tests/ipyrad-manuscript-refmap-results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results for ipyrad vs stacks vs dDocent simulated and empirical reference sequence assembly\n",
    "I'm assuming here that you've already run the de novo assemblies and have already installed all the prereqs for\n",
    "analysing the results. If not run the ipyrad-manuscript-results.ipynb."
   ]
  },
  {
   "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",
    "import matplotlib\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 subprocess\n",
    "import collections\n",
    "import allel\n",
    "import vcfnp\n",
    "import shutil\n",
    "import gzip\n",
    "import glob\n",
    "import os\n",
    "from allel.util import * ## for ensure_square()\n",
    "\n",
    "WORK_DIR=\"/home/iovercast/manuscript-analysis/\"\n",
    "\n",
    "## Simulation dirs\n",
    "REFMAP_SIM_DIR = os.path.join(WORK_DIR, \"REFMAP_SIM/\")\n",
    "IPYRAD_SIM_DIR = os.path.join(REFMAP_SIM_DIR, \"ipyrad/reference-assembly/\")\n",
    "STACKS_SIM_DIR = os.path.join(REFMAP_SIM_DIR, \"stacks/\")\n",
    "DDOCENT_SIM_DIR = os.path.join(REFMAP_SIM_DIR, \"ddocent/\")\n",
    "## A list of the simluated assembler names for indexing dicts\n",
    "assemblers = [\"ipyrad-reference\", \"ipyrad-denovo_reference\", \"stacks\", \"ddocent\"]\n",
    "\n",
    "## Empirical dirs\n",
    "REFMAP_EMPIRICAL_DIR=os.path.join(WORK_DIR, \"Phocoena_empirical/\")\n",
    "IPYRAD_REFMAP_DIR=os.path.join(REFMAP_EMPIRICAL_DIR, \"ipyrad/\")\n",
    "STACKS_REFMAP_DIR=os.path.join(REFMAP_EMPIRICAL_DIR, \"stacks/\")\n",
    "DDOCENT_REFMAP_DIR=os.path.join(REFMAP_EMPIRICAL_DIR, \"ddocent/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some helpful functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function for plotting PCA given an input vcf file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## LD pruning code from the scikit-allele cookbook\n",
    "def plot_ld(gn, title):\n",
    "    m = allel.stats.rogers_huff_r(gn) ** 2\n",
    "    ax = allel.plot.pairwise_ld(m)\n",
    "    ax.set_title(title)\n",
    "def ld_prune(gn, size=1000, step=1000, threshold=.3, n_iter=5):\n",
    "    for i in range(n_iter):\n",
    "        loc_unlinked = allel.stats.ld.locate_unlinked(gn, size=size, step=step, threshold=threshold)\n",
    "        n = np.count_nonzero(loc_unlinked)\n",
    "        n_remove = gn.shape[0] - n\n",
    "        print('iteration', i+1, 'retaining', n, 'removing', n_remove, 'variants')\n",
    "        gn = gn.compress(loc_unlinked, axis=0)\n",
    "    return gn\n",
    "\n",
    "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",
    "    \n",
    "    ## Test w/ ld pruning. Doesn't appreciably change the results.\n",
    "    #gnu = ld_prune(gn)\n",
    "    #gn = gnu\n",
    "\n",
    "    coords1, model1 = allel.stats.pca(gn, n_components=10, scaler='patterson')\n",
    "    return coords1, model1\n",
    "\n",
    "## We don't actually use this function bcz the allele.plot.pairwise_distance()\n",
    "## call returns a raster which looks nasty in print.\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",
    "\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",
    "\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": 3,
   "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(prog, distvar, distpis):\n",
    "    \n",
    "    ## The last position to consider\n",
    "    maxend = np.array(distvar.keys()).max()\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",
    "    axes.label.text = prog\n",
    "\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": 4,
   "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, np.array(snp_counts.keys()).max()+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, np.array(counts.keys()).max()+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",
    "# Results from simulated data\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": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_minus_reference-sim_outfiles/denovo_minus_reference-sim.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles/refmap-sim.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1.vcf\n"
     ]
    }
   ],
   "source": [
    "## Make a new pandas dataframe for holding the coverage results\n",
    "sim_vcf_dict = {}\n",
    "sim_vcf_dict[\"ipyrad-reference\"] = os.path.join(IPYRAD_SIM_DIR, \"refmap-sim_outfiles/refmap-sim.vcf\")\n",
    "sim_vcf_dict[\"ipyrad-denovo_plus_reference\"] = os.path.join(IPYRAD_SIM_DIR, \"denovo_plus_reference-sim_outfiles/denovo_plus_reference-sim.vcf\")\n",
    "sim_vcf_dict[\"ipyrad-denovo_plus_reference\"] = os.path.join(IPYRAD_SIM_DIR, \"denovo_minus_reference-sim_outfiles/denovo_minus_reference-sim.vcf\")\n",
    "sim_vcf_dict[\"stacks\"] = os.path.join(STACKS_SIM_DIR, \"batch_1.vcf\")\n",
    "sim_vcf_dict[\"ddocent\"] = os.path.join(DDOCENT_SIM_DIR, \"TotalRawSNPs.snps.vcf.recode.vcf\")\n",
    "## Make sure we have all the vcf files\n",
    "for k, f in sim_vcf_dict.items():\n",
    "    if os.path.exists(f):\n",
    "        print(\"found - {}\".format(f))\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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing - ddocent\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.vcf\n",
      "Doing - ipyrad-denovo_reference\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_ref-sim_outfiles/denovo_ref-sim.vcf\n",
      "Doing - ipyrad-reference\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles/refmap-sim.vcf\n",
      "Doing - stacks\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1.vcf\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(\"  {}\".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",
    "        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)"
   ]
  },
  {
   "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. The ddocent results for locus coverage and number of loci per sample are ugly because for reference sequence mapping the ddocent output vcf doesn't record this information. It only retains CHROM and POS, CHROM being \"MT\" here and POS being the base position of each snp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddocent sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 0.0833333333333\n",
      "ipyrad-denovo_reference sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000] 83.3333333333\n",
      "ipyrad-reference sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500] 41.6666666667\n",
      "stacks sim_loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 992] 82.8333333333\n",
      "------------------------------------------------------\n",
      "ddocent sim_sample_nlocs\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] 1.0\n",
      "ipyrad-denovo_reference sim_sample_nlocs\t[1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000] 1000.0\n",
      "ipyrad-reference sim_sample_nlocs\t[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500] 500.0\n",
      "stacks sim_sample_nlocs\t[994, 993, 994, 993, 994, 994, 994, 994, 994, 994, 994, 994] 993.833333333\n",
      "------------------------------------------------------\n",
      "ddocent sim_sample_nsnps\t[4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4838] 4839.83333333\n",
      "ipyrad-denovo_reference sim_sample_nsnps\t[9541, 9541, 9540, 9541, 9540, 9541, 9541, 9541, 9541, 9541, 9541, 9541] 9540.83333333\n",
      "ipyrad-reference sim_sample_nsnps\t[4776, 4776, 4775, 4776, 4775, 4776, 4776, 4776, 4776, 4776, 4776, 4776] 4775.83333333\n",
      "stacks sim_sample_nsnps\t[4648, 4642, 4648, 4643, 4647, 4647, 4648, 4647, 4647, 4648, 4648, 4646] 4646.58333333\n",
      "------------------------------------------------------\n",
      "ddocent sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4838] 403.333333333\n",
      "ipyrad-denovo_reference sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9539] 795.083333333\n",
      "ipyrad-reference sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4774] 398.0\n",
      "stacks sim_snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 14, 4633] 387.333333333\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 prog in sim_vcf_dict.keys():\n",
    "        try:\n",
    "            print(prog + \" \" + statname + \"\\t\"),\n",
    "            print(stat[prog ]),\n",
    "            print(np.mean(stat[prog]))\n",
    "        except:\n",
    "            print(\"No {} stats for {}\".format(statname, prog))\n",
    "    print(\"------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pairwise difference and PCA plots for each simulation treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddocent /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.vcf\n",
      "ipyrad-denovo_reference /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_ref-sim_outfiles/denovo_ref-sim.vcf\n",
      "ipyrad-reference /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles/refmap-sim.vcf\n",
      "stacks /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1.vcf\n"
     ]
    }
   ],
   "source": [
    "## Load the calldata into a dict so we don't have to keep loading and reloading it\n",
    "calldata = {}\n",
    "for prog in sim_vcf_dict.keys():\n",
    "    print(\"{}\".format(prog)),\n",
    "    print(\"{}\".format(sim_vcf_dict[prog]))\n",
    "    c = vcfnp.calldata_2d(sim_vcf_dict[prog], verbose=False).view(np.recarray)\n",
    "    calldata[prog] = c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set sample names and populations for nice plotting\n",
    "Some housekeeping with sample names to make the PCA plots prettier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "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",
    "pops = {\"pop1\":pop1, \"pop2\":pop2, \"pop3\":pop3}\n",
    "pop_colors = {\"pop1\":\"r\", \"pop2\":\"b\", \"pop3\":\"g\"}\n",
    "\n",
    "flt = np.in1d(np.array(sim_sample_names), pop1)\n",
    "print(flt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAPfCAYAAAAxMRGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab66dac090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2,2, figsize=(10,10), dpi=1000)\n",
    "axarr = [a for b in axarr for a in b]\n",
    "for prog, ax in zip(assemblers, axarr):\n",
    "\n",
    "    coords1, model1 = getPCA(calldata[prog])\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, 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": 279,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAANPCAYAAAAbtQq8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aabd2fc3990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, 2, figsize=(10, 10), dpi=1000)\n",
    "axarr = [a for b in axarr for a in b]\n",
    "for prog, ax in zip(assemblers, axarr):\n",
    "\n",
    "    ## Calculate pairwise distances\n",
    "    dist = getDistances(calldata[prog])\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, style=\"italic\")\n",
    "    ax.axison = False"
   ]
  },
  {
   "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Blank the ordered dicts for gathering locus coverage and sample nlocs\n",
    "sim_full_loc_cov = collections.OrderedDict()\n",
    "sim_full_sample_nlocs = collections.OrderedDict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ipyrad simulated results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "denovo_ref-sim\n",
      "[1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]\n",
      "mean sample coverage - 1000.0\tmin/max - 1000/1000\t\n",
      "refmap-sim\n",
      "[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]\n",
      "mean sample coverage - 500.0\tmin/max - 500/500\t\n",
      "[('ipyrad-denovo_reference', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000]), ('ipyrad-reference', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500])]\n",
      "[('ipyrad-denovo_reference', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]), ('ipyrad-reference', [500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500])]\n"
     ]
    }
   ],
   "source": [
    "assembly_methods = {\"ipyrad-reference\":\"refmap-sim\", \"ipyrad-denovo_reference\":\"denovo_ref-sim\"}\n",
    "\n",
    "for name, method in assembly_methods.items():\n",
    "    print(method)\n",
    "    simdir = os.path.join(IPYRAD_SIM_DIR, method + \"_outfiles/\")\n",
    "    statsfile = simdir + \"{}_stats.txt\".format(method)\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[name] = sample_coverage\n",
    "    \n",
    "    nmissing = [int(x.strip().split()[1]) for x in infile[38:50]]\n",
    "    sim_full_loc_cov[name] = nmissing\n",
    "\n",
    "## Just look at the ones we care about for ipyrad\n",
    "print(sim_full_loc_cov.items())\n",
    "print(sim_full_sample_nlocs.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## stacks simulated results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('ipyrad-denovo_reference', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000]), ('ipyrad-reference', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500]), ('stacks', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 997])]\n",
      "[('ipyrad-denovo_reference', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]), ('ipyrad-reference', [500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]), ('stacks', [1163, 1165, 1162, 1154, 1154, 1154, 1153, 1160, 1144, 1144, 1149, 1135])]\n"
     ]
    }
   ],
   "source": [
    "method = \"stacks\"\n",
    "simdir = STACKS_SIM_DIR\n",
    "try:\n",
    "\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[method] = [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[method] = []\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[method].append(len(lines) - 1)\n",
    "except Exception as inst:\n",
    "    print(\"sample_nlocs - {} - {}\".format(inst, simdir))\n",
    "\n",
    "print(sim_full_loc_cov.items())\n",
    "print(sim_full_sample_nlocs.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dDocent simulated results\n",
    "It is not straightforward how to get locus coverage and nloci per sample information from ddocent results. See the manuscript-analysis notebook for more explanation about why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 - 1000.66666667\n",
      "min/max - 1000/1002\n",
      "[('ddocent', [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1007])]\n",
      "[('ddocent', [1007, 1007, 1007, 1007, 1007, 1007, 1007, 1008, 1008, 1007, 1007, 1007])]\n"
     ]
    }
   ],
   "source": [
    "DDOCENT_DIR = \"/home/iovercast/manuscript-analysis/dDocent/\"\n",
    "\n",
    "def ddocent_stats(RUNDIR):\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(RUNDIR + \"/*-RG.bam\"):\n",
    "        cmd = \"{}samtools view {} | cut -f 4 | 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",
    "    cmd = \"{}samtools view {}cat-RRG.bam | cut -f 3,4 | uniq\".format(DDOCENT_DIR, RUNDIR)\n",
    "    locus_positions = subprocess.check_output(cmd, shell=True).split(\"\\n\")\n",
    "    \n",
    "    locus_counter = collections.Counter()    # Num loci per sample\n",
    "    coverage_counter = collections.Counter() # Coverage per locus\n",
    "    for loc in locus_positions:\n",
    "        if not loc:\n",
    "            continue\n",
    "        chrom = loc.split()[0]\n",
    "        pos = int(loc.split()[1])\n",
    "        cmd = \"{}samtools view {}cat-RRG.bam {}:{}-{} | cut -f 1 | cut -f 3 -d \\\"_\\\" | sort | uniq\".format(DDOCENT_DIR, RUNDIR, chrom, pos, pos+1)\n",
    "        res = subprocess.check_output(cmd, shell=True).split()\n",
    "        locus_counter.update(res)\n",
    "        coverage_counter.update([len(res)])\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(coverage_counter[i])\n",
    "            except:\n",
    "                dat.append(0)\n",
    "\n",
    "    return dat, locus_counter.values()\n",
    "\n",
    "loc_cov, sample_nlocs = ddocent_stats(DDOCENT_SIM_DIR)\n",
    "\n",
    "sim_full_loc_cov[\"ddocent\"] = loc_cov\n",
    "sim_full_sample_nlocs[\"ddocent\"] = sample_nlocs\n",
    "print(sim_full_loc_cov.items())\n",
    "print(sim_full_sample_nlocs.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting simulation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of loci recovered per sample.\n",
      "                        simulated\n",
      "ipyrad-reference              500\n",
      "ipyrad-denovo_reference      1000\n",
      "stacks                    1153.08\n",
      "ddocent                   1007.17\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWUAAALtCAYAAADwjnWTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaba3f88bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "simlevel = [\"simulated\"]\n",
    "sim_sample_nlocs_df = pd.DataFrame(index=assemblers, columns=simlevel)\n",
    "\n",
    "for sim in simlevel:\n",
    "    for ass in assemblers:\n",
    "        simstring = ass\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 = 1000\n",
    "#    if \"ipyrad-reference\" == sim\n",
    "    dat[sim] = dat[sim]/scale\n",
    "    dat[sim][\"ipyrad-reference\"] *= 2\n",
    "sns.heatmap(dat, square=True, center=1, linewidths=2, annot=True)\n",
    "print(sim_sample_nlocs_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Much better! Do the spline plot version of the above plot\n",
    "It just looks nicer, makes more sense.\n",
    "\n",
    "__NB__: This is broken for refmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "left cannot be >= right",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-630bba5c72ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfsns2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"simtype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"assembler\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"bin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"simdata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"bin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"simdata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m#axs = g.axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/iovercast/opt/miniconda/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 731\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/iovercast/opt/miniconda/lib/python2.7/site-packages/seaborn/axisgrid.pyc\u001b[0m in \u001b[0;36m_finalize_grid\u001b[0;34m(self, axlabels)\u001b[0m\n\u001b[1;32m    820\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_titles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 822\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfacet_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/iovercast/opt/miniconda/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mtight_layout\u001b[0;34m(self, renderer, pad, h_pad, w_pad, rect)\u001b[0m\n\u001b[1;32m   1752\u001b[0m                                          rect=rect)\n\u001b[1;32m   1753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1754\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1755\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/iovercast/opt/miniconda/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36msubplots_adjust\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1609\u001b[0m         \"\"\"\n\u001b[0;32m-> 1610\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplotpars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1611\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1612\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubplotBase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/iovercast/opt/miniconda/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, left, bottom, right, top, wspace, hspace)\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleft\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m                 \u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'left cannot be >= right'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbottom\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: left cannot be >= right"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAALsAAAB8CAYAAAAxfcFmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab6669ab50>"
      ]
     },
     "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\n",
    "        ## Normalize values so different sim sizes print the same\n",
    "        max = 1000.\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\", size=1)\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 (Phocoena)\n",
    "This section is incomplete. See below for empirical and simulated results that are combined."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process the vcf output from all the runs\n",
    "Here we'll pull together all the output vcf files. This takes a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "emp_vcf_dict = {}\n",
    "emp_vcf_dict[\"ipyrad-reference\"] = os.path.join(IPYRAD_REFMAP_DIR, \"refmap-empirical_outfiles/refmap-empirical.vcf\")\n",
    "emp_vcf_dict[\"ipyrad-denovo_reference\"] = os.path.join(IPYRAD_REFMAP_DIR, \"denovo_ref-empirical_outfiles/denovo_ref-empirical.vcf\")\n",
    "emp_vcf_dict[\"stacks\"] = os.path.join(STACKS_REFMAP_DIR, \"batch_1.vcf\")\n",
    "emp_vcf_dict[\"ddocent\"] = os.path.join(DDOCENT_REFMAP_DIR, \"TotalRawSNPs.snps.vcf.recode.vcf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in vcf for each analysis and pull in coverage/depth stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[vcfnp] 2016-12-29 22:07:19.464202 :: caching is disabled\n",
      "[vcfnp] 2016-12-29 22:07:19.464703 :: building array\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing - ipyrad-denovo_reference\n",
      "\t/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/denovo_ref-empirical_outfiles/denovo_ref-empirical.vcf\n",
      "file not found: /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/denovo_ref-empirical_outfiles/denovo_ref-empirical.vcf\n",
      "Doing - ipyrad-reference\n",
      "\t/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/refmap-empirical_outfiles/refmap-empirical.vcf\n",
      "file not found: /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/refmap-empirical_outfiles/refmap-empirical.vcf\n",
      "Doing - stacks\n",
      "\t/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1.vcf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[vcfnp] 2016-12-29 22:07:22.543297 :: caching is disabled\n",
      "[vcfnp] 2016-12-29 22:07:22.543798 :: building array\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "global name 'species' is not defined\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdEAAAHFCAYAAACthMMWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAHEdJREFUeJzt3HGM1/V9+PHXHR5zlaJwcKUt8Q9cLOw4eqNp6yHidmwj\nMpcidhyJw2BHE1iKlrTR0lJQBAEZZVJ0CWKHUTBoELBCNJZ0GsmFFRakMP1DRlfoaO6+59VWKOV6\nfn5/NN4vlMPyfQl3R308Ev/o5/P+fL+v7zvSp5/vfY6KoiiKAADKVtnbAwDApUpEASBJRAEgSUQB\nIElEASBJRAEgSUQBIElEASBJRAEgSUQBIKnsiO7duzdmz54dN9xwQ4wcOTJ27dr1B6/Zs2dPTJ06\nNerq6mLSpEmxdevW1LAA0JeUHdGTJ0/GqFGjYtGiRVFRUfEH1x87dixmz54d1113XWzfvj1uv/32\nWLBgQezevTs1MAD0FZeVe8GECRNiwoQJERFxPn93/VNPPRXDhw+Pu+++OyIiRowYEfv27YsNGzbE\n9ddfX+7bA0CfcdF/Jvraa6/FuHHjzjg2fvz42L9//8V+awC4qC56RFtbW6O6uvqMY9XV1fHOO+/E\n6dOnL/bbA8BFU/bXub2lpaUlWltbuz23YMGCqKqqiqeffrqHpwLgw+yiR3To0KHR1tZ2xrG2trYY\nMGBA9O/f/7xfZ/PmzbF27dpznh84cGB6RgDIuOgRra+vj1deeeWMY7t37476+vqyXqepqSkaGxu7\nPTdnzpyorPQrrwD0rLIjevLkyfjpT3/a9WTu0aNH44033ogrr7wyPv7xj8eqVauipaUlVqxYERER\n06dPj40bN8bKlSvj1ltvjebm5njxxRdj3bp1Zb1vTU1N1NTUdHuuqqqq3I8BAB9Y2RE9ePBg3H77\n7VFRUREVFRVdsZwyZUosW7YsSqVSHD9+vGv98OHDY926dbFs2bJ44oknYtiwYbFkyZKzntgFgEtN\nRXE+v+zZx02cODEi4rz+9iQAuFD8IBEAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACS\nRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJE\nFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQU\nAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQA\nkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJEFACS\nRBQAklIR3bhxYzQ2NsaYMWNi2rRpceDAgfdd/9xzz8UXvvCFqK+vj/Hjx8c3v/nN+MUvfpEaGAD6\nirIjunPnzli+fHnceeedsXXr1hg5cmTMmjUr3nrrrW7X79u3L77xjW/EtGnTYseOHbFmzZr48Y9/\nHAsXLvzAwwNAbyo7ohs2bIimpqaYMmVKXHPNNXHffffF5ZdfHlu2bOl2/WuvvRbDhw+P2267LT75\nyU/G2LFjo6mp6Q/evQJAX1dWRDs6OuLQoUPR0NDQdayioiLGjRsX+/fv7/aa+vr6OH78eLz88ssR\nEVEqleKFF16IG2+88QOMDQC977JyFre3t0dnZ2cMGTLkjOPV1dVx5MiRbq8ZO3ZsrFy5MubNmxen\nT5+O3/72t9HY2Fj217ktLS3R2tra7bmOjo6orPSMFAA9q6yIZrz55puxdOnSmDt3blx//fXR2toa\nK1asiIULF8bSpUvP+3U2b94ca9euPef5gQMHXohxAeC8lRXRQYMGRb9+/aJUKp1xvK2t7ay70/es\nW7cuxo4dG3fccUdERFx77bWxaNGiuO2222LevHnnvO73NTU1RWNjY7fn5syZ404UgB5XVkSrqqqi\ntrY2mpubY+LEiRERURRFNDc3x4wZM7q95tSpU1FVVXXGscrKyqioqIiiKM77vWtqaqKmpuaccwFA\nTyv79m3mzJnxzDPPxLZt2+Lw4cOxaNGiOHXqVEydOjUiIlatWhX33HNP1/q/+qu/ihdffDGeeuqp\nOHr0aOzbty+WLl0an/70p2Po0KEX7pMAQA8r+2eikydPjvb29lizZk2USqUYNWpUrF+/PgYPHhwR\nv3v69vjx413rb7nlljh58mRs2rQpHnzwwfjoRz8aDQ0N8bWvfe3CfQoA6AUVRTnfqfZR7321vGvX\nrl6eBIAPE0/jAECSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKI\nAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogC\nQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJA\nkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCS\niAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQFIqohs3bozGxsYYM2ZM\nTJs2LQ4cOPC+60+fPh2rV6+OxsbGqKuri4kTJ8azzz6bGhgA+orLyr1g586dsXz58rj//vujrq4u\nHn/88Zg1a1a88MILMXjw4G6vueuuu6K9vT0eeOCBuPrqq6O1tTXefffdDzw8APSmsiO6YcOGaGpq\niilTpkRExH333Rf/8R//EVu2bIkvf/nLZ61/5ZVXYt++ffGDH/wgBg4cGBERn/jEJz7g2ADQ+8r6\nOrejoyMOHToUDQ0NXccqKipi3LhxsX///m6v+eEPfxijR4+ORx99NCZMmBCTJk2KFStWxG9+85sP\nNjkA9LKy7kTb29ujs7MzhgwZcsbx6urqOHLkSLfXHD16NPbu3Rv9+/ePhx9+ONrb2+Pee++Nt99+\nOx544IHzfu+WlpZobW3t9lxHR0dUVnpGCoCeVfbXueUqiiIqKytj1apVccUVV0RExPz58+Ouu+6K\ne++9N/r3739er7N58+ZYu3btOc+/91UxAPSUsiI6aNCg6NevX5RKpTOOt7W1nXV3+p6hQ4fGxz72\nsa6ARkSMGDEiiqKIn//853H11Vef13s3NTVFY2Njt+fmzJnjThSAHldWRKuqqqK2tjaam5tj4sSJ\nEfG7O83m5uaYMWNGt9eMHTs2Xnzxxfj1r38df/qnfxoREUeOHInKysoYNmzYeb93TU1N1NTUnHMu\nAOhpZd++zZw5M5555pnYtm1bHD58OBYtWhSnTp2KqVOnRkTEqlWr4p577ulaf/PNN8dVV10V8+fP\nj8OHD8ePfvSjWLlyZdx6663n/VUuAPRFZf9MdPLkydHe3h5r1qyJUqkUo0aNivXr13f9jmipVIrj\nx493rf/IRz4S3/ve92LJkiXxxS9+Ma666qq46aab4qtf/eqF+xQA0AsqiqIoenuID+q9r5Z37drV\ny5MA8GHiaRwASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEA\nSBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBI\nElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgS\nUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJR\nAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASEpFdOPGjdHY2Bhj\nxoyJadOmxYEDB87run379kVtbW3ccsstmbcFgD6l7Iju3Lkzli9fHnfeeWds3bo1Ro4cGbNmzYq3\n3nrrfa/71a9+Fd/4xjeioaEhPSwA9CVlR3TDhg3R1NQUU6ZMiWuuuSbuu+++uPzyy2PLli3ve92i\nRYvi7//+76O+vj49LAD0JWVFtKOjIw4dOnTG3WRFRUWMGzcu9u/ff87rtmzZEseOHYuvfOUr+UkB\noI+5rJzF7e3t0dnZGUOGDDnjeHV1dRw5cqTba37yk5/E6tWrY9OmTVFZmX+OqaWlJVpbW7s919HR\n8YFeGwAyyopoud599934+te/HnPnzo2rr746IiKKoki91ubNm2Pt2rXnPD9w4MDU6wJAVlkRHTRo\nUPTr1y9KpdIZx9va2s66O42IOHHiRBw8eDDeeOONWLx4cUT8LqxFUcTo0aPjsccei89//vPn9d5N\nTU3R2NjY7bk5c+a4EwWgx5UV0aqqqqitrY3m5uaYOHFiRPzuzrK5uTlmzJhx1voBAwbE888/f8ax\njRs3xp49e+K73/1ufPKTnzzv966pqYmamppzzgUAPa3sr3NnzpwZ8+fPj9GjR0ddXV08/vjjcerU\nqZg6dWpERKxatSpaWlpixYoVUVFREX/2Z392xvXV1dXxJ3/yJ3HNNddcmE8AAL2k7IhOnjw52tvb\nY82aNVEqlWLUqFGxfv36GDx4cERElEqlOH78+AUfFAD6mooi+6RPH/LeV8u7du3q5UkA+DDxNA4A\nJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAk\niSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJ\nKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIko\nACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkiSgA\nJIkoACSJKAAkiSgAJIkoACSJKAAkiSgAJIkoACSJKAAkpSK6cePGaGxsjDFjxsS0adPiwIED51z7\n0ksvxZe+9KVoaGiIz3zmMzF9+vR49dVX0wMDQF9RdkR37twZy5cvjzvvvDO2bt0aI0eOjFmzZsVb\nb73V7fof/ehHcf3118ejjz4aW7dujc9//vMxe/bseOONNz7w8ADQmyqKoijKuWDatGkxZsyYWLBg\nQUREFEURN954Y8yYMSO+/OUvn9dr3HzzzTF58uT453/+5/In7sbEiRMjImLXrl0X5PUA4HyUdSfa\n0dERhw4dioaGhq5jFRUVMW7cuNi/f/95vUZRFHHixIm48sory5sUAPqYsiLa3t4enZ2dMWTIkDOO\nV1dXR6lUOq/XWL9+fZw8eTJuuummct4aAPqcy3ryzb7//e/HI488Ev/2b/8WgwcPLuvalpaWaG1t\n7fZcR0dHVFZ60BiAnlVWRAcNGhT9+vU7666zra3trLvT37djx45YuHBhPPTQQ3HdddeVPejmzZtj\n7dq15zw/cODAsl8TAD6IsiJaVVUVtbW10dzc3PUwT1EU0dzcHDNmzDjndc8//3wsWLAgVq9eHRMm\nTEgN2tTUFI2Njd2emzNnjjtRAHpc2V/nzpw5M+bPnx+jR4+Ourq6ePzxx+PUqVMxderUiIhYtWpV\ntLS0xIoVKyLid1/hzp8/P771rW9FXV1d113s5ZdfHgMGDDjv962pqYmamppuz1VVVZX7MQDgAys7\nopMnT4729vZYs2ZNlEqlGDVqVKxfv77rZ5ylUimOHz/etf7pp5+Ozs7OWLx4cSxevLjr+JQpU2LZ\nsmUX4CMAQO8o+/dE+yK/JwpAb/CDRABIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgS\nUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJR\nAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEA\nSBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBI\nElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgSUQBIElEASBJRAEgS\nUQBISkV048aN0djYGGPGjIlp06bFgQMH3nf9nj17YurUqVFXVxeTJk2KrVu3poYFgL6k7Iju3Lkz\nli9fHnfeeWds3bo1Ro4cGbNmzYq33nqr2/XHjh2L2bNnx3XXXRfbt2+P22+/PRYsWBC7d+/+wMMD\nQG8qO6IbNmyIpqammDJlSlxzzTVx3333xeWXXx5btmzpdv1TTz0Vw4cPj7vvvjtGjBgRt912W0ya\nNCk2bNjwQWcHgF5VVkQ7Ojri0KFD0dDQ0HWsoqIixo0bF/v37+/2mtdeey3GjRt3xrHx48efcz0A\nXCouK2dxe3t7dHZ2xpAhQ844Xl1dHUeOHOn2mtbW1qiurj5r/TvvvBOnT5+O/v37n9d7t7S0RGtr\na7fnOjo6orLSM1IA9KyyItqbNm/eHGvXrj3n+U984hM9OA0AlBnRQYMGRb9+/aJUKp1xvK2t7ay7\n0/cMHTo02trazlo/YMCA874LjYhoamqKxsbGc54fOnToeb8WAFwIZUW0qqoqamtro7m5OSZOnBgR\nEUVRRHNzc8yYMaPba+rr6+OVV14549ju3bujvr6+rEFramqipqamrGsA4GIq+weJM2fOjGeeeSa2\nbdsWhw8fjkWLFsWpU6di6tSpERGxatWquOeee7rWT58+PY4ePRorV66M//mf/4mNGzfGiy++GHfc\ncceF+xQA0AvK/pno5MmTo729PdasWROlUilGjRoV69evj8GDB0dERKlUiuPHj3etHz58eKxbty6W\nLVsWTzzxRAwbNiyWLFly1hO7AHCpqSiKoujtIQDgUnTJPJ2b9Y//+I9n3BkD8OH18Y9/PJ588skL\n9np/9L9cefr06fjlL38ZnZ2dvT3KJaWzs9O+lcme5di38tmznM7OzvjZz34WLS0tF+5Fiz9yBw8e\nLK699tri4MGDvT3KJcW+lc+e5di38tmznIuxb3/0d6IAcLGIKAAkiSgAJIkoACSJKAAkiSgAJPW7\n99577+3tIS62K664Ij73uc/FFVdc0dujXFLsW/nsWY59K589y7nQ++av/QOAJF/nAkCSiAJAkogC\nQJKIAkCSiAJAkogCQJKIAkCSiAJAkogCQJKIAkDSH0VEN27cGI2NjTFmzJiYNm1aHDhw4H3X79mz\nJ6ZOnRp1dXUxadKk2Lp1aw9N2neUs2cvvfRSfOlLX4qGhob4zGc+E9OnT49XX321B6ftO8r9d+09\n+/bti9ra2rjlllsu8oR9T7l7dvr06Vi9enU0NjZGXV1dTJw4MZ599tkemrbvKHffnnvuufjCF74Q\n9fX1MX78+PjmN78Zv/jFL3po2t63d+/emD17dtxwww0xcuTI2LVr1x+85oK0oLjE7dixoxg9enSx\ndevW4s033yy+/e1vF5/97GeLtra2btcfPXq0qK+vL1asWFEcPny4ePLJJ4s///M/L1599dUenrz3\nlLtnS5cuLdavX1/8+Mc/Lv73f/+3+M53vlPU1tYWr7/+eg9P3rvK3bf3/PKXvyz++q//uvinf/qn\nYsqUKT00bd+Q2bPZs2cXTU1NRXNzc/Gzn/2s2L9/f/Ff//VfPTh17yt33/bu3VuMGjWqePLJJ4tj\nx44V+/btK26++eZi7ty5PTx573n55ZeLf/3Xfy1eeumlYuTIkcUPfvCD911/oVpwyUf0H/7hH4r7\n77+/63+/++67xQ033FCsW7eu2/UPPvhgcfPNN59xbN68ecWsWbMu6px9Sbl71p2/+7u/Kx5++OGL\nMV6fld23efPmFQ899FDx3e9+90MX0XL37OWXXy4++9nPFm+//XZPjdgnlbtvjz32WPE3f/M3Zxx7\n4oknihtvvPFijtlnfepTn/qDEb1QLbikv87t6OiIQ4cORUNDQ9exioqKGDduXOzfv7/ba1577bUY\nN27cGcfGjx9/zvV/bDJ79vuKoogTJ07ElVdeebHG7HOy+7Zly5Y4duxYfOUrX+mJMfuUzJ798Ic/\njNGjR8ejjz4aEyZMiEmTJsWKFSviN7/5TU+N3esy+1ZfXx/Hjx+Pl19+OSIiSqVSvPDCC3HjjTf2\nyMyXogvVgssu5FA9rb29PTo7O2PIkCFnHK+uro4jR450e01ra2tUV1eftf6dd96J06dPR//+/S/a\nvH1BZs9+3/r16+PkyZNx0003XYwR+6TMvv3kJz+J1atXx6ZNm6Ky8pL+79WUzJ4dPXo09u7dG/37\n94+HH3442tvb495774233347HnjggZ4Yu9dl9m3s2LGxcuXKmDdvXpw+fTp++9vfRmNjYyxcuLAn\nRr4kXagWfPj+ZPOBfP/7349HHnkkHnrooRg8eHBvj9Nnvfvuu/H1r3895s6dG1dffXVE/O4OnvdX\nFEVUVlbGqlWroq6uLiZMmBDz58+Pbdu2xenTp3t7vD7rzTffjKVLl8bcuXPj2WefjcceeyyOHTsm\noj3gko7ooEGDol+/flEqlc443tbWdtZ/xb1n6NCh0dbWdtb6AQMG/NHfhUbk9uw9O3bsiIULF8ZD\nDz0U11133cUcs88pd99OnDgRBw8ejPvvvz9qa2ujtrY2HnnkkXj99ddj9OjRsWfPnp4avddk/3x+\n7GMfiyuuuKLr2IgRI6Ioivj5z39+UeftKzL7tm7duhg7dmzccccdce2118b1118fixYtii1btpz1\nOvzOhWrBJR3RqqqqqK2tjebm5q5jRVFEc3Nz/MVf/EW319TX15+xPiJi9+7dUV9ff1Fn7SsyexYR\n8fzzz8e3vvWt+M53vhMTJkzoiVH7lHL3bcCAAfH888/Htm3bYvv27bF9+/aYPn16jBgxIrZv3x6f\n/vSne3L8XpH5d23s2LHR0tISv/71r7uOHTlyJCorK2PYsGEXfea+ILNvp06dissuO/Onc5WVlVFR\nUeEbkHO4YC0o6zGkPmjHjh3FmDFjzngU/HOf+1zXo+D/8i//Utx9991d6997rPnBBx/seqy5tra2\n2L17d299hB5X7p4999xzRW1tbbFp06aitbW1659f/epXvfURekW5+/b7PoxP55a7ZydOnCj+8i//\nsrjrrruKN998s/jP//zP4m//9m+Lb3/72731EXpFufv27LPPdv0Z/elPf1rs3bu3uPXWW4umpqbe\n+gg97sSJE8Xrr79e/Pd//3fxqU99qvj3f//34vXXXy/+7//+ryiKi9eCS/rBooiIyZMnR3t7e6xZ\nsyZKpVKMGjUq1q9f3/XzulKpFMePH+9aP3z48Fi3bl0sW7YsnnjiiRg2bFgsWbLkrKe0/piVu2dP\nP/10dHZ2xuLFi2Px4sVdx6dMmRLLli3r8fl7S7n7Rvl79pGPfCS+973vxZIlS+KLX/xiXHXVVXHT\nTTfFV7/61d76CL2i3H275ZZb4uTJk7Fp06Z48MEH46Mf/Wg0NDTE1772td76CD3u4MGDcfvtt0dF\nRUVUVFTEihUrIuL////UxWpBRVG41weAjEv6Z6IA0JtEFACSRBQAkkQUAJJEFACSRBQAkkQUAJJE\nFACSRBQAkkQUAJJEFACS/h9VIPhRZtMw+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aab66bdce50>"
      ]
     },
     "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 emp_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": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAEsCAYAAAA7Ldc6AAAABmJLR0QA/wD/AP+gvaeTAAAACXBI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\n",
      "text/html": [
       "<div align=\"center\" class=\"toyplot\" id=\"t83c93ae41ce241759198e4857edcaf57\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t6166ac4c26174f46a875f859461aa4a6\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 800.0 300.0\" width=\"800.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t2fd35a2e10ba4599ae0f20f74bb3b0c5\"><clipPath id=\"t0f97d5d6e8ec4c939e8d5649842b883a\"><rect height=\"190.0\" width=\"690.0\" x=\"55.0\" y=\"55.0\"></rect></clipPath><g clip-path=\"url(#t0f97d5d6e8ec4c939e8d5649842b883a)\"><g class=\"toyplot-mark-FillBoundaries\" id=\"t144ead642f6a4ee7b63891ee4c27bd39\" style=\"stroke:none\"><polygon points=\"65.0,235.0 74.436619718309856,235.0 83.873239436619727,235.0 93.309859154929583,235.0 102.74647887323943,235.0 112.18309859154931,235.0 121.61971830985915,235.0 131.05633802816902,235.0 140.49295774647888,235.0 149.92957746478874,235.0 159.36619718309859,235.0 168.80281690140845,235.0 178.2394366197183,235.0 187.67605633802816,235.0 197.11267605633805,235.0 206.54929577464787,235.0 215.98591549295776,235.0 225.42253521126759,235.0 234.85915492957747,235.0 244.2957746478873,235.0 253.73239436619718,235.0 263.16901408450701,235.0 272.6056338028169,235.0 282.04225352112678,235.0 291.47887323943661,235.0 300.91549295774649,235.0 310.35211267605632,235.0 319.78873239436621,235.0 329.22535211267609,235.0 338.66197183098592,235.0 348.09859154929575,235.0 357.53521126760558,235.0 366.97183098591552,235.0 376.40845070422534,235.0 385.84507042253517,235.0 395.28169014084506,235.0 404.71830985915494,235.0 414.15492957746477,235.0 423.5915492957746,235.0 433.02816901408454,235.0 442.46478873239442,235.0 451.90140845070425,235.0 461.33802816901408,235.0 470.77464788732397,235.0 480.21126760563379,235.0 489.64788732394368,235.0 499.08450704225351,235.0 508.52112676056339,235.0 517.95774647887322,235.0 527.3943661971831,235.0 536.83098591549299,235.0 546.26760563380276,235.0 555.70422535211264,235.0 565.14084507042253,235.0 574.57746478873241,235.0 584.0140845070423,235.0 593.45070422535218,235.0 602.88732394366195,235.0 612.32394366197184,235.0 621.76056338028161,235.0 631.19718309859149,235.0 640.63380281690138,235.0 650.07042253521115,235.0 659.50704225352115,235.0 668.94366197183103,235.0 678.38028169014081,235.0 687.81690140845069,235.0 697.25352112676046,235.0 706.69014084507035,235.0 716.12676056338034,235.0 725.56338028169012,235.0 725.56338028169012,224.17666666666665 716.12676056338034,209.10333333333335 706.69014084507035,73.641666666666666 697.25352112676046,87.185000000000002 687.81690140845069,96.053333333333327 678.38028169014081,101.15333333333334 668.94366197183103,107.84 659.50704225352115,115.26333333333332 650.07042253521115,120.24999999999999 640.63380281690138,128.92000000000002 631.19718309859149,130.36499999999998 621.76056338028161,129.88333333333333 612.32394366197184,135.06833333333333 602.88732394366195,137.47666666666666 593.45070422535218,139.23333333333332 584.0140845070423,139.45999999999998 574.57746478873241,141.66999999999999 565.14084507042253,142.74666666666667 555.70422535211264,142.86000000000001 546.26760563380276,147.78999999999999 536.83098591549299,147.73333333333335 527.3943661971831,146.71333333333334 517.95774647887322,149.26333333333332 508.52112676056339,147.875 499.08450704225351,148.44166666666666 489.64788732394368,147.30833333333334 480.21126760563379,151.30333333333334 470.77464788732397,148.89500000000001 461.33802816901408,148.41333333333333 451.90140845070425,148.81 442.46478873239442,150.56666666666666 433.02816901408454,150.99166666666667 423.5915492957746,150.28333333333336 414.15492957746477,151.785 404.71830985915494,152.72 395.28169014084506,151.30333333333334 385.84507042253517,148.07333333333332 376.40845070422534,150.19833333333332 366.97183098591552,150.02833333333334 357.53521126760558,152.06833333333333 348.09859154929575,151.785 338.66197183098592,151.38833333333332 329.22535211267609,151.33166666666668 319.78873239436621,154.61833333333334 310.35211267605632,150.14166666666668 300.91549295774649,151.13333333333333 291.47887323943661,155.12833333333333 282.04225352112678,153.62666666666667 272.6056338028169,153.99500000000003 263.16901408450701,153.82499999999999 253.73239436619718,153.68333333333334 244.2957746478873,154.59 234.85915492957747,152.94666666666666 225.42253521126759,154.845 215.98591549295776,152.74833333333333 206.54929577464787,153.51333333333332 197.11267605633805,151.35999999999999 187.67605633802816,150.87833333333333 178.2394366197183,153.85333333333332 168.80281690140845,154.02333333333334 159.36619718309859,154.5333333333333 149.92957746478874,156.34666666666666 140.49295774647888,155.55333333333334 131.05633802816902,156.26166666666666 121.61971830985915,159.06666666666666 112.18309859154931,155.44 102.74647887323943,156.0916666666667 93.309859154929583,162.04166666666666 83.873239436619727,160.99333333333334 74.436619718309856,165.01666666666668 65.0,168.8416666666667\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>total variable sites</title></polygon></g><g class=\"toyplot-mark-FillBoundaries\" id=\"tb82488643cbc445f962193b1e6d93896\" style=\"stroke:none\"><polygon points=\"65.0,235.0 74.436619718309856,235.0 83.873239436619727,235.0 93.309859154929583,235.0 102.74647887323943,235.0 112.18309859154931,235.0 121.61971830985915,235.0 131.05633802816902,235.0 140.49295774647888,235.0 149.92957746478874,235.0 159.36619718309859,235.0 168.80281690140845,235.0 178.2394366197183,235.0 187.67605633802816,235.0 197.11267605633805,235.0 206.54929577464787,235.0 215.98591549295776,235.0 225.42253521126759,235.0 234.85915492957747,235.0 244.2957746478873,235.0 253.73239436619718,235.0 263.16901408450701,235.0 272.6056338028169,235.0 282.04225352112678,235.0 291.47887323943661,235.0 300.91549295774649,235.0 310.35211267605632,235.0 319.78873239436621,235.0 329.22535211267609,235.0 338.66197183098592,235.0 348.09859154929575,235.0 357.53521126760558,235.0 366.97183098591552,235.0 376.40845070422534,235.0 385.84507042253517,235.0 395.28169014084506,235.0 404.71830985915494,235.0 414.15492957746477,235.0 423.5915492957746,235.0 433.02816901408454,235.0 442.46478873239442,235.0 451.90140845070425,235.0 461.33802816901408,235.0 470.77464788732397,235.0 480.21126760563379,235.0 489.64788732394368,235.0 499.08450704225351,235.0 508.52112676056339,235.0 517.95774647887322,235.0 527.3943661971831,235.0 536.83098591549299,235.0 546.26760563380276,235.0 555.70422535211264,235.0 565.14084507042253,235.0 574.57746478873241,235.0 584.0140845070423,235.0 593.45070422535218,235.0 602.88732394366195,235.0 612.32394366197184,235.0 621.76056338028161,235.0 631.19718309859149,235.0 640.63380281690138,235.0 650.07042253521115,235.0 659.50704225352115,235.0 668.94366197183103,235.0 678.38028169014081,235.0 687.81690140845069,235.0 697.25352112676046,235.0 706.69014084507035,235.0 716.12676056338034,235.0 725.56338028169012,235.0 725.56338028169012,232.19499999999999 716.12676056338034,227.34999999999999 706.69014084507035,182.89499999999998 697.25352112676046,188.41999999999999 687.81690140845069,190.99833333333331 678.38028169014081,192.78333333333336 668.94366197183103,194.85166666666666 659.50704225352115,197.685 650.07042253521115,199.215 640.63380281690138,200.88666666666666 631.19718309859149,201.96333333333331 621.76056338028161,204.25833333333333 612.32394366197184,203.54999999999998 602.88732394366195,204.42833333333334 593.45070422535218,206.07166666666666 584.0140845070423,205.845 574.57746478873241,204.91 565.14084507042253,207.375 555.70422535211264,204.68333333333334 546.26760563380276,208.11166666666665 536.83098591549299,209.61333333333334 527.3943661971831,206.58166666666668 517.95774647887322,209.2166666666667 508.52112676056339,207.85666666666668 499.08450704225351,208.84833333333336 489.64788732394368,208.14000000000001 480.21126760563379,209.58500000000001 470.77464788732397,207.74333333333334 461.33802816901408,208.28166666666667 451.90140845070425,209.81166666666667 442.46478873239442,207.82833333333335 433.02816901408454,209.84 423.5915492957746,208.67833333333331 414.15492957746477,208.50833333333335 404.71830985915494,210.15166666666667 395.28169014084506,210.85999999999999 385.84507042253517,207.51666666666668 376.40845070422534,208.48000000000002 366.97183098591552,209.38666666666663 357.53521126760558,209.61333333333334 348.09859154929575,210.60499999999999 338.66197183098592,210.40666666666667 329.22535211267609,208.48000000000002 319.78873239436621,211.31333333333333 310.35211267605632,208.87666666666669 300.91549295774649,208.33833333333331 291.47887323943661,211.11499999999998 282.04225352112678,209.78333333333336 272.6056338028169,209.89666666666668 263.16901408450701,211.08666666666667 253.73239436619718,208.59333333333333 244.2957746478873,208.81999999999999 234.85915492957747,209.04666666666665 225.42253521126759,210.54833333333332 215.98591549295776,208.73499999999999 206.54929577464787,211.19999999999999 197.11267605633805,211.34166666666667 187.67605633802816,207.06333333333333 178.2394366197183,210.15166666666667 168.80281690140845,209.5 159.36619718309859,210.57666666666668 149.92957746478874,210.88833333333332 140.49295774647888,210.91666666666669 131.05633802816902,209.64166666666665 121.61971830985915,212.41833333333332 112.18309859154931,210.97333333333333 102.74647887323943,212.27666666666667 93.309859154929583,213.60833333333335 83.873239436619727,212.50333333333333 74.436619718309856,212.56 65.0,214.62833333333333\" style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>parsimony informative sites</title></polygon></g><g class=\"toyplot-mark-AxisLines\" id=\"t210720af5dae40ba973488f13cb6f9da\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"151.13333333333333\" y2=\"151.13333333333333\"></line></g></g><g class=\"toyplot-mark-AxisLines\" id=\"td1452437f04d4b5397f41d055bacee67\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"208.87666666666669\" y2=\"208.87666666666669\"></line></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t2349cf43109848a9bca38d7c94854e20\" transform=\"translate(65.0,235.0)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"660.5633802816901\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"47.183098591549296\" x2=\"47.183098591549296\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"94.36619718309859\" x2=\"94.36619718309859\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"141.54929577464787\" x2=\"141.54929577464787\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"188.73239436619718\" x2=\"188.73239436619718\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"235.91549295774647\" x2=\"235.91549295774647\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"283.09859154929575\" x2=\"283.09859154929575\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"330.28169014084506\" x2=\"330.28169014084506\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"377.46478873239437\" x2=\"377.46478873239437\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"424.6478873239437\" x2=\"424.6478873239437\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"471.83098591549293\" x2=\"471.83098591549293\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"519.0140845070422\" x2=\"519.0140845070422\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"566.1971830985915\" x2=\"566.1971830985915\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"613.3802816901408\" x2=\"613.3802816901408\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"660.5633802816901\" x2=\"660.5633802816901\" y1=\"0\" y2=\"-5\"></line></g><g><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(47.183098591549296,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">5</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(94.36619718309859,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">10</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(141.54929577464787,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">15</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(188.73239436619718,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">20</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(235.91549295774647,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">25</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(283.09859154929575,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">30</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(330.28169014084506,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">35</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(377.46478873239437,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">40</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(424.6478873239437,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">45</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(471.83098591549293,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">50</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(519.0140845070422,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">55</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(566.1971830985915,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">60</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(613.3802816901408,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">65</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(660.5633802816901,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">70</tspan></text></g><text style=\"dominant-baseline:hanging;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(335.0,22)translate(0,40.0)\"><tspan style=\"dominant-baseline:inherit\">Position along RAD loci</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t1887927ac1554fc9812cfed34989df51\" transform=\"translate(65.0,235.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"0\" x2=\"161.35833333333335\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"56.666666666666664\" x2=\"56.666666666666664\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"113.33333333333333\" x2=\"113.33333333333333\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"170.0\" x2=\"170.0\" y1=\"0\" y2=\"5\"></line></g><g><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(56.666666666666664,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">2000</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(113.33333333333333,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">4000</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(170.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">6000</tspan></text></g><text style=\"dominant-baseline:alphabetic;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(85.0,-22)translate(0,-40.0)\"><tspan style=\"dominant-baseline:inherit\">N variables sites</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-interactive\"><ul class=\"toyplot-mark-popup\" onmouseleave=\"this.style.visibility='hidden'\" style=\"background:rgba(0%,0%,0%,0.75);border:0;border-radius:6px;color:white;cursor:default;list-style:none;margin:0;padding:5px;position:fixed;visibility:hidden\">\n",
       "            <li class=\"toyplot-mark-popup-title\" style=\"color:lightgray;cursor:default;padding:5px;list-style:none;margin:0\"></li>\n",
       "            <li class=\"toyplot-mark-popup-save-csv\" onmouseout=\"this.style.color='white';this.style.background='steelblue'\" onmouseover=\"this.style.color='steelblue';this.style.background='white'\" style=\"border-radius:3px;padding:5px;list-style:none;margin:0\">\n",
       "                Save as .csv\n",
       "            </li>\n",
       "        </ul><script>\n",
       "        (function()\n",
       "        {\n",
       "          var data_tables = [{\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"t144ead642f6a4ee7b63891ee4c27bd39\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2335.0, 2470.0, 2612.0, 2575.0, 2785.0, 2808.0, 2680.0, 2779.0, 2804.0, 2776.0, 2840.0, 2858.0, 2864.0, 2969.0, 2952.0, 2876.0, 2903.0, 2829.0, 2896.0, 2838.0, 2870.0, 2865.0, 2859.0, 2872.0, 2819.0, 2960.0, 2995.0, 2837.0, 2953.0, 2951.0, 2937.0, 2927.0, 2999.0, 2993.0, 3068.0, 2954.0, 2904.0, 2937.0, 2990.0, 2965.0, 2980.0, 3042.0, 3056.0, 3039.0, 2954.0, 3095.0, 3055.0, 3075.0, 3026.0, 3116.0, 3080.0, 3078.0, 3252.0, 3256.0, 3294.0, 3372.0, 3380.0, 3442.0, 3527.0, 3710.0, 3693.0, 3744.0, 4050.0, 4226.0, 4488.0, 4724.0, 4904.0, 5217.0, 5695.0, 914.0, 382.0]], \"filename\": \"toyplot\"}, {\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"tb82488643cbc445f962193b1e6d93896\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [719.0, 792.0, 794.0, 755.0, 802.0, 848.0, 797.0, 895.0, 850.0, 851.0, 862.0, 900.0, 877.0, 986.0, 835.0, 840.0, 927.0, 863.0, 916.0, 924.0, 932.0, 844.0, 886.0, 890.0, 843.0, 941.0, 922.0, 836.0, 936.0, 868.0, 861.0, 896.0, 904.0, 936.0, 970.0, 852.0, 877.0, 935.0, 929.0, 888.0, 959.0, 889.0, 943.0, 962.0, 897.0, 948.0, 923.0, 958.0, 910.0, 1003.0, 896.0, 949.0, 1070.0, 975.0, 1062.0, 1029.0, 1021.0, 1079.0, 1110.0, 1085.0, 1166.0, 1204.0, 1263.0, 1317.0, 1417.0, 1490.0, 1553.0, 1644.0, 1839.0, 270.0, 99.0]], \"filename\": \"toyplot\"}];\n",
       "\n",
       "          function save_csv(data_table)\n",
       "          {\n",
       "            var uri = \"data:text/csv;charset=utf-8,\";\n",
       "            uri += data_table.names.join(\",\") + \"\\n\";\n",
       "            for(var i = 0; i != data_table.columns[0].length; ++i)\n",
       "            {\n",
       "              for(var j = 0; j != data_table.columns.length; ++j)\n",
       "              {\n",
       "                if(j)\n",
       "                  uri += \",\";\n",
       "                uri += data_table.columns[j][i];\n",
       "              }\n",
       "              uri += \"\\n\";\n",
       "            }\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = data_table.filename + \".csv\";\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "          }\n",
       "\n",
       "          function open_popup(data_table)\n",
       "          {\n",
       "            return function(e)\n",
       "            {\n",
       "              var popup = document.querySelector(\"#t83c93ae41ce241759198e4857edcaf57 .toyplot-mark-popup\");\n",
       "              popup.querySelector(\".toyplot-mark-popup-title\").innerHTML = data_table.title;\n",
       "              popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
       "              popup.style.left = (e.clientX - 50) + \"px\";\n",
       "              popup.style.top = (e.clientY - 20) + \"px\";\n",
       "              popup.style.visibility = \"visible\";\n",
       "              e.stopPropagation();\n",
       "              e.preventDefault();\n",
       "            }\n",
       "\n",
       "          }\n",
       "\n",
       "          for(var i = 0; i != data_tables.length; ++i)\n",
       "          {\n",
       "            var data_table = data_tables[i];\n",
       "            var event_target = document.querySelector(\"#\" + data_table.id);\n",
       "            event_target.oncontextmenu = open_popup(data_table);\n",
       "          }\n",
       "        })();\n",
       "        </script><script>\n",
       "        (function()\n",
       "        {\n",
       "            function _sign(x)\n",
       "            {\n",
       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
       "\n",
       "            function _mix(a, b, amount)\n",
       "            {\n",
       "                return ((1.0 - amount) * a) + (amount * b);\n",
       "            }\n",
       "\n",
       "            function _log(x, base)\n",
       "            {\n",
       "                return Math.log(Math.abs(x)) / Math.log(base);\n",
       "            }\n",
       "\n",
       "            function _in_range(a, x, b)\n",
       "            {\n",
       "                var left = Math.min(a, b);\n",
       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
       "            }\n",
       "\n",
       "            function inside(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.min, range, segment.range.max))\n",
       "                        return true;\n",
       "                }\n",
       "                return false;\n",
       "            }\n",
       "\n",
       "            function to_domain(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
       "                    {\n",
       "                        if(segment.scale == \"linear\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            return _mix(segment.domain.min, segment.domain.max, amount)\n",
       "                        }\n",
       "                        else if(segment.scale[0] == \"log\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            var base = segment.scale[1];\n",
       "                            return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            function display_coordinates(e)\n",
       "            {\n",
       "                var current = svg.createSVGPoint();\n",
       "                current.x = e.clientX;\n",
       "                current.y = e.clientY;\n",
       "\n",
       "                for(var axis_id in axes)\n",
       "                {\n",
       "                    var axis = document.querySelector(\"#\" + axis_id);\n",
       "                    var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
       "                    if(coordinates)\n",
       "                    {\n",
       "                        var projection = axes[axis_id];\n",
       "                        var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
       "                        if(inside(local.x, projection))\n",
       "                        {\n",
       "                            var domain = to_domain(local.x, projection);\n",
       "                            coordinates.style.visibility = \"visible\";\n",
       "                            coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
       "                            var text = coordinates.querySelector(\"text\");\n",
       "                            text.textContent = domain.toFixed(2);\n",
       "                        }\n",
       "                        else\n",
       "                        {\n",
       "                            coordinates.style.visibility= \"hidden\";\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var root_id = \"t83c93ae41ce241759198e4857edcaf57\";\n",
       "            var axes = {\"t1887927ac1554fc9812cfed34989df51\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 6000.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 170.0, \"min\": 0.0}, \"scale\": \"linear\"}], \"t2349cf43109848a9bca38d7c94854e20\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 71.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 670.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
       "\n",
       "            var svg = document.querySelector(\"#\" + root_id + \" svg\");\n",
       "            svg.addEventListener(\"click\", display_coordinates);\n",
       "        })();\n",
       "        </script></div></div>"
      ],
      "text/plain": [
       "<toyplot.canvas.Canvas at 0x2aaadbb9b510>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "## stacks empirical results"
   ]
  },
  {
   "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": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAEsCAYAAAA7Ldc6AAAABmJLR0QA/wD/AP+gvaeTAAAACXBI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\nOmAAAAAASUVORK5CYII=\n",
      "text/html": [
       "<div align=\"center\" class=\"toyplot\" id=\"t77dc68aeda1048aeb999325163f9d8b6\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t1c8bfb80f2074702a3adbc7238900eb3\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 800.0 300.0\" width=\"800.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t95f9fea3dfa441868ca00bb12b0abf45\"><clipPath id=\"t64c6ef54074441f5aa557a1e50ea027a\"><rect height=\"190.0\" width=\"690.0\" x=\"55.0\" y=\"55.0\"></rect></clipPath><g clip-path=\"url(#t64c6ef54074441f5aa557a1e50ea027a)\"><g class=\"toyplot-mark-FillBoundaries\" id=\"t05605829fbc8443f85506c892b0503a5\" style=\"stroke:none\"><polygon points=\"65.0,235.0 74.178082191780817,235.0 83.356164383561648,235.0 92.534246575342465,235.0 101.7123287671233,235.0 110.89041095890411,235.0 120.06849315068493,235.0 129.24657534246575,235.0 138.42465753424659,235.0 147.60273972602738,235.0 156.78082191780823,235.0 165.95890410958904,235.0 175.13698630136986,235.0 184.31506849315068,235.0 193.49315068493149,235.0 202.67123287671234,235.0 211.84931506849315,235.0 221.02739726027397,235.0 230.20547945205479,235.0 239.38356164383561,235.0 248.56164383561642,235.0 257.73972602739724,235.0 266.91780821917808,235.0 276.09589041095887,235.0 285.27397260273972,235.0 294.45205479452051,235.0 303.63013698630141,235.0 312.8082191780822,235.0 321.98630136986299,235.0 331.16438356164383,235.0 340.34246575342468,235.0 349.52054794520546,235.0 358.69863013698631,235.0 367.8767123287671,235.0 377.05479452054794,235.0 386.23287671232873,235.0 395.41095890410958,235.0 404.58904109589042,235.0 413.76712328767121,235.0 422.94520547945206,235.0 432.12328767123284,235.0 441.30136986301369,235.0 450.47945205479448,235.0 459.65753424657532,235.0 468.83561643835611,235.0 478.01369863013696,235.0 487.1917808219178,235.0 496.36986301369865,235.0 505.54794520547944,235.0 514.72602739726028,235.0 523.90410958904101,235.0 533.08219178082186,235.0 542.2602739726027,235.0 551.43835616438355,235.0 560.61643835616428,235.0 569.79452054794524,235.0 578.97260273972609,235.0 588.15068493150693,235.0 597.32876712328766,235.0 606.50684931506851,235.0 615.68493150684935,235.0 624.8630136986302,235.0 634.04109589041093,235.0 643.21917808219177,235.0 652.39726027397262,235.0 661.57534246575347,235.0 670.7534246575342,235.0 679.93150684931504,235.0 689.10958904109589,235.0 698.28767123287673,235.0 707.46575342465746,235.0 716.64383561643831,235.0 725.82191780821915,235.0 725.82191780821915,67.10799999999999 716.64383561643831,76.37299999999999 707.46575342465746,83.326000000000008 698.28767123287673,90.381 689.10958904109589,97.538000000000011 679.93150684931504,104.94999999999999 670.7534246575342,112.43000000000001 661.57534246575347,119.26400000000001 652.39726027397262,124.959 643.21917808219177,130.875 634.04109589041093,136.96100000000001 624.8630136986302,140.71800000000002 615.68493150684935,144.96800000000002 606.50684931506851,146.65100000000001 597.32876712328766,148.81 588.15068493150693,150.95200000000003 578.97260273972609,154.59 569.79452054794524,154.02900000000002 560.61643835616428,157.07200000000003 551.43835616438355,159.18000000000001 542.2602739726027,157.922 533.08219178082186,158.874 523.90410958904101,156.10300000000001 514.72602739726028,158.82300000000001 505.54794520547944,158.41499999999999 496.36986301369865,159.75800000000001 487.1917808219178,158.46600000000001 478.01369863013696,160.55700000000002 468.83561643835611,160.02999999999997 459.65753424657532,159.62199999999999 450.47945205479448,161.441 441.30136986301369,161.798 432.12328767123284,159.928 422.94520547945206,160.18300000000002 413.76712328767121,160.36999999999998 404.58904109589042,159.928 395.41095890410958,161.42399999999998 386.23287671232873,160.642 377.05479452054794,159.554 367.8767123287671,160.55700000000002 358.69863013698631,161.52600000000001 349.52054794520546,159.82600000000002 340.34246575342468,162.22300000000004 331.16438356164383,162.495 321.98630136986299,161.86599999999999 312.8082191780822,161.27100000000002 303.63013698630141,161.84900000000002 294.45205479452051,161.84900000000002 285.27397260273972,164.178 276.09589041095887,164.44999999999999 266.91780821917808,162.46099999999998 257.73972602739724,163.41299999999998 248.56164383561642,162.44399999999999 239.38356164383561,161.90000000000001 230.20547945205479,162.30799999999999 221.02739726027397,162.529 211.84931506849315,163.14099999999999 202.67123287671234,164.29700000000003 193.49315068493149,165.18099999999998 184.31506849315068,165.67400000000001 175.13698630136986,166.286 165.95890410958904,166.405 156.78082191780823,166.09900000000002 147.60273972602738,167.28899999999999 138.42465753424659,168.29199999999997 129.24657534246575,168.58099999999999 120.06849315068493,173.32399999999998 110.89041095890411,176.11199999999999 101.7123287671233,235.0 92.534246575342465,235.0 83.356164383561648,235.0 74.178082191780817,235.0 65.0,235.0\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>total variable sites</title></polygon></g><g class=\"toyplot-mark-FillBoundaries\" id=\"td0137d53efa94a7e9543b98fa972a909\" style=\"stroke:none\"><polygon points=\"65.0,235.0 74.178082191780817,235.0 83.356164383561648,235.0 92.534246575342465,235.0 101.7123287671233,235.0 110.89041095890411,235.0 120.06849315068493,235.0 129.24657534246575,235.0 138.42465753424659,235.0 147.60273972602738,235.0 156.78082191780823,235.0 165.95890410958904,235.0 175.13698630136986,235.0 184.31506849315068,235.0 193.49315068493149,235.0 202.67123287671234,235.0 211.84931506849315,235.0 221.02739726027397,235.0 230.20547945205479,235.0 239.38356164383561,235.0 248.56164383561642,235.0 257.73972602739724,235.0 266.91780821917808,235.0 276.09589041095887,235.0 285.27397260273972,235.0 294.45205479452051,235.0 303.63013698630141,235.0 312.8082191780822,235.0 321.98630136986299,235.0 331.16438356164383,235.0 340.34246575342468,235.0 349.52054794520546,235.0 358.69863013698631,235.0 367.8767123287671,235.0 377.05479452054794,235.0 386.23287671232873,235.0 395.41095890410958,235.0 404.58904109589042,235.0 413.76712328767121,235.0 422.94520547945206,235.0 432.12328767123284,235.0 441.30136986301369,235.0 450.47945205479448,235.0 459.65753424657532,235.0 468.83561643835611,235.0 478.01369863013696,235.0 487.1917808219178,235.0 496.36986301369865,235.0 505.54794520547944,235.0 514.72602739726028,235.0 523.90410958904101,235.0 533.08219178082186,235.0 542.2602739726027,235.0 551.43835616438355,235.0 560.61643835616428,235.0 569.79452054794524,235.0 578.97260273972609,235.0 588.15068493150693,235.0 597.32876712328766,235.0 606.50684931506851,235.0 615.68493150684935,235.0 624.8630136986302,235.0 634.04109589041093,235.0 643.21917808219177,235.0 652.39726027397262,235.0 661.57534246575347,235.0 670.7534246575342,235.0 679.93150684931504,235.0 689.10958904109589,235.0 698.28767123287673,235.0 707.46575342465746,235.0 716.64383561643831,235.0 725.82191780821915,235.0 725.82191780821915,187.48499999999996 716.64383561643831,190.137 707.46575342465746,191.87099999999998 698.28767123287673,192.84 689.10958904109589,195.52600000000001 679.93150684931504,198.33099999999999 670.7534246575342,200.33700000000002 661.57534246575347,201.66300000000001 652.39726027397262,205.23299999999998 643.21917808219177,205.148 634.04109589041093,207.85100000000003 624.8630136986302,209.279 615.68493150684935,209.29599999999999 606.50684931506851,210.26500000000001 597.32876712328766,212.06699999999998 588.15068493150693,210.77500000000001 578.97260273972609,212.37299999999999 569.79452054794524,213.12099999999998 560.61643835616428,212.42400000000001 551.43835616438355,214.15799999999999 542.2602739726027,213.376 533.08219178082186,213.13800000000001 523.90410958904101,213.869 514.72602739726028,214.15799999999999 505.54794520547944,212.934 496.36986301369865,214.32799999999997 487.1917808219178,213.83499999999998 478.01369863013696,214.124 468.83561643835611,215.297 459.65753424657532,213.80099999999999 450.47945205479448,213.631 441.30136986301369,215.31399999999999 432.12328767123284,216.58900000000003 422.94520547945206,214.09 413.76712328767121,214.209 404.58904109589042,214.24299999999999 395.41095890410958,213.95399999999998 386.23287671232873,215.535 377.05479452054794,215.05900000000003 367.8767123287671,213.85200000000003 358.69863013698631,215.70499999999998 349.52054794520546,214.00500000000002 340.34246575342468,214.34500000000003 331.16438356164383,215.92600000000002 321.98630136986299,215.26300000000001 312.8082191780822,213.971 303.63013698630141,215.19499999999999 294.45205479452051,214.66799999999998 285.27397260273972,214.49799999999999 276.09589041095887,215.12700000000001 266.91780821917808,215.38200000000001 257.73972602739724,214.31100000000001 248.56164383561642,216.23199999999997 239.38356164383561,215.892 230.20547945205479,213.85200000000003 221.02739726027397,215.042 211.84931506849315,215.14400000000001 202.67123287671234,215.535 193.49315068493149,215.875 184.31506849315068,216.453 175.13698630136986,215.10999999999999 165.95890410958904,216.30000000000001 156.78082191780823,216.38499999999999 147.60273972602738,216.334 138.42465753424659,217.11600000000001 129.24657534246575,216.81 120.06849315068493,217.15000000000001 110.89041095890411,218.96899999999999 101.7123287671233,235.0 92.534246575342465,235.0 83.356164383561648,235.0 74.178082191780817,235.0 65.0,235.0\" style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>parsimony informative sites</title></polygon></g><g class=\"toyplot-mark-AxisLines\" id=\"t4fe94c2d6eaa40e2b7c8e7e812c6f3ca\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"160.55700000000002\" y2=\"160.55700000000002\"></line></g></g><g class=\"toyplot-mark-AxisLines\" id=\"t3ae8bd8f30cf47f99629f6caadb4ed9c\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"214.24299999999999\" y2=\"214.24299999999999\"></line></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t6c54e78eae964261b1d70eaa63ef9a84\" transform=\"translate(65.0,235.0)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"660.8219178082192\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"45.890410958904106\" x2=\"45.890410958904106\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"91.78082191780821\" x2=\"91.78082191780821\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"137.6712328767123\" x2=\"137.6712328767123\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"183.56164383561642\" x2=\"183.56164383561642\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"229.45205479452054\" x2=\"229.45205479452054\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"275.3424657534246\" x2=\"275.3424657534246\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"321.23287671232873\" x2=\"321.23287671232873\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"367.12328767123284\" x2=\"367.12328767123284\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"413.013698630137\" x2=\"413.013698630137\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"458.90410958904107\" x2=\"458.90410958904107\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"504.79452054794524\" x2=\"504.79452054794524\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"550.6849315068492\" x2=\"550.6849315068492\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"596.5753424657535\" x2=\"596.5753424657535\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"642.4657534246575\" x2=\"642.4657534246575\" y1=\"0\" y2=\"-5\"></line></g><g><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(45.890410958904106,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">5</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(91.78082191780821,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">10</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(137.6712328767123,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">15</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(183.56164383561642,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">20</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(229.45205479452054,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">25</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(275.3424657534246,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">30</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(321.23287671232873,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">35</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(367.12328767123284,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">40</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(413.013698630137,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">45</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(458.90410958904107,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">50</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(504.79452054794524,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">55</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(550.6849315068492,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">60</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(596.5753424657535,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">65</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(642.4657534246575,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">70</tspan></text></g><text style=\"dominant-baseline:hanging;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(335.0,22)translate(0,40.0)\"><tspan style=\"dominant-baseline:inherit\">Position along RAD loci</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t517d825f444544bfbf1cfe74d8369920\" transform=\"translate(65.0,235.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"0\" x2=\"167.892\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"85.0\" x2=\"85.0\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"170.0\" x2=\"170.0\" y1=\"0\" y2=\"5\"></line></g><g><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(85.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">5000</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(170.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">10000</tspan></text></g><text style=\"dominant-baseline:alphabetic;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(85.0,-22)translate(0,-40.0)\"><tspan style=\"dominant-baseline:inherit\">N variables sites</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-interactive\"><ul class=\"toyplot-mark-popup\" onmouseleave=\"this.style.visibility='hidden'\" style=\"background:rgba(0%,0%,0%,0.75);border:0;border-radius:6px;color:white;cursor:default;list-style:none;margin:0;padding:5px;position:fixed;visibility:hidden\">\n",
       "            <li class=\"toyplot-mark-popup-title\" style=\"color:lightgray;cursor:default;padding:5px;list-style:none;margin:0\"></li>\n",
       "            <li class=\"toyplot-mark-popup-save-csv\" onmouseout=\"this.style.color='white';this.style.background='steelblue'\" onmouseover=\"this.style.color='steelblue';this.style.background='white'\" style=\"border-radius:3px;padding:5px;list-style:none;margin:0\">\n",
       "                Save as .csv\n",
       "            </li>\n",
       "        </ul><script>\n",
       "        (function()\n",
       "        {\n",
       "          var data_tables = [{\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"t05605829fbc8443f85506c892b0503a5\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 3464.0, 3628.0, 3907.0, 3924.0, 3983.0, 4053.0, 4035.0, 4042.0, 4078.0, 4107.0, 4159.0, 4227.0, 4263.0, 4276.0, 4300.0, 4268.0, 4211.0, 4267.0, 4150.0, 4166.0, 4303.0, 4303.0, 4337.0, 4302.0, 4265.0, 4281.0, 4422.0, 4322.0, 4379.0, 4438.0, 4374.0, 4328.0, 4416.0, 4390.0, 4401.0, 4416.0, 4306.0, 4327.0, 4434.0, 4410.0, 4379.0, 4502.0, 4426.0, 4505.0, 4481.0, 4641.0, 4478.0, 4534.0, 4460.0, 4584.0, 4763.0, 4730.0, 4944.0, 5070.0, 5197.0, 5296.0, 5546.0, 5767.0, 6125.0, 6473.0, 6808.0, 7210.0, 7650.0, 8086.0, 8507.0, 8922.0, 9331.0, 9876.0]], \"filename\": \"toyplot\"}, {\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"td0137d53efa94a7e9543b98fa972a909\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 943.0, 1050.0, 1070.0, 1052.0, 1098.0, 1095.0, 1100.0, 1170.0, 1091.0, 1125.0, 1145.0, 1168.0, 1174.0, 1244.0, 1124.0, 1104.0, 1217.0, 1154.0, 1169.0, 1206.0, 1196.0, 1165.0, 1237.0, 1161.0, 1122.0, 1215.0, 1235.0, 1135.0, 1244.0, 1173.0, 1145.0, 1238.0, 1221.0, 1223.0, 1230.0, 1083.0, 1158.0, 1257.0, 1247.0, 1159.0, 1228.0, 1245.0, 1216.0, 1298.0, 1226.0, 1243.0, 1286.0, 1272.0, 1226.0, 1328.0, 1287.0, 1331.0, 1425.0, 1349.0, 1455.0, 1512.0, 1513.0, 1597.0, 1756.0, 1751.0, 1961.0, 2039.0, 2157.0, 2322.0, 2480.0, 2537.0, 2639.0, 2795.0]], \"filename\": \"toyplot\"}];\n",
       "\n",
       "          function save_csv(data_table)\n",
       "          {\n",
       "            var uri = \"data:text/csv;charset=utf-8,\";\n",
       "            uri += data_table.names.join(\",\") + \"\\n\";\n",
       "            for(var i = 0; i != data_table.columns[0].length; ++i)\n",
       "            {\n",
       "              for(var j = 0; j != data_table.columns.length; ++j)\n",
       "              {\n",
       "                if(j)\n",
       "                  uri += \",\";\n",
       "                uri += data_table.columns[j][i];\n",
       "              }\n",
       "              uri += \"\\n\";\n",
       "            }\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = data_table.filename + \".csv\";\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "          }\n",
       "\n",
       "          function open_popup(data_table)\n",
       "          {\n",
       "            return function(e)\n",
       "            {\n",
       "              var popup = document.querySelector(\"#t77dc68aeda1048aeb999325163f9d8b6 .toyplot-mark-popup\");\n",
       "              popup.querySelector(\".toyplot-mark-popup-title\").innerHTML = data_table.title;\n",
       "              popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
       "              popup.style.left = (e.clientX - 50) + \"px\";\n",
       "              popup.style.top = (e.clientY - 20) + \"px\";\n",
       "              popup.style.visibility = \"visible\";\n",
       "              e.stopPropagation();\n",
       "              e.preventDefault();\n",
       "            }\n",
       "\n",
       "          }\n",
       "\n",
       "          for(var i = 0; i != data_tables.length; ++i)\n",
       "          {\n",
       "            var data_table = data_tables[i];\n",
       "            var event_target = document.querySelector(\"#\" + data_table.id);\n",
       "            event_target.oncontextmenu = open_popup(data_table);\n",
       "          }\n",
       "        })();\n",
       "        </script><script>\n",
       "        (function()\n",
       "        {\n",
       "            function _sign(x)\n",
       "            {\n",
       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
       "\n",
       "            function _mix(a, b, amount)\n",
       "            {\n",
       "                return ((1.0 - amount) * a) + (amount * b);\n",
       "            }\n",
       "\n",
       "            function _log(x, base)\n",
       "            {\n",
       "                return Math.log(Math.abs(x)) / Math.log(base);\n",
       "            }\n",
       "\n",
       "            function _in_range(a, x, b)\n",
       "            {\n",
       "                var left = Math.min(a, b);\n",
       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
       "            }\n",
       "\n",
       "            function inside(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.min, range, segment.range.max))\n",
       "                        return true;\n",
       "                }\n",
       "                return false;\n",
       "            }\n",
       "\n",
       "            function to_domain(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
       "                    {\n",
       "                        if(segment.scale == \"linear\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            return _mix(segment.domain.min, segment.domain.max, amount)\n",
       "                        }\n",
       "                        else if(segment.scale[0] == \"log\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            var base = segment.scale[1];\n",
       "                            return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            function display_coordinates(e)\n",
       "            {\n",
       "                var current = svg.createSVGPoint();\n",
       "                current.x = e.clientX;\n",
       "                current.y = e.clientY;\n",
       "\n",
       "                for(var axis_id in axes)\n",
       "                {\n",
       "                    var axis = document.querySelector(\"#\" + axis_id);\n",
       "                    var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
       "                    if(coordinates)\n",
       "                    {\n",
       "                        var projection = axes[axis_id];\n",
       "                        var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
       "                        if(inside(local.x, projection))\n",
       "                        {\n",
       "                            var domain = to_domain(local.x, projection);\n",
       "                            coordinates.style.visibility = \"visible\";\n",
       "                            coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
       "                            var text = coordinates.querySelector(\"text\");\n",
       "                            text.textContent = domain.toFixed(2);\n",
       "                        }\n",
       "                        else\n",
       "                        {\n",
       "                            coordinates.style.visibility= \"hidden\";\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var root_id = \"t77dc68aeda1048aeb999325163f9d8b6\";\n",
       "            var axes = {\"t517d825f444544bfbf1cfe74d8369920\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 10000.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 170.0, \"min\": 0.0}, \"scale\": \"linear\"}], \"t6c54e78eae964261b1d70eaa63ef9a84\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 73.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 670.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
       "\n",
       "            var svg = document.querySelector(\"#\" + root_id + \" svg\");\n",
       "            svg.addEventListener(\"click\", display_coordinates);\n",
       "        })();\n",
       "        </script></div></div>"
      ],
      "text/plain": [
       "<toyplot.canvas.Canvas at 0x2aab7727b4d0>"
      ]
     },
     "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": "markdown",
   "metadata": {},
   "source": [
    "# SKIP to HERE\n",
    "# Do both simulated and empirical at the same time\n",
    "Could make the plots prettier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.snps.vcf.recode.vcf\n",
      "found - /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_minus_reference-sim_outfiles/denovo_minus_reference-sim.vcf\n",
      "found - /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference-095_outfiles/denovo_plus_reference-095.vcf\n",
      "not found - /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1.vcf\n",
      "found - /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_outfiles/refmap-empirical.vcf\n",
      "found - /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_minus_reference_outfiles/denovo_ref-empirical.vcf\n",
      "found - /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/Final.recode.snps.vcf.recode.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles/refmap-sim.vcf\n",
      "found - /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_plus_reference-sim_outfiles/denovo_plus_reference-sim.vcf\n"
     ]
    }
   ],
   "source": [
    "vcf_dict = {}\n",
    "vcf_dict[\"ipyrad-reference-sim\"] = os.path.join(IPYRAD_SIM_DIR, \"refmap-sim_outfiles/refmap-sim.vcf\")\n",
    "vcf_dict[\"ipyrad-denovo_plus_reference-sim\"] = os.path.join(IPYRAD_SIM_DIR, \"denovo_plus_reference-sim_outfiles/denovo_plus_reference-sim.vcf\")\n",
    "vcf_dict[\"ipyrad-denovo_minus_reference-sim\"] = os.path.join(IPYRAD_SIM_DIR, \"denovo_minus_reference-sim_outfiles/denovo_minus_reference-sim.vcf\")\n",
    "vcf_dict[\"stacks-sim\"] = os.path.join(STACKS_SIM_DIR, \"batch_1.vcf\")\n",
    "vcf_dict[\"ddocent-tot-sim\"] = os.path.join(DDOCENT_SIM_DIR, \"TotalRawSNPs.snps.vcf.recode.vcf\")\n",
    "vcf_dict[\"ddocent-fin-sim\"] = os.path.join(DDOCENT_SIM_DIR, \"Final.recode.snps.vcf.recode.vcf\")\n",
    "vcf_dict[\"ipyrad-reference-empirical\"] = os.path.join(IPYRAD_REFMAP_DIR, \"reference-assembly/refmap-empirical_outfiles/refmap-empirical.vcf\")\n",
    "vcf_dict[\"ipyrad-denovo_plus_reference-empirical\"] = os.path.join(IPYRAD_REFMAP_DIR, \"reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.vcf\")\n",
    "vcf_dict[\"ipyrad-denovo_plus_reference-095-empirical\"] = os.path.join(IPYRAD_REFMAP_DIR, \"reference-assembly/denovo_plus_reference-095_outfiles/denovo_plus_reference-095.vcf\")\n",
    "vcf_dict[\"ipyrad-denovo_minus_reference-empirical\"] = os.path.join(IPYRAD_REFMAP_DIR, \"reference-assembly/denovo_minus_reference_outfiles/denovo_ref-empirical.vcf\")\n",
    "vcf_dict[\"stacks-empirical\"] = os.path.join(STACKS_REFMAP_DIR, \"batch_1.vcf\")\n",
    "vcf_dict[\"ddocent-fin-empirical\"] = os.path.join(DDOCENT_REFMAP_DIR, \"Final.recode.snps.vcf.recode.vcf\")\n",
    "\n",
    "# skipt the full ddocent vcf. It's huge and we know we don't want to use it.\n",
    "#vcf_dict[\"ddocent-tot-empirical\"] = os.path.join(DDOCENT_REFMAP_DIR, \"TotalRawSNPs.snps.vcf.recode.vcf\")\n",
    "\n",
    "## Make sure we have all the vcf files\n",
    "for k, f in vcf_dict.items():\n",
    "    if os.path.exists(f):\n",
    "        print(\"found - {}\".format(f))\n",
    "    else:\n",
    "        print(\"not found - {}\".format(f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some magic to get samples and populations properly grouped and colored for pretty PCA plots\n",
    "There has got to be a better way to do this for the empirical at least. It seems super hax, but it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## sim\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",
    "sim_sample_names = pop1 + pop2 + pop3\n",
    "sim_pops = {\"pop1\":pop1, \"pop2\":pop2, \"pop3\":pop3}\n",
    "sim_pop_colors = {\"pop1\":\"r\", \"pop2\":\"b\", \"pop3\":\"g\"}\n",
    "\n",
    "## empirical\n",
    "emp_pops = {}\n",
    "emp_sample_names = []\n",
    "popfile = os.path.join(STACKS_REFMAP_DIR, \"popmap.txt\")\n",
    "with open(popfile) as infile:\n",
    "    lines = [l.strip().split() for l in infile.readlines()]\n",
    "    emp_sample_names = [x[0] for x in lines]\n",
    "    pops = set([x[1] for x in lines])\n",
    "    for pop in pops: emp_pops[pop] = []\n",
    "    for line in lines:\n",
    "        p = line[1]\n",
    "        s = line[0]\n",
    "        emp_pops[p].append(s)\n",
    "emp_pop_colors = {k:v for (k,v) in zip(emp_pops, list(matplotlib.colors.cnames))}\n",
    "\n",
    "## Write out the samples to pop files for vcftools\n",
    "for outdir, pop_dict in {REFMAP_SIM_DIR:sim_pops, REFMAP_EMPIRICAL_DIR:emp_pops}.items():\n",
    "    for pop, samps in pop_dict.items():\n",
    "        with open(outdir + pop + \".txt\", \"w\") as outfile:\n",
    "            outfile.write(\"\\n\".join(samps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixing stacks vcf file format - stacks-empirical\n",
      "reordering\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1.reordered.vcf\n",
      "perl vcf-shuffle-cols -t /tmp/dummy.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1.vcf > tmp.vcf\n",
      "reordering\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference-095_outfiles/denovo_plus_reference-095.reordered.vcf\n",
      "perl vcf-shuffle-cols -t /tmp/dummy.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference-095_outfiles/denovo_plus_reference-095.vcf > tmp.vcf\n",
      "reordering\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.reordered.vcf\n",
      "perl vcf-shuffle-cols -t /tmp/dummy.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.vcf > tmp.vcf\n",
      "Fixing stacks vcf file format - stacks-sim\n",
      "reordering\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_outfiles/refmap-empirical.reordered.vcf\n",
      "perl vcf-shuffle-cols -t /tmp/dummy.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_outfiles/refmap-empirical.vcf > tmp.vcf\n",
      "reordering\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_minus_reference_outfiles/denovo_ref-empirical.reordered.vcf\n",
      "perl vcf-shuffle-cols -t /tmp/dummy.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_minus_reference_outfiles/denovo_ref-empirical.vcf > tmp.vcf\n",
      "reordering\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/Final.recode.snps.vcf.recode.reordered.vcf\n",
      "perl vcf-shuffle-cols -t /tmp/dummy.vcf /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/Final.recode.snps.vcf.recode.vcf > tmp.vcf\n"
     ]
    }
   ],
   "source": [
    "## The order of the samples in the vcf file is wonky. Reorder them so they're sorted by population. This\n",
    "## is roughly the order of populations from the manuscript.\n",
    "\n",
    "## If you run this multiple times, you should re-run the previous cell that resets\n",
    "## the vcf file to the original path, otherwise you get a bunch of *.reorder.reorder.vcf files.\n",
    "order = [\"WBS\", \"IS\", \"NOS\", \"SK1\", \"KB1\", \"BES2\", \"IBS\"]\n",
    "\n",
    "vcf_header = \"\"\"##fileformat=VCFv4.1\\n##fileDate=20161230\\n##source=\"Stacks v1.42\"\\n##INFO=<ID=NS,Number=1,Type=Integer,Description=\"Number of Samples With Data\">\\n##INFO=<ID=AF,Number=.,Type=Float,Description=\"Allele Frequency\">\\n##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">\\n##FORMAT=<ID=DP,Number=1,Type=Integer,Description=\"Read Depth\">\\n##FORMAT=<ID=AD,Number=1,Type=Integer,Description=\"Allele Depth\">\\n##FORMAT=<ID=GL,Number=.,Type=Float,Description=\"Genotype Likelihood\">\\n#CHROM\\tPOS\\tID\\tREF\\tALT\\tQUAL\\tFILTER\\tINFO\\tFORMAT\"\"\"\n",
    "with open(\"/tmp/dummy.vcf\", 'w') as outfile:\n",
    "    outfile.write(vcf_header)\n",
    "    for p in order:\n",
    "        outfile.write(\"\\t\" + \"\\t\".join(emp_pops[p]))\n",
    "for k, f in vcf_dict.items():\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",
    "        print(\"Fixing stacks vcf file format - {}\".format(k))\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",
    "    if \"empirical\" in k:\n",
    "        print(\"reordering\")\n",
    "        vcftools_path = os.path.join(DDOCENT_DIR, \"vcftools_0.1.11/perl/\")\n",
    "        os.chdir(vcftools_path)\n",
    "        tmpvcf = \"tmp.vcf\"\n",
    "        newvcf = f.rsplit(\".\", 1)[0] + \".reordered.vcf\"\n",
    "        print(newvcf)\n",
    "        cmd = \"perl vcf-shuffle-cols -t {} {} > {}\".format(\"/tmp/dummy.vcf\", f, tmpvcf)\n",
    "        print(cmd)\n",
    "        os.system(cmd)\n",
    "        !mv $tmpvcf $newvcf\n",
    "        ## Don't destroy the old one\n",
    "        vcf_dict[k] = newvcf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing - ddocent-fin-sim\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.snps.vcf.recode.vcf\n",
      "Doing - stacks-empirical\n",
      "  /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1.reordered.vcf\n",
      "Doing - ipyrad-denovo_minus_reference-sim\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_minus_reference-sim_outfiles/denovo_minus_reference-sim.vcf\n",
      "Doing - ipyrad-denovo_plus_reference-095-empirical\n",
      "  /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference-095_outfiles/denovo_plus_reference-095.reordered.vcf\n",
      "Doing - ipyrad-denovo_plus_reference-empirical\n",
      "  /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.reordered.vcf\n",
      "basic_string::substr: __pos (which is 18446744073709551615) > this->size() (which is 0)\n",
      "Doing - stacks-sim\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1.vcf\n",
      "Doing - ipyrad-reference-empirical\n",
      "  /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_outfiles/refmap-empirical.reordered.vcf\n",
      "Doing - ipyrad-denovo_minus_reference-empirical\n",
      "  /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_minus_reference_outfiles/denovo_ref-empirical.reordered.vcf\n",
      "Doing - ddocent-fin-empirical\n",
      "  /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/Final.recode.snps.vcf.recode.reordered.vcf\n",
      "Doing - ddocent-tot-sim\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.vcf\n",
      "Doing - ipyrad-reference-sim\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles/refmap-sim.vcf\n",
      "Doing - ipyrad-denovo_plus_reference-sim\n",
      "  /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_plus_reference-sim_outfiles/denovo_plus_reference-sim.vcf\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "## Load the calldata into a dict so we don't have to keep loading and reloading it\n",
    "## This can take several minutes for the large empirical datasets (like 20+ minutes)\n",
    "all_calldata = {}\n",
    "all_vardata = {}\n",
    "\n",
    "## Dicts for tracking all the stats\n",
    "loc_cov = collections.OrderedDict()\n",
    "snp_cov = collections.OrderedDict()\n",
    "samp_nsnps = collections.OrderedDict()\n",
    "samp_nlocs = collections.OrderedDict()\n",
    "\n",
    "for prog, filename in vcf_dict.items():\n",
    "    try:\n",
    "        print(\"Doing - {}\\n  {}\".format(prog, 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",
    "        all_calldata[prog] = c\n",
    "        all_vardata[prog] = v\n",
    "        \n",
    "        snp_cov[prog] = snp_coverage(c)\n",
    "        samp_nsnps[prog] = sample_nsnps(c)\n",
    "        loc_cov[prog] = loci_coverage(v, c, prog)\n",
    "        samp_nlocs[prog] = sample_nloci(v, c, prog)\n",
    "    except Exception as inst:\n",
    "        print(inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddocent-fin-sim sample_nlocs\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] 1.0\n",
      "stacks-empirical sample_nlocs\t[78511, 81962, 82414, 94151, 92146, 104316, 94099, 83252, 93109, 88319, 92008, 45195, 36564, 37502, 97311, 97110, 96071, 98059, 86883, 79414, 97308, 92421, 70530, 85021, 81766, 91929, 87529, 95557, 87105, 65463, 93372, 84925, 46207, 98960, 78322, 91547, 98216, 57176, 26729, 68932, 83942, 110102, 50223, 81625] 81438.7045455\n",
      "ipyrad-denovo_minus_reference-sim sample_nlocs\t[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500] 500.0\n",
      "ipyrad-denovo_plus_reference-095-empirical sample_nlocs\t[63041, 67885, 69761, 78541, 76574, 86265, 80607, 69913, 78769, 73461, 77077, 35786, 27485, 25629, 83193, 82596, 82414, 78615, 72002, 66468, 81868, 78135, 57845, 69802, 66292, 78313, 71215, 81069, 75198, 51989, 78737, 72108, 36670, 83460, 59866, 77037, 83158, 44554, 18856, 56152, 70249, 94988, 36984, 67930] 67467.2045455\n",
      "ipyrad-denovo_plus_reference-empirical sample_nlocs\tNo sample_nlocs stats for ipyrad-denovo_plus_reference-empirical\n",
      "stacks-sim sample_nlocs\t[994, 993, 994, 993, 994, 994, 994, 994, 994, 994, 994, 994] 993.833333333\n",
      "ipyrad-reference-empirical sample_nlocs\t[31030, 33423, 33693, 37242, 36927, 39491, 38589, 31674, 37857, 35265, 37421, 18602, 14897, 13209, 39165, 38832, 39065, 36030, 35081, 32160, 38806, 37707, 28368, 34076, 29465, 37217, 33201, 38057, 35794, 25000, 37581, 32377, 18832, 38576, 28166, 35939, 39433, 22276, 8975, 27358, 34143, 43137, 17783, 32452] 32144.8181818\n",
      "ipyrad-denovo_minus_reference-empirical sample_nlocs\t[30955, 33398, 34961, 39454, 37735, 44195, 40145, 35820, 38694, 36374, 38090, 17081, 12767, 12827, 41997, 41755, 41141, 40125, 35241, 33158, 40890, 38561, 28434, 33972, 34554, 39038, 36141, 40999, 37785, 25871, 38854, 37800, 17141, 42406, 30533, 39180, 41602, 21621, 9625, 28101, 34350, 48947, 18864, 33920] 33752.3181818\n",
      "ddocent-fin-empirical sample_nlocs\t[12408, 12421, 12410, 12444, 12438, 12446, 12450, 12424, 12449, 12429, 12439, 12348, 12262, 12235, 12447, 12445, 12456, 12456, 12422, 12432, 12450, 12446, 12400, 12433, 12423, 12451, 12438, 12459, 12442, 12396, 12440, 12434, 12151, 12456, 12412, 12452, 12453, 12362, 12086, 12418, 12434, 12461, 12337, 12422] 12407.2045455\n",
      "ddocent-tot-sim sample_nlocs\t[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] 1.0\n",
      "ipyrad-reference-sim sample_nlocs\t[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500] 500.0\n",
      "ipyrad-denovo_plus_reference-sim sample_nlocs\t[1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000] 1000.0\n",
      "------------------------------------------------------\n",
      "ddocent-fin-sim sample_nsnps\t[4722, 4722, 4722, 4722, 4722, 4722, 4722, 4722, 4722, 4722, 4722, 4720] 4721.83333333\n",
      "stacks-empirical sample_nsnps\t[127584, 132382, 132461, 151871, 147852, 166338, 150813, 137488, 150082, 142916, 147395, 75863, 61779, 63115, 155295, 155563, 155042, 158655, 140520, 129483, 156638, 149322, 115267, 138933, 136090, 148625, 142290, 154049, 141251, 109132, 150738, 139130, 77554, 158810, 127177, 148688, 157457, 94969, 48569, 113856, 136906, 175552, 84501, 133347] 132303.363636\n",
      "ipyrad-denovo_minus_reference-sim sample_nsnps\t[4765, 4765, 4765, 4765, 4765, 4765, 4765, 4765, 4765, 4765, 4765, 4765] 4765.0\n",
      "ipyrad-denovo_plus_reference-095-empirical sample_nsnps\t[137220, 146783, 149325, 169235, 165873, 185539, 173775, 153670, 170248, 159153, 166580, 77835, 59846, 56056, 179128, 177628, 177653, 170445, 156419, 143632, 176078, 168548, 125526, 151154, 146456, 169491, 154770, 174765, 162339, 114529, 170677, 156547, 79561, 180467, 131269, 167086, 178488, 97828, 42991, 122642, 152317, 203178, 81442, 147304] 146170.363636\n",
      "ipyrad-denovo_plus_reference-empirical sample_nsnps\tNo sample_nsnps stats for ipyrad-denovo_plus_reference-empirical\n",
      "stacks-sim sample_nsnps\t[4648, 4642, 4648, 4643, 4647, 4647, 4648, 4647, 4647, 4648, 4648, 4646] 4646.58333333\n",
      "ipyrad-reference-empirical sample_nsnps\t[68134, 73041, 72841, 81281, 81035, 86323, 84391, 70165, 83389, 77755, 82062, 40749, 32574, 28963, 85446, 84542, 85338, 79213, 77265, 70319, 84859, 82520, 62281, 74722, 65372, 81397, 73122, 82985, 77986, 55170, 82551, 70629, 41240, 84238, 62253, 78628, 86080, 49126, 20083, 59985, 74760, 94244, 39378, 71100] 70443.9772727\n",
      "ipyrad-denovo_minus_reference-empirical sample_nsnps\t[71317, 77190, 79627, 90622, 87474, 101779, 92398, 84924, 88994, 83694, 88136, 39198, 29297, 29794, 96825, 96303, 94582, 93041, 81786, 76200, 93959, 88312, 65666, 78307, 82013, 90542, 83851, 94569, 86947, 60858, 89912, 88383, 39578, 98424, 71052, 91057, 95441, 50570, 24554, 65478, 79379, 112805, 43932, 78162] 78112.0909091\n",
      "ddocent-fin-empirical sample_nsnps\t[171685, 172201, 171522, 172909, 173155, 173637, 173266, 172177, 173278, 172838, 173170, 168267, 165354, 166545, 173392, 173405, 173517, 173424, 172597, 172531, 173419, 173298, 171246, 172721, 172256, 173361, 173086, 173363, 172955, 170887, 173079, 172319, 161239, 173552, 172418, 173079, 173517, 168600, 159573, 171618, 172605, 173755, 169918, 172199] 171657.568182\n",
      "ddocent-tot-sim sample_nsnps\t[4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4840, 4838] 4839.83333333\n",
      "ipyrad-reference-sim sample_nsnps\t[4776, 4776, 4775, 4776, 4775, 4776, 4776, 4776, 4776, 4776, 4776, 4776] 4775.83333333\n",
      "ipyrad-denovo_plus_reference-sim sample_nsnps\t[9541, 9541, 9540, 9541, 9540, 9541, 9541, 9541, 9541, 9541, 9541, 9541] 9540.83333333\n",
      "------------------------------------------------------\n",
      "ddocent-fin-sim snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4720] 393.5\n",
      "stacks-empirical snp_cov\t[4332, 4871, 4460, 4037, 3726, 3595, 3412, 3352, 3475, 3433, 3462, 3505, 3495, 3582, 3618, 3742, 3772, 3742, 3834, 4201, 4199, 4501, 4519, 4811, 4995, 5290, 5531, 5858, 6115, 6392, 6910, 7214, 7428, 7672, 8174, 8440, 8524, 8645, 8752, 8328, 7460, 6158, 4035, 2142] 5175.88636364\n",
      "ipyrad-denovo_minus_reference-sim snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4765] 397.083333333\n",
      "ipyrad-denovo_plus_reference-095-empirical snp_cov\t[197, 351, 1457, 8374, 7055, 6189, 5742, 5437, 5295, 5155, 4906, 4728, 4597, 4706, 4725, 4665, 4807, 4678, 4897, 4976, 4995, 5352, 5479, 5441, 5918, 5902, 6178, 6198, 6658, 6864, 7191, 7438, 7973, 8232, 8723, 9050, 9462, 9790, 9673, 8969, 7716, 6075, 3534, 1374] 5843.68181818\n",
      "ipyrad-denovo_plus_reference-empirical snp_cov\tNo snp_cov stats for ipyrad-denovo_plus_reference-empirical\n",
      "stacks-sim snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 14, 4633] 387.333333333\n",
      "ipyrad-reference-empirical snp_cov\t[131, 148, 758, 5298, 4204, 3597, 3113, 3337, 2944, 2915, 2616, 2586, 2573, 2579, 2504, 2615, 2632, 2652, 2655, 2737, 2724, 2895, 3033, 3009, 3184, 3147, 3405, 3384, 3497, 3763, 3788, 3886, 4078, 4220, 4124, 4407, 4529, 4176, 4180, 3670, 2849, 1863, 808, 193] 2986.5\n",
      "ipyrad-denovo_minus_reference-empirical snp_cov\t[123, 296, 1219, 4780, 4096, 3341, 2934, 2490, 2570, 2584, 2448, 2294, 2018, 2236, 2142, 2234, 2235, 2231, 2193, 2208, 2286, 2387, 2345, 2330, 2755, 2669, 2857, 2988, 3104, 3285, 3366, 3644, 3878, 4128, 4391, 4881, 5290, 5592, 5693, 5686, 5376, 4327, 2735, 1034] 3038.61363636\n",
      "ddocent-fin-empirical snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6619, 8821, 13380, 27196, 118071] 3956.52272727\n",
      "ddocent-tot-sim snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4838] 403.333333333\n",
      "ipyrad-reference-sim snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 4774] 398.0\n",
      "ipyrad-denovo_plus_reference-sim snp_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 9539] 795.083333333\n",
      "------------------------------------------------------\n",
      "ddocent-fin-sim loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 0.0833333333333\n",
      "stacks-empirical loc_cov\t[2315, 2636, 2465, 2274, 2075, 1895, 1842, 1835, 1896, 1871, 1903, 1912, 1833, 1996, 1968, 2040, 2062, 2060, 2149, 2331, 2322, 2503, 2394, 2642, 2709, 3033, 3098, 3281, 3454, 3666, 3943, 4205, 4336, 4622, 5001, 5071, 5396, 5561, 5752, 5702, 5209, 4431, 2970, 1637] 3052.18181818\n",
      "ipyrad-denovo_minus_reference-sim loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500] 41.6666666667\n",
      "ipyrad-denovo_plus_reference-095-empirical loc_cov\t[26, 86, 513, 4305, 3689, 3128, 2931, 2738, 2654, 2551, 2453, 2388, 2287, 2257, 2260, 2314, 2344, 2214, 2260, 2357, 2362, 2413, 2516, 2512, 2585, 2750, 2794, 2838, 2975, 3183, 3205, 3354, 3574, 3690, 3900, 3962, 4286, 4480, 4409, 4175, 3624, 2844, 1671, 645] 2738.68181818\n",
      "ipyrad-denovo_plus_reference-empirical loc_cov\tNo loc_cov stats for ipyrad-denovo_plus_reference-empirical\n",
      "stacks-sim loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 992] 82.8333333333\n",
      "ipyrad-reference-empirical loc_cov\t[10, 42, 264, 2029, 1776, 1563, 1407, 1471, 1252, 1349, 1262, 1278, 1203, 1212, 1215, 1226, 1239, 1218, 1219, 1290, 1257, 1300, 1390, 1359, 1438, 1371, 1505, 1582, 1534, 1668, 1684, 1750, 1799, 1893, 1874, 1946, 2024, 1924, 1935, 1716, 1361, 906, 415, 99] 1346.70454545\n",
      "ipyrad-denovo_minus_reference-empirical loc_cov\t[9, 37, 250, 1530, 1381, 1253, 1191, 1003, 1011, 1063, 1032, 958, 893, 927, 889, 933, 969, 876, 880, 896, 952, 1012, 1023, 999, 1132, 1134, 1185, 1233, 1276, 1404, 1496, 1518, 1673, 1773, 1836, 2024, 2296, 2488, 2531, 2558, 2436, 1995, 1302, 485] 1266.86363636\n",
      "ddocent-fin-empirical loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 171, 240, 360, 826, 10870] 283.340909091\n",
      "ddocent-tot-sim loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] 0.0833333333333\n",
      "ipyrad-reference-sim loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500] 41.6666666667\n",
      "ipyrad-denovo_plus_reference-sim loc_cov\t[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000] 83.3333333333\n",
      "------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for statname, stat in {\"loc_cov\":loc_cov, \"snp_cov\":snp_cov,\\\n",
    "             \"sample_nsnps\":samp_nsnps, \"sample_nlocs\":samp_nlocs}.items():\n",
    "    for prog in vcf_dict.keys():\n",
    "        try:\n",
    "            print(prog + \" \" + statname + \"\\t\"),\n",
    "            print(stat[prog]),\n",
    "            print(np.mean(stat[prog]))\n",
    "        except:\n",
    "            print(\"No {} stats for {}\".format(statname, prog))\n",
    "    print(\"------------------------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter linked snps? Doesn't make a difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('ipyrad-reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa170410>)\n",
      "('ipyrad-denovo_plus_reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9bf26d0>)\n",
      "('ipyrad-denovo_minus_reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9dca250>)\n",
      "('stacks-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa1c25d0>)\n",
      "('ddocent-fin-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa1b8550>)\n",
      "('ipyrad-reference-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa17b690>)\n",
      "('ipyrad-denovo_plus_reference-095-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9bc8c90>)\n",
      "('ipyrad-denovo_minus_reference-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9d7ab90>)\n",
      "('stacks-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf81e1850>)\n",
      "('ddocent-fin-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf811c350>)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8AAAAMRCAYAAACTbayTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaba9a44450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, 5, figsize=(20,8), dpi=1000)\n",
    "## Make a list out of the axes\n",
    "axarr = [a for b in axarr for a in b]\n",
    "## Set them in order so the plot looks nice.\n",
    "progs = [\"ipyrad-reference-sim\", \"ipyrad-denovo_plus_reference-sim\", \"ipyrad-denovo_minus_reference-sim\", \"stacks-sim\", \"ddocent-fin-sim\",\\\n",
    "         \"ipyrad-reference-empirical\", \"ipyrad-denovo_plus_reference-095-empirical\", \"ipyrad-denovo_minus_reference-empirical\", \"stacks-empirical\", \"ddocent-fin-empirical\"]\n",
    "\n",
    "for prog, ax in zip(progs, axarr):\n",
    "    print(prog, ax)\n",
    "    if \"empirical\" in prog:\n",
    "        pop_colors = emp_pop_colors\n",
    "        pops = emp_pops\n",
    "        sample_names = emp_sample_names\n",
    "    else:\n",
    "        pop_colors = sim_pop_colors\n",
    "        pops = sim_pops\n",
    "        sample_names = sim_sample_names\n",
    "    ## Don't die if some of the runs aren't complete\n",
    "    try:\n",
    "        coords1, model1 = getPCA(all_calldata[prog])\n",
    "    except:\n",
    "        continue\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(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, style=\"italic\")\n",
    "    ax.axison = True\n",
    "\n",
    "axarr[0].legend(frameon=True)\n",
    "axarr[5].legend(loc='lower left', frameon=True)\n",
    "\n",
    "f.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('ipyrad-reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba0a6bc50>)\n",
      "('ipyrad-denovo_plus_reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9ca01d0>)\n",
      "('ipyrad-denovo_minus_reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa1e0210>)\n",
      "('stacks-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa00e050>)\n",
      "('ddocent-fin-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf90f04d0>)\n",
      "('ipyrad-reference-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf9498d50>)\n",
      "('ipyrad-denovo_plus_reference-095-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf964d210>)\n",
      "('ipyrad-denovo_minus_reference-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf95a6090>)\n",
      "('stacks-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf91a7d10>)\n",
      "('ddocent-fin-empirical', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabf94c00d0>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x2aabf9202750>,\n",
       " <matplotlib.text.Text at 0x2aabf9228750>]"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABvsAAALaCAYAAAAfqO9fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaba1df9310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2, 5, figsize=(20,8), dpi=1000)\n",
    "axarr = [a for b in axarr for a in b]\n",
    "## Set them in order so the plot looks nice.\n",
    "progs = [\"ipyrad-reference-sim\", \"ipyrad-denovo_plus_reference-sim\", \"ipyrad-denovo_minus_reference-sim\", \"stacks-sim\", \"ddocent-fin-sim\",\\\n",
    "         \"ipyrad-reference-empirical\", \"ipyrad-denovo_plus_reference-095-empirical\", \"ipyrad-denovo_minus_reference-empirical\", \"stacks-empirical\", \"ddocent-fin-empirical\"]\n",
    "\n",
    "for prog, ax in zip(progs, axarr):\n",
    "    print(prog, ax)\n",
    "    try:\n",
    "        ## Calculate pairwise distances\n",
    "        dist = getDistances(all_calldata[prog])\n",
    "    except:\n",
    "        continue\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, style=\"italic\")\n",
    "    ax.axison = False\n",
    "\n",
    "## Adjust margins to make room for the colorbar\n",
    "plt.subplots_adjust(left=0.05, bottom=0.1, right=0.8, top=0.9, wspace=0.2, hspace=0.3)\n",
    "\n",
    "## Add the colorbar\n",
    "cax = f.add_axes([0.87, 0.4, 0.03, 0.5])\n",
    "cb1 = matplotlib.colorbar.ColorbarBase(cax, cmap=\"jet\", orientation=\"vertical\", ticks=([0,1]))\n",
    "cb1.ax.set_yticklabels(['Similar', \"Dissimilar\"], weight=\"bold\", rotation=\"vertical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull in finer grained stats for each assembler\n",
    "For ipyrad and stacks this is very quick, but the whole step is slow because ddocent empirical\n",
    "stats take _forever_ to obtain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing - stacks-empirical\n",
      "[44367, 19212, 12649, 9571, 7961, 6694, 5997, 5625, 5210, 5136, 4765, 4653, 4400, 4499, 4465, 4347, 4215, 4264, 4418, 4501, 4596, 4603, 4712, 4891, 5055, 5402, 5737, 6002, 6479, 6757, 7147, 7807, 8294, 9141, 9957, 10916, 11935, 12668, 14520, 15065, 15006, 13576, 9744, 6215]\n",
      "[321973, 346480, 312450, 353761, 406143, 333570, 294514, 327705, 342850, 335347, 351034, 330976, 279661, 345149, 331976, 216198, 307288, 327835, 312977, 364989, 231650, 360533, 344603, 288889, 374219, 339563, 251650, 295388, 427019, 315171, 353471, 336082, 343184, 208747, 331309, 352628, 363792, 180628, 318019, 255977, 362677, 310417, 390315, 238011]\n",
      "Done - stacks-empirical\n",
      "\n",
      "\n",
      "Doing - stacks-sim\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 997]\n",
      "[1163, 1165, 1162, 1154, 1154, 1154, 1153, 1160, 1144, 1144, 1149, 1135]\n",
      "Done - stacks-sim\n",
      "\n",
      "\n",
      "Doing - ddocent-simulated\n",
      "wtf?\n",
      "no data for - ddocent-simulated\n",
      "Done - ddocent-simulated\n",
      "\n",
      "\n",
      "Doing - ipyrad_denovo_plus_reference-sim\n",
      "mean sample coverage - 1000.0\tmin/max - 1000/1000\t\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000]\n",
      "[1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]\n",
      "Done - ipyrad_denovo_plus_reference-sim\n",
      "\n",
      "\n",
      "Doing - ipyrad-reference-empirical\n",
      "mean sample coverage - 42086.5454545\tmin/max - 11632/57548\t\n",
      "[0, 0, 0, 4583, 3594, 2891, 2498, 2377, 2121, 1997, 1997, 1854, 1740, 1743, 1669, 1665, 1673, 1708, 1658, 1692, 1625, 1647, 1806, 1777, 1845, 1757, 1873, 2008, 1958, 2067, 2067, 2188, 2241, 2395, 2309, 2420, 2505, 2453, 2485, 2302, 1762, 1176, 550, 128]\n",
      "[48906, 44535, 19258, 24128, 48757, 47354, 51413, 51102, 17452, 51418, 49376, 36918, 43844, 45688, 41963, 51154, 44456, 50704, 47028, 49916, 49499, 32401, 38001, 40358, 43418, 48760, 24686, 42630, 44362, 57548, 11632, 35946, 51905, 28789, 50550, 47005, 22935, 42131, 36510, 48135, 52287, 46016, 41187, 49747]\n",
      "Done - ipyrad-reference-empirical\n",
      "\n",
      "\n",
      "Doing - ipyrad_denovo_minus_reference-empirical\n",
      "mean sample coverage - 44729.1363636\tmin/max - 12169/66795\t\n",
      "[0, 0, 0, 3475, 2742, 2341, 2149, 1850, 1751, 1620, 1597, 1474, 1368, 1353, 1315, 1345, 1345, 1254, 1259, 1262, 1277, 1347, 1365, 1349, 1428, 1468, 1524, 1579, 1648, 1756, 1878, 1901, 2113, 2292, 2363, 2595, 2882, 3220, 3252, 3325, 3122, 2660, 1770, 668]\n",
      "[52585, 46861, 16552, 22199, 50332, 53355, 55004, 55919, 16871, 56252, 51256, 37338, 44169, 46266, 43945, 54736, 44832, 56661, 50327, 54647, 51706, 33459, 45152, 40692, 47577, 51807, 22323, 50525, 45469, 66795, 12169, 37028, 55712, 27879, 53469, 51871, 24418, 44710, 39742, 49729, 59472, 48047, 46877, 51347]\n",
      "Done - ipyrad_denovo_minus_reference-empirical\n",
      "\n",
      "\n",
      "Doing - ipyrad_denovo_minus_reference-sim\n",
      "mean sample coverage - 500.0\tmin/max - 500/500\t\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500]\n",
      "[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]\n",
      "Done - ipyrad_denovo_minus_reference-sim\n",
      "\n",
      "\n",
      "Doing - ipyrad-reference-sim\n",
      "mean sample coverage - 500.0\tmin/max - 500/500\t\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500]\n",
      "[500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]\n",
      "Done - ipyrad-reference-sim\n",
      "\n",
      "\n",
      "Doing - ipyrad-denovo_reference-sim\n",
      "mean sample coverage - 1000.0\tmin/max - 1000/1000\t\n",
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000]\n",
      "[1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]\n",
      "Done - ipyrad-denovo_reference-sim\n",
      "\n",
      "\n",
      "Doing - ddocent-empirical\n",
      "wtf?\n",
      "no data for - ddocent-empirical\n",
      "Done - ddocent-empirical\n",
      "\n",
      "\n",
      "Doing - ipyrad_denovo_plus_reference-empirical\n",
      "mean sample coverage - 89181.8863636\tmin/max - 24088/128700\t\n",
      "[0, 0, 0, 9569, 7261, 5876, 5220, 4712, 4302, 4016, 3829, 3580, 3355, 3223, 3232, 3234, 3272, 3094, 3073, 3174, 3115, 3149, 3328, 3290, 3360, 3502, 3664, 3594, 3782, 3934, 4103, 4218, 4491, 4726, 4895, 5055, 5399, 5720, 5668, 5479, 4654, 3705, 2230, 848]\n",
      "[104327, 93135, 35643, 46544, 101562, 104417, 109738, 110064, 33754, 110746, 103584, 75765, 89784, 94538, 87661, 109230, 91956, 111073, 99810, 107757, 104609, 67453, 86352, 82809, 93855, 103568, 48108, 95916, 92502, 128700, 24088, 73980, 110872, 57707, 106908, 101847, 47662, 89130, 77879, 100780, 115635, 96817, 91406, 104332]\n",
      "Done - ipyrad_denovo_plus_reference-empirical\n",
      "\n",
      "\n",
      "[('stacks-empirical', [44367, 19212, 12649, 9571, 7961, 6694, 5997, 5625, 5210, 5136, 4765, 4653, 4400, 4499, 4465, 4347, 4215, 4264, 4418, 4501, 4596, 4603, 4712, 4891, 5055, 5402, 5737, 6002, 6479, 6757, 7147, 7807, 8294, 9141, 9957, 10916, 11935, 12668, 14520, 15065, 15006, 13576, 9744, 6215]), ('stacks-sim', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 997]), ('ipyrad_denovo_plus_reference-sim', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000]), ('ipyrad-reference-empirical', [0, 0, 0, 4583, 3594, 2891, 2498, 2377, 2121, 1997, 1997, 1854, 1740, 1743, 1669, 1665, 1673, 1708, 1658, 1692, 1625, 1647, 1806, 1777, 1845, 1757, 1873, 2008, 1958, 2067, 2067, 2188, 2241, 2395, 2309, 2420, 2505, 2453, 2485, 2302, 1762, 1176, 550, 128]), ('ipyrad_denovo_minus_reference-empirical', [0, 0, 0, 3475, 2742, 2341, 2149, 1850, 1751, 1620, 1597, 1474, 1368, 1353, 1315, 1345, 1345, 1254, 1259, 1262, 1277, 1347, 1365, 1349, 1428, 1468, 1524, 1579, 1648, 1756, 1878, 1901, 2113, 2292, 2363, 2595, 2882, 3220, 3252, 3325, 3122, 2660, 1770, 668]), ('ipyrad_denovo_minus_reference-sim', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500]), ('ipyrad-reference-sim', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500]), ('ipyrad-denovo_reference-sim', [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1000]), ('ipyrad_denovo_plus_reference-empirical', [0, 0, 0, 9569, 7261, 5876, 5220, 4712, 4302, 4016, 3829, 3580, 3355, 3223, 3232, 3234, 3272, 3094, 3073, 3174, 3115, 3149, 3328, 3290, 3360, 3502, 3664, 3594, 3782, 3934, 4103, 4218, 4491, 4726, 4895, 5055, 5399, 5720, 5668, 5479, 4654, 3705, 2230, 848])]\n",
      "[('stacks-empirical', [321973, 346480, 312450, 353761, 406143, 333570, 294514, 327705, 342850, 335347, 351034, 330976, 279661, 345149, 331976, 216198, 307288, 327835, 312977, 364989, 231650, 360533, 344603, 288889, 374219, 339563, 251650, 295388, 427019, 315171, 353471, 336082, 343184, 208747, 331309, 352628, 363792, 180628, 318019, 255977, 362677, 310417, 390315, 238011]), ('stacks-sim', [1163, 1165, 1162, 1154, 1154, 1154, 1153, 1160, 1144, 1144, 1149, 1135]), ('ipyrad_denovo_plus_reference-sim', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]), ('ipyrad-reference-empirical', [48906, 44535, 19258, 24128, 48757, 47354, 51413, 51102, 17452, 51418, 49376, 36918, 43844, 45688, 41963, 51154, 44456, 50704, 47028, 49916, 49499, 32401, 38001, 40358, 43418, 48760, 24686, 42630, 44362, 57548, 11632, 35946, 51905, 28789, 50550, 47005, 22935, 42131, 36510, 48135, 52287, 46016, 41187, 49747]), ('ipyrad_denovo_minus_reference-empirical', [52585, 46861, 16552, 22199, 50332, 53355, 55004, 55919, 16871, 56252, 51256, 37338, 44169, 46266, 43945, 54736, 44832, 56661, 50327, 54647, 51706, 33459, 45152, 40692, 47577, 51807, 22323, 50525, 45469, 66795, 12169, 37028, 55712, 27879, 53469, 51871, 24418, 44710, 39742, 49729, 59472, 48047, 46877, 51347]), ('ipyrad_denovo_minus_reference-sim', [500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]), ('ipyrad-reference-sim', [500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500]), ('ipyrad-denovo_reference-sim', [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]), ('ipyrad_denovo_plus_reference-empirical', [104327, 93135, 35643, 46544, 101562, 104417, 109738, 110064, 33754, 110746, 103584, 75765, 89784, 94538, 87661, 109230, 91956, 111073, 99810, 107757, 104609, 67453, 86352, 82809, 93855, 103568, 48108, 95916, 92502, 128700, 24088, 73980, 110872, 57707, 106908, 101847, 47662, 89130, 77879, 100780, 115635, 96817, 91406, 104332])]\n"
     ]
    }
   ],
   "source": [
    "## Blank the ordered dicts for gathering locus coverage and sample nlocs\n",
    "all_full_loc_cov = collections.OrderedDict()\n",
    "all_full_sample_nlocs = collections.OrderedDict()\n",
    "\n",
    "## Mapping between 'pretty names' and directory names. This is only really useful for ipyrad\n",
    "## where i named the directories a little silly. For stacks and ddocent this doesn't really do anything.\n",
    "assembly_methods = {\"ipyrad-reference-sim\":\"refmap-sim\", \"ipyrad-denovo_reference-sim\":\"denovo_ref-sim\",\\\n",
    "                    \"stacks-sim\":\"stacks-sim\", \"ipyrad-reference-empirical\":\"refmap-empirical\",\\\n",
    "                    \"stacks-empirical\":\"stacks-empirical\", \"ddocent-simulated\":\"ddocent-simulated\",\\\n",
    "                    \"ddocent-empirical\":\"ddocent-empirical\",\\\n",
    "                    \"ipyrad_denovo_minus_reference-sim\":\"denovo_minus_reference-sim\",\\\n",
    "                    \"ipyrad_denovo_plus_reference-sim\":\"denovo_plus_reference-sim\",\\\n",
    "                    \"ipyrad_denovo_minus_reference-empirical\":\"denovo_minus_reference\",\\\n",
    "                    \"ipyrad_denovo_plus_reference-empirical\":\"denovo_plus_reference-095\"\n",
    "                   }\n",
    "\n",
    "for name, method in assembly_methods.items():\n",
    "    print(\"Doing - {}\".format(name))\n",
    "    if \"ipyrad\" in name:\n",
    "        outdir = IPYRAD_SIM_DIR\n",
    "        firstsamp = 20\n",
    "        lastsamp = 32\n",
    "        if \"empirical\" in name:\n",
    "            outdir = IPYRAD_REFMAP_DIR + \"reference-assembly/\"\n",
    "            firstsamp = 20 #fixme\n",
    "            lastsamp = 64 # fixme\n",
    "        try:\n",
    "            nsamps = lastsamp - firstsamp\n",
    "            simdir = os.path.join(outdir, method + \"_outfiles/\")\n",
    "            statsfile = simdir + \"{}_stats.txt\".format(method)\n",
    "            infile = open(statsfile).readlines()\n",
    "            sample_coverage = [int(x.strip().split()[1]) for x in infile[firstsamp:lastsamp]]\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",
    "            all_full_sample_nlocs[name] = sample_coverage\n",
    "\n",
    "            nmissing = [int(x.strip().split()[1]) for x in infile[lastsamp+6:lastsamp+6+nsamps]]\n",
    "            all_full_loc_cov[name] = nmissing\n",
    "        except Exception as inst:\n",
    "            print(inst)\n",
    "    elif \"stacks\" in name:\n",
    "        outdir = STACKS_SIM_DIR\n",
    "        nsamps = 12\n",
    "        if \"empirical\" in name:\n",
    "            outdir = STACKS_REFMAP_DIR\n",
    "            nsamps = 44\n",
    "        try:\n",
    "            ## Effectively the same as thisjjjj\n",
    "            ## cut -f 2 batch_1.haplotypes.tsv | sort -n | uniq -c | less\n",
    "            lines = open(\"{}/batch_1.haplotypes.tsv\".format(outdir)).readlines()\n",
    "            cnts = [int(field.strip().split(\"\\t\")[1]) for field in lines[1:]]\n",
    "            all_full_loc_cov[method] = [cnts.count(i) for i in range(1,nsamps+1)]\n",
    "        except Exception as inst:\n",
    "            print(\"loc_cov - {} - {}\".format(inst, simdir))\n",
    "\n",
    "        try:\n",
    "            all_full_sample_nlocs[method] = []\n",
    "            ## This is actually number of loci\n",
    "            ## cut -f 3 *matches | sort -n | uniq | wc -l\n",
    "            \n",
    "            ## Right now this is actually counting number of snps\n",
    "            samp_haps = glob.glob(\"{}/*matches*\".format(outdir))\n",
    "            for f in samp_haps:\n",
    "                lines = gzip.open(f).readlines()\n",
    "                all_full_sample_nlocs[method].append(len(lines) - 1)\n",
    "        except Exception as inst:\n",
    "            print(\"sample_nlocs - {} - {}\".format(inst, outdir))\n",
    "#    elif \"ddocent\" in name:\n",
    "#        outdir = DDOCENT_SIM_DIR\n",
    "#        if \"empirical\" in name:\n",
    "#            outdir = DDOCENT_REFMAP_DIR\n",
    "#        loc_cov, sample_nlocs = ddocent_stats(outdir)\n",
    "#        all_full_loc_cov[method] = loc_cov\n",
    "#        all_full_sample_nlocs[method] = sample_nlocs\n",
    "    else:\n",
    "        print(\"wtf?\")\n",
    "    try:\n",
    "        print(all_full_loc_cov[name])\n",
    "        print(all_full_sample_nlocs[name])\n",
    "    except:\n",
    "        print(\"no data for - {}\".format(name))\n",
    "        pass\n",
    "    print(\"Done - {}\\n\\n\".format(name))\n",
    "print(all_full_loc_cov.items())\n",
    "print(all_full_sample_nlocs.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get pairwise fst for each population for simulated and empirical\n",
    "Using vcftools just to get the mean fst between each population pair."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing - ddocent-fin-sim\n",
      "('pop3-pop2', '0.17486/0.4497') ('pop3-pop1', '0.17204/0.45056') ('pop2-pop1', '0.12088/0.33013') \n",
      "\n",
      "Doing - stacks-empirical\n",
      "('KB1-SK1', '-0.01716/0.0033872') ('KB1-WBS', '0.076575/0.19859') ('KB1-IS', '-0.018957/0.01089') ('KB1-BES2', '-0.012275/0.0067543') ('KB1-IBS', '-0.014075/0.0045256') ('KB1-NOS', '-0.010345/0.014532') ('SK1-WBS', '0.081251/0.19717') ('SK1-IS', '-0.02681/0.0029578') ('SK1-BES2', '-0.0074286/0.013508') ('SK1-IBS', '-0.012173/0.008697') ('SK1-NOS', '-0.018733/0.0054864') ('WBS-IS', '0.11216/0.21698') ('WBS-BES2', '0.059879/0.19404') ('WBS-IBS', '0.059164/0.18995') ('WBS-NOS', '0.086069/0.20544') ('IS-BES2', '-0.0093259/0.026361') ('IS-IBS', '-0.016882/0.01746') ('IS-NOS', '-0.02235/0.0066823') ('BES2-IBS', '-0.0087638/0.0076244') ('BES2-NOS', '-0.0032056/0.024965') ('IBS-NOS', '-0.0087328/0.018391') \n",
      "\n",
      "Doing - ipyrad-denovo_minus_reference-sim\n",
      "('pop3-pop2', '0.1668/0.4316') ('pop3-pop1', '0.17018/0.44182') ('pop2-pop1', '0.12268/0.32948') \n",
      "\n",
      "Doing - ipyrad-denovo_plus_reference-095-empirical\n",
      "('KB1-SK1', '-0.023798/0.003303') ('KB1-WBS', '0.077415/0.23887') ('KB1-IS', '-0.025438/0.013112') ('KB1-BES2', '-0.01869/0.007567') ('KB1-IBS', '-0.019823/0.0054943') ('KB1-NOS', '-0.015872/0.017019') ('SK1-WBS', '0.083859/0.23675') ('SK1-IS', '-0.03404/0.0021198') ('SK1-BES2', '-0.013266/0.017269') ('SK1-IBS', '-0.017577/0.010677') ('SK1-NOS', '-0.025239/0.0049364') ('WBS-IS', '0.12264/0.26421') ('WBS-BES2', '0.05149/0.22469') ('WBS-IBS', '0.055395/0.22431') ('WBS-NOS', '0.089508/0.24784') ('IS-BES2', '-0.018112/0.029512') ('IS-IBS', '-0.023608/0.020038') ('IS-NOS', '-0.027994/0.0095543') ('BES2-IBS', '-0.014136/0.0096868') ('BES2-NOS', '-0.0078227/0.030793') ('IBS-NOS', '-0.01415/0.020284') \n",
      "\n",
      "Doing - ipyrad-denovo_plus_reference-empirical\n",
      "Failed - ipyrad-denovo_plus_reference-empirical - list index out of range\n",
      "Doing - stacks-sim\n",
      "('pop3-pop2', '0.17523/0.45058') ('pop3-pop1', '0.17249/0.45133') ('pop2-pop1', '0.12079/0.33051') \n",
      "\n",
      "Doing - ipyrad-reference-empirical\n",
      "('KB1-SK1', '-0.024338/0.0068613') ('KB1-WBS', '0.061695/0.21921') ('KB1-IS', '-0.029979/0.014001') ('KB1-BES2', '-0.022308/0.0044536') ('KB1-IBS', '-0.023808/0.0026994') ('KB1-NOS', '-0.015705/0.02254') ('SK1-WBS', '0.072674/0.22078') ('SK1-IS', '-0.03403/0.0091718') ('SK1-BES2', '-0.018931/0.01958') ('SK1-IBS', '-0.022596/0.012753') ('SK1-NOS', '-0.025798/0.010899') ('WBS-IS', '0.10708/0.24609') ('WBS-BES2', '0.040545/0.21383') ('WBS-IBS', '0.040374/0.20428') ('WBS-NOS', '0.074959/0.23353') ('IS-BES2', '-0.024111/0.031663') ('IS-IBS', '-0.031303/0.017557') ('IS-NOS', '-0.032434/0.0094771') ('BES2-IBS', '-0.017717/0.011511') ('BES2-NOS', '-0.011882/0.031635') ('IBS-NOS', '-0.017731/0.020207') \n",
      "\n",
      "Doing - ipyrad-denovo_minus_reference-empirical\n",
      "('KB1-SK1', '-0.024644/0.0036036') ('KB1-WBS', '0.089713/0.25488') ('KB1-IS', '-0.023546/0.014123') ('KB1-BES2', '-0.022548/0.0026067') ('KB1-IBS', '-0.022381/0.0043561') ('KB1-NOS', '-0.017197/0.014578') ('SK1-WBS', '0.088756/0.24235') ('SK1-IS', '-0.033035/0.0047607') ('SK1-BES2', '-0.01184/0.019395') ('SK1-IBS', '-0.015345/0.014307') ('SK1-NOS', '-0.025557/0.0073795') ('WBS-IS', '0.12954/0.27219') ('WBS-BES2', '0.056838/0.22986') ('WBS-IBS', '0.06109/0.23065') ('WBS-NOS', '0.099081/0.25648') ('IS-BES2', '-0.016472/0.032934') ('IS-IBS', '-0.021272/0.026445') ('IS-NOS', '-0.027781/0.0079985') ('BES2-IBS', '-0.015618/0.010091') ('BES2-NOS', '-0.010779/0.027077') ('IBS-NOS', '-0.014946/0.021999') \n",
      "\n",
      "Doing - ddocent-fin-empirical\n",
      "('KB1-SK1', '-0.001962/0.0029912') ('KB1-WBS', '0.085547/0.19715') ('KB1-IS', '-0.0017929/0.0090684') ('KB1-BES2', '0.00027415/0.0029637') ('KB1-IBS', '5.1773e-05/0.0033916') ('KB1-NOS', '0.0038877/0.011107') ('SK1-WBS', '0.089487/0.19613') ('SK1-IS', '-0.0076227/0.0021813') ('SK1-BES2', '0.0073208/0.01205') ('SK1-IBS', '0.0026361/0.0062348') ('SK1-NOS', '-0.00049069/0.0053676') ('WBS-IS', '0.12857/0.2216') ('WBS-BES2', '0.073132/0.19152') ('WBS-IBS', '0.067364/0.18469') ('WBS-NOS', '0.083301/0.19202') ('IS-BES2', '0.0085377/0.019996') ('IS-IBS', '0.0012964/0.011622') ('IS-NOS', '-0.0037559/0.0062206') ('BES2-IBS', '0.0014986/0.0040149') ('BES2-NOS', '0.011913/0.019716') ('IBS-NOS', '0.0082616/0.015119') \n",
      "\n",
      "Doing - ddocent-tot-sim\n",
      "('pop3-pop2', '0.17516/0.45038') ('pop3-pop1', '0.17211/0.45061') ('pop2-pop1', '0.12077/0.3301') \n",
      "\n",
      "Doing - ipyrad-reference-sim\n",
      "('pop3-pop2', '0.17509/0.4503') ('pop3-pop1', '0.17189/0.45029') ('pop2-pop1', '0.12032/0.32919') \n",
      "\n",
      "Doing - ipyrad-denovo_plus_reference-sim\n",
      "('pop3-pop2', '0.17095/0.44106') ('pop3-pop1', '0.17102/0.44598') ('pop2-pop1', '0.12149/0.32933') \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "## dict for storing fst values. This is a dict of dictionaries. The top level is for\n",
    "## each assembly type and within each assembly type is a dict of pop pairs and the fst between them.\n",
    "fsts = {}\n",
    "\n",
    "## Get pairwise fst for each pop for sim and empirical\n",
    "d = {REFMAP_SIM_DIR:sim_pops, REFMAP_EMPIRICAL_DIR:emp_pops}\n",
    "for k, v in vcf_dict.items():\n",
    "    if not os.path.exists(v):\n",
    "        continue\n",
    "    if \"sim\" in k:\n",
    "        indir = REFMAP_SIM_DIR\n",
    "        pop_dict = sim_pops\n",
    "    else:\n",
    "        indir = REFMAP_EMPIRICAL_DIR\n",
    "        pop_dict = emp_pops\n",
    "    os.chdir(indir)\n",
    "\n",
    "    try:\n",
    "        print(\"Doing - {}\".format(k))\n",
    "        fsts[k] = {}\n",
    "        combinations = list(itertools.combinations(pop_dict, 2))\n",
    "        for pair in combinations:\n",
    "            pop1 = indir + pair[0] + \".txt\"\n",
    "            pop2 = indir + pair[1] + \".txt\"\n",
    "            vcftools_cmd = DDOCENT_DIR + \"vcftools\"\n",
    "            cmd = \"{} --vcf {} --weir-fst-pop {} --weir-fst-pop {} --out {}\".format(vcftools_cmd, v, pop1, pop2, k)\n",
    "            ret = subprocess.check_output(cmd, shell=True)\n",
    "            ret = ret.split(\"\\n\")\n",
    "            ## Get the mean fst from the output\n",
    "            fst = [x for x in ret if \"Weir and Cockerham mean\" in x][0].split(\": \")[1]\n",
    "            weighted_fst = [x for x in ret if \"Weir and Cockerham weighted\" in x][0].split(\": \")[1]\n",
    "            print(\"-\".join(pair), \"{}/{}\".format(fst, weighted_fst)), \n",
    "            fsts[k][\"-\".join(pair)] = \"{}/{}\".format(fst, weighted_fst)\n",
    "        print(\"\\n\")\n",
    "    except Exception as inst:\n",
    "        print(\"Failed - {} - {}\".format(k, inst))\n",
    "        ## Allow it to fail if the vcf doesn't exist.\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ddocent-fin-sim\n",
      "stacks-empirical\n",
      "ipyrad-reference-sim\n",
      "ipyrad-denovo_minus_reference-sim\n",
      "ipyrad-denovo_plus_reference-095-empirical\n",
      "ipyrad-denovo_plus_reference-empirical\n",
      "ipyrad-reference-empirical\n",
      "ipyrad-denovo_minus_reference-empirical\n",
      "ddocent-fin-empirical\n",
      "ddocent-tot-sim\n",
      "stacks-sim\n",
      "ipyrad-denovo_plus_reference-sim\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop3</th>\n",
       "      <th>pop2</th>\n",
       "      <th>pop1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pop3</th>\n",
       "      <td></td>\n",
       "      <td>0.4497 0.4503 0.4316 0.4503 0.4505 0.4410</td>\n",
       "      <td>0.4505 0.4502 0.4418 0.4506 0.4513 0.4459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop2</th>\n",
       "      <td>0.1748 0.1750 0.1668 0.1751 0.1752 0.1709</td>\n",
       "      <td></td>\n",
       "      <td>0.3301 0.3291 0.3294 0.3301 0.3305 0.3293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pop1</th>\n",
       "      <td>0.1720 0.1718 0.1701 0.1721 0.1724 0.1710</td>\n",
       "      <td>0.1208 0.1203 0.1226 0.1207 0.1207 0.1214</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            pop3  \\\n",
       "pop3                                               \n",
       "pop2   0.1748 0.1750 0.1668 0.1751 0.1752 0.1709   \n",
       "pop1   0.1720 0.1718 0.1701 0.1721 0.1724 0.1710   \n",
       "\n",
       "                                            pop2  \\\n",
       "pop3   0.4497 0.4503 0.4316 0.4503 0.4505 0.4410   \n",
       "pop2                                               \n",
       "pop1   0.1208 0.1203 0.1226 0.1207 0.1207 0.1214   \n",
       "\n",
       "                                            pop1  \n",
       "pop3   0.4505 0.4502 0.4418 0.4506 0.4513 0.4459  \n",
       "pop2   0.3301 0.3291 0.3294 0.3301 0.3305 0.3293  \n",
       "pop1                                              "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>KB1</th>\n",
       "      <th>SK1</th>\n",
       "      <th>WBS</th>\n",
       "      <th>IS</th>\n",
       "      <th>BES2</th>\n",
       "      <th>IBS</th>\n",
       "      <th>NOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KB1</th>\n",
       "      <td></td>\n",
       "      <td>0.0033 0.0033 0.0068 0.0036 0.0029</td>\n",
       "      <td>0.1985 0.2388 0.2192 0.2548 0.1971</td>\n",
       "      <td>0.0108 0.0131 0.0140 0.0141 0.0090</td>\n",
       "      <td>0.0067 0.0075 0.0044 0.0026 0.0029</td>\n",
       "      <td>0.0045 0.0054 0.0026 0.0043 0.0033</td>\n",
       "      <td>0.0145 0.0170 0.0225 0.0145 0.0111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK1</th>\n",
       "      <td>-0.017 -0.023 -0.024 -0.024 -0.001</td>\n",
       "      <td></td>\n",
       "      <td>0.1971 0.2367 0.2207 0.2423 0.1961</td>\n",
       "      <td>0.0029 0.0021 0.0091 0.0047 0.0021</td>\n",
       "      <td>0.0135 0.0172 0.0195 0.0193 0.0120</td>\n",
       "      <td>0.0086 0.0106 0.0127 0.0143 0.0062</td>\n",
       "      <td>0.0054 0.0049 0.0108 0.0073 0.0053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBS</th>\n",
       "      <td>0.0765 0.0774 0.0616 0.0897 0.0855</td>\n",
       "      <td>0.0812 0.0838 0.0726 0.0887 0.0894</td>\n",
       "      <td></td>\n",
       "      <td>0.2169 0.2642 0.2460 0.2721 0.2216</td>\n",
       "      <td>0.1940 0.2246 0.2138 0.2298 0.1915</td>\n",
       "      <td>0.1899 0.2243 0.2042 0.2306 0.1846</td>\n",
       "      <td>0.2054 0.2478 0.2335 0.2564 0.1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS</th>\n",
       "      <td>-0.018 -0.025 -0.029 -0.023 -0.001</td>\n",
       "      <td>-0.026 -0.034 -0.034 -0.033 -0.007</td>\n",
       "      <td>0.1121 0.1226 0.1070 0.1295 0.1285</td>\n",
       "      <td></td>\n",
       "      <td>0.0263 0.0295 0.0316 0.0329 0.0199</td>\n",
       "      <td>0.0174 0.0200 0.0175 0.0264 0.0116</td>\n",
       "      <td>0.0066 0.0095 0.0094 0.0079 0.0062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BES2</th>\n",
       "      <td>-0.012 -0.018 -0.022 -0.022 0.0002</td>\n",
       "      <td>-0.007 -0.013 -0.018 -0.011 0.0073</td>\n",
       "      <td>0.0598 0.0514 0.0405 0.0568 0.0731</td>\n",
       "      <td>-0.009 -0.018 -0.024 -0.016 0.0085</td>\n",
       "      <td></td>\n",
       "      <td>0.0076 0.0096 0.0115 0.0100 0.0040</td>\n",
       "      <td>0.0249 0.0307 0.0316 0.0270 0.0197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBS</th>\n",
       "      <td>-0.014 -0.019 -0.023 -0.022 5.1773</td>\n",
       "      <td>-0.012 -0.017 -0.022 -0.015 0.0026</td>\n",
       "      <td>0.0591 0.0553 0.0403 0.0610 0.0673</td>\n",
       "      <td>-0.016 -0.023 -0.031 -0.021 0.0012</td>\n",
       "      <td>-0.008 -0.014 -0.017 -0.015 0.0014</td>\n",
       "      <td></td>\n",
       "      <td>0.0183 0.0202 0.0202 0.0219 0.0151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOS</th>\n",
       "      <td>-0.010 -0.015 -0.015 -0.017 0.0038</td>\n",
       "      <td>-0.018 -0.025 -0.025 -0.025 -0.000</td>\n",
       "      <td>0.0860 0.0895 0.0749 0.0990 0.0833</td>\n",
       "      <td>-0.022 -0.027 -0.032 -0.027 -0.003</td>\n",
       "      <td>-0.003 -0.007 -0.011 -0.010 0.0119</td>\n",
       "      <td>-0.008 -0.014 -0.017 -0.014 0.0082</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      KB1  \\\n",
       "KB1                                         \n",
       "SK1    -0.017 -0.023 -0.024 -0.024 -0.001   \n",
       "WBS    0.0765 0.0774 0.0616 0.0897 0.0855   \n",
       "IS     -0.018 -0.025 -0.029 -0.023 -0.001   \n",
       "BES2   -0.012 -0.018 -0.022 -0.022 0.0002   \n",
       "IBS    -0.014 -0.019 -0.023 -0.022 5.1773   \n",
       "NOS    -0.010 -0.015 -0.015 -0.017 0.0038   \n",
       "\n",
       "                                      SK1  \\\n",
       "KB1    0.0033 0.0033 0.0068 0.0036 0.0029   \n",
       "SK1                                         \n",
       "WBS    0.0812 0.0838 0.0726 0.0887 0.0894   \n",
       "IS     -0.026 -0.034 -0.034 -0.033 -0.007   \n",
       "BES2   -0.007 -0.013 -0.018 -0.011 0.0073   \n",
       "IBS    -0.012 -0.017 -0.022 -0.015 0.0026   \n",
       "NOS    -0.018 -0.025 -0.025 -0.025 -0.000   \n",
       "\n",
       "                                      WBS  \\\n",
       "KB1    0.1985 0.2388 0.2192 0.2548 0.1971   \n",
       "SK1    0.1971 0.2367 0.2207 0.2423 0.1961   \n",
       "WBS                                         \n",
       "IS     0.1121 0.1226 0.1070 0.1295 0.1285   \n",
       "BES2   0.0598 0.0514 0.0405 0.0568 0.0731   \n",
       "IBS    0.0591 0.0553 0.0403 0.0610 0.0673   \n",
       "NOS    0.0860 0.0895 0.0749 0.0990 0.0833   \n",
       "\n",
       "                                       IS  \\\n",
       "KB1    0.0108 0.0131 0.0140 0.0141 0.0090   \n",
       "SK1    0.0029 0.0021 0.0091 0.0047 0.0021   \n",
       "WBS    0.2169 0.2642 0.2460 0.2721 0.2216   \n",
       "IS                                          \n",
       "BES2   -0.009 -0.018 -0.024 -0.016 0.0085   \n",
       "IBS    -0.016 -0.023 -0.031 -0.021 0.0012   \n",
       "NOS    -0.022 -0.027 -0.032 -0.027 -0.003   \n",
       "\n",
       "                                     BES2  \\\n",
       "KB1    0.0067 0.0075 0.0044 0.0026 0.0029   \n",
       "SK1    0.0135 0.0172 0.0195 0.0193 0.0120   \n",
       "WBS    0.1940 0.2246 0.2138 0.2298 0.1915   \n",
       "IS     0.0263 0.0295 0.0316 0.0329 0.0199   \n",
       "BES2                                        \n",
       "IBS    -0.008 -0.014 -0.017 -0.015 0.0014   \n",
       "NOS    -0.003 -0.007 -0.011 -0.010 0.0119   \n",
       "\n",
       "                                      IBS                                  NOS  \n",
       "KB1    0.0045 0.0054 0.0026 0.0043 0.0033   0.0145 0.0170 0.0225 0.0145 0.0111  \n",
       "SK1    0.0086 0.0106 0.0127 0.0143 0.0062   0.0054 0.0049 0.0108 0.0073 0.0053  \n",
       "WBS    0.1899 0.2243 0.2042 0.2306 0.1846   0.2054 0.2478 0.2335 0.2564 0.1920  \n",
       "IS     0.0174 0.0200 0.0175 0.0264 0.0116   0.0066 0.0095 0.0094 0.0079 0.0062  \n",
       "BES2   0.0076 0.0096 0.0115 0.0100 0.0040   0.0249 0.0307 0.0316 0.0270 0.0197  \n",
       "IBS                                         0.0183 0.0202 0.0202 0.0219 0.0151  \n",
       "NOS    -0.008 -0.014 -0.017 -0.014 0.0082                                       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "import pandas as pd\n",
    "\n",
    "## Organize and pretty print the fsts dict\n",
    "\n",
    "df_sim = pd.DataFrame(index=sim_pops.keys(), columns=sim_pops.keys(), dtype=str).fillna(\"\")\n",
    "df_emp = pd.DataFrame(index=emp_pops.keys(), columns=emp_pops.keys(), dtype=str).fillna(\"\")\n",
    "for assembler, all_data in fsts.items():\n",
    "    print(\"{}\".format(assembler))\n",
    "    if \"sim\" in assembler:\n",
    "        indir = REFMAP_SIM_DIR\n",
    "        pop_dict = sim_pops\n",
    "        df = df_sim\n",
    "    else:\n",
    "        indir = REFMAP_EMPIRICAL_DIR\n",
    "        pop_dict = emp_pops\n",
    "        df = df_emp\n",
    "    ## Init the diagonal\n",
    "    for p in pop_dict.keys():\n",
    "        df[p][p] = \"\"\n",
    "    for p, fst_data in all_data.items():\n",
    "        #print(df)\n",
    "        p1 = p.split(\"-\")[0]\n",
    "        p2 = p.split(\"-\")[1]\n",
    "        df[p1][p2] += \" \"+fst_data.split(\"/\")[0][:6]\n",
    "        df[p2][p1] += \" \"+fst_data.split(\"/\")[1][:6]\n",
    "pd.set_option('display.max_colwidth',800)\n",
    "pd.set_option('display.width',80)\n",
    "df_sim.style.set_properties(**{'max-width':'10px'})\n",
    "display(df_sim)\n",
    "display(df_emp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick and dirty raxml trees\n",
    "ipyrad and stacks both kindly write out phylip files, but ddocent doesn't, so we'll have to make one ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm -f *.o raxmlHPC-PTHREADS-AVX2\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o axml.o axml.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o optimizeModel.o optimizeModel.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o multiple.o multiple.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o searchAlgo.o searchAlgo.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o topologies.o topologies.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o parsePartitions.o parsePartitions.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o treeIO.o treeIO.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o models.o models.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o bipartitionList.o bipartitionList.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o rapidBootstrap.o rapidBootstrap.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o evaluatePartialGenericSpecial.o evaluatePartialGenericSpecial.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o evaluateGenericSpecial.o evaluateGenericSpecial.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o newviewGenericSpecial.o newviewGenericSpecial.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o makenewzGenericSpecial.o makenewzGenericSpecial.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o classify.o classify.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX  -mavx -c -o fastDNAparsimony.o fastDNAparsimony.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o fastSearch.o fastSearch.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o leaveDropping.o leaveDropping.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o rmqs.o rmqs.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o rogueEPA.o rogueEPA.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o ancestralStates.o ancestralStates.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX  -mavx2 -D_FMA -march=core-avx2 -c -o avxLikelihood.o avxLikelihood.c\n",
      "gcc  -D_USE_PTHREADS  -D__SIM_SSE3 -O2 -D_GNU_SOURCE -msse3 -fomit-frame-pointer -funroll-loops  -D__AVX    -c -o mem_alloc.o mem_alloc.c\n",
      "gcc  -c -o eigen.o eigen.c \n",
      "gcc  -o raxmlHPC-PTHREADS-AVX2 axml.o  optimizeModel.o multiple.o searchAlgo.o topologies.o parsePartitions.o treeIO.o models.o bipartitionList.o rapidBootstrap.o evaluatePartialGenericSpecial.o evaluateGenericSpecial.o newviewGenericSpecial.o makenewzGenericSpecial.o   classify.o fastDNAparsimony.o fastSearch.o leaveDropping.o rmqs.o rogueEPA.o ancestralStates.o avxLikelihood.o  mem_alloc.o eigen.o   -lm -pthread   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘/home/iovercast/manuscript-analysis//miniconda/src’: File exists\n",
      "fatal: destination path 'standard-RAxML' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "%%bash -s \"$WORK_DIR\"\n",
    "## Install raxml\n",
    "mkdir $1/miniconda/src\n",
    "cd $1/miniconda/src\n",
    "git clone https://github.com/stamatak/standard-RAxML.git\n",
    "cd standard-RAxML\n",
    "make -f Makefile.PTHREADS.gcc\n",
    "cp raxml-PTHREADS $1/miniconda/bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1\n",
      "Loci file not provided. Converting only variable sites in VCF.\n",
      "\n",
      "/home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/Final.recode.snps.vcf.recode.reordered.phy\n",
      "Loci file not provided. Converting only variable sites in VCF.\n",
      "\n",
      "/home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.phy\n",
      "Loci file not provided. Converting only variable sites in VCF.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Get this script to convert from vcf to phy\n",
    "## This script wants python 3, so I had to go in and add the future import for\n",
    "## print by hand. Add this on line 17:\n",
    "##\n",
    "## from __future__ import print_function\n",
    "##\n",
    "## Also, had to update lines 32-34 like so:\n",
    "##\n",
    "## iupac = {\"AG\": \"R\", \"CT\": \"Y\", \"CG\": \"S\", \"AT\": \"W\", \"GT\": \"K\", \"AC\": \"M\",\n",
    "##          \"CGT\": \"B\", \"AGT\": \"D\", \"ACT\": \"H\", \"ACG\": \"V\", \"ACGT\": \"N\", \"AA\": \"A\",\n",
    "##          \"CC\": \"C\", \"GG\": \"G\", \"TT\":\"T\", \"GN\":\"N\", \"CN\":\"N\", \"AN\":\"N\", \"TN\":\"N\"}\n",
    "##          \"NT\":\"N\", \"NC\":\"N\", \"NG\":\"N\", \"NA\":\"N\", \"NN\":\"N\"}\n",
    "## Also, the when we decompose the ddocent complex genotypes vcftools splits\n",
    "## ./. data as '.', which VCF2phy.py kinda hates, so you have to add this piece\n",
    "## of code on lines 75/75:\n",
    "##\n",
    "##    if genotype_string == \".\":\n",
    "##        return \"N\"\n",
    "#!wget https://raw.githubusercontent.com/CoBiG2/RAD_Tools/master/VCF2phy.py\n",
    "\n",
    "for k, v in vcf_dict.items():\n",
    "    if \"stacks\" in k and \"sim\" in k:\n",
    "        outphy = v.rsplit(\".\", 1)[0]\n",
    "        print(outphy)\n",
    "        cmd = \"python VCF2phy.py -vcf {} -o {}\".format(v, outphy)\n",
    "        ret = subprocess.check_output(cmd, shell=True)\n",
    "        print(ret)\n",
    "    if \"ddocent\" in k:\n",
    "        ## Skip doing the big boy for now\n",
    "        if \"empirical\" in k and \"Total\" in v:\n",
    "            continue\n",
    "        ## Do not add .phy to outphy, the python script does it for us.\n",
    "        outphy = v.rsplit(\".\", 1)[0]\n",
    "        print(outphy)\n",
    "        cmd = \"python VCF2phy.py -vcf {} -o {}\".format(v, outphy)\n",
    "        ret = subprocess.check_output(cmd, shell=True)\n",
    "        print(ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run raxml\n",
    "For simulated datasets raxml runs very fast (<20 seconds per phylip file).\n",
    "\n",
    "For empirical it's much slower, maybe 1-2 days per assembler. Be sure to use the '.snps.phy' ipyrad file\n",
    "or else raxml runs _forever_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Can't find .phy or .phylip for - /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.snps.vcf.recode\n",
      "\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 2  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ddocent-fin-sim  -w /home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir  -s /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/Final.recode.snps.vcf.recode.phy\n",
      "ddocent-fin-sim\n",
      "\n",
      "Can't find .phy or .phylip for - /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1\n",
      "\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 20  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n stacks-empirical  -w /home/iovercast/manuscript-analysis/Phocoena_empirical/raxml_outdir  -s /home/iovercast/manuscript-analysis/Phocoena_empirical/stacks/batch_1.phylip\n",
      "stacks-empirical\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 2  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-denovo_minus_reference-sim  -w /home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir  -s /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_minus_reference-sim_outfiles/denovo_minus_reference-sim.snps.phy\n",
      "ipyrad-denovo_minus_reference-sim\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 20  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-denovo_plus_reference-095-empirical  -w /home/iovercast/manuscript-analysis/Phocoena_empirical/raxml_outdir  -s /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference-095_outfiles/denovo_plus_reference-095.snps.phy\n",
      "ipyrad-denovo_plus_reference-095-empirical\n",
      "\n",
      "Can't find .phy or .phylip for - /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.snps\n",
      "\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 20  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-denovo_plus_reference-empirical  -w /home/iovercast/manuscript-analysis/Phocoena_empirical/raxml_outdir  -s /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_plus_reference_outfiles/denovo_ref-empirical.snps.phy\n",
      "ipyrad-denovo_plus_reference-empirical\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 2  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n stacks-sim  -w /home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir  -s /home/iovercast/manuscript-analysis/REFMAP_SIM/stacks/batch_1.phylip\n",
      "stacks-sim\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 20  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-reference-empirical  -w /home/iovercast/manuscript-analysis/Phocoena_empirical/raxml_outdir  -s /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/refmap-empirical_outfiles/refmap-empirical.snps.phy\n",
      "ipyrad-reference-empirical\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 20  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-denovo_minus_reference-empirical  -w /home/iovercast/manuscript-analysis/Phocoena_empirical/raxml_outdir  -s /home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_minus_reference_outfiles/denovo_ref-empirical.snps.phy\n",
      "ipyrad-denovo_minus_reference-empirical\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 20  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ddocent-fin-empirical  -w /home/iovercast/manuscript-analysis/Phocoena_empirical/raxml_outdir  -s /home/iovercast/manuscript-analysis/Phocoena_empirical/ddocent/Final.recode.snps.vcf.recode.reordered.phy\n",
      "ddocent-fin-empirical\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 2  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ddocent-tot-sim  -w /home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir  -s /home/iovercast/manuscript-analysis/REFMAP_SIM/ddocent/TotalRawSNPs.snps.vcf.recode.phy\n",
      "ddocent-tot-sim\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 2  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-reference-sim  -w /home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir  -s /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/refmap-sim_outfiles/refmap-sim.snps.phy\n",
      "ipyrad-reference-sim\n",
      "/home/iovercast/manuscript-analysis//miniconda/bin/raxmlHPC-PTHREADS -f a  -T 2  -m GTRGAMMA  -N 100  -x 12345  -p 54321  -n ipyrad-denovo_plus_reference-sim  -w /home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir  -s /home/iovercast/manuscript-analysis/REFMAP_SIM/ipyrad/reference-assembly/denovo_plus_reference-sim_outfiles/denovo_plus_reference-sim.snps.phy\n",
      "ipyrad-denovo_plus_reference-sim\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for d in [REFMAP_SIM_DIR, REFMAP_EMPIRICAL_DIR]:\n",
    "    raxoutdir = d + \"raxml_outdir\"\n",
    "    if not os.path.exists(raxoutdir):\n",
    "        os.mkdir(raxoutdir)\n",
    "emp_trees = {}\n",
    "sim_trees = {}\n",
    "for assembler, vcffile in vcf_dict.items():\n",
    "    if \"sim\" in assembler:\n",
    "        outdir = REFMAP_SIM_DIR + \"raxml_outdir\"\n",
    "        ncores = 2\n",
    "        out_trees = sim_trees\n",
    "        physplit = 1\n",
    "    else:\n",
    "        outdir = REFMAP_EMPIRICAL_DIR + \"raxml_outdir\"\n",
    "        ncores = 20\n",
    "        out_trees = emp_trees\n",
    "        physplit = 2\n",
    "    if \"ipyrad\" in assembler:\n",
    "        inputfile = vcffile.rsplit(\".\", physplit)[0] + \".snps.phy\"\n",
    "    if \"stacks\" in assembler:\n",
    "        inputfile = vcffile.rsplit(\".\", 2)[0] + \".phylip\"        \n",
    "    if \"ddocent\" in assembler:\n",
    "        inputfile = vcffile.rsplit(\".\", 1)[0] + \".phy\"\n",
    "    if not os.path.exists(inputfile):\n",
    "        print(\"\\nCan't find .phy or .phylip for - {}\\n\".format(inputfile.rsplit(\".\", 1)[0]))\n",
    "    cmd = \"{}raxmlHPC-PTHREADS -f a \".format(WORK_DIR + \"/miniconda/bin/\") \\\n",
    "            + \" -T {} \".format(ncores) \\\n",
    "            + \" -m GTRGAMMA \" \\\n",
    "            + \" -N 100 \" \\\n",
    "            + \" -x 12345 \" \\\n",
    "            + \" -p 54321 \" \\\n",
    "            + \" -n {} \".format(assembler) \\\n",
    "            + \" -w {} \".format(outdir) \\\n",
    "            + \" -s {}\".format(inputfile)\n",
    "    print(cmd)\n",
    "    ## What's the difference?\n",
    "    ## out_trees[assembler] = \"{}/RAxML_bestTree.{}\".format(outdir, assembler)\n",
    "    out_trees[assembler] = \"{}/RAxML_bipartitions.{}\".format(outdir, assembler)\n",
    "    #continue\n",
    "    if \"sim\" in assembler:\n",
    "        print(assembler)\n",
    "        #!time $cmd\n",
    "    if \"empirical\" in assembler:\n",
    "        print(assembler)\n",
    "        #!time $cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('ddocent-fin-sim', '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ddocent-fin-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba1b1d890>)\n",
      "('ipyrad-denovo_minus_reference-sim', '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ipyrad-denovo_minus_reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9fa5110>)\n",
      "('stacks-sim', '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.stacks-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aaba9ec22d0>)\n",
      "('ipyrad-reference-sim', '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ipyrad-reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa229910>)\n",
      "('ipyrad-denovo_plus_reference-sim', '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ipyrad-denovo_plus_reference-sim', <matplotlib.axes._subplots.AxesSubplot object at 0x2aabaa14be10>)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB7YAAAMRCAYAAABoOllMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaba3ed5d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!conda install biopython\n",
    "from Bio import Phylo\n",
    "\n",
    "f, axarr = plt.subplots(1, 5, figsize=(20,8), dpi=1000)\n",
    "\n",
    "## For more than 1 row uncomment this\n",
    "#axarr = [a for b in axarr for a in b]\n",
    "\n",
    "## Pop membership and colors per pop were estabilshed above during the PCA\n",
    "#sim_sample_names = pop1 + pop2 + pop3\n",
    "#sim_pops = {\"pop1\":pop1, \"pop2\":pop2, \"pop3\":pop3}\n",
    "#sim_pop_colors = {\"pop1\":\"r\", \"pop2\":\"b\", \"pop3\":\"g\"}\n",
    "sim_colors_per_sample = {}\n",
    "for samp in sim_sample_names:\n",
    "    for pop in sim_pops:\n",
    "        if samp in sim_pops[pop]:\n",
    "            sim_colors_per_sample[samp] = sim_pop_colors[pop]\n",
    "\n",
    "## Set them in order so the plot looks nice.\n",
    "#progs = [\"ipyrad-reference-sim\", \"ipyrad-denovo_plus_reference-sim\", \"ipyrad-denovo_minus_reference-sim\", \"stacks-sim\", \"ddocent-fin-sim\",\\\n",
    "#         \"ipyrad-reference-empirical\", \"ipyrad-denovo_reference-empirical\", \"ipyrad-denovo_minus_reference-empirical\", \"stacks-empirical\", \"ddocent-fin-empirical\"]\n",
    "\n",
    "outgroup = [{'name': taxon_name} for taxon_name in pop3]\n",
    "\n",
    "for assembler, ax in zip(sim_trees.keys(), axarr):\n",
    "    print(assembler, sim_trees[assembler], ax)\n",
    "    tree = Phylo.read(sim_trees[assembler], 'newick')\n",
    "    tree.ladderize()\n",
    "    tree.root_with_outgroup(*outgroup)\n",
    "    ## This could be cool but doesn't work\n",
    "    Phylo.draw(tree, axes = ax, do_show=False, label_colors=sim_colors_per_sample)\n",
    "    ax.set_title(assembler)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESTING - Everything below here is crap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "{'ddocent-fin-sim': '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ddocent-fin-sim', 'ipyrad-denovo_minus_reference-sim': '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ipyrad-denovo_minus_reference-sim', 'stacks-sim': '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.stacks-sim', 'ipyrad-reference-sim': '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ipyrad-reference-sim', 'ipyrad-denovo_plus_reference-sim': '/home/iovercast/manuscript-analysis/REFMAP_SIM/raxml_outdir/RAxML_bipartitions.ipyrad-denovo_plus_reference-sim'}\n"
     ]
    }
   ],
   "source": [
    "del sim_trees['ddocent-tot-sim']\n",
    "print(len(sim_trees))\n",
    "print(sim_trees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Set empirical colors\n",
    "emp_colors_per_sample = {}\n",
    "for samp in emp_sample_names:\n",
    "    for pop in emp_pops:\n",
    "        if samp in emp_pops[pop]:\n",
    "            emp_colors_per_sample[samp] = emp_pop_colors[pop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import ete3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing ipyrad-reference-sim\n",
      "Doing ipyrad-denovo_reference-sim\n",
      "Doing stacks-sim\n",
      "Doing ddocent-simipyrad-reference-empirical\n",
      "Failed to load for ddocent-simipyrad-reference-empirical\n",
      "Doing ipyrad-denovo_reference-empirical\n",
      "Failed to load for ipyrad-denovo_reference-empirical\n",
      "Doing stacks-empirical\n",
      "Doing ddocent-fin-empirical\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div align=\"center\" class=\"toyplot\" id=\"tece85357d8fc44b893fe760fa5d58572\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t2a39968028d6475e8642256314ae3f10\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 800.0 300.0\" width=\"800.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t299847d5d6914b9bbec3cfc59dee517c\"><clipPath id=\"t36e05442afcb48b883e5163e53d9aa36\"><rect height=\"190.0\" width=\"690.0\" x=\"55.0\" y=\"55.0\"></rect></clipPath><g clip-path=\"url(#t36e05442afcb48b883e5163e53d9aa36)\"><g class=\"toyplot-mark-FillBoundaries\" id=\"t63f7e2bccdee4358baec4f34ea7160aa\" style=\"stroke:none\"><polygon points=\"65.0,235.0 68.602150537634415,235.0 72.204301075268816,235.0 75.806451612903231,235.0 79.408602150537632,235.0 83.010752688172047,235.0 86.612903225806448,235.0 90.215053763440864,235.0 93.817204301075265,235.0 97.41935483870968,235.0 101.02150537634409,235.0 104.6236559139785,235.0 108.2258064516129,235.0 111.82795698924731,235.0 115.43010752688173,235.0 119.03225806451613,235.0 122.63440860215054,235.0 126.23655913978493,235.0 129.83870967741936,235.0 133.44086021505376,235.0 137.04301075268816,235.0 140.64516129032259,235.0 144.24731182795699,235.0 147.84946236559139,235.0 151.45161290322579,235.0 155.05376344086022,235.0 158.65591397849462,235.0 162.25806451612902,235.0 165.86021505376345,235.0 169.46236559139786,235.0 173.06451612903226,235.0 176.66666666666669,235.0 180.26881720430109,235.0 183.87096774193549,235.0 187.47311827956989,235.0 191.07526881720429,235.0 194.67741935483869,235.0 198.27956989247309,235.0 201.88172043010752,235.0 205.48387096774195,235.0 209.08602150537632,235.0 212.68817204301075,235.0 216.29032258064515,235.0 219.89247311827955,235.0 223.49462365591398,235.0 227.09677419354841,235.0 230.69892473118279,235.0 234.30107526881721,235.0 237.90322580645159,235.0 241.50537634408602,235.0 245.10752688172042,235.0 248.70967741935482,235.0 252.31182795698925,235.0 255.91397849462368,235.0 259.51612903225811,235.0 263.11827956989248,235.0 266.72043010752691,235.0 270.32258064516128,235.0 273.92473118279571,235.0 277.52688172043014,235.0 281.12903225806451,235.0 284.73118279569894,235.0 288.33333333333331,235.0 291.93548387096774,235.0 295.53763440860217,235.0 299.13978494623655,235.0 302.74193548387098,235.0 306.34408602150535,235.0 309.94623655913978,235.0 313.54838709677421,235.0 317.15053763440864,235.0 320.75268817204301,235.0 324.35483870967738,235.0 327.95698924731181,235.0 331.55913978494618,235.0 335.16129032258067,235.0 338.76344086021504,235.0 342.36559139784947,235.0 345.9677419354839,235.0 349.56989247311827,235.0 353.1720430107527,235.0 356.77419354838707,235.0 360.3763440860215,235.0 363.97849462365593,235.0 367.58064516129031,235.0 371.18279569892474,235.0 374.78494623655911,235.0 378.38709677419354,235.0 381.98924731182797,235.0 385.59139784946234,235.0 389.19354838709683,235.0 392.7956989247312,235.0 396.39784946236563,235.0 400.0,235.0 403.60215053763437,235.0 407.2043010752688,235.0 410.80645161290317,235.0 414.40860215053766,235.0 418.01075268817203,235.0 421.61290322580646,235.0 425.21505376344084,235.0 428.81720430107521,235.0 432.41935483870964,235.0 436.02150537634407,235.0 439.6236559139785,235.0 443.22580645161293,235.0 446.82795698924735,235.0 450.43010752688178,235.0 454.03225806451616,235.0 457.63440860215053,235.0 461.23655913978496,235.0 464.83870967741933,235.0 468.44086021505382,235.0 472.04301075268819,235.0 475.64516129032262,235.0 479.24731182795699,235.0 482.84946236559136,235.0 486.45161290322579,235.0 490.05376344086022,235.0 493.65591397849465,235.0 497.25806451612902,235.0 500.8602150537634,235.0 504.46236559139783,235.0 508.0645161290322,235.0 511.66666666666669,235.0 515.26881720430106,235.0 518.87096774193549,235.0 522.4731182795698,235.0 526.07526881720435,235.0 529.67741935483878,235.0 533.27956989247321,235.0 536.88172043010763,235.0 540.48387096774195,235.0 544.08602150537638,235.0 547.6881720430107,235.0 551.29032258064524,235.0 554.89247311827955,235.0 558.49462365591398,235.0 562.09677419354841,235.0 565.69892473118273,235.0 569.30107526881727,235.0 572.90322580645159,235.0 576.50537634408602,235.0 580.10752688172045,235.0 583.70967741935476,235.0 587.3118279569893,235.0 590.91397849462362,235.0 594.51612903225805,235.0 598.11827956989237,235.0 601.72043010752679,235.0 605.32258064516134,235.0 608.92473118279565,235.0 612.52688172043008,235.0 616.12903225806451,235.0 619.73118279569894,235.0 623.33333333333337,235.0 626.9354838709678,235.0 630.53763440860212,235.0 634.13978494623655,235.0 637.74193548387098,235.0 641.3440860215054,235.0 644.94623655913983,235.0 648.54838709677415,235.0 652.15053763440858,235.0 655.75268817204301,235.0 659.35483870967744,235.0 662.95698924731187,235.0 666.55913978494618,235.0 670.16129032258061,235.0 673.76344086021504,235.0 677.36559139784947,235.0 680.9677419354839,235.0 684.56989247311822,235.0 688.17204301075265,235.0 691.77419354838707,235.0 695.37634408602162,235.0 698.97849462365593,235.0 702.58064516129036,235.0 706.18279569892468,235.0 709.78494623655922,235.0 713.38709677419365,235.0 716.98924731182797,235.0 720.5913978494624,235.0 724.19354838709671,235.0 727.79569892473125,235.0 731.39784946236557,235.0 731.39784946236557,107.5 727.79569892473125,137.25 724.19354838709671,133.0 720.5913978494624,116.00000000000001 716.98924731182797,154.25 713.38709677419365,133.0 709.78494623655922,107.5 706.18279569892468,99.0 702.58064516129036,116.00000000000001 698.97849462365593,145.75 695.37634408602162,128.75 691.77419354838707,128.75 688.17204301075265,145.75 684.56989247311822,167.0 680.9677419354839,133.0 677.36559139784947,137.25 673.76344086021504,158.5 670.16129032258061,111.75 666.55913978494618,150.0 662.95698924731187,99.0 659.35483870967744,133.0 655.75268817204301,90.5 652.15053763440858,86.25 648.54838709677415,150.0 644.94623655913983,154.25 641.3440860215054,137.25 637.74193548387098,120.24999999999999 634.13978494623655,124.5 630.53763440860212,86.25 626.9354838709678,107.5 623.33333333333337,120.24999999999999 619.73118279569894,111.75 616.12903225806451,111.75 612.52688172043008,116.00000000000001 608.92473118279565,154.25 605.32258064516134,103.25 601.72043010752679,137.25 598.11827956989237,90.5 594.51612903225805,141.5 590.91397849462362,137.25 587.3118279569893,86.25 583.70967741935476,158.5 580.10752688172045,150.0 576.50537634408602,137.25 572.90322580645159,124.5 569.30107526881727,137.25 565.69892473118273,133.0 562.09677419354841,133.0 558.49462365591398,103.25 554.89247311827955,167.0 551.29032258064524,99.0 547.6881720430107,154.25 544.08602150537638,128.75 540.48387096774195,141.5 536.88172043010763,133.0 533.27956989247321,120.24999999999999 529.67741935483878,128.75 526.07526881720435,124.5 522.4731182795698,116.00000000000001 518.87096774193549,107.5 515.26881720430106,116.00000000000001 511.66666666666669,94.75 508.0645161290322,124.5 504.46236559139783,128.75 500.8602150537634,133.0 497.25806451612902,141.5 493.65591397849465,86.25 490.05376344086022,124.5 486.45161290322579,107.5 482.84946236559136,158.5 479.24731182795699,133.0 475.64516129032262,128.75 472.04301075268819,154.25 468.44086021505382,141.5 464.83870967741933,150.0 461.23655913978496,116.00000000000001 457.63440860215053,133.0 454.03225806451616,167.0 450.43010752688178,94.75 446.82795698924735,145.75 443.22580645161293,133.0 439.6236559139785,158.5 436.02150537634407,116.00000000000001 432.41935483870964,133.0 428.81720430107521,124.5 425.21505376344084,116.00000000000001 421.61290322580646,107.5 418.01075268817203,141.5 414.40860215053766,128.75 410.80645161290317,145.75 407.2043010752688,150.0 403.60215053763437,150.0 400.0,111.75 396.39784946236563,133.0 392.7956989247312,116.00000000000001 389.19354838709683,103.25 385.59139784946234,235.0 381.98924731182797,235.0 378.38709677419354,235.0 374.78494623655911,141.5 371.18279569892474,111.75 367.58064516129031,86.25 363.97849462365593,82.0 360.3763440860215,103.25 356.77419354838707,111.75 353.1720430107527,124.5 349.56989247311827,86.25 345.9677419354839,99.0 342.36559139784947,154.25 338.76344086021504,116.00000000000001 335.16129032258067,124.5 331.55913978494618,124.5 327.95698924731181,141.5 324.35483870967738,141.5 320.75268817204301,167.0 317.15053763440864,120.24999999999999 313.54838709677421,103.25 309.94623655913978,111.75 306.34408602150535,116.00000000000001 302.74193548387098,111.75 299.13978494623655,145.75 295.53763440860217,107.5 291.93548387096774,120.24999999999999 288.33333333333331,124.5 284.73118279569894,137.25 281.12903225806451,150.0 277.52688172043014,128.75 273.92473118279571,116.00000000000001 270.32258064516128,120.24999999999999 266.72043010752691,99.0 263.11827956989248,137.25 259.51612903225811,90.5 255.91397849462368,94.75 252.31182795698925,145.75 248.70967741935482,107.5 245.10752688172042,133.0 241.50537634408602,116.00000000000001 237.90322580645159,128.75 234.30107526881721,120.24999999999999 230.69892473118279,116.00000000000001 227.09677419354841,137.25 223.49462365591398,103.25 219.89247311827955,99.0 216.29032258064515,145.75 212.68817204301075,116.00000000000001 209.08602150537632,124.5 205.48387096774195,124.5 201.88172043010752,116.00000000000001 198.27956989247309,128.75 194.67741935483869,124.5 191.07526881720429,124.5 187.47311827956989,128.75 183.87096774193549,154.25 180.26881720430109,141.5 176.66666666666669,111.75 173.06451612903226,99.0 169.46236559139786,154.25 165.86021505376345,133.0 162.25806451612902,137.25 158.65591397849462,111.75 155.05376344086022,99.0 151.45161290322579,107.5 147.84946236559139,133.0 144.24731182795699,128.75 140.64516129032259,103.25 137.04301075268816,73.500000000000014 133.44086021505376,133.0 129.83870967741936,150.0 126.23655913978493,82.0 122.63440860215054,141.5 119.03225806451613,141.5 115.43010752688173,103.25 111.82795698924731,116.00000000000001 108.2258064516129,116.00000000000001 104.6236559139785,103.25 101.02150537634409,137.25 97.41935483870968,145.75 93.817204301075265,120.24999999999999 90.215053763440864,103.25 86.612903225806448,116.00000000000001 83.010752688172047,116.00000000000001 79.408602150537632,107.5 75.806451612903231,116.00000000000001 72.204301075268816,120.24999999999999 68.602150537634415,111.75 65.0,235.0\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>total variable sites</title></polygon></g><g class=\"toyplot-mark-FillBoundaries\" id=\"tbf5a1ebeee1f42ca821514a5fb4cd60f\" style=\"stroke:none\"><polygon points=\"65.0,235.0 68.602150537634415,235.0 72.204301075268816,235.0 75.806451612903231,235.0 79.408602150537632,235.0 83.010752688172047,235.0 86.612903225806448,235.0 90.215053763440864,235.0 93.817204301075265,235.0 97.41935483870968,235.0 101.02150537634409,235.0 104.6236559139785,235.0 108.2258064516129,235.0 111.82795698924731,235.0 115.43010752688173,235.0 119.03225806451613,235.0 122.63440860215054,235.0 126.23655913978493,235.0 129.83870967741936,235.0 133.44086021505376,235.0 137.04301075268816,235.0 140.64516129032259,235.0 144.24731182795699,235.0 147.84946236559139,235.0 151.45161290322579,235.0 155.05376344086022,235.0 158.65591397849462,235.0 162.25806451612902,235.0 165.86021505376345,235.0 169.46236559139786,235.0 173.06451612903226,235.0 176.66666666666669,235.0 180.26881720430109,235.0 183.87096774193549,235.0 187.47311827956989,235.0 191.07526881720429,235.0 194.67741935483869,235.0 198.27956989247309,235.0 201.88172043010752,235.0 205.48387096774195,235.0 209.08602150537632,235.0 212.68817204301075,235.0 216.29032258064515,235.0 219.89247311827955,235.0 223.49462365591398,235.0 227.09677419354841,235.0 230.69892473118279,235.0 234.30107526881721,235.0 237.90322580645159,235.0 241.50537634408602,235.0 245.10752688172042,235.0 248.70967741935482,235.0 252.31182795698925,235.0 255.91397849462368,235.0 259.51612903225811,235.0 263.11827956989248,235.0 266.72043010752691,235.0 270.32258064516128,235.0 273.92473118279571,235.0 277.52688172043014,235.0 281.12903225806451,235.0 284.73118279569894,235.0 288.33333333333331,235.0 291.93548387096774,235.0 295.53763440860217,235.0 299.13978494623655,235.0 302.74193548387098,235.0 306.34408602150535,235.0 309.94623655913978,235.0 313.54838709677421,235.0 317.15053763440864,235.0 320.75268817204301,235.0 324.35483870967738,235.0 327.95698924731181,235.0 331.55913978494618,235.0 335.16129032258067,235.0 338.76344086021504,235.0 342.36559139784947,235.0 345.9677419354839,235.0 349.56989247311827,235.0 353.1720430107527,235.0 356.77419354838707,235.0 360.3763440860215,235.0 363.97849462365593,235.0 367.58064516129031,235.0 371.18279569892474,235.0 374.78494623655911,235.0 378.38709677419354,235.0 381.98924731182797,235.0 385.59139784946234,235.0 389.19354838709683,235.0 392.7956989247312,235.0 396.39784946236563,235.0 400.0,235.0 403.60215053763437,235.0 407.2043010752688,235.0 410.80645161290317,235.0 414.40860215053766,235.0 418.01075268817203,235.0 421.61290322580646,235.0 425.21505376344084,235.0 428.81720430107521,235.0 432.41935483870964,235.0 436.02150537634407,235.0 439.6236559139785,235.0 443.22580645161293,235.0 446.82795698924735,235.0 450.43010752688178,235.0 454.03225806451616,235.0 457.63440860215053,235.0 461.23655913978496,235.0 464.83870967741933,235.0 468.44086021505382,235.0 472.04301075268819,235.0 475.64516129032262,235.0 479.24731182795699,235.0 482.84946236559136,235.0 486.45161290322579,235.0 490.05376344086022,235.0 493.65591397849465,235.0 497.25806451612902,235.0 500.8602150537634,235.0 504.46236559139783,235.0 508.0645161290322,235.0 511.66666666666669,235.0 515.26881720430106,235.0 518.87096774193549,235.0 522.4731182795698,235.0 526.07526881720435,235.0 529.67741935483878,235.0 533.27956989247321,235.0 536.88172043010763,235.0 540.48387096774195,235.0 544.08602150537638,235.0 547.6881720430107,235.0 551.29032258064524,235.0 554.89247311827955,235.0 558.49462365591398,235.0 562.09677419354841,235.0 565.69892473118273,235.0 569.30107526881727,235.0 572.90322580645159,235.0 576.50537634408602,235.0 580.10752688172045,235.0 583.70967741935476,235.0 587.3118279569893,235.0 590.91397849462362,235.0 594.51612903225805,235.0 598.11827956989237,235.0 601.72043010752679,235.0 605.32258064516134,235.0 608.92473118279565,235.0 612.52688172043008,235.0 616.12903225806451,235.0 619.73118279569894,235.0 623.33333333333337,235.0 626.9354838709678,235.0 630.53763440860212,235.0 634.13978494623655,235.0 637.74193548387098,235.0 641.3440860215054,235.0 644.94623655913983,235.0 648.54838709677415,235.0 652.15053763440858,235.0 655.75268817204301,235.0 659.35483870967744,235.0 662.95698924731187,235.0 666.55913978494618,235.0 670.16129032258061,235.0 673.76344086021504,235.0 677.36559139784947,235.0 680.9677419354839,235.0 684.56989247311822,235.0 688.17204301075265,235.0 691.77419354838707,235.0 695.37634408602162,235.0 698.97849462365593,235.0 702.58064516129036,235.0 706.18279569892468,235.0 709.78494623655922,235.0 713.38709677419365,235.0 716.98924731182797,235.0 720.5913978494624,235.0 724.19354838709671,235.0 727.79569892473125,235.0 731.39784946236557,235.0 731.39784946236557,213.75 727.79569892473125,201.0 724.19354838709671,226.5 720.5913978494624,222.25 716.98924731182797,205.25 713.38709677419365,205.25 709.78494623655922,196.75 706.18279569892468,196.75 702.58064516129036,201.0 698.97849462365593,213.75 695.37634408602162,213.75 691.77419354838707,192.5 688.17204301075265,205.25 684.56989247311822,205.25 680.9677419354839,222.25 677.36559139784947,213.75 673.76344086021504,213.75 670.16129032258061,213.75 666.55913978494618,213.75 662.95698924731187,167.0 659.35483870967744,205.25 655.75268817204301,192.5 652.15053763440858,205.25 648.54838709677415,213.75 644.94623655913983,209.5 641.3440860215054,213.75 637.74193548387098,213.75 634.13978494623655,213.75 630.53763440860212,218.0 626.9354838709678,213.75 623.33333333333337,218.0 619.73118279569894,209.5 616.12903225806451,213.75 612.52688172043008,213.75 608.92473118279565,209.5 605.32258064516134,213.75 601.72043010752679,209.5 598.11827956989237,192.5 594.51612903225805,209.5 590.91397849462362,205.25 587.3118279569893,213.75 583.70967741935476,192.5 580.10752688172045,222.25 576.50537634408602,201.0 572.90322580645159,201.0 569.30107526881727,209.5 565.69892473118273,213.75 562.09677419354841,205.25 558.49462365591398,184.0 554.89247311827955,213.75 551.29032258064524,218.0 547.6881720430107,209.5 544.08602150537638,209.5 540.48387096774195,213.75 536.88172043010763,205.25 533.27956989247321,205.25 529.67741935483878,218.0 526.07526881720435,188.25 522.4731182795698,205.25 518.87096774193549,205.25 515.26881720430106,201.0 511.66666666666669,205.25 508.0645161290322,218.0 504.46236559139783,205.25 500.8602150537634,213.75 497.25806451612902,222.25 493.65591397849465,201.0 490.05376344086022,196.75 486.45161290322579,209.5 482.84946236559136,226.5 479.24731182795699,196.75 475.64516129032262,205.25 472.04301075268819,213.75 468.44086021505382,213.75 464.83870967741933,213.75 461.23655913978496,226.5 457.63440860215053,205.25 454.03225806451616,213.75 450.43010752688178,209.5 446.82795698924735,209.5 443.22580645161293,209.5 439.6236559139785,205.25 436.02150537634407,201.0 432.41935483870964,209.5 428.81720430107521,201.0 425.21505376344084,213.75 421.61290322580646,188.25 418.01075268817203,218.0 414.40860215053766,196.75 410.80645161290317,209.5 407.2043010752688,213.75 403.60215053763437,201.0 400.0,201.0 396.39784946236563,226.5 392.7956989247312,213.75 389.19354838709683,205.25 385.59139784946234,235.0 381.98924731182797,235.0 378.38709677419354,235.0 374.78494623655911,201.0 371.18279569892474,201.0 367.58064516129031,205.25 363.97849462365593,188.25 360.3763440860215,196.75 356.77419354838707,218.0 353.1720430107527,222.25 349.56989247311827,192.5 345.9677419354839,188.25 342.36559139784947,209.5 338.76344086021504,201.0 335.16129032258067,201.0 331.55913978494618,209.5 327.95698924731181,213.75 324.35483870967738,205.25 320.75268817204301,213.75 317.15053763440864,209.5 313.54838709677421,192.5 309.94623655913978,218.0 306.34408602150535,213.75 302.74193548387098,196.75 299.13978494623655,213.75 295.53763440860217,205.25 291.93548387096774,222.25 288.33333333333331,222.25 284.73118279569894,209.5 281.12903225806451,205.25 277.52688172043014,213.75 273.92473118279571,205.25 270.32258064516128,213.75 266.72043010752691,205.25 263.11827956989248,213.75 259.51612903225811,201.0 255.91397849462368,201.0 252.31182795698925,213.75 248.70967741935482,209.5 245.10752688172042,205.25 241.50537634408602,213.75 237.90322580645159,213.75 234.30107526881721,188.25 230.69892473118279,196.75 227.09677419354841,201.0 223.49462365591398,205.25 219.89247311827955,201.0 216.29032258064515,213.75 212.68817204301075,205.25 209.08602150537632,209.5 205.48387096774195,201.0 201.88172043010752,196.75 198.27956989247309,222.25 194.67741935483869,222.25 191.07526881720429,201.0 187.47311827956989,209.5 183.87096774193549,218.0 180.26881720430109,209.5 176.66666666666669,196.75 173.06451612903226,201.0 169.46236559139786,218.0 165.86021505376345,201.0 162.25806451612902,222.25 158.65591397849462,201.0 155.05376344086022,201.0 151.45161290322579,192.5 147.84946236559139,179.75 144.24731182795699,222.25 140.64516129032259,192.5 137.04301075268816,196.75 133.44086021505376,213.75 129.83870967741936,222.25 126.23655913978493,209.5 122.63440860215054,226.5 119.03225806451613,209.5 115.43010752688173,213.75 111.82795698924731,213.75 108.2258064516129,192.5 104.6236559139785,209.5 101.02150537634409,218.0 97.41935483870968,218.0 93.817204301075265,205.25 90.215053763440864,192.5 86.612903225806448,184.0 83.010752688172047,192.5 79.408602150537632,184.0 75.806451612903231,218.0 72.204301075268816,196.75 68.602150537634415,222.25 65.0,235.0\" style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>parsimony informative sites</title></polygon></g><g class=\"toyplot-mark-AxisLines\" id=\"t1cfe928bd79142ac948b41001dfa478f\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"124.5\" y2=\"124.5\"></line></g></g><g class=\"toyplot-mark-AxisLines\" id=\"tf555537bae2b4c2784c503fb070ac8e7\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"209.5\" y2=\"209.5\"></line></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t8ef13ed9457f4f3d9959374f6ef17afc\" transform=\"translate(65.0,235.0)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"666.3978494623656\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"18.010752688172044\" x2=\"18.010752688172044\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"36.02150537634409\" x2=\"36.02150537634409\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"54.03225806451613\" x2=\"54.03225806451613\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"72.04301075268818\" x2=\"72.04301075268818\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"90.05376344086021\" x2=\"90.05376344086021\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"108.06451612903226\" x2=\"108.06451612903226\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"126.0752688172043\" x2=\"126.0752688172043\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"144.08602150537635\" x2=\"144.08602150537635\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"162.09677419354838\" x2=\"162.09677419354838\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"180.10752688172042\" x2=\"180.10752688172042\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"198.11827956989248\" x2=\"198.11827956989248\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"216.1290322580645\" x2=\"216.1290322580645\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"234.13978494623657\" x2=\"234.13978494623657\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"252.1505376344086\" x2=\"252.1505376344086\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"270.1612903225806\" x2=\"270.1612903225806\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"288.1720430107527\" x2=\"288.1720430107527\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"306.18279569892474\" x2=\"306.18279569892474\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"324.19354838709677\" x2=\"324.19354838709677\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"342.2043010752688\" x2=\"342.2043010752688\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"360.21505376344084\" x2=\"360.21505376344084\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"378.2258064516129\" x2=\"378.2258064516129\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"396.23655913978496\" x2=\"396.23655913978496\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"414.247311827957\" x2=\"414.247311827957\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"432.258064516129\" x2=\"432.258064516129\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"450.26881720430106\" x2=\"450.26881720430106\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"468.27956989247315\" x2=\"468.27956989247315\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"486.2903225806452\" x2=\"486.2903225806452\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"504.3010752688172\" x2=\"504.3010752688172\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"522.3118279569892\" x2=\"522.3118279569892\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"540.3225806451612\" x2=\"540.3225806451612\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"558.3333333333334\" x2=\"558.3333333333334\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"576.3440860215054\" x2=\"576.3440860215054\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"594.3548387096774\" x2=\"594.3548387096774\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"612.3655913978495\" x2=\"612.3655913978495\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"630.3763440860215\" x2=\"630.3763440860215\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"648.3870967741935\" x2=\"648.3870967741935\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"666.3978494623656\" x2=\"666.3978494623656\" y1=\"0\" y2=\"-5\"></line></g><g><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(18.010752688172044,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">5</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(36.02150537634409,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">10</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(54.03225806451613,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">15</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(72.04301075268818,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">20</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(90.05376344086021,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">25</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(108.06451612903226,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">30</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(126.0752688172043,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">35</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(144.08602150537635,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">40</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(162.09677419354838,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">45</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(180.10752688172042,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">50</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(198.11827956989248,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">55</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(216.1290322580645,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">60</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(234.13978494623657,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">65</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(252.1505376344086,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">70</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(270.1612903225806,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">75</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(288.1720430107527,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">80</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(306.18279569892474,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">85</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(324.19354838709677,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">90</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(342.2043010752688,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">95</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(360.21505376344084,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">100</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(378.2258064516129,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">105</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(396.23655913978496,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">110</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(414.247311827957,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">115</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(432.258064516129,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">120</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(450.26881720430106,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">125</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(468.27956989247315,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">130</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(486.2903225806452,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">135</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(504.3010752688172,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">140</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(522.3118279569892,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">145</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(540.3225806451612,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">150</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(558.3333333333334,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">155</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(576.3440860215054,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">160</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(594.3548387096774,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">165</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(612.3655913978495,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">170</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(630.3763440860215,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">175</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(648.3870967741935,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">180</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(666.3978494623656,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">185</tspan></text></g><text style=\"dominant-baseline:hanging;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(335.0,22)translate(0,40.0)\"><tspan style=\"dominant-baseline:inherit\">Position along RAD loci</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t14d1ffc8c9864b318cfa174089efd5e8\" transform=\"translate(65.0,235.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"0\" x2=\"161.5\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"42.5\" x2=\"42.5\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"85.0\" x2=\"85.0\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"127.5\" x2=\"127.5\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"170.0\" x2=\"170.0\" y1=\"0\" y2=\"5\"></line></g><g><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(42.5,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">10</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(85.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">20</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(127.5,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">30</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(170.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">40</tspan></text></g><text style=\"dominant-baseline:alphabetic;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(85.0,-22)translate(0,-40.0)\"><tspan style=\"dominant-baseline:inherit\">N variables sites</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g><text style=\"dominant-baseline:middle;font-size:14px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(400.0,65.0)translate(0,-14.0)\"><tspan style=\"dominant-baseline:inherit\">ipyrad-reference-sim</tspan></text></g></svg><div class=\"toyplot-interactive\"><ul class=\"toyplot-mark-popup\" onmouseleave=\"this.style.visibility='hidden'\" style=\"background:rgba(0%,0%,0%,0.75);border:0;border-radius:6px;color:white;cursor:default;list-style:none;margin:0;padding:5px;position:fixed;visibility:hidden\">\n",
       "            <li class=\"toyplot-mark-popup-title\" style=\"color:lightgray;cursor:default;padding:5px;list-style:none;margin:0\"></li>\n",
       "            <li class=\"toyplot-mark-popup-save-csv\" onmouseout=\"this.style.color='white';this.style.background='steelblue'\" onmouseover=\"this.style.color='steelblue';this.style.background='white'\" style=\"border-radius:3px;padding:5px;list-style:none;margin:0\">\n",
       "                Save as .csv\n",
       "            </li>\n",
       "        </ul><script>\n",
       "        (function()\n",
       "        {\n",
       "          var data_tables = [{\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"t63f7e2bccdee4358baec4f34ea7160aa\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0, 176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 29.0, 27.0, 28.0, 30.0, 28.0, 28.0, 31.0, 27.0, 21.0, 23.0, 31.0, 28.0, 28.0, 31.0, 22.0, 22.0, 36.0, 20.0, 24.0, 38.0, 31.0, 25.0, 24.0, 30.0, 32.0, 29.0, 23.0, 24.0, 19.0, 32.0, 29.0, 22.0, 19.0, 25.0, 26.0, 26.0, 25.0, 28.0, 26.0, 26.0, 28.0, 21.0, 32.0, 31.0, 23.0, 28.0, 27.0, 25.0, 28.0, 24.0, 30.0, 21.0, 33.0, 34.0, 23.0, 32.0, 27.0, 28.0, 25.0, 20.0, 23.0, 26.0, 27.0, 30.0, 21.0, 29.0, 28.0, 29.0, 31.0, 27.0, 16.0, 22.0, 22.0, 26.0, 26.0, 28.0, 19.0, 32.0, 35.0, 26.0, 29.0, 31.0, 36.0, 35.0, 29.0, 22.0, 0.0, 0.0, 0.0, 31.0, 28.0, 24.0, 29.0, 20.0, 20.0, 21.0, 25.0, 22.0, 30.0, 28.0, 26.0, 24.0, 28.0, 18.0, 24.0, 21.0, 33.0, 16.0, 24.0, 28.0, 20.0, 22.0, 19.0, 25.0, 24.0, 18.0, 30.0, 26.0, 35.0, 22.0, 24.0, 25.0, 26.0, 33.0, 28.0, 30.0, 28.0, 26.0, 25.0, 27.0, 24.0, 22.0, 25.0, 19.0, 32.0, 16.0, 31.0, 24.0, 24.0, 23.0, 26.0, 23.0, 20.0, 18.0, 35.0, 23.0, 22.0, 34.0, 23.0, 31.0, 19.0, 28.0, 29.0, 29.0, 27.0, 30.0, 35.0, 26.0, 27.0, 23.0, 19.0, 20.0, 35.0, 34.0, 24.0, 32.0, 20.0, 29.0, 18.0, 23.0, 24.0, 16.0, 21.0, 25.0, 25.0, 21.0, 28.0, 32.0, 30.0, 24.0, 19.0, 28.0, 24.0, 23.0, 30.0]], \"filename\": \"toyplot\"}, {\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"tbf5a1ebeee1f42ca821514a5fb4cd60f\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0, 176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 3.0, 9.0, 4.0, 12.0, 10.0, 12.0, 10.0, 7.0, 4.0, 4.0, 6.0, 10.0, 5.0, 5.0, 6.0, 2.0, 6.0, 3.0, 5.0, 9.0, 10.0, 3.0, 13.0, 10.0, 8.0, 8.0, 3.0, 8.0, 4.0, 8.0, 9.0, 6.0, 4.0, 6.0, 8.0, 3.0, 3.0, 9.0, 8.0, 6.0, 7.0, 5.0, 8.0, 7.0, 8.0, 9.0, 11.0, 5.0, 5.0, 7.0, 6.0, 5.0, 8.0, 8.0, 5.0, 7.0, 5.0, 7.0, 5.0, 7.0, 6.0, 3.0, 3.0, 7.0, 5.0, 9.0, 5.0, 4.0, 10.0, 6.0, 5.0, 7.0, 5.0, 6.0, 8.0, 8.0, 6.0, 11.0, 10.0, 3.0, 4.0, 9.0, 11.0, 7.0, 8.0, 8.0, 0.0, 0.0, 0.0, 7.0, 5.0, 2.0, 8.0, 8.0, 5.0, 6.0, 9.0, 4.0, 11.0, 5.0, 8.0, 6.0, 8.0, 7.0, 6.0, 6.0, 6.0, 5.0, 7.0, 2.0, 5.0, 5.0, 5.0, 7.0, 9.0, 2.0, 6.0, 9.0, 8.0, 3.0, 5.0, 7.0, 4.0, 7.0, 8.0, 7.0, 7.0, 11.0, 4.0, 7.0, 7.0, 5.0, 6.0, 6.0, 4.0, 5.0, 12.0, 7.0, 5.0, 6.0, 8.0, 8.0, 3.0, 10.0, 5.0, 7.0, 6.0, 10.0, 6.0, 5.0, 6.0, 5.0, 5.0, 6.0, 4.0, 5.0, 4.0, 5.0, 5.0, 5.0, 6.0, 5.0, 7.0, 10.0, 7.0, 16.0, 5.0, 5.0, 5.0, 5.0, 3.0, 7.0, 7.0, 10.0, 5.0, 5.0, 8.0, 9.0, 9.0, 7.0, 7.0, 3.0, 2.0, 8.0, 5.0]], \"filename\": \"toyplot\"}];\n",
       "\n",
       "          function save_csv(data_table)\n",
       "          {\n",
       "            var uri = \"data:text/csv;charset=utf-8,\";\n",
       "            uri += data_table.names.join(\",\") + \"\\n\";\n",
       "            for(var i = 0; i != data_table.columns[0].length; ++i)\n",
       "            {\n",
       "              for(var j = 0; j != data_table.columns.length; ++j)\n",
       "              {\n",
       "                if(j)\n",
       "                  uri += \",\";\n",
       "                uri += data_table.columns[j][i];\n",
       "              }\n",
       "              uri += \"\\n\";\n",
       "            }\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = data_table.filename + \".csv\";\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "          }\n",
       "\n",
       "          function open_popup(data_table)\n",
       "          {\n",
       "            return function(e)\n",
       "            {\n",
       "              var popup = document.querySelector(\"#tece85357d8fc44b893fe760fa5d58572 .toyplot-mark-popup\");\n",
       "              popup.querySelector(\".toyplot-mark-popup-title\").innerHTML = data_table.title;\n",
       "              popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
       "              popup.style.left = (e.clientX - 50) + \"px\";\n",
       "              popup.style.top = (e.clientY - 20) + \"px\";\n",
       "              popup.style.visibility = \"visible\";\n",
       "              e.stopPropagation();\n",
       "              e.preventDefault();\n",
       "            }\n",
       "\n",
       "          }\n",
       "\n",
       "          for(var i = 0; i != data_tables.length; ++i)\n",
       "          {\n",
       "            var data_table = data_tables[i];\n",
       "            var event_target = document.querySelector(\"#\" + data_table.id);\n",
       "            event_target.oncontextmenu = open_popup(data_table);\n",
       "          }\n",
       "        })();\n",
       "        </script><script>\n",
       "        (function()\n",
       "        {\n",
       "            function _sign(x)\n",
       "            {\n",
       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
       "\n",
       "            function _mix(a, b, amount)\n",
       "            {\n",
       "                return ((1.0 - amount) * a) + (amount * b);\n",
       "            }\n",
       "\n",
       "            function _log(x, base)\n",
       "            {\n",
       "                return Math.log(Math.abs(x)) / Math.log(base);\n",
       "            }\n",
       "\n",
       "            function _in_range(a, x, b)\n",
       "            {\n",
       "                var left = Math.min(a, b);\n",
       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
       "            }\n",
       "\n",
       "            function inside(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.min, range, segment.range.max))\n",
       "                        return true;\n",
       "                }\n",
       "                return false;\n",
       "            }\n",
       "\n",
       "            function to_domain(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
       "                    {\n",
       "                        if(segment.scale == \"linear\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            return _mix(segment.domain.min, segment.domain.max, amount)\n",
       "                        }\n",
       "                        else if(segment.scale[0] == \"log\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            var base = segment.scale[1];\n",
       "                            return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            function display_coordinates(e)\n",
       "            {\n",
       "                var current = svg.createSVGPoint();\n",
       "                current.x = e.clientX;\n",
       "                current.y = e.clientY;\n",
       "\n",
       "                for(var axis_id in axes)\n",
       "                {\n",
       "                    var axis = document.querySelector(\"#\" + axis_id);\n",
       "                    var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
       "                    if(coordinates)\n",
       "                    {\n",
       "                        var projection = axes[axis_id];\n",
       "                        var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
       "                        if(inside(local.x, projection))\n",
       "                        {\n",
       "                            var domain = to_domain(local.x, projection);\n",
       "                            coordinates.style.visibility = \"visible\";\n",
       "                            coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
       "                            var text = coordinates.querySelector(\"text\");\n",
       "                            text.textContent = domain.toFixed(2);\n",
       "                        }\n",
       "                        else\n",
       "                        {\n",
       "                            coordinates.style.visibility= \"hidden\";\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var root_id = \"tece85357d8fc44b893fe760fa5d58572\";\n",
       "            var axes = {\"t14d1ffc8c9864b318cfa174089efd5e8\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 40.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 170.0, \"min\": 0.0}, \"scale\": \"linear\"}], \"t8ef13ed9457f4f3d9959374f6ef17afc\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 186.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 670.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
       "\n",
       "            var svg = document.querySelector(\"#\" + root_id + \" svg\");\n",
       "            svg.addEventListener(\"click\", display_coordinates);\n",
       "        })();\n",
       "        </script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div align=\"center\" class=\"toyplot\" id=\"t85a7e21009a04f138c5807fb2e89c398\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t0b720b7a1c0845269ff322dcadb67b95\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 800.0 300.0\" width=\"800.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t0b53850bbfb4462796304f050c5d30c2\"><clipPath id=\"tcd26523340d643509acc41aa5528e26d\"><rect height=\"190.0\" width=\"690.0\" x=\"55.0\" y=\"55.0\"></rect></clipPath><g clip-path=\"url(#tcd26523340d643509acc41aa5528e26d)\"><g class=\"toyplot-mark-FillBoundaries\" id=\"t7bac5bb25e114b228cd6ea05221e2313\" style=\"stroke:none\"><polygon points=\"65.0,235.0 68.602150537634415,235.0 72.204301075268816,235.0 75.806451612903231,235.0 79.408602150537632,235.0 83.010752688172047,235.0 86.612903225806448,235.0 90.215053763440864,235.0 93.817204301075265,235.0 97.41935483870968,235.0 101.02150537634409,235.0 104.6236559139785,235.0 108.2258064516129,235.0 111.82795698924731,235.0 115.43010752688173,235.0 119.03225806451613,235.0 122.63440860215054,235.0 126.23655913978493,235.0 129.83870967741936,235.0 133.44086021505376,235.0 137.04301075268816,235.0 140.64516129032259,235.0 144.24731182795699,235.0 147.84946236559139,235.0 151.45161290322579,235.0 155.05376344086022,235.0 158.65591397849462,235.0 162.25806451612902,235.0 165.86021505376345,235.0 169.46236559139786,235.0 173.06451612903226,235.0 176.66666666666669,235.0 180.26881720430109,235.0 183.87096774193549,235.0 187.47311827956989,235.0 191.07526881720429,235.0 194.67741935483869,235.0 198.27956989247309,235.0 201.88172043010752,235.0 205.48387096774195,235.0 209.08602150537632,235.0 212.68817204301075,235.0 216.29032258064515,235.0 219.89247311827955,235.0 223.49462365591398,235.0 227.09677419354841,235.0 230.69892473118279,235.0 234.30107526881721,235.0 237.90322580645159,235.0 241.50537634408602,235.0 245.10752688172042,235.0 248.70967741935482,235.0 252.31182795698925,235.0 255.91397849462368,235.0 259.51612903225811,235.0 263.11827956989248,235.0 266.72043010752691,235.0 270.32258064516128,235.0 273.92473118279571,235.0 277.52688172043014,235.0 281.12903225806451,235.0 284.73118279569894,235.0 288.33333333333331,235.0 291.93548387096774,235.0 295.53763440860217,235.0 299.13978494623655,235.0 302.74193548387098,235.0 306.34408602150535,235.0 309.94623655913978,235.0 313.54838709677421,235.0 317.15053763440864,235.0 320.75268817204301,235.0 324.35483870967738,235.0 327.95698924731181,235.0 331.55913978494618,235.0 335.16129032258067,235.0 338.76344086021504,235.0 342.36559139784947,235.0 345.9677419354839,235.0 349.56989247311827,235.0 353.1720430107527,235.0 356.77419354838707,235.0 360.3763440860215,235.0 363.97849462365593,235.0 367.58064516129031,235.0 371.18279569892474,235.0 374.78494623655911,235.0 378.38709677419354,235.0 381.98924731182797,235.0 385.59139784946234,235.0 389.19354838709683,235.0 392.7956989247312,235.0 396.39784946236563,235.0 400.0,235.0 403.60215053763437,235.0 407.2043010752688,235.0 410.80645161290317,235.0 414.40860215053766,235.0 418.01075268817203,235.0 421.61290322580646,235.0 425.21505376344084,235.0 428.81720430107521,235.0 432.41935483870964,235.0 436.02150537634407,235.0 439.6236559139785,235.0 443.22580645161293,235.0 446.82795698924735,235.0 450.43010752688178,235.0 454.03225806451616,235.0 457.63440860215053,235.0 461.23655913978496,235.0 464.83870967741933,235.0 468.44086021505382,235.0 472.04301075268819,235.0 475.64516129032262,235.0 479.24731182795699,235.0 482.84946236559136,235.0 486.45161290322579,235.0 490.05376344086022,235.0 493.65591397849465,235.0 497.25806451612902,235.0 500.8602150537634,235.0 504.46236559139783,235.0 508.0645161290322,235.0 511.66666666666669,235.0 515.26881720430106,235.0 518.87096774193549,235.0 522.4731182795698,235.0 526.07526881720435,235.0 529.67741935483878,235.0 533.27956989247321,235.0 536.88172043010763,235.0 540.48387096774195,235.0 544.08602150537638,235.0 547.6881720430107,235.0 551.29032258064524,235.0 554.89247311827955,235.0 558.49462365591398,235.0 562.09677419354841,235.0 565.69892473118273,235.0 569.30107526881727,235.0 572.90322580645159,235.0 576.50537634408602,235.0 580.10752688172045,235.0 583.70967741935476,235.0 587.3118279569893,235.0 590.91397849462362,235.0 594.51612903225805,235.0 598.11827956989237,235.0 601.72043010752679,235.0 605.32258064516134,235.0 608.92473118279565,235.0 612.52688172043008,235.0 616.12903225806451,235.0 619.73118279569894,235.0 623.33333333333337,235.0 626.9354838709678,235.0 630.53763440860212,235.0 634.13978494623655,235.0 637.74193548387098,235.0 641.3440860215054,235.0 644.94623655913983,235.0 648.54838709677415,235.0 652.15053763440858,235.0 655.75268817204301,235.0 659.35483870967744,235.0 662.95698924731187,235.0 666.55913978494618,235.0 670.16129032258061,235.0 673.76344086021504,235.0 677.36559139784947,235.0 680.9677419354839,235.0 684.56989247311822,235.0 688.17204301075265,235.0 691.77419354838707,235.0 695.37634408602162,235.0 698.97849462365593,235.0 702.58064516129036,235.0 706.18279569892468,235.0 709.78494623655922,235.0 713.38709677419365,235.0 716.98924731182797,235.0 720.5913978494624,235.0 724.19354838709671,235.0 727.79569892473125,235.0 731.39784946236557,235.0 731.39784946236557,101.26666666666668 727.79569892473125,139.80000000000001 724.19354838709671,117.13333333333333 720.5913978494624,103.53333333333333 716.98924731182797,126.19999999999999 713.38709677419365,105.8 709.78494623655922,110.33333333333334 706.18279569892468,108.06666666666666 702.58064516129036,112.60000000000001 698.97849462365593,123.93333333333334 695.37634408602162,133.0 691.77419354838707,105.8 688.17204301075265,126.19999999999999 684.56989247311822,137.53333333333333 680.9677419354839,110.33333333333334 677.36559139784947,126.19999999999999 673.76344086021504,142.06666666666666 670.16129032258061,94.466666666666669 666.55913978494618,112.60000000000001 662.95698924731187,103.53333333333333 659.35483870967744,99.0 655.75268817204301,96.73333333333332 652.15053763440858,103.53333333333333 648.54838709677415,121.66666666666667 644.94623655913983,133.0 641.3440860215054,114.86666666666667 637.74193548387098,126.19999999999999 634.13978494623655,142.06666666666666 630.53763440860212,80.866666666666674 626.9354838709678,119.39999999999999 623.33333333333337,110.33333333333334 619.73118279569894,108.06666666666666 616.12903225806451,101.26666666666668 612.52688172043008,114.86666666666667 608.92473118279565,117.13333333333333 605.32258064516134,128.46666666666664 601.72043010752679,114.86666666666667 598.11827956989237,110.33333333333334 594.51612903225805,117.13333333333333 590.91397849462362,112.60000000000001 587.3118279569893,71.800000000000011 583.70967741935476,139.80000000000001 580.10752688172045,133.0 576.50537634408602,123.93333333333334 572.90322580645159,110.33333333333334 569.30107526881727,121.66666666666667 565.69892473118273,135.26666666666665 562.09677419354841,123.93333333333334 558.49462365591398,108.06666666666666 554.89247311827955,121.66666666666667 551.29032258064524,121.66666666666667 547.6881720430107,142.06666666666666 544.08602150537638,101.26666666666668 540.48387096774195,121.66666666666667 536.88172043010763,105.8 533.27956989247321,121.66666666666667 529.67741935483878,105.8 526.07526881720435,130.73333333333335 522.4731182795698,112.60000000000001 518.87096774193549,112.60000000000001 515.26881720430106,128.46666666666664 511.66666666666669,105.8 508.0645161290322,121.66666666666667 504.46236559139783,114.86666666666667 500.8602150537634,133.0 497.25806451612902,110.33333333333334 493.65591397849465,85.400000000000006 490.05376344086022,128.46666666666664 486.45161290322579,128.46666666666664 482.84946236559136,123.93333333333334 479.24731182795699,121.66666666666667 475.64516129032262,119.39999999999999 472.04301075268819,135.26666666666665 468.44086021505382,130.73333333333335 464.83870967741933,126.19999999999999 461.23655913978496,114.86666666666667 457.63440860215053,123.93333333333334 454.03225806451616,139.80000000000001 450.43010752688178,101.26666666666668 446.82795698924735,123.93333333333334 443.22580645161293,146.59999999999999 439.6236559139785,126.19999999999999 436.02150537634407,112.60000000000001 432.41935483870964,135.26666666666665 428.81720430107521,99.0 425.21505376344084,119.39999999999999 421.61290322580646,114.86666666666667 418.01075268817203,119.39999999999999 414.40860215053766,101.26666666666668 410.80645161290317,126.19999999999999 407.2043010752688,130.73333333333335 403.60215053763437,119.39999999999999 400.0,110.33333333333334 396.39784946236563,130.73333333333335 392.7956989247312,112.60000000000001 389.19354838709683,94.466666666666669 385.59139784946234,235.0 381.98924731182797,235.0 378.38709677419354,235.0 374.78494623655911,121.66666666666667 371.18279569892474,108.06666666666666 367.58064516129031,87.666666666666657 363.97849462365593,112.60000000000001 360.3763440860215,112.60000000000001 356.77419354838707,119.39999999999999 353.1720430107527,114.86666666666667 349.56989247311827,89.933333333333323 345.9677419354839,114.86666666666667 342.36559139784947,128.46666666666664 338.76344086021504,130.73333333333335 335.16129032258067,117.13333333333333 331.55913978494618,103.53333333333333 327.95698924731181,128.46666666666664 324.35483870967738,105.8 320.75268817204301,144.33333333333334 317.15053763440864,126.19999999999999 313.54838709677421,121.66666666666667 309.94623655913978,114.86666666666667 306.34408602150535,126.19999999999999 302.74193548387098,92.200000000000017 299.13978494623655,119.39999999999999 295.53763440860217,105.8 291.93548387096774,114.86666666666667 288.33333333333331,110.33333333333334 284.73118279569894,128.46666666666664 281.12903225806451,119.39999999999999 277.52688172043014,85.400000000000006 273.92473118279571,119.39999999999999 270.32258064516128,126.19999999999999 266.72043010752691,105.8 263.11827956989248,135.26666666666665 259.51612903225811,110.33333333333334 255.91397849462368,108.06666666666666 252.31182795698925,128.46666666666664 248.70967741935482,119.39999999999999 245.10752688172042,128.46666666666664 241.50537634408602,96.73333333333332 237.90322580645159,105.8 234.30107526881721,103.53333333333333 230.69892473118279,117.13333333333333 227.09677419354841,139.80000000000001 223.49462365591398,105.8 219.89247311827955,87.666666666666657 216.29032258064515,137.53333333333333 212.68817204301075,117.13333333333333 209.08602150537632,126.19999999999999 205.48387096774195,110.33333333333334 201.88172043010752,110.33333333333334 198.27956989247309,119.39999999999999 194.67741935483869,117.13333333333333 191.07526881720429,126.19999999999999 187.47311827956989,105.8 183.87096774193549,135.26666666666665 180.26881720430109,142.06666666666666 176.66666666666669,121.66666666666667 173.06451612903226,114.86666666666667 169.46236559139786,144.33333333333334 165.86021505376345,101.26666666666668 162.25806451612902,142.06666666666666 158.65591397849462,96.73333333333332 155.05376344086022,103.53333333333333 151.45161290322579,130.73333333333335 147.84946236559139,130.73333333333335 144.24731182795699,130.73333333333335 140.64516129032259,99.0 137.04301075268816,69.533333333333331 133.44086021505376,128.46666666666664 129.83870967741936,128.46666666666664 126.23655913978493,99.0 122.63440860215054,135.26666666666665 119.03225806451613,110.33333333333334 115.43010752688173,119.39999999999999 111.82795698924731,110.33333333333334 108.2258064516129,121.66666666666667 104.6236559139785,96.73333333333332 101.02150537634409,139.80000000000001 97.41935483870968,112.60000000000001 93.817204301075265,103.53333333333333 90.215053763440864,101.26666666666668 86.612903225806448,119.39999999999999 83.010752688172047,133.0 79.408602150537632,119.39999999999999 75.806451612903231,105.8 72.204301075268816,137.53333333333333 68.602150537634415,101.26666666666668 65.0,235.0\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>total variable sites</title></polygon></g><g class=\"toyplot-mark-FillBoundaries\" id=\"t91084815576c432d9d7b2bb25df751eb\" style=\"stroke:none\"><polygon points=\"65.0,235.0 68.602150537634415,235.0 72.204301075268816,235.0 75.806451612903231,235.0 79.408602150537632,235.0 83.010752688172047,235.0 86.612903225806448,235.0 90.215053763440864,235.0 93.817204301075265,235.0 97.41935483870968,235.0 101.02150537634409,235.0 104.6236559139785,235.0 108.2258064516129,235.0 111.82795698924731,235.0 115.43010752688173,235.0 119.03225806451613,235.0 122.63440860215054,235.0 126.23655913978493,235.0 129.83870967741936,235.0 133.44086021505376,235.0 137.04301075268816,235.0 140.64516129032259,235.0 144.24731182795699,235.0 147.84946236559139,235.0 151.45161290322579,235.0 155.05376344086022,235.0 158.65591397849462,235.0 162.25806451612902,235.0 165.86021505376345,235.0 169.46236559139786,235.0 173.06451612903226,235.0 176.66666666666669,235.0 180.26881720430109,235.0 183.87096774193549,235.0 187.47311827956989,235.0 191.07526881720429,235.0 194.67741935483869,235.0 198.27956989247309,235.0 201.88172043010752,235.0 205.48387096774195,235.0 209.08602150537632,235.0 212.68817204301075,235.0 216.29032258064515,235.0 219.89247311827955,235.0 223.49462365591398,235.0 227.09677419354841,235.0 230.69892473118279,235.0 234.30107526881721,235.0 237.90322580645159,235.0 241.50537634408602,235.0 245.10752688172042,235.0 248.70967741935482,235.0 252.31182795698925,235.0 255.91397849462368,235.0 259.51612903225811,235.0 263.11827956989248,235.0 266.72043010752691,235.0 270.32258064516128,235.0 273.92473118279571,235.0 277.52688172043014,235.0 281.12903225806451,235.0 284.73118279569894,235.0 288.33333333333331,235.0 291.93548387096774,235.0 295.53763440860217,235.0 299.13978494623655,235.0 302.74193548387098,235.0 306.34408602150535,235.0 309.94623655913978,235.0 313.54838709677421,235.0 317.15053763440864,235.0 320.75268817204301,235.0 324.35483870967738,235.0 327.95698924731181,235.0 331.55913978494618,235.0 335.16129032258067,235.0 338.76344086021504,235.0 342.36559139784947,235.0 345.9677419354839,235.0 349.56989247311827,235.0 353.1720430107527,235.0 356.77419354838707,235.0 360.3763440860215,235.0 363.97849462365593,235.0 367.58064516129031,235.0 371.18279569892474,235.0 374.78494623655911,235.0 378.38709677419354,235.0 381.98924731182797,235.0 385.59139784946234,235.0 389.19354838709683,235.0 392.7956989247312,235.0 396.39784946236563,235.0 400.0,235.0 403.60215053763437,235.0 407.2043010752688,235.0 410.80645161290317,235.0 414.40860215053766,235.0 418.01075268817203,235.0 421.61290322580646,235.0 425.21505376344084,235.0 428.81720430107521,235.0 432.41935483870964,235.0 436.02150537634407,235.0 439.6236559139785,235.0 443.22580645161293,235.0 446.82795698924735,235.0 450.43010752688178,235.0 454.03225806451616,235.0 457.63440860215053,235.0 461.23655913978496,235.0 464.83870967741933,235.0 468.44086021505382,235.0 472.04301075268819,235.0 475.64516129032262,235.0 479.24731182795699,235.0 482.84946236559136,235.0 486.45161290322579,235.0 490.05376344086022,235.0 493.65591397849465,235.0 497.25806451612902,235.0 500.8602150537634,235.0 504.46236559139783,235.0 508.0645161290322,235.0 511.66666666666669,235.0 515.26881720430106,235.0 518.87096774193549,235.0 522.4731182795698,235.0 526.07526881720435,235.0 529.67741935483878,235.0 533.27956989247321,235.0 536.88172043010763,235.0 540.48387096774195,235.0 544.08602150537638,235.0 547.6881720430107,235.0 551.29032258064524,235.0 554.89247311827955,235.0 558.49462365591398,235.0 562.09677419354841,235.0 565.69892473118273,235.0 569.30107526881727,235.0 572.90322580645159,235.0 576.50537634408602,235.0 580.10752688172045,235.0 583.70967741935476,235.0 587.3118279569893,235.0 590.91397849462362,235.0 594.51612903225805,235.0 598.11827956989237,235.0 601.72043010752679,235.0 605.32258064516134,235.0 608.92473118279565,235.0 612.52688172043008,235.0 616.12903225806451,235.0 619.73118279569894,235.0 623.33333333333337,235.0 626.9354838709678,235.0 630.53763440860212,235.0 634.13978494623655,235.0 637.74193548387098,235.0 641.3440860215054,235.0 644.94623655913983,235.0 648.54838709677415,235.0 652.15053763440858,235.0 655.75268817204301,235.0 659.35483870967744,235.0 662.95698924731187,235.0 666.55913978494618,235.0 670.16129032258061,235.0 673.76344086021504,235.0 677.36559139784947,235.0 680.9677419354839,235.0 684.56989247311822,235.0 688.17204301075265,235.0 691.77419354838707,235.0 695.37634408602162,235.0 698.97849462365593,235.0 702.58064516129036,235.0 706.18279569892468,235.0 709.78494623655922,235.0 713.38709677419365,235.0 716.98924731182797,235.0 720.5913978494624,235.0 724.19354838709671,235.0 727.79569892473125,235.0 731.39784946236557,235.0 731.39784946236557,210.06666666666666 727.79569892473125,201.0 724.19354838709671,216.86666666666667 720.5913978494624,214.60000000000002 716.98924731182797,196.46666666666664 713.38709677419365,189.66666666666669 709.78494623655922,201.0 706.18279569892468,196.46666666666664 702.58064516129036,201.0 698.97849462365593,205.53333333333333 695.37634408602162,214.60000000000002 691.77419354838707,201.0 688.17204301075265,198.73333333333332 684.56989247311822,196.46666666666664 680.9677419354839,207.80000000000001 677.36559139784947,210.06666666666666 673.76344086021504,212.33333333333334 670.16129032258061,201.0 666.55913978494618,207.80000000000001 662.95698924731187,191.93333333333331 659.35483870967744,201.0 655.75268817204301,189.66666666666669 652.15053763440858,201.0 648.54838709677415,210.06666666666666 644.94623655913983,201.0 641.3440860215054,207.80000000000001 637.74193548387098,210.06666666666666 634.13978494623655,214.60000000000002 630.53763440860212,201.0 626.9354838709678,203.26666666666665 623.33333333333337,216.86666666666667 619.73118279569894,210.06666666666666 616.12903225806451,210.06666666666666 612.52688172043008,207.80000000000001 608.92473118279565,201.0 605.32258064516134,212.33333333333334 601.72043010752679,198.73333333333332 598.11827956989237,203.26666666666665 594.51612903225805,205.53333333333333 590.91397849462362,201.0 587.3118279569893,207.80000000000001 583.70967741935476,203.26666666666665 580.10752688172045,216.86666666666667 576.50537634408602,207.80000000000001 572.90322580645159,196.46666666666664 569.30107526881727,210.06666666666666 565.69892473118273,216.86666666666667 562.09677419354841,205.53333333333333 558.49462365591398,196.46666666666664 554.89247311827955,205.53333333333333 551.29032258064524,210.06666666666666 547.6881720430107,212.33333333333334 544.08602150537638,196.46666666666664 540.48387096774195,205.53333333333333 536.88172043010763,201.0 533.27956989247321,198.73333333333332 529.67741935483878,212.33333333333334 526.07526881720435,198.73333333333332 522.4731182795698,210.06666666666666 518.87096774193549,212.33333333333334 515.26881720430106,207.80000000000001 511.66666666666669,210.06666666666666 508.0645161290322,207.80000000000001 504.46236559139783,196.46666666666664 500.8602150537634,216.86666666666667 497.25806451612902,214.60000000000002 493.65591397849465,207.80000000000001 490.05376344086022,198.73333333333332 486.45161290322579,210.06666666666666 482.84946236559136,219.13333333333333 479.24731182795699,207.80000000000001 475.64516129032262,210.06666666666666 472.04301075268819,198.73333333333332 468.44086021505382,207.80000000000001 464.83870967741933,198.73333333333332 461.23655913978496,210.06666666666666 457.63440860215053,194.19999999999999 454.03225806451616,207.80000000000001 450.43010752688178,205.53333333333333 446.82795698924735,198.73333333333332 443.22580645161293,219.13333333333333 439.6236559139785,196.46666666666664 436.02150537634407,203.26666666666665 432.41935483870964,203.26666666666665 428.81720430107521,196.46666666666664 425.21505376344084,216.86666666666667 421.61290322580646,194.19999999999999 418.01075268817203,219.13333333333333 414.40860215053766,189.66666666666669 410.80645161290317,207.80000000000001 407.2043010752688,212.33333333333334 403.60215053763437,198.73333333333332 400.0,196.46666666666664 396.39784946236563,216.86666666666667 392.7956989247312,212.33333333333334 389.19354838709683,207.80000000000001 385.59139784946234,235.0 381.98924731182797,235.0 378.38709677419354,235.0 374.78494623655911,201.0 371.18279569892474,201.0 367.58064516129031,187.39999999999998 363.97849462365593,196.46666666666664 360.3763440860215,212.33333333333334 356.77419354838707,219.13333333333333 353.1720430107527,207.80000000000001 349.56989247311827,196.46666666666664 345.9677419354839,207.80000000000001 342.36559139784947,205.53333333333333 338.76344086021504,207.80000000000001 335.16129032258067,207.80000000000001 331.55913978494618,198.73333333333332 327.95698924731181,210.06666666666666 324.35483870967738,194.19999999999999 320.75268817204301,207.80000000000001 317.15053763440864,212.33333333333334 313.54838709677421,201.0 309.94623655913978,216.86666666666667 306.34408602150535,216.86666666666667 302.74193548387098,194.19999999999999 299.13978494623655,203.26666666666665 295.53763440860217,205.53333333333333 291.93548387096774,210.06666666666666 288.33333333333331,212.33333333333334 284.73118279569894,203.26666666666665 281.12903225806451,203.26666666666665 277.52688172043014,198.73333333333332 273.92473118279571,210.06666666666666 270.32258064516128,214.60000000000002 266.72043010752691,205.53333333333333 263.11827956989248,216.86666666666667 259.51612903225811,214.60000000000002 255.91397849462368,205.53333333333333 252.31182795698925,203.26666666666665 248.70967741935482,207.80000000000001 245.10752688172042,203.26666666666665 241.50537634408602,201.0 237.90322580645159,210.06666666666666 234.30107526881721,194.19999999999999 230.69892473118279,198.73333333333332 227.09677419354841,205.53333333333333 223.49462365591398,203.26666666666665 219.89247311827955,201.0 216.29032258064515,203.26666666666665 212.68817204301075,212.33333333333334 209.08602150537632,210.06666666666666 205.48387096774195,201.0 201.88172043010752,194.19999999999999 198.27956989247309,214.60000000000002 194.67741935483869,207.80000000000001 191.07526881720429,207.80000000000001 187.47311827956989,212.33333333333334 183.87096774193549,219.13333333333333 180.26881720430109,207.80000000000001 176.66666666666669,205.53333333333333 173.06451612903226,207.80000000000001 169.46236559139786,212.33333333333334 165.86021505376345,205.53333333333333 162.25806451612902,219.13333333333333 158.65591397849462,194.19999999999999 155.05376344086022,205.53333333333333 151.45161290322579,196.46666666666664 147.84946236559139,189.66666666666669 144.24731182795699,219.13333333333333 140.64516129032259,201.0 137.04301075268816,201.0 133.44086021505376,216.86666666666667 129.83870967741936,198.73333333333332 126.23655913978493,203.26666666666665 122.63440860215054,214.60000000000002 119.03225806451613,205.53333333333333 115.43010752688173,216.86666666666667 111.82795698924731,201.0 108.2258064516129,205.53333333333333 104.6236559139785,196.46666666666664 101.02150537634409,205.53333333333333 97.41935483870968,203.26666666666665 93.817204301075265,198.73333333333332 90.215053763440864,194.19999999999999 86.612903225806448,191.93333333333331 83.010752688172047,201.0 79.408602150537632,201.0 75.806451612903231,212.33333333333334 72.204301075268816,207.80000000000001 68.602150537634415,203.26666666666665 65.0,235.0\" style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;opacity:0.5;stroke:none\"><title>parsimony informative sites</title></polygon></g><g class=\"toyplot-mark-AxisLines\" id=\"ta15e84aaaee643e68d810cd25bc2fac4\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"119.39999999999999\" y2=\"119.39999999999999\"></line></g></g><g class=\"toyplot-mark-AxisLines\" id=\"t910f38656ebb4d2297b0d23a837dc685\" style=\"stroke-dasharray:4, 4\"><g class=\"toyplot-Series\"><line class=\"toyplot-Datum\" style=\"opacity:0.9;stroke:rgb(16.1%,15.3%,14.1%);stroke-dasharray:4, 4;stroke-opacity:1.0\" x1=\"65.0\" x2=\"735.0\" y1=\"205.53333333333333\" y2=\"205.53333333333333\"></line></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tb599969d8e7249e58345e62c83f9140c\" transform=\"translate(65.0,235.0)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"666.3978494623656\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"18.010752688172044\" x2=\"18.010752688172044\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"36.02150537634409\" x2=\"36.02150537634409\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"54.03225806451613\" x2=\"54.03225806451613\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"72.04301075268818\" x2=\"72.04301075268818\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"90.05376344086021\" x2=\"90.05376344086021\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"108.06451612903226\" x2=\"108.06451612903226\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"126.0752688172043\" x2=\"126.0752688172043\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"144.08602150537635\" x2=\"144.08602150537635\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"162.09677419354838\" x2=\"162.09677419354838\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"180.10752688172042\" x2=\"180.10752688172042\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"198.11827956989248\" x2=\"198.11827956989248\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"216.1290322580645\" x2=\"216.1290322580645\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"234.13978494623657\" x2=\"234.13978494623657\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"252.1505376344086\" x2=\"252.1505376344086\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"270.1612903225806\" x2=\"270.1612903225806\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"288.1720430107527\" x2=\"288.1720430107527\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"306.18279569892474\" x2=\"306.18279569892474\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"324.19354838709677\" x2=\"324.19354838709677\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"342.2043010752688\" x2=\"342.2043010752688\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"360.21505376344084\" x2=\"360.21505376344084\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"378.2258064516129\" x2=\"378.2258064516129\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"396.23655913978496\" x2=\"396.23655913978496\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"414.247311827957\" x2=\"414.247311827957\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"432.258064516129\" x2=\"432.258064516129\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"450.26881720430106\" x2=\"450.26881720430106\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"468.27956989247315\" x2=\"468.27956989247315\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"486.2903225806452\" x2=\"486.2903225806452\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"504.3010752688172\" x2=\"504.3010752688172\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"522.3118279569892\" x2=\"522.3118279569892\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"540.3225806451612\" x2=\"540.3225806451612\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"558.3333333333334\" x2=\"558.3333333333334\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"576.3440860215054\" x2=\"576.3440860215054\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"594.3548387096774\" x2=\"594.3548387096774\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"612.3655913978495\" x2=\"612.3655913978495\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"630.3763440860215\" x2=\"630.3763440860215\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"648.3870967741935\" x2=\"648.3870967741935\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"666.3978494623656\" x2=\"666.3978494623656\" y1=\"0\" y2=\"-5\"></line></g><g><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(18.010752688172044,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">5</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(36.02150537634409,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">10</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(54.03225806451613,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">15</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(72.04301075268818,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">20</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(90.05376344086021,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">25</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(108.06451612903226,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">30</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(126.0752688172043,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">35</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(144.08602150537635,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">40</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(162.09677419354838,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">45</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(180.10752688172042,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">50</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(198.11827956989248,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">55</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(216.1290322580645,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">60</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(234.13978494623657,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">65</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(252.1505376344086,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">70</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(270.1612903225806,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">75</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(288.1720430107527,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">80</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(306.18279569892474,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">85</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(324.19354838709677,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">90</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(342.2043010752688,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">95</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(360.21505376344084,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">100</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(378.2258064516129,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">105</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(396.23655913978496,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">110</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(414.247311827957,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">115</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(432.258064516129,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">120</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(450.26881720430106,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">125</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(468.27956989247315,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">130</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(486.2903225806452,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">135</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(504.3010752688172,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">140</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(522.3118279569892,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">145</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(540.3225806451612,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">150</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(558.3333333333334,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">155</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(576.3440860215054,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">160</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(594.3548387096774,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">165</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(612.3655913978495,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">170</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(630.3763440860215,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">175</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(648.3870967741935,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">180</tspan></text><text style=\"dominant-baseline:hanging;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(666.3978494623656,6)translate(0,2.5)\"><tspan style=\"dominant-baseline:inherit\">185</tspan></text></g><text style=\"dominant-baseline:hanging;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(335.0,22)translate(0,40.0)\"><tspan style=\"dominant-baseline:inherit\">Position along RAD loci</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tb8d3dbe3a407402b903016609c3698bb\" transform=\"translate(65.0,235.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"0\" x2=\"165.46666666666667\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"0.0\" x2=\"0.0\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"56.666666666666664\" x2=\"56.666666666666664\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"113.33333333333333\" x2=\"113.33333333333333\" y1=\"0\" y2=\"5\"></line><line style=\"\" x1=\"170.0\" x2=\"170.0\" y1=\"0\" y2=\"5\"></line></g><g><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(0.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">0</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(56.666666666666664,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">25</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(113.33333333333333,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">50</tspan></text><text style=\"dominant-baseline:alphabetic;font-size:12px;font-weight:normal;stroke:none;text-anchor:middle\" transform=\"translate(170.0,-6)translate(0,-5.0)\"><tspan style=\"dominant-baseline:inherit\">75</tspan></text></g><text style=\"dominant-baseline:alphabetic;font-size:16px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(85.0,-22)translate(0,-40.0)\"><tspan style=\"dominant-baseline:inherit\">N variables sites</tspan></text><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g><text style=\"dominant-baseline:middle;font-size:14px;font-weight:bold;stroke:none;text-anchor:middle\" transform=\"translate(400.0,65.0)translate(0,-14.0)\"><tspan style=\"dominant-baseline:inherit\">ipyrad-denovo_reference-sim</tspan></text></g></svg><div class=\"toyplot-interactive\"><ul class=\"toyplot-mark-popup\" onmouseleave=\"this.style.visibility='hidden'\" style=\"background:rgba(0%,0%,0%,0.75);border:0;border-radius:6px;color:white;cursor:default;list-style:none;margin:0;padding:5px;position:fixed;visibility:hidden\">\n",
       "            <li class=\"toyplot-mark-popup-title\" style=\"color:lightgray;cursor:default;padding:5px;list-style:none;margin:0\"></li>\n",
       "            <li class=\"toyplot-mark-popup-save-csv\" onmouseout=\"this.style.color='white';this.style.background='steelblue'\" onmouseover=\"this.style.color='steelblue';this.style.background='white'\" style=\"border-radius:3px;padding:5px;list-style:none;margin:0\">\n",
       "                Save as .csv\n",
       "            </li>\n",
       "        </ul><script>\n",
       "        (function()\n",
       "        {\n",
       "          var data_tables = [{\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"t7bac5bb25e114b228cd6ea05221e2313\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0, 176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 59.0, 43.0, 57.0, 51.0, 45.0, 51.0, 59.0, 58.0, 54.0, 42.0, 61.0, 50.0, 55.0, 51.0, 55.0, 44.0, 60.0, 47.0, 47.0, 73.0, 60.0, 46.0, 46.0, 46.0, 58.0, 61.0, 41.0, 59.0, 40.0, 53.0, 50.0, 41.0, 44.0, 57.0, 48.0, 52.0, 51.0, 55.0, 55.0, 48.0, 52.0, 43.0, 65.0, 57.0, 42.0, 52.0, 58.0, 57.0, 61.0, 47.0, 51.0, 47.0, 56.0, 55.0, 44.0, 57.0, 48.0, 51.0, 66.0, 51.0, 47.0, 55.0, 53.0, 57.0, 51.0, 63.0, 48.0, 53.0, 50.0, 48.0, 40.0, 57.0, 47.0, 58.0, 52.0, 46.0, 47.0, 53.0, 64.0, 53.0, 51.0, 54.0, 54.0, 65.0, 56.0, 50.0, 0.0, 0.0, 0.0, 62.0, 54.0, 46.0, 55.0, 51.0, 46.0, 48.0, 59.0, 51.0, 53.0, 51.0, 60.0, 44.0, 54.0, 48.0, 39.0, 49.0, 59.0, 42.0, 49.0, 53.0, 48.0, 46.0, 44.0, 51.0, 50.0, 49.0, 47.0, 47.0, 66.0, 55.0, 45.0, 53.0, 50.0, 57.0, 47.0, 54.0, 54.0, 46.0, 57.0, 50.0, 57.0, 50.0, 59.0, 41.0, 50.0, 50.0, 56.0, 49.0, 44.0, 50.0, 55.0, 49.0, 45.0, 42.0, 72.0, 54.0, 52.0, 55.0, 53.0, 47.0, 52.0, 53.0, 59.0, 56.0, 55.0, 51.0, 68.0, 41.0, 48.0, 53.0, 45.0, 50.0, 58.0, 61.0, 60.0, 58.0, 54.0, 62.0, 41.0, 48.0, 55.0, 43.0, 48.0, 57.0, 45.0, 49.0, 54.0, 56.0, 55.0, 57.0, 48.0, 58.0, 52.0, 42.0, 59.0]], \"filename\": \"toyplot\"}, {\"title\": \"Fill Data\", \"names\": [\"x\", \"y0\", \"y1\"], \"id\": \"t91084815576c432d9d7b2bb25df751eb\", \"columns\": [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0, 131.0, 132.0, 133.0, 134.0, 135.0, 136.0, 137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 153.0, 154.0, 155.0, 156.0, 157.0, 158.0, 159.0, 160.0, 161.0, 162.0, 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 169.0, 170.0, 171.0, 172.0, 173.0, 174.0, 175.0, 176.0, 177.0, 178.0, 179.0, 180.0, 181.0, 182.0, 183.0, 184.0, 185.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 14.0, 12.0, 10.0, 15.0, 15.0, 19.0, 18.0, 16.0, 14.0, 13.0, 17.0, 13.0, 15.0, 8.0, 13.0, 9.0, 14.0, 16.0, 8.0, 15.0, 15.0, 7.0, 20.0, 17.0, 13.0, 18.0, 7.0, 13.0, 10.0, 12.0, 13.0, 12.0, 7.0, 10.0, 12.0, 12.0, 9.0, 18.0, 15.0, 11.0, 10.0, 14.0, 15.0, 14.0, 13.0, 16.0, 18.0, 11.0, 15.0, 14.0, 12.0, 14.0, 13.0, 9.0, 8.0, 13.0, 9.0, 11.0, 16.0, 14.0, 14.0, 10.0, 11.0, 13.0, 14.0, 18.0, 8.0, 8.0, 15.0, 10.0, 12.0, 18.0, 11.0, 16.0, 12.0, 12.0, 13.0, 12.0, 17.0, 12.0, 7.0, 10.0, 17.0, 21.0, 15.0, 15.0, 0.0, 0.0, 0.0, 12.0, 10.0, 8.0, 17.0, 16.0, 10.0, 12.0, 20.0, 7.0, 18.0, 8.0, 17.0, 14.0, 14.0, 17.0, 7.0, 16.0, 13.0, 12.0, 18.0, 11.0, 16.0, 12.0, 16.0, 11.0, 12.0, 7.0, 11.0, 16.0, 12.0, 9.0, 8.0, 17.0, 12.0, 11.0, 12.0, 10.0, 11.0, 16.0, 10.0, 16.0, 15.0, 13.0, 17.0, 10.0, 11.0, 13.0, 17.0, 13.0, 8.0, 11.0, 17.0, 12.0, 8.0, 14.0, 12.0, 15.0, 13.0, 14.0, 16.0, 10.0, 15.0, 12.0, 11.0, 11.0, 8.0, 14.0, 15.0, 9.0, 11.0, 12.0, 15.0, 11.0, 15.0, 20.0, 15.0, 19.0, 12.0, 15.0, 10.0, 11.0, 12.0, 17.0, 16.0, 15.0, 9.0, 13.0, 15.0, 17.0, 15.0, 20.0, 17.0, 9.0, 8.0, 15.0, 11.0]], \"filename\": \"toyplot\"}];\n",
       "\n",
       "          function save_csv(data_table)\n",
       "          {\n",
       "            var uri = \"data:text/csv;charset=utf-8,\";\n",
       "            uri += data_table.names.join(\",\") + \"\\n\";\n",
       "            for(var i = 0; i != data_table.columns[0].length; ++i)\n",
       "            {\n",
       "              for(var j = 0; j != data_table.columns.length; ++j)\n",
       "              {\n",
       "                if(j)\n",
       "                  uri += \",\";\n",
       "                uri += data_table.columns[j][i];\n",
       "              }\n",
       "              uri += \"\\n\";\n",
       "            }\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = data_table.filename + \".csv\";\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "          }\n",
       "\n",
       "          function open_popup(data_table)\n",
       "          {\n",
       "            return function(e)\n",
       "            {\n",
       "              var popup = document.querySelector(\"#t85a7e21009a04f138c5807fb2e89c398 .toyplot-mark-popup\");\n",
       "              popup.querySelector(\".toyplot-mark-popup-title\").innerHTML = data_table.title;\n",
       "              popup.querySelector(\".toyplot-mark-popup-save-csv\").onclick = function() { popup.style.visibility = \"hidden\"; save_csv(data_table); }\n",
       "              popup.style.left = (e.clientX - 50) + \"px\";\n",
       "              popup.style.top = (e.clientY - 20) + \"px\";\n",
       "              popup.style.visibility = \"visible\";\n",
       "              e.stopPropagation();\n",
       "              e.preventDefault();\n",
       "            }\n",
       "\n",
       "          }\n",
       "\n",
       "          for(var i = 0; i != data_tables.length; ++i)\n",
       "          {\n",
       "            var data_table = data_tables[i];\n",
       "            var event_target = document.querySelector(\"#\" + data_table.id);\n",
       "            event_target.oncontextmenu = open_popup(data_table);\n",
       "          }\n",
       "        })();\n",
       "        </script><script>\n",
       "        (function()\n",
       "        {\n",
       "            function _sign(x)\n",
       "            {\n",
       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
       "\n",
       "            function _mix(a, b, amount)\n",
       "            {\n",
       "                return ((1.0 - amount) * a) + (amount * b);\n",
       "            }\n",
       "\n",
       "            function _log(x, base)\n",
       "            {\n",
       "                return Math.log(Math.abs(x)) / Math.log(base);\n",
       "            }\n",
       "\n",
       "            function _in_range(a, x, b)\n",
       "            {\n",
       "                var left = Math.min(a, b);\n",
       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
       "            }\n",
       "\n",
       "            function inside(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.min, range, segment.range.max))\n",
       "                        return true;\n",
       "                }\n",
       "                return false;\n",
       "            }\n",
       "\n",
       "            function to_domain(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(_in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
       "                    {\n",
       "                        if(segment.scale == \"linear\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            return _mix(segment.domain.min, segment.domain.max, amount)\n",
       "                        }\n",
       "                        else if(segment.scale[0] == \"log\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            var base = segment.scale[1];\n",
       "                            return _sign(segment.domain.min) * Math.pow(base, _mix(_log(segment.domain.min, base), _log(segment.domain.max, base), amount));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            function display_coordinates(e)\n",
       "            {\n",
       "                var current = svg.createSVGPoint();\n",
       "                current.x = e.clientX;\n",
       "                current.y = e.clientY;\n",
       "\n",
       "                for(var axis_id in axes)\n",
       "                {\n",
       "                    var axis = document.querySelector(\"#\" + axis_id);\n",
       "                    var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
       "                    if(coordinates)\n",
       "                    {\n",
       "                        var projection = axes[axis_id];\n",
       "                        var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
       "                        if(inside(local.x, projection))\n",
       "                        {\n",
       "                            var domain = to_domain(local.x, projection);\n",
       "                            coordinates.style.visibility = \"visible\";\n",
       "                            coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
       "                            var text = coordinates.querySelector(\"text\");\n",
       "                            text.textContent = domain.toFixed(2);\n",
       "                        }\n",
       "                        else\n",
       "                        {\n",
       "                            coordinates.style.visibility= \"hidden\";\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var root_id = \"t85a7e21009a04f138c5807fb2e89c398\";\n",
       "            var axes = {\"tb599969d8e7249e58345e62c83f9140c\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 186.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 670.0, \"min\": 0.0}, \"scale\": \"linear\"}], \"tb8d3dbe3a407402b903016609c3698bb\": [{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 75.0, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 170.0, \"min\": 0.0}, \"scale\": \"linear\"}]};\n",
       "\n",
       "            var svg = document.querySelector(\"#\" + root_id + \" svg\");\n",
       "            svg.addEventListener(\"click\", display_coordinates);\n",
       "        })();\n",
       "        </script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for prog in all_calldata.keys():\n",
    "#for prog in [\"ipyrad-reference-sim\"]:\n",
    "    print(\"Doing {}\".format(prog))\n",
    "    try:\n",
    "        c = all_calldata[prog]\n",
    "        v = all_vardata[prog]\n",
    "    except:\n",
    "        print(\"Failed to load for {}\".format(prog))\n",
    "        continue\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), 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(prog, distvar, distpis)\n",
    "\n",
    "    ## save fig\n",
    "    #toyplot.html.render(canvas, 'snp_positions.html')\n",
    "\n",
    "    canvas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Checking out differences between 0.85 and 0.95 clustering threshold for ipyrad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2aaba8c59b90>"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABn0AAALKCAYAAAD3ZZSCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqE9INM9hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aabf8a94a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filename = \"/home/iovercast/manuscript-analysis/Phocoena_empirical/ipyrad/reference-assembly/denovo_minus_reference-0.95_outfiles/denovo_ref-empirical.vcf\"\n",
    "## Only need to load once\n",
    "#myv = vcfnp.variants(filename, verbose=False, dtypes={\"CHROM\":\"a24\"}).view(np.recarray)\n",
    "#myc = vcfnp.calldata_2d(filename, verbose=False).view(np.recarray)\n",
    "\n",
    "f, axarr = plt.subplots(1, 2, figsize=(20,8), dpi=1000)\n",
    "## Make a list out of the axes\n",
    "## Set them in order so the plot looks nice.\n",
    "progs = [\"ipyrad-reference-sim\", \"ipyrad-denovo_plus_reference-sim\", \"ipyrad-denovo_minus_reference-sim\", \"stacks-sim\", \"ddocent-fin-sim\",\\\n",
    "         \"ipyrad-reference-empirical\", \"ipyrad-denovo_reference-empirical\", \"ipyrad-denovo_minus_reference-empirical\", \"stacks-empirical\", \"ddocent-fin-empirical\"]\n",
    "rundict = {\"0.85\":all_calldata[\"ipyrad-denovo_minus_reference-empirical\"], \"0.95\":myc}\n",
    "\n",
    "for prog, ax in zip(rundict.keys(), axarr):\n",
    "    pop_colors = emp_pop_colors\n",
    "    pops = emp_pops\n",
    "    sample_names = emp_sample_names\n",
    "\n",
    "    ## Don't die if some of the runs aren't complete\n",
    "    try:\n",
    "        coords1, model1 = getPCA(rundict[prog])\n",
    "    except:\n",
    "        continue\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(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, style=\"italic\")\n",
    "    ax.axison = True\n",
    "\n",
    "axarr[0].legend(frameon=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_ld(gn, title):\n",
    "    m = allel.stats.rogers_huff_r(gn) ** 2\n",
    "    ax = allel.plot.pairwise_ld(m)\n",
    "    ax.set_title(title)\n",
    "def ld_prune(gn, size=1000, step=1000, threshold=.3, n_iter=5):\n",
    "    for i in range(n_iter):\n",
    "        loc_unlinked = allel.stats.ld.locate_unlinked(gn, size=size, step=step, threshold=threshold)\n",
    "        n = np.count_nonzero(loc_unlinked)\n",
    "        n_remove = gn.shape[0] - n\n",
    "        print('iteration', i+1, 'retaining', n, 'removing', n_remove, 'variants')\n",
    "        gn = gn.compress(loc_unlinked, axis=0)\n",
    "    return gn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "g = allel.GenotypeArray(myc.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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('iteration', 1, 'retaining', 70451, 'removing', 12654, 'variants')\n",
      "('iteration', 2, 'retaining', 60084, 'removing', 10367, 'variants')\n",
      "('iteration', 3, 'retaining', 51456, 'removing', 8628, 'variants')\n",
      "('iteration', 4, 'retaining', 44316, 'removing', 7140, 'variants')\n",
      "('iteration', 5, 'retaining', 38444, 'removing', 5872, 'variants')\n"
     ]
    }
   ],
   "source": [
    "gnu = ld_prune(gn, size=50, step=200, threshold=.1, n_iter=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coords1, model1 = allel.pca(gnu, n_components=10, scaler='patterson')"
   ]
  }
 ],
 "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
}