{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Results for ipyrad/pyrad/stacks/aftrRAD/dDocent simulated & emprical analyses" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting numpy-indexed\n", "Collecting future (from numpy-indexed)\n", " Downloading future-0.16.0.tar.gz (824kB)\n", "\u001b[K 100% |████████████████████████████████| 829kB 426kB/s \n", "\u001b[?25hRequirement already satisfied (use --upgrade to upgrade): pyyaml in /home/iovercast/opt/miniconda/lib/python2.7/site-packages (from numpy-indexed)\n", "Building wheels for collected packages: future\n", " Running setup.py bdist_wheel for future ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\n", "\u001b[?25h Stored in directory: /home/iovercast/.cache/pip/wheels/c2/50/7c/0d83b4baac4f63ff7a765bd16390d2ab43c93587fac9d6017a\n", "Successfully built future\n", "Installing collected packages: future, numpy-indexed\n", "Successfully installed future-0.16.0 numpy-indexed-0.3.4\n", "\u001b[33mYou are using pip version 8.1.1, however version 9.0.1 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "## Prereqs for plotting and analysis\n", "#!conda install matplotlib\n", "#!conda install libgcc # for vcfnp\n", "#!pip install vcfnp\n", "#!pip install scikit-allel\n", "#!conda install -y seaborn\n", "#!pip install toyplot\n", "#!pip install numpy_indexed" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Imports and working/output directories directories\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.rcParams[\"figure.figsize\"] = [12,9]\n", "\n", "from collections import Counter\n", "import seaborn as sns\n", "sns.set_style('white')\n", "sns.set_style('ticks')\n", "import toyplot\n", "import toyplot.html ## toypot sublib for saving html plots\n", "import pandas as pd\n", "import numpy as np\n", "import collections\n", "import allel\n", "import vcfnp\n", "import shutil\n", "import glob\n", "import os\n", "from allel.util import * ## for ensure_square()\n", "\n", "## Set the default directories for exec and data. \n", "WORK_DIR=\"/home/iovercast/manuscript-analysis/\"\n", "EMPERICAL_DATA_DIR=os.path.join(WORK_DIR, \"example_empirical_rad/\")\n", "IPYRAD_DIR=os.path.join(WORK_DIR, \"ipyrad/\")\n", "PYRAD_DIR=os.path.join(WORK_DIR, \"pyrad/\")\n", "STACKS_DIR=os.path.join(WORK_DIR, \"stacks/\")\n", "AFTRRAD_DIR=os.path.join(WORK_DIR, \"aftrRAD/\")\n", "DDOCENT_DIR=os.path.join(WORK_DIR, \"dDocent/\")\n", "\n", "## tmp for testing\n", "#IPYRAD_OUTPUT = \"./arch/ipyrad/REALDATA/\"\n", "#PYRAD_OUTPUT = \"./arch/pyrad/REALDATA/\"\n", "\n", "os.chdir(WORK_DIR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get sample names, species names, and the species_dict\n", "Next will do some housekeeping to get our empirical samples sorted into species." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OrderedDict([('29154_superba', 'superba'), ('30556_thamno', 'thamno'), ('30686_cyathophylla', 'cyathophylla'), ('32082_przewalskii', 'przewalskii'), ('33413_thamno', 'thamno'), ('33588_przewalskii', 'przewalskii'), ('35236_rex', 'rex'), ('35855_rex', 'rex'), ('38362_rex', 'rex'), ('39618_rex', 'rex'), ('40578_rex', 'rex'), ('41478_cyathophylloides', 'cyathophylloides'), ('41954_cyathophylloides', 'cyathophylloides')])\n" ] } ], "source": [ "## Get sample names and assign them to a species dict\n", "IPYRAD_STATS=os.path.join(IPYRAD_DIR, \"REALDATA/REALDATA_outfiles/REALDATA_stats.txt\")\n", "infile = open(IPYRAD_STATS).readlines()\n", "sample_names = [x.strip().split()[0] for x in infile[20:33]]\n", "species = set([x.split(\"_\")[1] for x in sample_names])\n", "species_dict = collections.OrderedDict([])\n", "\n", "## Ordered dict of sample names and their species, in same order\n", "## as the vcf file\n", "for s in sample_names:\n", " species_dict[s] = s.split(\"_\")[1]\n", "print(species_dict)\n", "\n", "## Map species names to groups of individuals\n", "#for s in species:\n", "# species_dict[s] = [x for x in sample_names if s in x]\n", "#print(species_dict)\n", "species_colors = {\n", " 'rex': '#FF0000',\n", " 'cyathophylla': '#008000',\n", " 'thamno': '#00FFFF',\n", " 'cyathophylloides': '#90EE90',\n", " 'przewalskii': '#FFA500',\n", " 'superba': '#8B0000',\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function for plotting PCA given an input vcf file" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plotPCA(call_data, title):\n", " c = call_data\n", " g = allel.GenotypeArray(c.genotype)\n", " ac = g.count_alleles()\n", " ## Filter singletons and multi-allelic snps\n", " flt = (ac.max_allele() == 1) & (ac[:, :2].min(axis=1) > 1)\n", " gf = g.compress(flt, axis=0)\n", " gn = gf.to_n_alt()\n", " coords1, model1 = allel.stats.pca(gn, n_components=10, scaler='patterson')\n", " fig = plt.figure(figsize=(5, 5))\n", " ax = fig.add_subplot(1, 1, 1)\n", " sns.despine(ax=ax, offset=5)\n", " x = coords1[:, 0]\n", " y = coords1[:, 1]\n", " \n", " ## We know this works because the species_dict and the columns in the vcf\n", " ## are in the same order. \n", " for sp in species:\n", " flt = (np.array(species_dict.values()) == sp)\n", " ax.plot(x[flt], y[flt], marker='o', linestyle=' ', color=species_colors[sp], label=sp, markersize=10, mec='k', mew=.5)\n", " ax.set_xlabel('PC%s (%.1f%%)' % (1, model1.explained_variance_ratio_[0]*100))\n", " ax.set_ylabel('PC%s (%.1f%%)' % (2, model1.explained_variance_ratio_[1]*100))\n", " ax.legend(bbox_to_anchor=(1, 1), loc='upper left')\n", " fig.suptitle(title+\" pca\", y=1.02, style=\"italic\", fontsize=20, fontweight='bold')\n", " fig.tight_layout()\n", "\n", "def getPCA(call_data):\n", " c = call_data\n", " g = allel.GenotypeArray(c.genotype)\n", " ac = g.count_alleles()\n", " ## Filter singletons and multi-allelic snps\n", " flt = (ac.max_allele() == 1) & (ac[:, :2].min(axis=1) > 1)\n", " gf = g.compress(flt, axis=0)\n", " gn = gf.to_n_alt()\n", " coords1, model1 = allel.stats.pca(gn, n_components=10, scaler='patterson')\n", " return coords1, model1\n", "\"\"\"\n", " fig = plt.figure(figsize=(5, 5))\n", " x = coords1[:, 0]\n", " y = coords1[:, 1]\n", " \n", " ## We know this works because the species_dict and the columns in the vcf\n", " ## are in the same order. \n", "# for sp in species:\n", "# flt = (np.array(species_dict.values()) == sp)\n", "# ax.plot(x[flt], y[flt], marker='o', linestyle=' ', color=species_colors[sp], label=sp, markersize=10, mec='k', mew=.5)\n", " ax.plot(x, y, marker='o', markersize=10)\n", " ax.set_xlabel('PC%s (%.1f%%)' % (1, model1.explained_variance_ratio_[0]*100))\n", " ax.set_ylabel('PC%s (%.1f%%)' % (2, model1.explained_variance_ratio_[1]*100))\n", "\"\"\"\n", "\n", "def plotPairwiseDistance(call_data, title):\n", " c = call_data\n", " #c = vcfnp.calldata_2d(filename).view(np.recarray)\n", " g = allel.GenotypeArray(c.genotype)\n", " gn = g.to_n_alt()\n", " dist = allel.stats.pairwise_distance(gn, metric='euclidean')\n", " allel.plot.pairwise_distance(dist, labels=species_dict.keys())\n", "\n", "def getDistances(call_data):\n", " c = call_data\n", " #c = vcfnp.calldata_2d(filename).view(np.recarray)\n", " g = allel.GenotypeArray(c.genotype)\n", " gn = g.to_n_alt()\n", " dist = allel.stats.pairwise_distance(gn, metric='euclidean')\n", " return(dist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function for plotting distribution of variable sites across loci" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Inputs to this function are two Counters where keys\n", "## are the base position and values are snp counts.\n", "## The counter doesn't have to be sorted because we sort internally.\n", "def SNP_position_plot(distvar, distpis):\n", " \n", " ## The last position to consider\n", " maxend = max(distvar.keys())\n", " \n", " ## This does two things, first it sorts in increasing\n", " ## order. Second, it creates a count bin for any position\n", " ## without snps and sets the count to 0.\n", " distvar = [distvar[x] for x in xrange(maxend)]\n", " distpis = [distpis[x] for x in xrange(maxend)]\n", "\n", " ## set color theme\n", " colormap = toyplot.color.Palette()\n", "\n", " ## make a canvas\n", " canvas = toyplot.Canvas(width=800, height=300)\n", "\n", " ## make axes\n", " axes = canvas.cartesian(xlabel=\"Position along RAD loci\",\n", " ylabel=\"N variables sites\",\n", " gutter=65)\n", " ## x-axis\n", " axes.x.ticks.show = True\n", " axes.x.label.style = {\"baseline-shift\":\"-40px\", \"font-size\":\"16px\"}\n", " axes.x.ticks.labels.style = {\"baseline-shift\":\"-2.5px\", \"font-size\":\"12px\"}\n", " axes.x.ticks.below = 5\n", " axes.x.ticks.above = 0\n", " axes.x.domain.max = maxend\n", " axes.x.ticks.locator = toyplot.locator.Explicit(\n", " range(0, maxend, 5), \n", " map(str, range(0, maxend, 5)))\n", " \n", " ## y-axis\n", " axes.y.ticks.show=True\n", " axes.y.label.style = {\"baseline-shift\":\"40px\", \"font-size\":\"16px\"}\n", " axes.y.ticks.labels.style = {\"baseline-shift\":\"5px\", \"font-size\":\"12px\"}\n", " axes.y.ticks.below = 0\n", " axes.y.ticks.above = 5\n", "\n", " ## add fill plots\n", " x = np.arange(0, maxend)\n", " f1 = axes.fill(x, distvar, color=colormap[0], opacity=0.5, title=\"total variable sites\")\n", " f2 = axes.fill(x, distpis, color=colormap[1], opacity=0.5, title=\"parsimony informative sites\")\n", "\n", " ## add a horizontal dashed line at the median Nsnps per site\n", " axes.hlines(np.median(distvar), opacity=0.9, style={\"stroke-dasharray\":\"4, 4\"})\n", " axes.hlines(np.median(distpis), opacity=0.9, style={\"stroke-dasharray\":\"4, 4\"})\n", " \n", " return canvas, axes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions for polling stats from vcf call data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy_indexed as npi\n", " \n", "## Get the number of samples with data at each snp\n", "def snp_coverage(call_data):\n", " snp_counts = collections.Counter([x.sum() for x in call_data[\"GT\"] != \"./.\"])\n", " ## Fill zero values\n", " return [snp_counts[x] for x in xrange(1, max(snp_counts.keys())+1)]\n", "\n", "## Get the number of samples with data at each locus\n", "def loci_coverage(var_data, call_data, assembler):\n", " if \"stacks\" in assembler:\n", " loci = zip(*npi.group_by(map(lambda x: x.split(\"_\")[0],var_data[\"ID\"]))(call_data[\"GT\"] != \"./.\"))\n", " else:\n", " loci = zip(*npi.group_by(var_data[\"CHROM\"])(call_data[\"GT\"] != \"./.\"))\n", " counts_per_snp = []\n", " for z in xrange(0, len(loci)):\n", " counts_per_snp.append([x.sum() for x in loci[z][1]])\n", " counts = collections.Counter([np.max(x) for x in counts_per_snp])\n", " \n", " ## Fill all zero values\n", " return [counts[x] for x in xrange(1, max(counts.keys())+1)]\n", "\n", "## Get total number of snps per sample\n", "def sample_nsnps(call_data):\n", " return [x.sum() for x in call_data[\"GT\"].T != \"./.\"]\n", "\n", "## Get total number of loci per sample\n", "def sample_nloci(var_data, call_data, assembler):\n", " if \"stacks\" in assembler:\n", " locus_groups = npi.group_by(map(lambda x: x.split(\"_\")[0],var_data[\"ID\"]))(call_data[\"GT\"] != \"./.\")\n", " else:\n", " locus_groups = npi.group_by(v[\"CHROM\"])(c[\"GT\"] != \"./.\")\n", " \n", " by_locus = [x.T for x in locus_groups[1]]\n", " by_sample = np.array([(x).any(axis=1) for x in by_locus])\n", " return [x.sum() for x in by_sample.T]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## End housekeeping. Begin actual analysis of results.\n", "\n", "## First lets look at results just from the vcf files (so this is only looking at variable loci).\n", "The first thing we'll do is create a dataframe for storing a bunch\n", "of coverage information from the runs for each method." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "## Make a new pandas dataframe for holding the coverage results\n", "## this is going to \n", "sim_vcf_dict = {}\n", "for sim in [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]:\n", " sim_vcf_dict[\"ipyrad-\"+sim] = os.path.join(IPYRAD_DIR, \"SIMDATA/{}/{}_outfiles/{}.vcf\".format(sim, sim, sim))\n", " sim_vcf_dict[\"pyrad-\"+sim] = os.path.join(PYRAD_DIR, \"SIMDATA/{}/outfiles/c85d6m2p3H3N3.vcf\".format(sim))\n", " sim_vcf_dict[\"stacks_ungapped-\"+sim] = os.path.join(STACKS_DIR, \"SIMDATA/ungapped/{}/batch_1.vcf\".format(sim))\n", " sim_vcf_dict[\"stacks_gapped-\"+sim] = os.path.join(STACKS_DIR, \"SIMDATA/gapped/{}/batch_1.vcf\".format(sim))\n", " sim_vcf_dict[\"stacks_defualt-\"+sim] = os.path.join(STACKS_DIR, \"SIMDATA/default/{}/batch_1.vcf\".format(sim))\n", " sim_vcf_dict[\"aftrrad-\"+sim] = os.path.join(AFTRRAD_DIR, \"SIMDATA/{}/Formatting/{}.vcf\".format(sim, sim))\n", " sim_vcf_dict[\"ddocent_full-\"+sim] = os.path.join(DDOCENT_DIR, \"SIMDATA/{}/{}_fastqs/TotalRawSNPs.vcf\".format(sim, sim))\n", " sim_vcf_dict[\"ddocent_filt-\"+sim] = os.path.join(DDOCENT_DIR, \"SIMDATA/{}/{}_fastqs/Final.recode.vcf\".format(sim, sim))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clean up the vcf data\n", "Each program writes out a vcf with some little quirks, so we have to smooth them all out.\n", "gunzip and filter out all non-biallelic loci. You only need to run this cell once, if you are\n", "reanalyzing the simdata." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found - /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlarge/simlarge_outfiles/simlarge-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simhi/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlo/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/Final.recode-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simno/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlarge/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simno/Formatting/simno-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlo/Formatting/simlo-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/Final.recode-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlo/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlo/simlo_outfiles/simlo-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simhi/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/Final.recode-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simhi/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simno/simno_outfiles/simno-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlarge/Formatting/simlarge-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simhi/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simhi/Formatting/simhi-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simno/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlarge/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlo/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simhi/simhi_outfiles/simhi-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simno/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simno/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlarge/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlarge/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlo/batch_1-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/Final.recode-biallelic.recode.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/TotalRawSNPs-biallelic.recode.vcf\n" ] } ], "source": [ "\n", "for k, f in sim_vcf_dict.items():\n", " ## If it's gzipped then unzip it (only applies to ipyrad)\n", " if os.path.exists(f + \".gz\"):\n", " print(\"gunzipping - {}\".format(f+\".gz\"))\n", " cmd = \"gunzip -c {}.gz > {}\".format(f, f)\n", " !$cmd\n", " f = f.split(\".gz\")[0]\n", " sim_vcf_dict[k] = f\n", "\n", " if os.path.exists(f):\n", " print(\"found - {}\".format(f))\n", " if \"stacks\" in k:\n", " ## The version of vcftools we have here doesn't like the newer version of vcf stacks\n", " ## writes, so we have to 'fix' the stacks vcf version\n", " shutil.copy2(f, f+\".bak\")\n", " with open(f) as infile:\n", " dat = infile.readlines()[1:]\n", " with open(f, 'w') as outfile:\n", " outfile.write(\"##fileformat=VCFv4.1\\n\")\n", " outfile.write(\"\".join(dat))\n", "\n", "## Actually don't do this, it removes information about the assembly. \n", "## I don't know why i wanted to do this in the first place. Habit?\n", "## try:\n", "## ## Remove all but biallelic. \n", "## outfile = f.split(\".vcf\")[0]+\"-biallelic\"\n", "## cmd = \"{}vcftools --vcf {} --min-alleles 2 --max-alleles 2 --recode --out {}\" \\\n", "## .format(DDOCENT_DIR, f, outfile)\n", "## #print(cmd)\n", "## os.system(cmd)\n", "## \n", "## ## update the vcf_dict\n", "## sim_vcf_dict[k] = outfile + \".recode.vcf\"\n", "## except Exception as inst:\n", "## print(inst)\n", " else:\n", " print(\"not found - {}\".format(f))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pull depth and coverage stats out of the vcf files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get number of loci per sample, and locus coverage for each vcf file from each assembler and each simulated dataset. This is reading in from the vcf files from each assembly method. This is nice because vcf is relatively standard and all the tools can give us a version of vcf. It's not perfect though because it doesn't include information about monomorphic sites, so it doesn't tell us the true number of loci recovered. We can get an idea of coverage and depth at snps, but to get coverage and depth stats across all loci we need to dig into the guts of the output of each method (which we'll do later)." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:53:46.063946 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:53:46.064616 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ipyrad-simlarge\n", "\t/home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlarge/simlarge_outfiles/simlarge-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:53:50.046048 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:53:50.046543 :: building array\n", "[vcfnp] 2016-11-27 16:54:20.066691 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:54:20.067386 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - pyrad-simhi\n", "\t/home/iovercast/manuscript-analysis/pyrad/SIMDATA/simhi/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:54:20.493348 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:54:20.493821 :: building array\n", "[vcfnp] 2016-11-27 16:54:23.319907 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:54:23.320572 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - pyrad-simlo\n", "\t/home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlo/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:54:23.765458 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:54:23.765922 :: building array\n", "[vcfnp] 2016-11-27 16:54:26.717321 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:54:26.717919 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_full-simlarge\n", "\t/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/TotalRawSNPs-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:54:38.023720 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:54:38.024183 :: building array\n", "[vcfnp] 2016-11-27 16:55:53.857176 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:55:53.857903 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_filt-simlo\n", "\t/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/Final.recode-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:55:55.048663 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:55:55.049266 :: building array\n", "[vcfnp] 2016-11-27 16:56:02.929772 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:56:02.930457 :: building array\n" ] }, { "name": 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"output_type": "stream", "text": [ "Doing - ipyrad-simlo\n", "\t/home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlo/simlo_outfiles/simlo-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:57:15.318753 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:57:15.319228 :: building array\n", "[vcfnp] 2016-11-27 16:57:18.347953 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:57:18.348643 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_defualt-simhi\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simhi/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:57:18.757473 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:57:18.757896 :: building array\n", "[vcfnp] 2016-11-27 16:57:22.446983 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:57:22.447555 :: building array\n" ] }, { "name": "stdout", 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"\t/home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simno/simno_outfiles/simno-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:58:47.749539 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:58:47.750019 :: building array\n", "[vcfnp] 2016-11-27 16:58:50.666550 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:58:50.667183 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - aftrrad-simlarge\n", "\t/home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlarge/Formatting/simlarge-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:58:52.924963 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:58:52.925433 :: building array\n", "[vcfnp] 2016-11-27 16:59:07.577632 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:07.578327 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_ungapped-simhi\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simhi/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:59:08.104825 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:08.105282 :: building array\n", "[vcfnp] 2016-11-27 16:59:12.857512 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:12.858353 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - aftrrad-simhi\n", "\t/home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simhi/Formatting/simhi-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:59:13.149329 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:13.149779 :: building array\n", "[vcfnp] 2016-11-27 16:59:14.874231 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:14.875006 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_ungapped-simno\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simno/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:59:15.416360 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:15.416977 :: building array\n", "[vcfnp] 2016-11-27 16:59:20.232799 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:20.233563 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_defualt-simlarge\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlarge/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 16:59:24.182469 :: caching is disabled\n", "[vcfnp] 2016-11-27 16:59:24.183060 :: building array\n", "[vcfnp] 2016-11-27 17:00:01.487897 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:01.488714 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_ungapped-simlo\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlo/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:00:02.032021 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:02.032495 :: building array\n", "[vcfnp] 2016-11-27 17:00:07.044474 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:07.045104 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ipyrad-simhi\n", "\t/home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simhi/simhi_outfiles/simhi-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:00:07.802941 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:07.803469 :: building array\n", "[vcfnp] 2016-11-27 17:00:10.822673 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:10.823271 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - pyrad-simno\n", "\t/home/iovercast/manuscript-analysis/pyrad/SIMDATA/simno/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:00:11.291555 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:11.292032 :: building array\n", "[vcfnp] 2016-11-27 17:00:14.220085 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:14.220821 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_full-simhi\n", "\t/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/TotalRawSNPs-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:00:15.503526 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:15.503993 :: building array\n", "[vcfnp] 2016-11-27 17:00:23.207787 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:23.208546 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_defualt-simno\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simno/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:00:23.640669 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:23.641165 :: building array\n", "[vcfnp] 2016-11-27 17:00:27.520616 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:27.521816 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - pyrad-simlarge\n", "\t/home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlarge/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:00:31.749149 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:31.749765 :: building array\n", "[vcfnp] 2016-11-27 17:00:59.712859 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:00:59.713690 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_full-simlo\n", "\t/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/TotalRawSNPs-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:01:00.898081 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:01:00.898895 :: building array\n", "[vcfnp] 2016-11-27 17:01:08.917655 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:01:08.918244 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_gapped-simlarge\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlarge/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:01:14.105084 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:01:14.105781 :: building array\n", "[vcfnp] 2016-11-27 17:01:59.933549 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:01:59.934387 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_defualt-simlo\n", "\t/home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlo/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:02:00.344161 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:02:00.344609 :: building array\n", "[vcfnp] 2016-11-27 17:02:04.000344 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:02:04.000860 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_filt-simhi\n", "\t/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/Final.recode-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:02:05.309855 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:02:05.310451 :: building array\n", "[vcfnp] 2016-11-27 17:02:13.334594 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:02:13.335213 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_full-simno\n", "\t/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/TotalRawSNPs-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-11-27 17:02:14.594919 :: caching is disabled\n", "[vcfnp] 2016-11-27 17:02:14.595400 :: building array\n" ] } ], "source": [ "import collections\n", "sim_loc_cov = collections.OrderedDict()\n", "sim_snp_cov = collections.OrderedDict()\n", "sim_sample_nsnps = collections.OrderedDict()\n", "sim_sample_nlocs = collections.OrderedDict()\n", "## Try just doing them all the same\n", "for prog, filename in sim_vcf_dict.items():\n", " try:\n", " print(\"Doing - {}\".format(prog))\n", " print(\"\\t{}\".format(filename))\n", " v = vcfnp.variants(filename, verbose=False, dtypes={\"CHROM\":\"a24\"}).view(np.recarray)\n", " c = vcfnp.calldata_2d(filename, verbose=False).view(np.recarray)\n", "\n", " sim_snp_cov[prog] = snp_coverage(c)\n", " sim_sample_nsnps[prog] = sample_nsnps(c)\n", " ## aftrRAD doesn't retain the kind of info we'd need for the next two\n", " if \"aftrrad\" in prog:\n", " continue\n", " sim_loc_cov[prog] = loci_coverage(v, c, prog)\n", " sim_sample_nlocs[prog] = sample_nloci(v, c, prog)\n", " except Exception as inst:\n", " print(inst)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write out the results to a file\n", "In case we need to restart the notebook then we won't have to come back and reload all the data" ] }, { "cell_type": "code", "execution_count": 496, "metadata": { "collapsed": false }, "outputs": [], "source": [ "res_dict = {\"sim_loc_cov\":sim_loc_cov, \"sim_snp_cov\":sim_snp_cov,\\\n", " \"sim_sample_nsnps\":sim_sample_nsnps, \"sim_sample_nlocs\":sim_sample_nlocs}\n", "\n", "res = WORK_DIR + \"RESULTS/\"\n", "if not os.path.exists(res):\n", " os.mkdir(res)\n", "\n", "for k,v in res_dict.items():\n", " df = pd.DataFrame(v)\n", " df.to_csv(res + k + \".csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reload the data from csv files\n", "If you ever need to do this." ] }, { "cell_type": "code", "execution_count": 497, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 ipyrad-simlarge pyrad-simhi pyrad-simlo \\\n", "0 0 0 0 0 \n", "1 1 12 0 0 \n", "2 2 35 0 0 \n", "3 3 436 0 0 \n", "4 4 137 0 0 \n", "5 5 70 2 0 \n", "6 6 256 0 0 \n", "7 7 1737 0 0 \n", "8 8 1688 0 0 \n", "9 9 2327 0 0 \n", "10 10 9920 1 0 \n", "11 11 81527 9875 9884 \n", "\n", " ddocent_full-simlarge ddocent_filt-simlo stacks_gapped-simno \\\n", "0 1 0 0 \n", "1 2 0 0 \n", "2 38 0 0 \n", "3 433 0 0 \n", "4 139 0 0 \n", "5 70 0 0 \n", "6 258 0 0 \n", "7 1732 0 0 \n", "8 1689 0 0 \n", "9 2322 0 5 \n", "10 9808 0 329 \n", "11 81397 9853 9559 \n", "\n", " stacks_ungapped-simlarge ddocent_filt-simno stacks_gapped-simlo \\\n", "0 5 0 0 \n", "1 14 0 0 \n", "2 42 0 0 \n", "3 440 0 0 \n", "4 140 0 0 \n", "5 82 0 0 \n", "6 296 0 0 \n", "7 1743 0 1 \n", "8 1729 0 0 \n", "9 2627 0 2 \n", "10 12400 0 57 \n", "11 78902 9852 1452 \n", "\n", " ... ipyrad-simhi pyrad-simno ddocent_full-simhi \\\n", "0 ... 0 0 0 \n", "1 ... 0 0 0 \n", "2 ... 0 0 0 \n", "3 ... 1 0 2 \n", "4 ... 0 0 0 \n", "5 ... 0 0 0 \n", "6 ... 0 0 0 \n", "7 ... 1 0 0 \n", "8 ... 0 0 1 \n", "9 ... 0 0 0 \n", "10 ... 1 0 4 \n", "11 ... 9842 9894 9844 \n", "\n", " stacks_defualt-simno pyrad-simlarge ddocent_full-simlo \\\n", "0 350 0 0 \n", "1 259 13 0 \n", "2 365 38 0 \n", "3 528 446 0 \n", "4 68 141 0 \n", "5 60 72 0 \n", "6 153 258 0 \n", "7 668 1748 0 \n", "8 315 1704 0 \n", "9 338 2342 0 \n", "10 1311 9952 0 \n", "11 6352 81709 9853 \n", "\n", " stacks_gapped-simlarge stacks_defualt-simlo ddocent_filt-simhi \\\n", "0 5 356 0 \n", "1 14 278 0 \n", "2 42 386 0 \n", "3 440 532 0 \n", "4 140 74 0 \n", "5 82 64 0 \n", "6 296 178 0 \n", "7 1743 690 0 \n", "8 1729 332 0 \n", "9 2627 371 0 \n", "10 12400 1447 4 \n", "11 78902 6105 9844 \n", "\n", " ddocent_full-simno \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "5 0 \n", "6 0 \n", "7 0 \n", "8 0 \n", "9 0 \n", "10 0 \n", "11 9852 \n", "\n", "[12 rows x 29 columns]\n", " Unnamed: 0 ipyrad-simlarge pyrad-simhi pyrad-simlo \\\n", "0 0 95399 9877 9884 \n", "1 1 95428 9877 9884 \n", "2 2 95405 9877 9884 \n", "3 3 95456 9877 9884 \n", "4 4 95450 9877 9884 \n", "5 5 95412 9876 9884 \n", "6 6 95437 9877 9884 \n", "7 7 95418 9877 9884 \n", "8 8 95283 9877 9884 \n", "9 9 95294 9877 9884 \n", "10 10 95288 9877 9884 \n", "11 11 95302 9877 9884 \n", "\n", " ddocent_full-simlarge ddocent_filt-simlo stacks_gapped-simno \\\n", "0 95169 9853 9870 \n", "1 95200 9853 9862 \n", "2 95167 9853 9865 \n", "3 95218 9853 9863 \n", "4 95203 9853 9862 \n", "5 95164 9853 9864 \n", "6 95198 9853 9864 \n", "7 95177 9853 9867 \n", "8 95048 9853 9865 \n", "9 95056 9853 9867 \n", "10 95049 9853 9863 \n", "11 95052 9853 9865 \n", "\n", " stacks_ungapped-simlarge ddocent_filt-simno stacks_gapped-simlo \\\n", "0 95366 9852 1512 \n", "1 95401 9852 1503 \n", "2 95391 9852 1507 \n", "3 95400 9852 1503 \n", "4 95394 9852 1507 \n", "5 95367 9852 1504 \n", "6 95411 9852 1508 \n", "7 95405 9852 1508 \n", "8 95232 9852 1507 \n", "9 95276 9852 1506 \n", "10 95268 9852 1508 \n", "11 95271 9852 1506 \n", "\n", " ... ipyrad-simhi pyrad-simno ddocent_full-simhi \\\n", "0 ... 9844 9894 9848 \n", "1 ... 9844 9894 9848 \n", "2 ... 9844 9894 9848 \n", "3 ... 9844 9894 9850 \n", "4 ... 9844 9894 9850 \n", "5 ... 9843 9894 9850 \n", "6 ... 9844 9894 9848 \n", "7 ... 9844 9894 9848 \n", "8 ... 9844 9894 9849 \n", "9 ... 9844 9894 9850 \n", "10 ... 9844 9894 9850 \n", "11 ... 9844 9894 9850 \n", "\n", " stacks_defualt-simno pyrad-simlarge ddocent_full-simlo \\\n", "0 9320 95646 9853 \n", "1 9303 95675 9853 \n", "2 9253 95649 9853 \n", "3 9096 95708 9853 \n", "4 9042 95695 9853 \n", "5 9021 95659 9853 \n", "6 8958 95686 9853 \n", "7 8934 95672 9853 \n", "8 8846 95543 9853 \n", "9 8823 95552 9853 \n", "10 8767 95544 9853 \n", "11 8687 95558 9853 \n", "\n", " stacks_gapped-simlarge stacks_defualt-simlo ddocent_filt-simhi \\\n", "0 95366 9275 9848 \n", "1 95401 9254 9848 \n", "2 95391 9207 9848 \n", "3 95400 9055 9848 \n", "4 95394 9020 9848 \n", "5 95367 8993 9848 \n", "6 95411 8929 9846 \n", "7 95405 8888 9847 \n", "8 95232 8807 9847 \n", "9 95276 8779 9848 \n", "10 95268 8741 9848 \n", "11 95271 8645 9848 \n", "\n", " ddocent_full-simno \n", "0 9852 \n", "1 9852 \n", "2 9852 \n", "3 9852 \n", "4 9852 \n", "5 9852 \n", "6 9852 \n", "7 9852 \n", "8 9852 \n", "9 9852 \n", "10 9852 \n", "11 9852 \n", "\n", "[12 rows x 29 columns]\n", " Unnamed: 0 ipyrad-simlarge pyrad-simhi pyrad-simlo \\\n", "0 0 399293 42421 43106 \n", "1 1 399339 42421 43105 \n", "2 2 399224 42417 43105 \n", "3 3 399319 42423 43099 \n", "4 4 399355 42416 43100 \n", "5 5 399039 42421 43101 \n", "6 6 399259 42421 43104 \n", "7 7 399070 42421 43101 \n", "8 8 398377 42427 43103 \n", "9 9 398531 42423 43106 \n", "10 10 398376 42425 43106 \n", "11 11 398234 42423 43103 \n", "\n", " ddocent_full-simlarge ddocent_filt-simlo stacks_gapped-simno \\\n", "0 381136 39879 43704 \n", "1 381212 39877 43636 \n", "2 381074 39878 43669 \n", "3 381093 39875 43674 \n", "4 381176 39879 43644 \n", "5 380907 39876 43677 \n", "6 381106 39879 43668 \n", "7 380897 39876 43692 \n", "8 380142 39879 43668 \n", "9 380310 39877 43694 \n", "10 380280 39879 43676 \n", "11 380121 39875 43692 \n", "\n", " stacks_ungapped-simlarge aftrrad-simno aftrrad-simlo \\\n", "0 416856 40658 40007 \n", "1 416936 40658 40007 \n", "2 416890 40658 40007 \n", "3 416846 40658 40007 \n", "4 416861 40658 40007 \n", "5 416524 40658 40007 \n", "6 416844 40658 40007 \n", "7 416795 40658 40007 \n", "8 415751 40658 40007 \n", "9 416064 40658 40007 \n", "10 415945 40658 40007 \n", "11 415788 40658 40007 \n", "\n", " ... ipyrad-simhi pyrad-simno ddocent_full-simhi \\\n", "0 ... 40402 43855 40041 \n", "1 ... 40402 43854 40045 \n", "2 ... 40402 43855 40041 \n", "3 ... 40402 43855 40049 \n", "4 ... 40402 43855 40054 \n", "5 ... 40399 43855 40057 \n", "6 ... 40402 43855 40043 \n", "7 ... 40402 43855 40041 \n", "8 ... 40400 43854 40053 \n", "9 ... 40400 43855 40060 \n", "10 ... 40400 43854 40054 \n", "11 ... 40400 43855 40054 \n", "\n", " stacks_defualt-simno pyrad-simlarge ddocent_full-simlo \\\n", "0 32242 418730 39889 \n", "1 32199 418786 39887 \n", "2 32099 418656 39888 \n", "3 31740 418768 39887 \n", "4 31694 418782 39890 \n", "5 31644 418459 39889 \n", "6 31502 418659 39888 \n", "7 31443 418481 39887 \n", "8 30932 417726 39898 \n", "9 30922 417896 39893 \n", "10 30868 417747 39893 \n", "11 30683 417623 39888 \n", "\n", " stacks_gapped-simlarge stacks_defualt-simlo ddocent_filt-simhi \\\n", "0 416856 31446 40008 \n", "1 416936 31372 40010 \n", "2 416890 31298 40003 \n", "3 416846 30969 40003 \n", "4 416861 30944 40008 \n", "5 416524 30881 40008 \n", "6 416844 30741 40002 \n", "7 416795 30662 40001 \n", "8 415751 30184 40005 \n", "9 416064 30149 40010 \n", "10 415945 30129 40004 \n", "11 415788 29904 40002 \n", "\n", " ddocent_full-simno \n", "0 39981 \n", "1 39980 \n", "2 39980 \n", "3 39980 \n", "4 39980 \n", "5 39979 \n", "6 39980 \n", "7 39977 \n", "8 39981 \n", "9 39979 \n", "10 39980 \n", "11 39980 \n", "\n", "[12 rows x 33 columns]\n", " Unnamed: 0 ipyrad-simlarge pyrad-simhi pyrad-simlo \\\n", "0 0 0 1 1 \n", "1 1 22 10 0 \n", "2 2 46 6 1 \n", "3 3 786 6 1 \n", "4 4 305 1 1 \n", "5 5 175 14 0 \n", "6 6 721 0 0 \n", "7 7 5301 10 2 \n", "8 8 5886 7 1 \n", "9 9 8687 6 2 \n", "10 10 39920 60 19 \n", "11 11 346258 42337 43081 \n", "\n", " ddocent_full-simlarge ddocent_filt-simlo stacks_gapped-simno \\\n", "0 3 0 0 \n", "1 3 0 0 \n", "2 53 0 0 \n", "3 758 0 0 \n", "4 299 0 0 \n", "5 164 0 0 \n", "6 696 0 0 \n", "7 5106 0 0 \n", "8 5745 0 0 \n", "9 8397 0 24 \n", "10 37784 19 1362 \n", "11 330563 39860 42406 \n", "\n", " stacks_ungapped-simlarge aftrrad-simno aftrrad-simlo \\\n", "0 6 0 0 \n", "1 25 0 0 \n", "2 56 0 0 \n", "3 819 0 0 \n", "4 317 0 0 \n", "5 201 0 0 \n", "6 880 0 0 \n", "7 5515 0 0 \n", "8 6288 0 0 \n", "9 10290 0 0 \n", "10 52091 0 0 \n", "11 350753 40658 40007 \n", "\n", " ... ipyrad-simhi pyrad-simno ddocent_full-simhi \\\n", "0 ... 0 0 25 \n", "1 ... 0 0 3 \n", "2 ... 0 0 7 \n", "3 ... 3 0 11 \n", "4 ... 0 0 2 \n", "5 ... 0 0 2 \n", "6 ... 0 0 0 \n", "7 ... 5 0 8 \n", "8 ... 0 0 20 \n", "9 ... 0 0 7 \n", "10 ... 3 3 80 \n", "11 ... 40394 43852 39940 \n", "\n", " stacks_defualt-simno pyrad-simlarge ddocent_full-simlo \\\n", "0 469 0 11 \n", "1 327 24 0 \n", "2 520 50 1 \n", "3 840 827 2 \n", "4 128 319 2 \n", "5 134 180 0 \n", "6 348 761 0 \n", "7 1715 5535 2 \n", "8 920 6183 2 \n", "9 1063 9158 1 \n", "10 4497 41802 19 \n", "11 23829 363123 39866 \n", "\n", " stacks_gapped-simlarge stacks_defualt-simlo ddocent_filt-simhi \\\n", "0 6 472 0 \n", "1 25 347 0 \n", "2 56 539 0 \n", "3 819 833 0 \n", "4 317 143 0 \n", "5 201 134 0 \n", "6 880 397 0 \n", "7 5515 1754 0 \n", "8 6288 957 0 \n", "9 10290 1155 0 \n", "10 52091 4861 80 \n", "11 350753 22550 39932 \n", "\n", " ddocent_full-simno \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "5 0 \n", "6 0 \n", "7 1 \n", "8 0 \n", "9 1 \n", "10 9 \n", "11 39970 \n", "\n", "[12 rows x 33 columns]\n" ] } ], "source": [ "for k,v in res_dict.items():\n", " df = pd.read_csv(res + k + \".csv\")\n", " print(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Print out the results\n", "This isn't very helpful, but you get an idea of what's going on. Values here for stacks gapped analysis look weird because they are bad. The populations program was segfaulting during creation of the vcf, so it's super truncated. Not sure what the problem is. The results down below the plots pull in stats from other files so they are better." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ipyrad-simno sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9870] 822.5\n", "pyrad-simno sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9894] 824.5\n", "stacks_ungapped-simno sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 329, 9559] 824.416666667\n", "stacks_gapped-simno sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 329, 9559] 824.416666667\n", "stacks_defualt-simno sim_loc_cov\t[350, 259, 365, 528, 68, 60, 153, 668, 315, 338, 1311, 6352] 897.25\n", "ddocent_filt-simno sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9852] 821.0\n", "ddocent_full-simno sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9852] 821.0\n", "aftrrad-simno sim_loc_cov\tNo sim_loc_cov stats for aftrrad-simno\n", "------------------------------------------------------\n", "ipyrad-simlo sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9859] 821.583333333\n", "pyrad-simlo sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9884] 823.666666667\n", "stacks_ungapped-simlo sim_loc_cov\t[24, 30, 31, 38, 6, 2, 8, 48, 48, 66, 573, 9131] 833.75\n", "stacks_gapped-simlo sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 57, 1452] 126.0\n", "stacks_defualt-simlo sim_loc_cov\t[356, 278, 386, 532, 74, 64, 178, 690, 332, 371, 1447, 6105] 901.083333333\n", "ddocent_filt-simlo sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9853] 821.083333333\n", "ddocent_full-simlo sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9853] 821.083333333\n", "aftrrad-simlo sim_loc_cov\tNo sim_loc_cov stats for aftrrad-simlo\n", "------------------------------------------------------\n", "ipyrad-simhi sim_loc_cov\t[0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 9842] 820.416666667\n", "pyrad-simhi sim_loc_cov\t[0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 9875] 823.166666667\n", "stacks_ungapped-simhi sim_loc_cov\t[54, 59, 66, 96, 17, 12, 18, 138, 106, 132, 943, 8509] 845.833333333\n", "stacks_gapped-simhi sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 56] 5.0\n", "stacks_defualt-simhi sim_loc_cov\t[369, 288, 402, 556, 86, 78, 209, 714, 367, 444, 1614, 5738] 905.416666667\n", "ddocent_filt-simhi sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9844] 820.666666667\n", "ddocent_full-simhi sim_loc_cov\t[0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 4, 9844] 820.916666667\n", "aftrrad-simhi sim_loc_cov\tNo sim_loc_cov stats for aftrrad-simhi\n", "------------------------------------------------------\n", "ipyrad-simlarge sim_loc_cov\t[0, 12, 35, 436, 137, 70, 256, 1737, 1688, 2327, 9920, 81527] 8178.75\n", "pyrad-simlarge sim_loc_cov\t[0, 13, 38, 446, 141, 72, 258, 1748, 1704, 2342, 9952, 81709] 8201.91666667\n", "stacks_ungapped-simlarge sim_loc_cov\t[5, 14, 42, 440, 140, 82, 296, 1743, 1729, 2627, 12400, 78902] 8201.66666667\n", "stacks_gapped-simlarge sim_loc_cov\t[5, 14, 42, 440, 140, 82, 296, 1743, 1729, 2627, 12400, 78902] 8201.66666667\n", "stacks_defualt-simlarge sim_loc_cov\t[3666, 2729, 3623, 5435, 913, 878, 2294, 7764, 4321, 5593, 17308, 52404] 8910.66666667\n", "ddocent_filt-simlarge sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9808, 81396] 7600.33333333\n", "ddocent_full-simlarge sim_loc_cov\t[1, 2, 38, 433, 139, 70, 258, 1732, 1689, 2322, 9808, 81397] 8157.41666667\n", "aftrrad-simlarge sim_loc_cov\tNo sim_loc_cov stats for aftrrad-simlarge\n", "------------------------------------------------------\n", "ipyrad-simno sim_sample_nlocs\t[9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870] 9870.0\n", "pyrad-simno sim_sample_nlocs\t[9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894] 9894.0\n", "stacks_ungapped-simno sim_sample_nlocs\t[9870, 9862, 9865, 9863, 9862, 9864, 9864, 9867, 9865, 9867, 9863, 9865] 9864.75\n", "stacks_gapped-simno sim_sample_nlocs\t[9870, 9862, 9865, 9863, 9862, 9864, 9864, 9867, 9865, 9867, 9863, 9865] 9864.75\n", "stacks_defualt-simno sim_sample_nlocs\t[9320, 9303, 9253, 9096, 9042, 9021, 8958, 8934, 8846, 8823, 8767, 8687] 9004.16666667\n", "ddocent_filt-simno sim_sample_nlocs\t[9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852] 9852.0\n", "ddocent_full-simno sim_sample_nlocs\t[9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852] 9852.0\n", "aftrrad-simno sim_sample_nlocs\tNo sim_sample_nlocs stats for aftrrad-simno\n", "------------------------------------------------------\n", "ipyrad-simlo sim_sample_nlocs\t[9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859] 9859.0\n", "pyrad-simlo sim_sample_nlocs\t[9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884] 9884.0\n", "stacks_ungapped-simlo sim_sample_nlocs\t[9819, 9808, 9818, 9812, 9832, 9825, 9826, 9801, 9811, 9808, 9817, 9801] 9814.83333333\n", "stacks_gapped-simlo sim_sample_nlocs\t[1512, 1503, 1507, 1503, 1507, 1504, 1508, 1508, 1507, 1506, 1508, 1506] 1506.58333333\n", "stacks_defualt-simlo sim_sample_nlocs\t[9275, 9254, 9207, 9055, 9020, 8993, 8929, 8888, 8807, 8779, 8741, 8645] 8966.08333333\n", "ddocent_filt-simlo sim_sample_nlocs\t[9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853] 9853.0\n", "ddocent_full-simlo sim_sample_nlocs\t[9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853] 9853.0\n", "aftrrad-simlo sim_sample_nlocs\tNo sim_sample_nlocs stats for aftrrad-simlo\n", "------------------------------------------------------\n", "ipyrad-simhi sim_sample_nlocs\t[9844, 9844, 9844, 9844, 9844, 9843, 9844, 9844, 9844, 9844, 9844, 9844] 9843.91666667\n", "pyrad-simhi sim_sample_nlocs\t[9877, 9877, 9877, 9877, 9877, 9876, 9877, 9877, 9877, 9877, 9877, 9877] 9876.91666667\n", "stacks_ungapped-simhi sim_sample_nlocs\t[9741, 9769, 9746, 9746, 9756, 9747, 9756, 9745, 9727, 9731, 9717, 9715] 9741.33333333\n", "stacks_gapped-simhi sim_sample_nlocs\t[60, 60, 59, 60, 60, 60, 60, 60, 60, 60, 59, 58] 59.6666666667\n", "stacks_defualt-simhi sim_sample_nlocs\t[9187, 9208, 9138, 8985, 8955, 8925, 8878, 8842, 8737, 8716, 8648, 8582] 8900.08333333\n", "ddocent_filt-simhi sim_sample_nlocs\t[9848, 9848, 9848, 9848, 9848, 9848, 9846, 9847, 9847, 9848, 9848, 9848] 9847.66666667\n", "ddocent_full-simhi sim_sample_nlocs\t[9848, 9848, 9848, 9850, 9850, 9850, 9848, 9848, 9849, 9850, 9850, 9850] 9849.08333333\n", "aftrrad-simhi sim_sample_nlocs\tNo sim_sample_nlocs stats for aftrrad-simhi\n", "------------------------------------------------------\n", "ipyrad-simlarge sim_sample_nlocs\t[95399, 95428, 95405, 95456, 95450, 95412, 95437, 95418, 95283, 95294, 95288, 95302] 95381.0\n", "pyrad-simlarge sim_sample_nlocs\t[95646, 95675, 95649, 95708, 95695, 95659, 95686, 95672, 95543, 95552, 95544, 95558] 95632.25\n", "stacks_ungapped-simlarge sim_sample_nlocs\t[95366, 95401, 95391, 95400, 95394, 95367, 95411, 95405, 95232, 95276, 95268, 95271] 95348.5\n", "stacks_gapped-simlarge sim_sample_nlocs\t[95366, 95401, 95391, 95400, 95394, 95367, 95411, 95405, 95232, 95276, 95268, 95271] 95348.5\n", "stacks_defualt-simlarge sim_sample_nlocs\t[90013, 89989, 89284, 87596, 87548, 87334, 86818, 86236, 85427, 85225, 84641, 83680] 86982.5833333\n", "ddocent_filt-simlarge sim_sample_nlocs\t[90677, 90706, 90329, 89908, 90701, 90653, 90336, 89898, 90677, 90684, 90271, 89800] 90386.6666667\n", "ddocent_full-simlarge sim_sample_nlocs\t[95169, 95200, 95167, 95218, 95203, 95164, 95198, 95177, 95048, 95056, 95049, 95052] 95141.75\n", "aftrrad-simlarge sim_sample_nlocs\tNo sim_sample_nlocs stats for aftrrad-simlarge\n", "------------------------------------------------------\n", "ipyrad-simno sim_sample_nsnps\t[41893, 41893, 41893, 41893, 41893, 41893, 41893, 41893, 41893, 41893, 41893, 41893] 41893.0\n", "pyrad-simno sim_sample_nsnps\t[43855, 43854, 43855, 43855, 43855, 43855, 43855, 43855, 43854, 43855, 43854, 43855] 43854.75\n", "stacks_ungapped-simno sim_sample_nsnps\t[43704, 43636, 43669, 43674, 43644, 43677, 43668, 43692, 43668, 43694, 43676, 43692] 43674.5\n", "stacks_gapped-simno sim_sample_nsnps\t[43704, 43636, 43669, 43674, 43644, 43677, 43668, 43692, 43668, 43694, 43676, 43692] 43674.5\n", "stacks_defualt-simno sim_sample_nsnps\t[32242, 32199, 32099, 31740, 31694, 31644, 31502, 31443, 30932, 30922, 30868, 30683] 31497.3333333\n", "ddocent_filt-simno sim_sample_nsnps\t[39974, 39973, 39973, 39973, 39974, 39973, 39974, 39971, 39974, 39973, 39974, 39973] 39973.25\n", "ddocent_full-simno sim_sample_nsnps\t[39981, 39980, 39980, 39980, 39980, 39979, 39980, 39977, 39981, 39979, 39980, 39980] 39979.75\n", "aftrrad-simno sim_sample_nsnps\t[40658, 40658, 40658, 40658, 40658, 40658, 40658, 40658, 40658, 40658, 40658, 40658] 40658.0\n", "------------------------------------------------------\n", "ipyrad-simlo sim_sample_nsnps\t[41126, 41126, 41126, 41126, 41126, 41126, 41126, 41126, 41126, 41126, 41126, 41126] 41126.0\n", "pyrad-simlo sim_sample_nsnps\t[43106, 43105, 43105, 43099, 43100, 43101, 43104, 43101, 43103, 43106, 43106, 43103] 43103.25\n", "stacks_ungapped-simlo sim_sample_nsnps\t[42878, 42825, 42865, 42829, 42921, 42896, 42910, 42819, 42825, 42861, 42865, 42806] 42858.3333333\n", "stacks_gapped-simlo sim_sample_nsnps\t[6664, 6634, 6642, 6619, 6645, 6637, 6645, 6646, 6646, 6649, 6646, 6638] 6642.58333333\n", "stacks_defualt-simlo sim_sample_nsnps\t[31446, 31372, 31298, 30969, 30944, 30881, 30741, 30662, 30184, 30149, 30129, 29904] 30723.25\n", "ddocent_filt-simlo sim_sample_nsnps\t[39879, 39877, 39878, 39875, 39879, 39876, 39879, 39876, 39879, 39877, 39879, 39875] 39877.4166667\n", "ddocent_full-simlo sim_sample_nsnps\t[39889, 39887, 39888, 39887, 39890, 39889, 39888, 39887, 39898, 39893, 39893, 39888] 39889.75\n", "aftrrad-simlo sim_sample_nsnps\t[40007, 40007, 40007, 40007, 40007, 40007, 40007, 40007, 40007, 40007, 40007, 40007] 40007.0\n", "------------------------------------------------------\n", "ipyrad-simhi sim_sample_nsnps\t[40402, 40402, 40402, 40402, 40402, 40399, 40402, 40402, 40400, 40400, 40400, 40400] 40401.0833333\n", "pyrad-simhi sim_sample_nsnps\t[42421, 42421, 42417, 42423, 42416, 42421, 42421, 42421, 42427, 42423, 42425, 42423] 42421.5833333\n", "stacks_ungapped-simhi sim_sample_nsnps\t[42007, 42161, 42001, 42096, 42063, 42034, 42104, 42068, 41959, 41942, 41970, 42002] 42033.9166667\n", "stacks_gapped-simhi sim_sample_nsnps\t[250, 250, 247, 250, 250, 250, 250, 250, 250, 250, 246, 243] 248.833333333\n", "stacks_defualt-simhi sim_sample_nsnps\t[30282, 30375, 30183, 29875, 29828, 29762, 29663, 29607, 29029, 28982, 28947, 28822] 29612.9166667\n", "ddocent_filt-simhi sim_sample_nsnps\t[40008, 40010, 40003, 40003, 40008, 40008, 40002, 40001, 40005, 40010, 40004, 40002] 40005.3333333\n", "ddocent_full-simhi sim_sample_nsnps\t[40041, 40045, 40041, 40049, 40054, 40057, 40043, 40041, 40053, 40060, 40054, 40054] 40049.3333333\n", "aftrrad-simhi sim_sample_nsnps\t[39027, 39027, 39027, 39027, 39027, 39027, 39027, 39027, 39027, 39027, 39027, 39027] 39027.0\n", "------------------------------------------------------\n", "ipyrad-simlarge sim_sample_nsnps\t[399293, 399339, 399224, 399319, 399355, 399039, 399259, 399070, 398377, 398531, 398376, 398234] 398951.333333\n", "pyrad-simlarge sim_sample_nsnps\t[418730, 418786, 418656, 418768, 418782, 418459, 418659, 418481, 417726, 417896, 417747, 417623] 418359.416667\n", "stacks_ungapped-simlarge sim_sample_nsnps\t[416856, 416936, 416890, 416846, 416861, 416524, 416844, 416795, 415751, 416064, 415945, 415788] 416508.333333\n", "stacks_gapped-simlarge sim_sample_nsnps\t[416856, 416936, 416890, 416846, 416861, 416524, 416844, 416795, 415751, 416064, 415945, 415788] 416508.333333\n", "stacks_defualt-simlarge sim_sample_nsnps\t[306434, 306316, 304795, 300499, 300565, 300050, 299270, 297832, 292556, 292370, 291639, 289438] 298480.333333\n", "ddocent_filt-simlarge sim_sample_nsnps\t[366270, 366351, 364928, 363394, 366352, 366085, 365000, 363294, 366128, 366304, 364722, 362908] 365144.666667\n", "ddocent_full-simlarge sim_sample_nsnps\t[381136, 381212, 381074, 381093, 381176, 380907, 381106, 380897, 380142, 380310, 380280, 380121] 380787.833333\n", "aftrrad-simlarge sim_sample_nsnps\t[336112, 336112, 336112, 336112, 336112, 336112, 336112, 336112, 336112, 336112, 336112, 336112] 336112.0\n", "------------------------------------------------------\n", "ipyrad-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41893] 3491.08333333\n", "pyrad-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 43852] 3654.58333333\n", "stacks_ungapped-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 1362, 42406] 3649.33333333\n", "stacks_gapped-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 1362, 42406] 3649.33333333\n", "stacks_defualt-simno sim_snp_cov\t[469, 327, 520, 840, 128, 134, 348, 1715, 920, 1063, 4497, 23829] 2899.16666667\n", "ddocent_filt-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 39965] 3331.16666667\n", "ddocent_full-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 9, 39970] 3331.75\n", "aftrrad-simno sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40658] 3388.16666667\n", "------------------------------------------------------\n", "ipyrad-simlo sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41126] 3427.16666667\n", "pyrad-simlo sim_snp_cov\t[1, 0, 1, 1, 1, 0, 0, 2, 1, 2, 19, 43081] 3592.41666667\n", "stacks_ungapped-simlo sim_snp_cov\t[28, 36, 42, 67, 12, 2, 20, 145, 150, 237, 2306, 40279] 3610.33333333\n", "stacks_gapped-simlo sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 3, 0, 6, 233, 6422] 555.333333333\n", "stacks_defualt-simlo sim_snp_cov\t[472, 347, 539, 833, 143, 134, 397, 1754, 957, 1155, 4861, 22550] 2845.16666667\n", "ddocent_filt-simlo sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 39860] 3323.25\n", "ddocent_full-simlo sim_snp_cov\t[11, 0, 1, 2, 2, 0, 0, 2, 2, 1, 19, 39866] 3325.5\n", "aftrrad-simlo sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40007] 3333.91666667\n", "------------------------------------------------------\n", "ipyrad-simhi sim_snp_cov\t[0, 0, 0, 3, 0, 0, 0, 5, 0, 0, 3, 40394] 3367.08333333\n", "pyrad-simhi sim_snp_cov\t[1, 10, 6, 6, 1, 14, 0, 10, 7, 6, 60, 42337] 3538.16666667\n", "stacks_ungapped-simhi sim_snp_cov\t[58, 103, 112, 222, 54, 41, 50, 437, 403, 528, 3826, 37297] 3594.25\n", "stacks_gapped-simhi sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 236] 20.8333333333\n", "stacks_defualt-simhi sim_snp_cov\t[477, 350, 564, 862, 169, 166, 462, 1802, 1037, 1398, 5298, 20663] 2770.66666667\n", "ddocent_filt-simhi sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 39932] 3334.33333333\n", "ddocent_full-simhi sim_snp_cov\t[25, 3, 7, 11, 2, 2, 0, 8, 20, 7, 80, 39940] 3342.08333333\n", "aftrrad-simhi sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39027] 3252.25\n", "------------------------------------------------------\n", "ipyrad-simlarge sim_snp_cov\t[0, 22, 46, 786, 305, 175, 721, 5301, 5886, 8687, 39920, 346258] 34008.9166667\n", "pyrad-simlarge sim_snp_cov\t[0, 24, 50, 827, 319, 180, 761, 5535, 6183, 9158, 41802, 363123] 35663.5\n", "stacks_ungapped-simlarge sim_snp_cov\t[6, 25, 56, 819, 317, 201, 880, 5515, 6288, 10290, 52091, 350753] 35603.4166667\n", "stacks_gapped-simlarge sim_snp_cov\t[6, 25, 56, 819, 317, 201, 880, 5515, 6288, 10290, 52091, 350753] 35603.4166667\n", "stacks_defualt-simlarge sim_snp_cov\t[4983, 3452, 5104, 8857, 1725, 1881, 5371, 20413, 12908, 18084, 60319, 194817] 28159.5\n", "ddocent_filt-simlarge sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37780, 330513] 30691.0833333\n", "ddocent_full-simlarge sim_snp_cov\t[3, 3, 53, 758, 299, 164, 696, 5106, 5745, 8397, 37784, 330563] 32464.25\n", "aftrrad-simlarge sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 336112] 28009.3333333\n", "------------------------------------------------------\n" ] } ], "source": [ "for statname, stat in {\"sim_loc_cov\":sim_loc_cov, \"sim_snp_cov\":sim_snp_cov,\\\n", " \"sim_sample_nsnps\":sim_sample_nsnps, \"sim_sample_nlocs\":sim_sample_nlocs}.items():\n", "\n", " for sim in [\"-simno\", \"-simlo\", \"-simhi\", \"-simlarge\"]:\n", " for prog in [\"ipyrad\", \"pyrad\", \"stacks_ungapped\", \"stacks_gapped\", \"stacks_defualt\", \"ddocent_filt\", \"ddocent_full\", \"aftrrad\"]:\n", " try:\n", " print(prog+sim + \" \" + statname + \"\\t\"),\n", " print(stat[prog + sim]),\n", " print(np.mean(stat[prog + sim]))\n", " except:\n", " print(\"No {} stats for {}\".format(statname, prog + sim))\n", " print(\"------------------------------------------------------\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pairwise difference and PCA plots for each simulation treatment" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ipyrad-simno /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simno/simno_outfiles/simno-biallelic.recode.vcf\n", "pyrad-simno /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simno/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simno /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simno/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simno /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simno/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simno /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simno/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simno /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simno /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simno /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simno/Formatting/simno-biallelic.recode.vcf\n", "ipyrad-simlo /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlo/simlo_outfiles/simlo-biallelic.recode.vcf\n", "pyrad-simlo /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlo/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simlo /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlo/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simlo /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlo/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simlo /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlo/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simlo /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simlo /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simlo /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlo/Formatting/simlo-biallelic.recode.vcf\n", "ipyrad-simhi /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simhi/simhi_outfiles/simhi-biallelic.recode.vcf\n", "pyrad-simhi /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simhi/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simhi /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simhi/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simhi /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simhi/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simhi /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simhi/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simhi /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simhi /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simhi /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simhi/Formatting/simhi-biallelic.recode.vcf\n", "ipyrad-simlarge /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlarge/simlarge_outfiles/simlarge-biallelic.recode.vcf\n", "pyrad-simlarge /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlarge/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simlarge /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlarge/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simlarge /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlarge/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simlarge /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlarge/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simlarge /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simlarge /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simlarge /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlarge/Formatting/simlarge-biallelic.recode.vcf\n" ] } ], "source": [ "## Load the calldata into a dict so we don't have to keep loading and reloading it\n", "calldata = {}\n", "for sim in [\"-simno\", \"-simlo\", \"-simhi\", \"-simlarge\"]:\n", " for prog in [\"ipyrad\", \"pyrad\", \"stacks_ungapped\", \"stacks_gapped\", \\\n", " \"stacks_defualt\", \"ddocent_filt\", \"ddocent_full\", \"aftrrad\"]:\n", " print(\"{}\".format(prog+sim)),\n", " print(\"{}\".format(sim_vcf_dict[prog+sim]))\n", " c = vcfnp.calldata_2d(sim_vcf_dict[prog+sim], verbose=False).view(np.recarray)\n", " calldata[prog+sim] = c" ] }, { "cell_type": "code", "execution_count": 617, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ True True True True False False False False False False False False]\n" ] } ], "source": [ "pop1 = [\"1A_0\", \"1B_0\", \"1C_0\", \"1D_0\"]\n", "pop2 = [\"2E_0\", \"2F_0\", \"2G_0\", \"2H_0\"]\n", "pop3 = [\"3I_0\", \"3J_0\", \"3K_0\", \"3L_0\"]\n", "sim_sample_names = pop1 + pop2 + pop3\n", "flt = np.in1d(np.array(sim_sample_names), pop1)\n", "print(flt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot PCAs for each assembler and each simulated datatype" ] }, { "cell_type": "code", "execution_count": 619, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing - -simno\n", "Doing - -simlo\n", "Doing - -simhi\n", "Doing - -simlarge\n" ] }, { "data": { "image/png": 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u7o69e/fCzc0Nq1evBlByec+ePXsQGRmJ77//HvPmzYMBe1FGLTY2VrqRX2VW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\n66ZMmYKPP/4Y06dPl7KgsLAQAODh4YGUlJRKWVmTkpIS2Nvb49KlS3j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BQggUFhYiNze3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BHyBoCZyBwAvrRp0yZ9+OGHkqRt27Zp3rx5evvtt/1cFRAaXC6X7JPsKr6i+OdGjiRZJGeUU8VX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\n5NdERESoqKhIF110k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ExsaqPbyutD///BP29vY4f/48Pv30Uy1XRlT1mDm6V/pNT6WtXLkSb775JgYP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\nQ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+fWRnZ3MKiVrK0tAFlLt58yYSEhLg6OiIrKwsafLI5s2bIzs7G0DZrNavvvqq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\nenq6UlNT9eyzz/rWkTdA6CJzyBzATFYcV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pn8Wj2adSqeDg4IBnn332sVsIDxs2TLouTlhYGNq2bauTfc2bN5fGbEDRtUsm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W7d2jvu8Xj04Ycf6s0339SePXt07733BrxQAOGNvAFgJjIHgJnIHMA66uvrVbCqQItXLVZ1fbVi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\n/VsNH6L6q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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Some housekeeping with sample names to make the PCA plots prettier\n", "pop1 = [\"1A_0\", \"1B_0\", \"1C_0\", \"1D_0\"]\n", "pop2 = [\"2E_0\", \"2F_0\", \"2G_0\", \"2H_0\"]\n", "pop3 = [\"3I_0\", \"3J_0\", \"3K_0\", \"3L_0\"]\n", "pops = {\"pop1\":pop1, \"pop2\":pop2, \"pop3\":pop3}\n", "pop_colors = {\"pop1\":\"r\", \"pop2\":\"b\", \"pop3\":\"g\"}\n", "sim_sample_names = pop1 + pop2 + pop3\n", "\n", "for sim in [\"-simno\", \"-simlo\", \"-simhi\", \"-simlarge\"]:\n", "#for sim in [\"-simhi\"]:\n", " print(\"Doing - {}\".format(sim))\n", " f, axarr = plt.subplots(2, 4, figsize=(16,8), dpi=1000)\n", " axarr = [a for b in axarr for a in b]\n", "\n", " for prog, ax in zip([\"ipyrad\", \"pyrad\", \"stacks_ungapped\", \"stacks_gapped\", \\\n", " \"stacks_defualt\", \"ddocent_filt\", \"ddocent_full\", \"aftrrad\"], axarr):\n", " ## Annoying debug messages\n", " #print(\"{}\".format(prog+sim)),\n", " #print(\"{}\".format(sim_vcf_dict[prog+sim]))\n", "\n", " coords1, model1 = getPCA(calldata[prog+sim])\n", "\n", " x = coords1[:, 0]\n", " y = coords1[:, 1]\n", "\n", " ax.scatter(x, y, marker='o')\n", " ax.set_xlabel('PC%s (%.1f%%)' % (1, model1.explained_variance_ratio_[0]*100))\n", " ax.set_ylabel('PC%s (%.1f%%)' % (2, model1.explained_variance_ratio_[1]*100))\n", "\n", " for pop in pops.keys():\n", " flt = np.in1d(np.array(sim_sample_names), pops[pop])\n", " ax.plot(x[flt], y[flt], marker='o', linestyle=' ', color=pop_colors[pop], label=pop, markersize=10, mec='k', mew=.5)\n", " \n", " ax.set_title(prog+sim, style=\"italic\")\n", " ax.axison = True\n", " f.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot pairwise distances for each assembler and each simulated datatype" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing - -simno\n", "ipyrad-simno /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simno/simno_outfiles/simno-biallelic.recode.vcf\n", "pyrad-simno /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simno/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simno /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simno/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simno /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simno/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simno /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simno/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simno /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simno /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simno /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simno/Formatting/simno-biallelic.recode.vcf\n", "Doing - -simlo\n", "ipyrad-simlo /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlo/simlo_outfiles/simlo-biallelic.recode.vcf\n", "pyrad-simlo /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlo/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simlo /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlo/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simlo /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlo/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simlo /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlo/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simlo /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simlo /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simlo /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlo/Formatting/simlo-biallelic.recode.vcf\n", "Doing - -simhi\n", "ipyrad-simhi /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simhi/simhi_outfiles/simhi-biallelic.recode.vcf\n", "pyrad-simhi /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simhi/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simhi /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simhi/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simhi /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simhi/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simhi /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simhi/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simhi /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simhi /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simhi /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simhi/Formatting/simhi-biallelic.recode.vcf\n", "Doing - -simlarge\n", "ipyrad-simlarge /home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlarge/simlarge_outfiles/simlarge-biallelic.recode.vcf\n", "pyrad-simlarge /home/iovercast/manuscript-analysis/pyrad/SIMDATA/simlarge/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n", "stacks_ungapped-simlarge /home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simlarge/batch_1-biallelic.recode.vcf\n", "stacks_gapped-simlarge /home/iovercast/manuscript-analysis/stacks/SIMDATA/gapped/simlarge/batch_1-biallelic.recode.vcf\n", "stacks_defualt-simlarge /home/iovercast/manuscript-analysis/stacks/SIMDATA/default/simlarge/batch_1-biallelic.recode.vcf\n", "ddocent_filt-simlarge /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/Final.recode-biallelic.recode.vcf\n", "ddocent_full-simlarge /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/TotalRawSNPs-biallelic.recode.vcf\n", "aftrrad-simlarge /home/iovercast/manuscript-analysis/aftrRAD/SIMDATA/simlarge/Formatting/simlarge-biallelic.recode.vcf\n" ] }, { "data": { "image/png": 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4uKP+XlJSot27d6tdu3YutOqIk+lfXnrpJbVq1UqvvfbaUdOq5qEk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\nOa4+V1GQVhEbG6s//elP+uijj7Rt2zY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677zzjmu+q666SsuXLz+hZaakpKhRo0Y655xzTmj+00ltrxtwsnnw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\n++23x1z2u+++W2U7s7KyVLduXdWrV8/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Lzmi+Fi1aaOXKlWe1zKSkJF122WWqX79+udMHDRp00vy7mHg73YDz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\nIAUAAKBCkAIAAFAhSAEAAKgQpAAAAFQIUgAAACo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AWxBIAQAAAAC2IJACAAAAAGxBIAUAAAAA2IJACgAAAACwBYEUAAAA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\n66u+ffvK7XardevWKi4u1lNPPaU5c+a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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for sim in [\"-simno\", \"-simlo\", \"-simhi\", \"-simlarge\"]:\n", "#for sim in [\"-simhi\"]:\n", " print(\"Doing - {}\".format(sim))\n", " f, axarr = plt.subplots(2, 4, figsize=(16,8), dpi=1000)\n", " axarr = [a for b in axarr for a in b]\n", "\n", " for prog, ax in zip([\"ipyrad\", \"pyrad\", \"stacks_ungapped\", \"stacks_gapped\", \\\n", " \"stacks_defualt\", \"ddocent_filt\", \"ddocent_full\", \"aftrrad\"], axarr):\n", " print(\"{}\".format(prog+sim)),\n", " print(\"{}\".format(sim_vcf_dict[prog+sim]))\n", "\n", " ## Calculate pairwise distances\n", " dist = getDistances(calldata[prog+sim])\n", "\n", " ## Doing it this way works, but allel uses imshow internally which rasterizes the image\n", " #allel.plot.pairwise_distance(dist, labels=None, ax=ax, colorbar=False)\n", "\n", " ## Create the pcolormesh by hand\n", " dat = ensure_square(dist)\n", " \n", " ## for some reason np.flipud(dat) is chopping off one row of data\n", " p = ax.pcolormesh(np.arange(0,len(dat[0])), np.arange(0,len(dat[0])), dat,\\\n", " cmap=\"jet\", vmin=np.min(dist), vmax=np.max(dist))\n", " ## Clip all heatmaps to actual sample size\n", " p.axes.axis(\"tight\")\n", " \n", " ax.set_title(prog+sim, style=\"italic\")\n", " ax.axison = False\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Go through and pull in more fine grained results\n", "What we want to know is total number of loci recovered (not just variable loci). First we'll create some dictionaries just like above, but we'll call them *_full_* to indicate that they include monomorphic sites. Because snps don't occur in monomorphic sites kind of by definition, we only really are iterested in the depth across loci and the number of loci recovered per sample.\n" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing - ipyrad-simlarge\tDoing - pyrad-simhi\tDoing - pyrad-simlo\tDoing - ddocent_full-simlarge\tDoing - ddocent_filt-simlo\tDoing - stacks_gapped-simno\tDoing - stacks_ungapped-simlarge\tDoing - aftrrad-simno\tDoing - aftrrad-simlo\tDoing - ddocent_filt-simno\tDoing - stacks_gapped-simlo\tDoing - ipyrad-simlo\tDoing - stacks_defualt-simhi\tDoing - ddocent_filt-simlarge\tDoing - stacks_gapped-simhi\tDoing - ipyrad-simno\tDoing - aftrrad-simlarge\tDoing - stacks_ungapped-simhi\tDoing - aftrrad-simhi\tDoing - stacks_ungapped-simno\tDoing - stacks_defualt-simlarge\tDoing - stacks_ungapped-simlo\tDoing - ipyrad-simhi\tDoing - pyrad-simno\tDoing - ddocent_full-simhi\tDoing - stacks_defualt-simno\tDoing - pyrad-simlarge\tDoing - ddocent_full-simlo\tDoing - stacks_gapped-simlarge\tDoing - stacks_defualt-simlo\tDoing - ddocent_filt-simhi\tDoing - ddocent_full-simno\t" ] } ], "source": [ "sim_full_loc_cov = collections.OrderedDict()\n", "sim_full_sample_nlocs = collections.OrderedDict()\n", "## Try just doing them all the same\n", "for prog, filename in sim_vcf_dict.items():\n", " print(\"Doing - {}\\t\".format(prog)),\n", " sim_full_loc_cov[prog] = []\n", " sim_full_sample_nlocs[prog] = []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ipyrad simulated results" ] }, { "cell_type": "code", "execution_count": 407, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing - simno\tmean sample coverage - 10000.0\tmin/max - 10000/10000\t\n", "Doing - simlo\tmean sample coverage - 10000.0\tmin/max - 10000/10000\t\n", "Doing - simhi\tmean sample coverage - 9999.91666667\tmin/max - 9999/10000\t\n", "Doing - simlarge\tmean sample coverage - 96942.6666667\tmin/max - 96917/96986\t\n", "[('ipyrad-simlarge', [0, 27, 61, 600, 158, 80, 274, 1851, 1742, 2385, 10113, 82659]), ('ipyrad-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad-simhi', [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 9998]), ('ipyrad_simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad_simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad_simhi', [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 9998]), ('ipyrad_simlarge', [0, 27, 61, 600, 158, 80, 274, 1851, 1742, 2385, 10113, 82659])]\n", "[('ipyrad-simlarge', [96923, 96959, 96931, 96986, 96972, 96933, 96958, 96945, 96917, 96928, 96926, 96934]), ('ipyrad-simlo', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('ipyrad-simno', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('ipyrad-simhi', [10000, 10000, 10000, 10000, 10000, 9999, 10000, 10000, 10000, 10000, 10000, 10000])]\n" ] } ], "source": [ "## ipyrad stats\n", "IPYRAD_SIMOUT=os.path.join(IPYRAD_DIR, \"SIMDATA/\")\n", "for sim in [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]:\n", " print(\"Doing - {}\\t\".format(sim)),\n", " simstring = \"ipyrad-\" + sim\n", " simdir = os.path.join(IPYRAD_SIMOUT, sim)\n", " statsfile = simdir + \"/{}_outfiles/{}_stats.txt\".format(sim, sim)\n", " infile = open(statsfile).readlines()\n", " sample_coverage = [int(x.strip().split()[1]) for x in infile[20:32]]\n", " #print(sample_coverage)\n", " print(\"mean sample coverage - {}\\t\".format(np.mean(sample_coverage))),\n", " print(\"min/max - {}/{}\\t\".format(np.min(sample_coverage), np.max(sample_coverage)))\n", " sim_full_sample_nlocs[simstring] = sample_coverage\n", " \n", " nmissing = [int(x.strip().split()[1]) for x in infile[38:50]]\n", " sim_full_loc_cov[simstring] = nmissing\n", "\n", "## Just look at the ones we care about for ipyrad\n", "print([(x,y) for x,y in sim_full_loc_cov.items() if \"ipyrad\" in x])\n", "print([(x,y) for x,y in sim_full_sample_nlocs.items() if \"ipyrad\" in x])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pyrad simulated results" ] }, { "cell_type": "code", "execution_count": 406, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing - simno\tmean sample coverage - 10000.0\tmin/max - 10000/10000\t\n", "Doing - simlo\tmean sample coverage - 10000.0\tmin/max - 10000/10000\t\n", "Doing - simhi\tmean sample coverage - 9999.91666667\tmin/max - 9999/10000\t\n", "Doing - simlarge\tmean sample coverage - 96943.6666667\tmin/max - 96918/96987\t\n", "[('ipyrad-simlarge', []), ('pyrad-simhi', [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 9998]), ('pyrad-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad-simlo', []), ('ipyrad-simno', []), ('ipyrad-simhi', []), ('pyrad-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('pyrad-simlarge', [0, 27, 61, 600, 158, 80, 274, 1851, 1742, 2385, 10113, 82660]), ('ipyrad_simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad_simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('ipyrad_simhi', [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 9998]), ('ipyrad_simlarge', [0, 27, 61, 600, 158, 80, 274, 1851, 1742, 2385, 10113, 82659]), ('pyrad_simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('pyrad_simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10000]), ('pyrad_simhi', [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 9998]), ('pyrad_simlarge', [0, 27, 61, 600, 158, 80, 274, 1851, 1742, 2385, 10113, 82660])]\n", "[('ipyrad-simlarge', [96923, 96959, 96931, 96986, 96972, 96933, 96958, 96945, 96917, 96928, 96926, 96934]), ('pyrad-simhi', [10000, 10000, 10000, 10000, 10000, 9999, 10000, 10000, 10000, 10000, 10000, 10000]), ('pyrad-simlo', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('ipyrad-simlo', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('ipyrad-simno', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('ipyrad-simhi', [10000, 10000, 10000, 10000, 10000, 9999, 10000, 10000, 10000, 10000, 10000, 10000]), ('pyrad-simno', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('pyrad-simlarge', [96924, 96960, 96932, 96987, 96973, 96934, 96959, 96946, 96918, 96929, 96927, 96935]), ('pyrad_simno', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('pyrad_simlo', [10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10000]), ('pyrad_simhi', [10000, 10000, 10000, 10000, 10000, 9999, 10000, 10000, 10000, 10000, 10000, 10000]), ('pyrad_simlarge', [96924, 96960, 96932, 96987, 96973, 96934, 96959, 96946, 96918, 96929, 96927, 96935])]\n" ] } ], "source": [ "## ipyrad stats\n", "PYRAD_SIMOUT=os.path.join(PYRAD_DIR, \"SIMDATA/\")\n", "for sim in [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]:\n", " simstring = \"pyrad-\"+sim\n", " print(\"Doing - {}\\t\".format(sim)),\n", " simdir = os.path.join(PYRAD_SIMOUT, sim)\n", " statsfile = simdir + \"/stats/c85d6m2p3H3N3.stats\"\n", " infile = open(statsfile).readlines()\n", " sample_coverage = [int(x.strip().split()[1]) for x in infile[8:20]]\n", " print(\"mean sample coverage - {}\\t\".format(np.mean(sample_coverage))),\n", " print(\"min/max - {}/{}\\t\".format(np.min(sample_coverage), np.max(sample_coverage)))\n", " sim_full_sample_nlocs[simstring] = sample_coverage\n", " \n", " nmissing = [0] + [int(x.strip().split()[1]) for x in infile[26:37]]\n", " sim_full_loc_cov[simstring] = nmissing\n", "\n", "## Just look at the ones we care about for pyrad\n", "print([(x,y) for x,y in sim_full_loc_cov.items() if \"pyrad\" in x])\n", "print([(x,y) for x,y in sim_full_sample_nlocs.items() if \"pyrad\" in x])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## stacks simulated results" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('stacks_gapped-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 336, 9652]), ('stacks_ungapped-simlarge', [49, 29, 67, 593, 156, 92, 311, 1840, 1767, 2674, 12583, 79747]), ('stacks_gapped-simlo', [19, 5, 1, 3, 1, 1, 1, 1, 1, 15, 348, 9623]), ('stacks_defualt-simhi', []), ('stacks_gapped-simhi', [241, 28, 25, 28, 5, 1, 4, 29, 21, 39, 506, 9387]), ('stacks_ungapped-simhi', [1322, 174, 123, 148, 19, 13, 21, 148, 112, 137, 956, 8611]), ('stacks_ungapped-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 336, 9652]), ('stacks_defualt-simlarge', []), ('stacks_ungapped-simlo', [589, 79, 54, 56, 7, 2, 8, 52, 50, 67, 584, 9231]), ('stacks_defualt-simno', []), ('stacks_gapped-simlarge', [49, 29, 67, 593, 156, 92, 311, 1840, 1767, 2674, 12583, 79747]), ('stacks_defualt-simlo', []), ('stacks_default-simno', [3307, 709, 652, 748, 85, 66, 175, 727, 332, 353, 1349, 6446]), ('stacks_default-simlo', [3868, 754, 699, 773, 92, 73, 200, 749, 354, 387, 1488, 6207]), ('stacks_default-simhi', [4592, 837, 740, 833, 105, 90, 239, 783, 390, 465, 1660, 5842]), ('stacks_default-simlarge', [33633, 7241, 6542, 7614, 1102, 1025, 2505, 8307, 4487, 5750, 17674, 53253])]\n", "[('stacks_gapped-simno', [11381, 11435, 11446, 11406, 11400, 11432, 11468, 11442, 11393, 11438, 11466, 11365]), ('stacks_ungapped-simlarge', [110839, 110710, 110603, 110764, 110698, 110572, 110670, 110795, 110708, 110617, 110649, 110860]), ('stacks_gapped-simlo', [11365, 11399, 11420, 11375, 11381, 11395, 11435, 11423, 11354, 11408, 11428, 11343]), ('stacks_defualt-simhi', []), ('stacks_gapped-simhi', [11311, 11365, 11389, 11342, 11332, 11371, 11406, 11400, 11336, 11382, 11424, 11320]), ('stacks_ungapped-simhi', [11371, 11444, 11438, 11413, 11403, 11426, 11466, 11451, 11382, 11428, 11472, 11355]), ('stacks_ungapped-simno', [11381, 11435, 11446, 11406, 11400, 11432, 11468, 11442, 11393, 11438, 11466, 11365]), ('stacks_defualt-simlarge', []), ('stacks_ungapped-simlo', [11388, 11426, 11458, 11407, 11409, 11432, 11471, 11444, 11384, 11434, 11469, 11365]), ('stacks_defualt-simno', []), ('stacks_gapped-simlarge', [110839, 110710, 110603, 110764, 110698, 110572, 110670, 110795, 110708, 110617, 110649, 110860]), ('stacks_defualt-simlo', []), ('stacks_default-simno', [11381, 11434, 11372, 11250, 11132, 10993, 10974, 10982, 11010, 10897, 10824, 10702]), ('stacks_default-simlo', [11385, 11426, 11388, 11256, 11155, 11010, 10991, 10997, 11013, 10905, 10843, 10724]), ('stacks_default-simhi', [11371, 11443, 11368, 11271, 11155, 11017, 11002, 11025, 11022, 10924, 10878, 10739]), ('stacks_gapped-large', []), ('stacks_ungapped-large', []), ('stacks_default-large', []), ('stacks_default-simlarge', [110835, 110690, 109833, 109066, 108063, 106263, 105795, 106033, 106842, 104942, 104216, 104106])]\n" ] } ], "source": [ "import gzip\n", "\n", "## stacks stats\n", "STACKS_SIMOUT=os.path.join(STACKS_DIR, \"SIMDATA/\")\n", "STACKS_GAP_SIMOUT=os.path.join(STACKS_SIMOUT, \"gapped/\")\n", "STACKS_UNGAP_SIMOUT=os.path.join(STACKS_SIMOUT, \"ungapped/\")\n", "STACKS_DEFAULT_SIMOUT=os.path.join(STACKS_SIMOUT, \"default/\")\n", "\n", "for dir in [STACKS_GAP_SIMOUT, STACKS_UNGAP_SIMOUT, STACKS_DEFAULT_SIMOUT]:\n", " stacks_method = dir.split(\"/\")[-2]\n", " for sim in [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]:\n", " #print(\"Doing - {}-{}\".format(stacks_method, sim)),\n", " simstring = \"stacks_\"+stacks_method+\"-\"+sim\n", " try:\n", " simdir = os.path.join(dir, sim)\n", " lines = open(\"{}/batch_1.haplotypes.tsv\".format(simdir)).readlines()\n", " cnts = [int(field.strip().split(\"\\t\")[1]) for field in lines[1:]]\n", " sim_full_loc_cov[simstring] = [cnts.count(i) for i in range(1,13)]\n", " except Exception as inst:\n", " print(\"loc_cov - {} - {}\".format(inst, simdir))\n", "\n", " try:\n", " sim_full_sample_nlocs[simstring] = []\n", " samp_haps = glob.glob(\"{}/*matches*\".format(simdir))\n", " for f in samp_haps:\n", " lines = gzip.open(f).readlines()\n", " sim_full_sample_nlocs[simstring].append(len(lines) - 1)\n", " except Exception as inst:\n", " print(\"sample_nlocs - {} - {}\".format(inst, simdir))\n", "## Just look at the stacks results to reduce the clutter\n", "print([(x,y) for x,y in sim_full_loc_cov.items() if \"stacks\" in x])\n", "print([(x,y) for x,y in sim_full_sample_nlocs.items() if \"stacks\" in x])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AftrRAD simulated results\n", "AftrRAD doesn't natively support vcf. the vcf files we made\n", "are in sim\\*/Formatting/sim\\*.vcf\n", "\n", "It's not straightforward how to get the locations for snps\n", "from the output files. aftrRAD does calculate and write these out\n", "as part of the Genotype.pl phase to a file called __/TempFiles/SNPLocationsToPlot.txt__\n", "\n", "It's also not straightforward how to get the number of loci recovered for each invidual.\n", "You have to total up the counts from two different files for each individual. The\n", "`Outputs/Genotypes` directory has two files, one for `Haplotypes` and one for `Monomorphics`.\n", "The haplotypes file is a giant matrix of concatenated snps per locus so you have to \n", "search through each column to count up all the `NA`'s (missing data per locus per individual).\n", "Then you have to count up the number of non-zero monomorphic sites." ] }, { "cell_type": "code", "execution_count": 192, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Doing - aftrrad-simlarge mean sample coverage aftrrad-simlarge - 100172.75 min/max - 100132/100249\n", "Locus coverage\n", "[('aftrrad-simno', [0, 1, 17, 29, 5, 1, 3, 27, 15, 9, 36, 9910]), ('aftrrad-simlo', [0, 3, 20, 37, 7, 0, 4, 34, 18, 16, 58, 9870]), ('aftrrad-simlarge', [0, 19, 226, 838, 213, 117, 332, 2078, 1878, 2529, 10526, 81656]), ('aftrrad-simhi', [0, 11, 35, 57, 5, 1, 5, 53, 30, 22, 129, 9761])]\n", "Sample nloci\n", "[('aftrrad-simno', [10051, 10051, 10051, 10051, 10050, 10050, 10050, 10050, 10052, 10053, 10053, 10052]), ('aftrrad-simlo', [10059, 10060, 10058, 10059, 10059, 10059, 10059, 10058, 10061, 10062, 10062, 10059]), ('aftrrad-simlarge', [100138, 100141, 100137, 100135, 100141, 100134, 100137, 100132, 100244, 100244, 100249, 100241]), ('aftrrad-simhi', [10088, 10089, 10083, 10082, 10087, 10085, 10082, 10081, 10089, 10090, 10087, 10085])]\n" ] } ], "source": [ "AFTRRAD_SIMOUT=os.path.join(AFTRRAD_DIR, \"SIMDATA/\")\n", "for sim in [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]:\n", "#for sim in [\"simlo\"]:\n", " simstring = \"aftrrad-{}\".format(sim)\n", " print(\"Doing - {}\".format(simstring))\n", " simdir = os.path.join(AFTRRAD_SIMOUT, sim)\n", " ## The `17` in the file name is the min % of samples w/ data\n", " hapsfile = simdir + \"/Output/Genotypes/Haplotypes_17.All.txt\"\n", " monofile = simdir + \"/Output/Genotypes/Monomorphics_17.txt\"\n", "\n", " ## Set low_memory=False or else simlarge crashes pandas\n", " mono_df = pd.read_csv(monofile, delim_whitespace=True, header=0, low_memory=False)\n", " haps_df = pd.read_csv(hapsfile, delim_whitespace=True, header=0, index_col=0, low_memory=False)\n", "\n", " sample_coverage = {}\n", " ## Get the list of all the simulated sample names\n", " samplenames = mono_df.columns.values[1:]\n", " ## Do haplotypes file first because it is phased so there are 2 rows per sample\n", " ## so we end up double counting NA's. If you read the # of monomorphics first\n", " ## then you have to figure out some way to prevent the double counting, here\n", " ## we just count twice and overwrite.\n", " for row in haps_df.itertuples(): \n", " sample_coverage[row[0].split(\"Individual\")[1]] = len(pd.Series(row).nonzero()[0])\n", "\n", " for sname in samplenames:\n", " ## nonzero returns a tuple of which we are only interested in the first element\n", " sample_coverage[sname] += len(mono_df[sname].nonzero()[0])\n", "\n", " ## To sort or not to sort the coverage_df?\n", " print(\"mean sample coverage - {}\".format(np.mean(sample_coverage.values()))),\n", " print(\"min/max - {}/{}\".format(np.min(sample_coverage.values()), np.max(sample_coverage.values())))\n", "\n", " ## Put them in the dict sorted by sample name\n", " sim_full_sample_nlocs[simstring] = [sample_coverage[x] for x in sorted(sample_coverage.keys())]\n", "\n", " ## Now do locus coverage\n", " ## For the same reason as above, do the haplotypes file first. We create the tmp_counts\n", " ## counter and then make a new counter with the index being 1/2 to account for the\n", " ## doublecounting of NaN in the haplotypes file.\n", " ## Also, note that annoyingly pandas reads in \"NA\" as float('nan') rather than string \"NA\"\n", " ## which took me a _while_ to figure out.\n", " hap_nonzero = collections.Counter(haps_df.count())\n", " tmp_counts = collections.Counter(hap_nonzero)\n", " hap_counts = collections.Counter()\n", " for x in tmp_counts:\n", " hap_counts.update({x/2:tmp_counts[x]})\n", "\n", " ## Get monomorphics\n", " ## Get the sum of non-zero elements per row (the fancy .ix slicing drops the \n", " ## first column which is the sequence)\n", " mono_nonzero = (mono_df.ix[:, 1:] != 0).astype(int).sum(axis=1)\n", " mono_counts = collections.Counter(mono_nonzero)\n", "\n", " tot_counts = hap_counts + mono_counts\n", " ## Collections are unordered, and also any coverage level with no count\n", " ## will not be in the collection, so we have to order it and insert zeros at the appropriate locations\n", " dat = []\n", " for i in xrange(1,13):\n", " try:\n", " dat.append(tot_counts[i])\n", " except:\n", " dat.append(0)\n", " sim_full_loc_cov[simstring] = dat\n", "\n", "print(\"Locus coverage\")\n", "print([(x,y) for x,y in sim_full_loc_cov.items() if \"aftrrad\" in x])\n", "print(\"Sample nloci\")\n", "print([(x,y) for x,y in sim_full_sample_nlocs.items() if \"aftrrad\" in x])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## dDocent simulated results\n", "~~Here's what's weird. The dDocent reference.fasta file which is generated by the cd-hit clustering looks like it contains the right number of clusters. Similarly, if you use samtools to view the sample `-RG.bam`, and the `mapped.bed` file has 10000 loci as well. But if you look at the Contigs that make it to the final vcf files (either TotalRawSNPs or Final.recode) there are ~100+ contigs missing. I'm sure freebayes is filtering loci, but i diff'd the loci in the bed file and the vcf, and looked at the sequences of one of the missing loci in the cat-RRG.bam file and they look more or less normal. It's not the mapping quality flaq (-m), because all sim seqs have high quality mapping. None of the other settings in the dDocent script or the default freebayes settings seem particularly conspicuous.~~ Monomorphic loci obviously aren't going to end up in the final vcf. It is incredibly not straightforward how to actually get access to the monomorphic loci from the intermediary files that dDocent creates. The shortcuts that dDocent takes to run super fast mean it's really hard to get \"stacks\" of sequence data per locus (e.g. the ipyrad .loci file). \n", "\n", " grep Contig TotalRawSNPs.vcf | cut -f 1 | uniq -c | wc -l\n", " \n", "simno/simlo/simhi - 9887/9889/9890\n", "\n", "For simlarge there was a much bigger difference between the TotalRaw and Final.recode vcf files (98350/91620), TotalRaw recovers about 98.4% of true loci, but the final recode catches only about 92%.\n", "\n", "It's also not super straightforward how to get # loci per sample from the dDocent output, so \n", "I had to gin something up. Right now because it uses samtools to view the bam file and then does\n", "some shell manipulation, this is a little slow (20 seconds per simulation treatment), but tolerable.\n", "\n", "TODO: This is currently not tracking __which__ samples correspond with the number of loci recovered\n", "bcz the ddocent bam files use the weird naming scheme and I haven't back calculated the good names yet.\n", "\n", "This isn't exactly technically correct, because this is the counts of loci mapped for each individual,\n", "not the number of loci for each individual in the output!\n", "\n", "Also, this is a little slow. Takes about 30-40 minutes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing simno - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simno/simno_fastqs/\n", "This must be taken with a grain of salt because these aren't the actual counts you get out of the final data files.\n", "mean sample coverage - 10000.0\n", "min/max - 10000/10000\n", "Reading VCF\n", "Doing ddocent_full-simno\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Doing ddocent_filt-simno\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Locus coverage\n", "[('ddocent_full-simlarge', []), ('ddocent_filt-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886]), ('ddocent_filt-simlarge', []), ('ddocent_full-simhi', [0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 4, 9881]), ('ddocent_full-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simhi', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9881]), ('ddocent_full-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886])]\n", "Sample nloci\n", "[('ddocent_full-simlarge', []), ('ddocent_filt-simlo', []), ('ddocent_filt-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886]), ('ddocent_filt-simlarge', []), ('ddocent_full-simhi', []), ('ddocent_full-simlo', []), ('ddocent_filt-simhi', []), ('ddocent_full-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886])]\n", "Doing simlo - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlo/simlo_fastqs/\n", "This must be taken with a grain of salt because these aren't the actual counts you get out of the final data files.\n", "mean sample coverage - 10000.0\n", "min/max - 10000/10000\n", "Reading VCF\n", "Doing ddocent_full-simlo\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Doing ddocent_filt-simlo\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Locus coverage\n", "[('ddocent_full-simlarge', []), ('ddocent_filt-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886]), ('ddocent_filt-simlarge', []), ('ddocent_full-simhi', [0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 4, 9881]), ('ddocent_full-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simhi', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9881]), ('ddocent_full-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886])]\n", "Sample nloci\n", "[('ddocent_full-simlarge', []), ('ddocent_filt-simlo', [9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888]), ('ddocent_filt-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886]), ('ddocent_filt-simlarge', []), ('ddocent_full-simhi', []), ('ddocent_full-simlo', [9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888]), ('ddocent_filt-simhi', []), ('ddocent_full-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886])]\n", "Doing simhi - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/\n", "This must be taken with a grain of salt because these aren't the actual counts you get out of the final data files.\n", "mean sample coverage - 9998.75\n", "min/max - 9997/10000\n", "Reading VCF\n", "Doing ddocent_full-simhi\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Doing ddocent_filt-simhi\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 Locus coverage\n", "[('ddocent_full-simlarge', []), ('ddocent_filt-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886]), ('ddocent_filt-simlarge', []), ('ddocent_full-simhi', [0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 4, 9881]), ('ddocent_full-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simhi', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9881]), ('ddocent_full-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886])]\n", "Sample nloci\n", "[('ddocent_full-simlarge', []), ('ddocent_filt-simlo', [9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888]), ('ddocent_filt-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886]), ('ddocent_filt-simlarge', []), ('ddocent_full-simhi', [9885, 9885, 9886, 9887, 9888, 9888, 9886, 9886, 9886, 9887, 9887, 9888]), ('ddocent_full-simlo', [9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888]), ('ddocent_filt-simhi', [9885, 9885, 9885, 9885, 9885, 9885, 9883, 9884, 9884, 9885, 9885, 9885]), ('ddocent_full-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886])]\n", "Doing simlarge - /home/iovercast/manuscript-analysis/dDocent/SIMDATA/simlarge/simlarge_fastqs/\n", "This must be taken with a grain of salt because these aren't the actual counts you get out of the final data files.\n", "mean sample coverage - 96949.5\n", "min/max - 96919/96988\n", "Reading VCF\n", "Doing ddocent_full-simlarge\n", "0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 27000 28000 29000 30000 31000 32000 33000 34000 35000 36000 37000 38000 39000 40000 41000 42000 43000 44000 45000 46000 47000 48000 49000 50000 51000 52000 53000 54000 55000 56000 57000 58000 59000 60000 61000 62000 63000 64000 65000 66000 67000 68000 69000 70000 71000 72000 73000 74000 75000 76000 77000 78000 79000 80000 81000 82000 83000 84000 85000 86000 87000 88000 89000 90000 91000 92000 93000 94000 95000 96000 97000 10000 11000 12000 13000 14000 15000 16000 17000 18000 19000 20000 21000 22000 23000 24000 25000 26000 27000 28000 29000 30000 31000 32000 33000 34000 35000 36000 37000 38000 39000 40000 41000 42000 43000 44000 45000 46000 47000 48000 49000 50000 51000 52000 53000 54000 55000 56000 57000 58000 59000 60000 61000 62000 63000 64000 65000 66000 67000 68000 69000 70000 71000 72000 73000 74000 75000 76000 77000 78000 79000 80000 81000 82000 83000 84000 85000 86000 87000 88000 89000 90000 91000 Locus coverage\n", "[('ddocent_full-simlarge', [1, 2, 38, 434, 141, 71, 258, 1748, 1703, 2334, 9853, 81766]), ('ddocent_filt-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886]), ('ddocent_filt-simlarge', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9853, 81766]), ('ddocent_full-simhi', [0, 0, 0, 2, 0, 1, 0, 0, 1, 0, 4, 9881]), ('ddocent_full-simlo', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9888]), ('ddocent_filt-simhi', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 9881]), ('ddocent_full-simno', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9886])]\n", "Sample nloci\n", "[('ddocent_full-simlarge', [95609, 95639, 95606, 95654, 95650, 95609, 95639, 95625, 95492, 95499, 95494, 95502]), ('ddocent_filt-simlo', [9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888]), ('ddocent_filt-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886]), ('ddocent_filt-simlarge', [91091, 91119, 90741, 90312, 91116, 91065, 90744, 90308, 91090, 91095, 90682, 90212]), ('ddocent_full-simhi', [9885, 9885, 9886, 9887, 9888, 9888, 9886, 9886, 9886, 9887, 9887, 9888]), ('ddocent_full-simlo', [9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888, 9888]), ('ddocent_filt-simhi', [9885, 9885, 9885, 9885, 9885, 9885, 9883, 9884, 9884, 9885, 9885, 9885]), ('ddocent_full-simno', [9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886, 9886])]\n" ] } ], "source": [ "import subprocess\n", "\n", "DDOCENT_SIMOUT=os.path.join(DDOCENT_DIR, \"SIMDATA/\")\n", "for sim in [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]:\n", "#for sim in [\"simlo\"]:\n", " simdir = os.path.join(DDOCENT_SIMOUT, sim + \"/\" + sim + \"_fastqs/\")\n", " print(\"Doing {} - {}\".format(sim, simdir))\n", "\n", " ## This is hackish. You have to dip into the bam file to get locus counts that\n", " ## include counts for monomorphics. Note! that field 3 of the bam file is not\n", " ## the sequence data, but rather the dDocent mock-contig ('dDocent_Contig_3') \n", " ## the read maps to. So really this is kind of by proxy counting the number of loci.\n", " sample_coverage = []\n", " for samp in glob.glob(simdir + \"/*-RG.bam\"):\n", " cmd = \"{}samtools view {} | cut -f 3 | uniq | wc -l\".format(DDOCENT_DIR, samp)\n", " res = subprocess.check_output(cmd, shell=True)\n", " sample_coverage.append(int(res.strip()))\n", " print(\"This must be taken with a grain of salt because these aren't the actual counts you get out of the final data files.\")\n", " print(\"mean sample coverage - {}\".format(np.mean(sample_coverage)))\n", " print(\"min/max - {}/{}\".format(np.min(sample_coverage), np.max(sample_coverage)))\n", " \n", " print(\"Reading VCF\")\n", " vcf_filt = pd.read_csv(\"{}/Final.recode.vcf\".format(simdir), delim_whitespace=True, header=60, index_col=0)\n", " vcf_full = pd.read_csv(\"{}/TotalRawSNPs.vcf\".format(simdir), delim_whitespace=True, header=60, index_col=0)\n", "\n", " for vcf_string, vcf_df in zip([\"full\", \"filt\"], [vcf_full, vcf_filt]):\n", " simstring = \"ddocent_\" + vcf_string + \"-\" + sim\n", " print(\"Doing {}\".format(simstring))\n", " ## Sample coverage is the same for both conditions here, because they are based on the\n", " ## same raw data.\n", " idxs = set(vcf_df.index)\n", "\n", " ## Here we are going to simultaneously accumulate information about nloci per sample and locus coverage counts.\n", " c = []\n", " sample_cov_counter = collections.Counter()\n", " for i, idx in enumerate(idxs):\n", " ## Poor man's progress bar. For the impatient.\n", " if not i % 1000:\n", " print(i),\n", "\n", " ## Complicated song and dance here because if the locus only has one snp you have to do\n", " ## some bullshit to reshape the dataframe.\n", " tmp_df = vcf_df.loc[idx]\n", " if isinstance(tmp_df, pd.Series):\n", " tmp_df = tmp_df.to_frame().T\n", " \n", " ## The double apply is applying split() to all rows and columns to get the genotype data\n", " nonzero_samps = (tmp_df.iloc[:, 8:20].apply(lambda x: x.apply(lambda y: y.split(\":\")[0])) != \"./.\").\\\n", " astype(int).sum().nonzero()[0]\n", " sample_cov_counter.update(nonzero_samps)\n", " c.append(len(nonzero_samps))\n", " \n", " ## The double apply is applying split() to all rows and columns to get the genotype data\n", "# count = len((tmp_df.iloc[:, 8:20].\\\n", "# apply(lambda x: x.apply(lambda y: y.split(\":\")[0])) != \"./.\").astype(int).sum().nonzero()[0])\n", "# c.append(count)\n", " counts = collections.Counter(c)\n", "\n", " ## Fill in zero values that aren't in the locus counter\n", " dat = []\n", " for i in xrange(1,13):\n", " try:\n", " dat.append(counts[i])\n", " except:\n", " dat.append(0)\n", "\n", " sim_full_loc_cov[simstring] = dat\n", " sim_full_sample_nlocs[simstring] = sample_cov_counter.values()\n", "\n", "# Here's a different stupid way to do this...\n", "#\n", "# sample_counts = {}\n", "# sample_names = list(vcf.columns)[8:]\n", "# print(sample_names)\n", "# print(\"num loci - {}\".format(len(set(vcf.index))))\n", "# for name in sample_names:\n", "# print(name)\n", "# sample_counts[name] = 0\n", "# for locus in set(vcf.index):\n", "# snps = vcf[name][locus]\n", "# if any(map(lambda x: x.split(\":\")[0] != \"./.\", snps)):\n", "# sample_counts[name] += 1\n", "# else:\n", "# pass\n", "# #print(\"{} {} {}\".format(name, locus, snps))\n", "# print(sample_counts)\n", "\n", " print(\"Locus coverage\")\n", " print([(x,y) for x,y in sim_full_loc_cov.items() if \"ddocent\" in x])\n", " print(\"Sample nloci\")\n", " print([(x,y) for x,y in sim_full_sample_nlocs.items() if \"ddocent\" in x])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Write out results of the simulations\n", "This data is worth its weight in gold, it takes forever to acquire,\n", "so lets save it off to disk so we don't lose it." ] }, { "cell_type": "code", "execution_count": 502, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sim_full_loc_cov\n", "sim_sample_nlocs_df\n" ] } ], "source": [ "import pickle\n", "sim_res_dict = {\"sim_sample_nlocs_df\":sim_full_sample_nlocs, \"sim_full_loc_cov\":sim_full_loc_cov}\n", "\n", "for k,v in sim_res_dict.items():\n", " print(k)\n", " pickle.dump(v, open(WORK_DIR + \"RESULTS/\" + k + \".p\", 'wb'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting simulation results" ] }, { "cell_type": "code", "execution_count": 443, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean number of loci recovered per sample.\n", " simno simlo simhi simlarge\n", "ipyrad 10000 10000 9999.92 96942.7\n", "pyrad 10000 10000 9999.92 96943.7\n", "stacks_gapped 11422.7 11393.8 11364.8 110707\n", "stacks_ungapped 11422.7 11423.9 11420.8 110707\n", "stacks_default 11079.2 11091.1 11101.2 107224\n", "aftrrad 10051.2 10059.6 10085.7 100173\n", "ddocent_full 9886 9888 9886.58 95584.8\n", "ddocent_filt 9886 9888 9884.67 90797.9\n" ] }, { "data": { "image/png": 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iLy/vinYYFxfHpEmTrmjb8iAkJISsrCx3l1Gsoe+8zrTULby4c5Xpcv/mjXn+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\n3GQ2JiBojPHFsFJ9Ho0NDWhqaoR/QADOnfkrtO3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "simlevel = [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]\n", "assemblers = [\"ipyrad\", \"pyrad\", \"stacks_gapped\", \"stacks_ungapped\",\\\n", " \"stacks_default\", \"aftrrad\", \"ddocent_full\", \"ddocent_filt\"]\n", "\n", "sim_sample_nlocs_df = pd.DataFrame(index=assemblers, columns=simlevel)\n", "\n", "for sim in simlevel:\n", " for ass in assemblers:\n", " simstring = \"-\".join([ass, sim])\n", " sim_sample_nlocs_df[sim][ass] = np.mean(sim_full_sample_nlocs[simstring])\n", "print(\"Mean number of loci recovered per sample.\")\n", "## Normalize all bins\n", "dat = sim_sample_nlocs_df[sim_sample_nlocs_df.columns].astype(float)\n", "for sim in simlevel:\n", " scale = 10000\n", " if sim == \"simlarge\":\n", " scale = 100000\n", " dat[sim] = dat[sim]/scale\n", "sns.heatmap(dat, square=True, center=1, linewidths=2, annot=True)\n", "print(sim_sample_nlocs_df)\n", "#print(dat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ugly plot for locus coverage. " ] }, { "cell_type": "code", "execution_count": 552, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 552, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "simlevel = [\"simno\", \"simlo\", \"simhi\", \"simlarge\"]\n", "assemblers = [\"ipyrad\", \"pyrad\", \"stacks_gapped\", \"stacks_ungapped\",\\\n", " \"stacks_default\", \"aftrrad\", \"ddocent_full\", \"ddocent_filt\"]\n", "df = pd.DataFrame(index=assemblers, columns=simlevel)\n", "df2 = pd.DataFrame(columns = [\"assembler\", \"simtype\"] + [x for x in xrange(1,13)])\n", "df3 = pd.DataFrame(columns = [\"assembler\", \"simtype\", \"simdata\"])\n", "## Try to get a dataframe in the shape seaborn will be happy with\n", "dfsns = pd.DataFrame(columns = [\"assembler\", \"simtype\", \"bin\", \"simdata\"])\n", "\n", "for sim in simlevel:\n", " for ass in assemblers:\n", " simstring = ass + \"-\" + sim\n", " df[sim][ass] = np.array(sim_full_loc_cov[simstring])\n", " df2.loc[simstring] = [ass, sim] + sim_full_loc_cov[simstring]\n", " df3.loc[simstring] = [ass, sim, np.array(sim_full_loc_cov[simstring])]\n", "\n", " for i, val in enumerate(sim_full_loc_cov[simstring]):\n", " ## Normalize values so different sim sizes print the same\n", " max = 10000.\n", " if \"large\" in simstring:\n", " max = 100000.\n", " dfsns.loc[simstring + \"-\" + str(i)] = [ass, sim, i+1, val/max]\n", "\n", "sns.set(style=\"white\")\n", "\n", "## This kinda sucks, too spread out.\n", "#g = sns.FacetGrid(dfsns, row=\"assembler\", col=\"simtype\", margin_titles=True)\n", "#g.map(sns.barplot, \"bin\", \"simdata\", color=\"steelblue\", lw=0)\n", "plt.rcParams['figure.figsize']=(20,20)\n", "g = sns.FacetGrid(dfsns, col=\"simtype\", margin_titles=True, col_wrap=2, size=4, aspect=2)\n", "g.map(sns.barplot, \"bin\", \"simdata\", \"assembler\", lw=0.5, palette=\"bright\").add_legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Another ugly plot for locus coverage. " ] }, { "cell_type": "code", "execution_count": 553, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 553, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Barplots of assembly method x simdata type\n", "g = sns.FacetGrid(dfsns, col=\"assembler\", margin_titles=True, col_wrap=4, size=3, aspect=1.5)\n", "g.map(sns.barplot, \"bin\", \"simdata\", \"simtype\", lw=0.5, palette=\"bright\").add_legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Much better! Do the spline plot version of the above plot\n", "It just looks nicer, makes more sense." ] }, { "cell_type": "code", "execution_count": 598, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VNm/eTHx8PABxcXFs2rQJgB07djB48ODmPTghhBAOcln/NtQed3p3iMHF0QjRds2ePZuU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7feeisul4uxY8cyffp0XnjhBQwGA5MmTSI+Pp4bbriB8ePH88UXX2AwGHjttdd46aWXGDduHBMm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\noTH85S9/YenSpdhsNnr06MHzzz+Pu7t7k5/31EjT8ePHCQkJ4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfsns2 = pd.DataFrame(columns = [\"assembler\", \"simtype\", \"bin\", \"simdata\"])\n", "\n", "\n", "for sim in simlevel:\n", " for ass in assemblers:\n", " simstring = ass + \"-\" + sim\n", " ## Normalize values so different sim sizes print the same\n", " max = 10000.\n", " if \"large\" in simstring:\n", " max = 100000.\n", " \n", " newdat = sim_full_loc_cov[simstring]\n", " newdat = [sum(newdat)-sum(newdat[:i-1]) for i in range(1,13)]\n", " for i, val in enumerate(newdat):\n", " dfsns2.loc[simstring + \"-\" + str(i)] = [ass, sim, i+1, val]\n", "\n", "g = sns.FacetGrid(dfsns2, col=\"simtype\", hue=\"assembler\", sharex=True, sharey=False, size=4, col_wrap=2)\n", "g.map(plt.scatter, \"bin\", \"simdata\")\n", "g.map(plt.plot, \"bin\", \"simdata\").add_legend()\n", "axs = g.axes\n", "for ax in axs:\n", " ax.set_xlim(0,12.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Empirical Results (Pedicularis)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Process the vcf output from all the runs\n", "Here we'll pull together all the output vcf files and filter\n", "out everything except biallelic snps. This takes a few minutes to run." ] }, { "cell_type": "code", "execution_count": 603, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "found - /home/iovercast/manuscript-analysis/stacks/REALDATA/default/batch_1.vcf\n", "/home/iovercast/manuscript-analysis/dDocent/vcftools --vcf /home/iovercast/manuscript-analysis/stacks/REALDATA/default/batch_1.vcf --min-alleles 2 --max-alleles 2 --recode --out /home/iovercast/manuscript-analysis/stacks/REALDATA/default/batch_1-biallelic\n", "\n", "VCFtools - v0.1.11\n", "(C) Adam Auton 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf /home/iovercast/manuscript-analysis/stacks/REALDATA/default/batch_1.vcf\n", "\t--max-alleles 2\n", "\t--min-alleles 2\n", "\t--out /home/iovercast/manuscript-analysis/stacks/REALDATA/default/batch_1-biallelic\n", "\t--recode\n", "\n", "Reading Index file.\n", "File contains 149570 entries and 13 individuals.\n", "Applying Required Filters.\n", "Filtering sites by number of alleles\n", "After filtering, kept 13 out of 13 Individuals\n", "After filtering, kept 149570 out of a possible 149570 Sites\n", "Outputting VCF file... Done\n", "Run Time = 5.00 seconds\n", "found - /home/iovercast/manuscript-analysis/dDocent/REALDATA/TotalRawSNPs.vcf\n", "/home/iovercast/manuscript-analysis/dDocent/vcftools --vcf /home/iovercast/manuscript-analysis/dDocent/REALDATA/TotalRawSNPs.vcf --min-alleles 2 --max-alleles 2 --recode --out /home/iovercast/manuscript-analysis/dDocent/REALDATA/TotalRawSNPs-biallelic\n", "\n", "VCFtools - v0.1.11\n", "(C) Adam Auton 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf /home/iovercast/manuscript-analysis/dDocent/REALDATA/TotalRawSNPs.vcf\n", "\t--max-alleles 2\n", "\t--min-alleles 2\n", "\t--out /home/iovercast/manuscript-analysis/dDocent/REALDATA/TotalRawSNPs-biallelic\n", "\t--recode\n", "\n", "Reading Index file.\n", "File contains 238142 entries and 13 individuals.\n", "Applying Required Filters.\n", "Filtering sites by number of alleles\n", "After filtering, kept 13 out of 13 Individuals\n", "After filtering, kept 190405 out of a possible 238142 Sites\n", "Outputting VCF file... Done\n", "Run Time = 15.00 seconds\n", "found - /home/iovercast/manuscript-analysis/pyrad/REALDATA/outfiles/c85d6m2p3H3N3.vcf\n", "/home/iovercast/manuscript-analysis/dDocent/vcftools --vcf /home/iovercast/manuscript-analysis/pyrad/REALDATA/outfiles/c85d6m2p3H3N3.vcf --min-alleles 2 --max-alleles 2 --recode --out /home/iovercast/manuscript-analysis/pyrad/REALDATA/outfiles/c85d6m2p3H3N3-biallelic\n", "\n", "VCFtools - v0.1.11\n", "(C) Adam Auton 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf /home/iovercast/manuscript-analysis/pyrad/REALDATA/outfiles/c85d6m2p3H3N3.vcf\n", "\t--max-alleles 2\n", "\t--min-alleles 2\n", "\t--out /home/iovercast/manuscript-analysis/pyrad/REALDATA/outfiles/c85d6m2p3H3N3-biallelic\n", "\t--recode\n", "\n", "Reading Index file.\n", "File contains 319936 entries and 13 individuals.\n", "Applying Required Filters.\n", "Filtering sites by number of alleles\n", "After filtering, kept 13 out of 13 Individuals\n", "After filtering, kept 308247 out of a possible 319936 Sites\n", "Outputting VCF file... Done\n", "Run Time = 5.00 seconds\n", "not found - /home/iovercast/manuscript-analysis/aftrRAD/REALDATA/Formatting/REALDATA.vcf\n", "found - /home/iovercast/manuscript-analysis/ipyrad/REALDATA/REALDATA_outfiles/REALDATA.vcf\n", "/home/iovercast/manuscript-analysis/dDocent/vcftools --vcf /home/iovercast/manuscript-analysis/ipyrad/REALDATA/REALDATA_outfiles/REALDATA.vcf --min-alleles 2 --max-alleles 2 --recode --out /home/iovercast/manuscript-analysis/ipyrad/REALDATA/REALDATA_outfiles/REALDATA-biallelic\n", "\n", "VCFtools - v0.1.11\n", "(C) Adam Auton 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf /home/iovercast/manuscript-analysis/ipyrad/REALDATA/REALDATA_outfiles/REALDATA.vcf\n", "\t--max-alleles 2\n", "\t--min-alleles 2\n", "\t--out /home/iovercast/manuscript-analysis/ipyrad/REALDATA/REALDATA_outfiles/REALDATA-biallelic\n", "\t--recode\n", "\n", "Reading Index file.\n", "File contains 4554084 entries and 13 individuals.\n", "Applying Required Filters.\n", "Filtering sites by number of alleles\n", "After filtering, kept 13 out of 13 Individuals\n", "After filtering, kept 220118 out of a possible 4554084 Sites\n", "Outputting VCF file... Done\n", "Run Time = 17.00 seconds\n", "not found - /home/iovercast/manuscript-analysis/stacks/REALDATA/gapped/batch_1.vcf\n", "found - /home/iovercast/manuscript-analysis/dDocent/REALDATA/Final.recode.vcf\n", "/home/iovercast/manuscript-analysis/dDocent/vcftools --vcf /home/iovercast/manuscript-analysis/dDocent/REALDATA/Final.recode.vcf --min-alleles 2 --max-alleles 2 --recode --out /home/iovercast/manuscript-analysis/dDocent/REALDATA/Final.recode-biallelic\n", "\n", "VCFtools - v0.1.11\n", "(C) Adam Auton 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf /home/iovercast/manuscript-analysis/dDocent/REALDATA/Final.recode.vcf\n", "\t--max-alleles 2\n", "\t--min-alleles 2\n", "\t--out /home/iovercast/manuscript-analysis/dDocent/REALDATA/Final.recode-biallelic\n", "\t--recode\n", "\n", "Reading Index file.\n", "File contains 99971 entries and 13 individuals.\n", "Applying Required Filters.\n", "Filtering sites by number of alleles\n", "After filtering, kept 13 out of 13 Individuals\n", "After filtering, kept 80040 out of a possible 99971 Sites\n", "Outputting VCF file... Done\n", "Run Time = 6.00 seconds\n", "found - /home/iovercast/manuscript-analysis/stacks/REALDATA/ungapped/batch_1.vcf\n", "/home/iovercast/manuscript-analysis/dDocent/vcftools --vcf /home/iovercast/manuscript-analysis/stacks/REALDATA/ungapped/batch_1.vcf --min-alleles 2 --max-alleles 2 --recode --out /home/iovercast/manuscript-analysis/stacks/REALDATA/ungapped/batch_1-biallelic\n", "\n", "VCFtools - v0.1.11\n", "(C) Adam Auton 2009\n", "\n", "Parameters as interpreted:\n", "\t--vcf /home/iovercast/manuscript-analysis/stacks/REALDATA/ungapped/batch_1.vcf\n", "\t--max-alleles 2\n", "\t--min-alleles 2\n", "\t--out /home/iovercast/manuscript-analysis/stacks/REALDATA/ungapped/batch_1-biallelic\n", "\t--recode\n", "\n", "Reading Index file.\n", "Building new index file.\n", "\tScanning Chromosome: un\n", "Writing Index file.\n", "File contains 344633 entries and 13 individuals.\n", "Applying Required Filters.\n", "Filtering sites by number of alleles\n", "After filtering, kept 13 out of 13 Individuals\n", "After filtering, kept 344633 out of a possible 344633 Sites\n", "Outputting VCF file... Done\n", "Run Time = 11.00 seconds\n" ] } ], "source": [ "vcf_dict = {}\n", "vcf_dict[\"ipyrad\"] = os.path.join(IPYRAD_DIR, \"REALDATA/REALDATA_outfiles/REALDATA.vcf\")\n", "vcf_dict[\"pyrad\"] = os.path.join(PYRAD_EMPIRICAL_OUTPUT, \"outfiles/c85d6m2p3H3N3.vcf\")\n", "vcf_dict[\"stacks_ungapped\"] = os.path.join(STACKS_DIR, \"REALDATA/ungapped/batch_1.vcf\")\n", "vcf_dict[\"stacks_gapped\"] = os.path.join(STACKS_DIR, \"REALDATA/gapped/batch_1.vcf\")\n", "vcf_dict[\"stacks_default\"] = os.path.join(STACKS_DIR, \"REALDATA/default/batch_1.vcf\")\n", "vcf_dict[\"aftrrad\"] = os.path.join(AFTRRAD_DIR, \"REALDATA/Formatting/REALDATA.vcf\")\n", "vcf_dict[\"ddocent_full\"] = os.path.join(DDOCENT_DIR, \"REALDATA/TotalRawSNPs.vcf\")\n", "vcf_dict[\"ddocent_filt\"] = os.path.join(DDOCENT_DIR, \"REALDATA/Final.recode.vcf\")\n", "for k, f in vcf_dict.items():\n", " if os.path.exists(f):\n", " print(\"found - {}\".format(f))\n", " \n", " ## If it's gzipped then unzip it (only applies to ipyrad i think)\n", " if \".gz\" in f:\n", " print(\"gunzipping\")\n", " cmd = \"gunzip {}\".format(f)\n", " !cmd\n", " vcf_dict[k] = f.split(\".gz\")[0]\n", " \n", " ## Remove all but biallelic (for ipyrad this also removes all the monomorphic)\n", " #outfile = f.split(\".vcf\")[0]+\"-biallelic\"\n", " #cmd = \"{}vcftools --vcf {} --min-alleles 2 --max-alleles 2 --recode --out {}\" \\\n", " # .format(DDOCENT_DIR, f, outfile)\n", " #print(cmd)\n", " #!$cmd\n", " \n", " ## update the vcf_dict\n", " #vcf_dict[k] = outfile + \".recode.vcf\"\n", " else:\n", " print(\"not found - {}\".format(f))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in vcf for each analysis and pull in coverage/depth stats" ] }, { "cell_type": "code", "execution_count": 604, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:08:57.760390 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:08:57.760901 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_default\n", "\t/home/iovercast/manuscript-analysis/stacks/REALDATA/default/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:08:59.403870 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:08:59.404332 :: building array\n", "[vcfnp] 2016-10-18 11:09:19.246532 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:09:19.247164 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - ddocent_full\n", "\t/home/iovercast/manuscript-analysis/dDocent/REALDATA/TotalRawSNPs-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:09:25.020836 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:09:25.021373 :: building array\n", "[vcfnp] 2016-10-18 11:10:08.979173 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:10:08.979877 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - pyrad\n", "\t/home/iovercast/manuscript-analysis/pyrad/REALDATA/outfiles/c85d6m2p3H3N3-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:10:12.003180 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:10:12.003658 :: building array\n", "[vcfnp] 2016-10-18 11:10:36.855671 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:10:36.856239 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - aftrrad\n", "\t/home/iovercast/manuscript-analysis/aftrRAD/REALDATA/Formatting/REALDATA.vcf\n", "file not found: /home/iovercast/manuscript-analysis/aftrRAD/REALDATA/Formatting/REALDATA.vcf\n", "Doing - ipyrad\n", "\t/home/iovercast/manuscript-analysis/ipyrad/REALDATA/REALDATA_outfiles/REALDATA-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:10:39.041659 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:10:39.042141 :: building array\n", "[vcfnp] 2016-10-18 11:10:57.915668 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:10:57.916290 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_gapped\n", "\t/home/iovercast/manuscript-analysis/stacks/REALDATA/gapped/batch_1.vcf\n", "file not found: /home/iovercast/manuscript-analysis/stacks/REALDATA/gapped/batch_1.vcf\n", "Doing - ddocent_filt\n", "\t/home/iovercast/manuscript-analysis/dDocent/REALDATA/Final.recode-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:11:00.415481 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:11:00.416015 :: building array\n", "[vcfnp] 2016-10-18 11:11:19.294158 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:11:19.294788 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Doing - stacks_ungapped\n", "\t/home/iovercast/manuscript-analysis/stacks/REALDATA/ungapped/batch_1-biallelic.recode.vcf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-18 11:11:23.090947 :: caching is disabled\n", "[vcfnp] 2016-10-18 11:11:23.091441 :: building array\n" ] }, { "data": { "image/png": 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GsXTp0nLbR0vLysqicePGmJubc+HCBeLj4yuNtbYZNZEeOnQIa2trHn30Ub1V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\n2+1GQUFBn++jo2+fHzYajTh48CAA4NChQ/K+u3tN+sL69es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UXns3A5U806+Ig4MDDg4OANja2tK+fXt++eUX2rdvrxyzZcsWVq1aVfPgv2mw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\ns/nb3/6GiUneyyxYsICIiAjWr1/PiRMnCA8PR1EUZs2axZQpU9SM4l7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g2LFjAHTs2BE/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\nbi6Nq6srk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WrQsUPqoSFBSEhYUFc+fONajym6I0i2HvCAoi2s+Plnl3H3Z+3soKty1b0N6j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\nPXv2AACuXLmC1157DTt27MDWrVtRW1uLsLAwvPTSS/L6o0a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Ry0myF0IIIWo5SfZCCCFELSfJXgghhKjlZICeqLS8KZBnW/VxC3TGmwtv8oXx5vC/h/Ha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\nddmyZdDS0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Tnp5eabFt3ryZw4cPm69HjhzJ/v37b/i6J06cwMfHx6ZzLq67X79+/PLLL6WW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\nEZmIxZOIiMhELJ5EREQm+n8nrjDUjZwZSAAAAABJRU5ErkJggg==\n", 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9Sm3P6dOniY2Nxd7enoCAAHbu3Imrqyv379+nXbt2TJw4EUBZRnf27Nn07NkT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\nv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F06ZNmTVrFgCtW7fGx8cHX19fHB0dmT59umbaiohIpauxy/MdOnSIAQMGaNhWRERsZvdH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\nSEgItmzZ0id5tm7dirCwMGi1Wrz11ltoampSRK7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D7733HoCyje78+fPp3bs3ADNmzGDWrFk0adKEpKQkZs6cyZo1a3BycuLSpUuk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fz4cSxdupR7LFBRUYGYmBj88Y9/FCT/f5HnMyQlJWHHjh2oq6uDn58fNDU1cfLkSUGa6fxSNO2H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\nIT6CSqDb+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AuXLlqF69OoMHDyY8PNwYJze/+1ANGjTg3//+N127dsXV1RVnZ2fat2/PggULaNOmjbFd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\n+G1/B9lQ1wAAAABJRU5ErkJggg==\n", 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MTKzrZgghhBAKKeMLIYQQ9ZwkeyGEEKKek2QvhBBC1HOS7IUQQoh6TgboiRpbqXoOe3Xt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\nCQE1kCFEU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CgoODCQoKYt26daSlpRX5u3rWJHkKIUQxytBmFD6lCoDmr/WK6ODBgwwaNIiq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\nV1fb9PnGsnXrVgQFBeH555+XxqKjo1FeXg4AqKiokDKN97p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EoSpbxq8sZ86cISQkRCmfG41GrK2tlXnwQgghxJ9Nkn0Fc3FxITY2trKbIYQQQiikjC+E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\nr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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "emp_loc_cov = collections.OrderedDict()\n", "emp_snp_cov = collections.OrderedDict()\n", "emp_sample_nsnps = collections.OrderedDict()\n", "emp_sample_nlocs = collections.OrderedDict()\n", "## Try just doing them all the same\n", "for prog, filename in vcf_dict.items():\n", " try:\n", " print(\"Doing - {}\".format(prog))\n", " print(\"\\t{}\".format(filename))\n", " v = vcfnp.variants(filename, dtypes={\"CHROM\":\"a24\"}).view(np.recarray)\n", " c = vcfnp.calldata_2d(filename).view(np.recarray)\n", "\n", " emp_loc_cov[prog] = loci_coverage(v, c, prog)\n", " emp_snp_cov[prog] = snp_coverage(c)\n", " emp_sample_nsnps[prog] = sample_nsnps(c)\n", " emp_sample_nlocs[prog] = sample_nloci(v, c, prog)\n", " \n", " plotPCA(c, prog)\n", " plotPairwiseDistance(c, prog)\n", " except Exception as inst:\n", " print(inst)" ] }, { "cell_type": "code", "execution_count": 608, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "stacks_default [22215, 32074, 25377, 16640, 23557, 17426, 32172, 34354, 31333, 29809, 34792, 24791, 25256]\n", "ddocent_full [34816, 37668, 35523, 25583, 36635, 25751, 38051, 39179, 38946, 38744, 39209, 37612, 37240]\n", "pyrad [22826, 34866, 30179, 19710, 19076, 21977, 36830, 36774, 37212, 29720, 38012, 35350, 31155]\n", "ipyrad [18710, 28396, 24635, 15667, 15301, 17830, 29885, 29492, 30213, 24043, 30671, 29076, 25580]\n", "ddocent_filt [18640, 18904, 18745, 18471, 18713, 18553, 18969, 19050, 19096, 19068, 19086, 19182, 19164]\n", "stacks_ungapped [23103, 35494, 30792, 19377, 19547, 21469, 36663, 36949, 36703, 29789, 37968, 33544, 31931]\n" ] } ], "source": [ "for i in emp_sample_nlocs:\n", " print(i),\n", " print(emp_sample_nlocs[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ipyrad Empirical results\n", "First we load in the variant info and call data for all the snps" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[21163, 31863, 27902, 20852, 17779, 23008, 33289, 33697, 35765, 29184, 35096, 38144, 34746]\n", "mean sample coverage - 29422.1538462\n", "min/max - 17779/38144\n" ] } ], "source": [ "IPYRAD_EMPIRICAL_OUTPUT=os.path.join(IPYRAD_DIR, \"REALDATA/\")\n", "IPYRAD_STATS = os.path.join(IPYRAD_EMPIRICAL_OUTPUT, \"REALDATA_outfiles/REALDATA_stats.txt\")\n", "\n", "infile = open(IPYRAD_STATS).readlines()\n", "sample_coverage = [int(x.strip().split()[1]) for x in infile[20:33]]\n", "print(sample_coverage)\n", "print(\"mean sample coverage - {}\".format(np.mean(sample_coverage)))\n", "print(\"min/max - {}/{}\".format(np.min(sample_coverage), np.max(sample_coverage)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the biallelic vcf" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-12 12:42:26.099077 :: caching is disabled\n", "[vcfnp] 2016-10-12 12:42:26.099836 :: building array\n", "[vcfnp] 2016-10-12 12:42:29.441796 :: caching is disabled\n", "[vcfnp] 2016-10-12 12:42:29.442748 :: building array\n" ] } ], "source": [ "filename = os.path.join(IPYRAD_EMPIRICAL_OUTPUT, \"REALDATA_outfiles/REALDATA.biallelic.vcf\")\n", "# filename = vcf_dict[\"ipyrad\"]\n", "v = vcfnp.variants(filename).view(np.recarray)\n", "c = vcfnp.calldata_2d(filename).view(np.recarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of snps along loci\n", "Getting variable sites and parsimony informative sites from the vcf is kind of annoying\n", "because all the programs export __slightly__ different formats, so you need to\n", "parse them in slightly different ways. There's a better way to do this for ipyrad\n", "but i figure i'll do it the same way for all of them so it's more clear what's happening." ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/html": [ "
total variable sitesparsimony informative sites0510152025303540455055606570Position along RAD loci0200040006000N variables sites
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" ], "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Get only parsimony informative sites\n", "## Get T/F values for whether each genotype is ref or alt across all samples/loci\n", "is_alt_allele = map(lambda x: map(lambda y: 1 in y, x), c[\"genotype\"])\n", "## Count the number of alt alleles per snp (we only want to retain when #alt > 1)\n", "alt_counts = map(lambda x: np.count_nonzero(x), is_alt_allele)\n", "## Create a T/F mask for snps that are informative\n", "only_pis = map(lambda x: x < 2, alt_counts)\n", "## Apply the mask to the variant array so we can pull out the position of each snp w/in each locus\n", "## Also, compress() the masked array so we only actually see the pis\n", "pis = np.ma.array(np.array(v[\"POS\"]), mask=only_pis).compressed()\n", "\n", "## Now have to massage this into the list of counts per site in increasing order \n", "## of position across the locus\n", "distpis = Counter([int(x) for x in pis])\n", "#distpis = [x for x in sorted(counts.items())]\n", "\n", "## Getting the distvar is easier\n", "distvar = Counter([int(x) for x in v.POS])\n", "#distvar = [x for x in sorted(counts.items())]\n", "\n", "canvas, axes = SNP_position_plot(distvar, distpis)\n", "\n", "## save fig\n", "#toyplot.html.render(canvas, 'snp_positions.html')\n", "\n", "canvas" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cOXKEjh072i1+ERG5Mdh9UYKJEyfy4osvkpeXZ4yB5+fnM2bMGKKiomjatCmz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\nw4YNG8wdwtJ4eXmZO0I3zNQ7zNQ7zNQ7lpiJzIP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## Have to gunzip the ipyrad REALDATA vcf\n", "plotPCA(c, \"ipyrad\")" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [ { "data": { "image/png": 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HvvzyS5YvX079+vVJSEgA4MCBA6xevZqUlBQ+/PBD3nrrLWw2GxaLhYkTJ7Jg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ZVpvJUH1PvsgoODcfz4cWa5LYSsE0c16joOmtiRDoPFm5vw8HBER0fDzMwM165dQ0xMDAYNGoRH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\nRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotPairwiseDistance(c, \"pyrad\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## pyRAD empirical results\n", "\n", "#### TODO: Figure out why vcfnp is failing to read in pyrad vcf" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[23924, 37002, 31852, 23698, 20063, 26050, 38931, 39423, 41250, 33230, 40809, 42948, 38645]\n", "mean sample coverage - 33678.8461538\n", "min/max - 20063/42948\n" ] } ], "source": [ "PYRAD_EMPIRICAL_OUTPUT=os.path.join(PYRAD_DIR, \"REALDATA/\")\n", "PYRAD_STATS = os.path.join(PYRAD_EMPIRICAL_OUTPUT, \"stats/c85d6m2p3H3N3.stats\")\n", "\n", "infile = open(PYRAD_STATS).readlines()\n", "sample_coverage = [int(x.strip().split()[1]) for x in infile[8:21]]\n", "print(sample_coverage)\n", "print(\"mean sample coverage - {}\".format(np.mean(sample_coverage)))\n", "print(\"min/max - {}/{}\".format(np.min(sample_coverage), np.max(sample_coverage)))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Pull in the pyrad vcf\n", "Be careful because sometimes the pyrad vcf gets a newline inserted between the last\n", "line of metadata and the first line of genotypes, which causes vcfnp to silently fail.\n", "Also there are a couple flags you can pass to vcfnp for debugging, but the print \n", "hella data to the console.\n", "\n", " v = vcfnp.variants(filename, progress=1, verbose=True).view(np.recarray)" ] }, { "cell_type": "code", "execution_count": 230, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-08 15:40:30.499958 :: caching is disabled\n", "[vcfnp] 2016-10-08 15:40:30.500883 :: building array\n", "[vcfnp] 2016-10-08 15:40:37.399061 :: caching is disabled\n", "[vcfnp] 2016-10-08 15:40:37.417059 :: building array\n" ] } ], "source": [ "filename = os.path.join(PYRAD_EMPIRICAL_OUTPUT, \"outfiles/c85d6m2p3H3N3.vcf\")\n", "v = vcfnp.variants(filename).view(np.recarray)\n", "c = vcfnp.calldata_2d(filename).view(np.recarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of snps along loci" ] }, { "cell_type": "code", "execution_count": 231, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/html": [ "
total variable sitesparsimony informative sites05101520253035404550556065707580Position along RAD loci0250050007500N variables sites
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" ], "text/plain": [ "" ] }, "execution_count": 231, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Get only parsimony informative sites\n", "## Get T/F values for whether each genotype is ref or alt across all samples/loci\n", "is_alt_allele = map(lambda x: map(lambda y: 1 in y, x), c[\"genotype\"])\n", "## Count the number of alt alleles per snp (we only want to retain when #alt > 1)\n", "alt_counts = map(lambda x: np.count_nonzero(x), is_alt_allele)\n", "## Create a T/F mask for snps that are informative\n", "only_pis = map(lambda x: x < 2, alt_counts)\n", "## Apply the mask to the variant array so we can pull out the position of each snp w/in each locus\n", "## Also, compress() the masked array so we only actually see the pis\n", "pis = np.ma.array(np.array(v[\"POS\"]), mask=only_pis).compressed()\n", "\n", "## Now have to massage this into the list of counts per site in increasing order \n", "## of position across the locus\n", "distpis = Counter([int(x) for x in pis])\n", "#distpis = [x for x in sorted(counts.items())]\n", "\n", "## Getting the distvar is easier\n", "distvar = Counter([int(x) for x in v.POS])\n", "#distvar = [x for x in sorted(counts.items())]\n", "\n", "canvas, axes = SNP_position_plot(distvar, distpis)\n", "\n", "## save fig\n", "#toyplot.html.render(canvas, 'snp_positions.html')\n", "\n", "canvas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## stacks empirical results (ungapped)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "STACKS_OUTPUT=os.path.join(STACKS_DIR, \"REALDATA/\")\n", "STACKS_GAP_OUT=os.path.join(STACKS_OUTPUT, \"gapped/\")\n", "STACKS_UNGAP_OUT=os.path.join(STACKS_OUTPUT, \"ungapped/\")\n", "STACKS_DEFAULT_OUT=os.path.join(STACKS_OUTPUT, \"default/\")\n", "\n", "#lines = open(\"SIMsmall/stackf_high/batch_1.haplotypes.tsv\").readlines()\n", "#cnts = [int(field.strip().split(\"\\t\")[1]) for field in lines[1:]]\n", "#shigh = [cnts.count(i) for i in range(1,13)]" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-12 10:48:17.512074 :: caching is disabled\n", "[vcfnp] 2016-10-12 10:48:17.512879 :: building array\n", "[vcfnp] 2016-10-12 10:48:25.055213 :: caching is disabled\n", "[vcfnp] 2016-10-12 10:48:25.061273 :: building array\n" ] } ], "source": [ "filename = os.path.join(STACKS_UNGAP_OUT, \"batch_1.vcf\")\n", "v = vcfnp.variants(filename).view(np.recarray)\n", "c = vcfnp.calldata_2d(filename).view(np.recarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of snps along loci" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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total variable sitesparsimony informative sites0510152025303540455055606570Position along RAD loci0500010000N variables sites
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" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Get only parsimony informative sites\n", "## Get T/F values for whether each genotype is ref or alt across all samples/loci\n", "is_alt_allele = map(lambda x: map(lambda y: 1 in y, x), c[\"genotype\"])\n", "## Count the number of alt alleles per snp (we only want to retain when #alt > 1)\n", "alt_counts = map(lambda x: np.count_nonzero(x), is_alt_allele)\n", "## Create a T/F mask for snps that are informative\n", "only_pis = map(lambda x: x < 2, alt_counts)\n", "## Apply the mask to the variant array so we can pull out the position of each snp w/in each locus\n", "## Also, compress() the masked array so we only actually see the pis\n", "pis = np.ma.array(np.array(v[\"ID\"]), mask=only_pis).compressed()\n", "\n", "## Now have to massage this into the list of counts per site in increasing order \n", "## of position across the locus\n", "distpis = Counter([int(x.split(\"_\")[1]) for x in pis])\n", "#distpis = [x for x in sorted(counts.items())]\n", "\n", "## Getting the distvar is easier\n", "distvar = Counter([int(x.split(\"_\")[1]) for x in v.ID])\n", "#distvar = [x for x in sorted(counts.items())]\n", "\n", "canvas, axes = SNP_position_plot(distvar, distpis)\n", "\n", "## save fig\n", "#toyplot.html.render(canvas, 'snp_positions.html')\n", "\n", "canvas" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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rM3z4cEJDQwHTRHTy5ElGjhyJTqcjKiqK06dPF3lcNzc3ANq1a8fFixcBOHTo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\nERHJYFkSERHJ+H8kgGqFlFt2mgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotPCA(c, \"stacks ungapped\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-09-03 19:04:05.829749 :: caching is disabled\n", "[vcfnp] 2016-09-03 19:04:05.830278 :: building array\n" ] }, { "data": { "image/png": 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jI4P333+fV199FZvNhsViYcKECcyfP58lS5YQHBzM/PnzAWjdujWpqaksWbKE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W0tVzC0dckN1q2O/P39X1v6a+8aCy0TJzc3N1haWmL16tV4+vQp89z/FOtnL/T4CwsLcfLkSQDA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\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotPairwiseDistance(c, \"stacks ungapped\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## stacks empirical results (defaults)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-12 10:53:17.257960 :: caching is disabled\n", "[vcfnp] 2016-10-12 10:53:17.259180 :: building array\n", "[vcfnp] 2016-10-12 10:53:20.403885 :: caching is disabled\n", "[vcfnp] 2016-10-12 10:53:20.409034 :: building array\n" ] } ], "source": [ "filename = os.path.join(STACKS_DEFAULT_OUT, \"batch_1.vcf\")\n", "v = vcfnp.variants(filename).view(np.recarray)\n", "c = vcfnp.calldata_2d(filename).view(np.recarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Some informative stats\n", "TODO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of snps along loci" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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total variable sitesparsimony informative sites0510152025303540455055606570Position along RAD loci0100020003000N variables sites
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" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Get only parsimony informative sites\n", "## Get T/F values for whether each genotype is ref or alt across all samples/loci\n", "is_alt_allele = map(lambda x: map(lambda y: 1 in y, x), c[\"genotype\"])\n", "## Count the number of alt alleles per snp (we only want to retain when #alt > 1)\n", "alt_counts = map(lambda x: np.count_nonzero(x), is_alt_allele)\n", "## Create a T/F mask for snps that are informative\n", "only_pis = map(lambda x: x < 2, alt_counts)\n", "## Apply the mask to the variant array so we can pull out the position of each snp w/in each locus\n", "## Also, compress() the masked array so we only actually see the pis\n", "pis = np.ma.array(np.array(v[\"ID\"]), mask=only_pis).compressed()\n", "\n", "## Now have to massage this into the list of counts per site in increasing order \n", "## of position across the locus\n", "distpis = Counter([int(x.split(\"_\")[1]) for x in pis])\n", "#distpis = [x for x in sorted(counts.items())]\n", "\n", "## Getting the distvar is easier\n", "distvar = Counter([int(x.split(\"_\")[1]) for x in v.ID])\n", "#distvar = [x for x in sorted(counts.items())]\n", "\n", "canvas, axes = SNP_position_plot(distvar, distpis)\n", "\n", "## save fig\n", "toyplot.html.render(canvas, 'snp_positions.html')\n", "\n", "canvas" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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EdYy59m6vXr14/vnnmTp1Ku7u7rz33nsAaDQasrOzcXV1VfZ96aWX8PHxYfLk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\nUVlZqdT/+c9/CovFIhITE8X69ev7tB+TyaRcVevPHhwOh/I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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotPCA(c, \"stacks default\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-09-03 18:56:31.855138 :: caching is disabled\n", "[vcfnp] 2016-09-03 18:56:31.855756 :: building array\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xxhvZvHkzAEOGDCE3N5dt27YxaNAgYmNjGTx4MPv376+ybRUxY2NjOXbsWKX3\nt23bRmxsLHl5eWRkZDBlyhQAUlNTefPNN538bYiIiLgwt2q+aqiwsJBhw4YRGRmJxWLh7bffBmDM\nmDHExMQQExND7969iYmJAaCgoIBOnTo53js9/zibOltQtNlsxMbGkpubS1xcHMHBwZXeX7ZsGffc\ncw8Ahw4donPnzo73/Pz8OHToEJ06dSIlJQWLxULDhg25/vrrq/xQli1bRkhIyDnb891335GVlYW/\nvz/x8fGsWbOGsLAwjh8/TufOnXnyyScByMzMBMq7jXv16gXAU089xeTJk2nZsiXbtm1j0qRJvPXW\nWwQGBrJnzx7y8vLo0KEDW7ZsITg4mMLCQlq2bEnTpk155513MJvNfPnll0yfPp2ZM2dWatf8+fNJ\nSUnh5ptv5vjx445kFWDr1q3885//ZM6cOfj5+bF582ZMpjo+OEBERKS2VWfyhB2oYQedm5sbycnJ\ntG/fnpKSEmJjY+nRowevvPKK45ypU6dy5ZVXOn5u2bIlGRkZTseos4md2WwmMzOT4uJi/va3v7F7\n925at24NwJw5c/Dw8HAkdmdTVlbG4sWLWb58OQEBAUyZMoW5c+fyyCOPOM5Zvnw5P/zwgyNrPpvg\n4GCaN28OQGRkJFu2bCEsLAw3NzfCwsIqnZuVlcWOHTuYP38+x44dY+vWrTz66KOOCmJFBbJLly7k\n5OSQn5/PqFGjWLp0KV27duWmm24C4OjRozz55JMcOHAAAKvVeka7brnlFp5//nksFgthYWGOiuSe\nPXt4+umnmT9/Pr6+vud8tnOZNWuWY4yjiIhIbaro2TpdQkICiYmJBrTmwvn6+jq+k729vWnVqhW/\n/PILrVq1cpyzevVqFixYUOMYdTaxq+Dj48Ntt93Ghg0baN26Nenp6Xz22WeVHtrPz6/S5IfCwkL8\n/PzYsWMHJpOJgIAAAMLDwx2TBwA2btzIa6+9xsKFC/HwcHbN6XIVlS8vL69KVbBdu3Yxe/ZsFi1a\nhMlkwmaz0ahRoyqz7a5du7J48WKKiop49NFHeeONN8jJyaFr164AzJgxg27dupGamkpBQQHDhg07\n4x4PPfQQd911F+vWrWPw4MHMmzcPKP/HU1payvbt2x2Vw5pITEw84w8oPz+/yj82ERGRiyk7O9vx\nHX5JuVO9il3phYfMz89n586dlXokN2/ezDXXXEPLli0rnRcTE4OPjw+PPvqoI0c4mzo5xu7w4cMc\nPXoUgBNWNwV7AAAgAElEQVQnTrBx40YCAwNZv3498+bNY86cOXh6ejrO7927N1lZWZSWlpKXl0du\nbi7BwcH4+fmxe/dujhw5AsAXX3xBYGAgANu3byclJYU5c+Zw9dVXn7dN3333HQUFBdhsNrKyshwf\nbEUVDsorbI8//jhTp07lqquuAsoT04CAAD788EPHeTt37gTKq4Bbt27FbDbj6elJu3btHFU7oNKY\nwPT09CrblZeXR5s2bRg5ciQdO3Zk7969ADRq1IjXXnuNl19+mZycnPM+n4iISL1lxvnxdRchcyop\nKSEpKYkJEybg7e3tOL5q1apKvZHNmjVj3bp1ZGRkMH78eMaOHUtJSck5710nK3ZFRUWMHz8em82G\nzWYjIiKCXr16ERYWxqlTp3jwwQcB6NSpE5MmTaJ169aEh4cTGRmJu7s7KSkpmEwmmjVrRkJCAnFx\ncXh4eODv788LL7wAwLRp0zh+/Liji9Tf359//etfZ21Tx44dmTJlCgcOHKBbt2706dMHoFK1Ljs7\nm59//pmnnnoKu92OyWQiIyODadOmMWnSJObMmYPVaiUiIoJ27drh6emJv7+/Y3xg165dycrKom3b\ntgDEx8fz5JNPMmfOnLNW3d566y02bdqEyWSiTZs2hISEsHXrVgCaNGlCWloaDz30EM8+++wF/lZE\nRERclDvOJ2z/O76upt3EZWVlJCUlERUV5cgloHy41ccff1ypkOPh4UHjxo0B6NChAy1atGD//v10\n6NDhrPc32U8vOUmVcnJymD9/PnPnzjW6KXVCRVds9vi9BDQpq/X49rxaD1k5/mLjYrt/+bRxwQHr\nz5MNjR/uW3Xlujas7hNrWGwAWp7/lEumvYGxAToaGPsTA2MDphsMDF77//PukP+nO6GLAi95V6zj\n+6zxXgLcnHvgfKs7oX/UvG3jxo3j6quvJjk5udLx9evX8/rrr1ca83/48GGuuuoqzGYzeXl5DBky\nhJUrV9KoUaOz3r9OVuxEREREas0FLmPirC1btrBy5UqCgoKIjo7GZDIxZswYQkJCWL169RmTQjdv\n3szMmTPx8PDAZDIxefLkcyZ1oMSukl27djFu3DhH96rdbsfLy4ulS5dy6623Gtw6ERERuSQqFii+\nxLp06cKOHTuqfO/5558/41hYWNgZK2+cjxK70wQFBTnWoRMREZF64jLYKsxZLvIYIiIiIjVUnYpd\nHZ+ZoMRORERE6rfqjLGr29vCK7ETERGReq46FTsldiIiIiJ1WHXG2CmxExEREanDqtMVWwvLolwI\nJXYiIiJSv1WnK9Z6KRty4ZTYiYiISP1Wna5YJXYiIiIidZgZ57tYnd1T1iBK7KTmrgAa1H5YU8/a\nj1kp/jLjYpcdNHavVrdrDd6rdqNx+7WajN4vtY2BsZsbGBsg0MDYhw2MDVBiYGwjK1OmWo5XnYpd\nHc+c6njzRERERC6x6oyx0+QJERERkTpMFTsRERERF6ExdiIiIiIuQhU7ERERERehMXYiIiIiLkI7\nT4iIiIi4CFXsRERERFyExtiJiIiIuAhV7ERERERchMbYiYiIiLgIF+qKrePL7NVcaWkpAwcOJDo6\nGovFQmpqKgAzZsygX79+REdHEx8fT1FRUaXrDh48yM0338ybb77pOPbKK69w5513csstt1Q6d8mS\nJVgsFqKjo4mLi2PPnj1nbU9BQQGrVq1y/JyRkcGUKVMuxqOKiIjIhajoinXmVccrdi6b2Hl6erJg\nwQIyMzPJzMxk/fr1bNu2jREjRrBixQoyMzO58847HQlfhRdeeIFevXpVOhYaGsqyZWfu/G6xWFi5\nciWZmZnEx8fz/PPPn7U9+fn5lRI7AJOptnc5FhERkTO4VfNVh9XxguKFadCgAVBevSsrKwPA29vb\n8f7x48cxm/8vt127di0tWrRwXFchODi4yvuffq9jx45Vutd/mz59Onv37iUmJobo6GgaNWrEoUOH\nGDFiBHl5efTp04cnnngCgEmTJvH9999z8uRJ+vbtS0JCAgC9e/fmnnvuYf369bi7uzN58mRefvll\n8vLyiI+P53/+53/Iyclh1qxZXH311fz000907NiRadOmAfDll1/y4osvYrVauemmm5g0aRIeHh5O\nf54iIiIuyYUmT7hsxQ7AZrMRHR1Nz5496dmzpyNBq+haXblyJUlJSUB5YvbGG284kihnLVq0iL/+\n9a+8/PLLTJw48aznPf7443Tp0oWMjAzuv/9+AHbu3MmMGTNYuXIlq1ev5tChQwD8/e9/Z9myZSxf\nvpxNmzaxa9cux32aN29OZmYmXbp0ITk5mdTUVJYsWcLMmTMd5+zcuZOJEyeSlZVFXl4e33zzDaWl\npSQnJzNjxgxWrFhBWVkZixcvrtazioiIuCRnu2GrMxbPIC6d2JnNZkc37Lfffsvu3bsBGDNmDOvW\nrcNisbBw4UIAZs2axQMPPOCo1tntdqdixMXF8fHHHzN27Fj+9a9/Vat93bt3x9vbG09PT1q1akVB\nQQEAH3zwAbGxsURHR7Nnzx5HuwHuuusuAIKCgujUqRMNGjSgSZMmeHl5UVxcDJRXGJs1a4bJZKJd\nu3YUFBSwd+9eWrRoQcuWLQGIjo5m8+bN523jrFmzaNu2baVXaGhotZ5TRESkJkJDQ8/4Dpo1a9bF\nD2TG+W7YOp451fG88+Lw8fHhtttuY8OGDbRu3dpx3GKx8NBDD5GYmMi2bdtYs2YN06ZN488//8Rs\nNuPl5UVcXJxTMSIiIkhJSalWuzw9PR3/7ebmhtVqJT8/nzfffJP09HR8fHxITk6mtLT0jGvMZnOl\n600mk6O7+fTu1Yr7gvPJ6ukSExNJTEysdCw/P1/JnYiIXHLZ2dkEBARc+kCaFVv3HT58mKNHjwJw\n4sQJNm7cSGBgIAcOHHCcs3btWgIDA4HyLtXs7Gyys7O5//77efjhh89I6v47MTr9Xp9++inXX3/9\nWdvj7e1NSUnJedtdXFxMw4YN8fb25tdff2X9+vXnvaaqtv23wMBADh48SF5eHgArVqzgL3/5i1P3\nFhERcWm1NCu2sLCQYcOGERkZicVi4e233wYgNTWVkJAQYmJiiImJqfTdn5aWRlhYGOHh4Xz++efn\njVHH886aKyoqYvz48dhsNmw2GxEREfTq1YukpCT27duH2WzG39+fZ5555rz3mjZtGqtWreLkyZPc\neeedDBgwgISEBBYuXMiXX36Jh4cHjRo1YurUqWe9R9u2bTGbzURHRxMTE0Pjxo2rPK9du3a0b9+e\n8PBwrr32Wrp06eJ471yzaM/2XsVxT09PnnvuOZKSkhyTJwYNGnTeZxcREXF5tVSxc3NzIzk5mfbt\n21NSUkJsbCw9evQAYPjw4QwfPrzS+Xv27GH16tVkZWVRWFjI8OHDWbNmzbnzAXtN+uekXqvois2e\ntJeApmW13wDf2g9ZyRjjQtuWGBcbwN3/aUPjWzdONiy26W3DQpdrY2Ds5gbGBuhoYOzzD0W+tM7f\n0XPpnDQudP6f7oQuDLzkXbGO77Nxewlo4tz3Wf5hd0JfvDht+9vf/sbQoUPZsmULDRs25MEHH6z0\n/muvvQbAQw89BMCIESNITEykU6dOZ72ny3bFioiIiDjFgFmx+fn57Ny507Fix8KFC4mKiuIf//iH\nYyjZoUOHuPbaax3X+Pn5OVbQONejyEW0a9cuxo0b5yiT2u12vLy8WLp0qcEtExERkSrVYB27qiYR\nJiQknDHhsColJSUkJSUxYcIEvL29ue+++xg9ejQmk4lXXnmFF154gWeffdb59p9Gid1FFhQURGZm\nptHNEBEREWdVZ0eJ/z2vpl2xZWVlJCUlERUVRZ8+fQBo0qSJ4/17772Xhx9+GCiv0P3888+O9woL\nC/Hz8zvn/dUVKyIiIvVbLXbFTpgwgdatWzs2KwAq7Vv/8ccfExQUBJTvOJWVlUVpaSl5eXnk5uae\ndTes0x9FREREpP6qpS3FtmzZwsqVKwkKCiI6OhqTycSYMWNYtWoVO3bswGw207x5cyZPLp8o1rp1\na8LDw4mMjMTd3Z2UlJTz7jOvxE5ERETqtxp0xdZEly5d2LFjxxnHQ0JCznrNqFGjGDVqlNMxlNiJ\niIhI/VZLFbvaoMRORERE6jdtKSYiIiIidU0dzztFRERELjF1xYqIiIi4iFqaPFEblNhJze0HDhsQ\nd40BMU9jzzUudoRfunHBgbIvYg2N79bduL1qbTcYt08tADsNjN3ewNgAvxgYO9vA2GDsHsFWA2Mf\nq+V4qtiJiIiIuAgXmjxRx5snIiIicomZcb4SV8ennSqxExERkfpNFTsRERERF6ExdiIiIiIuQhU7\nEREREddgdyt/OXtuXabETkREROo1mxtYncyIbErsREREROouq7vziZ2z5xmljjdPRERE5NKyms2U\nuTm3jonVXLfXO1FiJyIiIvWa1d29GhW7up061cm0s7S0lIEDBxIdHY3FYiE1NRWAGTNm0K9fP6Kj\no4mPj6eoqAiAgoICOnXqRExMDDExMUyaNMlxr1WrVmGxWIiKimLkyJH8/vvvAPz8888MGzaMmJgY\noqKi+Oyzz2r9OWsqJyeHhx9+uNrvf/LJJ7z++usALFmyhOXLl1+yNoqIiFwurGYzVjc3516q2FWf\np6cnCxYsoEGDBlitVgYPHkxISAgjRozg0UcfBeDtt98mNTWVZ555BoCWLVuSkZFR6T5Wq5XnnnuO\n1atX07hxY6ZNm8bChQtJSEhgzpw5REREMGjQIPbs2cPIkSP55JNPLqjdNpsNcx3+hffu3ZvevXsD\nMGjQIINbIyIiUjfYcHN6a1xbHV/Irs5mIQ0aNADKq3dlZWUAeHt7O94/fvz4eZMou90OQElJCXa7\nneLiYvz8/AAwmUwUFxcD8OeffzqOVyUnJ4chQ4YwatQo7r777koVwZtvvpmpU6cSHR3Nf/7zH6Kj\no4mJicFisdC+ffnO2Xl5eYwYMYL+/fszZMgQ9u3bh81mIzQ01BH/xhtvZPPmzQAMGTKE3Nxctm3b\nxqBBg4iNjWXw4MHs37+/yrZVxIyNjeXYsco7J2/bto3Y2Fjy8vLIyMhgypQpAKSmpvLmm2+e8/MT\nERGpD8owU4abk686mzoBdbRiB+XVr9jYWHJzc4mLiyM4OBiAV155heXLl3PllVeyYMECx/n5+fnE\nxMTg4+PDo48+SteuXXF3dyclJQWLxULDhg25/vrrHUlZQkICDz74IG+//TYnTpw4b5Lz3XffkZWV\nhb+/P/Hx8axZs4awsDCOHz9O586defLJJwHIzMwE4MUXX6RXr14APPXUU0yePJmWLVuybds2Jk2a\nxFtvvUVgYCB79uwhLy+PDh06sGXLFoKDgyksLKRly5Y0bdqUd955B7PZzJdffsn06dOZOXNmpXbN\nnz+flJQUbr75Zo4fP46Xl5fjva1bt/LPf/6TOXPm4Ofnx+bNmzGZTBf2ixEREXExNtyxYnPy3Lqd\n2NXZ1pnNZjIzM1m/fj3ffvstu3fvBmDMmDGsW7cOi8XCwoULAfD19WXdunVkZGQwfvx4xo4dS0lJ\nCWVlZSxevJjly5ezYcMGgoKCSEtLA+CDDz6gf//+fPbZZ6SlpfHEE0+csz3BwcE0b94ck8lEZGQk\nW7ZsAcDNzY2wsLBK52ZlZbFjxw4ef/xxjh07xtatW3n00UeJjo7m6aef5rfffgOgS5cu5OTk8PXX\nXzNq1Cg2b97Md999x0033QTA0aNHSUpKwmKx8Nxzzzk+g9PdcsstPP/887z99tv8+eefjirmnj17\nePrpp5k7d+45q5EiIiL1nRW3ar3qsjqb2FXw8fHhtttuY8OGDZWOWywW1qxZA5SPyWvcuDEAHTp0\noEWLFuzfv58dO3ZgMpkICAgAIDw8nK1btwKwbNkywsPDAejcuTMnT57k8OHDTrerovLl5eVVqQq2\na9cuZs+ezSuvvILJZMJms9GoUSMyMjLIzMwkMzOTVatWAdC1a1dHMhcSEsLRo0fJycmha9euQPlk\nkW7durFy5Urmzp3LyZMnz2jHQw89xLPPPsuJEycYPHgw+/btA8qTXS8vL7Zv3+70M1Vl1qxZtG3b\nttKrogtZRETkUgoNDT3jO2jWrFkXPY4VczUSu7qdOtXJ1h0+fJijR48CcOLECTZu3EhgYCAHDhxw\nnLN27VoCAwMd59ts5SXUvLw8cnNzadGiBX5+fuzevZsjR44A8MUXXziu8ff3Z+PGjUB5dau0tJQm\nTZqctU3fffcdBQUF2Gw2srKyHMlXxTg+KK+wPf7440ydOpWrrroKKE9MAwIC+PDDDx3n7dy5Eyiv\nAm7duhWz2Yynpyft2rVj6dKljnufPiYwPT29ynbl5eXRpk0bRo4cSceOHdm7dy8AjRo14rXXXuPl\nl18mJyfnPJ/42SUmJvLjjz9WemVnZ9f4fiIiIs7Kzs4+4zsoMTHxosexVaNaV9cnT9TJMXZFRUWM\nHz8em82GzWYjIiKCXr16kZSUxL59+zCbzfj7+ztmxG7evJmZM2fi4eGByWRi8uTJNGrUiEaNGpGQ\nkEBcXBweHh74+/vzwgsvAPDkk08yceJE/v3vf2M2m5k6deo529SxY0emTJnCgQMH6NatG3369AGo\nVK3Lzs7m559/5qmnnsJut2MymcjIyGDatGlMmjSJOXPmYLVaiYiIoF27dnh6euLv70/nzp2B8gpe\nVlYWbdu2BSA+Pp4nn3ySOXPmOMbr/be33nqLTZs2YTKZaNOmDSEhIY6qZJMmTUhLS3NU9URERORM\n5ZMnnD23bjPZTy85SZVycnKYP38+c+fONbopdUJ+fj6hoaFkP7CXgEYG/BPfV/shT2evunhaK8L3\nGBgcyPoq1tD47j2eNiy27YbJhsUG4AYDY7c3MLbR8Y3uoGhjYGxn1/+4BPKPuROaHUh2drZjONUl\nifO/32dzs600czLML/nwcKjbJW9bTdXJip2IiIhIbbFWYx07A/NdpyixO82uXbsYN26co3vVbrfj\n5eXF0qVLufXWWw1unYiIiFwK5ZMnnFsOzIodnFwa5b8VFhYybtw4fvvtN8xmM/feey9Dhw7lxRdf\n5NNPP8XT05OWLVvy/PPP4+PjQ0FBAREREY75AZ06daq0lm5VlNidJigoyLEOnYiIiNQPVtwoq4XE\nzs3NjeTkZNq3b09JSQmxsbH06NGD22+/nbFjx2I2m3nppZdIS0vj8ccfB6reWetc6uSsWBEREZHa\nUt4V6+7kq+azYn19fR27Unl7e9OqVSt++eUXevTo4ViHtnPnzhQWFtY4hhI7ERERqdeMWO4kPz+f\nnTt3OnbWqrBs2TJCQkIqnRcTE8PQoUMdW4+ei7piRUREpF6rWKDYuXMvXElJCUlJSUyYMAFvb2/H\n8Tlz5uDh4YHFYgGgWbNmrFu3jsaNG/PDDz8wevRoPvjgg0rX/DcldiIiIlKvVWersIrErqpdmBIS\nEs67gHJZWRlJSUlERUU51sSF8o0IPvvsMxYsWOA45uHhUeXOWh06dDjr/ZXYiYiISL1WPnmieold\nTdexmzBhAq1bt+b+++93HFu/fj3z5s1j4cKFeHp6Oo4fPnyYq666CrPZXGlnrXNRYiciIiL1WsXk\nCefOrbktW7awcuVKgoKCiI6OxmQy8dhjj/Hss89y6tQpHnzwQeD/ljU5285a56LETkREROo1WzW6\nYm3UfMOuLl26sGPHjjOOn23b0LCwMMLCwqoVQ4mdiIiI1GvVmzxRszXsaosSO6m5mdR0jcYLYvev\n/Zinm2zgXrWr+xi7V6vJ4D1Dy643br9W8z7j9qkFsP5h3LObDhsWutxB40LbdxkXG2C/8+vSXnTH\njQvNL+5AYO3Fq94YOyV2IiIiInVWxeLDzp1b867Y2qDETkREROq16nXFXoyV7C4dJXYiIiJSr1Vv\n8sTF2XniUlFiJyIiIvVaGWanx9iV1fHdWJXYiYiISL1mq8YYO5u6YkVERETqruptKaauWBEREZE6\nq3qTJ9QVKyIiIlJnafKEiIiIiIvQ5AkRERERF1G9yRN1O3Wq22mniIiIiDitbqeddVBpaSlxcXGc\nOnUKq9VK3759SUhIIDU1lXfffZemTZsCMGbMGEJCQti4cSMvvfQSZWVleHh48MQTT9CtWzcARowY\nwa+//orVaqVLly6kpKRgMpkAyMrKYvbs2ZjNZtq2bctLL71k2DOLiIi4Ms2Krcc8PT1ZsGABDRo0\nwGq1MnjwYEJCQgAYPnw4w4cPr3R+kyZNSEtLw9fXl59++on4+HjWr18PwIwZM/D29gYgKSmJ1atX\nExERwYEDB3jjjTdYunQpPj4+HD7s3A7cVqsVN7e6/Q9ORESkrnGlWbF1u3V1VIMGDYDy6l1ZWZnj\nuN1+5sbA7dq1w9fXF4A2bdpw8uRJTp06BeBI6k6dOkVpaamjWvfuu+9y33334ePjA5Qnh2eTk5ND\nXFwcjzzyCJGRkQCsWLGCgQMHEhMTQ0pKCna7nYMHD9K3b19+//137HY7cXFxbNy48UI/ChERkcue\nFTfKnHzV9YqdErsasNlsREdH07NnT3r27ElwcDAACxcuJCoqin/84x8cPXr0jOs+/PBDOnTogIeH\nh+NYfHw8t99+Oz4+Ptx9990A7N+/n3379jF48GAGDRrEhg0bztme7du389RTT/Hhhx+yZ88esrKy\nWLJkCRkZGZjNZlasWIG/vz8jR44kJSWF+fPn07p1a3r06HERPxUREZHLU3lXrLuTr7qd2KkrtgbM\nZjOZmZkUFxczevRodu/ezX333cfo0aMxmUy88sorPP/88zz33HOOa3766SemT5/O/PnzK91r3rx5\nlJaWMnbsWL766iu6d++O1WolNzeXRYsWcfDgQYYMGcKqVascFbz/FhwcjL+/PwBfffUV27dvZ8CA\nAdjtdk6ePOkY9zdgwABWr17N0qVLyczMdOpZZ82aRWpqak0+JhERkQsSGhp6xrGEhAQSExMvahyt\nYycA+Pj4cOutt7Jhw4ZKY+vuvfdeHn74YcfPhYWFJCQk8OKLLxIQEHDGfTw9PenduzfZ2dl0794d\nPz8/OnfujNlsJiAggOuvv579+/fTsWPHKttR0TUM5d3BMTExjBkz5ozzTpw4waFDhwA4duwYDRs2\nPO8zJiYmnvEHlJ+fX+Ufm4iIyMWUnZ1d5ffmxeZKkyfUFVtNhw8fdnSznjhxgo0bNxIYGEhRUZHj\nnI8//pigoCAA/vzzT0aNGsUTTzxB586dHeccO3bMcU1ZWRmfffYZN9xwAwB9+vRh06ZNjngHDhyg\nRYsWTrWve/fufPjhh44JF3/88QcHDx4E4KWXXqJfv34kJSUxceLEC/kYREREXEbF5AnnXnU7dVLF\nrpqKiooYP348NpsNm81GREQEvXr1Yty4cezYsQOz2Uzz5s2ZPHkyAIsWLSI3N5fZs2eTmpqKyWRi\n3rx52O12HnnkEU6dOoXNZuO2225j8ODBANxxxx188cUXREZG4ubmxrhx42jcuLFT7WvVqhWPPfYY\nDz74IDabDQ8PD1JSUigoKOD7779n8eLFmEwm1qxZQ0ZGBjExMZfssxIREbkcVEyecPbcusxkr2oq\np8g5VHTFZv+5lwBb2fkvuMjs/rUespLJ/zEu9tO9jYsNYGpvbHzbB8bFdt//tHHBAWuTyYbFNrU0\nLHS5G4wLbd9lXGyA/T8YF/u4caH5xd2dUYGBl7wrtuL77M7sATQMuNKpa47lH2Vd6LJa6yauLlXs\nREREpF7T5Ampdbt27WLcuHGOte7sdjteXl4sXbrU4JaJiIhc3lxp8oQSu8tEUFCQ00uUiIiIiPPK\nMDs9xq5MkydERERE6i7b/y4+7Oy5dVndbp2IiIjIJaa9YkVERERchM3pNezcLmjyRGFhIcOGDSMy\nMhKLxcKCBQuA8jVnH3zwQfr27Ut8fHylbUnT0tIICwsjPDyczz///LwxlNiJiIhIveb84sTOT7Ko\nipubG8nJyXzwwQcsWbKERYsWsWfPHl577TW6d+/ORx99xG233UZaWhoAu3fvZvXq1WRlZfH666/z\nzDPPcL5V6pTYiYiISL1WMXnCuVfNUydfX1/aty9fENTb25tWrVpx6NAhsrOzHRsGxMTEsHbtWgA+\n+eQTIiIicHd3JyAggOuuu45t27adM4YSOxEREanXKiZPOPO6WJMn8vPz2blzJ506deK3337jmmuu\nAcqTv4ptQQ8dOsS1117ruMbPz8+x5/vZaPKEiIiI1Gs1mTwRGhp6xnsJCQkkJiae9x4lJSUkJSUx\nYcIEvL29HWvUVvjvn6tDiZ2IiIjUazXZeaKmW4qVlZWRlJREVFQUffr0AaBp06b8+uuvXHPNNRQV\nFdGkSROgvEL3888/O64tLCzEz8/vnPdXYic11wxjOvM7GBDzNNcZuFes4Xt2tjY2vMnAPUNP/WHc\nXq0AboeN26vW1s3YZ8fIf/cPGhgbuG6YcbGPnzQutkctb+5QhhsmpxcovrDGTZgwgdatW3P//fc7\njvXu3Zv09HQeeughMjIyHNXA3r17M3bsWB544AEOHTpEbm4uwcHB57y/EjsRERGp16y4YXYyJbqQ\nWbFbtmxh5cqVBAUFER0djclkYsyYMYwcOZLHHnuM999/n+bNm/Pqq68C0Lp1a8LDw4mMjMTd3Z2U\nlJTzdtMqsRMREZF6rSZdsTXRpUsXduzYUeV7//73v6s8PmrUKEaNGuV0DCV2IiIiUq9ZMTvdFVvX\nd55QYiciIiL1Wnm1ztnErpYHAFaTEjsRERGp18qTtUs/xq42KLETERGRek0VOxEREREXYatGYnch\nkydqgxI7ERERqdesmLE7ndhp8oSIiIhInWXFzelKnLMJoFGU2ImIiEi9ZsUNk5MpkRI7F1NaWkpc\nXBynTp3CarXSt29fEhISSE1N5d1336Vp06YAjBkzhpCQEMrKypg4cSI//PADNpuNqKgoHnroIQCG\nDk8gKIIAACAASURBVB1KUVERV1xxBSaTiXnz5tGkSRMyMjL4/+zdeVyVdd7/8ddhFXFfy9QKTU2N\nrLTUcRsZVzYPQreMYz+XvMdRcCsx1AnL7sx1KimrqXRcRp0UBALRxLQmU1Mrt3HJNBfUsQEXUEDO\nOb8/uDm35AKY51x4eD8fj/PIc52L6/39XoF8/H6/13XNnj2b++67D4DBgwcTHh5uWJ9FRERcWXnW\n2JV9P2OosCsnLy8vlixZgo+PDxaLhcjISLp16wbAsGHDGDZsWIn909PTuXbtGikpKeTl5dG/f3+C\ngoJo1KgRAPPnz6d169Y35AQGBjJt2rRytc1iseDuXrG/4URERCqa8lwVC+4VurSr2CsAKygfHx+g\naPSusLDQvt1ms92wr8lk4sqVK1gsFq5evYqXlxfVqlWzf261Wm+acbNj3cyOHTsYPHgwf/rTnwgM\nDAQgOTmZiIgIzGYzcXFx2Gw2MjMz6dOnDxcuXMBmszF48GC2bt1a5j6LiIi4qkLcKMS9jK+KXTpV\n7NZVUFarlQEDBvCb3/yG3/zmN/j7+wOwbNkyQkNDmTp1KpcuXQKgT58++Pj40KVLF3r27MmIESOo\nUaOG/VixsbGYzWbefffdEhkbNmwgJCSEcePGcfbs2du258CBA/z5z38mPT2do0ePkpaWxsqVK0lM\nTMTNzY3k5GQaNWrEyJEjiYuL4+OPP6Z58+Z07tz5Lp8ZERGRe48VDyxlfFkr+GSnCrs74Obmxtq1\na/niiy/Ys2cPP/zwA7///e/JyMggKSmJevXq8cYbbwCwZ88e3N3d+eqrr8jIyOCjjz7i1KlTAMyb\nN4+UlBSWL1/Orl27SEpKAqBnz55s2rSJ5ORkOnfuzOTJk2/bHn9/f/vU7rZt2zhw4ADh4eEMGDCA\nbdu2cfLkSQDCw8PJyclh1apVpR5TRESksrDghgX3Mr4qdulUscvOCq5atWo8/fTTfPnllyXW1j37\n7LOMGjUKgE8//ZSuXbvi5uZGnTp1ePLJJ9m3bx+NGzemQYMGAFStWpWgoCD27t1LaGgoNWvWtB8r\nIiKCOXPm3LYdxVPDUDSFazabmTBhwg375eXlce7cOQCuXLlC1apVS+3jggULiI+PL3U/ERGRuy0g\nIOCGbVFRUURHR9/VHGs5bnfiVqFX2GnErtyysrK4fPkyUFQobd26FT8/P86fP2/f57PPPqNFixYA\n3H///Wzbtg0oKqa+//57/Pz8sFgsZGdnA3Dt2jU+//xzHnnkEYASx8rIyKB58+Zlbl+nTp1IT08n\nKysLgIsXL5KZmQnA3LlzCQkJYezYsWW+MCM6OppDhw6VeGVkZJS5PSIiIncqIyPjht9Bd7uoA7DY\n3LFYy/iyVezCTiN25XT+/HleeuklrFYrVquV/v370717d2JiYvjXv/6Fm5sbDzzwAK+++ipQdKuS\n2NhYgoKCgKLp0BYtWnD16lVGjBiBxWLBarXSqVMnnn32WQCWLl3Kpk2b8PDwoGbNmsycObPM7WvW\nrBnjx49n+PDhWK1WPD09iYuL4/Tp0+zbt48VK1ZgMpnYsGEDiYmJmM3mu3+SRERE7iGFhW5YC8s4\nYldYscfETLayXn4p8r9OnTpFQEAAGVV+pLFbYelfcJfZ2jg9soS/LTcue+hQ47IBeNzYeFuycdnW\n74zLBvDMftmwbGv/Vw3LBuAR46JtvzMuG8D2nHHZV/ONy8509yDofj8yMjJo3Lixw3KKf5+dS12N\npdH9Zfoa98wzNAwMd3jb7pRG7ERERKRSsxS6YSnjiB0VfMROhd094vDhw8TExGAymYCiiyS8vb1Z\ntWqVwS0TERG5t1kt7mUu7EwWrbGTu6BFixasXbvW6GaIiIi4nMJr7hReK2PBVtb9DKLCTkRERCo1\nq9Udq6VsJZHVWrELu4o9USwiIiIiZaYROxEREancCt2LXmXdtwJTYSciIiKVm8Wt7AWbpWJPdqqw\nExERkcqt0FT0Kuu+FZgKOxEREancLEBZ77dvcWRDfj0VdiIiIlK5FVL2wu5XPHBpypQpbN68mbp1\n65KSkgLAhAkTOH78OFD0fPeaNWuSmJjI6dOn6d+/P35+fgA8/vjjTJ8+vdQMFXYiIiJSuTlpxC4s\nLIwhQ4YQExNj3/aXv/zF/udZs2ZRvXp1+/umTZuSmJhYrgwVdnLnHgK8DMita0DmdZobGd7KyHCg\nkbHxpkeNy3bLMi4boLCjcc9rNaXFGZYNYFpg4HNy/27wFZB+xkVXdf6jwO2qWAFnPqv22v++yrrv\nHWrfvj2nT5++5efr1q1jyZIldx6A7mMnIiIilZ2VopG4srysjmnCzp07qVevHk2bNrVvO3XqFGaz\nmSFDhrBz584yHUcjdiIiIlK5OWmN3e18+umnBAUF2d83aNCAzZs3U7NmTfbv38+YMWNITU3F19f3\ntsdRYSciIiKV2x2ssQsICLjho6ioKKKjo8sfb7Hw2WefkZCQYN/m6elJzZo1AWjTpg1NmjTh+PHj\ntGnT5rbHUmEnIiIildsdjNhlZGTQuHHjckfZbLYbtn311Vf4+fnRsGFD+7asrCxq1aqFm5sbJ0+e\n5MSJEzRp0qTU46uwExERkcrNSVfFvvDCC2zfvp0LFy7Qo0cPoqOjGThwIOvWrSsxDQtFa+7efvtt\nPD09MZlMvPrqq9SoUaPUDBV2IiIiUrk5aY3dvHnzbrp95syZN2zr3bs3vXv3LneGCjsRERGp3PTk\nCREREREX4aT72DmDCjsRERGp3IrvY1fWfSswFXYiIiJSuVWA+9jdLSrsREREpHLTGrvKq6CggMGD\nB3Pt2jUsFgt9+vQhKiqKgwcPEhcXR35+Ph4eHsTFxfHYY4+xZ88eXn75/55zGBUVxe9+9zsArl27\nxowZM9i+fTvu7u5MmDCBXr16sXjxYj755BM8PDyoU6cOr7/+Ovfff79RXRYREXFtGrGrvLy8vFiy\nZAk+Pj5YLBYiIyPp2rUrb7/9NtHR0XTp0oUtW7Ywe/Zsli5dSsuWLUlISMDNzY3z588TGhpKz549\ncXNz47333qNu3bqsX78egAsXLgDQunVrEhIS8Pb2ZsWKFcyePZu//OUvpbbNYrHg7m7wA6tFRETu\nNS40YudmdAPuRT4+PkDR6F1hYSEmkwmTycTly5cBuHz5sv3u0d7e3ri5FZ3mvLw8+58B1qxZwx//\n+Ef7+1q1agHw9NNP4+3tDUC7du04d+7cLduyY8cOBg8ezJ/+9CcCAwMBSE5OJiIiArPZTFxcHDab\njczMTPr06cOFCxew2WwMHjyYrVu33q1TIiIicu8qLOerAtOI3R2wWq2EhYVx4sQJBg8ejL+/P7Gx\nsTz//PPMmjULm83GypUr7fvv2bOHKVOmkJmZyezZs3Fzc7MXgW+++SY7duygadOmvPzyy9SpU6dE\n1urVq+nWrdtt23PgwAFSU1Np1KgRR48eJS0tjZUrV+Lu7s4rr7xCcnIyoaGhjBw5kri4OPz9/Wne\nvDmdO3e++ydHRETkXuNCI3Yq7O6Am5sba9euJScnhzFjxnDkyBFWrVrF1KlT+d3vfkd6ejpTpkxh\n0aJFAPj7+/Ppp5/y448/MnnyZLp160ZhYSFnz57lqaee4qWXXmLx4sW88cYbzJ49256TlJTE/v37\nWbp06W3b4+/vT6NGjQDYtm0bBw4cIDw8HJvNRn5+PnXr1gUgPDycdevWsWrVKtauXVumvi5YsID4\n+Pg7OU0iIiK/SkBAwA3boqKiiI6OvrtBuo+dAFSrVo2nn36aL7/8kqSkJKZNmwZA3759mTp16g37\n+/n5UbVqVY4cOUKbNm3w8fGhV69e9q9Zs2aNfd+tW7fywQcfsGzZMjw9PW/bjuKpYSh6uLDZbGbC\nhAk37JeXl2ef1r1y5QpVq1YttY/R0dE3/ACdOnXqpj9sIiIid1NGRgaNGzd2fJAL3cdOa+zKKSsr\nyz6NmpeXx9atW2nWrBkNGjRgx44dAHz99dc89NBDQFERZLEUfbecPn2aY8eO8cADDwDQs2dPtm3b\nBmA/DhRNrcbFxbFw4UJq165drvZ16tSJ9PR0srKyALh48SKZmZkAzJ07l5CQEMaOHWsvQkVERCo9\nrbGrvM6fP89LL72E1WrFarXSv39/unfvTrVq1fif//kfrFYr3t7evPbaawDs2rWLv/71r3h6emIy\nmZg+fbr9IokXXniBmJgYZs6cSZ06dewPAZ4zZw5Xr15l3Lhx2Gw2GjVqxLvvvlum9jVr1ozx48cz\nfPhwrFYrnp6exMXFcfr0afbt28eKFSswmUxs2LCBxMREzGazY06UiIjIvcKF1tiZbDabzehGyL2l\neCo2o9WPNPZy/j9dbI2cHlnCV28bl93lDeOyAXjY4PwtxkXbvjIuG4z9vndfF2dcOGBa8HLpOzmI\ndauxt5CyHTYw3MCRqVNWD36X7+fwqdji32c/9s2g0LdsOR65p/BLD3DeNHE5acROREREKrdCyn5R\nhKZi5W44fPgwMTExmEwmoOgiCW9vb1atWmVwy0RERO5xFso+xVrBp2JV2N0jWrRoUeZblIiIiEg5\nuNAaOxV2IiIiUrnpWbEiIiIiLkJr7ERERERchNbYiYiIiLgIrbETERERcRGFQFlvWaipWBEREZEK\nrJCyP2RVhZ2IiIhIBaY1diIiIiIuQmvsRIDmgK8BuQY/K/YxbwPD2xqYDeBncP6/jYs2ZRqXDcCD\nBmb3M/ZZsbaxrxgXPs24aABTWacHHcHAAsaUD+x3YmAhYCrHvhWYCjsRERGp3MpTrFXwws7IfwuI\niIiIGM9SztcdmjJlCp07dyY4ONi+LT4+nm7dumE2mzGbzXzxxRf2z95//3169+5Nv379+Oc//1mm\nDI3YiYiISOVWnmLtVxR2YWFhDBkyhJiYmBLbhw0bxrBhw0psO3r0KOvWrSMtLY2zZ88ybNgwNmzY\ngMl0+zljjdiJiIhI5VZYztcdat++PTVq1Lhhu81mu2FbRkYG/fv3x8PDg8aNG/Pggw+yZ8+eUjNU\n2ImIiEjlVvys2LK8HLDGbtmyZYSGhjJ16lQuX74MwLlz57j//vvt+zRs2JBz586VeiwVdiIiIlK5\nOWmN3c38/ve/JyMjg6SkJOrVq8cbb7zxq46nwk5ERESknAICAmjZsmWJ14IFC8p9nDp16tjXzT37\n7LP26daGDRty5swZ+35nz56lYcOGpR5PF0+IiIhI5WYBrGXc93+Xw2VkZNC4ceNyR/1yPd358+ep\nX78+AJ999hktWrQAoGfPnrz44osMHTqUc+fOceLECfz9/Us9vgo7ERERqdzKc4PiG69zKLMXXniB\n7du3c+HCBXr06EF0dDTbt2/nX//6F25ubjzwwAO8+uqrADRv3px+/foRGBiIh4cHcXFxpV4RCyrs\nREREpLIr7wURd7iQbd68eTdsGzhw4C33/+Mf/8gf//jHcmWosBMREZHKrbwXRFTgKxRU2JVTQUEB\ngwcP5tq1a1gsFvr06UNUVBQHDx5k+vTpXLlyhQceeIC5c+fi61v0INXiz3JycnBzc2P16tV4eXnx\nl7/8haSkJC5dusTu3bvtGWfOnGHy5MlcvnwZq9XKxIkT6d69u1FdFhERcW0Wyj7FagI8HdiWX0mF\nXTl5eXmxZMkSfHx8sFgsREZG0rVrV2bMmMFLL71E+/btSUhI4MMPP2TcuHFYLBZiYmKYO3cuLVq0\n4OLFi3h6Fn1HBAQEMGTIEHr37l0iY+HChfTv359BgwZx9OhRRo4cyaZNm0ptm8Viwd3d3SH9FhER\ncVmFlK+wq8Aq8GBixeXj4wMUjd4VFhZiMpn46aefaN++PQCdO3dmw4YNAPzzn/+kVatW9qtcatas\naV/86O/vT7169W44vslkIicnB4BLly7d9vLmHTt2MHjwYP70pz8RGBgIQHJyMhEREZjNZuLi4rDZ\nbGRmZtKnTx8uXLiAzWZj8ODBbN269S6dERERkXuYwTcovps0YncHrFYrYWFhnDhxgsGDB+Pv70/z\n5s3JyMggICCAdevWcfbsWQCOHz8OwIgRI8jOzqZ///48//zztz1+VFQUw4cPZ+nSpeTl5bFo0aLb\n7n/gwAFSU1Np1KgRR48eJS0tjZUrV+Lu7s4rr7xCcnIyoaGhjBw5kri4OHt7O3fufFfOh4iIyD3t\nDm53UlGpsLsDbm5urF27lpycHEaPHs0PP/zA66+/zmuvvca7775Lz5497dOtFouF3bt3s2bNGry9\nvRk6dCht27alY8eOtzx+amoqAwcOZOjQoXz33XdMmjSJ1NTUW+7v7+9Po0aNANi2bRsHDhwgPDwc\nm81Gfn4+devWBSA8PJx169axatUq1q5dW6a+LliwgPj4+LKeGhERkbsmICDghm1RUVFER0ff3aDy\nPFGigq94UmH3K1SrVo1nnnmGL7/8kmHDhvHRRx8BRaN0W7ZsAeC+++6jQ4cO1KxZE4Bu3bpx4MCB\n2xZ2q1evth+rXbt25Ofnk5WVRZ06dW66f/HUMBTd+NBsNjNhwoQb9svLy7M/Z+7KlStUrVq11D5G\nR0ff8AN06tSpm/6wiYiI3E13ehPgciuk7IVdBR+x0xq7csrKyrI/oDcvL4+tW7fi5+dHVlYWUDRN\nu3DhQgYNGgRAly5dOHToEPn5+RQWFvLNN9/QrFmzEsf85V2oGzVqZF//dvToUQoKCm5Z1P1Sp06d\nSE9Pt7fn4sWLZGZmAjB37lxCQkIYO3Ys06ZNu8MzICIi4mK0xq7yOn/+PC+99BJWqxWr1Ur//v3p\n3r07S5YsYfny5ZhMJnr37k1YWBgANWrUYNiwYQwcOBCTyUT37t3tty6ZM2cOn376Kfn5+fTo0YPw\n8HCioqKYPHky06ZNY/Hixbi5uTFr1qwyt69Zs2aMHz+e4cOHY7Va8fT0JC4ujtOnT7Nv3z5WrFiB\nyWRiw4YNJCYmYjabHXKeRERE7hnlmYqt4FfFmmy/HC4SKUXxVGxG0I809nX+P11sjZweWcKl8cZl\n11xjXDYAfgbnZ1TSbMD2oHHZbs3KuqrcQca9Yli0bZpx2QD8aGB2eW/aexedyvcgYL+fw6dii3+f\n/fhjBoWFZcvx8DiFn1+A86aJy0lTsSIiIiIuQlOx94jDhw8TExNjvweezWbD29ubVatWGdwyERER\nqShU2N0jWrRoUeZblIiIiEjlpMJOREREKrniy2LLum/FpcJOREREKrlCyl6wqbATERERqcA0Yici\nIiLiIiyUvWAz8D4wZaDCTkRERCq54sdKlHXfikuFnYiIiFRymooVERERcRG6eEJERETERWjETgR2\nYsx30FMGZF5nS75x2SGfG5cNQJbB+ZsMzP7BwGzA9Lxx2dblxj590jbNuGy3GS8bFw5YH3nVuPA8\n46IxAVWcGagROxEREREXoRE7EREREReh252IiIiIuAjd7kRERETERWjETkRERMRFaMRORERExEU4\nZ8RuypQpbN68mbp165KSkgLA7Nmz+fzzz/Hy8qJp06bMnDmTatWqcfr0afr374+fnx8Ajz/+ONOn\nTy81w9hr2EVEREQMd62crzsTFhbGRx99VGJbly5dSE1NJSkpiQcffJD333/f/lnTpk1JTEwkMTGx\nTEUdqLATERGRSq94xK4srzsfsWvfvj01atQosa1z5864uRWVY+3atePs2bN3fHxQYSciIiKVXvF9\n7Mryctx97FavXk23bt3s70+dOoXZbGbIkCHs3LmzTMfQGjsRERGp5Mp/8URAQMANn0RFRREdHX1H\nLVi4cCGenp4EBwcD0KBBAzZv3kzNmjXZv38/Y8aMITU1FV9f39seR4XdHbJarQwcOJCGDRvy3nvv\ncfHiRSZMmMDp06dp3Lgxb775JtWrV7/l4sfc3FwGDx6MyWTCZrNx9uxZQkNDiY2N5cyZM0yePJnL\nly9jtVqZOHEi3bt3N7jHIiIirqr8F09kZGTQuHHju5KekJDAli1bWLJkiX2bp6cnNWvWBKBNmzY0\nadKE48eP06ZNm9seS4XdHVqyZAnNmjUjJycHgA8++IBOnToxcuRIPvjgA95//31efPFF4P8WP17P\n19eXtWvX2t+HhYXRu3dvoKhq79+/P4MGDeLo0aOMHDmSTZtKf0imxWLB3d39bnVRRESkknDe7U5s\nNluJ91988QUfffQRy5Ytw8vLy749KyuLWrVq4ebmxsmTJzlx4gRNmjQp9fhaY3cHzp49y5YtW4iI\niLBvy8jIwGw2A2A2m9m4cWOZj3fs2DGys7N56qmip9ubTCZ7wXjp0iUaNmx4y6/dsWMHgwcP5k9/\n+hOBgYEAJCcnExERgdlsJi4uDpvNRmZmJn369OHChQvYbDYGDx7M1q1by913ERER1+OciydeeOEF\nBg0axLFjx+jRowdr1qzhtdde48qVKwwfPhyz2Wy/+nXnzp2EhIRgNpsZN24cr7766g0XXtyMRuzu\nwOuvv05MTAyXL1+2b/vPf/5DvXr1AKhfvz5ZWVn2z4oXP1arVo1x48bRvn37EsdLS0ujX79+9vdR\nUVEMHz6cpUuXkpeXx6JFi27bngMHDpCamkqjRo04evQoaWlprFy5End3d1555RWSk5MJDQ1l5MiR\nxMXF4e/vT/PmzencufPdOB0iIiL3OOeM2M2bN++GbQMHDrzpvr1797bP5JWHCrty2rx5M/Xq1ePR\nRx9l+/btt9zPZDIBRUVeaYsf09LSmDNnjv19amoqAwcOZOjQoXz33XdMmjSJ1NTUW2b5+/vTqFEj\nALZt28aBAwcIDw/HZrORn59P3bp1AQgPD2fdunWsWrWqxDTw7SxYsID4+Pgy7SsiInI33e0LFG5N\njxSrtHbv3s2mTZvYsmUL+fn55ObmMmnSJOrVq8fPP/9MvXr1OH/+PHXq1AHAy8vLPmd+s8WPBw8e\nxGKx0Lp1a3vG6tWr7TcwbNeuHfn5+WRlZdmP+Us+Pj72P9tsNsxmMxMmTLhhv7y8PM6dOwfAlStX\nqFq1aqn9jY6OvuEH6NSpUzf9YRMREbmb7uYFCrdXfLuTsu5bcWmNXTlNnDiRzZs3k5GRwfz583nm\nmWeYM2cOv/3tb0lISAAgMTHRXvhkZWVhtVoBbrr4MTU1laCgoBIZjRo1sq9/O3r0KAUFBbcs6n6p\nU6dOpKen26eCL168SGZmJgBz584lJCSEsWPHMm3atF9xFkRERFxJWdfXFb8qLo3Y3SX//d//zfjx\n41mzZg0PPPAAb775JlC0+PHtt9/G09MTk8l0w+LH9PR0PvjggxLHmjx5MtOmTWPx4sW4ubkxa9as\nMrejWbNmjB8/nuHDh2O1WvH09CQuLo7Tp0+zb98+VqxYgclkYsOGDSQmJtov+BAREam8XGfEzmT7\n5XW3IqUonorNuO9HGns4/xvc9pTTI0tImVP6Po4S8oJx2QA8bnB+Yum7OMwRA7MB3jAu2rbcuGwA\nW3Pjsj1mvGxcOGB95FXjwvOMiz5l8iCgip/Dp2KLf5+dOPE7CgtLX54E4OFxhaZNNzpxmrh8NGIn\nIiIilVK1atWoWbMmTZuW/RZlADVr1qRatWoOatWvo8LuHnH48GFiYmLsV9vabDa8vb1ZtWqVwS0T\nERG5N9WqVYsNGzbY7x1bVtWqVaNWrVoOatWvo8LuHtGiRYsy36JEREREyqZWrVoVtki7E7oqVkRE\nRMRFqLATERERcREq7ERERERchAo7ERERERehwk5ERETERaiwExEREXERKuxEREREXIQKOxEREREX\noRsUy50bD9RzfqxpuvMzrxc8wsDwhwzMBsg1Nt7IZ4YeN/I5tcBDzxmXbfIzLhvAZOAQRGFzA5/V\nCpiOGPusWqN4eOTg5/ep0c24J2nETkRERMRFqLATERERcREq7ERERERchAo7ERERERehwk5ERETE\nRaiwExEREXERKuxEREREXIQKOxEREREXocJORERExEWosBMRERFxESrsRERERFxEmQs7q9XKgAED\nGDVqFADp6ekEBQXx6KOPsn///hv2z8zM5IknnmDRokUA5ObmMmDAAMxmMwMGDKBjx47MnDkTgDNn\nzvDcc89hNpsJDQ1ly5Ytd6Nvt5WYmMj58+ft73v27MmFCxd+9XF37NhhP0dldX32E088cdt9//3v\nfzNu3LibfjZkyJCb/r8QERGRysGjrDsuWbKE5s2bk5OTA0CLFi2Ij4/n5Zdv/oDiN954g+7du9vf\n+/r6snbtWvv7sLAwevfuDcDChQvp378/gwYN4ujRo4wcOZJNmzbdUYfKKiEhgUceeYT69esDYDKZ\nHJp3O9dnl9aOBg0a8NZbbzm6SSIiInIPKtOI3dmzZ9myZQsRERH2bX5+fjz00EPYbLYb9t+4cSNN\nmjShefPmNz3esWPHyM7O5qmnngKKipnigvHSpUs0bNjwtu354IMPCA4OZsCAAcyfP5+TJ08SFhZm\n//ynn36yv3/nnXeIiIggODjYXoSuX7+effv2MWnSJMxmM/n5+dhsNpYuXUpYWBghISEcO3YMgIsX\nLzJmzBhCQkIYNGgQhw8fBiA+Pp6YmBgGDRpEnz59+OSTT+z5ubm5jB07ln79+jFp0iQAtm3bxpgx\nY+z7bN26lejoaICbnkOAWbNmERwcTEhICGlpaQCcPn2a4OBgAPLz85k4cSKBgYFERUVRUFBg/9qv\nvvqKQYMGERYWxvjx47l69SoAc+fOJSgoiNDQUGbPnn3b8ywiIiL3ljKN2L3++uvExMRw+fLlUve9\ncuUKH374IYsWLeKjjz666T5paWn069fP/j4qKorhw4ezdOlS8vLy7NO3N/PFF1/w+eefs2bNGry8\nvLh06RI1atSgevXqHDx4kFatWpGQkMDAgQOBounJ4oIqJiaGzZs306dPH5YtW0ZsbCytW7e2H7tO\nnTokJCTw97//nY8//pgZM2awYMECWrduzTvvvMO2bduIiYmxjzwePnyYf/zjH+Tm5mI2m+nRTBJ0\nhQAAIABJREFUowcABw8eJDU1lfr16xMZGcnu3bvp2LEjr776KtnZ2dSuXZs1a9YQHh5+y36uX7+e\nw4cPk5KSwn/+8x/Cw8N5+umnS+yzYsUKfHx8SE1N5dChQ/ZiNjs7m4ULF7J48WKqVKnCX//6VxYt\nWsTvf/97Nm7cSHp6OoC9mBYRERHXUOqI3ebNm6lXrx6PPvroLUeWrrdgwQKGDh2Kj48PcPPRqLS0\nNIKCguzvU1NTGThwIFu2bOH999+3j3LdzNdff01YWBheXl4A1KhRA4Dw8HASEhKwWq0ljv/111/z\n7LPPEhwczPbt2zly5Ij9WL9sW69evQBo27Ytp0+fBmDXrl2EhoYC0LFjRy5evEhubi4AAQEBeHl5\nUbt2bTp27MiePXsA8Pf3p0GDBphMJlq1amU/VmhoKMnJyVy+fJnvv/+erl273rKfu3fvJjAwEIC6\ndevy9NNPs3fv3hL7fPPNN4SEhADQsmVLWrZsCcD333/PDz/8QGRkJAMGDCApKYkzZ85QvXp1qlSp\nwtSpU/nss8/w9va+ZX6xBQsW2I9d/AoICCj160RERH6tgICAG34HLViwwOhmVWiljtjt3r2bTZs2\nsWXLFvLz88nNzSUmJuaW03h79uxhw4YNzJkzh0uXLuHm5oa3tzeDBw8GikazLBZLiZGy1atX20f3\n2rVrR35+PllZWdSpU6fMHenTpw/x8fE888wztG3blpo1a1JQUMCrr75KQkICDRs2JD4+nvz8/Fse\no7hYdHNzo7CwsNTM69fD2Ww2+3tPT0/7dnd3dywWCwBms5lRo0bh5eVF3759cXMr+0XJZSmqr9/3\nN7/5DfPmzbvhs08++YSvv/6a9PR0li1bxt/+9rfbHis6Oto+ZVzs1KlTKu5ERMThMjIyaNy4sdHN\nuKeUWllMnDiRzZs3k5GRwfz583nmmWduKOquLzqWL19ORkYGGRkZ/L//9/8YNWqUvaiDotG560fr\nABo1asTWrVsBOHr0KAUFBbcs6jp37kxCQgJ5eXlA0Ro4KCrKunbtyvTp0+1Tkvn5+ZhMJmrXrk1u\nbi7r16+3H8fX17dMU5FPPfUUycnJAGzfvp3atWvj6+sLFH3DFRQUkJ2dzTfffMNjjz1222M1aNCA\nBg0a8N5775VYE3i94nPZvn170tLSsFqtZGVlsXPnTvz9/Uvs26FDB1JSUoCiaeFDhw4B8Pjjj/Pt\nt99y4sQJAK5evcrx48e5cuUKly9fplu3bsTGxtr3FxEREddQ5qtif2njxo3MmDGD7OxsRo0aRatW\nrfjwww9L/br09HQ++OCDEtsmT57MtGnTWLx4MW5ubsyaNeuWX9+1a1cOHjzIwIED8fLyolu3bkyY\nMAGA4OBgNm7cSJcuXQCoXr06ERERBAYGUr9+/RKFV1hYGHFxcfj4+LBy5cpbXo0aHR3NlClTCAkJ\noWrVqiXa1rJlS5577jmys7MZPXo09evXt190UeyXxw0JCeHChQv4+fnddJ/iP/fq1YvvvvuO0NBQ\nTCYTMTEx1K1b1z6tCxAZGUlsbCyBgYE0a9aMtm3bAkVrBWfOnMnEiRMpKCjAZDIxfvx4fH19GT16\ntH3UMjY29pbnWURERO49Jlt55vgquI8//picnBzGjh3r8Kz4+Hh8fX0ZNmxYub5uxowZtG7d2n5x\nx72oeCo2440faVyv9Cnru2668yOvZ3vYuGxTO+OygV/xT8G7w/ajcdnH5xiXDfBQ2Vem3HUmv9L3\ncaiWxkVbtxuXDeD+w81vKebqPDxy8PP7VFOxd8Dgv6bvnqioKE6ePFnqmjEjhYWF4evry0svvWR0\nU0RERMQFVdjC7vDhw8TExNinJm02G97e3qxateqm+8fHxzuzeURFRZX7axISEhzQEhEREZEiFbaw\na9GiRYknVYiIiIjI7ZX9fhsiIiIiUqGpsBMRERFxESrsRERERFyECjsRERERF6HCTkRERMRFqLAT\nERERcREq7ERERERchAo7ERERERdRYW9QLPeAa0CBAbn5BmRex3TFwHCLgdkABjwauAQD+3/VuOii\nfAO/76tW4v/vpjzjsv+3BUY3wCCVtd+/nkbsRERERFyECjsRERERF6HCTkRERMRFqLATERERcREq\n7ERERERchAo7ERERERehwk5ERETERaiwExEREXERKuxEREREXIQKOxEREREXUebCzmq1MmDAAEaN\nGgVAeno6QUFBPProo+zfv9++37Vr14iNjSU4OJgBAwawY8cO+2dDhgyhb9++DBgwALPZTFZWVomM\n9evX06pVqxLHc5TExETOnz9vf9+zZ08uXLjwq4+7Y8cO+zkqq+uzn3jiidvu++9//5tx48bd9LMh\nQ4Y45dyJiIhIxVTmZ8UuWbKE5s2bk5OTA0CLFi2Ij4/n5ZdfLrHfP/7xD0wmEykpKWRlZfH888+T\nkJBg/3z+/Pm0bt36huPn5uaydOlS2rVrd6d9KZeEhAQeeeQR6tevD4DJZNxz6a7PLq0dDRo04K23\n3nJ0k0REROQeVKYRu7Nnz7JlyxYiIiLs2/z8/HjooYew2Wwl9j169CgdO3YEoE6dOtSoUYO9e/fa\nP7darTfNeOuttxg5ciSenp6ltueDDz6wjwjOnz+fkydPEhYWZv/8p59+sr9/5513iIiIIDg42F6E\nrl+/nn379jFp0iTMZjP5+fnYbDaWLl1KWFgYISEhHDt2DICLFy8yZswYQkJCGDRoEIcPHwYgPj6e\nmJgYBg0aRJ8+ffjkk0/s+bm5uYwdO5Z+/foxadIkALZt28aYMWPs+2zdupXo6GiAG85hsVmzZhEc\nHExISAhpaWkAnD59muDgYADy8/OZOHEigYGBREVFUVBQYP/ar776ikGDBhEWFsb48eO5erXoEeZz\n584lKCiI0NBQZs+eXeq5FhERkXtHmQq7119/nZiYmDKNarVq1YpNmzZhsVg4efIk+/fv5+zZs/bP\nY2NjMZvNvPvuu/ZtBw4c4OzZs3Tv3r3U43/xxRd8/vnnrFmzhrVr1/L888/TpEkTqlevzsGDB4Gi\n0biBAwcCRdOTn3zyCSkpKeTl5bF582b69OlD27ZtmTdvHomJiXh7ewNFhWhCQgKDBg3i448/BmDB\nggW0bt2a5ORkxo8fT0xMjL0thw8fZsmSJaxcuZJ33nnHPrV78OBBpk2bRlpaGidPnmT37t107NiR\nY8eOkZ2dDcCaNWsIDw+/ZT/Xr1/P4cOHSUlJYdGiRcyZM4eff/65xD4rVqzAx8eH1NRUoqOj2bdv\nHwDZ2dksXLiQxYsXk5CQQJs2bVi0aBEXLlxg48aNfPrppyQlJTF69OhSz7eIiIjcO0ot7DZv3ky9\nevV49NFHbzmydL2BAwfSsGFDwsPDeeONN3jyySdxcyuKmTdvHikpKSxfvpxdu3aRlJSEzWZj5syZ\nvPTSS/Zj3C7n66+/JiwsDC8vLwBq1KgBQHh4OAkJCVitVtLS0ggKCrLv/+yzzxIcHMz27ds5cuTI\nLXN69eoFQNu2bTl9+jQAu3btIjQ0FICOHTty8eJFcnNzAQgICMDLy4vatWvTsWNH9uzZA4C/vz8N\nGjTAZDLRqlUr+7FCQ0NJTk7m8uXLfP/993Tt2vWW/dy9ezeBgYEA1K1bl6effrrEyCfAN998Q0hI\nCAAtW7akZcuWAHz//ff88MMPREZGMmDAAJKSkjhz5gzVq1enSpUqTJ06lc8++8xe0IqIiIhrKHWN\n3e7du9m0aRNbtmwhPz+f3NxcYmJibjmN5+7uTmxsrP39oEGDeOihh4Ci9WEAVatWJSgoiL179xIQ\nEMCRI0cYMmQINpuNn3/+mdGjR7Nw4ULatGlT5o706dOH+Ph4nnnmGdq2bUvNmjUpKCjg1VdfJSEh\ngYYNGxIfH09+fv4tj1FcLLq5uVFYWFhq5vUjmDabzf7++ulkd3d3LBYLAGazmVGjRuHl5UXfvn3t\nBW9ZlKWovn7f3/zmN8ybN++Gzz755BO+/vpr0tPTWbZsGX/7299ue6wFCxYQHx9f5mwREZG7JSAg\n4IZtUVFR9qVMcqNSK4uJEyeyefNmMjIymD9/Ps8888wNRd31RUdeXp59PddXX32Fp6cnzZo1w2Kx\n2Kchr127xueff84jjzxCtWrV2LZtGxkZGWzatInHH3+c995775ZFXefOnUlISCAvLw8oWgMHRUVZ\n165dmT59un19XX5+PiaTidq1a5Obm8v69evtx/H19bVfCHI7Tz31FMnJyQBs376d2rVr4+vrC0BG\nRgYFBQVkZ2fzzTff8Nhjj932WA0aNKBBgwa89957JdYEXq/4XLZv3560tDSsVitZWVns3LkTf3//\nEvt26NCBlJQUoGha+NChQwA8/vjjfPvtt5w4cQKAq1evcvz4ca5cucLly5fp1q0bsbGx9v1vJzo6\nmkOHDpV4ZWRklPp1IiIiv1ZGRsYNv4NU1N1ema+K/aWNGzcyY8YMsrOzGTVqFK1ateLDDz/kP//5\nDyNGjMDd3Z2GDRvai8CCggJGjBiBxWLBarXSqVMnnn322RuOazKZbjs61bVrVw4ePMjAgQPx8vKi\nW7duTJgwAYDg4GA2btxIly5dAKhevToREREEBgZSv379EoVXWFgYcXFx+Pj4sHLlyluuH4yOjmbK\nlCmEhIRQtWpVZs2aZf+sZcuWPPfcc2RnZzN69Gjq169vv+ji+v5cLyQkhAsXLuDn53fTfYr/3KtX\nL7777jtCQ0MxmUzExMRQt25d+7QuQGRkJLGxsQQGBtKsWTPatm0LFK0VnDlzJhMnTqSgoACTycT4\n8ePx9fVl9OjR9lHL60dWRURE5N5nspVnjq+C+/jjj8nJyWHs2LEOz4qPj8fX15dhw4aV6+tmzJhB\n69at7Rd33ItOnTpFQEAAGTN+pHHd0qes77o450eW0NTA7GcMzAYw+G8L20njsv8137hsgId8jcuu\n+ohx2QC0Mi7a9k/jsgHcThn9F54xPDxy8PNLISMjg8aNGxvdnHvKHY/YVTRRUVGcPHmy1DVjRgoL\nC8PX17fEhSIiIiIid0uFLewOHz5c4hYrNpsNb29vVq1addP9nb3APyoqqtxfc/2NmkVERETutgpb\n2LVo0YK1a9ca3QwRERGRe0bZ77chIiIiIhWaCjsRERERF6HCTkRERMRFqLATERERcREq7ERERERc\nhAo7ERERERehwk5ERETERVTY+9hJxWWxWAA4m23Qt4/FmFi7fAOzLxmYDcY/UuyKcdn/NvhvSy93\n47KrWI3LBgz9mbPd/DHiTuPhkWNsAwzi4VH0w178+0bKToWdlNv58+cBGDzfyIemGijLwOx9BmZX\ndn5GN8BAeQbn7zcw28fAbMDPL8XYBhjs/PnzPPjgg0Y3455istlsBv8bXO41eXl57Nu3j/r16+Pu\nXv5hhICAADIyMhzQMuVX5Gyj89X3ytl3o/PV9zvLtlgsnD9/nrZt21KlSpW73DLXphE7KbcqVarQ\nvn37X3WMxo0b36XWKP9eyjY6X303TmXOV9/vjEbq7owunhARERFxESrsRERERFyECjsRERERF+E+\nffr06UY3QiqfZ555RvmVMNvofPXdOJU5X30XZ9JVsSIiIiIuQlOxIiIiIi5ChZ2IiIiIi1BhJyIi\nIuIiVNiJiIiIuAgVdiIiIiIuQoWdiIiIiItQYSciIiLiIlTYiYiIiLgID6MbIJXL5s2bOXLkCPn5\n+fZtUVFRDs+9du0aK1asYOfOnQB06NCBQYMG4enp6fDs6/3nP/8p0fdGjRo5LCspKYnQ0FAWLVp0\n08+HDRvmsOzrGXXuK0L/t27dSufOnUtsS0xMxGw2Ozzb6Hz13bi+z549m9GjR+Pt7c3zzz/PoUOH\niI2NJTQ01KWzpYhG7MRpXn75ZdLS0li2bBkA69evJzMz0ynZ06dPZ//+/URGRhIZGcmBAwdw5tP0\nMjIy6N27NwEBAfzhD3+gZ8+ejBw50qGZV69eBSA3N/emL2cx6txXhP6/8847xMXFceXKFX7++WdG\njRrF559/7pRso/PVd+P6/tVXX1GtWjU2b97MAw88wGeffcZHH33k8tnyv2wiThIUFFTivzk5ObbI\nyEinZAcHB5dpmyPzs7KybKGhoTabzWb7+uuvbbGxsU7LN5LR595IVqvV9uGHH9p69epl69Wrly0l\nJaXS5KvvxvU9MDDQZrPZbFOmTLFt2bLFZrM572fOyGwpoqlYcZoqVaoA4OPjw7lz56hduzbnz593\nSra7uzsnTpygadOmAJw8eRJ3d3enZAN4eHhQu3ZtrFYrVquVjh078vrrrzs0869//SsjR45kxowZ\nmEymGz6fNm2aQ/OLGXXuK0L/L168yJ49e2jSpAnnzp0jMzMTm8120/a4Wr76blzfe/ToQd++falS\npQrTp08nKysLb29vl8+WIirsxGl69OjBpUuXGDFiBGFhYZhMJiIiIpySHRMTw3PPPUeTJk2w2Wxk\nZmY6vLC6Xo0aNcjNzaVDhw68+OKL1KlTh6pVqzo0s1mzZgC0bdvWoTmlMercV4T+/9d//RcjR44k\nPDycvLw85s6dS2RkJCtXrnT5fPXduL6/+OKLPP/881SvXh13d3eqVKnCu+++6/LZUsRks9lsRjdC\nKp+CggLy8/OpXr26UzN//PFHAPz8/PDy8nJa9pUrV/D29sZms5GSksLly5cJDg6mdu3aDs8+efIk\nTZo0KbFtz549+Pv7Ozy7mJHn3sj+Z2Zm3nCBzDfffEOHDh0cnm10vvpuXN+vXr3KokWLOHPmDDNm\nzOD48eMcO3aM3/72ty6dLUV08YQ4TX5+PosWLSIqKooXXniBNWvWlLhC1NH27dvHkSNHOHjwIGlp\naaxdu9Zp2VWrVsXd3R0PDw/MZjPPPfecU4o6gHHjxnHu3Dn7+x07djB16lSnZBcz8twb2f/777+f\npKQk4uPjgaJf+M6cljIyX303ru+xsbF4enry7bffAtCwYUPefPNNl8+WIirsxGliYmI4cuQIf/jD\nHxg8eDA//PADkyZNckr2pEmTmD17Nrt27WLv3r3s3buXffv2OTz3iSee4Mknn7zhVbzdGaZPn87o\n0aM5f/48W7Zs4bXXXuODDz5wSjYYd+6LGdn/6dOn891335GamgqAr68vr7zyilOyjc5X343r+4kT\nJxg5ciQeHkWrrXx8fHDW5JyR2VJEa+zEaY4cOUJaWpr9fceOHenfv79Tsvft20daWprTFi8XK/5X\nq5H8/f2ZNm0aw4cPx9vbm8WLF1OnTh2n5Rt17osZ2f89e/aQmJjIgAEDAKhZsybXrl1zSrbR+eq7\ncX338vIiLy/P/jN34sQJpy1/MDJbiqiwE6dp3bo13333He3atQPg+++/d9rC9kceeYTz58/ToEED\np+QVu3Dhwm0/r1WrlsOyR40aVeJ9Xl4e1atXZ8qUKQC89957Dsu+nlHnviL038PDA4vFYv8ll5WV\nhZub8yZKjMxX343re3R0NM8//zxnzpzhhRde4Ntvv2XmzJkuny1FdPGEOFxwcDAAhYWFHDt2zL6o\nODMzEz8/vxKjeI4yZMgQDh48iL+/f4knHjj6l3vPnj0xmUw3nYowmUxkZGQ4LHvHjh23/fzpp592\nWPb1jDr3FaH/ycnJpKWlceDAAcxmM+np6YwfP55+/fo5PNvofPXduL4DZGdn8/3332Oz2Xj88ced\nOkpvZLaosBMnOH369G0/f+CBBxzehlv9kndWcWOkK1euUKVKFdzc3Dh27Bg//vgj3bp1c9rj1Iw+\n90b3/+jRo2zbtg2bzUanTp3st2FxFiPz1XfnZu/fv/+2n7dp08Yls6UkFXbiFBaLhcDAQNLT0w1t\nR05ODoWFhfb3jpwKvV50dDTh4eF07drVqVMyAGFhYSxfvpxLly4RGRlJ27Zt8fT0ZN68eU5th1Hn\n3qj+G/09b2S++m5M9pAhQ4Ci2wvt27ePli1bAnDo0CHatm3LqlWrXDJbStIaO3EKd3d3Hn744Zve\n38kZVq1axdtvv423t7d9atTRU6HXi4yMZM2aNcyYMYO+ffsSFhaGn5+fU7JtNhs+Pj6sXr2ayMhI\nRo4cSUhIiFOywfhzb1T/jf6eNzJffTcme+nSpQBERUWRkJBgL64OHz5sv/WKK2ZLSSrsxGkuXbpE\nYGAg/v7++Pj42Lc7YxH7Rx99REpKimFrPTp37kznzp25fPkyn376KcOGDeP+++8nIiKCkJAQh04L\n2mw2vv32W1JSUvif//kf+zZnMfrcG9l/I7/njc5X343r+7Fjx+yFFUCLFi04evSoy2dLERV24jTj\nxo0zLLtJkyYl/oI1QnZ2NsnJySQlJfHoo48SEhLCrl27WLt2rf1fu44wZcoU3n//fX73u9/xyCOP\ncPLkSZ555hmH5f2S0efeyP4b+T1vdL76bpyWLVsydepU+8h0SkpKiWLLVbOliNbYiVOdPn2an376\nic6dO3P16lUsFgvVqlVzeO6BAweIjY3l8ccfL3FPJWc8CB5gzJgxHDt2jNDQUMxmc4lbf4SFhZGQ\nkOCUdhjB6HNfkf3Xf/2XoWuPjMxX3x2XnZ+fz4oVK/jmm28A6NChA5GRkU55+oWR2VJEI3biNP/4\nxz9YtWoVFy9eZOPGjZw7d464uDj+9re/OTz75ZdfpmPHjrRo0cLpFy9A0cLijh073vQzRxV1v7yP\n2y85a1rIqHNfUfp/O858pF5Fy1ffHcfb25uhQ4cydOhQh+ZUtGwposJOnGb58uV88sknPPvsswA8\n9NBDZGVlOSW7sLCQ2NhYp2TdTMeOHdm9ezenT5/GYrHYtxffmd4Rhg8f7rBjl4dR576i9P92jHoa\nR0XIV9/vvnHjxvHWW2/Z7x36SykpKQ7JNTpbSlJhJ07j5eVVYiru+ltfOFq3bt1YtWoVv/3tb0u0\nwVm33Jg0aRInT56kVatWuLu7A0V/uTuysKso9+gz6txXlP6LOMvUqVMBY0ajjcyWklTYidN06NCB\n9957j7y8PL766iv+/ve/07NnT6dkf/rppwC8//779m3OvOWGkc9LPX78OPPnz+eHH34oMQXkrL4b\nfe6N7v/tGL3E2ch89f3uK167+8ADD/Dzzz+zd+9eoOh5yXXr1nVIZkXIlpJ08YQ4jdVqZfXq1fzz\nn/8EoEuXLkRERBg+JeMMY8eOZdq0aU5/XioU3UNv7NixvP7667z33nskJCRgtVoNv3LPWSpC/3Ny\ncjh+/DhNmjShZs2a9u2HDx+mRYsWTmvHLxmZ78zsrKysG26346x8I7LT0tKYM2cOTz/9NDabjZ07\ndxITE0Pfvn0dllkRsqWICjtxqoKCAn788UdMJhMPP/xwiak5Rzt8+DA//PADBQUF9m2OnAqF/1vA\nn5uba8jzUuH/rroNDg62r3Nx9pW4Rpz7Ykb0/8UXX2TKlCnUqVOHL7/8kj//+c889NBD/PTTT8TE\nxDj8maGrV68mPDwcgLNnzzJ58mT2799P8+bNmTlzJg8//LDDss+cOcPs2bM5d+4c3bp1Y8SIEfbv\n+dGjR/Puu+86LBtgy5YtvPLKKzRs2JA///nPTJo0ifz8fAoKCpg1axadOnVyyezrhYSEsGjRIvtI\nWVZWFkOHDiU5Odmls6WIpmLFaTZv3kxcXBxNmzbFZrNx6tQpXnnlFbp37+7w7Pj4eLZv387Ro0fp\n3r07X3zxBU899ZTDi4uKsIDfy8sLq9XKgw8+yLJly2jYsCG5ublOyzfq3Bczov+HDh2yj9K88847\nLFu2jMaNG9t/yTm6sFu+fLm9sJs5cyb9+/dn0aJFZGRkMH36dIdeiT5lyhR69+5Nu3btWL16NUOG\nDGHhwoXUrl2bzMxMh+UWmz9/Pn/961+5dOkSw4YN4/3336ddu3YcPXqUF198kcTERJfMvp7NZisx\n/VmrVi2nTT0bmS1FVNiJ07zxxhssWbKEBx98EIATJ07w3//9304p7NavX09SUhIDBgxg5syZ/Pzz\nz0yaNMnhudcv4D9//jx79uzBZDLx2GOPUb9+fYfnQ9Ev2qtXrzJt2jTeeusttm/fzqxZs5ySDcad\n+2JG9N9qtZKTk0O1atUwmUz2R0vVqVOnxFXRznDs2DHeeustAHr16sU777zj0LysrCwiIyMB+POf\n/0xSUhJ/+MMfWLhwoVOWXbi5udGsWTMAqlSpQrt27QBo1qwZVqvVZbOv16VLF0aMGEFgYCBQND3a\nrVs3l8+WIirsxGl8fX3tRR0UPZHA19fXKdne3t64ubnh4eFBTk4OdevW5cyZM07JBvjkk0945513\n6NixIzabjddee43Ro0fbR1UcqVatWvj6+uLr68vMmTMdnvdLRp97I/o/ZswYnnvuOX7/+9/z5JNP\nMm7cOHr27Mn27dvp2rWrw/PPnj3La6+9hs1mIzs7m2vXrtmnQx19NXphYSH5+fn2G9KGhoZSv359\nRowYwdWrVx2aDVC9enVWrlxJTk4ONWrUYPHixfTr14+tW7dStWpVl82+3uTJk1m/fj27d+8Gim6I\n3KtXL5fPliIq7MRp2rZty8iRI+nXrx8mk4n09HQee+wxNmzYAEDv3r0dmn3p0iUiIiIICwujatWq\nPPHEEw7L+6UPP/yQxMREateuDRQ9XmzQoEFOKeymTJnC2bNneeyxx2jfvj3t27d36iN+jD73RvS/\nf//+tGnThn/84x8cP34ci8XCd999R2BgoFMKu5iYGPuf27Zty5UrV6hZsybnz593+JXoERERfP/9\n9yVGqzt37sxbb73FnDlzHJoNMGvWLPvo4Mcff0xqaiojRoygUaNGvPbaay6b/Ut9+vShT58+Ts2s\nCNmiiyfEiUq7Sa2zRlNOnTpFTk4OrVq1ckoewKBBg1iyZIn9YpGCggKee+45Vq5c6ZT8goIC9u7d\ny44dO1i1ahVXrlxhx44dTsm+nhHnHipO/0Uc6YknnrjpdLfNZsNkMtlH0VwtW0pSYSduvtpCAAAg\nAElEQVSVxrlz52548kOHDh2ckh0TE8Phw4cJCAiw38OtZcuW9pGjYcOGOSx7586d7Nq1i507d3L5\n8mVatWpF+/btCQoKcljmLxl57o3ov81mY926dZhMJvr27cu2bdvIyMjg4YcfJjIy0uGPVvvlLTaS\nkpLYu3cvjzzyCM8++6xD17p99tlndOjQgVq1apGVlcUbb7zBv/71L5o1a8ZLL73Efffd57Ds0sTH\nxxMVFeWw48+cOZPevXvz1FNPOSxDpDQq7MRpbjVi54yRujlz5rBu3TqaNWtmf/IDOO8u6fHx8bf9\n3JG/bFq3bk2bNm344x//SLdu3Zx6ixkw/twb0f/p06eTlZVFQUEB1apVo6CggJ49e7Jlyxbq1q3L\ntGnTHJpvNpvtV2C+++677Nq1i6CgID7//HPuu+8+pkyZ4rDs/v37k5aWBsD48eNp164dffv2ZevW\nraSkpLBo0SKHZZemR48ebN682WHH79ixI40aNSI7O5t+/foRFBRE69atHZb3SxcuXLjt54582ouR\n2VKS1tiJ0/To0cP+5/z8fDZu3Oi0G/Zu3LiR9PR0pxc1xYoLt+LbbDjrohGAbdu2sXv3br755huW\nLFmCm5sb7dq1Y/z48U7JN/rcG9H/Xbt2kZKSwrVr1+jSpQtffvklXl5eBAUFYTabHZZb7Pp/r3/2\n2WcsX76cqlWrEhQURFhYmEOzrx+VPXHiBG+++SZQdO9AR95mpdiTT/7/9u48Ksor3ff4t0AiCIIQ\nESecUIOtrXbsSIw44YADiCIkRA0OKHGMpkWitih60g5oBmc0TolD262WQxA14kQSo2mHBpU2GiPO\nAxGDTIpQdf+oSx1KyTnd92a/xSqez1pZod5ay9+7K1F3vXvv53m13OtGo9Gi84gKtWvXRq/Xc+3a\nNZKTk5k6dSolJSUEBQXRr18/pfUDwfQZ63S6csuLqO72Ys1sYUkmdkIzz2+mDQoKYvDgwZpke3t7\n8+zZM6tNLi5fvkxsbCw5OTkAuLu7s3DhQpo1a6Y829XVFW9vb+7evcu9e/c4d+6cpn16rf3ZW2P8\npU8mHRwcaNWqlXnsVapUUb4MC/DkyRMyMjIwGAwUFxebT2Q6ODgoz/fz82PJkiW8++67tG/fnkOH\nDtGzZ09OnjxJ9erVlWaD6b/3jh07qFmz5gvvqS6tVLrE3bhxY8aPH8/48eO5dOkS+/btIzo6mkOH\nDinNP3LkiNJfv6JmC0sysRNWk5mZycOHD5Vm/Nd//Rc6nQ4nJycGDBhAhw4dLCYYqpfESs2aNYtp\n06bx+uuvA3Dq1Cni4uI0OTzRvXt3mjRpQrt27Xj77beZP3++JpOsivLZW2P8NWvWJD8/H2dnZ9at\nW2e+npWVZdF5RBVPT0/zFocaNWrw4MEDatWqxaNHjyyWw1WIi4sjMTHR3EJq48aNODk5ERAQQEJC\ngtJsMJVXuXPnTrkTO9X7Sst7WuXr64uvry9TpkxRml3WxIkTCQsLo1OnTpp8kago2cJE9tgJzTx/\nasrT05M//elPSo/F/0+V3nU6nWbdD/r37/9CS53yrqlgMBis8gdsRfnsrTX+8hQUFFBYWGi1pugl\nJSUUFRXh5OSkSV5ubi7FxcXmMj+2rnQyb20nTpxg586dpKWl0bt3b0JDQ2nSpInNZwsTmdiJSuHz\nzz9n2LBh/+s1VcaPH8/vfvc7QkJCANi7dy8XL15U3gUATJ0H4uPjefjwIUlJSVy6dIkjR44wbtw4\n5dlg/c/emuMvWxi4VHlN4VW4c+cOLi4uuLq6cuvWLS5cuECTJk00aXxf2mXBzs6OoqIirly5Qr16\n9TTZQF9UVISDg4P5S+TJkyfJyMjAx8dHky43ZeXn55OZmYm3tzeurq6aZoNpYp2UlERiYiJ16tQh\nPDyc/v37a/LU2JrZlV3F+BorKoUzZ85QUFAAmMovzJ8/n9u3b2uSvXv37heuadW3EWDevHk8evSI\niRMnMnHiRLKzs5k3b54m2XFxcUyZMoUqVUw7L3x9fc2nFrVg7c/eGuM/efIknTt3xt/fn5EjR3Lr\n1i3ze1FRUUqzAdasWcPQoUN588032b59O6NGjSI1NZX3339f+anUlJQU/P396dy5MykpKQwZMoSE\nhAT69++vyT6ssLAwHj9+DJgKg3/66ac8efKEjRs3snjxYqXZ8fHx5p9Pnz5Nv379WLBgAcHBwRw/\nflxp9vMePXqEXq9n+/bttGjRgsjISDIyMjTpX23NbCF77ISG4uPj2bt3L5cuXWLDhg2Eh4fzwQcf\nsHnzZmWZSUlJJCUlcevWLcaMGWO+np+fj5ubm7Lc57m5uWm2p+x5hYWFtG7d2uKa6n1WUHE+e2uM\nf9GiRaxbt45mzZpx4MABRo4cSUJCAm3bttWkIfqePXtITk6msLCQgIAADh8+jIeHBwUFBbz55ptK\n6yYuX76cPXv28OTJE0JCQtixYwdNmjTh9u3bTJw4UXnnC4PBYP7/Kzk5ma1bt+Lo6EhxcTEDBw4k\nJiZGWXZaWpr55yVLlrBixQpatmzJzZs3mTRpkmZPDMePH8+1a9cICQkhMTHRXH2gb9++yk9FWzNb\nmMjETmimSpUq6HQ687f48PBwduzYoTTzD3/4A56enjx69Mji26Kzs7OmbbWuXbvG+vXruX37tsWJ\nzC+++EJ5tru7Ozdu3DAvTR04cABPT0/luRXls7fG+J89e2Y+8dy7d298fHyYMGECU6dOVVocuJSd\nnR2Ojo44ODjg6OhoXgLVql9p6edbt25d8/6qevXqaTKpdXFx4fLlyzRv3hx3d3eePn2Ko6MjJSUl\nmuSXysvLo2XLloDpZLiW2e+88475oNbz9Hq9zWYLE9ljJzQzdOhQOnXqhF6vZ/Pmzbz88suEhITw\n5ZdfWvvWeOutt/jb3/6m7Nfv378/ERERtGrVymIjf6tWrZRllrp58yZxcXGcO3cOV1dX6tevz6JF\ni6hfv77y7H+H6s/eGuMPDQ1l9erVFhPIe/fu8e6773Ljxg3OnTunLBtg2rRpPHv2jIKCApycnLC3\nt6dTp06cPHmS/Px8lixZoix7wIAB6PV67OzsSE9PNz8tLSkpISQkhKSkJGXZAJcuXSI2Ntbctu7s\n2bO89tpr/PDDD4wYMYLg4GBl2W3atKFBgwaAqX3esWPHcHNzw2Aw0L9/f+VjL+vs2bMvdHvR6sCS\nNbOFTOyEhrKyskhKSjI3Y79z5w7ff/99hfgNP2DAgHL3gv1WQkNDrf5ttaCgAIPBgIuLi1Xv43mq\nP/tSWo7/xIkTeHh4vNAT9/Hjx2zZsoWxY8cqzS8uLubAgQPodDoCAwNJT08nKSmJOnXqMGTIEKVP\n7tLT03nllVeoWrWqxfVbt25x5swZ8wEilUpKSvjmm2/IzMykpKSE2rVr4+/vr/wAw/N7hj09PXnp\npZfIzs7m9OnT9OrVS2l+qalTp3Lz5k18fX3N2w50Op0m20GsmS1MZGInKgzVT27+J2VbMP2WStvs\nbNq0CQ8PD3r27GlRQ02LU4ItWrQgKiqKKVOmmJcBVY33/4Xqe6no4xfit9anTx+Sk5M1WfavSNnC\nRPbYiQpDdbsfa3i+zU7ZYrVatdlp2rQpBoOBkSNH8sknn1CjRg1N9/tYmzXGn5qaSufOnQFT2Yf5\n8+dz/vx5mjdvzvTp08stnqsq//HjxyxYsECz/PPnz5OQkICXlxdTpkxhxowZpKen06hRIz788ENa\ntGihLBtMh3PWrl3LV199xb1793BwcKBBgwZEREQo37yfm5vL6tWrSUlJITs7G51Oh4eHB927dyc6\nOlqzkifNmjUjKytLs5aNFSVbmMjETlQY1vyGp+ov+tLyDk+fPn1haUqriWyVKlWIjY0lOTmZIUOG\nsHDhwgr1bVr1JMsa4//kk0/ME6sFCxbg6elJYmIihw4dYtasWaxcuVKz/IULF2qaP2fOHCZOnEhu\nbi4RERFMnz6dDRs28N133xEfH6/8qXxMTAw9e/Zk3bp17N+/n4KCAvr168eqVavIzMzkT3/6k7Ls\nyZMn4+fnx6ZNm8z7K7Oysti1axeTJ09m/fr1yrIB8+nz/Px8+vXrR+vWrS3qxiUmJtpktrAkEztR\nqeTl5ZkLhpYtuaG61VFERMQLS3/lXVOhdOLUt29fmjZtypQpU7h7967y3H+X6s/e2uO/cOECe/bs\nAWD48OGaLwFrnV9cXGwu67F48WJza7EOHTqwcOFCpdlg2udW+mRuxIgRDBo0iPHjxzN//nz69u2r\ndGJ369Yti6fyYNpnFx0dzc6dO5XllrJmnTipUVdxyMROVBgqntzExMQwY8YMPDw8+Prrr4mLi6NR\no0Zcv36d2NhY+vTpA6CsGn9WVhb37983N2UvHWNeXh6FhYVKMssyGAzMmjXL/Lp58+Zs3bpVkyXg\nq1evMn/+fOzs7Jg5cyYrV64kJSWFRo0asXDhQnx8fMz3pIq1xv/w4UM2bNiA0WgkNzcXo9FofkpY\n2pXBVvOrVq3KN998Q25urrm8UY8ePfj+++81ae1WrVo1Tp8+zR//+EcOHz5s3sdqZ2en/OlwvXr1\n+Oyzzxg4cKB5ufvnn39Gr9dTp04dpdkA7du3N/+clZVFeno6Op2O3//+98pL/FgzW1iSwxPCKi5e\nvGiu8VSqtPbUbyk4ONhcTiUiIoLFixdTv359srOzGT58uPJerbt27UKv13PhwgWL0ibOzs6EhoZq\nckpOq1OnzxsyZAhRUVEUFBTw0UcfERMTQ9++fTl69Ciff/45n3/+uSb3YY3xL1++3OL14MGD8fDw\nICsri0WLFil/SmnN/EuXLrFo0SJ0Oh3Tp0/nr3/9K3v27KFWrVrMnTuXdu3aKcsuzZ85cybXr1+n\na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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotPairwiseDistance(c, \"stacks default\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## AftrRAD empirical results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "AFTRRAD_OUTPUT=os.path.join(AFTRRAD_DIR, \"REALDATA/\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filename = os.path.join(STACKS_DEFAULT_OUT, \"batch_1.vcf\")\n", "v = vcfnp.variants(filename).view(np.recarray)\n", "c = vcfnp.calldata_2d(filename).view(np.recarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of snps along loci\n", "NOT TESTED\n", "\n", "It's not very straightforward how to extract information about\n", "whether each snp is informative or not, given the output files\n", "that aftrRAD generates. It does create a file with all the snp\n", "locations so we can use this to generate the distribution of \n", "variable sites, but the pis will be empty." ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": true }, "outputs": [ { "data": { "image/png": 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total variable sitesparsimony informative sites0510152025303540455055606570Position along RAD loci0100020003000N variables sites
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" ], "text/plain": [ "" ] }, "execution_count": 184, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = os.path.join(AFTRRAD_OUTPUT, \"TempFiles/SNPLocationsToPlot.txt\")\n", "with open(f) as infile:\n", " snplocs = infile.read().split()\n", "## Now have to massage this into the list of counts per site in increasing order \n", "## of position across the locus\n", "distpis = Counter([])\n", "\n", "## Getting the distvar is easier\n", "distvar = Counter(map(int, snplocs))\n", "\n", "canvas, axes = SNP_position_plot(distvar, distpis)\n", "\n", "## save fig\n", "## toyplot.html.render(canvas, 'snp_positions.html')\n", "\n", "canvas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## dDocent empirical results\n", "HAVE TO MAKE SURE THE SAMPLES IN THE DDOCENT OUTPUT VCF ARE IN THE SAME ORDER AS THOSE IN THE IPYRAD/STACKS\n", "There is now code in the horserace notebook to rename samples in the ddocent\n", "vcf and to reorder the samples to match ipyrad/stacks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Getting max loci per sample from bam files.\n", "nloci 37358 - Pop1_Sample1-RG.bam\n", "nloci 41001 - Pop3_Sample1-RG.bam\n", "nloci 29663 - Pop2_Sample2-RG.bam\n", "nloci 42706 - Pop3_Sample2-RG.bam\n", "nloci 29490 - Pop2_Sample1-RG.bam\n", "nloci 38202 - Pop4_Sample2-RG.bam\n", "nloci 42543 - Pop3_Sample3-RG.bam\n", "nloci 42667 - Pop3_Sample4-RG.bam\n", "nloci 42999 - Pop3_Sample5-RG.bam\n", "nloci 43950 - Pop4_Sample1-RG.bam\n", "nloci 44300 - Pop4_Sample3-RG.bam\n", "nloci 40740 - Pop5_Sample1-RG.bam\n", "nloci 39368 - Pop5_Sample2-RG.bam\n", "[44300, 43950, 42999, 42706, 42667, 42543, 41001, 40740, 39368, 38202, 37358, 29663, 29490]\n", "mean sample coverage - 39614.3846154\n", "min/max - 29490/44300\n" ] } ], "source": [ "import subprocess\n", "\n", "DDOCENT_OUTPUT=os.path.join(DDOCENT_DIR, \"REALDATA/\")\n", "os.chdir(DDOCENT_OUTPUT)\n", "print(\"Getting max loci per sample from bam files.\")\n", "sample_coverage = []\n", "for samp in glob.glob(\"*-RG.bam\"):\n", " cmd = \"{}samtools view {} | cut -f 3 | uniq | wc -l\".format(DDOCENT_DIR, samp)\n", " res = subprocess.check_output(cmd, shell=True)\n", " print(\"nloci {} - {}\".format(res.strip(), samp))\n", " sample_coverage.append(int(res.strip()))\n", " \n", "print(sorted(sample_coverage, reverse=True))\n", " \n", "print(\"mean sample coverage - {}\".format(np.mean(sample_coverage)))\n", "print(\"min/max - {}/{}\".format(np.min(sample_coverage), np.max(sample_coverage)))\n", "#sim_coverage_df[\"ddocent_\"+sim] = sample_coverage\n", " \n", "vcf_filt = pd.read_csv(\"Final.recode.vcf\", delim_whitespace=True, header=60, index_col=0)\n", "vcf_full = pd.read_csv(\"TotalRawSNPs.vcf\", delim_whitespace=True, header=60, index_col=0)\n", "\n", "## There's gotta be a faster way to do this...\n", "## It's really fuckin slow on real data, like hours.\n", "for vcf in [vcf_full, vcf_filt]:\n", " sample_counts = {}\n", " sample_names = list(vcf.columns)[8:]\n", " print(sample_names)\n", " print(\"num loci in vcf - {}\".format(len(set(vcf.index))))\n", " for name in sample_names:\n", " print(name)\n", " sample_counts[name] = 0\n", " for locus in set(vcf.index):\n", " snps = vcf[name][locus]\n", " if any(map(lambda x: x.split(\":\")[0] != \"./.\", snps)):\n", " sample_counts[name] += 1\n", " else:\n", " pass\n", " print(sample_counts)\n", "print(sim_coverage_df)" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-15 15:13:53.339632 :: caching is disabled\n", "[vcfnp] 2016-10-15 15:13:53.340435 :: building array\n", "[vcfnp] 2016-10-15 15:14:20.818988 :: caching is disabled\n", "[vcfnp] 2016-10-15 15:14:20.832036 :: building array\n", "[vcfnp] 2016-10-15 15:15:48.271026 :: caching is disabled\n", "[vcfnp] 2016-10-15 15:15:48.272360 :: building array\n", "[vcfnp] 2016-10-15 15:15:59.214829 :: caching is disabled\n", "[vcfnp] 2016-10-15 15:15:59.224579 :: building array\n" ] } ], "source": [ "## Basic difference btw Final.recode and TotalRawSNPs is that final recode\n", "## only includes individuals with snps called in > 90% of samples\n", "\n", "f1 = os.path.join(DDOCENT_OUTPUT, \"TotalRawSNPs.vcf\")\n", "v_full = vcfnp.variants(f1, dtypes={\"CHROM\":\"a24\"}).view(np.recarray)\n", "c_full = vcfnp.calldata_2d(f1).view(np.recarray)\n", "f2 = os.path.join(DDOCENT_OUTPUT, \"Final.recode.vcf\")\n", "v_filt = vcfnp.variants(f2, dtypes={\"CHROM\":\"a24\"}).view(np.recarray)\n", "c_filt = vcfnp.calldata_2d(f2).view(np.recarray)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distribution of snps along loci" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
total variable sitesparsimony informative sites0510152025303540455055606570Position along RAD loci010002000N variables sites
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total variable sitesparsimony informative sites0510152025303540455055606570Position along RAD loci050010001500N variables sites
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for dset in [[v_full, c_full], [v_filt, c_filt]]:\n", " ## Get only parsimony informative sites\n", " ## Get T/F values for whether each genotype is ref or alt across all samples/loci\n", " is_alt_allele = map(lambda x: map(lambda y: 1 in y, x), dset[1][\"genotype\"])\n", " ## Count the number of alt alleles per snp (we only want to retain when #alt > 1)\n", " alt_counts = map(lambda x: np.count_nonzero(x), is_alt_allele)\n", " ## Create a T/F mask for snps that are informative\n", " only_pis = map(lambda x: x < 2, alt_counts)\n", " ## Apply the mask to the variant array so we can pull out the position of each snp w/in each locus\n", " ## Also, compress() the masked array so we only actually see the pis\n", " pis = np.ma.array(np.array(dset[0][\"POS\"]), mask=only_pis).compressed()\n", "\n", " ## Now have to massage this into the list of counts per site in increasing order \n", " ## of position across the locus\n", " distpis = Counter([int(x) for x in pis])\n", "\n", " ## Getting the distvar is easier\n", " distvar = Counter([int(x) for x in v_filt.POS])\n", "\n", " canvas, axes = SNP_position_plot(distvar, distpis)\n", "\n", " ## save fig\n", " ## toyplot.html.render(canvas, 'snp_positions.html')\n", "\n", " canvas" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "99971\n", "60996\n" ] } ], "source": [ "#Tmp delete me\n", "print(np.sum(distvar.values()))\n", "print(np.sum(distpis.values()))" ] }, { "cell_type": "code", "execution_count": 238, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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KE088oZw8eVJRFEV57bXXlK1btyqKoiharVb5z3/+oyiKonz66afK+++/b/Q9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\n", 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nzrgV6kq9Grf16xnRpcsNH9vDw4OMjAzg2qF26dIlTCYTd955JxkZGUa3rL0q\nV65MtWrVjAW8V6xYQfv27Uvk2OWBWqQOEBMRQe1du2zdRspdu3axKioK83XW4hOR8ivU358ZY8aw\nrX37qw84ysjAd/lyQopxffGOO+6gdevWmM1m3Nzc8PT0NF4rWIKsSpUqDBw4kMDAQGrWrMn9999/\nxTZXY+u1AtOmTTMGG9WrV4/333//ht9DeaVJ6x0waf0zgYE0iY293qBdDgQG8nlMTKnWIiJlKzU1\nleApU9g1YMDvt8BYrbitX4/v8uWsnDRJ04WWM2qROoAlM/PP3EaKJVP3kYrc6mrVqsWWWbOIjItj\n4YQJxsxGI7p0IWTWLI2VKIcUpA7g5O7+Z24jxcld95GK3A6cnJwYEBDAgICAsi5FSoC++jiAefhw\nEt1s30d6zM2N4BG6j1REpLxRkDpAUGgop3x9ybnG6znAaV9fAkN0H6mISHmjIHUAJycnZq9cyY4O\nHTjq5kbB6C4rcNTNjR0dOjB75UpdGxERKYfK9Brp6dOnefXVVzl37hxOTk4MGjSIYcOGceHCBf76\n17+SnJyMt7c3H3/8sTFn49y5c1m+fDnOzs68+eabdO7cuSzfwp9Wq1Ytlm3ZQkxkJNELF2LJzMTJ\n3Z3gESMIDAlRiIrcRvLy8ohZE8Oqb1dhcbbglOdEULcggvoE6bOgHCrT35izszPjx49n1apVLF26\nlCVLlnD48GHmzZvHAw88QFxcHB06dGDu3LkAHDp0iNWrVxMbG8vnn3/OlClTytVsGk5OTgQPGMDn\nMTHMX7eOz2NiMIeG6h+OyG0kNTWVh0c9zPq89bR4rQX3/+1+WrzWgnW56xj8/OBiL6NWUuLj4zl8\n+LDxeOjQoezdu9fu4yYnJ2M2m29on8Ln9vPz49dff7W5/aOPPnrV58ePH8/atWtv6Nw3okw/wWvW\nrEnz5s2B/Fk57r33XlJSUkhISKB///5A/pyN8fHxAKxbt46+ffvi4uKCt7c39evXZ/fu3WVWv4jI\njbBYLIyaOIr2E9vToEsDY6IDk8lEgy4NaD+xPaMmjirTWc4SEhI4dOhQmZ3/Wv7MpBAFy8g52k3T\nFDpx4gT79u3D19eXc+fOUaNGDSA/bM+fPw9ASkoKtWvXNvbx8vIiJSWlTOoVEblRMWti8A70pqJH\nxau+XtGjInX71mVV3KpinyMqKorg4GBCQkIYPXo0PXv2NObJTU9PNx4vW7aMgQMHEhISwtixY7l0\n6RI7d+6qAlfGAAAgAElEQVRk3bp1fPjhh/Tv35+kpCQAVq9ezaBBg+jTp48xDWBOTg7jx4/HbDYT\nGhrKtm3bAIiMjGTUqFEMHToUf39/wsLCjNry8vKYOHEiQUFBPPXUU+Tk5JCUlERooRndEhMTizwu\nULj3ceHChZjNZsxmM1988YXxfKtWrYy/v/322wQEBDBixAjOnTtnPL93716GDh3KgAEDePrppzl7\n9iwAixcvJjAwkH79+jFu3Lgb+pnfFEGakZHB2LFjeeONN/Dw8Ljim8ef+SYiInKzi/kmhns632Nz\nmwZdGhC9IbpYxz906BBz5szhyy+/JCoqivfee48OHToYS6XFxsbSu3dvnJ2d6d27N+Hh4URFRdGw\nYUPCw8Np1aoVfn5+vPrqq0RGRlKvXj0AI3jHjx9vBOOSJUtwcnIiOjqaGTNm8Prrr5OTk39vwo8/\n/sinn37KypUriYuLM7pnExMTr1iCrV69elSpUoV9+/YBEBERwYABA675Hvfu3UtkZCTh4eH85z//\nYdmyZca+BVmxdu1aEhMTWb16NdOmTWPnzp0A5Obm8s477/DJJ5+wfPlyQkND+cc//gHA559/TlRU\nFCtWrGDKlCk39HMv8yDNzc1l7Nix9OvXj4ceeggAT09P41vCmTNnqF69OpDfAi1YygfyByt5eXld\n9xyzZs2iadOmRf707NmzFN6NiMi1WZwt120YmEwmLM7F69rdunUrffr0oVq1agBUrVqVgQMHEhER\nARQNqf379zNkyBDMZjMxMTFFllP7o969ewNw3333cfLkSQB27NhBcHAwAA0bNqRu3bocO3YMgE6d\nOlG1alUqVqxIr169jFast7f3FUuwAUaNFouF2NhYgoKCrvpzKThvr169qFixIu7u7vTq1euKJdu+\n//57AgMDgfyBnh07dgTg6NGjHDx4kBEjRhASEsKcOXOMa9LNmjVj3LhxrCzGHRRlHqRvvPEGjRo1\n4oknnjCe8/PzM37xkZGRRuj5+fkRGxtrdAccP34cHx+f655jzJgx7N+/v8ifhISE0nlDIiLX4JTn\ndN0BklarFae8kvtobt26NcnJyWzfvh2LxUKjRo2A/AE4kyZNIjo6mhdeeKHIcmp/VHh5tdxrLPFW\n+H1dq1fxj0uwFRzL39+fb775hvXr13PfffcZXwRKmtVqpXHjxkRGRhIVFcXKlSv55z//CcC8efN4\n/PHH+emnnxg4cOANXacu0yDdsWMH0dHRbN26lZCQEPr378+3337LM888w5YtW/D392fr1q08++yz\nADRq1IiAgAACAwN59tlnmTRpkrp9RaTcCOoWxLFNx2xuc3TjUczdb2x0a4GOHTuyZs0aY3TrhQsX\nAIzrfoW7TDMzM6lRowaXL18mOvr3rmQPDw/S09Ove662bdsa+x09epRTp07RoEEDADZv3szFixfJ\nzs4mPj6e1q1b2zyWq6srXbp0YfLkyVe9Pgq/B3Xbtm2Jj4/n0qVLZGZmEh8fbywoXrBNu3btiI2N\nxWKxkJqaaly/bdCgAb/88gv/+9//gPwe0YKBVSdPnqR9+/aMGzeO9PR0Mm9g7vMyvY+0TZs2/Pzz\nz1d9bdGiRVd9/rnnnuO5554rxapEREpHUJ8gFj+/mDqt61x1wNGljEskxyYTODuwWMdv1KgRI0eO\nZOjQoTg7O9O8eXPef/99zGYzM2fONLo7AV588UUGDRqEp6cnPj4+xlqmffv2ZeLEiXz11VfMnDnz\nmo2Vxx57jEmTJmE2m6lQoQLTp083FgP38fFh9OjRpKSk0K9fvyLduNdiNpuJj48vMjdA4XMX/L1F\nixb079+fgQMHAjB48GCaNWtWZJtevXqxdetWAgMDqVOnjjEIqUKFCsycOZOpU6eSlpaGxWJh2LBh\n3HPPPfztb38jPT0dq9XKsGHDqFy58p/+uWsZNQcsoyYiUiA1NZVRE0dRt29d4xYYq9XK0Y1HSY5N\n5rN3PivxZdTWrFnD+vXrmT59eoke92oiIyPZu3cvEyZMuKH9FixYQHp6OmPHji2lykqPVn8REXGg\nWrVq8fXsr4lZE0PM9BhjZiNzdzOBswNLfIKWqVOnsnHjRubNm1eixy1Jo0ePJikpqcitLOWJWqRq\nkYqIiB3KfNSuiIhIeaYgFRERsYOCVERExA4abCQi4mB5eXnExUSwOW4RLtZMck3udO4zHP8grQZV\nHuk3JiLiQKmpqYwd0olKO4YxtVMsU7psYGqnWNy+H8qYxx4s82XUSsr1lk271ut79uzh3XffBfJX\n/Pr8889LrcaSohapiIiDWCwWpowN5oPe2/Bw+/15kwl6NMum/T3beHVsMLP+taXUWqYWi+WmbvXe\nd9993HfffUD+tLB+fn5lXNH13bw/TRGRW0xcTAQDm+wqEqKFebjBgCa7WBsbVazjJycnExAQwCuv\nvELfvn158cUXyc7Oxs/PjxkzZhAaGsrq1auNKVlDQkJo0aIFp06d4vz584wdO5ZBgwYxaNAgY8UU\ns9lsTBnYoUMHVqxYAcBrr73Gd999R3JyMkOGDCE0NJTQ0FBj+r3CDh06xKBBg+jfvz/9+vXj+PHj\nRV5PSkqif//+7Nmzh+3btzNy5Eggf3KHd955p1g/C0dSi1RExEE2rVnI1E7ZNrfp0TSbCbEL6BN0\n9Tlnr+fo0aO8//77tGzZkjfffJN//etfmEwm7rzzTmMxkIKpApcsWcKOHTuoXbs248aN48knn6R1\n69acOnWKp556itjYWNq0acOOHTuoU6cOd999Nzt27KBfv37873//Y8qUKZhMJhYuXIirqyuJiYm8\n/PLLLF++vEhNS5cu5YknniAoKIjc3FwsFgtnzpwx6n355ZeZPn06TZo0Yfv27UX2LQ/zqStIRUQc\nxMWayfVywWTK36646tSpQ8uWLYH81uSXX34J5M+hW9iOHTsIDw/n3//+NwDfffcdR44cMSZ+z8zM\nJCsrizZt2vDf//6XOnXq8Mgjj7Bs2TJSUlKoVq0abm5upKen8/bbb/Pzzz/j7OxMYmLiFTW1bNmS\nOXPmcOrUKXr37k39+vUBOH/+PC+88AKzZs3i3nvvLfZ7LmsKUhERB8k1uWO1YjNMrdb87UpKQYuu\nUqVKxnOpqalMnDiROXPm4Obm9tt5rXz99dfGxPMF2rVrx5IlS6hbty5//etf+b//+z/i4uJo06YN\nkL/ASI0aNYiOjiYvLw9fX98raggKCsLX15cNGzbw7LPP8vbbb+Pt7U3lypWpXbs2O3bsKNdBqmuk\nIiIO0rnPcDbsv8YF0t+s3+9Gl74jin2OkydPsmvXLgBiYmKMJcYK5Obm8tJLL/HKK69w9913G893\n6tSJxYsXG4/37dsHwF133cUvv/xCYmIi3t7etGnThgULFtCuXTsA0tLSjEn2o6KiyMvLu6KmpKQk\n6tWrx9ChQ/Hz82P//v1A/vJpn376KVFRUcTExBT7PZc1BamIiIP4B4USfsCXjGtcJs3IhuUHfOnd\nN6TY52jQoAFLliyhb9++pKWl8cgjjxR5fefOnezdu5dZs2YZg47OnDnDm2++yZ49ewgODiYoKIil\nS5ca+7Rs2dJYa7Rt27akpqYaLdLHHnuMiIgIQkJCOHbsWJGWb4HVq1cTFBRESEgIhw4dIiTk9/fn\n5ubG3Llz+eKLL1i/fn2x33dZ0qT1mrReRBwoNTWVKWODGdBkFz2aZmMy5Xfnrt/vxvIDvkz6ZGWx\nl1FLTk5m5MiRRRbqltKna6QiIg5Uq1YtZv1rC3GrIpmweqExs1GXviOYNTnkpr7HU65OLVK1SEVE\nxA766iMiImIHBamIiIgdFKQiIiJ20GAjEREHy8vLIyYigphFi7BkZuLk7o55+HCCQrWMWnmkIBUR\ncaDU1FSeDw6m9q5dNMnOxgRYgbXr1vHFjBnMXln8218crVWrVsbk9rczffUREXEQi8XC88HBtNm2\njXt+C1EAE3BPdjZttm3j+eBgLBZLWZb5p1it1nIxobwjKEhFRBwkJiKC2rt24XqN112Bu3btYlVU\n8ZZRy8rK4rnnniMkJASz2UxsbCx+fn78+uuvQP6i2UOHDgUgLCyMV199lUceeQR/f3+WLVtmHGf+\n/PkMHDiQfv36ERYWBuRP9tCnTx9ee+01zGYzp06dwmq18v777xMUFMTw4cP55ZdfAFi2bBkDBw4k\nJCSEsWPHcunSpWK9n/JCQSoi4iDRCxdSP9v2Mmr3ZGezcsGCYh1/48aNeHl5ERUVRXR0NF27dr2i\n1Vj48YEDB1i8eDFLly7l008/5cyZM2zevJnExETCw8OJiopiz549fP/99wAcP36cIUOGEB0dTZ06\ndcjKysLHx8eY07cgdHv37m3s37BhQ8LDw4v1fsoLXSMVEXEQS2Ym1+sMNf22XXE0adKE6dOn8/e/\n/51u3brRtm1bbM2507NnT1xdXXF1daVjx47s3r2b77//ns2bN9O/f3+sVitZWVkkJiZSu3Zt6tSp\ng4+Pj7G/s7MzAQEBAAQHBzN27FgA9u/fz8yZM7l48SJZWVl07ty5WO+nvFCQiog4iJO7O1awGabW\n37YrjnvuuYfIyEi++eYbZs6cSceOHalQoYJxzfWPXayFW6eFr3k+99xzDB48uMi2ycnJV52Q/mrH\nGz9+PLNnz6ZJkyZERkZesVj3rUZduyIiDmIePpxEN9vLqB1zcyN4RPGWUUtNTcXNzQ2z2cxTTz3F\nTz/9RN26ddmzZw8Aa9euLbJ9QkICOTk5/PLLL/z3v//l/vvvp3PnzixfvpzM31rFKSkpnD9//qrn\ny8vLY82aNQBER0cbK8JkZmZSo0YNLl++fFtMoK8WqYiIgwSFhvLFjBnU2bbtqgOOcoDTvr4EhhRv\nGbUDBw7wwQcf4OTkRIUKFZg8eTJZWVm8+eabfPLJJ7Rv377I9k2bNmXYsGH88ssvjBo1ipo1a1Kz\nZk2OHDnCww8/DICHhwcffvjhVe9vdXd358cff2T27Nl4enry0UcfAfDiiy8yaNAgPD098fHxISMj\no1jvp7zQpPWatF5EHKjgPtK7du0yboGxkt8SPe3r67D7SMPCwvDw8GD48OGlfq5bnVqkIiIOVKtW\nLZZt2UJMZCTRCxcaMxsFjxhBYIiWUSuP1CJVi1REROygrz4iIiJ2UJCKiIjYQUEqIiJiBwWpiIiI\nHRSkIiIidlCQioiI2EFBKiIiYgcFqYiIiB0UpCIiInZQkIqIiNhBQSoiImIHBamIiIgdFKQiIiJ2\nUJCKiIjYQUEqIiJiBwWpiIiIHco8SN944w0efPBBzGaz8dyFCxcYMWIE/v7+PPXUU6SlpRmvzZ07\nl969exMQEMCmTZvKomQRERHDnw7So0eP8t1337Fz507S09NLrIDQ0FDmz59f5Ll58+bxwAMPEBcX\nR4cOHZg7dy4Ahw4dYvXq1cTGxvL5558zZcoUrFZridUiIiJyo1xsvZiens7ChQsJDw/H1dUVT09P\ncnJySEpKwtfXl6effpqOHTvaVUDbtm1JTk4u8lxCQgJfffUVAP3792fo0KG88sorrFu3jr59++Li\n4oK3tzf169dn9+7d+Pr62lWDiIhIcdkM0ieeeIJ+/fqxfPlyatSoYTxvsVjYsWMHS5cuJTExkYcf\nfrhEizp//rxxvpo1a3L+/HkAUlJSaNmypbGdl5cXKSkpJXpuERGRG2EzSP/973/j6up6xfNOTk60\na9eOdu3akZOTU2rFFTCZTKV+DhERkeKwGaRXC9Ht27eTmZlJly5dcHZ2vuo29vL09OTs2bPUqFGD\nM2fOUL16dSC/BXrq1Clju9OnT+Pl5XXd482aNYuwsLASr1NEROSGRu1+/PHHREREEBcXx9ixY0us\niD8OGPLz8yMiIgKAyMhIevbsaTwfGxtrXKc9fvw4Pj4+1z3+mDFj2L9/f5E/CQkJJVa/iIjcvmy2\nSKOjo4vclpKYmMhHH30EQL9+/UqkgHHjxrFt2zZ+/fVXunfvzpgxY3j22Wd58cUXWb58OXXr1uXj\njz8GoFGjRgQEBBAYGIiLiwuTJk1St6+IiJQpk9XG/SNhYWHs2bOHN998k3r16vHRRx+RmpqKyWTi\nl19+Yfbs2Y6stUSdOHGCnj17kpCQgLe3d1mXIyIi5ZTNFuno0aM5evQo77zzDq1atWL06NF8//33\nZGVl0aVLF0fVKCIictO67jXSBg0aMG/ePGrXrs2IESOoUKECfn5+VKhQwRH1iYiI3NRsBunmzZsZ\nMGAAjz76KPfccw9hYWFERUUxYcIELly44KgaRUREblo2g3TatGmEhYUxdepU3n//fapVq8bUqVMJ\nCQlh9OjRjqpRRETkpnXdrl0nJydMJlORW1Tatm3LggULSrUwERGR8sDmYKNXXnmFUaNGUaFCBV59\n9dUir+kaqYiIyHWCtFu3bnTr1s1RtYiIiJQ7NoMUYPfu3axcuZKTJ0/i4uLCvffey2OPPUbNmjUd\nUZ+IiMhNzeY10gULFjBx4kQAjhw5wp133smvv/5KaGgo27dvd0iBIiIiNzObLdLw8HCWL19OpUqV\nOH/+PK+88goLFizg4Ycf5o033jDmwxUREbld2WyROjs7U6lSJQCqVq3KuXPnAGjWrJlDlk8TERG5\n2dlskbZo0YKJEyfSuXNn4uLiaNOmDQCXLl3i8uXLDilQRETkZmazRTpp0iSqV69OREQE99xzj3EL\nzOXLl5k5c6ZDChQREbmZ2WyRuru789e//vWK5ytXrkyzZs1KrSgREZHy4oYW9i5s/fr1JVmHiIhI\nuVTsIE1ISCjJOkRERMqlYgfp1KlTS7IOERGRcumGgjQ3N5effvqJtLS00qpHRESkXLEZpN999x0d\nO3bkwQcf5L///S+PPvoo48aN46GHHmLr1q2OqlFEROSmZXPU7j/+8Q8WLVpEWloao0eP5pNPPqFD\nhw78+OOPvPvuuyxdutRRdYqIiNyUbAbp5cuXjdtcqlatSocOHQC4//77yc7OLv3qREREbnI2u3Yt\nFovxd7PZXOS1vLy80qlIRESkHLEZpG3btiU9PR2AsWPHGs8fOXKEatWqlW5lIiIi5YDJarVai7Oj\n1WrFZDKVdD0Oc+LECXr27ElCQgLe3t5lXY6IiJRTxb6PNDMzsyTrEBERKZeKHaSBgYElWYeIiEi5\nZHPU7jfffHPN1y5dulTixYiIiJQ3NoN05MiRtGvXjqtdRs3IyCi1okRERMoLm0Fav3593n33XerV\nq3fFa926dSu1okRERMoLm9dIBw8ezIULF6762rBhw0qlIBERkfKk2Le/lHe6/UVEREqCzRbpn5kG\nUFMFiojI7cxmkA4ZMoR58+Zx6tSpIs9fvnyZzZs3M3r0aGJiYkq1QBERkZuZzcFGS5Ys4csvv2TY\nsGFkZWVRo0YNLl26xJkzZ+jQoQNPP/00rVq1clStIiIiN50/fY309OnTnD59Gjc3Nxo0aEDFihVL\nu7ZSpWukIiJSEmy2SAu76667uOuuu0qzFhERkXKn2FMEioiIiIJURETELgpSEREROxQ7SK8145GI\niMjtxGaQ7tmzh169euHj48PYsWM5f/688dqTTz5Z2rWJiIjc9GwG6Xvvvcebb77Jt99+S5MmTRgy\nZIgxOcNtOrOgiIhIETZvf8nMzKR79+4AjB49mgYNGvDEE08wf/58TCaTI+oTERG5qdkM0kuXLpGX\nl4ezszMAgYGBuLq68uSTT5Kbm+uQAkVERG5mNrt2H3jgATZt2lTkuV69ejFhwgRycnJKtTAREZHy\nQMuoaYpAERGxg80WaUJCAitWrLji+aioKNatW1dqRYmIiJQXNoN0/vz5dO7c+Yrnu3btyrx580qt\nKBERkfLCZpDm5OTg6el5xfPVq1cnMzOz1IoSEREpL2wGqa3Zi7Kyskq8mD/r22+/pU+fPvj7+6tl\nLCIiZcpmkDZt2pTo6Ogrnl+1ahWNGzcutaJssVgsvPPOO8yfP5+YmBhWrVrF4cOHy6QWERERm/eR\njhs3jqFDh7JhwwZ8fX0B2LVrF9u2bePLL790SIF/tHv3burXr0/dunWB/HtbExISuPfee8ukHhER\nub3ZbJE2aNCAiIgI6tWrx6ZNm9i0aRP16tUjIiKCBg0aOKrGIlJSUqhdu7bx2MvLi9TU1DKpRURE\nxGaLFMDV1ZWHHnqIp59+msqVKzuiJhERkXLDZpDGxsYyfvx4PDw8yMnJYdasWTzwwAOOqu2qvLy8\nOHnypPE4JSWFWrVq2dxn1qxZhIWFlXZpIiJyG7LZtTt79myWLl3Kli1bCAsL47PPPnNUXdd0//33\nc/z4cZKTk8nJyWHVqlX07NnT5j5jxoxh//79Rf4kJCQ4qGIREbmV2QxSJycnmjdvDkDHjh1JT093\nSFG2ODs7M3HiREaMGEFQUBCBgYEaaCQiImXGZtfu5cuXOXz4sLH26KVLl4o8btSoUelXeBVdu3al\na9euZXJuEbl95OXlEbsinP8snMalX4/j5gpud9QnZNjrBPYbXNblyU3CZpBmZ2fzzDPPFHmu4LHJ\nZFL3qIjcslJTU3nt2QByz+1kRFcrPVqAyQRW63nWbn0EFKTyG5tBqonpReR2ZLFYmDzGTMW0Hwgb\nDh5uv79mMoH//bflollyDde9/UVE5HYTFxPBPS4/0K5j0RAVuRoFqYjIH2xasxBrei7dW5R1JVIe\nKEhFRP7AxZoJzvnduCLXoyAVEfmDXJM71jywWhWmcn027yMVEbkdde4znDsqu7Dhp7KuRMoDBamI\nyB/4B4VyLLc1/9kKGdllXY3c7BSkIiJ/4OTkxORZ0Vyq0prnFppI2JPfzQv5/12zW/298jtdIxUR\nuYpatWoxP+K/rI5ezqL57zN743EqVbDiduc99H9yfFmXJzcRBamIyDU4OTkR2G8Qgf0GlXUpchNT\n166IiIgdFKQiIiJ2UJCKiIjYQUEqIiJiBwWpiIiIHRSkIiIidlCQioiI2EFBKiIiYgcFqYiIiB0U\npCIiInZQkIqIiNhBQSoiImIHBamIiIgdFKQiIiJ2UJCKiIjYQUEqIiJiBwWpiIiIHRSkIiIidlCQ\nioiI2EFBKiIiYgcFqYiIiB0UpCIiInZQkIqIiNhBQSoiImIHBamIiIgdFKQiIiJ2UJCKiIjYQUEq\nIiJiBwWpiIiIHRSkIiIidlCQioiI2EFBKiIiYgcFqYiIiB0UpCIiInZQkIqIiNhBQSoiImIHBamI\niIgdFKQiIiJ2KLMgXbNmDUFBQTRv3py9e/cWeW3u3Ln07t2bgIAANm3aZDy/d+9ezGYz/v7+vPvu\nu44uWURE5AplFqRNmjQhLCyMdu3aFXn+8OHDrF69mtjYWD7//HOmTJmC1WoFYPLkybz77rvExcVx\n7NgxNm7cWBali4iIGMosSBs2bMg999xjhGSBhIQE+vbti4uLC97e3tSvX5/du3dz5swZMjIy8PHx\nASAkJIT4+PiyKF1ERMRw010jTUlJoXbt2sZjLy8vUlJSSElJ4a677rrieRERkbLkUpoHHz58OGfP\nnr3i+b/+9a/4+fmV5qlFREQcolSDdOHChTe8j5eXF6dOnTIenz59Gi8vryueT0lJwcvL608dc9as\nWYSFhd1wLSIiItdzU3TtFr5O6ufnR2xsLDk5OSQlJXH8+HF8fHyoWbMmVapUYffu3VitVqKioujZ\ns+efOv6YMWPYv39/kT8JCQml9XZEROQ2UqotUlvi4+N55513+OWXXxg5ciTNmjXjn//8J40aNSIg\nIIDAwEBcXFyYNGkSJpMJgLfeeovx48dz6dIlunbtSteuXcuqfBEREQBM1j8Om71NnDhxgp49e5KQ\nkIC3t3dZlyMiIuXUTdG1KyIiUl4pSEVEROygIBUREbFDmQ02utXl5eURERfHos2byXRxwT03l+Gd\nOxPq74+Tk76/iIjcKhSkpSA1NZXgKVPYNXAg2VOngskEVivrNmxgxpgxrJw0iVq1apV1mSIiUgLU\nNCphFouF4ClT2PbBB2T36JEfogAmE9k9erDtgw8InjIFi8VStoWKiEiJUJCWsIi4OHYNHAgeHlff\nwMODXQMGELV2rWMLExGRUqEgLWELN20iu3t3m9tk9+jBAi0BJyJyS1CQlrBMF5ffu3OvxWTK305E\nRMo9BWkJc8nIgOtNFmW14p6b65iCRESkVClIS5DFYiE3Lg6n1attbue2bh0junRxUFUiIlKaFKQl\naHV4OHX37KH1xImQkXH1jTIyuPvzzwnp3duxxYmISKlQkJagpdOmMQJY9cMPdOjaFbdVq37v5rVa\ncVu1imZdu+Lz00+alEFE5BahES8lKDsxkR6ACdjyww9EDhrEwsaNyaxaFfeLFxlx8CD9srJ42NOz\nrEsVEZESoiAtQW7khyjkN/UHZGUxYPfuK7e7PVeuExG5Jal/sQRdqlaN60WkFci54w5HlCMiIg6g\nIC1BrtWqsf4626z7bTsREbk1KEhLUMNq1VgOXGO8LhlAxG/biYjIrUHXSEtQnocHbwKhlSpxoXFj\nKv02yOjJgwe5MyuLSOAt4JNrzcMrIiLljoK0BN0XGkrP1FSOTZlCdkCAsXxa3OrVNJo4kQ0//MBe\nNze6jBhR1qWKiEgJUZCWEIvFwsc7drBvw4aiK7+YTOT17cv+bt0wd+1KOxcXwkJCyqxOEREpWQrS\nEhIRF8fuQYNsLp/2wzvv8HxamiZjEBG5hegTvYT8meXTLAEBRFzlvlIRESm/FKQlRMuniYjcnhSk\nJcQ9N1fLp4mI3IYUpCUk9P77cVqzxuY2buvXa/k0EZFbjIK0BFgsFnZ89BGtJ0ywuXyaT3i4lk8T\nEbnFKEhLQFxEBAN27bK9fFr37vy1XTuN2BURucVo5EsJ2LhgAe9eunTd5dMmhofzyPDhZV2uiIiU\nIAVpCbh4/PifWj7t4vHjDq1LRERKn/oZS8Cp1NQ/tXzaqdRUR5QjIiIOpCAtAZVr1mTDdbZZD1Sp\nWTXy9dEAAA+7SURBVNMB1YiIiCMpSEtA3fr1WYbt5dPCgTr16zuuKBERcQgFaQnoMmIED1WsyKvk\nL9xd0M1r/e3xq0DPihXp+tRTZVWiiIiUEgVpCfAPDSWhZUumAZeACcCk3/6bA0wD1rVsSW+t+iIi\ncstRkJYAJycnJq1cyesdOlDRzY2pwBRgKuDq5sbrHTowaeVK3UMqInIL0u0vJaRWrVrM2rKFuMhI\nJixciEtmJrnu7nQZMYJZISEKURGRW5SCtAQ5OTkRMGAAAQMGlHUpIiLiIGomiYiI2EFBKiIiYgcF\nqYiIiB0UpCIiInZQkIqIiNhBQSoiImIHBamIiIgdFKQiIiJ2UJCKiIjYQUEqIiJiBwWpiIiIHcos\nSD/44AMCAgLo168fY8aMIT093Xht7ty59O7dm4CAADZt2mQ8v3fvXsxmM/7+/rz77rtlUbaIiEgR\nZRaknTt3ZtWqVaxYsYL69eszd+5cAA4dOsTq1auJjY3l888/Z8qUKVit+UtlT548mXfffZe4uDiO\nHTvGxo0by6p8ERERoAyD9MEHHzSWFmvZsiWnT58GYN26dfTt2xcXFxe8vb2pX78+u3fv5syZM2Rk\nZODj4wNASEgI8fHxZVW+iIgIcJNcIw0PD6dbt27/3969x1Rd/38Af6KAmNBSLgcWhQS1wEAdjtM8\n4uUAB4Fz43K2cisHtmozj6mZgmatlm1QmUIROHLZxVJGZxqsrTjz4CUxuoCKliQbIJ6DhHLHc5DX\n7w9+fMYR8CsczwHk9djcOO/PZ5/n5/3h4Ot8Luf9BgCYTCb4+fkJy0QiEUwmE0wmE3x9fYe1M8YY\nYxPJrvORpqWloaWlZVj7pk2bIJVKAQB5eXlwcXGBXC63564wxhhjdmHXQnrgwIG7Li8uLobBYMDB\ngweFNpFIhGvXrgmvjUYjRCLRsHaTyQSRSHRP+5GTk4Pc3Nwx7j1jjDH2v03Ypd3y8nIUFhYiLy8P\nrq6uQrtUKkVpaSnMZjMaGhpQX1+P8PBweHt7w8PDA9XV1SAi6HQ6REdH31PWhg0b8Pfff1v9u3Dh\nAsrKyqwuFzPGGGNj5USDj8Q6mEwmg8ViwSOPPAIAWLhwId555x0AA19/KSoqgrOzM3bs2IFly5YB\nAM6fP4+MjAzcunULy5cvx86dOydi1xljjDHBhBVSxhhj7EFg13ukU01fX5/wNRzGGLsbX19fODvz\nf6GMC6kVo9F4z/ddGWPTW1lZGfz9/Sd6N9gkwIV0iMEHj8rKyhyaGx0d7fDM6ZbLfeXc+53JDyqy\nQVxIhxi8TDMRnzIn6pPtdMrlvnLu/cSXddmgSTGyEWOMMTZVcSFljDHGbMCFlDHGGLPBzHcGR0Fg\nArFYPC0yp1su95Vzp3omm5x4QAbGGGPMBnxplzHGGLMBF1LGGGPMBlxIGWOMMRtwIWWMMcZswIWU\nMcYYs8G0LaRZWVmIj4+HSqXChg0b0NnZKSzLz8+HTCZDfHw8Tp48KbRfuHABCoUCcXFxeP/998eV\n+9NPP0EulyMkJAQXLlywWmbP3KHKy8uxevVqxMXFoaCgwObtDZWZmYmlS5dCoVAIbW1tbUhPT0dc\nXBzWrVuHjo4OYdlofR4Lo9GIF198EYmJiVAoFDh48KBDcs1mMzQaDdRqNRQKBXJzcx2SCwD9/f1I\nSkrCq6++6rBMqVQKpVIJtVqN1NRUh+V2dHRAq9UiPj4eiYmJqKqqsmtuXV0d1Go1kpKSoFarERER\ngYMHDzqkr2yKomnq1KlTdPv2bSIiys7Opg8//JCIiC5fvkwqlYosFgs1NDRQTEwM9ff3ExFRamoq\nVVVVERHRSy+9ROXl5WPO/ffff6muro5eeOEFOn/+vNBeW1tr19xBt2/fppiYGGpsbCSz2UxKpZJq\na2vHvb07/fbbb1RTU0NyuVxoy8rKooKCAiIiys/Pp+zsbCK6+7Eei+bmZqqpqSEios7OTpLJZFRb\nW2v3XCKi7u5uIiLq6+sjjUZDVVVVDsk9cOAAbdmyhV555RUisv8xJiKSSqV08+ZNqzZH5G7bto2K\nioqIiMhisVB7e7tDcokG/l4kEgk1NTU5LJNNPdP2jHTp0qWYMWOg+4sWLRLmIdXr9UhISICzszP8\n/f0REBCA6upqXL9+HV1dXQgPDwcAqNVq/PLLL2POfeKJJzB//nzQHV/fLSsrs2vuoOrqagQEBODR\nRx+Fi4sLEhMT7+vMGUuWLMHDDz9s1VZWVoakpCQAQFJSkrD/ox3rsfL29kZISAgAYM6cOQgKCoLJ\nZLJ7LgDMnj0bwMDZaV9fn0P6azQaYTAYoNFohDZH9JWI0N/fb9Vm79zOzk5UVlYiJSUFwMBA8R4e\nHg7pLwCcPn0ajz/+OPz8/ByWyaaeaVtIhyoqKsKKFSsAACaTCX5+fsIykUgEk8kEk8lkNW3SYPv9\n4qjckXKam5vHvb170draCi8vLwADRa+1tXXUfbH1mDY2NuLSpUtYuHAh/vvvP7vn9vf3Q61WQyKR\nQCKRIDw83O65u3fvxptvvgknJyehzRF9dXJyQnp6OlJSUnDkyBGH5DY2NmLu3LnIyMhAUlIS3nrr\nLfT09DikvwBQWloKuVwOwDHHmE1ND/Q8QGlpaWhpaRnWvmnTJkilUgBAXl4eXFxchD8WR+VOZ0ML\nwP3U1dUFrVaLzMxMzJkzZ1iOPXJnzJgBnU6Hzs5OrF+/HpcvX7Zr7vHjx+Hl5YWQkBBUVFSMup49\n+nro0CH4+PigtbUV6enpCAwMtPsx7uvrQ01NDXbt2oWwsDDs3r0bBQUFDvndWiwW6PV6vPHGGyNm\n2Ot9zKaeB7qQHjhw4K7Li4uLYTAYhIdTgIFPk9euXRNeG41GiESiYe0mkwkikWhcuSO5H7n3mtPU\n1GS1PR8fn3Fv7154enqipaUFXl5euH79OubNmyfsy0h9Ho++vj5otVqoVCrExMQ4LHeQu7s7IiMj\nceLECbvm/vHHH9Dr9TAYDLh16xa6urqwdetWeHl52b2vg++TefPmISYmBtXV1XY/xr6+vvD19UVY\nWBgAQCaTYf/+/Q753ZaXl2PBggXCth35fmJTy7S9tFteXo7CwkLk5eXB1dVVaJdKpSgtLYXZbEZD\nQwPq6+sRHh4Ob29veHh4oLq6GkQEnU6H6Ohom/Zh6H1SR+WGhYWhvr4eV69ehdlsRklJic39uNOd\n93+lUimKi4sBAD/88IOQN1qfxyMzMxPBwcFYu3atw3JbW1uFJzd7e3tx+vRpBAUF2TV38+bNOH78\nOMrKyvDxxx9DLBYjOzsbq1atsmtfe3p60NXVBQDo7u7GyZMn8dRTT9n9GHt5ecHPzw91dXUAgDNn\nziA4ONgh76mSkhKrK1WOyGRT1EQ+6TSRYmNjaeXKlaRWq0mtVtPbb78tLPv8888pJiaGVq9eTSdO\nnBDaz507R3K5nGJjY+m9994bV+7PP/9My5cvp7CwMJJIJLRu3TqH5A5lMBhIJpNRbGws5efn27y9\noTZv3kwSiYQWLFhAK1asoKKiIrp58yatXbuWZDIZpaWlUVtbm7D+aH0ei8rKSnr66adJqVSSSqUi\ntVpNBoOBbty4YdfcS5cukVqtJqVSSXK5nD777DMiIrvnDqqoqBCe2rV3Zn19vXB85XK58L5xRF8v\nXrxIycnJpFQqaf369dTe3m733O7ubhKLxdTR0SG0Oer3yqYenv2FMcYYs8G0vbTLGGOM3Q9cSBlj\njDEbcCFljDHGbMCFlDHGGLMBF1LGGGPMBlxIGWOMMRtwIWWTmlQqRUJCAlQqFRQKBUpLS4VldXV1\neO211xAbG4vU1FSsWbNGGID/6NGjUCqVWLBgAb755pu7ZvT29iIlJQW9vb0AgHfffVeYLkyj0eDX\nX38V1v3rr7/w3HPPQalUQqPRoKamZsRtXrp0Cc8//zwWLVqEjRs3Wi0zGAxQKBRQKBQ4deqU0J6b\nm4tjx44Jr81mM1JSUqym+GOMTUIT/UVWxu5m1apVwjRvNTU1FB4eTjdu3CCTyUQSiYSOHj0qrNvS\n0kI6nY6IBqa2qq2tpW3bttHXX39914yCggKrgSmGfgn/4sWLJBaLhddRUVFUWVlJRAMDQSQkJIy4\nzebmZqqqqqLvv/+etFqt1bLk5GQyGo3U1NREycnJRER05coVYXCFob788kvat2/fXfefMTax+IyU\nTXr0/2OGhISEYM6cOWhsbMS3334LsVhsNYG4p6cnVCoVACA4OBhBQUH3NLD44cOHrbbj7u4u/NzR\n0SFMtzc4JGBERAQAICIiAkajccSzUm9vb4SHh8PFxWXYMhcXF3R1daG7u1sYnvKDDz7Ajh07hq2b\nkJCAoqKi/9kHxtjEeaAHrWcPljNnzsBsNmP+/PmoqanBsmXLbN6m0WhET0+P1TRYALBv3z4cO3YM\n7e3tyM3NBTAwWPvcuXOh1+shlUqh1+vR3d2NpqYmhIaG3nPm1q1bsX37djg5OSEjIwM6nQ6LFy/G\nY489NmxdLy8vuLq6oq6uDoGBgbZ1ljFmF1xI2aSn1Woxa9YsuLu7Iycnx+qM0VZGo1GYY/LOTK1W\ni4qKCmRlZeHQoUNwdnbGp59+iqysLOTm5mLRokUIDg7GzJkzx5QZERGBw4cPAwDa2trw0Ucf4Ysv\nvsCePXtQX1+PgIAAvP7668L6np6eMBqNXEgZm6S4kLJJLycnB0FBQVZtoaGhqKqqsnnbbm5uuHXr\n1qjLxWIxOjs78c8//yA0NBQhISHCNHkWiwUSiQTBwcHjzs/OzsbGjRtRWVmJ5uZm7NmzB9u3b8fZ\ns2cRGRkJYOChIzc3t3FnMMbsi++RskmPRphXYc2aNaioqEBJSYnQ1traCp1ON6ZtBwYG4vr167BY\nLELblStXhJ/PnTuH1tZW4bLr0Anb8/PzERkZOeIl2aH7PtL+A0BlZSUAYMmSJejp6RHu5zo5OaG7\nuxsA0N/fj4aGBjz55JNj6hdjzHH4jJRNaqM9LOTj44OvvvoK2dnZ+OSTTzB79mw89NBDePnllwEM\nzCWZlZWF9vZ26PV67N+/H4WFhcPObGfNmgWxWIyzZ89CIpGAiLBr1y60tbVh5syZcHNzw969e+Hh\n4QEA+O677/Djjz+CiPDMM89g9+7dwrZ27tyJ6OhorFq1ClevXsWaNWvQ29sLs9mMlStXYsOGDUhJ\nSQEwcDa7d+9e4f5rVFQUjhw5ApVKBX9/f0RFRQEAfv/9dyxcuPC+Xs5mjN1fPI0am/b+/PNPFBYW\nCkVtMtmyZQs0Gg2effbZid4Vxtgo+NIum/YWL16MlStXCgMyTBZmsxmRkZFcRBmb5PiMlDHGGLMB\nn5EyxhhjNuBCyhhjjNmACyljjDFmAy6kjDHGmA24kDLGGGM24ELKGGOM2eD/AOaQZ7LoX7/gAAAA\nAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for dset in [[v_full, c_full], [v_filt, c_filt]]:\n", " plotPCA(dset[1], \"ddocent\")" ] }, { "cell_type": "code", "execution_count": 239, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3j4ULF5Zo14oVK4iKiuKpp57i3r17yhcYgGPHjvGvf/2LJUuW4OLiwpEjRx5q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\nYwzLly/Hg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sIDIykg4dOpCXl6d8gQE4fvw4H374IUuXLsXR0ZGjR4+WuxZTCCFEGSwAQ39C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\ngg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plotPairwiseDistance(c_full, \"dDocent Full\")\n", "plotPairwiseDistance(c_filt, \"dDocent Filtered\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Everything Below here is crap!" ] }, { "cell_type": "code", "execution_count": 584, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-17 14:32:50.401685 :: caching is disabled\n", "[vcfnp] 2016-10-17 14:32:50.402203 :: building array\n", "[vcfnp] 2016-10-17 14:32:50.880046 :: caching is disabled\n", "[vcfnp] 2016-10-17 14:32:50.880506 :: building array\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "('loc_cov', [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 1, 9881])\n", "('snp_cov', [1, 10, 6, 6, 1, 14, 0, 10, 7, 6, 61, 43054])\n", "('snps', [43139, 43139, 43135, 43141, 43134, 43139, 43139, 43139, 43144, 43141, 43143, 43141])\n", "('nlocs', [9883, 9883, 9883, 9883, 9883, 9882, 9883, 9883, 9883, 9883, 9883, 9883])\n" ] } ], "source": [ "## Testing for different vcf formats\n", "prog = \"stacks-simhi\"\n", "filename = \"/home/iovercast/manuscript-analysis/stacks/SIMDATA/ungapped/simhi/batch_1.vcf\"\n", "#prog = \"dDocent-simhi\"\n", "#filename = \"/home/iovercast/manuscript-analysis/dDocent/SIMDATA/simhi/simhi_fastqs/TotalRawSNPs.vcf\"\n", "#prog = \"ipyrad-simlarge\"\n", "#filename = \"/home/iovercast/manuscript-analysis/ipyrad/SIMDATA/simlarge/simlarge_outfiles/simlarge-biallelic.recode.vcf\"\n", "prog = \"pyrad-simhi\"\n", "filename = \"/home/iovercast/manuscript-analysis/pyrad/SIMDATA/simhi/outfiles/c85d6m2p3H3N3.vcf\"\n", "v = vcfnp.variants(filename, dtypes={\"CHROM\":\"a24\"}).view(np.recarray)\n", "c = vcfnp.calldata_2d(filename).view(np.recarray)\n", "\n", "print(\"loc_cov\", loci_coverage(v, c, prog))\n", "print(\"snp_cov\", snp_coverage(c))\n", "print(\"snps\", sample_nsnps(c))\n", "print(\"nlocs\", sample_nloci(v, c, prog))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Everything Below here is crap!" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Everything Below here is crap!" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['1_6' '1_14' '1_27' ..., '614361_45' '614386_71' '614406_58']\n", "344633\n", "76984\n" ] } ], "source": [ "## Maybe useful\n", "print(v.ID)\n", "print(len(v.ID))\n", "print(len(set([x.split(\"_\")[0] for x in v.ID])))\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['4_35' '4_62' '5_56' ..., '496594_58' '496602_66' '496604_59']\n", "149570\n", "82955\n", "82955\n", "(numpy.record, [('CHROM', 'S12'), ('POS', '" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = allel.GenotypeArray(cmask.genotype)\n", "gn = g.to_n_alt()\n", "m = allel.stats.rogers_huff_r(gn[:1000]) ** 2\n", "ax = allel.plot.pairwise_ld(m)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## Some crap from when i was counting loci a very stupid and slow way for stacks.\n", " ### This doesn't' count monomorphics, but could be useful still. It's dog slow.\n", " #vcf = pd.read_csv(\"{}/batch_1.vcf\".format(simdir), delim_whitespace=True, header=9, index_col=2)\n", " #sample_counts = {}\n", " #sample_names = list(vcf.columns)[8:]\n", " #print(sample_names)\n", " #uniq_loci = set([x.split(\"_\")[0] for x in vcf.index])\n", " #print(\"num loci - {}\".format(len(uniq_loci)))\n", " ## Replace the ID index in the stacks vcf file so we can actually evaluate loci\n", " #vcf.index = pd.Index([x.split(\"_\")[0] for x in vcf.index])\n", " #for name in sample_names:\n", " # print(name),\n", " # sample_counts[name] = 0\n", " # for locus in uniq_loci:\n", " # snps = vcf[name][locus]\n", " # if any(map(lambda x: x.split(\":\")[0] != \"./.\", snps)):\n", " # sample_counts[name] += 1\n", " # else:\n", " # pass\n", " # #print(\"{} {} {}\".format(name, locus, snps))\n", " #print(sample_counts)\n", " #sim_full_sample_nlocs[\"stacks_\"+stacks_method+\"-\"+sim] = sample_coverage\n", " " ] }, { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[vcfnp] 2016-10-06 21:43:30.965523 :: caching is disabled\n", "[vcfnp] 2016-10-06 21:43:30.966488 :: building array\n", "[vcfnp] 2016-10-06 21:43:42.021230 :: caching is disabled\n", "[vcfnp] 2016-10-06 21:43:42.029759 :: building array\n" ] } ], "source": [ "filename = os.path.join(DDOCENT_OUTPUT, \"Final.recode.snps.vcf\")\n", "v_filt = vcfnp.variants(filename).view(np.recarray)\n", "c_filt = vcfnp.calldata_2d(filename).view(np.recarray)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(numpy.record, [('CHROM', 'S24'), ('POS', ' at 0x2aab77fca870>\n", "[96757, 98257, 97393, 95620, 97197, 95823, 98629, 98982, 99257, 99071, 99157, 99736, 99578]\n" ] } ], "source": [ "print(c.dtype)\n", "print(len(c))\n", "print(c[\"GT\"][0:10][:,0])\n", "print((c[\"GT\"][0:10000][:,0] == \"./.\").sum())\n", "\n", "print(c[\"GT\"][0:5] != \"./.\")\n", "print((x != \"./.\").sum() for x in c[\"GT\"][0:5])\n", "\n", "from collections import Counter\n", "\n", "loci_coverage = [x.sum() for x in c_filt[\"GT\"].T != \"./.\"]\n", "print(loci_coverage)" ] }, { "cell_type": "code", "execution_count": 599, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ipyrad-simno\t[9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870, 9870] 9870.0\n", "pyrad-simno\t[9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894, 9894] 9894.0\n", "stacks_ungapped-simno\t[9870, 9862, 9865, 9863, 9862, 9864, 9864, 9867, 9865, 9867, 9863, 9865] 9864.75\n", "stacks_gapped-simno\t[9870, 9862, 9865, 9863, 9862, 9864, 9864, 9867, 9865, 9867, 9863, 9865] 9864.75\n", "stacks_defualt-simno\t[9320, 9303, 9253, 9096, 9042, 9021, 8958, 8934, 8846, 8823, 8767, 8687] 9004.16666667\n", "ddocent_filt-simno\t[9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852] 9852.0\n", "ddocent_full-simno\t[9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852, 9852] 9852.0\n", "aftrrad-simno\tNo aftrrad-simno\n", "------------------------------------------------------\n", "ipyrad-simlo\t[9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859, 9859] 9859.0\n", "pyrad-simlo\t[9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884, 9884] 9884.0\n", "stacks_ungapped-simlo\t[9819, 9808, 9818, 9812, 9832, 9825, 9826, 9801, 9811, 9808, 9817, 9801] 9814.83333333\n", "stacks_gapped-simlo\t[1512, 1503, 1507, 1503, 1507, 1504, 1508, 1508, 1507, 1506, 1508, 1506] 1506.58333333\n", "stacks_defualt-simlo\t[9275, 9254, 9207, 9055, 9020, 8993, 8929, 8888, 8807, 8779, 8741, 8645] 8966.08333333\n", "ddocent_filt-simlo\t[9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853] 9853.0\n", "ddocent_full-simlo\t[9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853, 9853] 9853.0\n", "aftrrad-simlo\tNo aftrrad-simlo\n", "------------------------------------------------------\n", "ipyrad-simhi\t[9844, 9844, 9844, 9844, 9844, 9843, 9844, 9844, 9844, 9844, 9844, 9844] 9843.91666667\n", "pyrad-simhi\t[9877, 9877, 9877, 9877, 9877, 9876, 9877, 9877, 9877, 9877, 9877, 9877] 9876.91666667\n", "stacks_ungapped-simhi\t[9741, 9769, 9746, 9746, 9756, 9747, 9756, 9745, 9727, 9731, 9717, 9715] 9741.33333333\n", "stacks_gapped-simhi\t[60, 60, 59, 60, 60, 60, 60, 60, 60, 60, 59, 58] 59.6666666667\n", "stacks_defualt-simhi\t[9187, 9208, 9138, 8985, 8955, 8925, 8878, 8842, 8737, 8716, 8648, 8582] 8900.08333333\n", "ddocent_filt-simhi\t[9848, 9848, 9848, 9848, 9848, 9848, 9846, 9847, 9847, 9848, 9848, 9848] 9847.66666667\n", "ddocent_full-simhi\t[9848, 9848, 9848, 9850, 9850, 9850, 9848, 9848, 9849, 9850, 9850, 9850] 9849.08333333\n", "aftrrad-simhi\tNo aftrrad-simhi\n", "------------------------------------------------------\n", "ipyrad-simlarge\t[95399, 95428, 95405, 95456, 95450, 95412, 95437, 95418, 95283, 95294, 95288, 95302] 95381.0\n", "pyrad-simlarge\t[95646, 95675, 95649, 95708, 95695, 95659, 95686, 95672, 95543, 95552, 95544, 95558] 95632.25\n", "stacks_ungapped-simlarge\t[95366, 95401, 95391, 95400, 95394, 95367, 95411, 95405, 95232, 95276, 95268, 95271] 95348.5\n", "stacks_gapped-simlarge\t[95366, 95401, 95391, 95400, 95394, 95367, 95411, 95405, 95232, 95276, 95268, 95271] 95348.5\n", "stacks_defualt-simlarge\t[90013, 89989, 89284, 87596, 87548, 87334, 86818, 86236, 85427, 85225, 84641, 83680] 86982.5833333\n", "ddocent_filt-simlarge\t[90677, 90706, 90329, 89908, 90701, 90653, 90336, 89898, 90677, 90684, 90271, 89800] 90386.6666667\n", "ddocent_full-simlarge\t[95169, 95200, 95167, 95218, 95203, 95164, 95198, 95177, 95048, 95056, 95049, 95052] 95141.75\n", "aftrrad-simlarge\tNo aftrrad-simlarge\n", "------------------------------------------------------\n" ] } ], "source": [ "#x = sim_loc_cov\n", "#x = sim_snp_cov\n", "#x = sim_sample_nsnps\n", "x = sim_sample_nlocs\n", "for sim in [\"-simno\", \"-simlo\", \"-simhi\", \"-simlarge\"]:\n", " for prog in [\"ipyrad\", \"pyrad\", \"stacks_ungapped\", \"stacks_gapped\", \"stacks_defualt\", \"ddocent_filt\", \"ddocent_full\", \"aftrrad\"]:\n", " try:\n", " print(prog+sim+\"\\t\"),\n", " print(x[prog+sim]),\n", " print(np.mean(x[prog+sim]))\n", " except:\n", " print(\"No {}\".format(prog+sim))\n", " print(\"------------------------------------------------------\")" ] }, { "cell_type": "code", "execution_count": 338, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "two methods of splitting by group\n", "as list\n", "('a', array([ 1.]))\n", "('b', array([ 4.5, 4.3]))\n", "('c', array([ 2.]))\n", "('d', array([ 5.67]))\n", "('e', array([ 1.2 , 8.08, 9.01]))\n", "as iterable\n", "[1 2 1 1 3]\n", "('a', [1])\n", "('b', [4.5, 4.3])\n", "('c', [2.0])\n", "('d', [5.67])\n", "('e', [1.2, 8.08, 9.01])\n", "iterable as iterable\n", "('a', [1])\n", "('b', [4.5, 4.3])\n", "('c', [2.0])\n", "('d', [5.67])\n", "('e', [1.2, 8.08, 9.01])\n" ] } ], "source": [ "if True:\n", " keys = [\"e\", \"b\", \"b\", \"c\", \"d\", \"e\", \"e\", 'a']\n", " values = [1.2, 4.5, 4.3, 2.0, 5.67, 8.08, 9.01,1]\n", "\n", " print('two methods of splitting by group')\n", " print('as list')\n", " for k,v in zip(*npi.group_by(keys)(values)):\n", " print(k, v)\n", " print('as iterable')\n", " g = npi.group_by(keys)\n", " for k,v in zip(g.unique, g.split_sequence_as_iterable(values)):\n", " print(k, list(v))\n", " print('iterable as iterable')\n", " for k, v in zip(g.unique, g.split_iterable_as_iterable(values)):\n", " print(k, list(v))\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }