{ "metadata": { "name": "", "signature": "sha256:f3af29148f267e08a848db2139d16b2243afa78c254306c7580b59a85ae3a1c7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Figures for:\n", "\n", "These are not the k-mers you are looking for: efficient online k-mer counting using a probabilistic data structure\n", "==================================================================================================================\n", "\n", "See the paper at: http://arxiv.org/abs/1309.2975" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Table of Contents\n", "=================\n", "\n", "[Figure 1 - time usage of different k-mer counting tools](#Figure-1---time-usage-of-different-k-mer-counting-tools)\n", "\n", "[Figure 2 - memory usage of different k-mer counting tools](#Figure-2---Memory-usage-of-different-k-mer-counting-tools)\n", "\n", "[Figure 3 - Disk storage usage of different k-mer counting tools](#Figure-3---Disk-storage-usage-of-different-k-mer-counting-tools)\n", "\n", "[Figure 4 - Comparison of time it takes to count k-mers](#Figure-4---Comparison-of-time-it-takes-to-count-k-mers)\n", "\n", "[Figure 5 - relation between average miscount and counting error rate](#Figure-5---relation-between-average-miscount-and-counting-error-rate)\n", "\n", "[Figure 6 - counting error rate vs miscount](#Figure-6---counting-error-rate-vs-miscount)\n", "\n", "[Figure 7 - Percentage of the unique k-mers starting in different position in reads](#Figure-7---Percentage-of-the-unique-k-mers-starting-in-different-position-in-reads)\n", "\n", "[Tables](#Tables)\n", "\n", "[Supplimentary - determine the optimal parameters of hash tables to use](#Supplimentary---determine-the-optimal-parameters-of-hash-tables-to-use)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#%pylab inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import seaborn" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "import seaborn as sns" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# you may need to:\n", "#\n", "#!pip install --upgrade six\n", "# !pip install --upgrade statsmodels\n", "#\n", "# and then restart the ipython notebook kernel." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy\n", "import seaborn as sns\n", "\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "\n", "datadir = '../data/'\n", "figsize(12,6)\n", "\n", "# also try \"whitegrid\" for white background\n", "sns.set(style=\"darkgrid\", font=\"Serif\")\n", "sns.set_color_palette(\"deep\", n_colors=10, desat=.8)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/qingpeng/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/seaborn/rcmod.py:465: UserWarning: set_color_palette is deprecated, use set_palette instead.\n", " UserWarning)\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# palette can be changed around\n", "# see other options: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps\n", "c_khmer, c_ty, c_jf, c_dsk, c_kmc, c_bfc, c_t, c_k = sns.color_palette(\"Set1\", 8)\n", "c_meta,c_rk, c_re , c_ec =sns.color_palette(\"Set1\", 4)\n", "k17, k32 =sns.color_palette(\"Set1\", 2)\n", "# set all widths the same\n", "lineparams = {'markersize':8.0, 'lw':2.0}\n", "\n", "# keep each tool's format consistent\n", "s_khmer = {'marker': 'o', 'c': c_khmer}\n", "s_khmer.update(lineparams)\n", "\n", "s_ty = {'marker': '^', 'c': c_ty}\n", "s_ty.update(lineparams)\n", "\n", "s_jf = {'marker': 'h', 'c': c_jf}\n", "s_jf.update(lineparams)\n", "\n", "s_dsk = {'marker': 's', 'c': c_dsk}\n", "s_dsk.update(lineparams)\n", "\n", "s_kmc = {'marker': 'v', 'c': c_kmc}\n", "s_kmc.update(lineparams)\n", "\n", "s_bfc = {'marker': 'D', 'c': c_bfc}\n", "s_bfc.update(lineparams)\n", "\n", "s_t = {'marker': 'p', 'c': c_t}\n", "s_t.update(lineparams)\n", "\n", "s_k = {'marker': 'p', 'c': c_k}\n", "s_t.update(lineparams)\n", "\n", "# \n", "s_meta = {'marker': 'h', 'c': c_meta}\n", "s_meta.update(lineparams)\n", "\n", "s_rk = {'marker': 's', 'c': c_rk}\n", "s_rk.update(lineparams)\n", "\n", "s_re = {'marker': 'v', 'c': c_re}\n", "s_re.update(lineparams)\n", "\n", "#s_rwe = {'marker': 'D', 'c': c_rwe}\n", "#s_rwe.update(lineparams)\n", "\n", "s_ec = {'marker': 'p', 'c': c_ec}\n", "s_ec.update(lineparams)\n", "\n", "s_k17 = {'c': k17}\n", "s_k17.update(lineparams)\n", "\n", "s_k32 = {'c': k32}\n", "s_k32.update(lineparams)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def get_time_mem(filename):\n", " \"Extract the user time and max memory as generated by 'time' command\"\n", " for line in open(filename):\n", " line = line.rstrip()\n", " if 'system' in line:\n", " fields1 = line.split('user')\n", " time1 = float(fields1[0])\n", " fields1b = line.split('system')[0].split()[-1]\n", " time2 = float(fields1b)\n", " \n", " walltime = line.split('elapsed')[0].split()[-1].rsplit(':')\n", " assert len(walltime) <= 3\n", " hours = 0.\n", " minutes = 0.\n", " seconds = walltime[-1]\n", " if len(walltime) == 3:\n", " hours = float(walltime[0])\n", " minutes = float(walltime[1])\n", " elif len(walltime) == 2:\n", " minutes = float(walltime[0])\n", " \n", " wall_seconds = hours*60*60 + minutes*60 + float(walltime[1])\n", " \n", " time = wall_seconds\n", " fields2 = line.split('avgdata ')\n", " fields3 = fields2[1].split('max')\n", " mem = str(int(fields3[0])/4)\n", " return float(time), float(mem)\n", " raise Exception(filename)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# read time files\n", "\n", "\n", "\n", "mkindex_time={}\n", "mkindex_mem={}\n", "suffix_time={}\n", "suffix_mem={}\n", "\n", "# tallymer runtime info\n", "# part=1 k=22 \n", "\n", "for i1 in range(1,6):\n", " for i2 in [1]:\n", " for i3 in [22]:\n", " name = \"%d_%d_%d\" % (i1, i2, i3)\n", " filename = 'mkindex_%d_part%d_%d.time' % (i1, i2, i3)\n", " filename = datadir + filename\n", " mkindex_time[name],mkindex_mem[name] = get_time_mem(filename)\n", "\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir + 'suffix_%d_part%d.time' % (i1, i2)\n", " suffix_time[name],suffix_mem[name] = get_time_mem(filename)\n", "\n", "# read jellyfish \n", "# k=22 and k=31\n", "\n", "jelly_count_mem = {}\n", "jelly_count_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22,31]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir + 'jelly_%d_%d.time1' % (i1, i2)\n", " jelly_count_time[name],jelly_count_mem[name] = get_time_mem(filename)\n", "\n", " \n", "jelly_hist_mem = {}\n", "jelly_hist_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22,31]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir + 'jelly_%d_%d.time2' % (i1, i2)\n", " jelly_hist_time[name],jelly_hist_mem[name] = get_time_mem(filename)\n", "\n", "# DSK use k=22\n", "\n", "dsk_mem = {}\n", "dsk_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir+'dsk_%d_%d.time' % (i1, i2)\n", " dsk_time[name], dsk_mem[name] = get_time_mem(filename)\n", "\n", "# khmer use k=22 only, error rate=1%\uff0c 5% and 20%\n", "\n", "khmer_mem1 = {}\n", "khmer_time1 = {}\n", "\n", "for i1 in range(1,6):\n", " for i2 in [1,5,20]:\n", " for i3 in [22]:\n", " name = \"%d_%d_%d\" % (i1, i2, i3)\n", " filename = datadir +'bloom_%d_%d_%d.hist.time' % (i1, i2, i3)\n", " khmer_time1[name],khmer_mem1[name] = get_time_mem(filename)\n", "\n", " \n", "#khmer_mem2 = {}\n", "#khmer_time2 = {}\n", "\n", "#for i1 in range(1,6):\n", "# for i2 in [1,5,20]:\n", "# for i3 in [22]:\n", "# name = \"%d_%d_%d\" % (i1, i2, i3)\n", "# filename = datadir +'bloom_%d_%d_%d.time2' % (i1, i2, i3)\n", "# khmer_time2[name],khmer_mem2[name] = get_time_mem(filename)\n", "\n", "\n", "kmc_mem = {}\n", "kmc_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir+'kmc_%d_%d.time' % (i1, i2)\n", " kmc_time[name], kmc_mem[name] = get_time_mem(filename)\n", " \n", "\n", "kmc_dump_mem = {}\n", "kmc_dump_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir+'kmc_dump_%d_%d.time' % (i1, i2)\n", " kmc_dump_time[name], kmc_dump_mem[name] = get_time_mem(filename)\n", " \n", "bfcount_mem = {}\n", "bfcount_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir+'BF_count_%d.time' % (i1)\n", " bfcount_time[name], bfcount_mem[name] = get_time_mem(filename)\n", " \n", "bfdump_mem = {}\n", "bfdump_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir+'BF_dump_%d.time' % (i1)\n", " bfdump_time[name], bfdump_mem[name] = get_time_mem(filename)\n", " \n", "turtle_mem = {}\n", "turtle_time = {}\n", "for i1 in range(1,6):\n", " for i2 in [22]:\n", " name = \"%d_%d\" % (i1, i2)\n", " filename = datadir+'turtle_%d_%d.time' % (i1, i2)\n", " turtle_time[name], turtle_mem[name] = get_time_mem(filename) \n", "\n", "kanalyze_mem = {}\n", "kanalyze_time = {}\n", "for i1 in range(1,6):\n", " name = \"%d_%d\" % (i1, 22)\n", " filename = datadir+'kanalyze_%d.time' % (i1,)\n", " print filename\n", " kanalyze_time[name], kanalyze_mem[name] = get_time_mem(filename) \n", "print kanalyze_mem\n", "print \"1\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "../data/kanalyze_1.time\n", "../data/kanalyze_2.time\n", "../data/kanalyze_3.time\n", "../data/kanalyze_4.time\n", "../data/kanalyze_5.time\n", "{'5_22': 7378396.0, '1_22': 7221044.0, '2_22': 7106256.0, '3_22': 7119244.0, '4_22': 7122028.0}\n", "1\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# number of distinct 22-mers in different data sets\n", "\n", "def get_total_kmers(filename):\n", " total = 0\n", " for line in open(filename):\n", " line = line.rstrip()\n", " fields = line.split()\n", " total = total + int(fields[1])\n", " return total\n", "total_list = []\n", "for i in range(1,6):\n", " filename = datadir+'jelly_%d_22.hist' % (i)\n", " total = get_total_kmers(filename)\n", " total_list.append(float(total) / 1e9)\n", "\n", "print kmc_mem,kmc_time,\"test\"\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'5_22': 4962980.0, '1_22': 2741064.0, '2_22': 4710916.0, '3_22': 5075284.0, '4_22': 5122148.0} {'5_22': 353.9, '1_22': 44.69, '2_22': 94.94, '3_22': 146.56, '4_22': 229.79} test\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 1 - time usage of different k-mer counting tools" ] }, { "cell_type": "code", "collapsed": false, "input": [ "time_khmer = [] # 1% error rate , k=22\n", "time_tallymer = [] # k=22, time usage is the same for part=1 or part=4, only use part=1 here\n", "time_jellyfish_k22 = [] # use k=22, but memory change with different k size. k=31. \n", "time_dsk = [] # use k=22.\n", "time_kmc = []\n", "time_BF = []\n", "time_turtle = []\n", "time_kanalyze = []\n", "for i in range(1,6):\n", " time_khmer.append(khmer_time1[str(i)+'_1_22'])\n", " time_tallymer.append(suffix_time[str(i)+'_1']+mkindex_time[str(i)+'_1_22'])\n", " time_jellyfish_k22.append(jelly_count_time[str(i)+'_22'] + jelly_hist_time[str(i)+'_22'])\n", " time_dsk.append(dsk_time[str(i)+'_22'])\n", " time_kmc.append(kmc_time[str(i)+'_22']+kmc_dump_time[str(i)+'_22'])\n", " time_BF.append(bfcount_time[str(i)+'_22']+ bfdump_time[str(i)+'_22'])\n", " time_turtle.append(turtle_time[str(i)+'_22'])\n", " time_kanalyze.append(kanalyze_time[str(i)+'_22'])\n", "#for i in range(1,3):\n", " # L_turtle.append(turtle_time[str(i)+'_22'])\n", "time_khmer" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[392.33, 803.79, 1160.09, 1482.18, 1845.94]" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# fig 1\n", "\n", "#f, axarr = plt.subplots(2, sharex=True)\n", "#f.set_size_inches(10,7)\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "fig.set_size_inches(10,7)\n", "\n", "\n", "\n", "read_counts = [9744399, 19488798, 29233197, 38977596, 48721995] \n", "read_counts = [ float(x) / 1e6 for x in read_counts ]\n", "\n", "#############\n", "# top subplot\n", "#############\n", "\n", "#ax = axarr[0]\n", "ax.set_ylabel('Time (s)',fontsize=12)\n", "\n", "ax.plot(read_counts, time_khmer,'-', label='khmer (1% false positive rate)', **s_khmer)\n", "ax.plot(read_counts, time_tallymer,'-', label='Tallymer', **s_ty)\n", "ax.plot(read_counts, time_jellyfish_k22,'-', label='Jellyfish', **s_jf)\n", "ax.plot(read_counts, time_dsk,'-', label='DSK', **s_dsk)\n", "ax.plot(read_counts, time_kmc,'-', label='KMC', **s_kmc)\n", "ax.plot(read_counts, time_BF,'-', label='BFCounter', **s_bfc)\n", "ax.plot(read_counts, time_turtle,'-', label='scTurtle', **s_t)\n", "ax.plot(read_counts, time_kanalyze,'-', label='KAnalyze', **s_k)\n", "ax.set_xlim(0,50)\n", "ax.legend(loc='best',handlelength=4,prop={'size':12}) #,prop={'size':8})\n", "\n", "\n", "################\n", "# bottom subplot\n", "################\n", "\n", "#ax = axarr[1]\n", "\n", "#ax.plot(read_counts, time_khmer,'-', label='khmer (1% error rate)', **s_khmer)\n", "\n", "#ax.plot(read_counts, time_jellyfish_k22,'-', label='Jellyfish', **s_jf)\n", "#ax.plot(read_counts, time_dsk,'-', label='DSK', **s_dsk)\n", "#ax.plot(read_counts, time_kmc,'-', label='KMC', **s_kmc)\n", "#ax.plot(read_counts, time_BF,'-', label='BFCounter', **s_bfc)\n", "#ax.plot(read_counts, time_turtle,'-', label='scTurtle', **s_t)\n", "#ax.plot(read_counts, time_kanalyze,'-', label='KAnalyze', **s_k)\n", "ax.set_xlim(0,50)\n", "ax.set_xlabel('Total number of reads (millions)',fontsize=12)\n", "ax.set_ylabel('Time (s)',fontsize=12)\n", "\n", "ax.legend(loc='best',handlelength=4,prop={'size':12}) #,prop={'size':8})\n", "fig_file = '../figure/time_benchmark.eps'\n", "#fig_file = '../figure/time_benchmark.pdf'\n", "\n", "plt.savefig(fig_file,dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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WscKgVqsxNjbGzMyczz77koUL5xIb+/xfyC8bonv+kF3WvpYt23Do0H6Sk5MA+O239eze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\n9yQdJ/uERSz5/h/iy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"text": [ "" ] } ], "prompt_number": 7 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 2 - Memory usage of different k-mer counting tools" ] }, { "cell_type": "code", "collapsed": false, "input": [ "mem_khmer_1p = [] # 1% error rate , k=22\n", "mem_khmer_5p = [] # 5% error rate, k=22\n", "mem_khmer_20p = [] # 20% error rate, k=22\n", "mem_tallymer = [] # k=22, pick biggest memory, suffix 1 part and 4 parts different memory ,but all smaller than mkindex step(same with part1 or part4). \n", "mem_jellyfish_k22 = [] # use k=22, but memory change with different k size. k=31. \n", "mem_jellyfish_k31 = []\n", "mem_dsk = [] # use k=22.\n", "mem_kmc = []\n", "mem_BF = []\n", "mem_turtle = []\n", "mem_kanalyze = []\n", "\n", "for i in range(1,6):\n", "\n", " mem_khmer_1p.append(khmer_mem1[str(i)+'_1_22']/1000000)\n", " mem_khmer_5p.append(khmer_mem1[str(i)+'_5_22']/1000000)\n", " mem_khmer_20p.append(khmer_mem1[str(i)+'_20_22']/1000000)\n", " mem_tallymer.append(mkindex_mem[str(i)+'_1_22']/1000000) # memory usage of mkindex is always bigger than suffix(part1/4)\n", " if jelly_count_mem[str(i)+'_22'] > jelly_hist_mem[str(i)+'_22']:\n", " mem_jellyfish_k22.append(jelly_count_mem[str(i)+'_22']/1000000)\n", " else:\n", " mem_jellyfish_k22.append(jelly_hist_mem[str(i)+'_22']/1000000)\n", "\n", " if jelly_count_mem[str(i)+'_31'] > jelly_hist_mem[str(i)+'_31']:\n", " mem_jellyfish_k31.append(jelly_count_mem[str(i)+'_31']/1000000)\n", " else:\n", " mem_jellyfish_k31.append(jelly_hist_mem[str(i)+'_31']/1000000)\n", " \n", " mem_dsk.append(dsk_mem[str(i)+'_22']/1000000)\n", " \n", " if kmc_mem[str(i)+'_22'] > kmc_dump_mem[str(i)+'_22']:\n", " mem_kmc.append(kmc_mem[str(i)+'_22']/1000000)\n", " else:\n", " mem_kmc.append(kmc_dump_mem[str(i)+'_22']/1000000)\n", " \n", " if bfcount_mem[str(i)+'_22'] > bfdump_mem[str(i)+'_22']:\n", " mem_BF.append(bfcount_mem[str(i)+'_22']/1000000)\n", " else:\n", " mem_BF.append(bfdump_mem[str(i)+'_22']/1000000)\n", " mem_turtle.append(turtle_mem[str(i)+'_22']/1000000)\n", " mem_kanalyze.append(kanalyze_mem[str(i)+'_22']/1000000)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "# 2. memory usage of different k-mer counting tools, growing\n", "\n", "#f, axarr = plt.subplots(2, sharex=True)\n", "#f.set_size_inches(10,7)\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "fig.set_size_inches(10,7)\n", "\n", "\n", "\n", "#############\n", "# top subplot\n", "#############\n", "\n", "\n", "\n", "ax.set_ylabel('Memory usage(G)',fontsize=12)\n", "ax.set_xlabel('Total number of distinct k-mers (billions)',fontsize=12)\n", "ax.plot(total_list, mem_khmer_1p,'-', label='khmer (1% false positive rate)', **s_khmer)\n", "ax.plot(total_list, mem_khmer_5p,'--', label='khmer (5% false positive rate)', **s_khmer)\n", "ax.plot(total_list, mem_khmer_20p,':', label='khmer (20% false positive rate)', **s_khmer)\n", "ax.plot(total_list, mem_tallymer,'-', label='Tallymer', **s_ty)\n", "ax.plot(total_list, mem_jellyfish_k22,'--', label='Jellyfish', **s_jf)\n", "ax.plot(total_list, mem_dsk,'-', label='DSK', **s_dsk)\n", "ax.plot(total_list, mem_kmc,'-', label='KMC', **s_kmc)\n", "ax.plot(total_list, mem_BF,'-', label='BFCounter', **s_bfc)\n", "ax.plot(total_list, mem_turtle,'-', label='scTurtle', **s_t)\n", "ax.plot(total_list, mem_kanalyze,'-', label='KAnalyze', **s_k)\n", "#print L_turtle\n", "ax.set_xlim(0,2.3)\n", "ax.set_ylim(0,35)\n", "ax.legend(loc='upper left',handlelength=4,prop={'size':12}) #,prop={'size':8})\n", "\n", "################\n", "# bottom subplot\n", "################\n", "\n", "\n", "#ax = axarr[1]\n", "#ax.set_xlabel('Total number of distinct k-mers (billions)',fontsize=12)\n", "#ax.set_ylabel('Memory usage(G)',fontsize=12)\n", "\n", "#ax.plot(total_list, mem_khmer_1p,'-', label='khmer (1% error rate)', **s_khmer)\n", "#ax.plot(total_list, mem_khmer_5p,'--', label='khmer (5% error rate)', **s_khmer)\n", "#ax.plot(total_list, mem_khmer_20p,':', label='khmer (20% error rate)', **s_khmer)\n", "\n", "#ax.plot(total_list, mem_jellyfish_k22,'--', label='Jellyfish', **s_jf)\n", "#ax.plot(total_list, mem_jellyfish_k31,'-', label='Jellyfish k=31', **s_jf)\n", "#ax.plot(total_list, mem_dsk,'-', label='DSK', **s_dsk)\n", "#ax.plot(total_list, mem_kmc,'-', label='KMC', **s_kmc)\n", "#ax.plot(total_list, mem_BF,'-', label='BFCounter', **s_bfc)\n", "#ax.plot(total_list, mem_turtle,'-', label='scTurtle', **s_t)\n", "#ax.plot(total_list, mem_kanalyze,'-', label='kanalyze', **s_k)\n", "#ax.set_xlim(0,2.3)\n", "#ax.legend(loc='upper left',handlelength=4,prop={'size':12}) #,prop={'size':8})\n", "\n", "\n", "#fig_file = '../figure/memory_benchmark.pdf'\n", "fig_file = '../figure/memory_benchmark.eps'\n", "plt.savefig(fig_file,dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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nefz4MQB+fqf47bdNxd7Ly/VVWp0aGRmTnv48tkXKe96/fz9yuRyFQsHMmT9w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\nGyTAiB5P4m0PYDoe6OpudACuABHVUqxV5CSigUiSMVhIsWZWK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"text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "memory usage of khmer is larger than predicted, which is the \"maxresident\" in time output." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 3 - Disk storage usage of different k-mer counting tools" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", " \n", "\n", "file_ls = open(\"../data/ls.log\",'r')\n", "size = {}\n", "file_ls.readline()\n", "for line in file_ls:\n", "# print line\n", " line = line.rstrip()\n", " fields = line.split()\n", " size[fields[-1]] = int(fields[-4])\n", "\n", "\n", "# BFCount: \n", "# ----\n", "# bloom filter based, only count non-unique k-mers: (frequency>1)\n", "# \n", "# two steps: \n", "# \n", "# - BFCounter count - counting - output temparary binary file\n", "# - BFCounter dump - get the frequency of non-unique k-mers - write to hard disk the text files\n", "# \n", "# Estimated number of k-mers (upper bound) was used, which is acquired from actual distinct k-mers in test datasets.\n", "# This will influence the memory usage and disk usage.\n", "\n", "\n", "\n", "L_BF = []\n", "for i in range(1,6):\n", " size_total = size['iowa.'+str(i)]+size['iowa.'+str(i)+'.txt']\n", " L_BF.append(size_total/1000000000.0)\n", "print L_BF\n", " \n", "\n", "\n", "# KMC\n", "# ----------\n", "# like DSK, hard disk based\n", "# \n", "# two steps:\n", "# \n", "# - kmc - the main program for counting k-mer occurrences - output temparary binary files \\*.kmc_pre and \\*.kmc_suf\n", "# - kmc_dump - the program listing k-mers in a database produced by kmc - output text file to contain k-mers and counts\n", "# \n", "\n", "\n", "L_KMC = []\n", "for i in range(1,6):\n", " size_total = size['kmc_'+str(i)+'_22.out.kmc_pre']+size['kmc_'+str(i)+'_22.out.kmc_suf']+size['kmc_dump_'+str(i)+'_22.out']\n", " L_KMC.append(size_total/1000000000.0)\n", "print L_KMC\n", " \n", "\n", "\n", "# Turtle\n", "# --------\n", "# like BFCounter, only counts frequent k-mers, count>1\n", "# \n", "# based on enhanced Bloom Filter\n", "# \n", "# Two subprograms:\n", "# \n", "# - scTurtle: reports k-mers with frequency >1 with counts\n", "# - aTurtle: reports all k-mers with counts\n", "# - cTurtle: reports k-mers with frequency >1 but not their counts\n", "# \n", "# Tried aTurtle , failed to finish dataset 3, 4, 5\n", "# \n", "# Tried scTurtle, the same, shown in figure\n", "# \n", "\n", "\n", "L_Turtle = []\n", "for i in range(1,6):\n", " size_total = 0\n", " for k in range(7):\n", " size_total = size_total+ size['turtle_'+str(i)+'_22.out'+str(k)]\n", " \n", " L_Turtle.append(size_total/1000000000.0)\n", "print L_Turtle\n", "\n", "\n", "\n", "# kanalyze , use 2G memory, also lots of temperatre files, as large as output file\n", "\n", "\n", "L_kanalyze = []\n", "for i in range(1,6):\n", " size_total = size['kanalyze_'+str(i)+'.out']\n", " L_kanalyze.append(size_total/1000000000.0)\n", "print L_kanalyze\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1.412243247, 3.563975899, 5.437263035, 7.185738078, 9.38215238]\n", "[1.197719135, 3.072405453, 4.676246871, 6.206675487, 8.141617581]\n", "[1.31980792, 3.455289434, 5.433532237, 8.192208869, 9.853386717]\n", "[14.611555334, 28.006493339, 38.427155048, 47.27821484, 57.005033241]\n" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# 3. disk usage of different k-mer counting tools\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "fig.set_size_inches(10,7)\n", "\n", "\n", "# from size of intermediant files on hard disk, will modify to automatically generate\n", "L_khmer = [5.5,11,15,18,21] # 1% error rate , k=22\n", "L_tallymer = [7.8,17,23,28,36] # k=22, pick biggest memory, suffix 1 part and 4 parts different memory ,but all smaller than mkindex step. \n", "L_jellyfish_k22 = [5.3,9.9,14,17,20] # use k=22, but memory change with different k size. k=31. \n", "L_dsk = [1.1,2.1,3.0,3.8,4.7] # use k=22. by default, use the size of reads file as disk usage parameter\n", "L_khmer_gz = [1284585409. / 1e9,2491325096. / 1e9, 3439743439./1e9,4254327173./1e9,5151176537./1e9]\n", "\n", "ax.set_xlabel('Total number of distinct k-mers (billions)',fontsize=12)\n", "ax.set_ylabel('Disk usage (GB)',fontsize=12)\n", "\n", "ax.plot(total_list,L_khmer, linestyle='-', label='khmer (1% false positive rate)*', **s_khmer)\n", "ax.plot(total_list,L_khmer_gz, linestyle='--', label='khmer (1% false positive rate), gzip-compressed', **s_khmer)\n", "ax.plot(total_list,L_tallymer, linestyle='-', label='Tallymer', **s_ty)\n", "ax.plot(total_list,L_jellyfish_k22, linestyle='-', label='Jellyfish', **s_jf)\n", "ax.plot(total_list,L_dsk, linestyle='-', label='DSK', **s_dsk)\n", "ax.plot(total_list,L_KMC,linestyle='-', label='KMC',**s_kmc)\n", "ax.plot(total_list,L_BF,linestyle='-', label='BFCounter',**s_bfc)\n", "ax.plot(total_list,L_Turtle,linestyle='-', label='scTurtle',**s_t)\n", "ax.plot(total_list,L_kanalyze,'-', label='KAnalyze', **s_k)\n", "\n", "ax.set_ylim(0,60)\n", "ax.legend(loc='upper left', handlelength=4,prop={'size':12}) #,prop={'size':8})\n", "fig_file = '../figure/disk_benchmark.eps'\n", "#fig_file = '../figure/disk_benchmark.pdf'\n", "plt.savefig(fig_file,dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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ZREZGkpR0m8TEBNasWUl4eBhWVpYcP36Uv/46wNWrV6hd24MZM6YSERFGYGAAbdq0p25d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\nBJKECSGEEEIYwf8D2xfGFcegffMAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 43 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 4 - Comparison of time it takes to count k-mers" ] }, { "cell_type": "code", "collapsed": false, "input": [ "tally_count = [ get_time_mem('../data/1_part1_22.count.time')[0],\n", " get_time_mem('../data/2_part1_22.count.time')[0],\n", " get_time_mem('../data/3_part1_22.count.time')[0],\n", " get_time_mem('../data/4_part1_22.count.time')[0],\n", " get_time_mem('../data/5_part1_22.count.time')[0] ]\n", "\n", "khmer_count = [ get_time_mem('../data/bloom_1_1_22.count.time')[0],\n", " get_time_mem('../data/bloom_2_1_22.count.time')[0],\n", " get_time_mem('../data/bloom_3_1_22.count.time')[0],\n", " get_time_mem('../data/bloom_4_1_22.count.time')[0],\n", " get_time_mem('../data/bloom_5_1_22.count.time')[0] ]\n", "\n", "jelly_count = [ get_time_mem('../data/jelly_1_22.count.time')[0],\n", " get_time_mem('../data/jelly_2_22.count.time')[0],\n", " get_time_mem('../data/jelly_3_22.count.time')[0],\n", " get_time_mem('../data/jelly_4_22.count.time')[0],\n", " get_time_mem('../data/jelly_5_22.count.time')[0] ]\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "fig.set_size_inches(10,7)\n", "\n", "ax.plot(total_list, khmer_count, linestyle='-', label='khmer (1% false positive rate)', **s_khmer)\n", "ax.plot(total_list, tally_count, linestyle='-', label='Tallymer', **s_ty)\n", "ax.plot(total_list, jelly_count, linestyle='-', label='Jellyfish', **s_jf)\n", "\n", "ax.legend(loc='best',handlelength=4,prop={'size':12})#,prop={'size':8}\n", "ax.set_ylim(0,1600)\n", "ax.set_xlabel('Total number of distinct k-mers (billions)',fontsize=12)\n", "ax.set_ylabel('Time (s)', fontsize=12)\n", "#fig_file = '../figure/count_benchmark.pdf'\n", "fig_file = '../figure/count_benchmark.eps'\n", "plt.savefig(fig_file,dpi=300)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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BBytXLmXr1s2cPXsWNzc3Tp8+SUxMNLt370ChsOKHH5aQlpZKZmYGbm7N+PzzuSQnJ1NQ\nkE9eXi4//ricCxfOY2dny4EDv/Pbb7s4c+YU7doFMXfux1y8eJ74+DgefPAhOnQIQq225csvF7Bt\n21ZUKhsmTXoGhULBtGkvkZqaQnx8HD4+Gry9faqM18rKinXr1jBiRISpB+jQoSi++GI+Z86c5sKF\n81y8eKFSLxDArFnv8dpr03FxcQXA19efNWtWsWnTOiZMmETbtu1uuNbHH/+T6OiDnD59krS0VO69\ntyf+/i3ZuHEd69atwc7Ojri4oyQkxNGmTVs+/fTfpKWlcubMaR55ZBCJiQksXvwl27f/gkqlYvLk\nZ1GrbWndOqDa53C91NQUYmKiadOmLQsWfEp6ehpvv/130ztjnTt3ITk5ma+//oLjxxN49dXpBAS0\nwcFBzblzF1m4cAHbt/9CUVEhzzzzPOXl5cyY8SZZWZkcPnyIgQMH06yZO7t27eD33/fQsWMnoqOj\n+OWXnzh79iwODg6m5Ke4WEt5uYHBgx8zxdeihSc+Phr++98v2bx5A3l5+Tz//CtVTrFS3c+4umfa\np08/fHw0HDiwn717f8PX15dOncLo3LkLFy6cZ9Giz9m9ewe9et1P794PmK6zYsUyHnywT409pqJ2\nODioLfp7VTQdTbWtOTioq9yuMDakyaTuQGbm7ZXgapOHh5NFr99YLF36LUqlNWPHTrB0KHUuJiaa\njz6axerVG27ruKba1nbv3kFU1EFef/1vlg6lyWiqbU2YX1Ntax4eTlVul5KoqPcmTJiEtbU1xcXF\nlg5F1DMXLpyXZE0I0SRIwiYahCeeGGMahdpYJSbGs2DBHHJysnn//bctHU6D8OSTky0dghBCmIWM\nEhWinggKCmHJkuWWDkMIIUQ9JD1sQgghhBD1nPSwCSGEEELUIFWbzI9nlwIwpvWTeNlXPbtBXZGE\nTQghhBCiGlp9ERsurGFn6i8YjBWTdv897xgPeT/CMP+R2FvfuEpLXZCSqBBCCCFENXanbefXlJ9N\nyRqAwWjg15Sf2Z223WxxSA+bEEIIIcR1yo3l5OvyyCypfrUdc5KErYkZN24kzZtXLAh+4cI5jEZM\nM8Tn5GSzbFnkDcfs3bubL76YT/Pm7ixYsIj16//H0qXfEhbWlXfe+bs5wxdCCCFqTbmxnNzSbDJK\n0skoTqv0NbMkHV15/VlpQRK2JuZq0gUwe/ZMDAYD7703C4BXX32+ymPuv78Ply9f5qefNgIwfHg4\n2dlZpKWlmidoIYQQ4g4ZjAayS7LIKEkjozi90teskgz0Rn21xzrZOGOjsCFHl23GiKsmCZuZHLuU\nD0BHXxeLxvHccy+b/lyxKtmfK5NNmfJStcfd2qLvQgghhPnpy/VklWRUTspK0skoTie7NLPS+2fX\nc1G50sLWixa2nrSw+/Orh60n9tb2aPVaNl5Yw47UrabzKBVK+nkPpI/XAHPdoiRs5rL84EUAPrJw\nwhYa2rHaz86cOcXnn3+KSmWLv39LxowZj0bje9PzlZaW8uyzT3Hp0iUGDhzMW2+9y/Ll37N8+fc8\n+uhQSktL2bZtC+PHP0VCQjxJSad58slJNG/uzooVS1EoFLz66nTatGkLQFFRIevXr+X33/eg0fgy\nevQ4AgLakpAQx7//PZuiokJGjRrHzp2/cuzYEfbsiarV5yOEEKJ+0hl0ZJakVy5flqSRWZxOdmkW\nRqrvSGimbl6RiNl64WFX8bWFnScetp7YKm1vel17a3tGBzxJH6/+rDz7AyDTejRaxy7lE5dSYPqz\npXvZqlNeXs6nn36Bra0te/fuZvny73njjXeq3FehUACgVquZN+9LRo4cwl/+MgWAUaPGER9/jJdf\nfg2Ac+eSiImJ5l//+pS4uKO88cZUJk78C/Pnf8nixV+ydu1qXn+9YimmBQs+Ra1Ws2DBIi5ePM8z\nz0xk06ZtBAeHMnXqdKZPfwU3Nze++GIx8+Z9YoanIoQQwlxKDCVkXukhK8rN42zWeVOClqvLqfY4\nBQrc1S1oYed5Q0+Zu7oFKqXqrmPzsvfhtZC37vo8d0oStitmbkwg+nxenV/nnXXxdXbubi1d+ftj\nwXd8fEhIKJ9/Po/Tp09SXl5OSkpytQnbtdzc3OjWrQdbtmxmwoRJ7Nu3h169elfap1OnMFQqFcHB\noeh0Ojp1CgMgODiURYs+A8BgMLBv3x4++WQ+CoUCf/9WtG4dwJEjMXTv3tNUhn3wwYcAmDp1+h3f\nqxBCCMvQ6rVklqSTXpxmKmFmXvmaX1b9v8NKhRJ3tQcedjeWL93VHlhbNe6UpnHfnbgtH3zwLiNH\njmb69LdIS0vliSeGVbvv9e+wPfroEJYs+YoJEyaxc+evvPFG5cXLPT29ALC1tb3yvbfpe61WC8D5\n8+fIy8tl/vw5ph68kpJiTp8+TffuPYGKQRMq1d3/n5IQQoi6U1hWaCpXXv9e2eWygmqPs1ZY42Hr\nSQs7T1q6+eFEM1P5spnaHaVCaca7qF8kYbvibnqmquPh4cQzi/abyqFXhfo481F4aK1f73ZVJEUV\nidH58+e4ePEC/fo9DIBWW1TFvtV/37v3A/znP7M5ePAAVlZWODg43nY8LVu2wtXVjddff5tWrVoD\noNPpMBiqf1lUCCGE+RmNRi6XFdzwPtnVxEyrL6r2WJWVqiIpu66XrIWtF27qZlgpKub09/BwIjPz\nsrluqd6ThK0OHTqbc0OyBhCXUlAv3mW7dpSoRuOLi4sLMTHR9O//MDt3bq9i3+q/V6vVPPRQf2bP\nnnlDGbXqEaU3blMqlTzwQB9+/nkTU6a8iJWVFTNnvsukSc8QGNj+9m9QCCHEHTMajeTrcqtJytIp\nMRRXe6xaaWt6yb+FXeWvLipXU1Imbp3C2MjnZ7Bkdv7+pkQOn8ut8jNL97J98cU8tm79CYBHHx3K\nCy+8ws6dv7J06XeoVCo6dAgmMnIl3bv3JDz8CRYunE9OTg4DBw4iIKAtP/ywBJ1OR0TEaJ58cjIA\nR4/G8s47r7N+/VaUSqXpOhs2rMPd3Z2//e19li//nr17dxMcHMo77/ydv//9bS5cOE+/fg/z7rsf\nUFRUyMaN69izZzcODg707v0gw4eHc/ZsErNmzeDChfOEhHTkww//jbOzs8WeX30j/ycqzEXaWuNS\nMXFszjXTYPz5NbMkA115abXH2int8bxuGoyrPWbONi43VGJuV1Ntax4eTlVul4StDjW1xnbu3FnW\nrl3NtGlvWjqUJqeptTVhOdLWGh6D0UBOadYNk8ZWvOyfgd5YVu2xjtZO15Qtr/aUVXzvYO1410nZ\nzTTVtlZdwiYlUXHXfv11K3379mfjxrUMHlz9QAUhhBB1Q1+uJ6s088bllYrTyKpp4lgb1ytzkl1f\nvvTE3trBjHchbkYSNnHXTp48zvLlP9Ct2720b9/B0uEIIUS9l6pN5sezS4Fbn4S1rFxHZknmNWtd\n/tlbll2SRTnl1R7rpmp23Qv+f04iW9PEsaJ+kIRN3LUXX5xq6RCEEKJB0OqL2HBhDTtTfzH1ev09\n7xgPeT/CMP+RKBVKMksyrlteqeJrbml2tbP5V0wc6/HnLP7XJGYetp61MnGssCxJ2IQQQggz2Z22\nnV9Tfq60zWA08GvKz+xN33XTkZdWWOFu2+KG6TA8bb1obuuBjZVNXYcvLMgsCVtmZiZz587lxIkT\nREZGVvps4cKFfPfddxw4cMC0befOncTGxmIwGBg0aBAhISEAXLp0icjISBQKBRqNhvDwcKysZGiw\nEEKIhq/EUIxSobxmjrJr17z0ornavdHP5i+qZ5affExMDAMGDOD48eOVtv/xxx8UFBRUGmWSnp7O\n4sWLWbZsGTqdjmHDhrFlyxYA3nvvPWbNmoWfnx8zZszA29ub3r0rL4EkhBBCNESPah4jvNUYmaNM\nVMksrWLgwIHY29tX2paVlcXPP//MhAkTKk2sun//foKCggBQqVQ4OjqSlJSETqfj5MmT+Pn5ARAU\nFFSpV04IIYSo7242k5aDjaMka6JaFmkZ5eXlfPrpp0yfPv2GxpuYmIiXl5fpe41GQ0JCAklJSbi6\nulbaHh9fdwupCyGEELXNqoq1MJUKJQ/7DKaP1wALRCQaCosUwxMSErC2tmbFihUUFBRQWlrK119/\nzSOPPEJwcDBHjx417ZucnExISAgajYa8vLxK20NDa14pwM3NHmtryy0WW90EeJZ04MABPv/8c6Ki\nomjfvj2vv/46DzzwQJX7fvbZZ6xcuZIxY8bw8ssv88EHH7B582beeecdRowYUeO19u7dy9dff01B\nQQF9+/YlPj6eZ555hnvvvbfaY66/prg19bGticZJ2tqdSStKY9PFNQA82/E5YjKiAXi647P4Ovla\nMrR6S9ranyySsIWGhpqSrasDCZ599lkAbG1tWbVqFQClpaUUFhbSunXFQuDt27fnwoUL+Pv7k5iY\nyODBg2u8Vm6uto7uomb1dZbmNm1CmDPnCx54oDsvvDCVDh06Vxvn6NETOXPmHFqtjszMy7z00nSO\nHz/J5cslt3Rvn346j7/8ZQrduvVg06b1vPvuP7C3t7/psddfU9SsvrY10fhIW7sz5cZy5sTNocRQ\nwr3uvejh0pceLn0rPiyBzBJ5ptdrqm3NoisdREVFsWHDBrKysvjyyy+ZPHkyarWaCxcusGLFCkpL\nS/nyyy+ZOHEinp6ePPvss3z88ceUl5czZ84c03lmzZrF6tWrAQgLC6Nnz57mCP+u3ckEifXNna5g\nlpAQh6+vPwqFgscee7yWoxJCiIZhd9qvnMhPwMnGmbFtJlk6HNEAmSVh6969O927d79hu7+/P2+9\n9RZvvfVWpe19+/alb9++N+zv6+vLtGnT6irMWleoK2Rl0vfVTpBYn5b8SE1NYdWqFZw6dYJOncKI\niBiDm5vbTY+JizvG++//DYPBwMSJfyE8/AlmzpxBVNQBXnrpNX76aSNGo5EPPniXli1b0apVACtX\nLuXxx0fy9NNT0GqLmDv3Y9LT08jPz+fhhwcyfvxE0/m1Wi0zZ87gzJlT9O3bn6efnlLXj0EIIWpd\nZkkGkWeXAzChzdM42ThbOCLREMlwlDq09dwWfk35udIablcnSNydtt2Ckd1o2rSXCA29h88++wpv\nb2/ee++tGo8JDe3I1Kmv4+DgQHj4EwCMGTOB8PBRDBo0lAULFgEwc+ZHvPPO3xk37kl69rzPNI3L\npk3r8fBowbx5C/nss6/Ytm2r6dxGo5H9+/cxbdobLFz4X5Yu/Y6cnOw6uHMhhKg75cZyvju1iNLy\nUrq796Krew9LhyQaKJmB74p58f/iWG6s2a635twK1pxbUavn7OjWmakhNSda1zt9+hQFBQU89FB/\nAPr1e4R58z6htLQEtfrma8zdd9/9/PvfHxIfH0dISChbtmwmImL0LV3XxkZFdPRB+vbtT2BgOxYt\n+sb0mUKhIDg4BGdnFwD8/Pw5duwIffr0u+37E0IIS9mdtp3jV0qh49pMsnQ4ogGThE1w5MhhyssN\nTJ36gmlb8+YenDhxnHvu6XzTY21sbOjX7xG2bNlMhw5BpKYmo9HcfLTT1ffhRoyIQK1WM2vWDFQq\nNc8//xLdu//5XqKXl7fpz87Ozmi1lhtAIoQQtyurJJPIs8sAGC+lUHGXJGG74k56pmryW+5Wvo//\ntsrPRrYayyDfYbV+zTsRFtYFlUptKmECaLVFqFRq0/fXrkZxvUcfHcKbb06lW7fu9OhxX43Xu3qu\nrKwsBg9+jMGDH2PPnl289dZfWbv2J1xcXGu8phBC1GdGo5Fvr5RCu7n3pJuUQsVdknfY6tCjrQfx\nsM9glNdMlFgfJ0gMCGiLg4MD+/btAaCwsJDp019Br9cDFb94rh0lev2I0ZCQUFxcXFmw4FP693+4\niitUPvbq8V999TmHDkUBEBraCXt7B5RK61u6phBC1Ge7037leH48jtZOjG8z2dLhiEZAetjqkION\nA6MDnqSPV39Wnv0BqD/TelxNgK72Ys2Z8xmRkStZvvx7XFxcee65l7G1tWXJkq85eHA/KpUtLVp4\ncuLEcU6fPsWyZd/h6upGr14Va7kOHDiY48cTTe+c6fV6XnvtRRQKBR988C4jRkSQnZ1d6VwPPTSA\nr79eyOKZt9DsAAAgAElEQVTFC7Gzs+fVV6fj6OjIypVLTfu1bduOpKTTpmt6enrRpUs3yzw0IYS4\nBVklmaw+d2VUaFsphYraoTA28q4LS066Vx8n/dNqi7C3dyAzM4Pw8CGsX7+FZs2a3/V5166NxM3N\njb59+9dClOJ21ce2JhonaWs3ZzQamRM3m8T8OLq59+D5Dq9ZOqQGq6m2teomzpWSaBPz8ssVc5md\nOHEcPz//u07WtmzZDMCePbvp3fvBu45PCCEast/StpOYH4ejtRPjAqQUKmqPlESbGF9ffyZOHIO3\ntw/Tp//trs+3fv0a1q//HyNHjsLGxqYWIhRCiIYpuySTVeeujgqdjLPKxcIRicZEErYmZtasj2r1\nfAsXflPzTkII0cgZjUa+Pf0VpYYSujbvQXePXpYOSTQyUhIVQggh7tJv6TtIzIvD0dpRRoWKOiEJ\nmxBCCHEXsksyWXV2KQDj2jwtpVBRJyRhE0IIIe6Q0Wjku9NfXymF3kt39541HyTEHZCETQghhLhD\ne9J3kJB3DEdrR8a1mSwrtIg6IwmbEEIIcQeyS7JYdWWt0HFtJuOicrVwRKIxk4RNCCGEuE1Go5Hv\nT39NiaGYLs3vpbu7jAoVdUsSNiGEEOI27UnfSXzeURyujAqVUqioa5KwCSGEELehohR6dVToJCmF\nCrOQhE0IIYS4RdeWQsOad+de9/ssHZJoIiRhE0IIIW7R3vRdplLohDZPSylUmI0kbEIIIcQtyCnN\nZtXZHwAYFyClUGFekrAJIYQQNbhaCi02FBPWrBv3ekgpVJiXJGxCCCFEDfal7yIu9wj21g5MaPsX\nKYUKs5OETQghhLiJnNJsfpRSqLAwSdiEEEKIalSUQhdTbCimc7Nu9PDobemQRBMlCZsQQghRjX0Z\nu4nLjb1SCpVRocJyJGETQgghqpBTms2PSRWl0LEBE3FVuVk4ItGUScImhBBCXMdoNPLD6cUUG7R0\nbtaVnh73Wzok0cRJwiaEEEJc5/eM3ziWG4u9UkaFivpBEjYhhBDiGrmlOaxM+h6AsW2kFCrqB0nY\nhBBCiCuuLYV2atZFSqGi3pCETQghhLji94zfOJp7GHulA0+2kVKoqD8kYRNCCCGoXAodE/AUrupm\nFo5IiD9JwiaEEKLJu7YUeo9bF3q1eMDSIQlRiSRsQgghmrz9GXtMpdCnZFSoqIckYRNCCNGk5V1T\nCh0d8KSUQkW9JAmbEEKIJqtirdD/ojUUcY9bGPe1eNDSIQlRJUnYhBBCNFkHMvdyNDcGO6U9T7Z9\nRkqhot6ShE0IIUSTlKfLZcWZ74CKUaFuUgoVNTh2KZ9jl/Itcm1ri1xVCCGEsKCro0K1hiI6unWW\nUqi4JcsPXgTgI18Xs19betiEEEI0OQcy93Ikp6IU+lTbZ6UUKmp07FI+cSkFxKUUWKSXzSw9bJmZ\nmcydO5cTJ04QGRkJwOzZs9Hr9Xh4eJCTk8PUqVNxdHQEYOfOncTGxmIwGBg0aBAhISEAXLp0icjI\nSBQKBRqNhvDwcKysJOcUQghx6/J0uaxIqiiFjg54Ukqhoka5RTrm/HrK9P3ygxfN3stmlmwnJiaG\nAQMGYDQaTduMRiPvv/8+L7zwAnZ2dqxYsQKA9PR0Fi9ezLRp03j11VeZPn266Zj33nuPkSNHMnXq\nVGJjY9m/f785whdCCNFIGI1Glp7+L1p9EaFunendoo+lQxL1WHpBCQt3JzH5u2iyCnWm7ZboZTNL\nwjZw4EDs7e0rbXv33XcrfV9eXg7A/v37CQoKAkClUuHo6EhSUhI6nY6TJ0/i5+cHQFBQEAcOHDBD\n9EIIIRqLPzL3EZtzCDulHU/JqFBRjQs5WuZsO8WUH2L46VgahvIb97n6Ppu5WHzQQVZWFvHx8cyb\nNw+AxMREvLy8TJ9rNBoSEhLQ6XS4urpW2r59+3azxyuEEKJhytflsTzpWwBGt36SZurmlg1I1Dsn\n0y8TeSiZ/Uk5AFgpoLOfC7EXb+xNu9rL1tFMpVGLJmyXL19m9uzZfPjhh6b314KDgzl69Khpn+Tk\nZEJCQtBoNOTl5VXaHhoaWuM13NzssbZW1n7wt8jDw8li1xZNi7Q1YS4Nsa0ZjUa+/mMeWn0RXTy7\n8njoY9K71gCYo60ZjUZizuXw7W9niUrKBkBlbcVjYRrG927FP9bFVXvs6tgU+oX51nmMYMGELScn\nh9mzZ/PWW2/h6enJ1q1bGThwID179mTVqlUAlJaWUlhYSOvWrQFo3749Fy5cwN/fn8TERAYPHlzj\ndXJztXV6Hzfj4eFEZuZli11fNB3S1oS5NNS29kfGPv5IPYCd0o4x/pPJyiq0dEiiBnXd1sqNRqLO\n5bI6+hIn0ivag52NFYM7ejG8kw9uDirQG5g1NOim56ntGKtLUhXGa0cC1JGoqCjWrVvH3r17GTt2\nLJMnT2bs2LEYDAacnZ0BcHR0ZOHChQDs2rWL6OhoysvLGTp0KMHBwUDFKNHVq1cD4O/vz4gRI2oc\nJWrJXywN9RebaHikrQlzaYhtLV+Xx/sxb1CkL2Ri2yk84PWQpUMSt6Cu2pqh3MieU1lEHkrmfE5F\np46TrTXDOnkztKM3jraWfVvMogmbJUnCJpoCaWvCXBpaWzMajXyROIfDOdGEuN7DayF/k1JoA1Hb\nba3MUM72xAzWxCSTVlAKQHMHFSPCfBgY4omtjeVen7pWdQmbxQcdCCGEEHXlYNbvHM6JxlZpx8TA\nKZKsNUHFOgNb4tNYdziFHG0ZAN4utozsoqFfBw9slA1jPldJ2IQQQjRK+bo8lp/5FoBRrSfIqNAm\npqC4jE1HU9l4NI3CUj0ArZrbM6qbL/e1aY7SqmEl75KwCSGEaHSMRiNLz3xDkb6QENd7eMBT3ltr\nKrILdayLTWFLfBolZRUTqAV5OTGqmy9dW7o22F5WSdiEEEI0OlFZ+zmcHYWt0k7WCm0i0vJLWBOT\nzK+JGejLK17P7+LvyhNdNYT4ODf4NiAJmxBCiEalohS6BIBRrcfT3NbdwhGJunQuq4jImGT2nMqi\n3AgKoHeb5kR01dC2haOlw6s1krAJIYRoNIxGI8vOLKFQX0iwa0ce8Oxn6ZBEHTmedpnV0Zc4eC4X\nAKWVgv7t3RnZVYOfm30NRzc8krAJIYRoNKKyDhCTfbBiVGhbGRXa2BiNRo5cymd19CWOJhcAoFJa\n8UhIC0aEaWjhpLZwhHVHEjYhhBCNQoEun+VnvgHgCSmFNirlRiN/JOWw6tAlTmcUAWCvUjKkoxfD\nOnnjaq+ycIR1TxI2IYQQjcLVUmiQaygPSim0UdAbyvntyqoEF3OLAXCxs2Z4Jx8Gd/TCQd100pim\nc6dCCCEarajM/RzK/gO10pZJUgpt8Er1BiIPXuD735LIuFyxKoG7o4rwMA0PB7eoN6sSmJMkbEII\nIRq0Al0+y66OCm01nua2HhaOSNwprU7Pz3HprItNIe/KqgQaVzsiumro0869waxKUBckYRNCCNGg\nLU9aQqH+ckUp1Ku/pcMRdyC/uIyNR1LZdCyVolIDAO29nRnRyYueAQ1vVYK6IAmbEEKIBis66wDR\nWRWlUBkV2vBkFZay9nAKW+PTKdVXrEoQ4uPMqK4aHunqR1ZWoYUjrD8kYRNCCNEgXS4rYOnpK6NC\nW43DXUqhDUZKXjGRMcnsPJ5pWpWgW0s3nuimIdjbGUCS7+tIwiaEEKJBqhgVepkOLiH08Rpg6XDE\nLUjKLCIy5hL7TmebViV4ILA5EV18CfBwsHR49ZokbEIIIRqc6Kw/iM46gNpKzaTA56Q3pp5LSC1g\ndfQlos/nAWBtpWBAkAcju2jwcbWzcHQNgyRsQgghGpTLZQUsuzJBbkTr8VIKraeMRiOHL+Sx6lAy\n8SkVqxKora0YGOLJiDAf3B0b76oEdUESNiGEEA3K8jPfcrms4EopVEaF1jeGciMHkrJZdSiZpMyK\nVQkc1EqG3uPNY/d442JnY+EIGyZJ2IQQQjQYh7L+ICprP2orNRMDp2ClaLrzctU3ZYZydp/IJDIm\nheS8ilUJXO1teLyzD4NCPbFXScpxN+TpCSGEaBAulxWw1FQKHYeHbQsLRyQASsoMbEvI4H+Hk8kq\n1AHQwknNyC4a+gd5oLZueqsS1AVJ2IQQQjQIK0yl0GAZFVoPFJXq+elYGuuPpJBfrAfAz61iVYIH\nA92xbsKrEtQFSdiEEELUe4eyDnLQVAp9TkqhFpSn1bHhSCqbj6Wh1VWsShDYwpEnumno0boZVjJi\nt05IwiaEEKJeqyiF/heAka2kFGopGQUlrD2cwi8JGegMFasS3OPrwhNdNXTydZGpVeqYJGxCCCHq\ntRVJ33G5rID2LsH09ZZSqLldzNWy5lAyu05mYbiyKkGP1m5EdPWlg5eThaNrOiRhE0IIUW/FZEVx\nMPN3VFZqJsmoULM6nVFI5KFkfj+TjRGwUkCfdu5EdNHQyl1WJTA3SdiEEELUS4Vll/nhzGIAIlqN\nxcPW08IRNX5Go5H4lAJWH0om5sK1qxK0ILyLBm8XWwtH2HRJwiaEEKJeuloKbeccRF/vhy0dTqNm\nNBqJPp/L6uhkEtMuA2BrY8WgUC+Gd/KhuaPKwhEKSdiEEELUO4ezo/gjc9+VUqiMCq0rhnIjv5/J\nZvWhS5zN0gLgqLZmWCdvhnT0wllWJag3JGETQghRrxSWXeaH01dHhY6hhZ2UQmtbmaGcHcczWROT\nTGp+CQDN7G14PMyHgSFe2Ktkstv6RhI2IYQQ9cqKpO8oKMunnXMHHvJ+xNLhNColZQa2xqez9nAK\n2UUVqxJ4OV9dlaAFNjLZbb0lCZsQQoh643B29JVSqIpJgc9LKbSWFJbo2XQslQ1HUrlcUrEqQctm\n9kR01fBAoDtKK5lDrb6ThE0IIUS9UFhWyA+nK0aFhrcaK6XQWpBbpGP9kRR+OpZGcVnFZLftPR0Z\n1c2Xbq3cZFWCBkQSNiGEEPXCyiul0EDnDvSTUuhdSSso4X8xyfyamEGZoWKy285+Lozq6kuoxllW\nJWiAJGETQghhcbHZ0RzI3HulFCqjQu/UhRwtkYeS2X0ykyuLEtAroBkRXTW085RVCRoySdiEEEJY\nVEUptGJUaHjLMXjaeVk4oobnZPplVh9K5kBSDlCxKkG/9h6M7KrBv5m9haMTtUESNiGEEBb1Y9L3\n5JflEejcnn4+Ay0dToNhNBo5mlxA5KFLxF7MB8BGqeDhYE/Cw3zwdJZVCRoTSdiEEEJYTGz2IfZn\n7pFS6G0oNxqJOpfL6uhLnEgvBMDORsngjl4M7+SNm4OsStAYScImhBDCIq4dFTqi5Wg87bwtHFH9\nZig3sudUFpGHkjmfU7EqgZOtNcM7eTOkozeOtvJPemMmP10hhBAW8ePZilJoW+f29Pd51NLh1Fs6\nfTnbj2fwv5hk0gpKAWjuoCK8iw+PBHtiayOrEjQFkrAJIYQwu9jsQ+zP2IONlQ2TpRRaJa3OwJa4\nNNbHppCjLQPAx8WWkV01PNTeQ1YlaGIkYRNCCGFWRfpCfjhztRQ6Rkqh1ykoLmPT0VQ2Hk2jsLRi\nVYLW7vY80dWX+9o0l1UJmiizJGyZmZnMnTuXEydOEBkZCUBhYSErVqygtLQUa2trxo8fj5NTxRwx\nO3fuJDY2FoPBwKBBgwgJCQHg0qVLREZGolAo0Gg0hIeHY2Ul/4chhBANyY9JP5CvqyiFDmjCpdBj\nlypGdnb0dQEgu1DHutgUtsSnUXJlVYIgbydGdfWla0tXmey2iTNLwhYTE8OAAQM4fvy4adsPP/yA\nh4cHERERbNiwgcWLFzNt2jTS09NZvHgxy5YtQ6fTMWzYMLZs2QLAe++9x6xZs/Dz82PGjBl4e3vT\nu3dvc9yCEEKIWnA0J4bfM37DxsqmyY8KXX7wIgCvOqlZE5PM9sQM9Fdmu+3i78qobr6E+DhbMkRR\nj5jlb8rAgQOxt688cd8ff/xBUFAQAMHBwRw4cACA/fv3m7arVCocHR1JSkpCp9Nx8uRJ/Pz8AAgK\nCjIdI4QQov4r0hfy/TWjQr2acCn02KV84lIKiEsp4LkfYtgan46h3Ejvts35dNQ9zBwWLMmaqMRi\n77AlJCTg7V3xl9XHx4fExESMRiOJiYl4ef05y7VGoyEhIQGdToerq2ul7du3bzd73EIIIe7Mj0lL\nydPl0sapHQN8Blk6HIuJTyngo59PmL43AgOCWjCyiwZfNzvLBSbqNYslbMHBwaSkpNCsWTOSk5MJ\nCgpCoVAQHBzM0aNHTfslJycTEhKCRqMhLy+v0vbQ0NAar+PmZo+1teWGPHt4yNptwjykrQlzuZO2\nFp0Wxe8Zu1FZqfhrj7/i6eRSB5HVX0ajkX0nM/l+71mOXsi74fMRPVoS1rqZBSKr3+T32p8slrD1\n7NmTxMREQkNDSUhIoGfPnqbtq1atAqC0tJTCwkJat24NQPv27blw4QL+/v4kJiYyePDgGq+Tm6ut\nu5uogYeHE5mZly12fdF0SFsT5nInbU2rL2JBzHwAhvs/gbrEhcySptFeq5rs1kqBaWH2qxb+coKP\nwmvuhGhKmurvteqSVIXRaDRW+UktioqKYt26dezdu5exY8cyefJk9Ho9y5cvR6vVYmtry/jx43F0\ndARg165dREdHU15eztChQwkODgYqRomuXr0aAH9/f0aMGFHjKFFL/rCbamMT5idtTZjLnbS1JSe/\nZF/Gbto4BfLWPR80iYEGJWUGfk3MYO3hFDIu/znZbY/WbvwUl17lMbMfDzGNGBVN9/eaRRM2S5KE\nTTQF0taEudxuWzuac5j5Cf/GWmHDB2H/xMvepw6js7zCEj2bj6Wy4UgqBSUVc6hpXO0Y2cWHvu09\neH99AnEpBVUeG+rjLL1s12iqv9eqS9hk4lwhhBB1Qqsvumat0FGNOlnLLixlXWwqW+PTKL4yh1pg\nC0ciumro0bqZabJbScjEnZKETQghRJ1YdXYpubocApwCeVhT8zvHDdGl3GL+dziZncczTXOohfm5\nMrKrhns0zjLZrag1krAJIYSodcdyYtmbvgtrReNcK/Rk+mXWxCSz/0wORkAB3N+2OSO7aGjbwtHS\n4YlGSBI2IYQQtUqr1/L96a8BeLzlE3jbaywcUe0wGo0cuZRP5KFkjlxZVsraSkH/oBaEh/ng4ypz\nqIm6IwmbEEKIWvVnKbQtj2iGWDqcu2YoN3IgKZvIQ8mcziwCwM5GyeCOngzr5EMzB5WFIxRNgSRs\nQgghak1cbix703dirbBhUuDzDboUWmYoZ8fxTP4Xk0xKfgkArnY2DOvkzaCOXjiq5Z9QYT7S2oQQ\nQtQKrV7Ld6cqSqHDW0bg00BLoVqdni1x6ayPTSFHWwaAp7Oa8DAN/YM8UFtw9RzRdEnCJoQQolas\nvlIKbe3YpkGWQvO0OjYcSeWnuDSKSg0AtGpuT0RXDfe3dTdNzSGEJUjCJoQQ4q7F5R5hz5VS6OR2\nL6BUNJxeqLSCEtYeTuHXhAx0hoo51EJ8nInoqqGrv6tMzSHqBUnYhBBC3JWKUuhXQMMqhZ7NKmJN\nTDJ7TmWZ1vbs0dqNkV00BHk7WzY4Ia4jCZsQQoi7Enl2WYMphRqNRhJSLxN56BLR5/MAUFop6NfO\nnfAuGlo2t7dwhEJUTRI2IYQQdyw+9yi/pe/AWmHN5HbP19tSaLnRSPS5XCIPJZOYVrE+pdraioEh\nngzv7EMLJ7WFIxTi5iRhE0IIcUeK9Vq+O11RCh3mH4GPva+FI7qR3lDOb6eyWBOTzIWcYgAc1dY8\ndo8XQ+7xxsXOxsIRCnFrJGETQghxR1afW05OaTatHNsw0HeopcOppKTMwLaEDNbGJpN5WQeAu6OK\nxzv78EiwJ3aq+tkTKER1JGETQghx2+Jzj/Jb2vaKUmjgc/WmFHq5pIzNx9LYeCSVghI9AL5udozs\noqFPO3dslA13Il/RtEnCJoQQ4rZUlEIrJsgd5j8SjYOfhSOCrMJS1semsCU+nZKyiqk52nk6EtFV\nQ4/WzbCSqTlEAycJmxBCiNsSeW45OaVZtHIMYKDvYxaN5WKulrUxKew8kYn+ytwcXfxdieiiIVTj\nLHOoiUZDEjYhhBC3LDYjlt2mUqjlRoWeTL9M5KFkDiTlYASsFPBAYHNGdtHQxsPRIjEJUZckYRNC\nCHFLSvTFfHZkHgCPWaAUajQaib2YT2RMMkcv5QNgo1TQv0MLRoT54ONqZ9Z4hDAnSdiEEELcktXn\nlpNZnElLx9Y8asZSqKHcyO9nsomMSSYpswgAe5WSwaFeDOvkjZuDymyxCGEpkrAJIYSoUWJeHLvT\nfsVaYc3TgeZZK1SnL2fH8Qz+dziF1PwSAFztbRjWyZvBoV44qOWfMNF03FJrLywsJC4ujvj4eOLj\n4zEYDISEhJj+c3V1res4hRBCWEiJvphvTy0CYHTQ2DovhWp1en6OS2d9bAq52jIAvJzVhHfR0L9D\nC1TWMjWHaHpqTNh2797N22+/jZeXFz4+Pnh7e6NUKomLi2Pbtm1cunSJd999l6FD69ekiUIIIWpH\n5LnlZJdm4e/QmvDAkeRmF9fJdXK1OjYeSeWnY2kU6QwABLg7ENFVw31tmqO0khGfoum6acK2a9cu\ntm/fztatW3FycqpyH61Wy6effgogSZsQQjQyiXlx7Er7FaVCydPtnsfaqvbLkGn5JfzvcDK/JmZQ\nZqiYmqOjxpmILhrC/F1lag4hqCFhCwwMpG/fvjc9gb29Pe+++y4pKSm1GZcQQggLu7YU+phfOL4O\n/rV6/rNZRUQeSmbv6SyuTKFGz4BmjOyioYNX1Z0EQjRVN03YNBpNtZ8dP34cW1tbWrVqBYCPj0+t\nBiaEEMKy1pxfYSqFPuo7rFbOaTQaiU8pIDImmUPn8wBQWino396dkV00+DWzr5XrCNHY3HLf9kcf\nfcSuXbtYv34927dvZ8aMGTg7O/P0008zceLEuoxRCCGEmSXmxbEzdRtKhZLJ7Z6761JoudFI1Llc\nIg8lczztMgBqaysGhnjyeGcfPJzUtRG2EI3WLf8N3L9/P5s3b8ba2pr58+fzzTffEBISwpQpUyRh\nE0KIRqTEUMJ3p74CYKhfOH4OLe/4XHpDOb+dyiIyJpmLORWDFZzU1jzWyZshHb1wtrOplZiFaOxu\nOWFr1qwZ1tbWREdH4+LiQlhYGADFxXUzWkgIIYRlrDm3gqzSTPwdWjHoDkuhJWUGfklIZ+3hFLIK\ndQC4O6oYEebDI8Ge2NpYZkkrIRqqW07YAgICeOONNzh69ChvvPEGZWVlrF+/HrVaurGFEKKxOJ4X\nz87UX66UQm9/VGhBcRmbj6Wx8Wgql0v0APi52TGyi4YH27ljo5Q51IS4E7f8N3HGjBns2LGDoUOH\n0qdPH3Jzc0lLS2P69Ol1GZ8QQggzKTGU8K2pFDritkqhmZdLWRebwtb4dEr15QC093Qkoqsv97Z2\nw0qm5hDirtw0YSsqKsLBwQEAKysrBgwYYPrMzc2Nl19+2fS9VqvF3l5G9wghREP1v3MryCrNwM+h\nJYN8h9/SMRdztKyJSWbXySwMV+bm6NrSlYguGkJ8nGUONSFqyU0Ttq1bt5KSksILL7yAUln1+wZG\no5ElS5ZgbW3NU089VSdBCiGEqFvH8xLYcaUU+nTgCzWWQk+kXSYyJpkDSTkAWCngwcCKqTkCPBzM\nEbIQTcpN/0aGh4ezatUqBg8eTPv27fH29sbb2xsrKytSU1NJTU3l1KlTDB8+nKefftpcMQshhKhF\npYYS0wS5Q/wex8+x6lKo0WjkwOks/rvjFMeSCwCwUSoYENSCEWEavF1szRazEE1Nje+wjRo1irCw\nMI4cOUJCQgJbt27FYDAQFBTEfffdx3PPPUdQUJA5YhVCCFEH1lxTCh3s+/gNnxvKjfx+JpvIQ8kk\nZRUBYK9SMqSjF4918sbNXmXukIVocm5p0EFgYCCBgYF1HYsQQggzO5H/Zyl0cmDlUaE6fTnbj2ew\n9nAKqfklADR3VPHYPd48GuKJg7r21xUVQlRN/rYJIUQTdW0pdLDv4/g7tgKgqFTPz3FprD+SSp62\nDABvF1vCw3wYdX8ABXlaS4UsRJMlCZsQQjRRa86tJLOkohQ6xO9xcot0rD+Sys9xaWh1BgACPByI\n6KLhvjbNUVopUMuEt0JYhCRsQgjRBFWUQreiVCh5zPtpvvrtPL8mZlBmqJia4x6NMyO7+hLm5yJT\ncwhRD0jCJoQQTcCxS/kAdPR1uVIKrZgg16X0fmauyaLcCAqgV0AzRnbR0N7LyYLRCiGud1sJm06n\nIyoqihMnTjBmzBjOnTtHcHDwXQUQGRlJUlIS9vb2aLVa3nzzTQoLC1mxYgWlpaVYW1szfvx4nJwq\nfnns3LmT2NhYDAYDgwYNIiQk5K6uL4QQTcHygxcBmK1x5uu478ksSaesuDmJJ0OwVijo18GD8C4+\n+LnJBOhC1Ee3nLCdPHmSMWPGEBgYiFarZcKECSxcuJD777+f0aNH39HFCwsLmTt3Ljt27EClUvHU\nU08RFRVFdHQ0Hh4eREREsGHDBhYvXsy0adNIT09n8eLFLFu2DJ1Ox7Bhw9iyZcsdXVsIIZqKY5fy\niUupmDfthTUb0HvtxGhUUJLyCI938mV4Zx/cHWVdaCHqs1tehffbb78lMjKSH3/8EVdXV1QqFfPn\nz2fHjh13fHG1Wo1CoaCgoACj0Uhubi5OTk788ccfprndgoODOXDgAAD79+83bVepVDg6OpKUlHTH\n1xdCiMZOq9Mzf+9BXAM24BqwjpJmmwAIsO7H4tGD+Mv9rSVZE6IBuOUetosXL9K6detK28rLyyks\nLLzji9vY2PDJJ58wZcoU3NzceOKJJ+jQoQMJCQl4e3sD4OPjQ2JiIkajkcTERLy8vEzHazQaEhIS\nCAgIuOMYhBCiMbqYq2XD0bPsz92E2ucoaqty02dqKwde6foETiobC0YohLgdt5yw9e3blw8//JCR\nI7WrsSkAACAASURBVEcCcObMGdauXVtpQfjblZKSwt/+9jd++eUXrK2tefvtt1m9ejXBwcGkpKTQ\nrFkzkpOTCQoKQqFQEBwczNGjR03HJycnyztsQghxhaHcSPT5XDYdTSX2Yj72LQ7h5BN7w36l5UXs\ny9jNIN9hFohSCHEnbjlhGz16NB999BHPPPMMOTk5PPnkkwwYMIDnn3/+ji+em5uLs7Mz1tYVYbRo\n0YLMzEx69uxJYmIioaGhJCQk0LNnTwB69uzJqlWrACgtLaWwsPCGXr/rubnZY21tuXmDPDxkpJUw\nD2lrTVe+VsfGmGTWRF0kNf8ySnU+js3z8WiRTnVT3OaVGu64zUhbE+Yibe1PCqPRaLydAwwGA0lJ\nSQQEBKBU3n0i9P/s3Xl4k2W+PvA7S/e9NG3SlhbK0oW2ooCggrJDbVkERBjkjKMeZnTmOoo6o85x\nzjDX8aejzug4eo4zyBxnRlllp+wgiDrsiCl0YSnQJUn3LU2bNsnz+yNp2tId2iRt7891cbXN8uZJ\n8pLefb7P+30//vhjNDY2wt3dHSUlJXjppZcgk8mwYcMGGAwGeHp6YsWKFfD19QUAHD9+HOfOnYPF\nYkFaWlqXR6mWlNTc9RjvlELh59THp8GD+9rgIoRAdWMVLmhzcfxGDq5X5kPiVg6ZZwXk7jWApOuP\ndR/9NHw4d1WPH5v7GjnKYN3XOgqpPQ5st3vttdfw+9///m420acY2Ggw4L42MJksJhTX66A1aFBU\np4G2TgOdQYOCWg0aRV2795FCCoVnKJTe4TCYDLhand3u7RYPW35HJVHua+Qog3Vf6yiwdbskmpOT\ngw0bNuDChQuora21X15WVubSgY2IyJUJIaA31UBr0EBXZ/tn0EJXp0FpfTEssLR7P4vJA6IhCGFe\n4RirHI7Y4GiEeYUj1DPMfgJ3g8mAPXnb8JX2IMzCeqopmUSG6ao5eER55+uPicjxuh3Y3nrrLdx3\n3314+eWX4eXlZb/87bff7pOBERENJCaLCSX1RdDVaW2hrCmgaVFrav9oewkkCJQr0GgMRGm5Lxrr\nAmEyBiHMS4W0hBGYHhcKL/eOl6Z4y73xRMxKPKKcgU03PgcALBu+Ekrv8D55jkTUd7od2IQQeOGF\nF9pc/sEHH/TqgIiI+jN9Y401lBkK7eFMa5sta5rlup2XzAtKr3DrP+9whLgrUVDshW8yTcgpMQIA\npBJg4vBgpCWrkBTh36Pzeyq9w/HimFd75fkRkXN0O7A988wz+Mtf/oLx48cjPNz615kQAq+//jo2\nbdrUZwMkInI1ZmFGaX2xbW2Z1rq2zDZrpje1v+ZGAglCPEKh9FbZwpkKSq8IKL1VCHALhEQiQXGN\nEfszdFifWYSaeuvSEz9POeYkhCElSYlQPza4JRqsuh3YiouL8cknn8BoNLa6vCd/5RER9Se1Jr19\nPVlzGVOL4npdh7NlHlIPKL3D7TNmKu9whHmpEOapgrvMvc3thRBQF1Zjr1qL0zfKYbEdBjZC4YO0\nZBUeHhUCd3m3T0pDRANUtwPb2rVrsXbtWiQnJ7daw/aLX/yiTwZGROQIFmFBaX0JdHW2EqZtbZm2\nToOaxuoO7xfsEQKVlzWMqVoEtED3oG79IVvXYMaxnBKkZ2iRX2494lMulWDKqCFIS1IhVunLP4iJ\nyK7bgW306NG499574e7e+i/EZ599ttcHRUTU2wwmA3R1thJmizYZxXU6mISp3fu4Sz1spUtVq1mz\nMC8lPGSedzQOTWUd9mbocCSrGIYG6yxdsLcb5iYqMXdMGIJ82s7CERF1O7AlJyfjZz/7GcaNG4eI\niAgA1qn8Tz/9FPv27euzARIRdZdFWFBmLLWGsRZHYeoMGlQ1VnZ4vyD3YPuCf2tAswazII9gSCV3\nX460CIELtyqxR63FhbzmccSr/DAvWYVJMcFwk7HsSUQd63ZgW7duHeLi4nDq1KlWl5eUlPT6oIiI\nOlNvqmsOY7b1ZVqDBsX1OjRaGtu9j5vUDWGezTNlKltAC/NSwfMOZ8u6ojeacCSrGPsydNBW1QMA\n3GVSPDI6BKnJSoxQ+PbJ4xLRwNPtwJaamoo1a9a0ufy9997rzfEQEQGwzpZVGMugsx2F2TRrVlSn\nRUVDeYf3C3APtK0taw5lSq9wBHsM6ZXZsu64WVqLvRk6HMspgdFkbXwb6ueB1CQlZsaHwt/LzSHj\nIKKBo9uBrb2wBqDLk68TEXXGaK5vbo3R4ojMojotGiwN7d5HLpEjrJ21ZUovFbzk3g5+BlZmi8Cp\n3HLszdAio7D5YIWxQwOQlqzC+OggyKQ8iICI7kynge3s2bOYMGECAOtJ2tuzY8cOLFmypPdHRkQD\nhhACFQ3lLU67VGibNdOi3FjW4f383QJsoSzCHshU3uEY4qFw2GxZVyoNDTiUWYz9l3Qo1VsDppeb\nFNPjQpGapMTQYOcESCIaWDoNbJ9++ikSEhLg4+ODTZs2YcqUKa2uF0K06ctGRP2P1lCIzTe+AHB3\npy4ymo0ortM1lzBtAa2oTgOjpf3PCplEhlBPJZTe4S3aZERA6aWCt9znjp9TX7tSVIO9ah1OXC2F\nydY8LSLQE6lJKsyIV8DbvdsFDCKiLnX6ibJ27Vr790899VS7LTzWrVvX+6MiIocwmGqxO28bjmkP\n2RvB/rYyA9NUszE/anG7gUkIgaqGClt3f22Lc2JqUG4sg4Bo97F85X6t+pU1HZEZ4hkKmaTj82G6\nkkazBd9eK8NetRY5Rdbzf0oA3D8sCGnJKtwzNABS9k4joj7QaWBLSUlBSEgIXn755Q77rbEPG1H/\n9bXuKI5o9re6zCzMOKLZD1+5H8YOGdfmvJi6Oi3qzXXtbk8mkUHhGdZ86iXv5rVlvm5+jnhKfaJU\nb8SBS0U4eLkIlXXWo1B9PeSYlRCKR5OUUPr3zVGmRERNOg1sISEh+Pzzzx01FiJyITvztmBn3pZ2\nr/OW+0DlFdEqlKm8whHiGQq5dGCUAoUQuKypRnqGDievl9lPGTVsiDfSklV4ZHQIPN36x8wgEfV/\nd/3J+oc//AGvvPJKb4yFiFxMmKfS2h7D1q+sqU2Gr9xvwJ42qb7RjK+vlCJdrcXNMgMAQCoBJo8c\ngrRkFRJUA/e5E5Hr6jSw5ebm4le/+hWEaLsmRSKRQAiBkydPMrAR9UPVDVW4WHauw+sXRi1FWtRj\nDhyRc+mq6rE3Q4fDWUWoNVrX8wV6uWFOYhhSxoRhiK+Hk0dIRINZp4FNIpFAJpN1GtiIqH+pMxlw\nqHAvDhXutR+5KYHEfrCATCLDdNUcTA+f48xhOoRFCFzMq0R6hg7nblbYD5eIDfNFWrIKD40cwlNG\nEZFL6DSwDR8+HG+//XanG3j33Xd7dUBE1DcaLY04rj2Mvfk7oTfVAACSg+7FY8OegJvEDZtuWNer\n3k1bj/7C0GDCkawS7MvQorDSesoouVSCh0eHIC1JhVFhPGUUEbmWu17D9qtf/ao3xkFEfcQiLDhV\n/C125X2JMmMpAGCE32gsHrYcowPi7Ld7ccyrzhqiw+SXG5CeocOx7GLUNVpPGRXi645HE5WYPSYM\nATxlFBG5qE4DW0lJCVauXImXX34ZY8eOddSYiKgXCCHwQ/kF7Li1GYWGfABAuHckFkU/gXuCxw2a\nhfNmi8DZmxVIV2vxQ0GV/fLkCH+kJqswcXgwTxlFRC6v08B24MABR42DiHrR1apsbLu1CdeqcwAA\nwR4hWBC1BA+ETnGZUzr1teq6RhzKLML+S0UorrGu1fOQSzE9ToHUJCWih7juWRSIiG43MBomEREA\noKA2D9tvboa64gIAwFfui9Shj2GqaibcpO5OHp1jXC/RI12tw4krpWgwW8ueqgBPpCYpMSM+FL4e\n/Ngjov6Hn1xEA0BRbRE+y/k7TpV8CwEBD6kHZkWkYk5EKrzkA//k441mC05eL0e6WossXY398nHR\ngUhLUuG+6ECeMoqI+jUGNqJ+rKaxGul5O/C17ghMwgSZRIZHlDOQOvQxBLgHOnt4fa68tgEHLxdh\n/yUdKgzWU0b5uMswIz4UqUlKhAd6OXmERES9g4GNqB+qN9XhkGYvDhbuhdFcDwkkmKh4CAujH4fC\nM8zZw+tTQghk62qQrtbhu+tlMNvOGRUV7IW0ZBWmjlbAy52njCKigYWBjagfabQ04mvdUezN34Ga\nxmoAQGLQWDx779PwbVA4eXR9y2gy45srpUjP0OF6SS0A6ymjHogJRlqyCkkR/oPmyFciGnwY2Ij6\nAYuw4HTJd9h160uUGksAADF+o7B42DLEBiRAEeCHkpKaLrbSPxVX12PfpSIcyixCTb0JAODvKcec\nMWGYm6hEqB9PGUVEAx8DG5ELE0Igo+J7bLu5qUUvtQg8Fr0MYwdwLzUhBNQFVUjP0OHMjXLYqp4Y\nqfBBWrIKU0aFwF0+ONqTEBEBDGxELuta9RVsu7kBV+291IZgftQSPBj68IDtpVbXYMZXOcXYq9Yh\nv6IOgO2UUaOGIDVZhdgw3wEbUomIOsPARuRiCmvzsePWZlwsPw8A8JH7InXoQkxTzRqwvdQKK+qw\nN0OHo9nFMDSYAQDBPu5ISQzDnIQwBPkMzOdNRNRdDGxELqKsvgS78rbiZPE3EBBwl3pgdsSjmB2R\nBu8B2EvNbBE4f6sCezN0uJBXab88QeWHtGQVHogJhlw2MGcSiYh6ioGNyMlqGquxN38njmsP23up\nPaycjrShiwZkLzV9vQmHs4qwL0MHXbX1lFHucimmjg5BapIKMQqeMoqI6HYMbEROUm+ux+FCay+1\nerN1vdb9igexMGopQr0GXi+1G6W12KvW4diVEjSYrKeMCvXzQGqSErMSQuHn6ebkERIRuS4GNiIH\nM1lMOKE7ij3521v0UrsHi6KXIcp3mHMH18tMZgtO3yjHHrUOlzXV9svHDg3AvGQVxkUHQSblQQRE\nRF1hYCNyEIuw4EzJv7Dz1pcoNRYDAGL8RmJR9HLEBSY4eXS9q9LQdMqoIpTVNgAAvNykmBEXikeT\nlRgaNPDW5BER9SUGNqI+Zu2ldhHbb25CgSEPAKD0Csei6GW4d8j4AdWm4kpRDfaodfj2ailMtuZp\nEYFeSEtWYnqcAt7u/MghIroT/PQk6kPXq69g282NuFKdDQAIcg/GgqgleCDsYcgkA+N8l41mC765\nWop0tQ5Xi/UAAAmAicODkJaswj2RAQMqlBIROQMDG1Ef0BgKsP3mZlwsPwcA8Jb7IDVyIaapZsNd\nNjB6ipXqjdh/qQgHL+tQVWc9ZZSvhxyzE0KRkqSE0t/TySMkIho4GNiIelFZfamtl9oJWy81d8wM\nfxRzI9PgLe//7SqEELikqUa6WotTuc2njBoe4o20ZBUeHhUCT7eBMXNIRORKnB7YSkpKcPz4cej1\nenz77bd44YUXMGLECGzYsAFGoxFyuRwrVqyAn58fAODYsWO4ePEizGYzUlJSMGbMGCc/AyJA31iD\nvfk7cUx7GCbRCJlEhilh05AWtQiB7kHOHt5dq28043hOCfZm6HCzzAAAkEklmDIyGKlJKiSo/Fj2\nJCLqQ04PbK+99ho+/vhjeHl54dFHH4Wbmxv++c9/QqFQYMmSJdi9ezfWrVuH1atXo6ioCOvWrcP6\n9evR0NCA+fPn48CBA85+CjSIGc31OFy4DwcL01HX1Est5AEsiF6KMC+lk0fXMxkFVQCApMgA+2Xa\nqnrsy9DicFYxao3WU0YFerth7pgwzB2jxBDfgVHeJSJydU4NbHq9HuXl5fjss88ghEBycjKmTJmC\n06dP45e//CUAICEhAevXrwcAnDx5EvHx8QAAd3d3+Pr6Ijc3FzExMU57DjQ4mSwmfFP0FfbkbUd1\nozXojAlMxqJhyxDtO9zJo7szG87kAwD+X4Q/vs+rRLpah/O3KmCreiI2zBdpySo8NHII3HjKKCIi\nh3JqYDt37hyysrLwpz/9CVFRUXjuuefg5uaGzMxMqFQqAEB4eDiysrIghEBWVhaUyuZZi4iICGRm\nZjKwkcNYhAVnS09i560tKKm39lIb7jsCi4YtQ3xgopNHd+cyCqpwydbY9um/n0NZbSMAwE0mwZRR\nIUhLUmFUmK8zh0hENKg5NbBFRkYiLCwM0dHRAIBJkybhyJEjSEhIgEajQXBwMAoLCxEfHw+JRIKE\nhASo1Wr7/QsLC7tcwxYU5A253HmLoBUKP6c9NvUeIQS+L76Af17+B25U5QIAInwjsTLh3zAp/AGX\nWL91p/vajRI93j96zf5zWW0jwgI8sXjCUMy7LxJBPix7Umv8XCNH4b7WzKmBbeTIkZBKpaipqYGf\nnx9u3LiByZMnIyQkBFlZWUhMTERmZiYmTZoEwBrotmzZAgAwGo3Q6/UYPrzz8lNFhaHPn0dHFAo/\nlJTUOO3xqXdcr76K7bc2IacqE4C1l9q8qMV4KOwRyCQylJbqnTzCnu9rZovA+VsV2KPW4mJ+VZvr\nfzE1BmOHBsJkMKLEYOzNoVI/x881cpTBuq91FFIlQgjR7jUOcuHCBXzzzTeQy+UQQuC5555DfX09\nNmzYAIPBAE9PT6xYsQK+vtZyzPHjx3Hu3DlYLBakpaUhIaHzU/o4880erDvbQKExFGLHrc34vuws\nAGsvtUcjF2C6ao7L9VLr7r6mN5pwJLMYezO00FVbg5gEwO0fAonh/nh7Uf8t8VLf4ecaOcpg3ddc\nNrD1NQY26qlyYxl2523Dd0XH7b3UZoTPRUrkfJftpdbVvpZXbkC6WouvsktgNFkAAKF+HrgvKhAH\nLhe1e5+3Fo5pdcQoEcDPNXKcwbqvdRTYnN7Wg8hV6BtrsL9gN45qDsIkGiGFFA8rp2Pe0EUI9Ah2\n9vB6zGwROHuzAulqLX4oaC573hMZgHn3qDA+Oghv7Lzc4f03nMnH2wxsREQugYGNBj2juR5HNAdw\noGC3vZfa+JBJeCx6KcK8VE4eXc/p6004lFmEvRk6FNdYy54ecimmxymQmqRC9BBv+21Z9iQi6h8Y\n2GjQsvZSO4b0vO2oaqwEACQEJmHRsGUY5tv/WsXcKqvFHrUOx3Oay55Kfw+kJqswMz4Uvh78705E\n1F/xE5wGHYuw4Fzpaey8tQXF9ToAwDDfGCwetrzf9VIzWwTO3CjHwb3ZOH+j3H752KEBmJeswrjo\nIMikzm85QkREd4eBjQYNIQQyKzOw7eYm5NXeAACEeanwWPQTGDfkfpfopdZdNfWNOJRZjH0typ6e\nblJMjwtFWpISQ4O9u9gCERH1JwxsNCjk1lzD9psbkW3rpRboHmTtpRb6COTS/vPf4EZpLdLVWhy/\nUooGW9lTFeCJZQ8Mw8Sh/vBh2ZOIaEDipzsNaFpDIXbe2oLzZWcAAN4yH6QMnY/pqjnwkHk4eXTd\nY7YInMotR7paaz99FADcFxWIeckq3BcdiLBQ/0F5+DsR0WDBwEYDUrmxDHvytuG7oq9hgQVuUjfM\nCE9BSuQ8+Mj7xzkxq+oacSizCPsydCjVNwAAvNykmBEfitQkFSKDvJw8QiIichQGNhpQ9I167C/Y\nha+0B9FoaeqlNgPzhi5CUD/ppZZbYi17fn2lFA1ma9kzPMATackqzIhXwNud/22JiAYbfvLTgGA0\nG3FUcwAHCvbAYK4FAIwPmYiFUUuh9A538ui6ZjJbcOpGOdLVOlxuUfYcHx2ItGQV7o0KhLQfHRRB\nRES9i4GN+jWTxYTvio5jd/42VDVYe6nFByRi0bBlGO43wsmj61pVXSMOXraWPctqm8qeMsxKCEVq\nkhLhgSx7EhERAxv1UxZhwXlbL7UiWy+1KJ/hWDxsGcYEJTt5dF27VqxHulqLE1dL0Wi2ns43ItAL\n85KVmBYXCm93mZNHSEREroSBjfoday+1jbilt/ZSC/VU4rHopRgXMhFSidTJo+uYyWzBydxy7PlB\niyyd9YhOCYAJw4KQlqzC2KEBLHsSEVG7GNio37hZcx3bbm5CVtUlAECAeyDmD12Mh8KmunQvtQpD\nAw5eLsL+S0Uot5U9vd1lmBUfitRkFVQBnk4eIRERuTrX/S1HZKOr02Lnrc04V3oaAOAl80ZK5HzM\nCJ/r0r3UrhTVIF2twzdXS2GyWMueQ4O8kJaswrRYBbxY9iQiom5iYCOXVWEsx5787fhWd8zeS226\nag5SIhfA1801e6k1mi347loZ0tVa5BTpAVjLnhOHW8ue90QG9KtTYBERkWtgYCOXU2vSY3/BHhzV\n7EejpRESSDAlbBrmRS1GsMcQZw+vXRW1DThwuQj7L+lQYWgEAPh4yDArPgypSUooWfYkIqK7wMBG\nLsNoNuIr7UHsz99t76U2bsj9WBi9FCrvCCePrn05uhqkq7X49lqZvewZFeyFeckqTI1VwNONZU8i\nIrp7DGzkdGZhtvZSy9uGyoYKAEBcQAIWDVuOGL+RTh5dW41mC761lT2v2MqeUgkwKSYY85JVSIrw\nZ9mTiIh6FQMbOY0QAufLzmDHrc0oqtMCAKJ8hmHxsOVICExyudBTXtuA/Zd0OHC5CJW2sqevhxxz\nxoQiJVGJMH+WPYmIqG8wsJFTZFVewrabG3FTnwsAUHiG4bHopRgfMsmleqkJIZCj02OPWovvrpfB\nbCt7DhvijbRkFR4ZHcKyJxER9TkGNnKom/pcbL+5CZmVGQCAALdApEUtwpSwaS7VS63RbME3V0ux\nR63FtWLrejqpBHhwRDDSklVIDGfZk4iIHMd1fkPSgFZUp8WOW1twrvQUAMBL5oW5kfMxM3wuPGSu\nU0os0xux/1IRDlzWoarOBADw85BjzpgwpCQpEernun3fiIho4GJgoz5V2VCBPXnb8I2tl5pc4oYZ\n4XOQEjkfvm5+zh4eAGvZM8t2tOe/rpfby57DQ7wxL1mFh0eHwEPOsicRETkPAxv1CYOpFgcK9uCI\nZj8aLA2QQILJYVMxP2qJy/RSazBZcMJW9swtaS57PjRyCOYlq5Cg8mPZk4iIXAIDG/WqBnMDvtIe\nxL6CXTCYrCHo3iET8Fj0Ewh3kV5qpXoj9mXocPByEarrrWVPf09b2TNRCQXLnkRE5GIY2KhXWHup\nfY09edtQ0VAOABjtH4/Fw5ZjhP8oJ4/OWvbM1NZgj1qLk9fLYKt6IkbhYy17jgqBu9x1jk4lIiJq\niYGN7ooQAhfKzmLHrc3Q1WkAAEN9orF42DKMCbzH6SVFo8mME1esZc8bpQYA1rLnlFFDkJasQryS\nZU8iInJ9DGx0x7IrL2PbzY24ob8OAFB4hmJh9FJMCHnA6b3UimuM2J+hw8HMItTYyp4BXnLMHaNE\nSmIYhviy7ElERP0HAxv1WJ7+Brbd3ITLlWoAgL9bAOZFLcKUsOlO7aUmhMAlTTX2/KDF6Rvl9rLn\nSIUP5t2jwuSRLHsSEVH/xMBG3VZUp8OuW1twpvQkAGsvtTkRaZgZ8Sg8ndhLrb7RjK+vlCJdrcXN\nMmvZUyaV4GHb0Z6xSl+WPYmIqF9jYKMuVTVUWnupFR2DWZghl8gxTTUbjw5dAD83f6eNq6i6Hvsy\ndDiUWQy90Vr2DPR2w9wxYZg7Rokhvu5OGxsREVFvYmCjDhlMBhws3IPDhfvRYDFCAgkeCn0E86OW\nYIhniFPGJIRARmE19qi1ONOi7Dk6zBdpySpMHjkEbjKWPYmIaGBhYKM2Gi0N+EpzCPsKdqHWpAcA\njA0ej8eilyLCZ6hTxlTfaMaxnBLsVetwq9xa9pRLJXjYdrRnrNI1zppARETUFxjYyM4szDhZdAK7\n8ra26KUWZ+ulNtopY9JV12OvWofDWUWoNZoBAEHebkhJVGLumDAE+bDsSUREAx8DG0EIge/LzmHH\nrc3Q1hUCsPZSWxS9DIlBju+lJoSAuqDKVvasgK3qiVhb2fMhlj2JiGiQYWAb5HKqMrHt5ibk1lwF\nAIR4hGJh9OO4X/Ggw3up1TVYy57pGVrkl9cBsJY9p4wKQVqyEqPDWPYkIqLBiYFtkMrT38T2W5tw\nqeIHAICfmz/mDV2Eh5UzHN5LTVtVj71qLY5kFaO2wVr2DPZ2Q0qSEnPGhCHIm2VPIiIa3BjYBpni\nuiLszNuCMyX/AgB42nqpzXJwLzUhBL7Pr0K6WotzN5vLnvFKP8y7R4UHYoIhZ9mTiIgIAAPboFHV\nUIn0/B04oTtq76U2VTULqUMXOrSXmqHBjGPZxUjP0KGgorns+fDoEMxLVmFkqK/DxkJERNRfMLAN\ncAaTAYcK03G4cB+Mtl5qD4Y+jAVRSzDEU+GwcWgq67A3Q4cjWcUw2MqeQ3zc8ait7Bng5eawsRAR\nEfU3LhHY6uvr8fjjj2Py5Ml49dVXodfrsXHjRhiNRsjlcqxYsQJ+ftYF58eOHcPFixdhNpuRkpKC\nMWPGOHn0rqnR0oBj2sPYl78TensvtXF4LPoJh/VSswiB7/MqsUetxflblfbLE1TWsuek4Sx7EhER\ndYdLBLY//elPGDNmjL19xOeffw6FQoElS5Zg9+7dWLduHVavXo2ioiKsW7cO69evR0NDA+bPn48D\nBw44efSuxSIsOFn8DXblbUW5sRQAMMo/FouGLcco/1iHjMHQYMLRrBKkq7XQVNUDANxkEkwdrUBq\nshIjFCx7EhER9YTTA9uuXbswbtw45OTkwGCwdrA/ffo0fvnLXwIAEhISsH79egDAyZMnER8fDwBw\nd3eHr68vcnNzERMT45zBuxAhBC6Wn8eOW5ugMVh7qUV4D8XiYcuQFHSvQ3qpFVTUYa9ai6PZxahr\ntAAAQnytZc/ZCSx7EhFR/2TeugWWLRshrl4BAEhGjYZ06XLIlix12BicGtiuXbuG3NxcrF69GtnZ\n2RDCeqxgZmYmVCoVACA8PBxZWVkQQiArKwtKpdJ+/4iICGRmZg76wHalKgvbbm7EdXsvNQUWRD+O\niYqH+ryXmkUIXLhlLXteyGsueyaG+yMtWYVJMcGQSR3beJeIiKi3mLdugfnNNa0uE1dy7Jc5WxwE\ngQAAIABJREFUKrQ5NbAdOXIE7u7uWLt2LS5cuIDGxkb84x//QEJCAjQaDYKDg1FYWIj4+HhIJBIk\nJCRArVbb719YWNjlGragIG/I5bK+fiodUij6rtnrzaob+Oflf+B80TkAQIB7AB6PewJzh6XATda3\ns1n6+kbs/V6DL8/kocB2bk8PuRRzklV4fGI0RvHcng7Xl/saUUvc18hRXGFfK96+BeYOrpNu3wLF\nc884ZBxODWw/+9nP7N8bjUYYDAb8+Mc/Rl1dHbKyspCYmIjMzExMmjQJADBp0iRs2bLFfnu9Xo/h\nw4d3+hgVFYa+ewJdUCj8UFJSc1fb0BoKsfnGFwCAZcNXQukdjpL6Yuy8Ze2lJiDgIfPEnIhUzA5P\nhafcC5Xl9QDqe+EZtJVfbkB6hg5fZRej3lb2VPi5IzVJhVnxofC3lT3v9nlTz/TGvkbUHdzXyFFc\nYV8TJSVozMrq8PrGrKxeH2NHIVUimuqQTnTo0CGsX78eJpMJP/rRjzB16lRs2LABBoMBnp6eWLFi\nBXx9rQvVjx8/jnPnzsFisSAtLQ0JCQmdbtuZb/bd7GwGUy12523DMe0hmIU128skMoR7R6KwNh8W\nWCCTyDBNNQupQx/r015qZovA+VsV2KPW4mJ+lf3y5Ahr2fP+4Sx7OpsrfLDR4MB9jRzFkfuaEAIo\nyIfIzoIlKxMiJwsiOwsoK+v8jhIJ3L+/3KtjcenA1pf6a2DbX7Ab225u7PD6B0KnYEHU4wjpw15q\neqMJRzKLsTdDC121EQDgLpdiWqwCaUlKDAvx6bPHpp7hL1FyFO5r5Ch9ta+JxkaIG7kQ2dZQJnKy\nIHKyAb2+7Y19/QCzCaira3dbktGxcNuyo1fH11Fgc/pRotRzM8NTsCzm3/ps+3nlBqSrtfgquwRG\nk7XsGerngdQkJWYlhMLPk0d7EhGR6xN1dRBXrzSHs+wsiGtXgIaGtjdWKCCJjYckLh7SOOtXRETC\nsu3LNgcdNJEuXd6n42+Jga0fCnAP7PVtmi0CZ29WIF2txQ8FzWXPeyIDMO8eFcZHB7HsSURELktU\nVULkZENkZVpLmzlZwM2bgMXS9sZDh0ISawtm8QmQxMVDMiSk3e02HQU6qNt6kPPp6004lFmEvRk6\nFNdYy54ecimmxymQmqRC9BBvJ4+QiIiomRACKC6yz5hZbF+h1bS9sUwGyehY+8yZJC7e+rNfz44+\nlS1Z6tBw1h4GNhf1iHImqhuq8JX2YKuDDqar5uAR5cy73v6tslrsUetwPKe57Kn090Bqsgoz40Ph\n68Fdg4iInEtYLEB+nj2UNf1DRXnbG3t6tg1nI0dB4uHh+IH3Af5WdlHecm88EbMSjyhnYNONzwE0\nt/W4U2aLwJkb5UhXa6EurLZfPnZoAOYlqzCOZU8iInIS0dgAcf26LZRlouT6VTRevgwY2mnP5e/f\nHMpi4yGNTwCih0Eic17f1b7GwObilN7heHHMq3e1jZr6RhzKLMa+FmVPTzcppseFIi1JiaHBLHsS\nEZHjCEMtRE6OvX2G9WCAq4DJZL+N/bCA0DDrOrPYuOaDAVThDjnloithYBvAbpTWIl2txfErpWiw\nlT1VAZ5IS1ZiRlwofFj2JCKiPiYqKlq10LBkZwG3bgK3dxWTSIDoYfZQFjjxPlQrh0ESHOyUcbsa\n/sYeYMwWgVO51rLnJU1z2fO+qEDMS1bhvuhASAfZXyVERNT3hBCATtui+Wy2db1Zka7tjeVySEaM\nbC5rxiVY15/5NPf39FT4oYY9/+wY2AaIqrpGHMoswr4MHUr11olkLzcpZsSHIjVJhcggLyePkIiI\nBgphNgO3bjYfDNB0ZoCqqrY39vKyHQgQ1xzQRoyExM3d8QPvxxjY+rncEmvZ8+srpWgwW8ue4QGe\nSEtWYUa8At7ufIuJiOjOiYYGiGtXWzefvZID1LfT/T8oqNVRmtK4eGBo1IA+GMBR+Nu8H8iwNbJN\nigwAAJjMFpy6UY50tQ6XW5Q9x0cHIi1ZhXujWPYkIqKeE3q9vZRpnznLvd7qYAA7lapN81mEhg26\ngwEchYGtH9hwJh8A8NoQbxy8bC17ltU2lT1lmJUQitQkJcIDWfYkIqLuEWWlrWbNLNlZQH5e2xtK\nJEBMDKQt+5vFxkMS2Ptn3aGOMbC5uIyCKvvBAz/+7CxsVU9EBHphXrIS0+JC4e3OqWYiImqfEALQ\nFDafFSArEyInCygpaXtjNzdIRo2+7cwAoyHxYvsnZ2Ngc3H/96+b9u/NFmDCsCCkJaswdmgAy55E\nRNSKMJkgbt5ovd4sJxuoqW57Yx8fSGLjmsNZfAIkw2MgcXNz/MCpSwxsLiyjoArXimtbXfbY2HD7\nWjYiIhq8RH09xNUrrZvPXr0CGI1tbxw8BJL4+OY1Z3HxQORQSKRSxw+c7ggDmwtrWrt2+2VvM7AR\nEfVb5q1bYNmy0RquAEhGjYZ06fJOTy4uqqtbHAyQaf168wZgNre9cUSk/QjNptkzKBQ8GKCfY2Bz\nUS3XrrV0SVONjIIqzrIREfVD5q1bYH5zTavLxJUc+2WyJUshSkrs68ya+pyhsKDtxqTSts1nY+Mg\n8ffv66dBTsDA5qLam11reR1n2YiI+h/Llo0dXmf+4D2YP/kIKCtre6WHh/VggJZHaY4aDYmnZx+O\nllwJA5uLentRorOHQERE3SQaG6xd/quqIKqrgMpKiKoqoLoKorISqLZddyWn443U1lr/+fq1mDWz\n/Rs2HBI5f2UPZnz3iYiIbERjA1BdbQ1XVZW2r1VAVXMAQ2WlNZS1uA0Mhrt/cIkEbukHgfAIrjej\nNhjYiIhowBGNjdZWFu3MdDUFsHbDWG1t1xtvj1wO+AcAAQGQ+AcAgYHWrwEBkAQEAAGBQEAAzB/9\nCShof8mLZNRoSCIi7+JZ00DGwEZERC5LmEzN5cSW4arl7FZ7YexOg5dM1nHwCgy0XtcqjFmDGHx8\nujcrVlPT5qCDJtKly+9szDQoMLAREQ1gd9JCoi9Yg1d1q1DVafBqWvel19/ZA0ql1iDl3xyqmma6\nJLbL2w1jvr59Wo5set1d4T2h/oWBjYhogOpOC4meEiYTREVFc6hqucjeNhPWbhjT19zZk5BKAX9/\na/CyhapWwavV9y3CmK+vyzaFlS1ZynBGPcbARkQ0QHXaQmLLRkhnzm4RrtoJWe2EMU17pzjqDonE\nGrxahKpWAayjMObr57LBi8iRGNiIiAYQYbEApaUQWo295NauKzlonPpgzx9AIgH8/FqFKvs6rs5m\nwfz8GbyI7gIDGxFRPyKMRkCntQYyrfUrbF+FTgvotIDJ1L2N+fkDgQGQ+Ldc4xXQbhhrWoCvGB6O\n0vJeaGFBRD3CwEZE5CKEENYypE4DaKwBrCmYNYUylLfTBf92QcGQqFQQebc6XrQ/OhbuW3b0eIwS\nmazH9yGiu8fARkTkIMJkAkqKW4QwjX1mrGmmDHV1nW9ELgdCwyBRhUOiCgdUKuv3ShUk4eFAmBIS\nLy8A7R900ETGFhJE/QoDGxFRLxF1hnZmxppDGYqLAbO58434+NiCWIsQprSFMpUKCFF0e5aLLSSI\nBg4GNiKibhBCAOVlrUNYU5myaYasqqrrDSkU9hkx2EKYRNUcyuDn16t9wNhCgmhgYGAjIoLtHJJF\nRW1nxlqEMjQ0dL4Rd3db8FK1H8rClJC4uzvmCRHRgMLARkSDgqipaZ4N02hazYwJrRYoLQGE6Hwj\nAQFtQljLnxE8hK0riKhPMLARUb8nLBagpKTF4v12ypVdneJIKgXCwjotV0p8fBzzhIiIbsPARkQu\nT9TXA0U6+8yY0GlbLe5Hka7r3mOeXkC4ChKlLYgpVUB4uP1nhIZBIudHIhG5Jn46EZFT2XuP3TYz\n1jKUdav3WPCQFiGsxVGVTe0vAgL69KTeRER9iYGNiPqUMJmA4iJrEGvVENb2c3d7j4Upm8uVLUNZ\nU+8xT0/HPCEiIidgYCMa5Mxbt9xVny5hqG0+NVLLIyybypXFxYDF0vlGfH1brxVrMTMmUaqAkBB2\n2CeiQY2BjWgQa68TvriSY79Muvhxa+8xjaZVCGsZyrrsPSaRAIrQtgv4WzaE9fPri6dHRDRgMLAR\nDWLmLRs7vu6d/wfzu2/1oPeYNYS17T0WBokbe48REd0NBjaiAUwIAVSUQxQWQBQWAravQlMIUVgA\n5Od1fOfGRuvXgIA2p0pq03uMi/mJiPqUUwNbXl4e/vjHPyImJgZmsxkqlQrLly+HXq/Hxo0bYTQa\nIZfLsWLFCvjZSibHjh3DxYsXYTabkZKSgjFjxjjzKRA5naiuRkPRLVguXbEHMXs402iA+i4W9HdE\nIoHbd2cg8WbvMSIiZ3NqYKuqqsLMmTMxb948CCEwc+ZMTJ8+Hdu3b4dCocCSJUuwe/durFu3DqtX\nr0ZRURHWrVuH9evXo6GhAfPnz8eBAwec+RSI+pyoqwM0hbaZsQJrINNo7LNlqKlGSWcb8PeHJDwC\niIiEJCICkvCmrxFofO0VwHawwe0ko0YzrBERuQinBrakpCQkJSUBACQSCUy2xpenT5/GL3/5SwBA\nQkIC1q9fDwA4efIk4uPjAQDu7u7w9fVFbm4uYmJinDB6ot4hGhusR1m2LFW2nCnrqgeZpxfk0UNh\nDrOtIYuIhCQiEgi3hjKJv3+Hd5U98aM2Bx00kS5dfudPioiIepXLrGHbvXs3Fi5ciLCwMGRmZkKl\nUgEAwsPDkZWVBSEEsrKyoFQq7feJiIhAZmYmAxu5NGE2AyXF1kBWaJ0hQ2EhhNYWzrpqeyGXW9eL\nNc2QRURaZ8ts4QxBwQgN9UdJSU2Px9bUuuNu2noQEVHfc4nAdurUKajVarzxxhsArLNqGo0GwcHB\nKCwsRHx8PCQSCRISEqBWq+33Kyws7HINW1CQN+Ry5/VvUijYrmCgE0LAUloKc14+TPl5MOcXwJSf\n3/xzoaZ5AX97pFLIIiIgixoKeWQkZFFRkA8dav15aBSkyrBunVD8jve1556x/iPqJn6ukaNwX2vm\n9MB2/PhxnD9/Hm+88QaKioqg0WgwadIkZGVlITExEZmZmZg0aRIAYNKkSdiyZQsAwGg0Qq/XY/jw\n4Z1uv6LC0OfPoSMKhd8dzXqQ6xHV1RAa28xY05GWTaXL7izsHzLEOkMWHgnYZsnsM2RKpb3thcn2\nz9jyvmW1XY6P+xo5Cvc1cpTBuq91FFIlQgjh4LHYXbp0CStXrkRSUhKEEKirq8OTTz6JWbNmYcOG\nDTAYDPD09MSKFSvg6+sLwBrwzp07B4vFgrS0NCQkJHT6GM58swfrztYfiTqDdb2YprDtkZaFhYC+\ni/fR3791qTK8RflSFQ6Jl1efjp/7GjkK9zVylMG6r7lkYHMEBjYCblvYX1jQPFtmO/qyOwv7JRFt\nj7REuG22zMmd+rmvkaNwXyNHGaz7WkeBzeklUaLeIMxm6wnG7YGs0BbIbDNkxUVAZ3+buLk1L+xv\nKlVGRFqPsoyIBIKC2ByWiIichoGN+gUhhPWclk1rx1p27NcUAFotYGsL0y6p1LpWLLyDIy0Vod1a\n2E9EROQMDGzkMkR1VYsO/YWty5caDVBf3/kGQkKsgSw8onlhf1M4C1NC4ubmmCdCRETUyxjYyGFa\nLey//UjLHi/sv+1Iy/AISDw9HfNEiIiIHIyBjXqNaGwANJrmUyg1rSNrOtKyorzzDXh5tXukpT2g\nOXlhPxERkbMwsFG3tVnYb+9DVtjzhf1Npcqm0ydxYT8REVGHGNjITggBlJW2KVXaj7TUdWdhv6rt\nkZa2NhhQKLiwn4iI6A4wsLkw89YtvXqORyEEUF1l7cxvmyGzz5ZpC3u2sN/ek6xFOOPCfiIioj7B\nwOaizFu3wPzmmlaXiSs59ss6Cm32hf23HWnZ9DP0+s4fOCCgTamyVcd+LuwnIiJyOAY2F2XZsrHD\n68zr/wlJeERzqbJFOOvZwv7mIy3ts2W2U4ARERGR62Bgc1FNZdB23ciF6fl/b/86N7fm0yW1bHlh\nC2QIDOTCfiIion6Gga2fkoy/v0XHflv5kgv7iYiIBiQGNhclGTUa4kpO+9eNjoXbur87dkBERETk\nNJyKcVHSpcvv6DoiIiIaeDjD5qKajgLtzbYeRERE1D8xsLkw2ZKlDGdERETEkigRERGRq2NgIyIi\nInJxDGxERERELo6BjYiIiMjFMbARERERuTgGNiIiIiIXx8BGRERE5OIY2IiIiIhcHAMbERERkYtj\nYCMiIiJycQxsRERERC6OgY2IiIjIxTGwEREREbk4BjYiIiIiF8fARkREROTiGNiIiIiIXBwDGxER\nEZGLY2AjIiIicnEMbEREREQujoGNiIiIyMUxsBERERG5OAY2IiIiIhfHwEZERETk4hjYiIiIiFwc\nAxsRERGRi5M7ewB3IicnB+np6ZBKpUhISMCcOXOcPSQiIiKiPtMvA9srr7yCL7/8Ep6envjJT36C\n2NhYDBs2zNnDIiIiIuoT/a4kqtVqAQCenp4AgPj4eJw6dcqZQyIiIiLqU/0usF2+fBkqlcr+c0RE\nBC5fvuzEERERERH1rX5XEk1MTMSHH35o/7mgoACJiYkd3l6h8HPEsFz28Wnw4L5GjsJ9jRyF+1qz\nfjfDplQqAQB1dXUAgOzsbEyaNMmZQyIiIiLqUxIhhHD2IHrqypUr2LVrFyQSCZKTkzF79mxnD4mI\niIioz/TLwEZEREQ0mPS7kigRERHRYMPARkREROTiGNiIiIiIXFy/a+vhiro6Vdb27duxefNmuLu7\nAwCWLFmCBQsWOGOo1I+VlJTgT3/6E3JycrB169Y21zc2NmLLli0oLy+HEALLli1DaGioE0ZK/V1X\n+xo/06g35OXl4Y9//CNiYmJgNpuhUqmwfPnyVrfh51ozBrZe0NWpsiQSCd5//31EREQ4b5DU7124\ncAEzZ85EdnZ2u9fv378fJSUlePHFF3H+/Hm88847+OMf/+jgUdJA0NW+xs806g1VVVWYOXMm5s2b\nByEEZs6cienTpyMsLMx+G36uNWNgu0sdnSrr9nObfvHFFwgKCoJCocCsWbPg6+vr6KFSPzdnzhyc\nPn26w+tPnTqFqVOnAgDi4uJw5swZB42MBpqu9jWAn2l095KSkpCUlATA+keAyWRqcxt+rjXjGra7\n1J1TZU2YMAGrVq3CqlWrAADvvfeeQ8dIg0NWVpZ9X/Tx8YHJZEJxcbGTR0UDET/TqLft3r0bCxcu\nbDW7BvBzrSUGtruUmJhon2UD2j9VVmRkJIKCggAAjzzyCPbv3+/QMdLgkJCQAI1GAwCora2FXC4f\ntGs9qG/xM41606lTp6BWq7F69eo21/FzrRkD213q6FRZVVVV0Ov1AIB33nkHBoMBgPUAhXHjxjln\nsDTgtNzPJk2ahKysLADWv0onTpzozKHRAMPPNOoLx48fx3fffYc33ngDRUVFuHjxIj/XOsAzHfSC\n9k6V9d577yEwMBD//u//jn/+85+4ePEiYmJioNfrsWjRIowePdrZw6Z+5uzZs9i5cye+/fZbLF++\nHD/5yU/w5z//GQEBAVi1apX9aKqSkhJIJBL86Ec/gkKhcPawqR/qaF/jZxr1pkuXLmHlypVISkqC\nEAJ1dXVYsWIFrl27xs+1djCwEREREbk4lkSJiIiIXBwDGxEREZGLY2AjIiIicnEMbEREREQujoGN\niIiIyMUxsBERERG5OAY2IiIiIhfHwEbUS+bOnYuVK1di5cqVmDx5Mh566CH7zykpKb32OEePHrU/\nlrOtWbMGEyZMwI4dO5w2hkuXLuHZZ5/F4sWL8frrr3d62//+7/9uNV6j0YipU6fCaDT2+HH37NmD\nN954447G3BOu9H7fifz8fBw/fhxfffVVl89j1apVOHv2LADgF7/4BZKTk3H27Fk0NjZi5cqViIuL\ns5+m6Ny5c3jmmWf6dOzr168HW5WSq2BgI+olCoUCn3/+OT7//HNMmTIFDz30kP3nkJCQTu+7ffv2\nbv9CnjFjBn7605/2xpDv2po1axAXFweJROK0MXz22WeYPXs2tm3bhuTk5E5v+5vf/KbVeD08PJCe\nng4PD49O71dQUIC4uLhWl6WmpuI///M/727wHWy7JVd6v3sqPz8fv/3tb/Hggw9i+vTpXT6PDz74\nABMmTAAAfPzxx/b/N25ubvj8889b3Xb8+PH48MMP+2bgNjExMXj99dcZ2sglMLAR9ZKXX37Z/r0Q\notWHfMvregN/gTT74YcfEB0dDQBYvnx5j+/v6+t7R48rlUrh5eV1R/ftqf76fq9evRo//elP4e7u\nDqDr5+Hj49Oj7d/pe9ddDzzwAIQQ2LRpU58+DlF3MLAR9ZKxY8d2el1NTQ3+9re/4cknn8RLL72E\n8+fPAwDOnDmDTz/9FNnZ2Vi5ciXefPNNAMDmzZuxbNkyPPXUU/jd736H/Pz8bo1j8+bNmD59On71\nq1/hv/7rv7BgwQK8+eabMJlMAICnn37aXloyGo1YunSpfYanoaHBXnraunUr/u3f/g3z5s2DWq3G\nX//6Vzz++ON47bXXUF1d3eoxi4qK8Oyzz2LhwoX44osv0NjYaL/uwoULWL16NZ566ils3rwZDQ0N\nAJrLqWvXrsXzzz+PyZMn4+OPP27zfMxmM/bu3YtnnnkGzzzzDPbt2wez2QzAWjYrLi7G22+/jVWr\nVrX7euTn5+P111/HkiVL8Ic//KHVdS+//LK97AYA2dnZ+I//+A+sWLECTzzxBI4fPw69Xo+XXnoJ\nAOwl7uzsbCxYsADTp08HYC1bpqSkYOXKlXj//fexaNGiNq9Tfn4+3n77bTzxxBNYuHAhNmzY0O62\nLRZLp+/vl19+iQkTJmDZsmW4ePFiq+vUarV9XH//+9+xePFiPPXUUygvL8frr7+O+fPnt3mNe/r+\ntPcataehoQHZ2dlISkpqc90777yDJUuW4Ne//jUKCwsBAOvWretwH7hdeXl5q/0WsJa3N2/ejB//\n+Md47rnn8PXXXwNAq3Lq9u3b8fTTT+OJJ57A6dOn7ffdv38/Vq1ahccffxyrVq1Cbm6u/bqkpKQ2\nrzORUwgi6nWvvvqqeOWVV1pd9vrrr4vf/e53wmQyiWvXron7779faLVaIYQQ27dvF08++WSr269f\nv14YDAYhhBBHjhwRv/nNb+zXbdu2rc3tW/roo4/ExIkThVarFUajUUybNk18//339utjY2NFYWGh\nEEKIgoICERsb2+r+sbGx4oMPPhBCCPHnP/9ZTJkyRRw7dkxYLBbx5JNPir1799pv++STT4olS5aI\nyspKUV1dLZYvXy6++OILIYQQWq1WjB07VuTl5YnGxkbx2muviQ8//LDVfX/0ox+JmpoakZ2dLb78\n8ss2z2XHjh1i6dKloqKiQlRUVIhly5aJnTt32q+fNm2aOHPmTIevxYIFC8Rnn30mhBDixIkTIjEx\nUezYsaPd+z/33HPiX//6lxBCiFOnTonXXnutw9fo9OnTYtq0afaft2/fLpKTk0VmZqYwm81i+fLl\nIj093X797Nmz7T9fvXpVzJw5s8Nt367l+71jxw7xySefdHjb06dPi8TERHH8+HEhhBArV64UKSkp\noqioSNTW1op77rlHlJSUCCF6/v5s2bJFPP/88+2+RrdTq9Vi/PjxbZ5HYmKi/f7/93//JxYvXmy/\n/rXXXhMfffSR/efb39vO9tuPP/5YPP/886Kurk5oNBoxe/Zscfbs2Vb3/cMf/iCEEGLjxo3i6aef\nFkII+/+P2tpaIYR1f9++fbv9focPHxapqakdvt5EjsIZNiIHMJvNOHbsGObPnw+ZTIYRI0YgOTkZ\nBw8eBNB+qWjs2LF49913sXz5cqxduxZHjhzp9uMJITBq1CgolUq4u7tj9OjR+P777zu8bXsmT54M\nAEhOTkZlZSUmT54MiUSCpKQkXLhwodVtH3jgAQQEBMDPzw9Tp07FiRMnAACHDh3CpEmTMHToUMjl\ncqSkpOBf//pXq/tOnDgRvr6+iI2NxZIlS9qM4/Dhw5gxYwYCAwMRGBiI6dOn49ChQ916HUpKSpCT\nk4N58+YBAKZMmQJ/f/8Ob+/u7o7du3ejpKQEEydOxG9/+9sOX6PbLxNCYMiQIYiPj4dUKkVCQoL9\nNc/JyUFxcbH94JORI0fi97//fYfb7sju3btx/vx5/OxnP+vwNkIISCQSPPTQQwCsM0QKhQKhoaHw\n9vbGyJEj7e9fT9+fxx9/HG5ubu2+RrfLysqCUqlsc3lAQAAeeOABANZ1gJcvX0Z5eXm3X4OWz7Ol\nw4cP49FHH4WnpydUKhUeeOCBNvvJlClTAACJiYn290Ymk6GhoQHbtm1DXV0dnn/+eaSlpdnvEx4e\njtzc3Ds6MIWoNzGwETlAbm4uKioqMGLECPtlMTEx9lJce1566SWMHDkSGzduxPvvv9/jX2qRkZH2\n74OCglBbW9uj+4eHhwMAPD09ERISArlcDgDw8vJqs63hw4e3+r6phHTu3Dnk5OTYy31/+ctfUFFR\nYS+7AUBUVFSn47hw4QJiYmLsP8fExODcuXPdeg5qtRo+Pj4YMmRIq/t35K233oJSqcTixYvx85//\nHDqdrluP0yQiIsL+fWBgoP11OnfuHKKioiCVNn/kjhs3rkfbvnLlCtLT0/Htt9/at9tURl+5ciXe\nfvtt+21bvl+enp729xKwvn8Gg8E+rp6+P919jUwmE2QyWZvLW+4roaGh8PHxgVqt7tFrcbva2lpk\nZ2d3+f+r6f9EYGCg/TWQyWTYvHkzLl26hBkzZuC9995DfX29/T4ymQxCCPuSAiJnYWAj6gMSiaTV\nkZMxMTEIDg7GtWvX7Jddv34d999/f7v3v379Om7evGmfkbk9IHV1VGZX13t7e9vXV+Xl5XV62+5s\nu+Wan9zcXNx7770AgPvvvx9JSUn2o2U3bNiATZs22Rehd8eECRNw/fp1+8/Xr1+3H0nMBjR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"text": [ "" ] } ], "prompt_number": 44 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 5 - relation between average miscount and counting error rate" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ht_size = \"100000,200000,400000,600000,800000,1000000,1200000,1400000,1800000,2200000,2600000,3000000,4000000,6000000\"\n", "HT_SIZE_array = map(int,ht_size.split(','))\n", "\n", "\n", "file_obj = open(datadir+'fp_assessment.out','r')\n", "lines = file_obj.readlines()\n", "result1 = [map(float,lines[1].split()),map(float,lines[2].split())]\n", "result2 = [map(float,lines[5].split()),map(float,lines[6].split())]\n", "result3 = [map(float,lines[9].split()),map(float,lines[10].split())]\n", "result4 = [map(float,lines[13].split()),map(float,lines[14].split())]\n", "result5 = [map(float,lines[17].split()),map(float,lines[18].split())]\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "#fig.set_size_inches(6.83,4)\n", "fig.set_size_inches(10,7)\n", "\n", "\n", "ax.set_xlabel('Counting false positive rate (miscount>0)',fontsize=12)\n", "ax.set_ylabel('Average miscount',fontsize=12)\n", "#ax.plot(total_list, khmer_count, linestyle='-', label='khmer (1%)', **s_khmer)\n", "\n", "\n", "ax.plot(result1[1],result1[0],linestyle='-', label='metagenome data', **s_meta)\n", "ax.plot(result2[1],result2[0],linestyle='-', label='random k-mers', **s_rk)\n", "ax.plot(result3[1],result3[0],linestyle='-', label='reads with error', **s_re)\n", "ax.plot(result4[1],result4[0],linestyle='--', label='reads without error', **s_re)\n", "ax.plot(result5[1],result5[0],linestyle='-', label='E.coli reads', **s_ec)\n", "\n", "# result2[1],result2[0],'go-',result3[1],result3[0],'bo-',result4[1],result4[0],'yo-',result5[1],result5[0],'ko-')\n", "#ax.set_xlim(0,0.2)\n", "#ax.set_ylim(0,20)\n", "ax.axis(ymax=10, ymin=0)\n", "ax.legend(loc='upper left',handlelength=4,prop={'size':12})#,prop={'size':8}\n", "fig_file = '../figure/average_offset_vs_fpr.eps'\n", "plt.savefig(fig_file,dpi=300)\n", "\n", "#print result1\n", "#print result2\n", "#print result3\n", "#print result4" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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qV/1uL7/8PNu3byMqKpqRI98rcO7i77Br1066d7+Lli3b8OGHUzh37hxPP/0sTZs2AyAr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\nJobCoUR92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"text": [ "" ] } ], "prompt_number": 45 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 6 - counting error rate vs miscount" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "y-axis is the average of (offset/correct_count) for each k-mer\n", "\n", "Since the high diversity dataset, most of the accurate counts are 1, so for smaller hash table(high error rate), the offset(miscount) may be 2 or 3. That percentage will be 100%-200%, which is the case in the figure while counting error rate >0.7" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ht_size = \"100000,200000,400000,600000,800000,1000000,1200000\"\n", "HT_SIZE_array = map(int,ht_size.split(','))\n", "\n", "\n", "file_obj = open(datadir+'fp_assessment.out','r')\n", "lines = file_obj.readlines()\n", "result1 = numpy.array([map(float,lines[3].split()),map(float,lines[2].split())])\n", "result1[0] *= 100.\n", "result2 = numpy.array([map(float,lines[7].split()),map(float,lines[6].split())])\n", "result2[0] *= 100.\n", "result3 = numpy.array([map(float,lines[11].split()),map(float,lines[10].split())])\n", "result3[0] *= 100.\n", "result4 = numpy.array([map(float,lines[15].split()),map(float,lines[14].split())])\n", "result4[0] *= 100.\n", "result5 = numpy.array([map(float,lines[19].split()),map(float,lines[18].split())])\n", "result5[0] *= 100.\n", "\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111)\n", "#fig.set_size_inches(6.83,4)\n", "fig.set_size_inches(10,7)\n", "\n", "\n", "ax.set_xlabel('Counting false positive rate (miscount > 0)',fontsize=12)\n", "ax.set_ylabel('Average miscount (percent)',fontsize=12)\n", "\n", "ax.plot(result1[1],result1[0],linestyle='-', label='metagenome data', **s_meta)\n", "ax.plot(result2[1],result2[0],linestyle='-', label='random k-mers', **s_rk)\n", "ax.plot(result3[1],result3[0],linestyle='-', label='reads with error', **s_re)\n", "ax.plot(result4[1],result4[0],linestyle='--', label='reads without error', **s_re)\n", "ax.plot(result5[1],result5[0],linestyle='-', label='E.coli reads', **s_ec)\n", "\n", "\n", "#ax.plot(result1[1],result1[0],'ro-',result2[1],result2[0],'go-',result3[1],result3[0],'bo-',result4[1],result4[0],'yo-',result5[1],result5[0],'ko-')\n", "#ax.set_xlim(0,1)\n", "ax.set_ylim(0,100)\n", "ax.legend(loc='upper left',handlelength=4,prop={'size':12}) #,prop={'size':8}\n", "fig_file = '../figure/percent_offset_vs_fpr.eps'\n", "plt.savefig(fig_file,dpi=300)\n", "#ax.axis(ymax=1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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uZIaVjHAdQ3vrjnqJ83Y1t+v3KFEo7u5vvUTziCw6lZfX+K084cGrrKzE0NBQN7dsyZIF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HN/CEIIIU4aCW1CHIeC8jpeWpNKclE1CjBtQACzhwRiZWy5ia1eXIzl0QfR/9gMgOGyaRjv/T8U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\nTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 46 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 7 - Percentage of the unique k-mers starting in different position in reads" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "file17 = open(datadir+\"ecoli_ref.fastq.pos17.abund1\",'r')\n", "file32 = open(datadir+\"ecoli_ref.fastq.pos32.abund1\",'r')\n", "\n", "list1 = []\n", "list2 = []\n", "x1 = []\n", "x2 = []\n", "\n", "for line in file17:\n", " line = line.rstrip()\n", " fields = line.split()\n", " if fields[1] != '0':\n", " x1.append(float(fields[0]))\n", " list1.append(float(fields[1]))\n", "\n", "for line in file32:\n", " line = line.rstrip()\n", " fields = line.split()\n", " if fields[1] != '0':\n", " x2.append(float(fields[0]))\n", " list2.append(float(fields[1]))\n", "\n", "fig = plt.figure()\n", "\n", "ax = fig.add_subplot(111)\n", "fig.set_size_inches(10,7)\n", "ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n", "ax.set_xlabel(\"Starting position of k-mer in read\",fontsize=12)\n", "ax.set_ylabel(\"Number of abund=1 k-mers at that position\",fontsize=12)\n", "\n", "\n", "ax.plot(x1,list1,linestyle='-', label='k=17', **s_k17)\n", "ax.plot(x2,list2,linestyle='-', label='k=32', **s_k32)\n", "\n", "#ax.plot(x1,list1,'r-')\n", "#ax.plot(x2,list2,'b-')\n", "ax.legend(loc='upper left',prop={'size':12})\n", "#plt.ylim(0,10000000)\n", "ax.set_xlim(0,100)\n", "\n", "plt.savefig(\"../figure/perc_unique_pos.eps\",dpi=300)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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7+fkVeqDU1FSWLFnCyJEjC/3cIiJXs5RzWczbGAfAAzdWwabeNY9muFw5uxo4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\nc/8fCbjMEHDfEOEAAAAASUVORK5CYII=\n", "text": [ "" ] } ], "prompt_number": 20 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Tables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Table 3\n", "==========\n", "Iterative low-memory k-mer trimming. The results of trimming reads at unique (erroneous) k-mers from a 1.41Gbp short-read data set in under 30 MB of RAM. After each iteration, we measured the total number of distinct k-mers in the data set, the total number of unique (and likely erroneous, with frequency = 1) k-mers remaining, and the number of unique k-mers present at the 3\u2019 end of reads." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def human_format(num):\n", " magnitude = 0\n", " while num >= 1000: # TODO: handle negative numbers?\n", " magnitude += 1\n", " num /= 1000.0\n", " # add more suffixes if you need them\n", " return '%.1f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])\n", "\n", "print('the answer is %s' % human_format(7436313)) # prints 'the answer is 7.44M'" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "the answer is 7.4M\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# get fp rate of filter-abund, get discarded percentage of bases and get processed_bases(for other trimming method output file)\n", "# from ecoli_ref.fastq.r1.fq.out -< filter-abund-single.py\n", "def read_out_file(file_out):\n", "\n", " fp_out = open(file_out,'r')\n", " for line in fp_out:\n", " if \"discarded\" in line:\n", " percent = line.split()[1]\n", " if \"fp rate estimated to be\" in line:\n", " fp = float(line.split()[5])\n", " if \"processed\" in line:\n", " processed_bases = int(line.split()[1])\n", " \n", " return \"{:.1f}%\".format(fp * 100), percent, processed_bases\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "def read_diginorm_out_file(file_out):\n", "\n", " fp_out = open(file_out,'r')\n", " for line in fp_out:\n", " if \"DONE with\" in line:\n", " percent = float(line.split()[-2])\n", " kept_reads = line.split()[-6]\n", " all_reads = line.split()[-4]\n", " if \"fp rate estimated to be\" in line:\n", " fp = float(line.split()[5])\n", "\n", " return \"{:.1f}%\".format(fp * 100),\"{:.1f}%\".format(percent) , '{:,}'.format(int(kept_reads)),'{:,}'.format(int(all_reads))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "#file_out = \"../data/ecoli_ref.fastq.ka.r1.fq.hist\"\n", "# get number of unique k-mers(freq=1) and number of total distinct k-mers.\n", "def read_hist_file(file_out):\n", " fp_out = open(file_out,'r')\n", " for line in fp_out:\n", " line = line.rstrip()\n", " fields = line.split()\n", " if fields[0] == '1':\n", " unique_num = int(fields[1])\n", " all_num = int(fields[2])\n", " return human_format(unique_num),human_format(all_num)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "# ecoli_ref.fastq.kh.e_coli_ref_hist\n", "def read_e_coli_ref_hist_file(file_out):\n", " fp_out = open(file_out,'r')\n", " for line in fp_out:\n", " line = line.rstrip()\n", " fields = line.split()\n", " if fields[0] == '0':\n", " missing_kmers = str(fields[1])\n", " return missing_kmers" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "code", "collapsed": false, "input": [ "#file_out = \"../data/ecoli_ref.fastq.ka.r1.fq.endcount\"\n", "def read_endcount_file(file_out):\n", " \n", " fp_out = open(file_out,'r')\n", " for line in fp_out:\n", " line = line.rstrip()\n", " fields = line.split()\n", " perc = float(fields[-1])\n", " \n", " return \"{:.1f}%\".format(perc * 100)\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "------------\n", "start from raw data\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "out1 = read_out_file(\"../data/ecoli_ref.fastq.r1.fq.out\")\n", "hist1 = read_hist_file(\"../data/ecoli_ref.fastq.r1.fq.hist\")\n", "endcount1 = read_endcount_file(\"../data/ecoli_ref.fastq.r1.fq.endcount\")\n", "out2 = read_out_file(\"../data/ecoli_ref.fastq.r2.fq.out\")\n", "hist2 = read_hist_file(\"../data/ecoli_ref.fastq.r2.fq.hist\")\n", "endcount2 = read_endcount_file(\"../data/ecoli_ref.fastq.r2.fq.endcount\")\n", "out3 = read_out_file(\"../data/ecoli_ref.fastq.r3.fq.out\")\n", "hist3 = read_hist_file(\"../data/ecoli_ref.fastq.r3.fq.hist\")\n", "endcount3 = read_endcount_file(\"../data/ecoli_ref.fastq.r3.fq.endcount\")\n", "out4 = read_out_file(\"../data/ecoli_ref.fastq.r4.fq.out\")\n", "hist4 = read_hist_file(\"../data/ecoli_ref.fastq.r4.fq.hist\")\n", "endcount4 = read_endcount_file(\"../data/ecoli_ref.fastq.r4.fq.endcount\")\n", "out5 = read_out_file(\"../data/ecoli_ref.fastq.r5.fq.out\")\n", "hist5 = read_hist_file(\"../data/ecoli_ref.fastq.r5.fq.hist\")\n", "endcount5 = read_endcount_file(\"../data/ecoli_ref.fastq.r5.fq.endcount\")\n", "out6 = read_out_file(\"../data/ecoli_ref.fastq.r6.fq.out\")\n", "hist6 = read_hist_file(\"../data/ecoli_ref.fastq.r6.fq.hist\")\n", "endcount6 = read_endcount_file(\"../data/ecoli_ref.fastq.r6.fq.endcount\")\n", "\n", "# untrimmed\n", "endcount0 = read_endcount_file(\"../data/ecoli_ref.fastq.endcount\")\n", "hist0 = read_hist_file(\"../data/ecoli_ref.fastq.hist\")\n", "\n", "# quality filtered by FASTX\n", "endcount00 = read_endcount_file(\"../data/ecoli_ref-trim.fastq.endcount\")\n", "# to get number of freq=1 unique k-mers and total distinct k-mers in trimmed data set\n", "hist00 = read_hist_file(\"../data/ecoli_ref-trim.fastq.hist\")\n", "# to get processed bases(in trimmed data), to get % of trimmed bases compared to processed bases in full data \n", "out00 = read_out_file(\"../data/ecoli_ref-trim.fastq.r1.fq.out\")\n", "\n", "# quality filtered by seqtk using different parameters\n", "endcount_seqtk = read_endcount_file(\"../data/ecoli_ref.fastq.seqtk-trimmed.fastq.endcount\")\n", "hist_seqtk = read_hist_file(\"../data/ecoli_ref.fastq.seqtk-trimmed.fastq.hist\")\n", "out_seqtk = read_out_file(\"../data/ecoli_ref.fastq.seqtk-trimmed.r1.fastq.out\")\n", "\n", "endcount_seqtk_q001 = read_endcount_file(\"../data/ecoli_ref.fastq.seqtk-trimmed-q001.fastq.endcount\")\n", "hist_seqtk_q001 = read_hist_file(\"../data/ecoli_ref.fastq.seqtk-trimmed-q001.fastq.hist\")\n", "out_seqtk_q001 = read_out_file(\"../data/ecoli_ref.fastq.seqtk-trimmed-q001.r1.fastq.out\")\n", "\n", "endcount_seqtk_b3e5 = read_endcount_file(\"../data/ecoli_ref.fastq.seqtk-trimmed-b3e5.fastq.endcount\")\n", "hist_seqtk_b3e5 = read_hist_file(\"../data/ecoli_ref.fastq.seqtk-trimmed-b3e5.fastq.hist\")\n", "out_seqtk_b3e5 = read_out_file(\"../data/ecoli_ref.fastq.seqtk-trimmed-b3e5.r1.fastq.out\")\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "markdown", "metadata": {}, "source": [ "FASTX:\n", "\n", "fastq_quality_filter -Q33 -q 30 -p 50 -i ecoli_ref.fastq >ecoli_ref-trim.fastq\n", "\n", "SEQTK with different parameters:\n", "\n", "seqtk trimfq \n", "\n", "seqtk trimfq -q 0.01 \n", "\n", "seqtk trimfq -b 3 -e 5 \n", "\n", "\n", "total k-mers means \"total number of different k-mers\"\n", "\n", "unique k-mers means \"total number of k-mers with frequency as 1\"\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "table_format = '{:28s}{:>10s}{:>16s}{:>16s}{:>18s}{:>27s}'\n", "print table_format.format('method','FP rate','bases trimmed','distinct k-mers',\\\n", " 'unique k-mers','unique k-mers at 3\\' end')\n", "#print '{:35s}{:>10s}{:>20s}{:>20s}{:>20s}{:>20s}'.format('iteration','FP rate','bases trimmed','total k-mers',\\\n", "# 'unique k-mers','unique k-mers at 3\\' end')\n", "#print out1\n", "print '-'*120\n", "print table_format.format('data to process','-','-',hist0[1], hist0[0], endcount0)\n", "print table_format.format('khmer iteration 1',out1[0],out1[1],hist1[1], hist1[0], endcount1)\n", "print table_format.format('khmer iteration 2',out2[0],out2[1],hist2[1], hist2[0], endcount2)\n", "print table_format.format('khmer iteration 3',out3[0],out3[1],hist3[1], hist3[0], endcount3)\n", "print table_format.format('khmer iteration 4',out4[0],out4[1],hist4[1], hist4[0], endcount4)\n", "print table_format.format('khmer iteration 5',out5[0],out5[1],hist5[1], hist5[0], endcount5)\n", "print table_format.format('khmer iteration 6',out6[0],out6[1],hist6[1], hist6[0], endcount6)\n", "print table_format.format('filtered by FASTX','-',\"{:.1f}%\".format((1-float(out00[2])/out1[2]) * 100),hist00[1], hist00[0], endcount00)\n", "print table_format.format('filtered by seqtk(default)','-',\"{:.1f}%\".format((1-float(out_seqtk[2])/out1[2]) * 100),hist_seqtk[1], hist_seqtk[0], endcount_seqtk)\n", "print table_format.format('filtered by seqtk(-q 0.01)','-',\"{:.1f}%\".format((1-float(out_seqtk_q001[2])/out1[2]) * 100),hist_seqtk_q001[1], hist_seqtk_q001[0], endcount_seqtk_q001)\n", "print table_format.format('filtered by seqtk(-b 3 -e 5)','-',\"{:.1f}%\".format((1-float(out_seqtk_b3e5[2])/out1[2]) * 100),hist_seqtk_b3e5[1], hist_seqtk_b3e5[0], endcount_seqtk_b3e5)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "method FP rate bases trimmed distinct k-mers unique k-mers unique k-mers at 3' end\n", "------------------------------------------------------------------------------------------------------------------------\n", "data to process - - 41.6M 34.1M 30.4%\n", "khmer iteration 1 80.0% 13.5% 13.3M 6.5M 29.8%\n", "khmer iteration 2 40.2% 1.7% 7.6M 909.9K 12.3%\n", "khmer iteration 3 25.4% 0.3% 6.8M 168.1K 3.1%\n", "khmer iteration 4 23.2% 0.1% 6.7M 35.8K 0.7%\n", "khmer iteration 5 22.8% 0.0% 6.6M 7.9K 0.2%\n", "khmer iteration 6 22.7% 0.0% 6.6M 1.9K 0.0%\n", "filtered by FASTX - 9.1% 26.6M 20.3M 26.3%\n", "filtered by seqtk(default) - 8.9% 17.7M 12.1M 12.3%\n", "filtered by seqtk(-q 0.01) - 15.4% 9.9M 5.1M 5.2%\n", "filtered by seqtk(-b 3 -e 5) - 8.0% 34.5M 27.7M 25.3%\n" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we expect the fp rate for the first iteration is ~80%, with this fp rate, the optimal number of hash table is 1.\n", "\n", "The size of hash table is :\n", "\n", ">>> math.log(0.8,0.5)\n", "\n", "0.3219280948873623\n", "\n", ">>> math.log(0.2)\n", "\n", "-1.6094379124341003\n", "\n", ">>> 41553294/math.log(0.2)\n", "\n", "\n", "-25818513.208226312\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Table 4\n", "==========\n", "Low-memory digital normalization. The results of digitally normalizing a 5m read E. coli data set to C=20 with k=20 under several memory usage/error rates. The error rate (column 1) is empirically determined. We measured reads remaining, number of \u201ctrue\u201d k-mers that were removed during the normalization process, and the number of total k-mers remaining. Note: at high error rates, reads are erroneously removed due to inflation of k-mer counts." ] }, { "cell_type": "code", "collapsed": false, "input": [ "#ecoli_ref.fastq.keep20M.fq.kh.e_coli_ref_hist \n", "diginorm_file={}\n", "hist_file = {}\n", "missed_kmers = {}\n", "for size in ['10M','20M','40M','60M','120M','240M','2400M']:\n", " diginorm_file[size] = read_diginorm_out_file(\"../data/norm\"+size+\".out\")\n", " hist_file[size] = read_hist_file(\"../data/ecoli_ref.fastq.keep\"+size+\".fq.hist\")\n", " missed_kmers[size] = read_e_coli_ref_hist_file(\"../data/ecoli_ref.fastq.keep\"+size+\".fq.kh.e_coli_ref_hist\")\n", "missed_kmers['all'] = read_e_coli_ref_hist_file(\"../data/ecoli_ref.fastq.kh.e_coli_ref_hist\")\n", "hist_file['all'] = read_hist_file(\"../data/ecoli_ref.fastq.hist\")" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "table_format = '{:>20s}{:>10s}{:>16s}{:>20s}{:>25s}{:>27s}'\n", "print table_format.format('memory','FP rate','retained reads','retained reads %','true k-mers removed','retained total k-mers')\n", "\n", "print '-'*70\n", "# all 5m reads ecoli data set\n", "print table_format.format('[before diginorm]','-',diginorm_file['10M'][3],'100.0%',missed_kmers['all'],hist_file['all'][1])\n", "\n", "for size in ['10M','20M','40M','60M','120M','240M','2400M']:\n", " print table_format.format(size,diginorm_file[size][0],diginorm_file[size][2],diginorm_file[size][1],missed_kmers[size],hist_file[size][1])\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " memory FP rate retained reads retained reads % true k-mers removed retained total k-mers\n", "----------------------------------------------------------------------\n", " [before diginorm] - 5,000,000 100.0% 170 41.6M\n", " 10M 100.0% 1,076,958 21.0% 185 20.9M\n", " 20M 98.8% 1,460,936 29.0% 172 25.7M\n", " 40M 83.2% 1,602,437 32.0% 172 27.6M\n", " 60M 59.1% 1,633,182 32.0% 172 27.9M\n", " 120M 18.0% 1,652,273 33.0% 172 28.1M\n", " 240M 2.8% 1,655,988 33.0% 172 28.1M\n", " 2400M 0.0% 1,656,518 33.0% 172 28.1M\n" ] } ], "prompt_number": 30 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Supplementary - determine the optimal parameters of hash tables to use\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "\n", "n1 = 561178082\n", "n2 = 1060354144\n", "n3 = 1445923389\n", "n4 = 1770589216\n", "n5 = 2121474237\n", "ns = [n1,n2,n3,n4,n5]\n", "\n", "print ns" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[561178082, 1060354144, 1445923389, 1770589216, 2121474237]\n" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "if false positive rate is fixed, number of hash tables is fixed for minimum memory \n", "\n", "0.01 ~ 6\n", "\n", "0.05 ~ 4\n", "\n", "0.2 ~ 2\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def optimal(f,N):\n", " M = math.log(f,0.6185)*N\n", " print M/N\n", " Z = math.log(2)*(M/float(N))\n", " intZ = int(Z)\n", " if intZ == 0:\n", " intZ = 1\n", " H = int(M/intZ)\n", " f1 = (1-math.exp(-N/(M/Z)))**Z\n", " f2 = (1-math.exp(-N/float(H)))**intZ\n", " return M,Z,intZ, H, f1, f2\n", "\n", "def optimal2(f,N):\n", " Z = math.log(f,0.5)\n", " intZ = int(Z)\n", " if intZ == 0:\n", " intZ = 1\n", " M = Z/math.log(2)*N\n", " \n", " #print M/N\n", " H = int(M/intZ)\n", " f1 = (1-math.exp(-N/(M/Z)))**Z\n", " f2 = (1-math.exp(-N/float(H)))**intZ\n", " return M,Z,intZ, H, f1, f2\n", "\n", "m1 = []\n", "h1 = []\n", "n1 = []\n", "for n in ns:\n", " s= optimal2(0.01,n)\n", " m1.append(s[0])\n", " n1.append(str(s[2]))\n", " h1.append(str(s[3]))\n", "\n", "m5 = []\n", "h5 = []\n", "n5 = []\n", "for n in ns:\n", " s= optimal2(0.05,n)\n", " m5.append(s[0])\n", " n5.append(str(s[2]))\n", " h5.append(str(s[3]))\n", "\n", "m20 = []\n", "h20 = []\n", "n20 = []\n", "for n in ns:\n", " s= optimal2(0.2,n)\n", " m20.append(s[0])\n", " n20.append(str(s[2]))\n", " h20.append(str(s[3]))\n", "print h1, n1, h5,n5" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['896487446', '1693926061', '2309876682', '2828533499', '3389075734'] ['6', '6', '6', '6', '6'] ['874767793', '1652886462', '2253914137', '2760005195', '3306966891'] ['4', '4', '4', '4', '4']\n" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Table: optimal size and number of hash tables to use, to minimize memory usage for expected false positive rate \n", "-------\n", "used in benchmark\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "print 'optimal size and number of hash tables to use, to minimize memory usage for expected false positive rate'\n", "print '='*100\n", "print '{:10s}{:30s}{:20s}{:20s}{:20s}'.format('Data set','size of file(GB)','fpr=1%',\\\n", " 'fpr=5%','fpr=20%')\n", "print '-'*100\n", "print '{:10s}{:30s}{:20s}{:20s}{:20s}'.format('subset1','1.90',h1[0]+' X '+n1[0],h5[0]+' X '+n5[0],h20[0]+' X '+n20[0])\n", "print '{:10s}{:30s}{:20s}{:20s}{:20s}'.format('subset2','2.17',h1[1]+' X '+n1[1],h5[1]+' X '+n5[1],h20[1]+' X '+n20[1])\n", "print '{:10s}{:30s}{:20s}{:20s}{:20s}'.format('subset3','3.14',h1[2]+' X '+n1[2],h5[2]+' X '+n5[2],h20[2]+' X '+n20[2])\n", "print '{:10s}{:30s}{:20s}{:20s}{:20s}'.format('subset4','4.05',h1[3]+' X '+n1[3],h5[3]+' X '+n5[3],h20[3]+' X '+n20[3])\n", "print '{:10s}{:30s}{:20s}{:20s}{:20s}'.format('subset5','5.00',h1[4]+' X '+n1[4],h5[4]+' X '+n5[4],h20[4]+' X '+n20[4])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "optimal size and number of hash tables to use, to minimize memory usage for expected false positive rate\n", "====================================================================================================\n", "Data set size of file(GB) fpr=1% fpr=5% fpr=20% \n", "----------------------------------------------------------------------------------------------------\n", "subset1 1.90 896487446 X 6 874767793 X 4 939926751 X 2 \n", "subset2 2.17 1693926061 X 6 1652886462 X 4 1776005260 X 2 \n", "subset3 3.14 2309876682 X 6 2253914137 X 4 2421801771 X 2 \n", "subset4 4.05 2828533499 X 6 2760005195 X 4 2965590108 X 2 \n", "subset5 5.00 3389075734 X 6 3306966891 X 4 3553293421 X 2 \n" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "optimal number of hash tables is determined by expected false positive rate only\n", "---------" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "x=[]\n", "y=[]\n", "f=0.0001\n", "import matplotlib.pyplot as plt\n", "\n", "while f<1:\n", " Z = math.log(f,0.5)\n", " x.append(f)\n", " y.append(Z)\n", " f = f+0.0001\n", "fig = plt.figure(figsize=(6,5))\n", "ax = fig.add_subplot(111)\n", "\n", "x2=[0.001,0.005,0.01,0.03,0.05,0.1,0.2]\n", "y2 = []\n", "for f in x2:\n", " y2.append(math.log(f,0.5))\n", " \n", "ax.plot(x,y)\n", "for x, y in zip(x2, y2):\n", " ax.plot((x,),(y,), 'ro')\n", " print x,y\n", " ax.text(x, y, '%.3f, %.2f' % (float(x),float(y)))\n", "\n", " \n", "\n", "ax.set_xscale('log')\n", "ax.set_xlabel('false positive rate')\n", "ax.set_ylabel('optimal number of hash tables')\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "0.001 9.96578428466\n", "0.005 7.64385618977\n", "0.01 6.64385618977\n", "0.03 5.05889368905\n", "0.05 4.32192809489\n", "0.1 3.32192809489\n", "0.2 2.32192809489\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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/vfnmm1oPfPr0aXx9ffONoAkKCqJWrVpMnjwZR0dHli1b9nx+CpFPHVsLpgd0pmkDG86E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\nHdq4PnB91nKs14Uql5WSkm5/EzAxMZHQ0FDeeec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"text": [ "" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "estimated minimum required memory depends on expected false positive rate and number of unique k-mers in the data set to count\n", "----------" ] }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "\n", "def optimal3(f,N):\n", " Z = math.log(f,0.5)\n", " intZ = int(Z)\n", " if intZ == 0:\n", " intZ = 1\n", " M = Z/math.log(2)*N\n", " return M\n", "\n", "fig = plt.figure(figsize=(6,5))\n", "ax = fig.add_subplot(111)\n", "\n", "\n", "fs =[0.001,0.005,0.01,0.03,0.05,0.1,0.2]\n", "for f in fs:\n", " Ns = []\n", " Ms = []\n", " #print f\n", " N=0\n", " while N<1e10:\n", " # print N\n", " M = optimal3(f,N)\n", " Ns.append(N)\n", " Ms.append(M/1e9)\n", " N = N+1e9\n", " #print Ns,Ms\n", " ax.plot(Ns,Ms,'-',label='fpr='+str(f))\n", "#ax = fig.add_subplot(111)\n", "#ax.plot(5e9,5e10,'o')\n", "#ax.plot(4e9,4e10,'o')\n", "fs =[0.01,0.05,0.2]\n", "\n", "for f in fs:\n", " Ns = []\n", " Ms = []\n", " #print f\n", " for n in ns:\n", " # print N\n", " M = optimal3(f,n)\n", " Ms.append(M)\n", " Ns.append(n)\n", "\n", " #print Ns,Ms\n", " #ax.plot(Ns,Ms)\n", "\n", "ax.set_xlabel('# of unique k-mers')\n", "ax.set_ylabel('estimated minimum required memory (G)')\n", "ax.legend(loc='upper left')\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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1XFnN4dSpU5w5cwYjo58ffvvwn7sZO3YsY8eObbfN1NS0rSHcburUqUydOlVO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