{
 "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": 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BAAAAAHAgCm8AAAAAAByIwhsAAAAAAAei8AYAAAAAwIEovAEAAAAAcCAKbwAA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CjI0M9auXKl5s6dW+n+Bg0aGPr5AAD/dfbsWQ0ZMqTS/ZMmTdLkyZOrsSLvoXcDAPyB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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": 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cc87JysvLs8svv7xoX6ecckq2//77F132ne98JzvrrLNW/06CL8F7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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": {
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cN2/eXBs3bjQvHQDL+Pm5bPNyEkNFuUaNGjpy5IikK1vhli9fLulKB12+fHnz0gGAwxgq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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": 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      "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": 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EFACS/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": 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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 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       "                Save as .csv\n",
       "            </li>\n",
       "        </ul><script>\n",
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      ],
      "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": 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      "text/plain": [
       "<toyplot.canvas.Canvas at 0x2aab7727b4d0>"
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     },
     "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": 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kJLdd3u8MyG2kyxtzZmYml/fk7bxRtP2LFy+Qk5MDDw8PufWK+76Ji4vD48eP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\nBaivYuPitDU/X3F1WGJwi8mk2Ljaf/8AgDsrLCzUc889p1d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qFVq1aYNm0aGjdujJSUFJw6dQqjR49GeHg4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rePjhh23fd0JCAkeOHKF169asW7eOlJQU6tSpQ0BAAE8//TTVq1fP8/jyirOgdeLyK6f79evH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\nXuXtr2hvb09kZGSx++UGRwghIH7LFj51+nuFncLKYKoGo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XdxvLRaNh46xO+//84Py5ax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      "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": 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3ZGZmol+/fqhatSr+/fdfbN26FZmZmbKCw+HDhzFu3DipEubu3bsYP3486tWrZ1DPRrFS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\nmFJEREREREREpJuHhweeP3+O2rVro06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PAAAg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\nrS7d2liJa/FKXNskx7Vp+Fn0A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      "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": {
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       "<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",
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      "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                                              "
      ]
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     "output_type": "display_data"
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     "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": {
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      "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"
     ]
    },
    {
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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 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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\" 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       "                Save as .csv\n",
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       "        </ul><script>\n",
       "        (function()\n",
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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\" 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       "                Save as .csv\n",
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       "        (function()\n",
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       "                uri += data_table.columns[j][i];\n",
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       "              link.download = data_table.filename + \".csv\";\n",
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       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
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       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
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      ]
     },
     "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"
    },
    {
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uuCLV1dU5cuRIXnjhhZM9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\nWAc6h3UgP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      "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
}