHSPoDSWdJ+v70n39A0m2S3uK9f8I5d4akH/Te76xrsANGOiiQkJuyZEm5TAlTzcI0Myk/ZSx1\n2fpBCJ9TnP4Z23vbQ8n1gEUq/TMWftrWRu0iws/iB069urDu3v139t1mVSmfodxU/yba6FQxttx1\nTchNAa0jrTP3O6jp17AOC7wvpINWoE3poKWuDuq9f0LSOc654yQdP/3nR7z3jwb3acerCwAAAADo\nq1Q6qHNurXPuB733j3rvd0z/9+jCjxxu5M4jB3EDK2oCkYOaQFhxrIEVMdNdZWsCPyPpcefc7znn\nXl3ngAAAAAAA9SlbE/hDks6T9CZJp0v6W0k3SvqU9/6ROgfYIGoCgRoMotVDrO56I0stTqoNROiw\nlhDUAKJGcU1gWOu3PYrFsi0iYqk63nUrz5ldjmtzc1tLpFTRXqiqbVq227bavlBVdeJt3d8ooCaw\nfm2qCSx1JtB7/y/e+xu89/+HpBdK+m1J50p6yDnHZAkAAAAAhkTZdNBZ3vt9kj4m6SpJX5X0k1UP\nqi2o7UIO4gZW9GFCDmoCYcX3E6yoCeyuUlcHlSTnnJN0hnopoedN//kmSf93DeMC0HKWtKgq2kBY\nVNUyIiX1nN5113uy9h2mgJL+iSaFn6B1URuID556zexy3PahbOuWOOUz3OP2ex4srNm84ZK+j8v9\nbOemVVpSCS3HyCr233SqqKUlRxWpm5bnN+jXoun3Aiij1CTQOXetpF+U9DxJt0p6q6T/6b1/qsax\nNW758uX7mh4Dhg9xA6tdT91HzMDsQzuuJ25gwvcTrA4ddZCY6aiyZwJfJelKSX/kvf9WjeMBAAAA\nANSo7IVhXuW9/91RmwCSO48cxA2sqAlEDmoCYcX3E6yoCewuS03gD0p6pXpXB10SrvPeb654XABa\nzlKbkpJb9xeKL2G/NqphSu2j7OXnLbVIZeuU1h/bu98z/vVZeuGzj9a1V3y61OOAKsRtIfqJa/TO\nWzlXBxgfB7zm2k7Fn5nD6wDDdXOf0bVrVhke1/9zmVubXFU9Vx11YKmxta3urIrxDGtt3bCME6Ot\nbE3g8ZL+p6SXqHf28Onpxz4t6buSOjkJJHceOYgbWH332U8SMzCjJhBWfD/BiprA7irbIuIjku6X\n9BxJT0o6QdJqSQ9q7kqhAAAAAICWK5sO+kpJr/HeP+mc+76kI7z39zvnNkr6qKRX1DbCBk1NTY3x\nqxms2h43lsuWt1kq5fK8la/ruy5MV5OK6WSp1LLU/iwprWEbiOs23ShJunjirWPX7vhEa2MG7bRx\n4sIxy9nAsumfsc0bLu27LmyHIhU/X6k07W3RupCLbsfp3f32F2v6WFe21YHlvottETHI7yfL8+/H\nktI76Pc0ZZi+S1Mp3OtXnqslB5aNcTawm8qeCXSSvjO9/E1JPza9/A1JK6seFAAAAACgHmXPBH5N\nvbN9eyX9haR3O+e+J+lCSXtqGlvj2nw2B+1F3MCKs4DIQU0grPh+ghVnAbur7CTwSknLppffK+lP\nJH1B0j9I+oUaxgUAAAAAqEGpSaD3/tZgea+klzvnnivp297779c1uKa1vbZrmAzrZZ5ztD1u6nrt\ny9ZqpO4XX4p+85svmV3esOWq4n2TNUXFqqKwnm/b7lsL61L1g6G47i/cR7wN7+dqLOJxz3c/a20X\nINnjZu/Ohwq3V5w1VyO4LlF3F9fohdatPKdwO6wpiqv1UnWAof5VfvVJ1a/l1rbVdd/FbGOx30+W\n1yJ13C/7fC2Pq6IGcSFd/reLNH+NLTWB3VW6T2Bs1BrHAwAAAEAXlL0wzEhq89kctBdxAyvOAiIH\ncQMrvp9gxVnA7so+EwhYdD2FYlTkptuEj0u1U5hYs6pw+/xPXT27HF8mPpUOeu2mTxdu+8v7X7Y+\nFLeBCBNj4lQ2V7hfMYUmTGtdGz2nmbYQQN0WagkRxnAc+2Vbp8TryqZ8WoSfp80bLimsi/dfVirN\nsGwbgPi+lvYNKVV9X+am6Oe2qEhZbGuL+e4b329Y/p3RdJuL2L3775xd/uCp1/Rdh+7hTGDC1NTU\nWNNjwPAhbmC1ceJCYgZmxA2slhxYRszAhJjpLiaBAAAAADBCSk0CnXOnO+fWBLcvcM7d5Zy73jn3\n7PqG1yxy55GDuIEVtV3IQdzAivouWBEz3VW2JvAjki6XJOfccZJ+T9L/kPRqSddK+pVaRgdgaJSt\nqUnVHsWXog9bLaRq+VKXvo8fG9cWhvV8cTuHuJ6v+Lj+CuOO2l4Ag/LYzocLty+6/I1975tqnRLX\nvKbqelP23hZ8TqMPe/iZTdX/5tYAxlJtIBa6b9l1Ofer0qDHlltnWVVLjkG0iKhCm8f27ruKNbfx\n9zW6pWw66ApJfzm9fJ6k2733vybprZJeX8fA2oDaLuQgbmBFbRdyEDew4vsJVtQEdlfZSeD3NXfW\n8LWSbptefkLSc6seFAAAAACgHi5MW+p7J+c+J+kbkv5M0n+XdIL3fo9z7jWSPum9f0m9w2zEBa0R\n5QAAIABJREFUrqYHAFSljjSZ3NQfSypZ6cvNR4exvbf3Tw9dl0g1i9PQErtIpqemFFLigBrFLSIO\ni/1E64UwPTROCUt9hsN/U1z3/mI7lBVnzo0nTuEO00PjdO5Q/BxiLthQPO42p+E1ydKiIfW4WO7r\n3ea0zjaPrQoLpO2uHuBQKjU5Oek/9/QXmh6GJOm1R56h8fHxuPqlEWXPBL5D0omSPirpSu/9num/\n/7ykL9cxMAAAAABA9UpNAr33f+m9/wnv/XO895uCVRdJekstI2sBcueRg7iBFbVdyEHcwIrvJ1hR\nE9hdpj6BzrnVzrlfCNpClL26KAAAAACgBcrWBB4jaYekn1KvNGal9/4x59zHJT3lvX97vcNsRKma\nwNxc+ibUncs+TK9F3dr8WixmbAvVgMyIL+O+LqjN2bD5ytL7K2uhFhGpOqL1x86Nbeue4rjL1iSm\nLmlPDSAGKawDjItOtpx/WeF2+O0f3zes+wvbRcTrUp+R3NiPaxlD8Wc5HluoqmNbqt45tY9B1GKn\nVLHP3OdrGUvu46pSV01kW7Zp2Uf8/bw3aDPz/Z1/R01gBYaxJvC3JO1X70qgB4O//7Gks6seFAAA\nAACgHmUnga+VdKn3/tvR3x+T9MJqh9Qe5M4jB3EDK2q7kIO4gRXfT7C6eOKtxExHla3pe6akf5vn\n7z8q6anqhjN82pTmt5C6xzpMr0Xd2vxa1JEiFd933cpzCuu2BuljqdRJizAFNHVJ+Xgf8f7jFNAc\nuc8BqFrqcxGnacctFArrgjTpDVuuKqxbu2ZVuf3HaZ1+/vsNShVph5bjZx3fA01/t6RSXKsa26Cf\nY+7+hvX9Te1jy/nF2ydfdJoef/xxfeDX3lvzqNCEsmcC/1zRVUCdc0dKerekz1U8ptZYvnz5vqbH\ngOFD3MDqQzuuJ2ZgRtzAiu8nWD3/+c8nZjqq7JnAjZLudM69UtIzJF0raVzScySdWtPYAAAAAAAV\nK9sn8CFJP6FeY/jbJS2V9EeSVgWN4zuH3HnkIG5gRW0XchA3sOL7CVaPP/44MdNRpfv8ee+nJL2v\nxrEArVB3K422ya3xSF1WO1V7k3td5IXaQKSEl8KP6/fCGsGyLSGAtgo/F3E7BV/4JKStOWbu87x2\nTfm62RVnzu3f0iLC0hYilKpzHHSrnja3BkpJjTtV+12VYXmdRkG/98K5VnQ0QMX6TgKdcz9ZdiPe\n+/urGU67kDuPHMQNrKjtQg7iBlZ8P8GKmOmu1JnAUs3S1fuR/YgKxgIAAAAAqFmqJvClJf9bUfMY\nG0PuPHIQN7Citgs5iBtY8f0EK2Kmu/qeCfTe//UAx4GEUatRa9ogXuM2vadV9Q1MrTtv5etmly11\nSd7P3fe6RH2RpV4w7hOY6pVWGEt0O9Ub0FILBSxGXEuXqp+zuHf/nbPL4edXKn6GLZ+LkKUGMPyM\nLtTrMFWbXMdxt+5tVrndfprufQigGWX7BMo5N+ac+03n3E3Ouf9vernTvw6QB40cxA2sqO1CDuIG\nVnw/wYqY6a5Sk0Dn3JmS9kj6eUlPSvrO9PIe59zZ9Q0PAAAAAFAlF6Zb9b2Tcw+r1x/w7X76Aa53\nvdiPSDrLe//yWkfZjF1TU1Nj/AICq1GJm9xLhd+0++a+63LbNyyUAre+kM5WPObF6WWh7JYRwS7i\nVNX5bJy4cIyzOrDaOHHh2NanvlSIm/CzEKdxViX8DCc/I9E/L8LPQiodNL4YfZzCHUo9x9xU9zan\nii5WU99Pw9o+Y9TM972+5MCysUNHHdy35ujXrG5gSJWYnJz0n3v6C00PQ5L02iPP0Pj4eCt6bpRN\nB32xpN/2wYxxevl3ptcBAAAAAIZA2UngfZJeMc/fxyV1skegRB408hA3sOIsIHIQN7Di+wlWh446\nSMx0VKpPYOhjkn7LObdS0t3TfztF0q9Iek/YWL6rjeMBAAAAoAvKTgI/Pf3/KxPrpI41jh+V2i5U\ni7hJW39s8ZLu53/q6tnlzRsuLazbuqd/vV7K9nseLNwu1g0VU/FTl58Pa5HKXvpeKlcHGKImEBYz\n9XQXHD8x9k8/8reFuLHUyOXW9Za19lXFWr7twUcvrvPbEXy+Np9/WWFdVcUzqRq9sjVqltq2Nta9\nzXw/DbpeMRV7bXyd0LPm6Nfwb5oOKzsJfGmtowAAAAAADESpSeCoNo7nlw/kIG5gxVlA5LjhkR37\nqmoOj9HA9xOsiJnuKtUiQpKcc8dIOlXS0YouKOO9/53qh9a4XU0PADZcgjpPVWk5qe2k0s5SR6AN\nm+fLQO9J/eN3fZQSF6aTpVpUpMTjTKWH7r3Nlg4KpMTtFAbRBiIl9RkKPyepz0jq82t5TpZjDd8J\ng0XKZ+fQIqICQ9ciwjn3Bkl/Lekzkq6Q9N7ov06ampoaa3oMGD7EDaw2TlxIzMBs9dKTiBuY8P0E\nK2Kmu8rWBF4j6UOS3u+9f7rG8QAAAAAAalS2T+BzJH1y1CaA5EEjB3EDK2oCkWPXU/cRNzDh+wlW\nxEx3lT0T+IeSflrSR2scC7Ao1ByUY6mdzK2vsVx6fmtQX+Sii8FvCS8VH9Uvh+0jLDVE8X19UMUU\nt4gItSKBHyMpjr1UvIcxHLY/WYz4c5FqqxKOdUvU8mXDlquy9vfBU6+ZXV7o2DLK3wODrotfaH+j\n/F4Aw6DsmcB3SPo/nXPbnXO/6Zx7X/hfnQNsEnnQyEHcwIqaQORYcmAZcQMTvp9gRcx0V9kzgRdK\nOlPSP0g6VnMXAHPTy++vfmgAAAAAgKqVahHhnNsv6Rrv/YfrH1Jr0CICI6+qdNDwvpZU0VAqJc1y\nmfiU+NL32xKXuA/REgKLFuV8XvS+N84u77jnwcK6Qpp05OSjT5tdvnf/ncldpj6XuZ/ZML162+5b\nC+vWBZ/ZutKrm0xBpE0Rht0CMUyLiAoMXYsISUdI2lHnQAAAAAAA9Ss7CfykpDfVOI5WIg8aOYgb\nWFETiBzUBMKK7ydYETPdVbYm8JmSftk5d7akr0r6t+m/O0nee/9f6hgcAAAAAKBaZWsC7whuhg+Y\nmQSeUfG42oCaQCAht+4uVb+Xqvurah9hzZQkvfuuS2aX1608p7DupmA821P1gdFhdO9OagRhs+Ls\nEwq3w4KRtaecGK2bWxt/Rqqov61KXGNraeXSz0J1duFzpiYPqBQ1gRVoU01gqTOB3vvTax4HAAAA\nAGAAytYEjiTyoJGDuIEVNYHIsXrpScQNTPh+ghUx011lawLlnPsPkt4g6QWSnqFeAtRMOuh/qGd4\nGAVcVjtPbvuG1HYsj8vdR2rcuemfqf1J6RS5MAU0TkdNpeQV0kNbkdiBYRaH0OYNl84ub91zi/pZ\nTHuUsp/hOrZp2cag91/Vd1LTqalN7x9Au5U6E+ice4ukWyQ9W9IZkvZL+t8knSjp4boG17Tly5fv\na3oMGD7EDaw+tON6YgZmu566j7iBCd9PsCJmuqtsOujFkv6z9/4Nkg5JukS9CeCnJR2oaWwAAAAA\ngIqVnQS+VNLt08vflfRs37us6EclXVDHwNqAPGjkIG5gRU0gclATCCu+n2BFzHRX2ZrAb0n6oenl\nfZJ+Qr1+gc9Vr4cgBqSL9XNdeA5NqOp1q6LeZTH7S9WtVFXTEm4nvmx9KG4DEdYBUvaHxYrbQKyL\n6kxDzs1FXNxaYabNyeNTj9dSv2Zhqd9Lja2Odha5r01VNdVNf7c1vf+yuvjvGgtqN9GUsmcCvyTp\nzOnl/1fSf3PO3SDpDzV3hrBzyINGDuIGVtQEIsfzlz+fuIEJ30+wIma6q+yZwLdJWjq9fI2kpyW9\nWr0J4X+tYVwAAAAAgBq4Xmkf5rFrampqjF9AmjdsqRKjGjdl0zotUq0dUuviVg9h64k4HXT9sXPr\nNmy5qrAuTNfbFqWK7r3toeTYLTZOXDjG2cDhseKsYlrn3p1zsWBJ+QzjMo7ZMAW033HPeqwZdFsZ\ny/6G7ThfpzrTI2dihtcbZQXHmdVNjyXX5OSk/9zTX2h6GJKk1x55hsbHx1tRYVK2RcSPO+eOD26f\n5Zz7lHPuUufcEfUNDwAAAABQpbI1gb8vaZUkOedeIGm7en0Cf03SlfUMrXmjeDYHi0fcwIqzgMjB\nsQZWxAysiJnuKjsJPE7S/dPLPyvpXu/96yRtkPSGOgYGAAAAAKheqZpA59wBSa/w3v+Vc+5PJN3p\nvf+gc+5Fkh713i9dYBPDiJpAZOly3FRR95dbe2ThVTyuxfVW/cR1f2E9V3yk3P7lufuGNWE5qAls\nXlzLZxEWd2zecGlh3dY9c7EXt3oIzbR9mHHv/jtnl6uqCbQoW89nqdUt+9mvqnZxFFhfqy5/P6Ee\n1ARWa+hqAiV9TdKvOudOk/RaSbdO/31M0j/UMTAAAAAAQPXKTgLfJemtku6Q9Bnv/Ven/z4h6d4a\nxtUK/FqGHMQNrDgLiBwca2BFzMCKmOmuUn0Cvfd3OueeJ+mHvPf/GKz6PUkHaxkZMI86L51t3f8g\n9l3H861qm5Y0MMt2ykrtz6mYaZG6FH/YIiJOB41vhxabAorBs6R8plKBd0Rxsfn8y2aXc3N84pht\nWtnjQup+uceWYUr5bLrVwjC9VgDapeyZQHnvn44mgPLe/7X3fn/1w2qHqampsabHgOFD3MBq48SF\nxAzMONbAipiBFTHTXaUngQAAAACA4cckMIE8aOQgbmBFTSBycKyBFTEDK2Kmu0rVBAJt0XT9w6D3\nX8f+FrPNNtX4hLU4h9X5BTWA860vrNvTf11Y97firPwWAhicuO5v7ZpVs8vb73mw7+PCGsDYeVE8\nxdV7qWq+VFuIlCpaLSykilYMlrENS+sHy3Mq+3yHtYYcQHclzwQ6597nnHvWoAbTNuRBIwdxAytq\nApGDYw2siBlYETPdtVA66BWSRnYSCAAAAABd47yPL34drHTu+5Ke3+UrgCbsanoAWJw6UnGGNb1n\nEONO7aNsGwjL427afbNhdP2l2kCEvQFoCdEecWpuIW03SgdNpXmGctM2pcGkbobKfp7ragdTxTYB\nDJ3VTQ8g1+TkpP/c019oehiSpNceeYbGx8db0ROIC8MAAAAAwAgpMwn8inPurxL/PVb7KBtCHjRy\nEDewoiYQOZYcWEbcwITvJ1gRM91V5uqgN0j618T6/vmkAAAAAIBWoSawP2oCUZtB1xaWNYhL0Vvq\n/sLWDnHbBx/8/pRqAbGQVE3g3tuoAxyksNYvrsGMa/36SdUAplqHWGoCB12H16ZjRKwrbQlyX+82\nvU9tfi9yx9bm5zSCqAmsADWBAAAAAIBGMAlMIA8aOYgbWFETiBwca2BFzMCKmOmuhSaBvyzpwCAG\nAgAAAACoX5mawKck3S3pC5I+L+le7/33BjO8RlETCCSkajXK9gVczOPCeq51K8/pu05aoO4v7DEX\n95+jJnCgcuv+1h87V+u3dU/xvS9b62ep80s9tis99crW8Q7Tc+o66udQM2oCKzBMNYEvk/R2SU9I\n+jVJX5L0z865W5xzG51zq51zrXgiAAAAAICFJSeB3vs93vtPeO/f6L0fk3SCpI2S/kXSRZL+QtI/\n1j/MZpAHjRzEDayoCUQOjjWwImZgRcx0V5k+gbO89484576t3sTvnyX9oqRn1TEw59wlkq6U9DHv\n/a8Hf79C0lsl/YikeyW9zXv/ULD+w5J+SdKTkt7jvb8xWPd6Se/y3v/vdYwZGEaW1K7cVg+pdDlL\nCmgoTAFdTIuIi973xtnl6zbdmLgnqlA25TOWav0Qp4DmqCqNs462KnXtIyX1mR10miHpp+Xw2gCw\nWPDqoM65H3XO/axz7mPOuYck/a16KaL/IOnn1JuMVco5t0a9id5XFTSjd869W9I7Jf1nSa+UtF/S\n7c65Z0+vf72kN0g6U9K7JP1359xzp9cdJenD09stZfny5fuqeD4YLcQNrD6043piBmYca2BFzMCK\nmKmXc+4K59z3o/8G8ponzwQ65/5S0rGS7pN0h3qTvy9775+sa0DOuedI+pSkCyRdEfzdSXqHpKu9\n99um//ZL6k0E3yjpekkvl3SH9/5+Sfc75z4i6cWSviXpKklbvPeP1DV2AAAAADB4RNLpwe2BXIBz\noTOBKyR9W9JfSXpM0t46J4DTrpf0x977L0oKLzrzEknHSNo58wfv/VOS7pT0quk/PShptXPuh51z\nqyU9U9Ke6TOLp6s3ESyNPGjkIG5gRU0gcnCsgRUxAytiZiC+573fH/z3rUHsdKGawB+WdLKkMyS9\nWdLHnHPfVO+s4B3qnXV7rKrBOOfeKuml6p3Zk4JUUEnPn/7/E9HD9ksakyTv/U7n3KckfUXSdySd\nL+mgpI9L+r8k/Sfn3Nun//br3vu7qxr7MKCuAla5tUCWS8qnap9u2n1zqf3FUi0hYtvveTBrHyjH\nUgOYqvtLKdsGIlY2npuoiWvTMdoyljq+Z9r0WqC/qj4n/FsFI+ilzrlvSPquetc7udR7/1d17zQ5\nCfTeH5L059P/vd85t1TSKZJeo97FV37HOfeE9/5Fix2Ic+449S4E8+qgD6FT8Wxg36EGY94kaVOw\n3csk3aVe0/tNkv69pFdI+iPn3Eu890+HG5qcnLxCksbHx7V8+fJ9M7+AzORED/vtJQeW9X7ROVqt\nGE9Xb89oy3hSt5ccXKZDRx1Mxku/9WXjS0dodv3U96b67j/e3+qlJ41J0q6n7jPd3qYH9knSBcdP\njEnSDY/sKHV75ozcTI0etxd/+zkvfm7p1z/3/Zbm4ktS33iyxvN88SupNcfPmb8Ny/GE2+24PaOu\nf18s9v1PfV+04fUb1dvBv4+vEKp0j3pzqkfUy3j8DUlfds79uPe+1g4MyWbxh93ZuR9UL/XydPXO\nDp4i6Qe99wteYKbEtt8i6fdVzIM9Qr0J3vckjav3Ar3Se39f8Lg/lbTfe3/BPNt8maRbJK1Sr8bw\nVO/9L0yv2y/pDO/91/oMqXPN4vl1DfOxnKUr21B7mM4EhmgOXz3OBI4WvmdGF2cCO2+om8X/6Xf/\ntJF97921V4/dN5c0+aZT3qQLLrig7wku59wy9crwrvHe/1adY0tOAp1zR2ouHXRm0rdU0t9I+sLM\nf977v1/0QHoXhPmx8E+SbpD0dfVq+R6W9A1JH/XeXz39mKXqpYde7L3/RLQ9Nz2+j3jvt0+ngZ7u\nvV83ve4fJb3Ge//VPkPaFf7CCpRVZdy07R+fZb+cc9s+pMTHqvO3XDW7vDaaPMRHte2JSWEbJn4b\nJy4cG+YrhIYTvbVrVhXWxem2g57olZWb8hg/to7PbL9t1vkdxT/EFy/3NazzuD+omJGIm64IYmao\nJ4EaO9T0MHr2LdH4+Hgyy9E593lJD3vv31bnUBaqCfwnScvUS7X5gqRfl/T5OvJUvff/rF7vwVnO\nuYOSvj3TB3D6ap+XOucekbRbvVOmByTdOM8m/5Okb3nvt0/f/pJ6Ka2nqndm8JCkR6t+HgAAAABg\nNX2C6+WSPl/3vhaaBF6k3qRvd90D6cOrWO/3QefcMyV9TL3+hPdIOiu+Yqlz7hhJl2nuqqHy3t/n\nnLta0jZJ/yJpg/f+u6mdcxYQOYgbWA3zWUA0h2MNrIgZWBEz9XLOXSvps5L+TtLRkt6rXneDP6h7\n3wtdGObjdQ9ggf2fMc/fChd+6fO4J9RrKRH//RpJ11Q2QAAAAADI82OSPiPpRyV9U9LdktZ47/+u\n7h2bLgwzYlpTE0htxnBpS9zksNTyLabGpazUhWHCI1eq5k9qR91fyrDVBFou9hILCyHiWs7Q+pXn\nFm5v231r33VVsNS4DuI4XOa439SxJrc2uM76yC6r8t8Aw/z9hGZQE1ixEjWBg7Loq3oCAAAAAIYH\nk8AEfi1DDuIGVsN0FhDtwbEGVsQMrIiZ7lrowjBogVFId2mTQaffplIlLf32qrDQNsP9V9UG4uSj\nT5tdfvddlxTWhWl/W3ffUliXyqVoe/rnMFhxVjHlc+/O/q/pINo+pFJAq2jR0HTKZ2wQqZO5x5Pc\nfqFVGMXvwyaf8yim3wKjgjOBCVNTU2NNjwHDh7iB1caJC4kZmHGsgRUxAytipruYBAIAAADACGES\nmEAeNHIQN7CiJhA5ONbAipiBFTHTXbSI6G9X0wOwWqhGi1z+ag2iVqKqfeTWHuXWK6bEtX1hrZeX\n73vfbYk2ENQA1iAqujw2qBHcvOHSwrrzP3V1sK5Y1xm2dpCkD5w6d99799/Zd/e59WuWz0zT7Xfq\nrglEf9S6AWa0iKgCLSKGA3nQyEHcwIqaQOTgWAMrYgZWxEx3MQkEAAAAgBFCOmh/Q5cOWpVBX2K9\nrsuYD2JsVWhTWlJuKl0s9R6mUj5T4nRQUkCrt+LsuZRPS9uH8L2JH+eivNIPnnrN7HJVbUZCVX1+\n2pQqmpv+WtexpenXpuva9J0Q470faaSDVoF0UAAAAABAE5gEJpAHjRzEDayoCUQOjjWwImZgRcx0\nF5NAAAAAABgh1AT2N7I1gTFqAIZfVe9hFTVcJz/vtMLtldedXepx1ABWL6wBlNJ1gOetfN3ssqWu\nM3ycVE/N2rC2Wsj9XHJMLqfNtXVVaVMsjMLrPeKoCawCNYEAAAAAgCYwCUwgDxo5iBtYUROIHEsO\nLCNuYML3E6yIme46sukBoP2GJaWjzW0gqpJK/clNiRtEKt1Nu2+ed3khpIA2Z/3Kcwu3y75vcfpn\nLBVvVaS21dFipipVHWtmHjf1vanKxtPm1y3XsI7bok3PsU1jAbAwzgQmLF++fF/TY8DwIW5g9aEd\n1xMzMONYAytiBlbETHcxCQQAAACAEcIkMIE8aOQgbmBFTSBycKyBFTEDK2Kmu6gJbKFB1KgNUx1c\nWV2sIYrVcXn91DbrsO3uBwq3qftrJ0vbB4sqanUtsd+mz/Cgx7LQ6zTo163Nx9ZB6+J3MIDhwpnA\nBPKgkYO4gRU1gcjBsQZWxAysiJnuYhIIAAAAACOEdNCEqampsSZ+ARlEWgipJ/0t9rVpKm5CllS6\nqrbjg+XzN1/Zdx0Ot3HiwrG6zgauOPuE0vd1crPLHzj16sK6e/ffmbX/OtqTcPzqWehYM4jXaVhT\nc0dVG76fMFyIme7iTCAAAAAAjBAmgQn88oEcxA2sqAlEDo41sCJmYEXMdBeTQAAAAAAYIc57qnX6\n2EUedH1ya4GGQRvixvL61tEWIj6ubNhy1ewyLSEOV2dNYFDmJ0lacdZcjeC6U07s+zBLG4g6WpdY\njGrrgTYcazBciBlYBTGzuumx5JqcnPQaO9T0MHr2LdH4+Lhb+I7140wgAAAAAIwQJoEJ/FqGHMQN\nrKgJRA6ONbAiZmBFzHQXk0AAAAAAGCH0CUwgd97GUodm6R02LDU+M+NecmDZ2E+ueGUybqqoYUq9\nTqm6vzpqAGNb99xSuE0dYFrlNYFBtcHFl7+psGp9UOuXW5SQiq+29aZrcpt14zsKVsQMrIiZ7uJM\nIAAAAACMECaBCfzygRyHjjpI3MCEmkDk4DsKVsQMrIiZ7iIdFJWpKn1qGNKw5mMZd93PMZXaVlU6\n6E27b+67btvdD1SyD+QJ20DEtgbvm4sSQtevPHd22ZKynZKbZmlJdy67ndTjhvW4Mwpy040tx0He\nfwCjhjOBCVNTU2NNjwHDh7iB1caJC4kZmHGsgRUxAytipruYBAIAAADACGESmEAeNHIQN7CiJhA5\nONbAipiBFTHTXc573/QY2mpX0wMAYqnamCpq/bbuLrZ2CGvE4vXrjj2nsO78LVfNLk+sWVVYd92m\nGxc9NpS34uy5mkDninV/X7/o1tnle/ffWVhXtna06RYRVRnWcWP4UZOIIbS66QHkmpyc9Bo71PQw\nevYt0fj4eG6HpkpxJjCBPGjkIG5gRU0gcnCsgRUxAytipruYBAIAAADACCEdtL9WpoO2OYXEkj7W\nBXVdfryOlM/U2MIUzzj9Mz46nL/5ytnltaecWFgXtoXYu/Oh4gM5zNQqTP+UpHXBe7N+5esK63Jz\nUHJTRetQ1XGwjnTQNh+jq0Ia7Zw2vxZlx1ZHGxfL/jE0SAetAumgAAAAAIAmMAlMIA8aOYgbWFET\niBwca2BFzMCKmOkuJoEAAAAAMEKoCeyvlTWBMUv9GDn5zamq5qKsVCzEbSDWrTyn77rt9zxYuL02\nav0QKtQE3vZQ3/uhIkFFwcWXv6nv3c6LagLrUMexpYvHr2F9TsM67lxtrjkFGkRNYBWoCQQAAAAA\nNIFJYAJ50MhB3MCKmkDk4FgDK2IGVsRMdx3Z9ABgY0lT6WL6ieX5dzEVp+xl+lPrwvRPSdq2+9bZ\n5YVaRIQpnxisuA1EmEtiSfms4rOQm6Zs0ZXPbGjQz6mqtMYuvhcpVT3fKtJIF7MdAEjhTGDC8uXL\n9zU9Bgwf4gZWH9pxPTEDM441sCJmYEXMdBeTQAAAAAAYIUwCE8iDRg7iBlbUBCIHxxpYETOwIma6\ni5rAIZO69P8o1A1YnmNuG4Y6XseytYsLKdsS5OSjTyuse/ddl8wux3V/qRYRqWsY0wZidC3mM1LH\nMSv1GRqF42I/o/zcm0ANJoBhwpnABPKgkYO4gRU1gcjBsQZWxAysiJnuYhIIAAAAACPEeR9fBB7T\ndk1NTY214ReQQaR8lk0ztKQ1tikdsyplXov54qaO1hapFLg4rTNM+QxbQvTWzaWHbth8Zd9tHiY6\ndOzdSXporo0TF44tdDYwbhGx7pQTZ5ddlLj7wVOvmV2u6nOZs42F9l/HZ3+Yjidl9XtObfmOwvAg\nZmAVxMzqpseSa3Jy0mvsUNPD6Nm3ROPj46lqm4HhTCAAAAAAjBAmgQn8WoYcxA2sqAlEDo41sCJm\nYEXMdBeTQAAAAAAYIdQE9teamsBRV3dNYu7++pmamhr7myO+XjpuFlNf1c9Nu28u3D5v5etml31U\nzBfWD267+4HkdteuWTW7fN2mG0uNBQubqQk8Nqj727zh0sJ9tu4p1nmG72msbN1dV+rIWoZEAAAg\nAElEQVTnRq1VzoymvqMG/XqP6vtbB/5dAytqAitGTSAAAAAAoAlMAhP4tQw5iBtYUROIHBxrYEXM\nwIqY6a4jmx4AUJWq0oSq2M5C2wjTm8qmeFqsD9o+SIenh4YWSgENbb/nwewxwca5YrZIKv0z17Cm\n1nUljbWstqX0Dvr1Htb3lzRWzGfUjl9oL84EJkxNTY01PQYMH+IGVhsnLiRmYMaxBlbEDKyIme5i\nEggAAAAAI4RJYAJ50MhB3MCKmkDk4FgDK2IGVsRMd9Eior9dTQ8A7Wap04lZ7ttP2NpBKtYBpmoA\nY6mawL23PWQfGJJWBG0gYutOOXF2eTE1gNSY2KU+h7yeRdS6ASOJFhFVoEXEcCAPGjmIG1hdcPwE\nMQMzjjWwImZgRcx0F5NAAAAAABghpIP215p00GFNvRnEuHP3UffYFtP2oWyqqCXlM2ztsPnNlxTW\nbdhy1ewy6Z954hTPMK0zPsbuCN+L8y8rrEvlh+TG92K2k7O/YTpGDYtRuKT8oL8vYl18TevAZ32k\nkQ5aBdJBAQAAAABNYBKYQB40chA3sHrGgWXEDMw41sCKmIEVMdNdTAIBAAAAYIRQE9hfa2oCyxqF\nupGUqp5/FXWGg5CqCUy1fQjr1STp2k2fnl1ecWaxtm3vztGuEUy1cwjFr2mqvUN4xI2LAgZRnzpq\nx4U24b1AlxDPI4eawCpQEwgAAAAAaAKTwATyoJFjCfVdMCJmkIPvKFgRM7AiZrrryKYHgOosJnWx\nTWkcuZfxruo5lN1Ov9dw6ntTWn708uR9c+WmgK5ds2p2+bpNNxbWhSmgo5D+WTbFUzo8zTO0vpDy\nWUyr37r7luB+5xbWpXJA2nwJ+zaPbVikXqc2H5OB+RCjwHDjTGDC8uXL9zU9Bgwf4gZWh446SMzA\njGMNrIgZWBEz3cUkEAAAAABGCJPABPKgkYO4gRU1gcjBsQZWxAysiJnuoiZwkSy1XnVf/t2y/TZf\nir7NdQZl3++F7hc+x9R9w9oySVp/7Fx92YYtV/V93N7birV914W1flFXmEHUAa44q1zdYXi/+L6W\nWr5YWBO5/Z4H+94vVQMYc4lbHzj16tnle/ffWVg3X3zPV0dalUHXytYl91g3LLr4nAAA7cWZwATy\noJGDuIEVMYMcxA2siBlYETPdxSQQAAAAAEYIk8AE8qCRg7iBFTGDHMQNrIgZWBEz3UVN4CI1XcfR\nptq+pl+LXJb+Z6lavnfd9R5J0uqlJ+mFLzg6uZ2ytYU+KuA7P1EHGErV1jUi0RyvUC94e3Gci6kD\nLOzezQ0g3fuv2NMvrMk8r9AX0NbzbdDqrp9roqfdsB5f2qzrdZYAgP44E5hAHjRy7HrqPuIGJhxr\nkIO4gRUxAytipruYBAIAAADACHHe+4XvNZp2TU1NjbXhF5DclJ0mUrb6GcRYLPuwpID2e1y/FM8l\nB5aNHTrqYDJuwjTDOAUxdNPumwu3w0/r9rsfKKx7bOfDs8vvfN8bCuuu23RjajhZDkvVDAZnST8t\nm/K5NkrjjJ9/KJXyGad1lpVK6c2N5zhmRj0lL/dzOWhtOba25TsKw4OYgVUQM6ubHkuuyclJr7FD\nTQ+jZ98SjY+PJ4pkBoczgQAAAAAwQpgEJvBrGXIsdBYQiBEzyMF3FKyIGVgRM93FJBAAAAAARgg1\ngf21piawKmVrmFL1Lm2r7Us9LmcbC20nZabOb/XSk8Ze+IKj9823boalDjC0LVEHt/e2oA4vzjYP\nPuaLaR+R27Jh7ZpVs8vb73mwsC5Vv1fW+qjOb9DJ9ov9HHTtWFNWW2rrFtLWcY5q3CAfMQMragIr\nRk0gAAAAAKAJTAIT+LUMOegTCCuONchB3MCKmIEVMdNdpIP2t6vpAWBhZVNALSmudfAqfs7C9NC4\n1UHZT2ScxrnizBP6ritroVTR3HTQMOUzldLqXDFDYvOGS2aX45TawuOiBNA43fbko0+bXX73XZf0\nva+lDUQVLSIsclugtClluy6j/F4AwICQDloF0kGHw9TU1FjTY8DwWXJgGXEDE441yEHcwIqYgRUx\n011MAgEAAABghDAJTCAPGjno+QYrjjXIQdzAipiBFTHTXUc2PYAuy21ZUFWrg0Ftt8z+cltS1CXc\nZ1gvJhVrxlKtHCxS9WwTUYuEuEawr6h4MLcOsCDKUs+tAYyFz2lt4vmGrSR6w+mfNn9e0BYiVXMp\nFWMqfk9zW6DkPi63RqzperLccQ/i8527zTrq9Ya1XrCqcbe5PhMAMIczgQnkQSMHNYGw4liDHMQN\nrIgZWBEz3cUkEAAAAABGCC0i+qu1RQQpLNWoIi2pjhYRN+2+Obl+/bFzKYkbtlxVWBemdcYtGwr3\nu62C9M9IVemfsXVRCmgobBkR3y9MB/3AqVf3XbfQe1i2JUjTn8M2pQcOkzqOA7z+1SO+gaFGi4gq\n0CICAAAAANAEJoEJ5EEjBzWBsOJYgxzEDayIGVgRM93FJBAAAAAARgg1gf2VqgkcpjqSYanHyL1U\neYqlTqiKGsG4JnBb1PYhTAY/7BMY/MHS9iGsH4wfV1WtX1izF497e6K2L2V90OohN0l+oXhu0yXt\nsXh1HHebPpY3vf+u4PMM1IaawCpQEwgAAAAAaAKTwATyoJGDmkBYcaxBDuIGVsQMrIiZ7iIdtL9d\nU1NTY8uXL983iJ2RwlKvOtpAxGZSQFcvPWls11P3FeImTgcdFrlpnXGy6Nbdt/R9XNjqYf3Kcwvr\nUq0dcj8nudup43Ez65YcWDZ26KiD+4b1s8/xqxmD/I5CNxAzsApihnTQKpAOOhw4UCJHPAEEFnLo\nqIPEDMz4joIVMQMrYqa7mAQCAAAAwAhhEphAHjRyrF56EnEDE+pIkYPvKFgRM7AiZrrryKYHgJ46\n6mi6csnxVD1f2fYRg2gDYRG2cAhbO6TuZ7lv6n5rozq/3NYOMZe4dV6hXrDo5KNPm12+d/+dfe9n\neQ9TcWFpORLe1/L5KVsjN7Nu6ntTWn708tLbHwTL8aOO2k10G3ECtA+fy9HCmcAE8qCRg5pAWHGs\nQQ7iBlbEDKyIme5iEggAAAAAI4QWEf0NtEVEF9Vx2fhBtHpISaWAzrSBuOD4ibH3/rdrCnHzzsvf\nWLjv9i/PpWDuvb18ymcV4pTPbYZ00LCFQ9z2IZXymatsmuGwp7BwrOmp4pjR5lioemzEDaxofQUr\nWkRUjBYRAAAAAIAmMAlM4BdW5LjhkR3EDUw41iAHcQMrYgZWxEx3MQkEAAAAgBFCTWB/jdUEjlJN\ni3WfdbR6iGvbwro3SxuIazd9WpK0ceLCsa3f+VLpuNl7W1QTePbi20CkpOr+wucuDabuL9SmWLdY\n7Oeiy7VdgzhmjGrtUZfjpq3a/P1cBjEDK2oCK0ZNIAAAAACgCUwCE/i1DDk+tON64gYmHGuQg7iB\nFTEDK2Kmu0gH7W9XvxW56YmDThtpc9pKm9pHeBU/A3EKZChsp2ARp3wqSASwpHWuXbNqdnn7PQ+W\nflwqBbTuFE+p/tizxHqbPhddbnsBAOgU0kGrQDrocJiamhpregwYPhccP0HcwGTJgWXEDMz4joIV\nMQMrYqa7mAQCAAAAwAhhEphAHjRy0CcQVoeOOkjMwIzvKFgRM7AiZrqLmsD++tYEhnLrdqqqHRxE\nDeKgL79uqe0L6/fi9gb97rfQfcO2ENujGsCJoCbvuk039h9YlO19WN1f8LF77PaHC6veefkb++4/\npWzd38lHn1ZYd+/+O0vvI5Sqh61qXa42tSWgtq96bXtNR7VFBYCRQk1gFagJHA7kQSPHxokLiRuY\ncKxBDuIGVsQMrIiZ7mISCAAAAAAjpFWTQOfcJc65rzjn/tk5t98591nn3I/Pc78rnHPfcM4ddM59\nwTl3QrT+w865bznn/tY598Zo3eudc39eZjzkQSMHfQJhxbEGOYgbWBEzsCJmuqtVNYHOuVslfUbS\nV9SboL5f0imSTvDef3v6Pu+WdJmkX5L0dUnvk/RqScd57//VOfd6SddL+mlJL5P0+5Je4L3/lnPu\nKEn3S3q99/6RBYZTqk9grIp6EEvvsKr3XWY8Vewzt9eiRVjbF0vV2pX+RER33LtzrhdgXAMYrlvI\nsWfPPXYiqvNLjdu5uRTzzRsuKazbtvvW2eVUPeRCmo7FXNRszan7taiqXq9tdX+5iL3B4vUGakNN\nYBWoCZyf9/4c7/0feO8f8t5PStog6XmSXiVJrvev3HdIutp7v817/zX1JoNHSZo54/dySXd47+/3\n3v+hpH+R9OLpdVdJ2lJiAiiJPGjkoSYQVhxrkIO4gRUxAytiprtaNQmcxw+pN8ZvT99+iaRjJO2c\nuYP3/ilJd2p6oijpQUmrnXM/7JxbLemZkvY459ZIOl29iSAAAAAANMY5d9p0+dvfO+e+75z7pYHt\nu03poDHn3B9JWiFptffeO+deJelLkl7ovf/74H6/L2nMe3/O9O3LJb1Z0nckvVfSzeqld/6qpJ+Q\n9HZJByX9uvf+7j67L9UiIpcl1alNl9CvKlW1qnTQVMpnShj3H37/Zwrr3vm+N8wuH9YGIjiBf1jK\n521BOujZ/dfNt76fVNuHlLAlxGLktnPoSirfoFk+F7ymzeG9ADCCSAetQpQO6pw7V9Kpkh6QtFnS\nr3rvNw9iKEcOYic5nHMfVu/s3qt9uZnq7H2895skbQq2dZmkuyQdmP77v5f0Ckl/5Jx7iff+6SrH\nDgAAAAAp3vtbJN0iSc65Tw5y362cBDrnfkvSz0s6w3v/18Gqx6f/f4ykvw/+fkywLt7WyyT9R0mr\nJF0g6Yve+yck3e6ce4ak4yR9beb+k5OTV0jS+Ph4IQ965upIM39b7G0doX2StOTAst7to9X3/ksO\nLtOhow42tv/w9sz9+40nXt9v++H+p7431ff59tvezO3VS08ak6RdT91nuv2V7+zaJ0mvXLp67OKJ\nZ+vaHZ/YJ0kXT7x17KSlx+srT/XWz9T3zVzxM759wfETY5J0wyM7Ztef+JIfP+q9D3/80XD9b9z2\nUOHxW5/60ryPj2/nPj9JpV6/hW5b3//Fxteo3p75W1WvP7drfr9aEt979uw57lnPetaBpl8Pbg/P\n7SeffPKoY4899tG2jIfb7b8987fg38dXCJ3QunRQ59z/I+nn1JsAPhqtc5K+Iemj3vurp/+2VNIT\nki723n9invt/QdJHvPfbnXNvl3S6937d9Lp/lPQa7/1X5xnKrqmpqbGZD0PVSAftbjroxokLx2Ym\nefG62ceSDorAzLGGdNDh0Jb3os7vKHQTMQOrIGZIB61C4uqgzrkDkt42qHTQVk0CnXMfU6+Wb62k\nh4NVB7z3T07f512SLlXvrN5uSb+huRYRT0bb+2VJ53rvz5u+fZKkz0t6nXpnBt+nXn3hd+cZTq01\ngYMwiH+olJ2wWSYMuXzUs2Hr7ltKPW5bou1Cqg1EUvTxjieMKbkTv7D1Q9gSIl6XspgJeeoHgbpb\np4yCUX/+XcB7CGCIDfUkcHx8vJF933HHHbrjjjtmb7/oRS/SBRdc0IpJYNvSQX9VvX92fy76+xXq\n9QyU9/6DzrlnSvqYpB+RdI+ks+aZAB6jXj/BV838zXt/n3Puaknb1GsdsaHPBBAAAAAAsp1++uk6\n/fTTZ29PTk42N5hIq1pEeO9/wHt/xPT/w//eH91vk/d+zHv/TO/9Gd77w07PeO+f8N6/xHs/Ff39\nGu/90d77Y733O1PjCfOhgbLoEwgrjjXIQdzAipiBFTHTXW07EwhVlzI0iFSj3BTA3BTQOMUzTHNM\npX/GKZ/hefi1a1YV1l33/rk6wFQaZ7+6vue8+Lmm9M+U9ccW0zjP33LV7PLaKG3UBc8qTo2tQ269\nYKzs43K3udBj66i5rUObx4b+hiW+AACD55x7lqSV0zd/QNKLnHOrJH3Le/93de67VWcC24biaeSY\nubInUBbHGuQgbmBFzMCKmKndKyXdP/3fUvVa2d2voNVdXTgTCAAAAAAD5r2/Qw2dlONMYAJ50Mgx\n0+sPKItjDXIQN7AiZmBFzHQXZwIbkqpZarpupI56qtz+glKx1m/dsecU1m0Ia+Si2r6UsGIurAGM\nV8YtIcr291tIWM+3I6pXTPX423z+ZbPL815fuM82LK93v8fFLNtJxUkd8W7ZZtOfN7STpZZv0PEN\nDDtqZYHmcSYwgTxo5KAmEFYca5CDuIEVMQMrYqa7mAQCAAAAwAghHTRhampqbL5fQKpIgWxaVal8\nVWzzpt03l36cc8UkyIkgBdTUBmJTlAIaCNs7xOmge28Lbkf5mDOPu+D4ibH4bOC6qJ1DKG71EEq1\nxEjJbd+Q27pjoe3mthIZFf2ONWXxmlZv0CnFOe/hYuMGo6ctMcMxani0JWZQPc4EAgAAAMAIYRKY\nwC8fyEFNIKw41iAHcQMrYgZWxEx3MQkEAAAAgBFCTWBCvzzosrnsZWukLKqq/RnEZfnL1pfF7Qy8\n94Xb5wdtIGKpNgnhVrbf82Df+yXbPkQ7uOh9b+y7zZm6v9VLTxrb9dR92b+chXWAqRrA3Nc7ZRB1\nGk3XgrSxfm6xNRdteA7DoI3v/YycsVCrAytiBlbETHdxJhAAAAAARgiTwAR++UCOxZwFxGjiWIMc\nxA2siBlYETPdRTpoCXWkEKVS93LTSKsaZ2o7qXHnpiMu1CIi1ULh2k2f7rsubPWQUmj7oGIbijD9\nU5J2BCmg8bjC9NAtGy4trPMqpriGKZ9xOmzIkvJZ9n1ajLKtJlKPiw06Ja9NKYBtTk/sIl5fAAB6\nOBOYMDU1Ndb0GDB8LjjuZ4gbmCw5sIyYgRnfUbAiZmBFzHQXk0AAAAAAGCFMAhPIg0aOGx79LHED\nk0NHHSRmYMZ3FKyIGVgRM93l4svxY9auMney1M9Z6vnKPq7sNiz7r6r1QOq1WKgOsJ/DagCD8E21\neti7s1j3t+LME/qvC7YTt6CYCOoAd9z9QGFdWCOYqvOTijWC23bfWlhnaQvRVlV9LspuE83KrRUd\nhDaPDQCGyOqmB5BrcnLSj4+PNz0MSdLk5KTGx8dTHc4GhjOBCeRBI8fqpScRNzDhWIMcxA2siBlY\nETPdxSQQAAAAAEYI6aD9lUoHTbGkeJZNWVrMpf/LpuTltoiIhW0Q4hTHMB10W5RWabF2zarZ5es2\n3Vj+gcGJ+FQribgNRNgiYvObLyms27pn7vm6KJE0leJ58tGnFW7fu//OvvcN5bZoICUOaAc+lwCG\nCOmgFSAdFAAAAADQCCaBCeRBIwc1gbDiWIMcxA2siBlYETPdxSQQAAAAAEbIkU0PoM3a2BulrrqR\nOlpEhG0QcltCLOS69xvqAAOpOsBQnLQd1iA6N39K966n7tu3UIuIUFwDWEU7hVjd7UmwOFUfa6g1\nGw6LfW/a+B2FdiNmYEXMdBdnAgEAAABghDAJTCAPGjmoCYQVxxrkIG5gRczAipjpLtJBW6KOFMDF\ntJPIEad8bg9aP8StFixtIfbufGh2OU7jXHHmCfPebzHCsW6PxhmuC1tgSNJMCuiSA8t0SAcL6ywp\ntmVTc6uKC1pNLF5Vn9mqNL3/KrTtNQUAoEs4E5hAHjRyHDrqIHEDE441yEHcwIqYgRUx011MAgEA\nAABghDAJTCAPGjmWHFhG3MCEYw1yEDewImZgRcx0FzWBNUrVc6XqXXJrtCz7z2Vp9eCDZVMN4G39\na/tS62Irzi7XBiIWNn7YvOHSwrqte+bqAH3hGc69NquXnqT7Hr8/uY86Wj0MYhvUZc2P16V6vKYA\nANSHM4EJ5EEjx66n7iNuYMKxBjmIG1gRM7AiZrqLSSAAAAAAjBDSQROmpqbG6voFJJXqNOjWDlXt\nP9VOIV5nSessKzf9M2XbnlsLt2faQKQsObBs7MQVqwtx4wpJpkWWS+GXbdHA5fWHS53HmqrQHqR9\nhiFu0C7EDKyIme7iTCAAAAAAjBAmgQn88oEc9AmEFcca5CBuYEXMwIqY6S4mgQAAAAAwQqgJTJjJ\ng86t2UqpqrVDeN/F1BJu3T3X+mD9ynNLPy6s9ZsIagDjdabWDmcVa/v27uz/2Nw6wHXRWPuxvBYz\nlhxYNuaOdoVfzqpqCVL2vnXVbI1aXdignu8w1FyMwvs9bIYhbtAuxAysiJnu4kwgAAAAAIwQJoEJ\n/PKBHNQEwopjDXIQN7AiZmBFzHQXk0AAAAAAGCHUBCbM5EHn1sLkPi6uHwvr9T546jWl95eqEbxp\n981ZY4v5YDnuBZitf0s9k1SfwlBc9xe+3in9Xu8lB5aN6WjtS923CnX0F6xKV/oUVjHuMq8FNRfI\nQdzAipiBFTHTXZwJBAAAAIARwiQwgV8+kIOaQFhxrEEO4gZWxAysiJnuct77he81gu7Z/8VdZe5n\nScFsE6/i+x6mQG6/58HCus0bLp1d3rD5yr7btLSBCB3WEuL2h5Lr+4mzSLecf9nschznW/fMPd/z\nVr6u1PYXkmrXkYoTy7qyUvtfTMuTYU3rrAKvxWDxegNAq6xuegC5Jicn/fj4eNPDkCRNTk5qfHy8\nosKnxeFMYMKSA8vGmh4Dhs/U1BRxAxNiBjmIG1gRM7AiZrqLSSAAAAAAjBAmgQnUdiEH+fOwImaQ\ng7iBFTEDK2Kmu6gJ7K9vTWDZur+q6rmqEtb9xW0RwpYR26J2CmHichwtuXWASVGm9EWXv2l2OW71\nsC5oA3H4ZuY2lHq+qZpAS81nHfV7AAAALUBNYAWoCRwS5EEjB3EDK2IGOYgbWBEzsCJmuotJIAAA\nAACMkCObHkCb9cuDLpuuV1VaZ+ry/qkUz9i6lefMLofpkAupImH4sDYQO/unkcb33RGkgKbSP+Pn\nH742uSytFmZY8+eHKf2zivYVOBw1F8hB3MCKmIEVMdNdnAkEAAAAgBHCJDCBPGjkIG5gRcwgB3ED\nK2IGVsRMdzEJBAAAAIARQk1gwkwetKUtQG77iPBxllq2VB1gqu4vbgNRVlzLF9bvper89t4ePe7s\nE/rcMy3VziEWtoioo36t3zbny58fRBuIOtpXpLbT5tYWbR7bfKi5QA7iBlbEDKyIme7iTCAAAAAA\njBAmgQnkQSMHcQMrYgY5iBtYETOwIma6i3TQPu7Z/0UtObhMf7P/67VtP1S21YOltYPF3tv6p3Im\nH5dIAQ3FbR8s1ibaQqSEr+MgUiVDTaQjDjrlsc0plm0eGwAAQNM4E5hw6KiD5EHDjPx5WBEzyEHc\nwIqYgRUx011MAgEAAABghDAJTFhyYBl50DAjfx5WxAxyEDewImZgRcx0FzWBi5Sq/UrVofno9ra7\n7+9737C2La4XTLWTSLWByK0BrMo6Q51f2bYQue06LMJtpurOmq5Jy61JXOg1S21n0HWXAAAAyMOZ\nwARqApGD/HlYETPIQdzAipiBFTHTXUwCAQAAAGCEkA6asOTAsrH5zgamUj7LpiBuPazVg5td+sCp\nVxfWvPuuS2aXU+0jFjLoFNAVZ+e1hSib/iml34uyqbkWZdIhlxxYNvaTK165b751lu0sdL/ccdbx\nuMU+dhjU2fZjampqbKFfWwfddqSJNiewKRM3QIiYgRUx012cCQQAAACAEcIkMIGaQOQgbmDFr6zI\nQdzAipiBFTHTXUwCAQAAAGCEUBPYx5qjX1N5HnTYFiJu3+CC5Xv33xk9zgfLRa1qA+GKN8u2gbDU\nAKZYWjbU0T5iZh/TPXX2zbfOsp2FULM1WHW+vmWONYN+f4mn9qNWB1bEDKyIme7iTCAAAAAAjBAm\ngQn88oEcxA2siBnkIG5gRczAipjpLtJBaxSnU939xB2zy3t3FlM1V5w1105hw5arCuvWrlk1u3zq\nm99cWPfSs14+t81Bp38q3QZie5CqujZKDa0qBTSV1lm2fUTd+17osWXbRcT3JV0PAAAAOTgTmDBd\n2wWYLDmwjLiBCcca5CBuYEXMwIqY6S4mgQAAAAAwQpgEJpAHjRz0CYQVxxrkIG5gRczAipjpLmoC\n+3jXXe+ZXV6/8tysbWy8692F29sT7RzCdgqpNhAvPfPlhXVN1AH2E7eEWB/U/bn4zgmDaOdQtiav\nqsfFyj62qrq/1Lirek79tlnldruG16k/XhsAVanjew4YdpwJTFi99CTyoGFG/jysqCNFDo41sCJm\nYEXMdBeTQAAAAAAYIc77OPkQknTP/i/umlm2XMI/FL+2r4raO4RSrRaaTvkMxxanfIbitNltu2/t\nu86ibOpGKn0sN7WsrpS0KlJTSJcbTqQlAQCG0OqmB5BrcnLSj4+PNz0MSdLk5KTGx8ctVVK14Uwg\nAAAAAIwQJoEJ1OkgB/nzsCJmkIO4gRUxAytipruYBAIAAADACKFFRB8btlwlSfskacuG4rqydTxb\n99ySte+mawBje3fOjWd7tG5tUCPookYQi6kDrEJTtVdleupUMR7qyYbTfO8bfZiQY6G4oW4YMY41\nsCJmuoszgQAAAAAwQpgEJlxw3M+QBw0z8udhRcwgB3EDK2IGVsRMdzEJBAAAAIARQp/APrz3s30C\n7/3mnaUfd/LzTptdXnnd2YV1bav16yfuWbh2zarZ5R33PFhYt+X8y7L2kerhV/Zx8WOHqd5lWMcN\nAFWgXhEYOvQJrAB9AgEAAAAAjWASmPD444+TBw0z8udhRcwgB3EDK2IGVsRMd9Eioo/jPnyO3nLc\nz+iTj35WmzdcWli3dfdc64e4DcK7vvye2eU2p3/GKZ/heenDnm/Q6mJzIv3TkqpZVTpkKq100OlF\nM/tfcnCZlmt58r6kPg1WbryRtjs6mj5+jJrU67uY94LPLACUw5nAhE8++ll6o8Ds0FEHiRuY0IcJ\nOYgbWBEzsCJmuotJIAAAAACMECaBCW+hTyAyLDmwjLiBCTUXyEHcwIqYgRUx013UBPax57aH9E9L\nT9We2x7S1jW3FNatW3nO7PJNu28urNt29wMDGd9ixdemDesAwxrAhR4XstRx1MFoUFYAABmFSURB\nVFGrkVs3ktpOV+qEulAns5j3Ivc9HdbXCna81+1RVZ04AKA/zgQmXLvjE+RBw4yaQFhRc4EcxA2s\niBlYETPdxSQQAAAAAEYI6aB9rDjr5brg+ImxGx7ZsS9M/5SKLSLi9M9haQvho3Xnf+rq2eUtUYuI\nsiyX/K5ju5Y2FGVThnJSDqfz51v1y1mbUqSaTrFt02sxY2pqaoxfW2FVZ9x0IYUch+NYAytiprs4\nEwgAAAAAI4RJYMINj+zglw+Y8YsZrIgZ5CBuYEXMwIqY6S4mgQAAAAAwQqgJ7GPvzod18cRbx67d\n8Yl9Or/YGGFY2kDE/RzWnXJi5buoo7YuNmztHMifr0eX65KIGeSoM27aeGzF4nGsgcU9+7+oJQeW\njR066uA+Pvfdw5lAAAAAABghTAIT6BOIHPzKCitiBjmIG1gRM7Ci93F3kQ7ax0vPevns8tbdNxfW\nrV2zanb5uk03DmxMCwlbQCzkvJWvK33fKlIAUulEC7WPyG0RUXY8pDgMFq/3cGr6M9P0/kcZrzcw\nmvjsdxtnAhMuOH5irOkxYPhM9wkESiNmkIO4gRUxAytipruYBAIAAADACGESmECfQOSg5gJWxAxy\nEDewImZgRcx0FzWBfezd+dDs8rbEuqaFdYBxC4j1Ud1f1DGiEmXbN9Qldx/kucNq0JfJb9tl+Ud9\n/wAAdAlnAhM2TlxIHjTMyJ+HFTGDHMQNrIgZWBEz3cUkEAAAAABGiPPeNz2GVnJn/rtdTY9hPqk2\nEHE6aKoNRG47hTqQ5tUeC6UgVpH+a4mn3G0SUwAAVGp10wPINTk56cfHx5sehiRpcnJS4+PjdVRo\nmXEmEAAAAABGCJPABGoCkYP8eVgRM8hB3MCKmIEVMdNdTAIBAAAAYITQIiLhQzuub0VvlLAOcG1U\n97fj7gdml1M1gBZxfdWGzVfOLu/d+XDfx8X1iuvWzI11/cpzC+tSNVuDqO8K91H19rvUU+ddd72n\ncNtrroZ4exB7PVfNLUbZ7mvXrJp73D0PFtZt2XBp3/033YKkCmXiuUsxg8EhbmBFzMCKmOkuzgQC\nAAAAwAhhEphATSBykD8PK2IGOYgbWBEzsCJmuot00DaKUuni1g+hzedflrWLOEXtpt03zy5ft+nG\nwrqybUT23Pa1wu2tweO23VNMHVy35pbZZUuqaIoldXBYUgktUimuqdcmfO/jFM89Ox/qv0NDd5lr\ng9hwUYDftObmvuvi2AjV3bqkKl2MNQAAMNw4E5jQlppADBfy52FFzCAHcQMrYgZWxEx3MQkEAAAA\ngBHCJDCBmkDkIH8eVsQMchA3sCJmYEXMdBc1gW0RlEJdfPmb+t6tqjYQsfXHztVeXes/nbeRqEbs\nsaCdxF2f+lRhXVT2WIlRr70Kn7+lXi6MqTAOJGlD2PZBKrzHe1P1gpEVZ821D4nf+7IxbWkrAgAA\ngP44E5hATSBykD8PK2IGOYgbWBEzsCJmuotJIAAAAACMENJBEzZOXDhW19nAY88+oXB784ZLZ5ed\ny0uWtLQFiG3dc8vCdzLyQe7gtt3F7acu/T9o8eu02LTSqampsaZ/ObM8h/D5n3LM6YV1uy8qbufe\nb945u3zyxaf13c5hgpiOo7uKNF5L7Kfu21RKcRtiBsOHuIEVMQMrYqa7OBMIAAAAACOESWACNYHI\nwS9msCJmkIO4gRUxAytipruYBAIAAADACBnKmkDn3K9J2ijp+ZK+Jukd3vsvTa+7eHqdJH3Ae//h\n4HEnSrpR0irv/XcX2s+iawKj4qdU64dUHWDupf9TtU9RNwdtu/uB0tstLdjJtnuK2w9rAheqw0rV\nbFVRz1V1Hdiw5c+nnn8cl6n7xvWETbK8p21oLTJsMYN2IG5gRczAipjprqGbBDrnfkHSRyT9qqQv\nSXqbpFuccydI+hFJmyT9tHpnOf/EObfTez/pnDtC0ickva3MBBAAAAAAumjoJoGS3inpBu/9/5i+\n/V+cc+eoNyl8QNJXvfd3SJJz7quSjpM0Kekd0+s+X3ZH1AQiB7+YwYqYQQ7iBlbEDKyIme5y3seJ\nge3lnFsi6UlJv+i9vyn4+29LGpf0K5LukrRKvTOBD0g6RdIhSZ+TdJL3/tul9nXmv9tV7eh1+LXx\nC6vmVq446+WFdXt3Pjy7fNHlbyys215I41yotUTwXkdpfhNrVvXZZvFhe3c+1HfrK6K2F6F1p5zY\nd52Lxt2m9hEWqbTdVMph6r6WVgeDVnVrjbq2uZh95KYbD6LtRN1p0nVp89jqZvmsD+K1GeX3os2a\niIU2IS5ba3XTA8g1OTnpx8fHmx6GJGlyclLj4+OH/YM9VepWl2G7MMyPSjpC0hPR3/dLer73/hFJ\nl0q6XdJtkt7jvX9U0u9KukzSa5xzX3XO/aVzbmKhnW2cuHCs0tFjJExNTRE3MCFmkIO4gRUxAyti\npn5Bqdt/Ve9E1pfVK3V7QZ37HcZ00CTv/cclfXzmtnPuTZK+L+nPJH1d0hr1JpJ3Oede5r3/Zvj4\nycnJK8LbMxPBmdTQum5fu+MT+yTp4om3jv3wi56rGx7ZsU+SLjh+Yuyfnvnq2fWrl540Jkm7nrpv\ndr2k4P4/M337s31u/6/27jzMiurM4/j3BwpuuCRuYMRRJ4LIqCgoRAZ3xriMW0bjEteZaIwmcXmM\nC+OCitEYRI0axwzRuEUz47iODhrXuIG7uIAGDS4tKkbEBkXpd/44p6Eob3fT2rcvcH+f5zkPXVWn\nqk7dern3nltvncr1J+XpPmn5h0x9B2DQMpv3WqVv7wX2T8zfflvH8+X2pOmP8vbL7R+4zOa9hBaY\n7jZzOeb0mPUOQLeZy/UCFpvphoaGXo2NjT1YEYrLWZ15y2F+ekVDQ0OvbrMWPN6GuQ3zli/M/srb\n68zpjm5PZxxfa693R7WHrizU9ssfrl/19SrHV3v335nTxdd/UWhPZ063FU/tjbfGxsYexUEbFsX/\nb55u/3Tx/y/Q6udHe6cbGxt7kC0qx/t13z89XeXzkTV/P+7fv/8ZWEdr7Va3U6q10yUlHfRSoF9E\nbFuq/01gArANqWc9IiK2yMvGAyMj4o6K+3I6aMXVnA7aMqeDOh30667XHk4HXfw4HdQWhtNBHZeL\nKKeDdoByOmhbt7pFxDbVasti1QkEkPQ48FxEHFGYNxn4Y0ScWqp7NfBMRIyRtAdwekQMyMueBU6L\niNs6sflmZmZmZtZJFvFOYC/gLWBY8R5ASacB+0dE32q1ZXG7JxBgNHCIpMMlbSjpItJNlL8pVpK0\nA9AXuCjPmgD0kbRbvh+wDzC+tR2VU0PNFobjxtrLMWNfhePG2ssxY+3lmFlyLXZXAgEk/Qg4EegJ\nvAAcW+o9L0saGXTfiHiuMP8gYFSePDkirum8VpuZmZmZWWeaOHFizTo7EyZMYMKECfOmBw0axKGH\nHrow6aAVb3XrSItlJ9DMzMzMzGxx155b3TrSEjc6qJmZmZmZ2WJiNHBNHrTyUdJzz790q1tHcyfQ\nzMzMzMysBiLipvxEgxHMv9Vt54h4s5r7dTqomZmZmZlZHVkcRwftMJKOkvS6pNmSnpQ0tLDsBEnT\ncjmutN4ASS9L6t75rbbOIOlkSRMkzZD0nqTbJG1Uod4Zkt6WNEvS/ZL6lZaPljRd0lRJ+5eW7Sbp\n4Wofi9VGjqEmSZeU5jtmbAGSekq6Or/XzJb0oqRhpTqOGwNA0lKSRkmakuNliqSzJHUt1XPM1DFJ\nw/J3l7fyZ9HBFeq0FSPdJV0i6X1Jn0i6VdJapeXX5O9KkyRtX1r/GEnXVe8o7WuJiLoswL7AHOBw\n0uMiLgZmAmsDG5NG6tkG2A6YRXpgI0BX4Elgu1ofg0tV4+Nu4GCgH9AfuBloAFYp1Pk58DGwJ7AR\ncCPwNrBCXr5bXmcz4Ps5jr6Zl/UAXgX61vpYXaoSP4OBKcCzwMWOGZdWYmXlHCtXkR7GvA6wbfE8\nO25cSjFzGjAd2AXonc//dGCEY8alEAPfBc4G9s7faQ8qLW81RnKdy/O87YEBwP2k0fe75OXHAC8C\nGwLHA9MK664NvA6sWuvXwqWFGKl1A2p24PAEcEVp3mTSIyT+BXisMP9xYO/89/HA2Fq336XT42V5\n4Atglzyt/AF6cqHOMvkN9Yd5+kTghsLyd4HN89+XAKfV+rhcqhIrKwGvAVvnD8yLHTMurcTLKODh\nVpY7blzKMXE78LvSvKuB2x0zLi3EzEwKncCFjJGVgM+A/Qp1vgXMBXbM05cC5+a/lwWamP9jwu2U\nOp4ui1apy3TQ/EyOzYBxpUXjgO+QbsjcQNLaktYBNgAmSloX+DGpI2j1ZUVS+vTf8vS6wBoUYigi\nPgUeIsUQpKtAAyWtLGkg6Q3yNUmDSVeZm59ZaUuW/yAN6/wg6YO2mWPGKtkDGC/pxnz7wTOSflxY\n7rixsruA7ST1AcgpfNsCd+bljhlry8LEyObA0qU6bwEvF+o8BwyVtAywE/BOREyXtA/QPSJ+X+0D\nsa+uLjuBwKqktM5ppfnvAWtGxCvAKcA9wP8BJ0XEJNJl8VOBrSU9L+kFSbt3Yrutdi4ipUA8lqfX\nzP9WjCGAiBgHXAtMAMYCB5FSbq4AjgAOl/RSvh91SHWbb51B0r8B65FG+AIojrzlmLFK1gOOIl09\nHk56r/lFoSPouLEFRMRlwHXAy5LmABOBqyKieTh5x4y1pc0Yyf/OjYjppTrTCnXGkjqCLwEnAftI\nWhk4FzhS0un5XsEHm3+0sEWHHxHRgoi4gvRmCICkA0iXue8lpY0OJnUkH5G0QUS8X5OGWtVJGk36\n1Wto5ByHNsyrExFnAmcWtnUq8AgpNeNMYBPSPag3SVo3Ir7oyLZb58kfcOeQ4mRu82wWvBrYEsdM\n/eoCjI/5DwR+TtK3SVknl7axruOmDkn6CXAo6V6+F0n3al0k6Y2IGNvG6o4Za8tCPzYgx8TRxXmS\nriR9f+4H7EPKvNsfuAbYouOaaV9XvV4J/ICU07xGaf4apBzpBSg9u+Ms0sMbhwCvRsSkiHiJdPP0\nltVtrtWKpAtJgwhtFxFvFBa9m/+tFEPvUoGkDYDDSDdjbws8GBHTIuIeoDtpgCJbfA0hZRm8KOlz\nSZ8Dw4Cj8q/1H+R6jhkreof0K3rRK6QBP8DvNfZlpwKjIuKmiHgxIq4lPWz65LzcMWNtWZgYeRfo\nmr8DF61Jy3G0NanT9yvSwIp3REQjcD0p/Xj5Dmi7dZC67ARGxBzgKVLqTdGOwKMVVhlNGtxhKuk1\nW7qwrBt1+jou6SRdxPwO4OTS4tdJb4LDC/WXAYZSIYYkiXSv2PERMZN0dahbYdnSpCvLtvj6H9JI\nspvksilpJOEb8t+v4pixL3sE6FuatwHwRv7b7zVWJlJmUlET87MOHDPWloWJkaeAz0t1vkV6v6oU\nR92By0gDy8ylEEeFfx1Hi5B6TgcdDVwjaTwpmI8k/brxm2IlSTuQAv6QPGsC0EfSbqTOXx9gfCe1\n2TqJpEuBA0mDNsyQ1Jz/PjMiGiMiJI0BTpH0CukL/ghSGs31FTZ5ODA9Im7J038GRkraitRBmANM\nqt4RWbVFxAxgRnGepFnA33LWAI4Zq+BC4FFJpwA3kVL7jiFf1fF7jVVwC3CSpNdJV5EHAMeSRgh1\nzBgA+arbt/NkF2AdSZuSzvWbbcVIRMyQ9J/A+ZLeAz4kfXd+jnRrVNm/A3dHxFN5+s/AhZKuIv2g\nPjEiPq7CodpXVevhSWtZgB+Rfg35lNS5G1paviwpLWeT0vyDgLdy+UGtj8OlKrHRREoZbiqV00r1\nTielc80mPQ6gX4VtrZHjrGdp/kmkm7BfA4bX+phdqhJH8x4R4ZhxaSVOdiaN1jg7f+YcXaGO48al\n+XwuD1yQz/Us4C+k58F1c8y4FM7hNoXvLsXvM2MLdVqNEdIVvItJtzM0ArcCa1XYV3/SDwXLFuYJ\nGEPqPD4PDKj1a+KyYFE+UWZmZmZmZlYHfC+bmZmZmZlZHXEn0MzMzMzMrI64E2hmZmZmZlZH3Ak0\nMzMzMzOrI+4EmpmZmZmZ1RF3As3MzMzMzOqIO4FmZmZmZmZ1xJ1AM7NFkKQHJF1S63bUmqRDJM2s\ndTuqQVIXSVdI+kBSk6RhtW6TmZnVB3cCzcxqSNI2uQPwjdKiyMWWXDsDhwC7AGsCj9W0NWZmVjeW\nqnUDzMwMAHXKTqSlIuKLztiXtenvgYaIeKKlCpK6RcScTmyTmZnVAV8JNDOrIkndJY2R9K6k2ZIe\nk7RVXvZ3wH256vv5iuDYwupdJY2S9L6kaZJ+KUmFbXeTdJ6kNyU1ShovaXhhefNVxu/mZZ8Bw6lA\n0hGSJuc2vi/pbkld87JBksbl+TMkPSxpcGn9JklHSrott2VS3n/vvO4nkp6WtHFhnUMkzZS0a2Hf\n90lat43XdDdJT+X6UySdLWnpwvK9JD0vaZak6Tm1dvVWtnecpOdyG9+SdKWklfKyFfN+di2tM1zS\nHEmr5ukt8/HNlvSkpJ1aS/GUdBUwGuid603J8x+QdJmkCyS9Bzyc5/eTdKekj3MsXC9pjcL2uuZ1\nPszlQkmXS7q/tdfSzMzqkzuBZmbVdT6wD3AosCnwAnC3pDWBqcDeuV4/UkrgT/O0gAOAOcAQ4Gjg\nZ8C+hW3/DvhHYD9gI+Bq4PZiRyv7BXAK0AcYX26gpIHAr4HTgQ2A7YG7ClVWyNseCgwCngX+t0IK\n6wjgOmAT4EngBmAscAkwAGjI2ynqDpwGHJyPsytwc7mNhbb+E3AtcDHpNTsM+B4wKi9fE/hDfm36\nAsOA37e0vWwu6XXvB+wPbJHbTER8DNxGOhdFBwDjIuIDSSsAdwAvAZsBJwEX0Ho670+AkcBbpPM+\nqLDswLzuUOAgST2Bh4Dnc73tSefk1sKPAscD/wr8EBhMeh33b6MNZmZWryLCxcXFxaUKBVge+Aw4\nsDCvC/AacFae3gZoAr5RWvcB4JHSvHHAlfnv9Umdl7VLdW4BLi1te8822rkX8BGwwkIel4B3gAMK\n85qAcwrTG+V5PyvM27p4rKT74ZqAIYU6vYEvgO0KdWYWlj8EnFpqzx7NdUidsCag99c4bzsBnxam\ndwMam18fYFlgBvD9PH0EMB3oXlhnv9yOYa3s5wTg9Qrn/dnSvJHAvaV5q+TtD8zT7wAnl87RJOC+\nWv8/cHFxcXFZ9IqvBJqZVc/6wNLAI80zIqKJNABIvzbWDdKVn6IGoDmtcTPSF/2XckrlTKVRNHcG\n1iut92Qb+xoH/BV4XdK1kg7KV7cAkLS60iiWkyR9BHyc27F2aTvF9r6X/32hwrxiamYThauTETGV\n1KFp6fXZHBhROubrgOVyeuSzwL3AREn/lVNUV23t4CVtJ+menFb7MfDfwNL5qiLA3cAsYM88/c+k\n1/6WPN0XeCEiPits9ktXXBdSAE+V5m0ODCsd89Rcd/2currAwDIREcATdNK9pmZmtnjxwDBmZp1P\npM5PWz4vTQfzv9R3ydMDK9SbXZpubG0nEfGJpM1IqZM7AicDoyQNiojmFM7VSOmob5BSVP8EdGul\nvdHKvPIPkO1JWRRwBvDHCss+yJ3s4fmexeHA4cC5kraOiHKnGknrAHcCV5DSWaeTOl03kI8vIj6X\ndBMpBfSa/O/NEfFpqV0dpXy+REo3PaFC3fdIqZ+VuANoZmYV+UqgmVn1/IXUYRraPCMPtjKEdP8Y\neTm0/EW+Jc+QvuT3jIgppdLQ3oZGxNyIuD8iTgE2JqWy7pIXbwVcEhF3RcTLwCdAz/buowVdgC2b\nJyT1BnoBL7dQ/2lgwwrHPCUi5haO5/GIGBkRg0hXFvdtYXsDSVdrj42IJyLiNWCtCvWuBbaXtCHQ\nfF9is5eB/pKWKczborWDbqengf7A1ArH/ElEzCBdJR7SvEK+V3ALfE+gmZlV4E6gmVmVREQjcDlw\nntIInRvm6dWAy3K1v5K+qO8qaTVJy+f5ovKVHOVtTyalQV4laW9J60kaKOkESXtWWK9FknaR9FNJ\nA/KVsQOAHszviE0GfiBpQ0mDSAOvdNRjC74AxkgaLGlT0lXHiRHxpxbqjwT2l3SmpP6S+kr6nqTz\n8rEMljQivxa9Je1OSlt9sYXtTSZ9Fh4raV1J+zF/cJ55IuIx0rm6AXifdCW02fWk+zOvzKN47kAa\niAfa3wmrdN4vBVYCbpS0RT7XO+QU3ea03YuAE3Ms9AHGkFJEzczMvsSdQDOz6vo5cCNptMpnSFd0\ndoqIaQAR8TZpVM5zgHfJo1JS+WHx5XmH5u2eT+qw3U666vhGaZ22fATsDtyTt3MccHhENN/LeBhp\nNMqnSB2e35b20ZJK+y7P+ww4mzSC5+N53l4trRMR40hXKLcl3fP2BHAiqYPWfCzfIaVPTgZ+CYyM\niOsrNjDiBVKn7zhSR/EwUtplpbZfB/wD8Id8z13zNj4hDR6zEemq3XmkcwrwaXkjpeNq6xyTr+xu\nRUohvhuYSBrN9VPS6wfwK1Is/Jb5r+N1OCXUzMwqUOFzzMzMrNNIOoSUZtqj1m3paPkK5M3AahHx\nYY3a8Gtgo4jYthb7NzOzRZcHhjEzM/uaJB0MTAHeJF3tHQPcVqsOoJmZWWvcCTQzs1paUtJRVieN\nWtqTlNZ7BykVuJYqpZuamZk5HdTMzMzMzKyeeGAYMzMzMzOzOuJOoJmZmZmZWR1xJ9DMzMzMzKyO\nuBNoZmZmZmZWR9wJNDMzMzMzqyPuBJqZmZmZmdWR/wedpuL0t5BT9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa1ea9d80d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from matplotlib.colors import LogNorm\n",
    "import matplotlib as mpl\n",
    "import pandas as pd #insted of numpy for much faster csv loading\n",
    "#import matplotlib as mpl\n",
    "#mpl.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "inputdir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/heatmap/'\n",
    "\n",
    "#load result matrices\n",
    "m=pd.read_csv(inputdir+os.listdir(inputdir)[0],sep=' ',header=None)\n",
    "for filename in os.listdir(inputdir):\n",
    "    try:\n",
    "        m+=pd.read_csv(inputdir+filename,sep=' ',header=None)\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "\n",
    "#plot\n",
    "fig,ax=plt.subplots()\n",
    "fig.set_size_inches(16,16)\n",
    "\n",
    "# define the colormap\n",
    "cmap = plt.cm.Greens\n",
    "# extract all colors from the map\n",
    "cmaplist = [cmap(i) for i in range(cmap.N)]\n",
    "# force the first color entry to be white\n",
    "cmaplist[0] = (1.0,1.0,1.0,1.0)\n",
    "# create the new map\n",
    "cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)\n",
    "\n",
    "# define the bins and normalize\n",
    "bounds = [0,1,5,10,20,100]\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "#show the image\n",
    "cax = ax.imshow(m,interpolation='none',norm=norm,cmap=cmap,\n",
    "                vmin=0.2,vmax=30,alpha=0.8,origin='lower')\n",
    "cbar=fig.colorbar(cax,shrink=0.8)\n",
    "cbar.outline.set_edgecolor('lightgrey')\n",
    "\n",
    "#set grid\n",
    "ax.grid(True,c='lightgrey',lw=1,linestyle='dotted')\n",
    "ax.set_frame_on(False)\n",
    "tics=ax.xaxis.set_ticks(np.linspace(0,200,6))\n",
    "labs=ax.set_xticklabels(['0%','20%','40%','60%','80%','100%'], rotation='horizontal')\n",
    "tics=ax.yaxis.set_ticks(np.linspace(0,200,6))\n",
    "labs=ax.set_yticklabels(['0%','20%','40%','60%','80%','100%'], rotation='horizontal')\n",
    "\n",
    "ax.set_xlim(-1,201)\n",
    "ax.set_ylim(-1,201)\n",
    "\n",
    "#enlarge font\n",
    "mpl.rcParams['font.size']=14.0\n",
    "\n",
    "# remove tick marks\n",
    "ax.xaxis.set_tick_params(size=0)\n",
    "ax.yaxis.set_tick_params(size=0)\n",
    "\n",
    "#legend\n",
    "ax.plot([],[],c='g',lw=4,label='WT vs others avg freq')\n",
    "ax.legend(fancybox=True,loc='upper left')\n",
    "\n",
    "#annotate\n",
    "ax.set_title('')\n",
    "ax.set_xlabel('other samples avg freq')\n",
    "cax=ax.set_ylabel('WT samples avg freq')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "###Zoom on the interesting region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KKDMWz7ksx19JfaVcLjN//OxcM/5KmvLPoBl22S7X8SVpxq2DLx+sZ35sumNjPb7MsHjn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\nLOAkSZIkqU5YwEmSJElSnfj/yF+DhCymADsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa209887650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define the colormap\n",
    "cmap = plt.cm.Greens\n",
    "# extract all colors from the map\n",
    "cmaplist = [cmap(i) for i in range(cmap.N)]\n",
    "# force the first color entry to be white\n",
    "cmaplist[0] = (1.0,1.0,1.0,1.0)\n",
    "# create the new map\n",
    "cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)\n",
    "\n",
    "# define the bins and normalize\n",
    "bounds = [0,1,2,3,4]\n",
    "norm = mpl.colors.BoundaryNorm(bounds, cmap.N)\n",
    "\n",
    "\n",
    "#plot\n",
    "fig,ax=plt.subplots()\n",
    "cax = ax.imshow(np.array(m)[:,180:],interpolation='none',extent=[180,200,0,200],\n",
    "                aspect=0.1,vmin=0.2,vmax=4,alpha=0.8,origin='lower',\n",
    "                cmap=cmap,norm=norm)\n",
    "\n",
    "cbar=fig.colorbar(cax,shrink=0.8)\n",
    "cbar.outline.set_edgecolor('lightgrey')\n",
    "\n",
    "#legend\n",
    "ax.plot([],[],c='g',lw=4,label='WT vs others avg freq')\n",
    "ax.legend(fancybox=True,loc='lower left')\n",
    "\n",
    "#annotate\n",
    "ax.set_title('')\n",
    "ax.set_xlabel('other samples avg freq')\n",
    "ax.set_ylabel('WT samples avg freq')\n",
    "fig.set_size_inches(16,16)\n",
    "\n",
    "\n",
    "#set w grd\n",
    "ax.grid(True,c='lightgrey',lw=1,linestyle='dotted')\n",
    "ax.set_frame_on(False)\n",
    "tics=ax.xaxis.set_ticks(np.linspace(180,200,6))\n",
    "tics=ax.yaxis.set_ticks(np.linspace(0,200,6))\n",
    "\n",
    "ax.set_xlim(180,201)\n",
    "ax.set_ylim(0,205)\n",
    "\n",
    "labs=ax.set_yticklabels(['0%','20%','40%','60%','80%','100%'], rotation='horizontal')\n",
    "labs=ax.set_xticklabels(['90%','92%','94%','96%','98%','100%'], rotation='horizontal')\n",
    "\n",
    "\n",
    "# remove tick marks\n",
    "ax.xaxis.set_tick_params(size=0)\n",
    "ax.yaxis.set_tick_params(size=0)\n",
    "\n",
    "#50% ones\n",
    "rect=plt.Rectangle((196,90),4,30, fc='none',ec='r',lw=3, linestyle='dashed')\n",
    "cax=ax.add_patch(rect)\n",
    "\n",
    "#lower freq ones\n",
    "rect=plt.Rectangle((198,124),2,18, fc='none',ec='b',lw=3,linestyle='dashed')\n",
    "cax=ax.add_patch(rect)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Conclusion:\n",
    "---\n",
    "\n",
    "There is a nice 1,0.5 cluster, altough there is an other cluster clearly separated for this, at 1,0,7. This spot might be mostly due to more than diploid regions.  \n",
    "\n",
    "\n",
    "Clusters are at considerably higher frequencies, than at BRCA1 homo\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "#Collect the line specific mutations \n",
    "---\n",
    "- Write them to files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%writefile wt_vs_all_collect_mut.py\n",
    "#!/usr/bin/python\n",
    "\n",
    "#import modules\n",
    "import subprocess\n",
    "import sys\n",
    "import re\n",
    "import numpy as np\n",
    "import fnmatch\n",
    "import os\n",
    "\n",
    "#input ouput files\n",
    "#input_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/indel/indel_list/'\n",
    "input_dir='/nagyvinyok/adat83/sotejedlik/orsi/SNV/SNV_list_withB_allsamples/'\n",
    "output_dir='/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all'\n",
    "subprocess.call(['mkdir',output_dir+'/het_indel'])\n",
    "subprocess.call(['mkdir',output_dir+'/lowfreq_indel'])\n",
    "\n",
    "#which file to run on come in cmdline arg\n",
    "input_fname=sys.argv[1]\n",
    "\n",
    "#filenames for samplenames\n",
    "rm_dup_dir='/nagyvinyok/adat83/sotejedlik/orsi/bam_all_links'\n",
    "#collect filenames\n",
    "fnames=[]\n",
    "for fname in os.listdir(rm_dup_dir):\n",
    "    if (fnmatch.fnmatch(fname, '*.bam') and \n",
    "        not fnmatch.fnmatch(fname,\"*.bai\")): #strange .bai convention!!!\n",
    "        fnames.append(fname)\n",
    "fnames=sorted(fnames)\n",
    "\n",
    "#select the group samples set\n",
    "group=['Sample1_RMdup_picard_realign.bam','Sample4_RMdup_picard_realign.bam'] #R1\n",
    "for i in range(1,8)+[9]: #R2\n",
    "    group.append('DS00'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(10,12): #R2\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(41,51): #R3\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in [73,74]: #R4test\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(81,98): #R4\n",
    "    group.append('DS0'+str(i)+'_RMdup_picard_realign.bam')\n",
    "for i in range(141,145): #R5\n",
    "    group.append('DS'+str(i)+'_RMdup_picard_realign.bam')\n",
    "\n",
    "#create array to index into numpy arrays\n",
    "group_bool,else_bool,group=[],[],set(group)\n",
    "for sample in fnames:\n",
    "    group_bool.append(sample in group)\n",
    "    else_bool.append(not (sample in group))\n",
    "group_bool,else_bool=np.array(group_bool),np.array(else_bool)\n",
    "\n",
    "#filter for pileup lines\n",
    "# there is a # in the beggining of Orsis format lines\n",
    "cmd_filt_pup_lines= ' grep -v \\'#\\' '+ input_dir+input_fname\n",
    "\n",
    "#output files\n",
    "f_het=open(output_dir+'/het_snp/'+input_fname,'w')\n",
    "f_lowfreq=open(output_dir+'/lowfreq_snp/'+input_fname,'w')\n",
    "\n",
    "#run the pipeline\n",
    "from subprocess import Popen, PIPE\n",
    "p = Popen(cmd_filt_pup_lines, stdout=PIPE, bufsize=1,shell=True)\n",
    "with p.stdout:\n",
    "    for line in iter(p.stdout.readline, b''):\n",
    "        #parse line\n",
    "        linelist=line.strip().upper().split(' ')\n",
    "        covs=np.array(map(int,linelist[3::2]),dtype=np.int32)\n",
    "        bases=linelist[4::2]\n",
    "        ref_count=[]\n",
    "        for i in xrange(len(bases)):\n",
    "            ref_count.append(len(re.findall('[\\.\\,]',bases[i]))) \n",
    "        ref_freq=np.array(ref_count,dtype=np.double)/covs\n",
    "        \n",
    "        #calculate group freqs\n",
    "        group_freq=np.mean(ref_freq[group_bool])\n",
    "        else_freq=np.mean(ref_freq[else_bool])\n",
    "        \n",
    "        #save the line specific mutations\n",
    "        if(group_freq >= 0.45 and group_freq <= 0.60  and \n",
    "          else_freq >=0.98):\n",
    "            f_het.write(line)\n",
    "        if(group_freq >= 0.61 and group_freq <= 0.71  and \n",
    "          else_freq >= 0.99):\n",
    "            f_lowfreq.write(line)\n",
    "        \n",
    "p.wait() # wait for the subprocess to exit\n",
    "\n",
    "#close files\n",
    "f_het.close()\n",
    "f_lowfreq.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Run them in slurm\n",
    "\n",
    "import os\n",
    "import subprocess\n",
    "input_dir='/nagyvinyok/adat83/sotejedlik/orsi/indel/indel_list_withB_allsamples/'\n",
    "for filename in os.listdir(input_dir):\n",
    "    try:\n",
    "        print subprocess.check_output([ 'sbatch',\n",
    "                                       '--mem',str(1000),'./wt_vs_all_collect_mut.py' ,\n",
    "                                       filename],stderr=subprocess.STDOUT),\n",
    "    except subprocess.CalledProcessError, e:\n",
    "        print e.output,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of het SNPs:\n",
      "80\n",
      "\n",
      "The number of low frequency SNPs:\n",
      "44\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "rm /nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/het_snp/all.pup\n",
    "cat /nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/het_snp/* > \\\n",
    "/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/het_snp/all.pup \n",
    "\n",
    "echo The number of het SNPs:\n",
    "cat /nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/het_snp/all.pup | wc -l\n",
    "echo\n",
    "\n",
    "rm /nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/lowfreq_snp/all.pup \n",
    "cat /nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/lowfreq_snp/* > \\\n",
    "/nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/lowfreq_snp/all.pup \n",
    "\n",
    "echo The number of low frequency SNPs:\n",
    "cat /nagyvinyok/adat83/sotejedlik/ribli/dt40/snp/WT_vs_all/lowfreq_snp/all.pup | wc -l"
   ]
  }
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