001_testdata_explore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this notebook best viewed here: http://nbviewer.jupyter.org"
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    " "
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   "source": [
    "  "
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    " "
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Purpose\n",
    "\n",
    "We ran the same population as individuals (indSeq) and as a pool (poolSeq).\n",
    "\n",
    "Here I read in the data, filter for the intersection of common loci, and then explore the data a bit.\n",
    "\n",
    "I had coded the pipeline to also capture loci that are biallelic (two alleles) but neither of these alleles were the reference (since we're using a reference from a different species). Normally, pipelines will only keep biallelic SNPs where one of the alleles is the reference allele. In any case, I discard them here because there aren't very many of them.\n",
    "\n",
    "#### Conclusions\n",
    "\n",
    "It's probably OK to leave in the noREF SNPs, the frequency is estimated (more or less) pretty similarly between GATK and VarScan (even if the alleles are not the same), but since there aren't very many I decided to discard them from further analyses."
   ]
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   "source": [
    "  "
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    " "
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pythonimports import *\n",
    "methodsdir = makedir('/data/home/lindb/methods_paper_analysis')\n",
    "firstdir = makedir(op.join(methodsdir, '001_testdata_explore'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get the snptables, do some minor filtering\n",
    "- read in data\n",
    "- filter for intersecting loci\n",
    "- save to .pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the file names\n",
    "sppfiles = {'gatk':{'JP': '/data/projects/pool_seq/non-pangenome/gatk_diploid_testdata/JP_i101-gatk/filtered_snps/JP_i101_filtered_concatenated_snps_max-missing_table_biallelic-only_translated.txt',\n",
    "                   'DF': '/data/projects/pool_seq/non-pangenome/gatk_diploid_testdata/DF_i52-gatk/filtered_snps/DF_i52_filtered_concatenated_snps_max-missing_table_biallelic-only_p000_translated.txt'},\n",
    "           'varscan':{'JP': '/data/projects/pool_seq/non-pangenome/varscan_pooled/JP_pooled/snpsANDindels/JP_pooled-varscan_all_bedfiles_SNP_translated.txt',\n",
    "                      'DF': '/data/projects/pool_seq/non-pangenome/varscan_pooled/DF_p52/snpsANDindels/DF_p52-varscan_all_bedfiles_SNP.txt'}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure I copied them correctly\n",
    "for method,spp in sppfiles.items():\n",
    "    for sp,f in spp.items():\n",
    "        assert op.exists(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk\n",
      "\t JP\n",
      "\t DF\n",
      "varscan\n",
      "\t JP\n",
      "\t DF\n"
     ]
    }
   ],
   "source": [
    "# read in the tables\n",
    "tablecols = ['CHROM','POS','REF','ALT','AF','QUAL','TYPE','FILTER','ADP','WT','HET','HOM','NC']\n",
    "snps = {}\n",
    "for method in sppfiles:\n",
    "    print(method)\n",
    "    snps[method] = {}\n",
    "    for spp in sppfiles[method]:\n",
    "        print('\\t',spp)\n",
    "        snps[method][spp] = pd.read_table(sppfiles[method][spp])\n",
    "        if method == 'varscan' and spp == 'JP':\n",
    "            # i ran all pools, isolate JP_p101 calls\n",
    "            locuscols = [col for col in snps[method][spp] if 'locus' in col]\n",
    "            snps[method][spp] = snps[method][spp][tablecols + ['JP_p101.GT','JP_p101.GQ','JP_p101.SDP',\n",
    "                                                               'JP_p101.DP','JP_p101.FREQ','JP_p101.PVAL'] +locuscols]\n",
    "            # remove AF=0 (because SNPs were called across all pools, JP_p101 can have AF=0)\n",
    "            # AF=0 wouldn't be possible if calling AFs on a single pool\n",
    "            snps[method][spp] = snps[method][spp][snps[method][spp]['JP_p101.FREQ'] != \"0%\"]\n",
    "            # remove rows with no calls\n",
    "            snps[method][spp] = snps[method][spp][snps[method][spp]['JP_p101.FREQ'] == snps[method][spp]['JP_p101.FREQ']]\n",
    "            # fill in AF column\n",
    "            snps[method][spp]['AF'] = snps[method][spp]['JP_p101.FREQ'].str.replace(\"%\", \"\").astype(float)/100\n",
    "        elif method =='varscan' and spp == 'DF':\n",
    "            df = snps[method][spp]  # view, will changes go to snps['varscan']['DF']\n",
    "            # add in a \">\" so locus names match between gatk and varscan\n",
    "            df['CHROM'] = [f\">{chrom}\" for chrom in df['CHROM'].tolist()]\n",
    "            df['locus'] = [f\">{locus}\" for locus in df['locus'].tolist()]\n",
    "# note varscan will output AF=0 (fixed for REF), I'm leaving these in so we can compare AFs of these too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP gatk 0.025 1.0\n",
      "DF gatk 0.025 1.0\n",
      "JP varscan 0.0001 1.0\n",
      "DF varscan 0.0039 1.0\n"
     ]
    }
   ],
   "source": [
    "# look at allele freqs\n",
    "for method in snps:\n",
    "    for spp,df in snps[method].items():\n",
    "        print(spp, method, min(df['AF']), max(df['AF']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP gatk (378713, 117)\n",
      "DF gatk (1527720, 117)\n",
      "JP varscan (3686528, 21)\n",
      "DF varscan (1601285, 21)\n"
     ]
    }
   ],
   "source": [
    "# look at num snps and cols\n",
    "for method in snps:\n",
    "    for spp,df in snps[method].items():\n",
    "        print(spp, method, df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP ['locus', 'unstitched_locus']\n",
      "gatk DF ['locus', 'unstitched_locus']\n",
      "varscan JP ['locus', 'unstitched_locus']\n",
      "varscan DF ['locus']\n"
     ]
    }
   ],
   "source": [
    "# make sure columns are there that I want\n",
    "for method in snps:\n",
    "    for spp,df in snps[method].items():\n",
    "        cols = [col for col in df.columns if 'locus' in col]\n",
    "        print(method, spp, cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### filter for intersecting loci"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getlocuscol(df):\n",
    "    \"\"\"Determine which column contains the locus name.\"\"\"\n",
    "    return 'unstitched_locus' if 'unstitched_locus' in df else 'locus'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP unstitched_locus\n",
      "gatk DF unstitched_locus\n",
      "varscan JP unstitched_locus\n",
      "varscan DF locus\n"
     ]
    }
   ],
   "source": [
    "# get the list of loci\n",
    "methodloci = {}\n",
    "for method in snps:\n",
    "    for spp in snps[method]:\n",
    "        df = snps[method][spp]\n",
    "        locuscol = getlocuscol(df)\n",
    "        print(method, spp, locuscol)\n",
    "        if spp not in methodloci:\n",
    "            methodloci[spp] = []\n",
    "        methodloci[spp].append(df[locuscol].tolist())\n",
    "# for spp,lsts in methodloci.items():\n",
    "#     print(spp,len(lsts), type(lsts), type(lsts[0]), type(lsts[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['>jcf7190000000231-59482',\n",
       " '>jcf7190000000231-108467',\n",
       " '>jcf7190000000275-99191',\n",
       " '>jcf7190000000290-96627',\n",
       " '>jcf7190000000366-177185']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methodloci['DF'][0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['>jcf7190000000000-77616',\n",
       " '>jcf7190000000000-77656',\n",
       " '>jcf7190000000000-77657',\n",
       " '>jcf7190000000000-77729',\n",
       " '>jcf7190000000000-77738']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methodloci['DF'][1][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['>super808-865131',\n",
       " '>super819-359621',\n",
       " '>super896-944540',\n",
       " '>super1256-476207',\n",
       " '>super1349-138470']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methodloci['JP'][0][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['>super4-15421',\n",
       " '>super4-15496',\n",
       " '>super4-47156',\n",
       " '>super4-47333',\n",
       " '>super4-47395']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methodloci['JP'][1][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP 256950\n",
      "DF 637410\n"
     ]
    }
   ],
   "source": [
    "# get intersection\n",
    "# indexing assumes only two methods\n",
    "interloci = {}\n",
    "for spp in methodloci:\n",
    "    interloci[spp] = set(methodloci[spp][0]).intersection(methodloci[spp][1])\n",
    "    print(spp, len(interloci[spp]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP 378713\n",
      "\t gatk JP 256950\n",
      "gatk DF 1527720\n",
      "\t gatk DF 637410\n",
      "varscan JP 3686528\n",
      "\t varscan JP 256950\n",
      "varscan DF 1601285\n",
      "\t varscan DF 637410\n"
     ]
    }
   ],
   "source": [
    "# reduce to intersection\n",
    "for method,spp in snps.items():\n",
    "    for sp,df in spp.items():\n",
    "        locuscol = getlocuscol(df)\n",
    "        print(method, sp, nrow(df))\n",
    "        snps[method][sp] = df[df[locuscol].isin(interloci[sp])].copy()\n",
    "        print('\\t', method, sp, nrow(snps[method][sp]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save so I don't have to reload and refilter\n",
    "pklfile = op.join(firstdir, 'snps_dict_intersecting_loci.pkl')\n",
    "pkldump(snps, pklfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "snps = pklload(op.join(firstdir, 'snps_dict_intersecting_loci.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data after removing noREF snps is saved below as a pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# before removing noREF SNPs, compare to varscan\n",
    "\n",
    "noREF SNPs are a biallelic SNPs that do not have the reference allele (from gatk pipeline). I'll eventually remove these, but curious what's happening at the same loci called with VarScan (that only would have called true biallelic SNPs).\n",
    "\n",
    "- first compare to noREF snps in the gatk table (later expand to all data before filtering)\n",
    "- plot relationship\n",
    "- redo VariantsToTable on each bedfile-produced vcf, look at effect across all data \n",
    "\n",
    "Conclusion: Even if VarScan does not call a site multiallelic that was called multiallelic by gatk, the estimated AF is still similar when considering the alt. In other words, even when GATK and VarScan disagree on the specific alleles, they agree (more or less) on AF."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### first compare to noREF snps in the gatk table (later expand to all data before filtering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP 1244\n",
      "gatk DF 1131\n",
      "varscan JP 0\n",
      "varscan DF 0\n"
     ]
    }
   ],
   "source": [
    "# make sure only gatk has noREF snps\n",
    "for method,spp in snps.items():\n",
    "    for sp,df in spp.items():\n",
    "        print(method, sp, sum(snps[method][sp]['ALT'].str.contains('+',regex=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP 1244\n",
      "DF 1131\n"
     ]
    }
   ],
   "source": [
    "# get noREF SNPs (ploci)\n",
    "ploci = {}\n",
    "for sp,df in snps['gatk'].items():\n",
    "    # get ploci from gatk\n",
    "    locuscol = getlocuscol(df)\n",
    "    ploci[sp] = df.loc[df['ALT'].str.contains('+', regex=False), locuscol].tolist()\n",
    "    # reduce to intersection with varscan\n",
    "    locuscol = getlocuscol(snps['varscan'][sp])\n",
    "    ploci[sp] = list(set(ploci[sp]).intersection(snps['varscan'][sp][locuscol].tolist()))\n",
    "    print(sp, len(ploci[sp]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP 1244\n",
      "gatk DF 1131\n",
      "varscan JP 1244\n",
      "varscan DF 1131\n"
     ]
    }
   ],
   "source": [
    "# isolate ploci from each method/spp\n",
    "nrsnps = {}\n",
    "for method,spp in snps.items():\n",
    "    nrsnps[method] = {}\n",
    "    for sp,df in spp.items():\n",
    "        locuscol = getlocuscol(df)\n",
    "        nrsnps[method][sp] = df[df[locuscol].isin(ploci[sp])].copy()\n",
    "        nrsnps[method][sp].index = nrsnps[method][sp][locuscol].tolist()\n",
    "        print(method, sp, len(nrsnps[method][sp]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# match up alt alleles between gatk and varscan\n",
    "for sp,df in nrsnps['gatk'].items():\n",
    "    df['new_AF'] = df['AF']\n",
    "    locuscol = getlocuscol(df)\n",
    "    for locus in df[locuscol]:\n",
    "        alt = nrsnps['varscan'][sp].loc[locus, 'ALT']\n",
    "        if df.loc[locus, 'ALT'].split(\"+\")[1] != alt:\n",
    "            df.loc[locus, 'new_AF'] = 1 - df.loc[locus, 'AF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### plot relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def makesweetgraph(x=None,y=None,cmap='jet',ylab=None,xlab=None,bins=100,save=False,saveloc='',sets=(None,None),figsize=(5,4),snsbins=60,title=None):\n",
    "    \"\"\"Make 2D histogram.\"\"\"\n",
    "    import seaborn as sns\n",
    "    from matplotlib.colors import LogNorm\n",
    "    set1,set2 = sets\n",
    "    ax1 = sns.jointplot(x=x, y=y,marginal_kws=dict(bins=snsbins))\n",
    "    ax1.fig.set_size_inches(figsize[0], figsize[1])\n",
    "    ax1.ax_joint.cla()\n",
    "    plt.sca(ax1.ax_joint)\n",
    "    plt.hist2d(x,y,bins,norm=LogNorm(),cmap=cmap)\n",
    "    if title is None:\n",
    "        plt.title('%s vs\\n%s\\n' % (xlab,ylab),y=1.2,x=0.6)\n",
    "    else:\n",
    "        plt.title(title,y=1.2,x=0.6)\n",
    "    plt.ylabel(ylab,fontsize=12)\n",
    "    plt.xlabel(xlab,fontsize=12)\n",
    "    cbar_ax = ax1.fig.add_axes([1, 0.1, .03, .7])\n",
    "    cb = plt.colorbar(cax=cbar_ax)\n",
    "    cb.set_label(r'$\\log_{10}$ density of points',fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP (0.8334385558107021, 3.36e-322)\n",
      "DF (0.8678774163267831, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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O4zhO4fBlQ8dxhgVfAnSaic+8HMdxnMLhMy/HcVqCz7ScVuIzL8dxHKdwuPJyHMdxCocrL8dxHKdwuPJyHMdxCocbbDiOkxs3xnDajc+8HMdxnMLhMy/H6THKZ03lObN8VuUUAZ95OY7jOIXDZ16OU2DqzaIcp1uRmbVbhqFQaOEdJy++pDeQPbearHbL4LQHXzZ0HMdxCocvGzpdTaWZSruW1nzW5DjNw5WX4zQB33tynOHFlZdTaJqhNBqZEdXrx2dZjtNa3GCjx+ikZbRy/IHv5MUNNnoXn3kViJSHeyMOp3mVRiVl54rHcZzhpNAzr0tmPVJX+GYs7zSiEOpd4wrAcYaOz7x6l0IrL0nXABPaLccQmQDMa7cQTcTH09l023iWMbPN2i2EM/wUWnl1A5Jmm9nUdsvRLHw8nY2Px+kW3EnZcRzHKRzJykvSfpKuk/T3+P49knZrnWiO4ziOU5kk5SXpGOBLwIXAOrH4ceCIFsnVS5zVbgGajI+ns/HxOF1B0p6XpEeAt5vZM5KeM7OVJAmYb2YrtVxKx3Ecx8mQumy4FPBCPC5pu+WBF5sukeM4juPUIdVJ+RrgZEmHZMqOBn7XdIlyMG3aNLvmmmvaKYLTYsKKdcDsqDZKEqglT/ZcpfON9FGpvWrny+tWkmfp+S/0vX995R8OvvDafplHT31hcL0jwvm9Tzi7r+jy+Z8a1MxLS43rO355zGgAFo4c21c28cZnw8EdmYtWzRxfGf9u3V902SH/AcC9uqqv7Cizun5eW2yznR1+6oX1qrWdMt9P91+rQ+rM62vA2sDzwDhJLwAbAIe3SrAU5s3rJncVx3FawcIF89stgtMCcvl5SZoITAbmmtmjLZMqkalTp9rs2bPbLYbjOO2j7gxl3Y03t+PPv3o4ZBkSPvPKR67Yhmb2GPBYi2RxHMdpOuPHjO6Y4NNO86iqvCQtoXbUdgFmZiObLpXjOI7j1KDWzGv9YZPCcRzHcXJQVXmZ2f2VyiWtamZPt04kx3Ecx6lN0p6XpHHAj4FPAEuAMZI+DEy1TrBfdhzHqcL8l15vabqhdu2nrSfZywn1noD/BW4HZpjZjBaLNWykGmz8lOCQvAHwj1g2CzgFcOXlFJpm+Wc1Cx2ZeXNx/6E9UuH8iYP9zvrGk/HZ4oY6nZb62au/qOTLNYut+sruu25zAI7b8bD+MjbsO16Z4L81k537yo6Jj4hsO1sxC4A5vKmvbD36F3t2eyH4cj21wsp9ZdvzJwDuvW6LfiF37N2sGK8AX06o91/BNuGAVssz3KQqrw8Aa5nZ65IMwMyelrRa60RzHMdxqiFgVLuFaCOpyusFYDzwZKlA0iTgqVYI5TiO00y60VRe5PR16jJSA/MeCXwQOBKYAewAnAj83sxOaamENXAnZafXqRSuqtkhtfIsqw5FngaXb5OclB+4+x/1qnUadce1tmTfTGjoS3B7NybsTFXcJwKvAecCywCXAD8DKgRHcxzHcVpNry8b5goP1Wn4zMtxep6enXmtIyXNTfeDOcD19Iq1oaRtUxowsxubJ47jOE7zacRUvtP3yUYAy6ZVfb7XrA1/kXC9EQL1Oo7jOMNIry8b1oqwMWk4BXGcTqCewUG2vNY1lc5VOp/c9xGZ6zK+Xxs8EpbD7tNv+gtL/l37D2zfHgFd1/++lK8L4PWTVwgH22UuiNePvmNwvQ1O6F+Ge2j+lL7jKeMfAvr9uADeyS0AfPG6n/fLvePgZbxsO+8ZfxMAf7zuQ/0Vdhp8L+yEQc30DK68HMdxupxOXwJslF5+gNfa83qQ2lHlATCzdZsqkeM4LUE6ZmDUDafQ5Njz6kqqWhtKen9KA2b2x6ZKlAO3NnScQKXlyfKySgxnKKxmheEqW17tWWvDjSQ7N6Ghd/eatWE7lZLjON1DihJ18pMjwkbPWRsOQNJmwHuACWR+FZjZsS2Qy3GcHFSaxbQ7wHAlmiFT3jbGjxk95D47kV432BiRUknS54BbgZ2BbwHvAL4JbJrakaRpku6VNEfSoKgmktaW9EdJ/5B0g6S1Utt2HMfpNUp7XvVe3UrqzOubwM5mdoOk58zsw5J2AT6WcrGkkcDphJiIjwK3SZpuZv/KVDsZuNDMLpC0PSEk1d7JI3EcpyPpxBlgN9DrgXlTx76amd0Qj5dIGgH8DrgQ+GzC9VsCc8zsAQBJlwEfAbLKaxPgkHh8PXBVomyO0zQaCWqbGoy2olFFldxcfUyKMsyts2+U9QOL7bw/E4v1j5M/NKidDeyjfcelPF0AtiMD5a1koXhD5jjjd5b1CXttfPAJ6xvjiXX85Y5kMBcPfGuPDKyX4ueVkoyyiKb0vmyYxqOS1o7H/wY+BGwNvJF4/URgbra9WJblTvpnch8FxkpaGcdxHGcQInnZcJyksyR9uC2CtojUmdcpwGbAw8DxwC8JSv9rTZTlMOAnkvYFbgQeAxaXV5J0AHAAwOTJxfu15DhOoDSza8WyYvY5MWH18t/J3YGAUSlP8EXdaW3YUFR5ScsAS5vZ84n1twGONrOd4vsjAMzsxCr1lwfuMbOaRhvu5+V0O/WWMYeSu2sofldD9dlq4riS/LyOP//qmnU6cNmw7rjeOkL256XrNzTu1d7O5zUAM3sVeDXHJbcB60tahzCj2h3YM1tB0gRgvpktAY4AzmtENsdxnCzjx4zuROU0ZCQYNbLdUrSPYTFWMbNFkg4CrgVGAueZ2V2SjgVmm9l0QkjQEyUZYdnwy8Mhm+N0MvVmNENZcmvXtSnXu4VifUYIll0moeJLLRelLQybpaWZzQRmlpV9J3P8K+BXwyWP4zhOoRFhKtCj1FRekrYys1m16jiO43QyKaby0JH7XrXpcUevekP/A7DCcAjiOI7j5MCVV03qWrw4TifQiPWaMj+0Lf4wr1RWr78sgxySJw2UZVA/WefjIyrs85xYW+6SU/Drb+n/jdmXoHJyv+Nxqe/stexVoZ9KDsnZpJbxmtGHZRJUrvzDtDFk274hI1d0NM4TGd/3xAhP5wRrw26lrt6WNIkaSsys1r+44ziO0xLSZ17jJJ1Fr6REiYwBHqK68jJ6esvQ6USG4vvkdCeF289KIV159Z6TsqSFwIq1GjCzQVEwhgt3UnZK+PJSP+1U3ql9N1HGnk1GOXU52ewNExq6ozedlK2dyslxHMepwgggxc+rQ4mRmpaY2euNXO8GG07X0YuzrSztHH9q38MpY7cmowQKtWkj6XhgupndKmkH4LeELCUfM7Pr8rZXL6r85tVOSBol6ca8HTqO4zhNoLTnVe/VOewD3BOPvw0cToik9N1GGqs5NDN7sMbpEcC7GunUcZpNr8+2nB6keMuGK5jZC5LGAFsA28fQgac20lhn6WXHaRGNGBJkqZZQsl6bA/yq5lbwY6piBNfnD1bqL+s/lU3QOLeCP1mlxJWTKshXJcFl31hLSR/r9VdJ3irUS0BZyefLf5jUoEDLhsCzkjYipNeaFRXXso025srLcZyuplJ4qK4wnS9ehI1Tgdvj8afj322BfzXSWL3Yhsc2eq3jOI7TQgqmvMzsR5J+DyzKbEk9SEwampd6fl4/TxBov0Y6bgbu5+U4PU9DySgLMPOq7+e1smz2TgkNXcoc4HraHGFD0t1mtnGF8v8zszfnba+ewUZNxSSpnrWi4ziRavtfvqfTegqgrBqjWBE21spZXpOGJp2S3gx8hrBuuWYjbTiO4zhDoCD5vKQ+s5ylMscl1gPmNtJusvKStAqwJ8FWfwvgZuA/G+nUcZzGqRR93elBirPntUP8OypzDLAEeBL4bCON1jPYGAXsCuwL7ATMAS4F1gY+YWZPN9Kp4/QiA/MvuMJxhogohJ+Xmb0PQNKPzewrzWq3nt5+iqAdzweOMrP/jUJ8qVkCOE45zfLxqedzVGq/3l7UgHYy/lZ9PkmVcnOV/KAq+HYNkDEbFOeG+PdicqEjGZD3q9RPrnGV5M3m+CKMccBCT0m2KuOqmMesgf29Zvt5VcukXOi9sIIsG5YoKS5JY4GxZecez9tePeX1D+DdwFbAvyU9aGbP5e3EcZwWUSn5o9MbFGfZEABJWwMXAm/KFtNgaq2a1oJmtl3s6DrgMOBJSTMIeb5G5e3McTqNlNmZ43QkpUzK9V6dw1nA1cCbgXXja534Nzc1/bwGVZbeTbAy/CSwCDjPzL7RSMfNwP28nKHgIYi6gt7N57WmbPbnExo6tjPyecX8kCtYHqVTg1x+WmZ2c/QXWB34CkGDOo7jOMNN8aLKzwIS0mem0dDQzOxVgtXhpc0SxHHayVBmYRUNNjL4rM5pCQXb8wL+CEyXdCbBRL4PM7skb2PFGrrjNJF6keKd7qBrk1GW9ryKQynKR7m5vAGuvBzHcXqC9JnXOEln0ebYhma2TjPbGzblJWkacBrBJPIcMzup7Pxk4AJgxVjnm2Y2c7jkc3qXoS7r9Tsf+/KgM8ykGZh3SmzDppJLeUlaFVg+W2ZmDyRcNxI4nRAa5FHgNknTzSybx+W/gCvM7KeSNgFmAlPyyOc4raZbLRS7dVxdTQH2vCQdZmYnx+PyuIZ9mJVc/tNJGnqcNZ1LsDLMmnCmOpdtCcwpKTpJlwEfYWASMgNWiMfjgNwe147jOOWUJ6MsdFSNLCMowp7X9sDJ8XiHKnUMaI3yIsyajgMuMLNX8nYCTGRg5OBHCVE7shwNXCfpKwQn6A800E9P0opArc1qc6jtNMUKEAaEPaqVar6a8UalvivVHRT+qUqIpmpUG6PKnrdWFu2o0n1OKavYV506tUJPlc63Ep8lRgow8zKznTPH72tm26l+XisBP2tQcaWyB3C+ma0F7AxcVClfmKQDJM2WNPuZZ55poTiOU4FJRw2M2+d0JNnnxMIF89stTusolp8XAJJWkzRV0mpDaSd1aOcC+wHnNdjPY8CkzPu1YlmWzwHTAMzsr5KWASYAAyLXm9lZhDAjTJ06tSme2k4anorDKQrZ58S6G29uXbNUmKUYy4Z9SFoJuIgwOQEwSTOBfcws9y+MpPBQkm4i7Fs9zGDnsm0Trl8KuA94P0Fp3QbsaWZ3Zer8HrjczM6XtDHBoW1irVAiHh5qeHHl5XQgvRsean3Z7B8kNLRrx4SHOp8wITkEuJ8QN/dkYL6Z7Zu3vdSZ1znx1RBmtkjSQcC1BAOP88zsLknHArPNbDpwKHC2pK8RNvD2bVYMLMcZbjptX6bT5HGaRAcuC9ZgR2BjM3s+vr9P0j4MNNxLJmnoZnZBI42XtTGTYP6eLftO5vhfwLuG2o/TOvyh5zgdRMHyedGf/iTLEhJmmZVI1ttxc21LwrSvrzMza3QfzHEKRxFmMEWQcbgpmcp31d7XCAqRSTnDHwiGeF8DHiL48Z5CSLmVm1Q/r/8g5FD9N7ApcBewGXAzjRtxOE7X0mlKo9PkcZpEsWZeBxNiGM6hfwZ2HbB/I42lzryOB/Yzs19Kes7M3ippP4Iic5whU88YJNWPqWr7Gd/+/L78tUkO7Js1sZ9bfXZUz9csjy9aHhlr+W+58utACuDnlSVaFE6TtAbB+nyumT3RaHupQ59sZr8sK7uAYHl4WKOdO47jtJrxY0Z313JhiYIpLwBJywPvJbhLzZU008wWNtJW6tCflrSamT0FPCRpG2AeRZu0Oj3FgNnGET5zcLqMgqVEkTSVYLT3CvAIMBn4saSdzSy3z1Oqn9fhhNiEv5b0GYLz3xLgFDP7dt5Om4X7eTm1cL+0nqC+P1QxnxP1x7WZbPavEhrauGP8vG4Ffm1m38uUfQP4hJm9I297qaby38scXyjpBmCMmd2dt0PHcRynCRRv2XBjgnVhlh8ADU2AUq0N3wI8a2ZzAczsEUmTJG1hZnc20rHjtBqfaTldT7E2bu4gWKnfkSl7c9n7ZFL19sXArmVlowlxqjZvpGPHcRxnCBTPz+s64GpJ5xBCDU4BPgucJWnPUiUzuySlsTzWhgOSTprZ/ZKmJF7vOI7jNJP0ZcNxks4CZpjZjJbKVJvPAm8A+2TKFsXyEkbwBatLqvJ6VNLbzOx/SwWS3oYnjHQcp8Pp2mSUgKUtGz5vZge0WJS6mNk6zWwvVXn9EPitpO/THw34MOC7zRTG6W5qhSnOPxgAACAASURBVC3K66RcXq9uYsmSg3CVhJCp/VWsW3KAPjHdkbrPGXgvBp4/YaBDdamsHnWduOuMP+tAXZ7o0ulMbAS8Xqxlw6aSam14tqQFhJxbkwhZkQ81swRDTcdxHKfZmGDRyJR8wktaLks7SDa0jBE2yqNsOI7jOG3AJBYvlfIIf73lsrSDVCflPYA7zOxuSRsAZxPU+RfN7J4Wy1iVgjofOo7TPHo2GeVbp46wP82uv244Xq90hJNys8kTmPed8fgUQibkF4EzgO1bIJfTxTQr8sVQ26m3T5ba3nBd4zhZljCC1xidUPOVlstSDUm3laJnSDrKzBKjWNcnVXmtYmZPSVoGeDewG8HkcV6zBHEcx3HysbjzQ2ysL0kWlvgOBYZdeT0jaT2CN/RtZvaapOVoMAOm4zjOcDF+TMrspHgYYnHnh9iYBdwo6W5gmehvNohGTPlTlddxwO3AYuBTsewDgIeGcnLTrGWyobZT6fpG2hyuaxwnS0GU1+7AgcC68f2oZjVcV3lJEnAjwUR+kZm9HE/9LQrmDAPZJIElP5zUpITDFV29ri9WjYSQFX2SoKZflir5ms6t386g/a3E/srlHMoeW6Xra92/Rny/8tBIgkunvRhK3PNqH2b2HHAiQEyrtV+z2q6rvMzMJP0fMNbMlmTKn26WEI7jOE4+wsyr4/e8+jCzXSEoMfozKT/VaHupI/87sAHQNrN4x3GcRigPDwXdEyKqAMuGfUhakRDkfedYZJJmAvuY2fzc7SX6eR1PCGRzPiG6Rt9FZnZe3k6bhft5OU7Pk+Tndfz5Vw8oK4DyqjuujacuaxfOXq9uQ1vqnx3h5yXpfGACcAj9YQZPBuab2b5520udeb0LeBB4b1m5AW1TXk5t3JfIcbqZYi0bAjsCG5vZ8/H9fZL2Af7VSGOpsQ3f10jjjuM47Wb8mNFFmGnlpiDWhllEZtUusoQGXa5yq+1ofdjXWdaIw3EaZbgsIh2nmyiY8voDcJGkrwEPEZJRnkJIUpmbJOUlaSLwE2BbYMWy00l3T9I04LRY/xwzO6ns/A+B0gxvOWBVMyvvy8mBKwDH6V7Sw0N1DAcTEk3OoX8Gdh2wfyONpc68zgReBt4P/JmgxI4GZqZcLGkkcDqwA/AocJuk6WbWt9ZpZl/L1P8K8NZE2cr66s59nmaNq9lx+JoZX7BSebKMFfzg8vRdq588/lmD8nVVy59Vo89y/zV7pDX5zhqhW/+/ikqR9ryiReE0SWvQbyr/RKPtpY78ncBkM3sphqm6U9LngFsIEebrsSUwx8weAJB0GfARqm/U7QH4f0aPUTGJpOMMkaypfDftfRVwzwuAqLAaVlolUpXXYmBRPF4gaRXgBWBi4vUTCSb2JR4FtqpUUdLawDrAnxLbdhzH6TmKqryaRaqf1wzgPDP7jaSfAesT4uwvl2KJKGk3YJqZ7R/f7w1sZWYHVah7OLCWmX2lSlsHAAcATJ48+e0PP/xwXfkdx+laKlqqZZ8TE1af+PbTrroFKNTMq64F3pumrmgnzd62bkOf1IyO8PNqNqkzr72BUr7pgwmh7ccCpyZe/xhhjbPEWrGsErsDX67WkJmdBZwFwUk5sX/HcXqI8udEgZRWMr0+80pVXkuZ2TwAM3uFkJwyD7cR8rqsQ1BauwN7lleStBGwEvDXnO07juP0HEVSXpImmdnc+jXTGFG/CgCPSJopaS9JY/J2YmaLgIOAa4G7gSvM7C5Jx0raNVN1d+AyS1nLdBzH6WEMsYiRdV8dxANRj/xHtEAfEqkzr8nAJwl5WX4q6WqCvf7vo2Kqi5nNpMy03sy+U/b+6ER5HKcu7vjsdDOGeJ2l2y1GHjYCPkfwGf5pjHV4jpnd30hjSTMvM5tnZmeY2buBzQhJKL9LE8wdHcdxWkmlqPLdQGnPq96rUzCz+83sSMJk6ACCMrtL0v9I+pSkXE5rjXi4rQqsRogOvKCB650KVHJMHXC+hc6hzXLWHWrftdpuRt+V+kpyOB5CosZm3bN2JRT12Wrn0i6DDUnrAt8CxpnZbnmvN7Mlkv5AsG9Yl+AHvC5wsqTPmVlSuKikmZekTSQdJ2kOcFUs/g8zWz+v4I7jOM7QCZmUl677SkHSeZKelvTPsvJpku6VNEfSNwHM7AEz+1wjMkt6u6SfElbtvgqcAaxpZusCRwA/T20r1WDjL8AawBeASWb2NTO7NZ/YjuM4w0+3R5Vv0rLh+cC0bEEmrN8HgU2APSRt0qi8ku4Aries+O1gZlPN7Gdm9iKAmV0MjEptL3XZcDUzez23tE4y9eLxtXL5JrXtVsjQyr6bdc1Qxt2sezZcy3e+TFgsmrVsaGY3SppSVpw3rF89zgR+YWYLa8ixampjqQYbrrgcx3E6iBym8hMkzc68DkjsolJYv4mSVpZ0JvBWSUfkEPk/KykuSf+Xo40+ihOS2HEcx+kjh6n8vGaGhzKzZwluU3mpFgt3rUbkcOXlNESzU6u0k161sOuVcc9/qTsXjobB2jBPWL+qSDoyHo7KHJdYj4Gzu2SqKi9JfzOzrePxUWbmeSocx3E6iBYrr6SwfgnsEP+OyhwDLAGeBD7biHC1Zl4bSFrGzF4lBOJ15eU4jtMhlPa8Ehgn6SxghpnNqFRB0qXAdoT9sUeBo8zsXEmlsH4jCZlF7sotZ8w8IunH1bKFNEIt5fVb4D5JDwHLSrqximD1Y/I7XcdwWf8NB50qV6vp1XF3Czn2vJ43s5pGGma2R5XyQWH98iDpMDM7Ob59ssKyYamfE/K2XVV5mdl+kt4NTAHeAZybt3GnWNTKYNyuB91Qolt0E72yP+WkU5CUKNsDJeX1gSp1DGie8gIws5uBmyWNNrML8jbuOI7jtIYcy4Ztw8x2zhzXTVychyRrQzM7T9J2wGcI5o6PAReZ2fXNFMZxHMdJo4BR5QcgaRlgSaN+xEnKS9L+hGndOcAsQlTgSyV928zObqRjp/PoxKWoTpSpHfh9aJzxY0a3W4SWUJBlwz4kHQ9MN7NbJe1AsKtYIuljqcF4s6T6eX2DEIvqzowglwO/Blx5OY7jtIFE5VXX2nCY2Af4fjz+NnA48AIhvVbLlNfKDI5ndS8wPm+HjuM4ztDJsedV19pwmFjBzF6QNAbYAtjezBZJOrWRxlKV183ADyQdbmYvx85PBG5ppFOn/bj1mtMrlJJRdltk+QLueT0raSNCQuNZUXEt22hjqcrrQOBy4HlJ8wkzrluAir4BjuM4Tmsp2p4XcCpwezz+dPy7LQ1GqU+1NnwC2FbSWsCawONm9mgjHbaComWrHS6GEkuwnn9VxYzDmR+29VK85GVApum5tbMdt/Jzy9tPO75TnRpD0mkuRTCVz2JmP5L0e2CRmT0Yix8k5InMTa7AvFFhdYzSchrHH2qOU3wWFyy2upn9W9JYSWvGohcbbatYI3ccx8lJt2ZSXsIIXifJDaAjrA0lbQNcALwpW0yIsJF7CunKy3Ecp6AUzNrwZ8DVBH/hl4bamMxsyBK1i6lTp9rs2bPbLYbjOO1D9SoU9DlRd1zLT93QNp99Zt2G/qrtb29mMspGkbSQYC7fFKWTGmHjN4Tp3u/M7I1mdOx0H24oMJhuMwIqIt2cjDJx2bBTmAVsCNzTjMZSlw1vAr4DnCvpCkJcQ/fxchzHaRMFNJX/IzBd0pmEJJR9mNkleRtLNZX/AcFJeVNgL0Jcw9eBi4BfmNn99dqQNA04jbAxd46ZnVShzieBowkbeHeaWSNZO1uO/5p2HKfdGMl7Xp1Cad+tPCGlAa1RXn09hCyaR0iaCfwEOAo4VNJtwKHZ2IdZJI0ETiekgH4UuE3SdDP7V6bO+sARwLvM7DlJq+YdjNNeXIkPxu+J0zqUairfEdaGZrZOM9tLVl6SNiTMuvYESrOuXYBngC8BVwHVhNsSmGNmD8S2LgM+wkDP6s8Dp5vZcwBm9nSukbSRVu31tHKGN6DtSf3t5nUuriRjHrmH5EhdZv1sj1S/Z83+jFLHmCJPSjspcriirE73hodK2vPqFGvD0kRmK2CSmV0uaTnAzOyVvG2lGmzMJmRUvhzY08xmlVX5gaTyqWCWicDczPtHCQPIskHs6y+EpcWjzeyaCrIcQJx+Tp7cni9j9iFRK/uw4zjtIfucmLD6xDZL0xqKFmFD0psIpvJrEHTP5cCOwG6EiVEuRiTWOwlY08y+XEFxAU2ZEi4FrA9sR4iZeLakFSv0c5aZTTWzqausssoQu3QcpxvJPifGrtidyS8sLhvWe3UQPwYuI8TGLVmt3wC8p5HGkvy8JO0IPGRm92XKNgQmm9kfEq7fhjCT2im+PwLAzE7M1DmTEGn45/H9H4Fvmtlt1dotqP+G4zjNo2f9vEZPfbNNmD29bkNPaN1O8fOaB6weo8nPN7Pxsfx5MxuXt73UmdfpwMKysoWxPIXbgPUlrSNpNLA7UH7XryLMupA0gbCM+EBi+x2BdEzfy3Ecp5UsYQSvLxld99VBvAAMWE2LMQ6faqSxVOW1aowsn+UJYPWUi81sEXAQcC1wN3CFmd0l6VhJu8Zq1xLyvfwLuB74upk9myif4zhOb2GwaNHIuq8O4krgvJidBEkrE9KkXNZIY6kLog9I2t7M/pQp244Qzj4JM5sJzCwr+07m2IBD4stxHMepgZlYvKij9rTq8W1CXMOSTfPTBP+uExppLHXkRwNXSjoXuJ8QFXi/+HIibqrsOJ1H14aHMrG4s2ZWNYnm8J+W9FWCW9XDZvZMo+2lRtj4bTTa+CzwIYLZ+061jCmKTjv9ZyomemySPMM9rnr+UKnyDNUvqp3j9h81TiuwJeK1Vzo7JYpU7o3Zx9PAsqXzZvnT1ybPOc3sVuDWvB04juM4rUAsWZz0CG+nk/JDhPBP9WhNPq9oIbgv8BZg+ew5M/tM3k4dx3GGi/FjOsrirnkY0PnLhpMyx9MIeuQYgr3EOoR9sAsaaTjVz+tSYAtgBvBy9pyZtc0uvKD+G47jNI+e9fPSZlONKxPGtaE6xc/rHmDbbOg/SasBfzazjfK2l7psOA1Yx8wW5O3AcRzHaRGL2i1ALlanbPIT3ye5XJWT6uf1CLB0Ix04juM4LSDkRKn/6hxuBC6QNEXSCEnrAOcR8kXmJnXmdSHwW0mnUeYNXeb75TiO4wwHJeVVHD5P8Ot6gH4jjhsImUpyk6q8Dop/y53JDFi3kY4dx3GcIbAEeLXdQqRjZk8B748hodYCHjOzxxptL9XPq6lJxBzHcZwmUKyZFwBm9jjw+FDbyZOMchSwNSE1yuWSxkRBXhqqEL1AKxJL1nPcTXV2blZixOGiT95JA+XL7+bYBBlKfTfofJ2nn079PDqd+S+93pXJKAu4bNhUkgw2JL0ZuA84Gzg3Fr+XsNnmOI7jDDfpBhvjJJ0l6cPtEbQ1pM68fgp8x8wukvRcLPszQZk5juM4w036nlc7I2y0jFTltSlwcTw2CMuFkpZtiVRdSCuWfOq1Wel8alkn0wnyNnLvW9GP0+P08LJhqvJ6CHg70OfOLWlLYE4LZHIcx2ka48eM7r79LgjTiDfaLUTjSJoCrAw8a2YP5b0+VXl9G/idpDOB0ZKOAA4k2O07juM4w80S4LV2C5EfSdsBPwQeAxYAK0bz+UPN7PrUdlJN5a+WNI2grP4MrA18zMxuzyu44ziO0wSKa234XeADZvZsqUDSeELs3HelNpInJcrfgS/lkdBxHKfduKl8xyHghbKyhSQEI86SmhLl2GrnzOw7eTpsJrff/nifH0zeje1W+F21i2pjqeUjVOma4fBFG8rn1Oi4cvWXeb4Np9+Y4zREMZXXGcCtkm4AngPGA+8GTs3TSOrMa1LZ+9UJfl6/ydOZ4ziO0yQKFh6qhJldLGkGsBXRYAM4ysyez9NO6p7XfuVlcQ9sjzydOY7jOE2iwNaGUVFdN5Q2kpJRVrxQGgE8Z2bjhiLAUChokjnHcZpH7yajnDjV+HLCuL7VGcko6yHpt2b2kdT6qXte5ZHjlyOEsZ+bQzbHcRynmRRwz0vSkZWKgY3ztJO65zWHMEkt/Rp4Gfg7sE+ezhzHcZwmkb7nNU7SWcAMM5vRUpnSOBg4jMGzy1xea6l7XqkZlx3HcZzhIH3Pq9NiG94J/E9MjdKHpG3zNJLs5+U4juN0EAYsbrcQ+TGzHaqUfy5PO6l7XnPpT9tcS6iqXoDROvE0YCRwjpmdVHZ+X+C/CSFDAH5iZuekyOc4jtOTFGjPS1I1/fBazLKci9SZ12mE/a0fAQ8TwkMdBFxIJlhvNSSNBE4HdgAeBW6TNN3M/lVW9XIzOyhRJsdxnN5lCfBKu4XIxUNUmQRJeg24DDjYzMqjb1QkVXntC+xkZqVZEZJ+D1xjZqckXL8lMMfMHojXXgZ8BChXXh1Dt2awLVrW5Lx0U+QUpznMf+n1dovQGoq3bLg/sDdwHP2ToG8BVxAU23HAyUDS/lyq8loTeLGs7EVgYuL1ExloVv8owbu6nI/HTbv7gK+Z2SBTfEkHEAc3eXKXxSpzHKcpZJ8TE1ZPfUwVjOLFNvwG8G4zmxff3y/p/4CbzGwjSf8GbkxtLNWKcDowXdIOkjaWtCMhNNT0PJLXYQYwxcw2B/4AXFCpkpmdZWZTzWzqKqus0sTuHcfpFrLPibErjm+3OK3BCMuG9V6dw+oMluiVWE5cmUsOepE68zoQOBo4kzALexz4JVB5DWowjzEwPuJa9BtmAJANjw+cA3w/se2W0K3LTd06rhLdPj4nP+PHjG63CK2heMuGNwHnSzqMsBI3GTgpliPpzcCTqY2l+nm9CnwzvhrhNmB9SesQlNbuhAgdfUhaw8yeiG93Be5usC/HcZzup3jLhvsDlwAP0m+4cQP9umAEORIcJ/t5SdqBoHRWNbMPS5oKrGBmf6p3rZktknQQcC3BVP48M7srplqZbWbTga9K2pXwccwnGIk4juM4lSiY8orm8O+XNJFgB/FY1gjQzO7M016qn9dXgP8kLOftFotfIZjOvzOlDTObCcwsK/tO5vgI4IiUthzHcXoeo5ApUQgqdwlDVL2pBhsHE9I2nxQ7BbgH2HAonTuO4zgNUpp51Xt1CJJWknQ18ARwK/C4pBmSGrKoSVVeY+k3dS+tVY4CutSBwnEcp8MpmPICfhj/bkTQHxsTRvGDRhpL3fO6kWCs8d1M2VeB6xvptBcpsvNstzpsO/34Z1xAipeMckdg40zG5Psk7UODwSpSlddXgBmSPg+MlXQvsBDYpZFOHcdxnCFi5Ewi0nbE4PBQS0hIvFmJVOX1FPCO+FqbsIR4q5ktqXmV4ziO0xoKZm1ICD5xkaSvEcJBTQFOAa5rpLG6yisG1X0RWNHMbiVstDk5KfJSTJFld9Lwz7iAFG/Z8GCCn1c2ufF15PDtylJXeZnZYkn3ASsTIms4juM47WYJqcuGHZFJ2czmA9MkrUGIuDQ3E5giN6nLhr8ArpZ0GiGobt+6ZYqTsuM4jtMC0pYN25ZJWdKRdc4DYGYn5G07VXl9Mf49uqzcgHXzduo4juMMkWIsG1bMmlyGAc1TXpK2KIXrMLN18jbsOI7jtJACBOY1s/e1qu1aM6+bgBUAJP3bzNZvlRCO4zhOTpZQ1PBQTaGW8logaReCA9kaMSL8IHv8UnZkx3EcZxgpxrJhy6ilvP4TOJXg1zUCuL9CHSNEiXccx3GGmw5fNmwlVWMbmtlvzGw9MxsFvGxmIyq8XHE5juO0C0t4dSmpgXlXbqkUjuM4jpODJOVlZh493nEcx+kYkjMpO47jOJ1Eb1tsuPJyHMcpJEsICe17k6RlQ0nbVin/bHPF6RykY/pejuM4nUfxslE2k1SDjYslvT1bIOlA4NvNF8lxHMepT2nZsN6rO0lVXnsCV0raGEDSV4HDgJaF/nAcx3Fq0dvKK2nPy8xulvQFYKakK4CPAu8zs7ktlc5xHMepgtHLe161AvOWR4u/D/gZIfLGJ4BRktbt1vBQnpzPcZzOpniplJtJrZlXNttlOX+O5R4eynEcpy24qXxFzCx1P8xxHMcZdnp75pVqKv+jKuWnpnYkaZqkeyXNkfTNGvU+LskkTU1t23Ecp/co+XnVe3UnqbOrfauU751ysaSRwOnAB4FNgD0kbVKh3ljCntqsRLkcx3F6FLc2rErGCXmpCg7J6wLzEvvZEphTMu6QdBnwEUKusCzHAd8Dvp7YrpOTrNO1G6U4TpHp7WXDeqbypZnVaAbOsgx4CtgnsZ+JQNas/lFgq2wFSW8DJpnZ7yS58nIcx6mJG2xUxczeByDpeDP7r1YJIWkE8AOqL09m6x4AHAAwefLkVonkOE6B6Y3nhPt51SWruCSJjPm8mS1JaOIxYFLm/VqxrMRYYDPghtA8qwPTJe1qZrPLZDkLOAtg6tSpXZxqrTX4UqHTC/TGc6K3Z16p1oZrSvqNpGcJi6x5dwNvA9aXtI6k0cDuwPTSSTN73swmmNkUM5sC/A0YpLgcx3GcEh6YN4WfAa8D7wdeBN5GUD4HplxsZouAg4BrgbuBK8zsLknHSto1t9SO4zg9T2+byqfm83onMNnMXpJkZnanpM8BtwBnpzRgZjOBmWVl36lSd7tEuRzHcXqU3l42TFVei+mffy6QtArwAsGK0HEcxxl22mMqL2kMcAZhNe4GM/vFsAtB+rLhLGDneHwtcDlwJeB7UgXDk2w6TrfQPCdlSedJelrSP8vKK0VG+hjwKzP7PNC2bZ9U5bU3IRgvwMHA9cA/CXm+HMdxnGGnqXte5wPTsgU1IiOtRb/f7uKhjGAopJrKL8gcv0KIhOE4juO0jcevhf+akFBxGUnZVbKzoitBH2Z2o6QpZddVi4z0KEGB3UH6BKjpyKy+C4SkY6uceo0wkGvM7KlmCpaCpGeAh4e73yYzgfQwW0XAx9PZdNt4ljGzzWpVkHSNmU2rVceBqLyuLt1PSbsB08xs//h+b0JkpMOBnwCvAje3a88r1WBjA0L25FsJ08VJBK08A/gwcIakj5vZNS2Rsgpmtspw9tcKJM02s66JoO/j6Wy6cTz16rjiai5m9hKwX7vlSJ3yjQB2N7P3mNmeZvYe4JPAYjPbGvgScFKrhHQcx3GGnXqRkdpKqvLaiUxEjMjVhI08gIsJUeYdx3Gc7qBmZKR2k6q87ge+WFZ2YCyHsI7+crOE6jHOql+lUPh4OhsfjzMISZcCfwU2lPSopM9Vi4zUTjmzpBpsvI3g1zWSMG2cSDCR/JiZ/a+kbYENzSwp2objOI7jDIUk5QUgaRSwNbAm8ATwVzPr3dgkjuM4TttIttE3szfM7CYzu9zMbnTFlU4VL/XyOp+U9C9Jd0m6ZLhlzEu9MUn6oaQ74us+SQsqtdMpJIxnsqTrJf1d0j8k7VypnU4hYTxrS/pjHMsNktZqh5ypVIsAkTkvST+K4/1HXC1yuhkz81cLX4Sl1vsJBi2jgTuBTcrqrA/8HVgpvl+13XIPdUxl9b8CnNduuYf4GZ0FfDEebwI81G65hzieXwL7xOPtgYvaLXedMW1LyGbxzyrndwZ+T8g1uDUwq90y+6u1r7Z5R/cQfV7qZvY6UPJSz/J54HQzew7AzJ4eZhnzkjKmLHsAlw6LZI2RMh4DVojH44DHh1G+vKSMZxPgT/H4+grnOwozuxGYX6PKR4ALLfA3YEVJawyPdE47cOXVeibSHwcMQkSS8mj8GwAbSPqLpL9J6nSnypQxAWF5CliH/gdlJ5IynqOBvSQ9Skjt85XhEa0hUsZzJyHAKoQABGMlrTwMsrWK5O+k0x248uoMliIsHW5HmKWcLWnFtkrUPHYnRKBuWwDPJrEHcL6ZrUVYorpIUpH/fw4D3ivp78B7CVbERf+MnB4iNTyU0zgpXuqPEtbo3wAelHQfQZndNjwi5iaP5/3uwJdbLtHQSBnP54hRt83sr5KWIfg3duISb93xmNnjxJmXpOWBj1smAHcB6ehoEE7zKfIvx6KQ4qV+FWHWhaQJhGXEB4ZTyJwked5L2ghYieD82MmkjOcR4P0AkjYGlgGeGVYp06k7HkkTMjPHI4DzhlnGZjMd+Ey0OtwaeN7Mnmi3UE7rcOXVYqyKl7qkYyWVErldCzwr6V+EzfOvm9mz7ZG4PoljgvDQvMzM0pwJ20TieA4FPi/pToLxyb6dOq7E8WwH3Btn+asB322LsIlUigAh6UBJB8YqMwk/+OYAZxPirTpdTLKTsuM4juN0Cj7zchzHcQqHKy/HcRyncLjychzHcQqHKy/HcRyncLjychzHcQqHKy8nN5IOihG+X5Q0rt3ydBqSLpZ0dJVzI+N9m5zQznqSLPP+Zkn7Jly3lCSTNCW+P0fSkanyJ7T/bUlnNqs9x2kEV15dhqTt4oPr8LLyKbF8qbLyc+LD9EVJr0t6I/N+RoX2lwFOBt5nZsub2fOtHVF3YWaL4317ZBj73N/MTmhie8eZ2YEwWME6znDhyqsJlCuENrMPIfr2Z1Iqxwfb8ma2PPB94Bel92b24QqXrA4sbVXSgXfYvXAcp0vpWeUl6XBJvyorO03Sj+LxfpLulrRQ0gOSvpCpt1308j9c0pPAz2O4naslLZA0X9JNpfA7kiZJulLSM5KelfSTWP4mSX+KZfMk/SIbkFfSQ5IOi8n1npd0eZz5VBvTGGA3QizB9SVNbfI92xi4Kx6/KOm6zBLVlyTNAe6J5zeR9D/xXtwj6eOZdlaJ9+oFhSj635V0Qzw36Jd8+XKZpP1jm89J+r2kSbG8JMsXFJISPlf6PDPXfiFeu1DSPyVtIekISZeX1TtD0ilDuF2rKCR7XKiQxLJcxinx/a4KCTtfkPSIpG+ndlDtPlSoN2AZU9LHMn3OkbRjleuOlPR4rHePpO1i+fGSzo/Vboxlpdn6O2rJJmmEQtLIp+N3+h+SNkkds+P00e6EYu16AWsDLwNj4/uREjgS+QAABhlJREFUwBPA1vH9h4A3EZLbvTfWfVs8tx2wCPgesDSwLHAicCYwKr7eE68dSUg/8UNgDCEm3rtjO+sBO8Q2ViE8CE7NyPgQcCuwJjCeEOrnwBpj2juOYSQwA/hx5twUQk6qpWpcfzwhcnqt+7Ze+Nr0vV8qtnsNIY7hssDyhKCon4nn3w48C2wYr/kVIcTScsDmUeYbKrUfy24mhGMC+DhwL7BhbPto4KYyWX5LyLk1hTAL/UA8vwchbcbb42ezASGY61rAi8AKsd7oKO8WDX63LgaeB94VP9vTM+MryTglvt8e2JTwQ3ILYB6wS5V7nfc+TMnIc3Q8fiewgBCncUQc/4YVxrAp8DCweny/DrBu+fekyudVS7YPEb7T42L/m5T68Je/8rzaLkBbBx8eBp+JxzsA99eoexXwn/F4O+B1YJnM+WPjQ3O9suu2IQRwrao0MnX/A/h75v1DwF6Z998Hzqxx/f8QlV98UD8DjIrvp9Ba5bVtpuzTwPVl150LfIug2Bdl71Mc1w2V2s98TvvG4z8QMwBn+n+NkLupJMvWmfNXAofF4z8CX64yrj8A+2U+h38M4Xt1MXBx5v04YAmwBmWKpcK1PwH+u8q9znsfpmTkOTrzOfx3whg2BJ4iKLmlys7VU161ZNuRMDvfChjR6D32l796dtkwcgnhIQ+wZ3wPgKQPxiWt+ZIWEHI4Tchc+4yZvZp5/9+EoKDXxWXGb8byScDDFoKlDkDSapIuk/SYpBcID5kJZdWezBy/TJjVDCIuy7wP+EUs+i1hlvehKmNvNtlEgGsD74pLqAvi/fsU4eG9GmFmmK3/cI5+1gZOz7Q7j6AY1srUqXbPJgH3V2n3AmCveLwXcFGlSpL2UQ2Dlgx947Ng1PI8YQZd3t42km6IS8rPA/sz+DtQiZT7UIla96APM7uXEIz4WOBpSZdKWj1Brpqymdl1hBWKnwJPSTpT0tjEdh2nj15XXr8EtpO0FiGb7CUAkpYGfk2wqlvNzFYkRK1W5toB+zJmttDMDjWzdYFdgUMkvZ/wEJusyoYMJ8R23mxmKxAemqpQL4W9CZ/nDIV9uAcIymufBtvLS/Z+zAX+aGYrZl7Lm9lBhF/zSxiYeylrNv4SgKTlMmXZh+Zc4HNlbS9rZrMSZJxLWAquxJXA2yVtCnyQ/h8BAwdpdoHVNmgp0Tc+BXeCccDjFepdRviuTTKzccA5pH0HGr0Pte7BAMzsYjN7F2HJcCRhaXxQtbyymdmpZvY2YDPCsuEhKfI4TpaeVl5m9gxwA/Bz4EEzuzueGk3Yq3gGWCTpg4TljqpI2iUaG4jwK3sx4SF9K2FP5yRJYyQtI+ld8bKxhL2W5yVNBL4+hOHsAxwDvCXz+jiwswamd186ylB6teI7MB3YVNKekkbF15aSNrSQcPMq4BhJy0rajKB4SzwZX3sp+EQdQPglX+JM4FsKxiNIWlHSbolynQN8Q9JbFVi/ZEhgZi8DvyHsxf3FQrLGofDhOKtamrDMdpNVzi81FphvZq8q5KHaPbH9Ru/DucD+kt4XjSfWkrRheSVJG8c6SwOvxNeSCu09DZikdVNki9+DLeOPuZcIy++V2nWcmvS08opcAnyAzJKhmS0EvgpcATxHWFIclGyxjPUJe04vEvIOnWFm15vZYuDDhL2BRwhZkz8VrzkGeBtB2f2O8Os/N/GhtzZwupk9mXlNJyxl7pGp/iL9D6NXCAYDTSUuk+1EmEk+QVBGJxJ+EAB8kWDc8RThYfrzzLUGfB44krDctB4wK3P+l8APgF/GpdZ/xL5S5LqUYGRzOfAC4X6vlKlyAfBmqiwZ5uRigtKaRzBKqea68EXgREkLCWO+IqXxRu+Dmd1CuL8/InzvrmfgLLjE0oS9yHmEz28lwp5leXsLCZ/trLhMOLWObCsSPvMFhD3dJ2Jdx8mF5/Ny2o6k/QmGKdu1WY51CQ/a1czspXbK8v/t3LENwCAQBMHvvxA6cx8ET2yRwYmZJlY+MMA/X15Q/f9R9dnLEC64n9cQeN66UPFVz1hbEyRwltkQgDhmQwDiiBcAccQLgDjiBUAc8QIgjngBEGcCkc2NhSzh7xwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot GATK noREF snps vs same sites detected in VarScan - show pretty good agreement\n",
    "for sp,df in nrsnps['gatk'].items():\n",
    "    freqcol = [col for col in snps['varscan'][sp] if 'FREQ' in col][0]\n",
    "    x = nrsnps['varscan'][sp][freqcol].str.replace(\"%\",\"\").astype(float)/100\n",
    "    y = df['new_AF']\n",
    "    print(sp, pearsonr(x,y))\n",
    "    makesweetgraph(x, y,\n",
    "                   title='species = %s\\nsites called multiallelic by GATK but not varscan' % sp,\n",
    "                   xlab='varscan ALT frequency - biallelic sites',\n",
    "                   ylab='gatk frequency of varscan ALT allele')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getafcol(df):\n",
    "    \"\"\"Determine which Allele Frequency column to use.\"\"\"\n",
    "    return 'new_AF' if 'new_AF' in df.columns else 'AF'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JP gatk 22244 8.66%\n",
      "DF gatk 25680 4.03%\n",
      "JP varscan 21178 8.24%\n",
      "DF varscan 30832 4.84%\n"
     ]
    }
   ],
   "source": [
    "# how many are fixed in either method\n",
    "for method,spp in snps.items():\n",
    "    for sp,df in spp.items():\n",
    "        afcol = getafcol(df)\n",
    "        print(sp, method, sum(df[afcol]==1), \"%s%%\" % round(sum((df[afcol]==1)/nrow(df))*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP 256950\n",
      "gatk DF 637410\n",
      "varscan JP 256950\n",
      "varscan DF 637410\n"
     ]
    }
   ],
   "source": [
    "# print numer of snps in each dataset\n",
    "for method,spp in snps.items():\n",
    "    for sp,df in spp.items():\n",
    "        print(method, sp, nrow(snps[method][sp]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### remove noREF snps from analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP 255706\n",
      "gatk DF 636279\n",
      "varscan JP 255706\n",
      "varscan DF 636279\n"
     ]
    }
   ],
   "source": [
    "# remove noREF snps from analyses\n",
    "for method,spp in snps.items():\n",
    "    for sp,df in spp.items():\n",
    "        locuscol = getlocuscol(df)\n",
    "        snps[method][sp] = df[~df[locuscol].isin(ploci[sp])].copy()\n",
    "        print(method, sp, nrow(snps[method][sp]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save datasets without noREF SNPs as pkls\n",
    "norefpkl = op.join(firstdir, 'snps_dict_intersecting_no-noREFloci.pkl')\n",
    "pkldump(snps, norefpkl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/data/home/lindb/methods_paper_analysis/001_testdata_explore/snps_dict_intersecting_loci.pkl',\n",
       " '/data/home/lindb/methods_paper_analysis/001_testdata_explore/snps_dict_intersecting_noREFloci.pkl']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sure they're there\n",
    "fs(firstdirs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatk JP (255706, 117)\n",
      "gatk DF (636279, 117)\n",
      "varscan JP (255706, 21)\n",
      "varscan DF (636279, 21)\n"
     ]
    }
   ],
   "source": [
    "# read back in the data\n",
    "snps = pklload(op.join(firstdir, 'snps_dict_intersecting_no-noREFloci.pkl'))\n",
    "for method,spp in snps.items():\n",
    "    for sp,df in spp.items():\n",
    "        print(method, sp, df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### it looks like adjusting freqs makes things bad, let's see across all bedfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pythonimports import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 20\n"
     ]
    }
   ],
   "source": [
    "# load engines\n",
    "lview,dview = get_client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unzip(f):\n",
    "    \"\"\"gunzip to make reading vcf easier.\"\"\"\n",
    "    import os\n",
    "    import shutil\n",
    "    import subprocess\n",
    "    if os.path.exists(f):\n",
    "        os.chdir(os.path.dirname(f))\n",
    "        gunzip = shutil.which('gunzip')\n",
    "        output = subprocess.check_output([gunzip, '--force', f]).decode('utf-8').replace(\"\\n\", \"\").split()\n",
    "    return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = fs('/data/projects/pool_seq/non-pangenome/varscan_pooled/JP_pooled/snpsANDindels/raw_vcfs_and_tables',\n",
    "           endswith='.gz')\n",
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "# unzip files in parallel\n",
    "jobs = make_jobs(files, unzip, lview)\n",
    "watch_async(jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bgzip(f):\n",
    "    \"\"\"bgzip file f.\"\"\"\n",
    "    import os\n",
    "    import shutil\n",
    "    if os.path.exists(f):\n",
    "        os.chdir(os.path.dirname(f))\n",
    "        bgzip = shutil.which('bgzip')\n",
    "        os.system(f'{bgzip} --force {f}')\n",
    "    return f\n",
    "dview['bgzip'] = bgzip\n",
    "\n",
    "def create_table(f):\n",
    "    \"\"\"Create VariantsToTable.txt file from vcf.\"\"\"\n",
    "    import os\n",
    "    import subprocess\n",
    "    newf = f.replace('raw_vcfs_and_tables', 'test').replace('.vcf','_table.txt')\n",
    "    gatk = '/data/programs/gatk-4.1.0.0/gatk'\n",
    "    cmd = f'''{gatk} VariantsToTable \\\n",
    "--variant {f} \\\n",
    "-F CHROM -F POS -F REF -F ALT -F AF -F QUAL -F TYPE -F FILTER -F ADP -F WT -F HET -F HOM -F NC -GF GT -GF GQ \\\n",
    "-GF SDP -GF DP -GF FREQ -GF PVAL -GF AD -GF RD \\\n",
    "-O {newf} \\\n",
    "--split-multi-allelic'''\n",
    "    print(cmd)\n",
    "    output = subprocess.check_output(cmd.split()).decode('utf-8').replace(\"\\n\", \"\").split()[-1]\n",
    "    if os.path.exists(newf):\n",
    "        f = bgzip(f)\n",
    "    return newf,output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1101"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = fs('/data/projects/pool_seq/non-pangenome/varscan_pooled/JP_pooled/snpsANDindels/raw_vcfs_and_tables',\n",
    "          endswith='.vcf')\n",
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "764\n",
      "1101\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-7880b58460e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# jobs2 = make_jobs(files, create_table, lview)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mwatch_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjobs2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/mypy/pythonimports/pythonimports.py\u001b[0m in \u001b[0;36mwatch_async\u001b[0;34m(jobs, phase)\u001b[0m\n\u001b[1;32m    298\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m         \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\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    301\u001b[0m         \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mjobs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "jobs2 = make_jobs(files, create_table, lview)\n",
    "watch_async(jobs2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### not all jobs finished (764/1101), but let's see what we get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56 56\n"
     ]
    }
   ],
   "source": [
    "# reset engines\n",
    "lview,dview = get_client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getdf(f):\n",
    "    \"\"\"Read in VariantsToTable output, create a 'locus' column.\"\"\"\n",
    "    import pandas\n",
    "    \n",
    "    dfs = []\n",
    "    for df in pandas.read_table(f, chunksize=10000):\n",
    "        df['locus'] = [\"%s-%s\" % (chrom,pos) for (chrom,pos) in zip(df['CHROM'],df['POS'])]\n",
    "        df.index = df['locus'].tolist()\n",
    "        df = df[df.index.isin(varscanloci)].copy()\n",
    "        dfs.append(df)\n",
    "        \n",
    "    return pandas.concat(dfs)\n",
    "dview['varscanloci'] = snps['varscan']['JP']['locus'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "787"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the VariantsToTable output files that finished\n",
    "files = fs('/data/projects/pool_seq/non-pangenome/varscan_pooled/JP_pooled/snpsANDindels/test', endswith='.txt')\n",
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "787\n",
      "787\n"
     ]
    }
   ],
   "source": [
    "# read in VariantsToTable outputs in parallel\n",
    "jobs = make_jobs(files, getdf, lview)\n",
    "watch_async(jobs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "renaming varscan columns ...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(228278, 334)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the returns, convert column names\n",
    "varscandata = pd.concat([j.r for j in jobs])\n",
    "varscandata = get_varscan_names(varscandata,\n",
    "                                '/data/projects/pool_seq/non-pangenome/varscan_pooled/JP_pooled/poolsamps.pkl',\n",
    "                                'JP_pooled')\n",
    "varscandata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "228278"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to make sure all loci coming out are also in gatk\n",
    "len(set(varscandata['locus'].tolist()).intersection(snps['gatk']['JP']['locus'].tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### what does correlation look with raw intersection? (after adjusting noREF snp freq in gatk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy dataframe to easy-named object \"gatk\"\n",
    "snps['gatk']['JP'].index = snps['gatk']['JP']['locus'].tolist()\n",
    "gatk = snps['gatk']['JP'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((228278, 334), (228278, 114))"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy dataframe to easy-named object \"vdata\"\n",
    "vdata = varscandata[varscandata.index.isin(gatk.index.tolist())].copy()\n",
    "vdata.shape, gatk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reduce gatk data to the SNPs in the VarScan data\n",
    "gatk = gatk[gatk.index.isin(vdata.index)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228278, 114)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rows, cols\n",
    "gatk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reorient the VarScan data in the same order as the GATK data\n",
    "vdata = vdata.loc[gatk.index, :].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust freq in GATK\n",
    "gatk.loc[gatk['ALT'].str.contains(\"+\",regex=False), 'AF'] = 1 - gatk.loc[gatk['ALT'].str.contains(\"+\",regex=False), 'AF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9761389392068591, 0.0)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is the correlation of the data\n",
    "pearsonr(vdata['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gatk['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the data - bottom right isn't weird, just needs an adjustment (1-AF)\n",
    "makesweetgraph(vdata['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gatk['AF'],\n",
    "              xlab='varscan', ylab='gatk', title=\"total data, no adjustment/filtering\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### what if we adjust FREQ so denominator = AD + RD?  \n",
    "#### A: decreases correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9754240960429793, 0.0)"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adjust VarScan FREQ by dividing AD / (AD + RD) - instead of AD / DP\n",
    "vdata['JP_p101_new.FREQ'] = vdata['JP_p101.AD'] / (vdata['JP_p101.AD'] + vdata['JP_p101.RD'])\n",
    "pearsonr(vdata['JP_p101_new.FREQ'], gatk['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the bottom right now are closer to 1.0 frequency\n",
    "makesweetgraph(vdata['JP_p101_new.FREQ'], gatk['AF'],\n",
    "              xlab='varscan', ylab='gatk', title='total data, depth-adjusted, no filtering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228278, 335)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vdata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### what are the SNPs in the bottom right of unadjusted (\"total data, no adjustment/filtering\" figure)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try and isolate the bottom-right loci by figure coordinates\n",
    "weird_var = vdata[(vdata['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float) / 100) >0.7].copy()  # x-axis\n",
    "# weird_var = vdata.copy()\n",
    "weird_gatk = gatk[gatk['AF']<0.4].copy()  # y-axis\n",
    "interloci = set(weird_var.index).intersection(weird_gatk.index)\n",
    "len(interloci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((121, 335), (121, 114))"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get small dataframes with the weird loci\n",
    "weird_var = weird_var[weird_var.index.isin(interloci)].copy()\n",
    "weird_gatk = weird_gatk[weird_gatk.index.isin(interloci)].copy()\n",
    "weird_var.shape, weird_gatk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makesweetgraph(weird_var['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, weird_gatk['AF'],\n",
    "              xlab='varscan', ylab='gatk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.45617819641637225, 1.4531689901704265e-07)"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(1-weird_var['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, weird_gatk['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makesweetgraph(1-weird_var['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, weird_gatk['AF'],\n",
    "              xlab='varscan', ylab='gatk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHROM</th>\n",
       "      <th>POS</th>\n",
       "      <th>REF</th>\n",
       "      <th>ALT</th>\n",
       "      <th>AF</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>FILTER</th>\n",
       "      <th>ADP</th>\n",
       "      <th>WT</th>\n",
       "      <th>HET</th>\n",
       "      <th>HOM</th>\n",
       "      <th>NC</th>\n",
       "      <th>locus</th>\n",
       "      <th>JP_p101.GT</th>\n",
       "      <th>JP_p101.GQ</th>\n",
       "      <th>JP_p101.SDP</th>\n",
       "      <th>JP_p101.DP</th>\n",
       "      <th>JP_p101.FREQ</th>\n",
       "      <th>JP_p101.PVAL</th>\n",
       "      <th>JP_p101.AD</th>\n",
       "      <th>JP_p101.RD</th>\n",
       "      <th>JP_p101_new.FREQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Scaffold_35-476207</th>\n",
       "      <td>Scaffold_35</td>\n",
       "      <td>476207</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_35-476207</td>\n",
       "      <td>T/G</td>\n",
       "      <td>254.0</td>\n",
       "      <td>59</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.58%</td>\n",
       "      <td>3.809700e-26</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_151-668199</th>\n",
       "      <td>Scaffold_151</td>\n",
       "      <td>668199</td>\n",
       "      <td>T</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_151-668199</td>\n",
       "      <td>A/A</td>\n",
       "      <td>255.0</td>\n",
       "      <td>73</td>\n",
       "      <td>73.0</td>\n",
       "      <td>97.26%</td>\n",
       "      <td>2.683500e-42</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_154-4551691</th>\n",
       "      <td>Scaffold_154</td>\n",
       "      <td>4551691</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_154-4551691</td>\n",
       "      <td>T/T</td>\n",
       "      <td>255.0</td>\n",
       "      <td>63</td>\n",
       "      <td>63.0</td>\n",
       "      <td>96.83%</td>\n",
       "      <td>2.609000e-36</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_155-126984</th>\n",
       "      <td>Scaffold_155</td>\n",
       "      <td>126984</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>127</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_155-126984</td>\n",
       "      <td>C/T</td>\n",
       "      <td>255.0</td>\n",
       "      <td>80</td>\n",
       "      <td>80.0</td>\n",
       "      <td>78.75%</td>\n",
       "      <td>1.657000e-37</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_167-1649702</th>\n",
       "      <td>Scaffold_167</td>\n",
       "      <td>1649702</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_167-1649702</td>\n",
       "      <td>G/G</td>\n",
       "      <td>255.0</td>\n",
       "      <td>69</td>\n",
       "      <td>69.0</td>\n",
       "      <td>88.41%</td>\n",
       "      <td>2.000100e-33</td>\n",
       "      <td>61.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.953125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_4937-1236986</th>\n",
       "      <td>Scaffold_4937</td>\n",
       "      <td>1236986</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_4937-1236986</td>\n",
       "      <td>G/G</td>\n",
       "      <td>255.0</td>\n",
       "      <td>94</td>\n",
       "      <td>94.0</td>\n",
       "      <td>90.36%</td>\n",
       "      <td>1.469400e-38</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.903614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_4937-1236999</th>\n",
       "      <td>Scaffold_4937</td>\n",
       "      <td>1236999</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_4937-1236999</td>\n",
       "      <td>T/T</td>\n",
       "      <td>255.0</td>\n",
       "      <td>117</td>\n",
       "      <td>117.0</td>\n",
       "      <td>100%</td>\n",
       "      <td>6.952000e-70</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_5839-74825</th>\n",
       "      <td>Scaffold_5839</td>\n",
       "      <td>74825</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_5839-74825</td>\n",
       "      <td>A/A</td>\n",
       "      <td>255.0</td>\n",
       "      <td>85</td>\n",
       "      <td>85.0</td>\n",
       "      <td>83.53%</td>\n",
       "      <td>4.155400e-34</td>\n",
       "      <td>71.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.835294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_6215-664813</th>\n",
       "      <td>Scaffold_6215</td>\n",
       "      <td>664813</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_6215-664813</td>\n",
       "      <td>C/T</td>\n",
       "      <td>255.0</td>\n",
       "      <td>171</td>\n",
       "      <td>171.0</td>\n",
       "      <td>79.53%</td>\n",
       "      <td>1.130600e-62</td>\n",
       "      <td>136.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.795322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_6303-227203</th>\n",
       "      <td>Scaffold_6303</td>\n",
       "      <td>227203</td>\n",
       "      <td>T</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>SNP</td>\n",
       "      <td>PASS</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Scaffold_6303-227203</td>\n",
       "      <td>G/G</td>\n",
       "      <td>255.0</td>\n",
       "      <td>53</td>\n",
       "      <td>53.0</td>\n",
       "      <td>100%</td>\n",
       "      <td>1.594300e-31</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               CHROM      POS REF ALT  AF  QUAL TYPE FILTER  \\\n",
       "Scaffold_35-476207       Scaffold_35   476207   T   G NaN -10.0  SNP   PASS   \n",
       "Scaffold_151-668199     Scaffold_151   668199   T   A NaN -10.0  SNP   PASS   \n",
       "Scaffold_154-4551691    Scaffold_154  4551691   C   T NaN -10.0  SNP   PASS   \n",
       "Scaffold_155-126984     Scaffold_155   126984   C   T NaN -10.0  SNP   PASS   \n",
       "Scaffold_167-1649702    Scaffold_167  1649702   A   G NaN -10.0  SNP   PASS   \n",
       "...                              ...      ...  ..  ..  ..   ...  ...    ...   \n",
       "Scaffold_4937-1236986  Scaffold_4937  1236986   A   G NaN -10.0  SNP   PASS   \n",
       "Scaffold_4937-1236999  Scaffold_4937  1236999   C   T NaN -10.0  SNP   PASS   \n",
       "Scaffold_5839-74825    Scaffold_5839    74825   G   A NaN -10.0  SNP   PASS   \n",
       "Scaffold_6215-664813   Scaffold_6215   664813   C   T NaN -10.0  SNP   PASS   \n",
       "Scaffold_6303-227203   Scaffold_6303   227203   T   G NaN -10.0  SNP   PASS   \n",
       "\n",
       "                       ADP  WT  HET  HOM  NC                  locus  \\\n",
       "Scaffold_35-476207      93   0   21   19   0     Scaffold_35-476207   \n",
       "Scaffold_151-668199    121   0    0   40   0    Scaffold_151-668199   \n",
       "Scaffold_154-4551691    86   0    4   36   0   Scaffold_154-4551691   \n",
       "Scaffold_155-126984    127   0   34    6   0    Scaffold_155-126984   \n",
       "Scaffold_167-1649702   100   0    3   37   0   Scaffold_167-1649702   \n",
       "...                    ...  ..  ...  ...  ..                    ...   \n",
       "Scaffold_4937-1236986  107   0    4   36   0  Scaffold_4937-1236986   \n",
       "Scaffold_4937-1236999  126   0    0   40   0  Scaffold_4937-1236999   \n",
       "Scaffold_5839-74825    176   0   13   27   0    Scaffold_5839-74825   \n",
       "Scaffold_6215-664813   230   0   17   23   0   Scaffold_6215-664813   \n",
       "Scaffold_6303-227203    90   0    0   40   0   Scaffold_6303-227203   \n",
       "\n",
       "                      JP_p101.GT  JP_p101.GQ  JP_p101.SDP  JP_p101.DP  \\\n",
       "Scaffold_35-476207           T/G       254.0           59        59.0   \n",
       "Scaffold_151-668199          A/A       255.0           73        73.0   \n",
       "Scaffold_154-4551691         T/T       255.0           63        63.0   \n",
       "Scaffold_155-126984          C/T       255.0           80        80.0   \n",
       "Scaffold_167-1649702         G/G       255.0           69        69.0   \n",
       "...                          ...         ...          ...         ...   \n",
       "Scaffold_4937-1236986        G/G       255.0           94        94.0   \n",
       "Scaffold_4937-1236999        T/T       255.0          117       117.0   \n",
       "Scaffold_5839-74825          A/A       255.0           85        85.0   \n",
       "Scaffold_6215-664813         C/T       255.0          171       171.0   \n",
       "Scaffold_6303-227203         G/G       255.0           53        53.0   \n",
       "\n",
       "                      JP_p101.FREQ  JP_p101.PVAL  JP_p101.AD  JP_p101.RD  \\\n",
       "Scaffold_35-476207          74.58%  3.809700e-26        44.0         0.0   \n",
       "Scaffold_151-668199         97.26%  2.683500e-42        71.0         0.0   \n",
       "Scaffold_154-4551691        96.83%  2.609000e-36        61.0         0.0   \n",
       "Scaffold_155-126984         78.75%  1.657000e-37        63.0         0.0   \n",
       "Scaffold_167-1649702        88.41%  2.000100e-33        61.0         3.0   \n",
       "...                            ...           ...         ...         ...   \n",
       "Scaffold_4937-1236986       90.36%  1.469400e-38        75.0         8.0   \n",
       "Scaffold_4937-1236999         100%  6.952000e-70       117.0         0.0   \n",
       "Scaffold_5839-74825         83.53%  4.155400e-34        71.0        14.0   \n",
       "Scaffold_6215-664813        79.53%  1.130600e-62       136.0        35.0   \n",
       "Scaffold_6303-227203          100%  1.594300e-31        53.0         0.0   \n",
       "\n",
       "                       JP_p101_new.FREQ  \n",
       "Scaffold_35-476207             1.000000  \n",
       "Scaffold_151-668199            1.000000  \n",
       "Scaffold_154-4551691           1.000000  \n",
       "Scaffold_155-126984            1.000000  \n",
       "Scaffold_167-1649702           0.953125  \n",
       "...                                 ...  \n",
       "Scaffold_4937-1236986          0.903614  \n",
       "Scaffold_4937-1236999          1.000000  \n",
       "Scaffold_5839-74825            0.835294  \n",
       "Scaffold_6215-664813           0.795322  \n",
       "Scaffold_6303-227203           1.000000  \n",
       "\n",
       "[121 rows x 23 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goodcols = [col for col in vdata if '.' not in col]\n",
    "goodcols.extend([col for col in vdata if 'JP_p101' in col])\n",
    "weird_var[goodcols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>JP_p101.FREQ</th>\n",
       "      <th>AF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Scaffold_35-476207</th>\n",
       "      <td>0.7458</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_151-668199</th>\n",
       "      <td>0.9726</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_154-4551691</th>\n",
       "      <td>0.9683</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_155-126984</th>\n",
       "      <td>0.7875</td>\n",
       "      <td>0.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_167-1649702</th>\n",
       "      <td>0.8841</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_4937-1236986</th>\n",
       "      <td>0.9036</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_4937-1236999</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_5839-74825</th>\n",
       "      <td>0.8353</td>\n",
       "      <td>0.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_6215-664813</th>\n",
       "      <td>0.7953</td>\n",
       "      <td>0.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scaffold_6303-227203</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       JP_p101.FREQ     AF\n",
       "Scaffold_35-476207           0.7458  0.375\n",
       "Scaffold_151-668199          0.9726  0.075\n",
       "Scaffold_154-4551691         0.9683  0.053\n",
       "Scaffold_155-126984          0.7875  0.250\n",
       "Scaffold_167-1649702         0.8841  0.200\n",
       "...                             ...    ...\n",
       "Scaffold_4937-1236986        0.9036  0.150\n",
       "Scaffold_4937-1236999        1.0000  0.150\n",
       "Scaffold_5839-74825          0.8353  0.375\n",
       "Scaffold_6215-664813         0.7953  0.350\n",
       "Scaffold_6303-227203         1.0000  0.184\n",
       "\n",
       "[121 rows x 2 columns]"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare the freqs. Looks like the weird loci just need adjustment (1-AF)\n",
    "pd.concat([pd.DataFrame(weird_var['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100).T, \n",
    "          pd.DataFrame(weird_gatk['AF']).T]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98, 23)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count stuff\n",
    "lst = [1 if '+' in alt else 0 for alt in weird_gatk['ALT']]\n",
    "lst.count(1), lst.count(0)  # noREF, non noREF in wierd data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makesweetgraph(weird_var['JP_p101_new.FREQ'], weird_gatk['AF'],\n",
    "              xlab='varscan', ylab='gatk')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# are the weird loci due to weird RD vals? A: No, look pretty normal.\n",
    "plt.hist(vdata['JP_p101.RD'][vdata['JP_p101.RD']<1000], bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### what does it look like if I remove SNPs with depth probs ONLY when RD=0?   #  A: doesn't change much"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2324"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove SNPs that have FREQ != AD/DP !AND! have RD=0\n",
    "test = vdata[(vdata['JP_p101.RD']==0) & ((vdata['JP_p101.AD']+vdata['JP_p101.RD']) != vdata['JP_p101.DP'])].copy()\n",
    "nrow(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "92"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many intersect with the weird loci?\n",
    "len(set(test.index).intersection(weird_var.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9763035347076849, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see if adjusting works A: kinda meh\n",
    "vadjusted = vdata[~vdata.index.isin(test.index)].copy()\n",
    "gadjusted = gatk[~gatk.index.isin(test.index)].copy()\n",
    "print(pearsonr(vadjusted['JP_p101_new.FREQ'], gadjusted['AF']))\n",
    "makesweetgraph(vadjusted['JP_p101_new.FREQ'], gadjusted['AF'],\n",
    "              xlab='varscan', ylab='gatk', title=\"filtered SNPs, depth-adjusted freq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9763500243425801, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare to correlation of unadjusted FREQ - A: unadjusted is slightly worse, but prob not significantly\n",
    "print(pearsonr(vadjusted['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gadjusted['AF']))\n",
    "makesweetgraph(vadjusted['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gadjusted['AF'],\n",
    "              xlab='varscan', ylab='gatk', title='filtered SNPs, no adjustment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### what happens when I adjust freq to 100% for all the varscan SNPs with RD=0 AND DP!=AD+RD?\n",
    "A: r goes down a little bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9754656288531, 0.0)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using data: RD==0 && DP!=AD+RD, change freq to 100%\n",
    "v100 = vdata.copy()\n",
    "v100.loc[v100.index.isin(test.index), 'JP_p101.FREQ'] = '100%'\n",
    "pearsonr(v100['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gatk['AF'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### look at noREF snps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1106"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the noREF SNPs (these have 'alt1+alt2' ALT alleles (from our pipelines), note the '+')\n",
    "multigatk = gatk[gatk['ALT'].str.contains('+',regex=False)].copy()\n",
    "multivarscan = vdata[vdata.index.isin(multigatk.index)].copy()  # get intersection\n",
    "nrow(multigatk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### how many varscan SNPs have depth descrepancies (DP != AD + RD)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16339"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(vdata['JP_p101.DP'] != (vdata['JP_p101.AD']+vdata['JP_p101.RD']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### explore data when DP != AD + RD - A: nothing super weird going on, correlation decreases to about 0.955 (still high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16339, 335), (16339, 114))"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# isolate data\n",
    "dpvar = vdata[vdata['JP_p101.DP'] != (vdata['JP_p101.AD']+vdata['JP_p101.RD'])].copy()\n",
    "dpgatk = gatk[gatk.index.isin(dpvar.index)].copy()\n",
    "dpvar.shape, dpgatk.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15349, 5859.0)"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(dpvar['JP_p101.RD']<200), max(dpvar['JP_p101.RD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at RD histogram\n",
    "plt.hist(dpvar['JP_p101.RD'][dpvar['JP_p101.RD']<200],bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# look at DP histogram\n",
    "plt.hist(dpvar['JP_p101.DP'][dpvar['JP_p101.DP']<1000],bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9556260043913264, 0.0)"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(dpvar['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, dpgatk['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makesweetgraph(dpvar['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, dpgatk['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9778114258206512, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fix the bottom right stuff\n",
    "goodvar = vdata[~vdata.index.isin(dpvar.index)].copy()\n",
    "goodgatk = gatk[~gatk.index.isin(dpvar.index)].copy()\n",
    "print(pearsonr(goodvar['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, goodgatk['AF']))\n",
    "makesweetgraph(goodvar['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, goodgatk['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9761389392068591, 0.0)\n"
     ]
    }
   ],
   "source": [
    "print(pearsonr(vdata['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gatk['AF']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7458"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(weird_var.loc['Scaffold_35-476207', 'JP_p101.FREQ'].replace(\"%\",\"\")) / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.977248272136013, 0.0)"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gatk2 = gatk.copy()\n",
    "for locus in weird_var.index:\n",
    "    varfreq = float(weird_var.loc[locus, 'JP_p101.FREQ'].replace(\"%\",\"\")) / 100\n",
    "    gatkfreq = float(weird_gatk.loc[locus, 'AF'])\n",
    "    if (varfreq > 0.5 and gatkfreq < 0.5) or (varfreq < 0.5 and gatkfreq > 0.5):\n",
    "        gatk2.loc[locus, 'AF'] = 1 - gatk2.loc[locus, 'AF']\n",
    "\n",
    "pearsonr(vdata['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gatk2['AF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "makesweetgraph(vdata['JP_p101.FREQ'].str.replace(\"%\",\"\").astype(float)/100, gatk2['AF'],bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.9768311147493849, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pearsonr(vdata['JP_p101_new.FREQ'], gatk2['AF']))\n",
    "makesweetgraph(vdata['JP_p101_new.FREQ'], gatk2['AF'],\n",
    "              xlab='varscan', ylab='gatk', title='total data, depth-adjusted, no filtering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}