nb4_virentes_populations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live oaks introgression manuscript\n",
    "## Notebook 4: Population analyses \n",
    "\n",
    "#### D. Eaton, A. Hipp, A. Gonzalez-Rodriguez & J. Cavender-Bares\n",
    "##### contact: deren.eaton@yale.edu  \n",
    "\n",
    "-----------------------   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "mkdir -p analysis_treemix/\n",
    "mkdir -p analysis_structure/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import gzip\n",
    "from collections import OrderedDict, Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------   \n",
    "\n",
    "## Treemix analysis\n",
    "\n",
    "I run a custom python code below to convert the unlinked snps file from the pyrad output into a Treemix input file. This requires grouping sampled individuals into populations, and calling biallelic SNP frequencies for them. I grouped the four non-Virentes white oaks into the outgroup sample. Each ingroup taxon is its own population, except for _Q. fusiformis_ for which I split up the Texas and Mexico populations, for a total of 9 populations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## group sample names into taxon names\n",
    "## again I exclude the South Carolina sample of virginiana, which has less data.\n",
    "\n",
    "taxa = {\n",
    "    'virginiana' : [\"LALC2\",\"FLSF33\",\"FLBA140\"],\n",
    "    'minima'     : [\"FLCK216\",\"FLSA185\",\"FLMO62\",\"FLSF47\"],\n",
    "    'geminata'   : [\"FLCK18\",\"FLWO6\",\"FLSF54\",\"FLAB109\"],\n",
    "    'sagraeana'  : [\"CUSV6\",\"CUVN10\",\"CUCA4\"],\n",
    "    'oleoides'   : [\"MXSA3017\",\"BZBB1\",\"HNDA09\",\"CRL0001\",\"CRL0030\"],\n",
    "    'brandegeei' : [\"BJSB3\",\"BJSL25\",\"BJVL19\"],\n",
    "    'fusiformis.m' : [\"MXED8\",\"MXGT4\"],\n",
    "    'fusiformis.t' : [\"TXMD3\",\"TXGR3\"],\n",
    "    'outgroup'   : [\"EN\",\"AR\",\"DO\",\"DU\"],\n",
    "}\n",
    "\n",
    "## used to require that all populations have data for at least\n",
    "## one individual in each locus that we use\n",
    "minhits = [1]*9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## resolve ambiguity characters\n",
    "def resolve(base):\n",
    "    D = {'R':['G','A'],\n",
    "         'K':['G','T'],\n",
    "         'S':['G','C'],\n",
    "         'Y':['T','C'],\n",
    "         'W':['T','A'],\n",
    "         'M':['C','A'],}\n",
    "    if base in D:\n",
    "        return D[base]\n",
    "    else: \n",
    "        return [base,base]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t    geminata ['FLCK18', 'FLWO6', 'FLSF54', 'FLAB109'] minimum= 1\n",
      "\t    minima ['FLCK216', 'FLSA185', 'FLMO62', 'FLSF47'] minimum= 1\n",
      "\t    fusiformis.m ['MXED8', 'MXGT4'] minimum= 1\n",
      "\t    brandegeei ['BJSB3', 'BJSL25', 'BJVL19'] minimum= 1\n",
      "\t    outgroup ['EN', 'AR', 'DO', 'DU'] minimum= 1\n",
      "\t    oleoides ['MXSA3017', 'BZBB1', 'HNDA09', 'CRL0001', 'CRL0030'] minimum= 1\n",
      "\t    virginiana ['LALC2', 'FLSF33', 'FLBA140'] minimum= 1\n",
      "\t    fusiformis.t ['TXMD3', 'TXGR3'] minimum= 1\n",
      "\t    sagraeana ['CUSV6', 'CUVN10', 'CUCA4'] minimum= 1\n"
     ]
    }
   ],
   "source": [
    "## print groups and minhits \n",
    "for i,j in zip(taxa,minhits):\n",
    "    print \"\\t   \",i, taxa[i], 'minimum=',j\n",
    "    \n",
    "## read in data to sample names\n",
    "#infile = open(\"outfiles/c85d6m4p3.unlinked_snps\",'r')\n",
    "infile = open(\"analysis_pyrad/outfiles/virentes_c85d6m4p5.unlinked_snps\",'r')\n",
    "dat = infile.readlines()\n",
    "nsamp,nsnps = dat[0].strip().split(\" \")\n",
    "nsamp = int(nsamp)\n",
    "nsnps = int(nsnps)\n",
    "NDATA = np.empty([int(nsamp),int(nsnps)],dtype='object')\n",
    "\n",
    "## read SNP matrix into a numpy.array\n",
    "for line in range(len(dat[1:])):\n",
    "    a,b = dat[1:][line].split()\n",
    "    NDATA[line] = list(b)\n",
    "sites = np.transpose(NDATA)\n",
    "\n",
    "## unpack ambiguity bases and find two most common alleles\n",
    "## at every SNP site, save to a list\n",
    "alleles = []\n",
    "for site in sites:\n",
    "    ds = []\n",
    "    for s in site:\n",
    "        if s in list(\"RKSYWM\"):\n",
    "            ds.append(resolve(s)[0])\n",
    "            ds.append(resolve(s)[1])\n",
    "        else:\n",
    "            ds.append(s)\n",
    "            ds.append(s)\n",
    "    snp = [s for s in ds if s not in [\"N\",'-']]\n",
    "    a = Counter(snp).most_common(3)\n",
    "    alleles.append([a[0][0],a[1][0]])\n",
    "\n",
    "## create a dictionary mapping sample names to SNPs    \n",
    "SNPS = OrderedDict()\n",
    "for line in dat[1:]:\n",
    "    a,b = line.split()\n",
    "    SNPS[a] = b\n",
    "\n",
    "## create a dictionary with empty lists for each taxon \n",
    "FREQ = OrderedDict()\n",
    "for tax in taxa:\n",
    "    FREQ[tax] = []\n",
    "\n",
    "## fill the FREQ dictionary with SNPs for all \n",
    "## samples in that taxon\n",
    "keeps = []\n",
    "for snp in range(int(nsnps)):\n",
    "    GG = []\n",
    "    for tax,mins in zip(taxa,minhits):\n",
    "        GG.append( sum([SNPS[i][snp] not in [\"N\",\"-\"] for i in taxa[tax]]) >= int(mins))\n",
    "    if all(GG):\n",
    "        keeps.append(snp)\n",
    "\n",
    "for keep in keeps:\n",
    "    for tax in FREQ:\n",
    "        bunch = []\n",
    "        for i in taxa[tax]:\n",
    "            #print tax, i, SNPS[i][keep]\n",
    "            bunch.append(resolve(SNPS[i][keep])[0])\n",
    "            bunch.append(resolve(SNPS[i][keep])[1])\n",
    "        FREQ[tax].append(\"\".join(bunch))\n",
    "\n",
    "## output files\n",
    "outfile = gzip.open(\"analysis_treemix/treemix_SNPs.gz\",'w')\n",
    "\n",
    "## print header\n",
    "print >>outfile, \" \".join(FREQ.keys())\n",
    "\n",
    "## print data\n",
    "for i,j in enumerate(keeps):\n",
    "    a1 = alleles[j][0]\n",
    "    a2 = alleles[j][1]\n",
    "    H = [str(FREQ[tax][i].count(a1))+\",\"+str(FREQ[tax][i].count(a2)) for tax in FREQ]\n",
    "\n",
    "    ## exclude non-biallelic SNPs\n",
    "    if \"0,0\" not in \" \".join(H):\n",
    "        ## exclude if not variable in subsampling\n",
    "        if not all([i.split(\",\")[1] == '0' for i in H]):\n",
    "            print >>outfile, \" \".join(H)\n",
    "outfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  12061  108549  434320\r\n"
     ]
    }
   ],
   "source": [
    "! zcat analysis_treemix/treemix_SNPs.gz | wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1      -o analysis_treemix/SNPs_m0 > analysis_treemix/logSm0\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 1 -o analysis_treemix/SNPs_m1 > analysis_treemix/logSm1\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 2 -o analysis_treemix/SNPs_m2 > analysis_treemix/logSm2\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 3 -o analysis_treemix/SNPs_m3 > analysis_treemix/logSm3\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 4 -o analysis_treemix/SNPs_m4 > analysis_treemix/logSm4\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 5 -o analysis_treemix/SNPs_m5 > analysis_treemix/logSm5\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 6 -o analysis_treemix/SNPs_m6 > analysis_treemix/logSm6\n",
    "treemix -i analysis_treemix/treemix_SNPs.gz -root outgroup -k 1 -m 7 -o analysis_treemix/SNPs_m7 > analysis_treemix/logSm7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the treemix results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext rmagic\n",
    "## this requires that you have the python module rpy2 installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "source(\"~/local/src/treemix/src/plotting_funcs.R\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "echo -e \"virginiana\\nminima\\ngeminata\\nsagraeana\\noleoides\\nfusiformis.m\\nfusiformis.t\\nbrandegeei\\noutgroup\" >analysis_treemix/poporder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 400 -w 800\n",
    "## save figure\n",
    "#svg(\"figures/treemix_m0.svg\", width=8, height=4)\n",
    "par(mfrow=c(1,2));\n",
    "plot_tree(\"analysis_treemix/SNPs_m0\", lwd=3)\n",
    "plot_resid(\"analysis_treemix/SNPs_m0\",\"analysis_treemix/poporder\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"mse 0.000551605086419753\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "TkSuQmCC\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 400 -w 800\n",
    "## save figure\n",
    "#svg(\"figures/treemix_m1.svg\", width=8, height=4)\n",
    "par(mfrow=c(1,2));\n",
    "plot_tree(\"analysis_treemix/SNPs_m1\", lwd=3)\n",
    "plot_resid(\"analysis_treemix/SNPs_m1\",\"analysis_treemix/poporder\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"mse 0.000551605086419753\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 400 -w 800\n",
    "## save figure\n",
    "#svg(\"figures/treemix_m2.svg\", width=8, height=4)\n",
    "par(mfrow=c(1,2));\n",
    "plot_tree(\"analysis_treemix/SNPs_m2\", lwd=3)\n",
    "plot_resid(\"analysis_treemix/SNPs_m2\",\"analysis_treemix/poporder\")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"mse 0.000551605086419753\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "gg==\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 400 -w 800\n",
    "## save figure\n",
    "par(mfrow=c(1,2));\n",
    "plot_tree(\"analysis_treemix/SNPs_m3\", lwd=2)\n",
    "plot_resid(\"analysis_treemix/SNPs_m3\",\"analysis_treemix/poporder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"mse 0.000551605086419753\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 400 -w 800\n",
    "## save figure\n",
    "par(mfrow=c(1,2));\n",
    "plot_tree(\"analysis_treemix/SNPs_m4\", lwd=2)\n",
    "plot_resid(\"analysis_treemix/SNPs_m4\",\"analysis_treemix/poporder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"mse 0.000551605086419753\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "AACQ7P8BTUCvcOJyalQAAAAASUVORK5CYII=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 400 -w 800\n",
    "## save figure\n",
    "par(mfrow=c(1,2));\n",
    "plot_tree(\"analysis_treemix/SNPs_m5\", lwd=2)\n",
    "plot_resid(\"analysis_treemix/SNPs_m5\",\"analysis_treemix/poporder\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------   \n",
    "\n",
    "## _Structure_ analysis of RADseq SNPs \n",
    "\n",
    "Structure was run on the structure formatted ('k' option) SNP output from pyRAD in the file \"virentes\\_c85d6m20p5noutg.str\". Clustering assignment was measured for K values between 2-8, and the optimal K value was inferred by Evanno's K, as described in the text. Tthe optimal K value is 7. Here we run 10 replicate analyses at each value of K, and estimate average cluster assignment values by permuting across replicates using _CLUMPP_, and finally plot the results as a stacked barplot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## download fresh (default) params files (or you could get them elsewhere)\n",
    "dl = ! wget www.dereneaton.com/downloads/struct.main   -q -O analysis_structure/struct.main  \n",
    "dl = ! wget www.dereneaton.com/downloads/struct.extras -q -O analysis_structure/struct.extras  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## set the file format to match pyRAD structure format (6 blank columns)\n",
    "## meaning no populations, locations, names, or phenotypes are indicated.\n",
    "stderr = ! sed -i '/LABEL /c\\#define LABEL 1 //'         analysis_structure/struct.main\n",
    "stderr = ! sed -i '/POPDATA /c\\#define POPDATA 0 //'     analysis_structure/struct.main\n",
    "stderr = ! sed -i '/POPFLAG /c\\#define POPFLAG 0 //'     analysis_structure/struct.main\n",
    "stderr = ! sed -i '/LOCDATA /c\\#define LOCDATA 0 //'     analysis_structure/struct.main\n",
    "stderr = ! sed -i '/PHENOTYPE /c\\#define PHENOTYPE 0 //' analysis_structure/struct.main\n",
    "stderr = ! sed -i '/EXTRACOLS /c\\#define EXTRACOLS 0 //' analysis_structure/struct.main\n",
    "stderr = ! sed -i '/BURNIN /c\\#define BURNIN 50000 //'     analysis_structure/struct.main\n",
    "stderr = ! sed -i '/NUMREPS /c\\#define NUMREPS 500000 //'   analysis_structure/struct.main\n",
    "\n",
    "stderr = ! sed -i '/RANDOMIZE /c\\#define RANDOMIZE 0 //'   analysis_structure/struct.extras\n",
    "stderr = ! sed -i '/LOCISPOP /c\\#define LOCISPOP 0 //'     analysis_structure/struct.extras\n",
    "stderr = ! sed -i '/USEPOPINFO /c\\#define USEPOPINFO 0 //' analysis_structure/struct.extras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## create a Parallel computing client\n",
    "## can map a function across how ever many \n",
    "## parallel engines are turned on in the notebook\n",
    "## under the Clusters tab\n",
    "from IPython.parallel import Client\n",
    "PAR = Client()[:]\n",
    "LOADBALANCE = Client().load_balanced_view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## decorator adds parallel capability if run in IPython notebook\n",
    "@LOADBALANCE.parallel(block=True)\n",
    "def structure(arg):\n",
    "    ff = arg[1]\n",
    "    NUMINDS, NUMLOCI = (27,14011)\n",
    "    cmd = \"\"\" -m analysis_structure/struct.main \\\n",
    "              -e analysis_structure/struct.extras \\\n",
    "              -K %d \\\n",
    "              -N %s \\\n",
    "              -L %s \\\n",
    "              -i %s.str \\\n",
    "              -D %s \\\n",
    "              -o %s \"\"\" % (arg[0], NUMINDS, NUMLOCI, ff, str(arg[3]), arg[2]+\".K\"+str(arg[0]))\n",
    "    stderr = ! structure $cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "args = [[K, \"analysis_pyrad/outfiles/virentes_c85d6m20p5noutg\", \n",
    "        \"analysis_structure/RADrep\"+str(reps), np.random.randint(0,1e9,1)[0]] for K in range(2,9) \\\n",
    "        for reps in range(1,11)]\n",
    "for i in args[:]: print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(\"analysis_pyrad/outfiles/virentes_c85d6m20p5noutg.str\",'r') as ifile:\n",
    "    print len(ifile.readline().strip().split()), 'SNPs in structure data set'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## run these jobs in parallel\n",
    "quiet = structure.map(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use _CLUMPP_ to get means across permuted replicates  \n",
    "First I create concatenated indfiles by cutting out the relevant lines of the _STRUCTURE_ output and appending to a single file for all replicates with the same value of $K$. Then I create and edit a _CLUMPP_ params file, entering settings to perform permutations of all iterations using the greedy algorithm. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_CLUMPP(K): \n",
    "    repfiles = glob.glob(\"analysis_structure/RADrep*.K\"+str(K)+\"_f\")\n",
    "    outfile = open(\"analysis_structure/RADr\"+str(K)+\".indfile\",'w')\n",
    "    for ff in repfiles:\n",
    "        i = 1\n",
    "        for line in open(ff).readlines():\n",
    "            if \")   :  \" in line:\n",
    "                ll = line.strip().split()\n",
    "                print >>outfile, \" \".join([ll[0],ll[0],ll[2],ll[0].split('.')[0]])+\" :  \"+\" \".join(ll[4:])\n",
    "                i += 1\n",
    "        print >>outfile, \"\"\n",
    "    outfile.close()\n",
    "    \n",
    "    ## get template CLUMPP params file, I host a template file at this download\n",
    "    ! wget -q www.dereneaton.com/downloads/clumpp.paramfile -O analysis_structure/clumpp.paramsfile\n",
    "\n",
    "    ## fill in the template: tell it the format to use individuals\n",
    "    stderr = ! sed -i '/type of data /c\\DATATYPE 0    # individuals //' analysis_structure/clumpp.paramsfile\n",
    "    stderr = ! sed -i '/individuals or populations /c\\C 27    # N individuals //' analysis_structure/clumpp.paramsfile\n",
    "    stderr = ! sed -i '/Print the permuted /c\\PRINT_PERMUTED_DATA 0    # no print //' analysis_structure/clumpp.paramsfile\n",
    "    \n",
    "    ## call CLUMPP\n",
    "    NREPS = 10\n",
    "    NINDS = 27\n",
    "    args = \"-i analysis_structure/RADr%s.indfile \\\n",
    "            -o analysis_structure/RADr%s.outfile \\\n",
    "            -j analysis_structure/RADr%s.miscfile \\\n",
    "            -k %s -r %s -c %s \" % (K,K,K,K,NREPS,NINDS)\n",
    "    stderr = ! ~/local/src/CLUMPP_Linux64.1.1.2/CLUMPP analysis_structure/clumpp.paramsfile $args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## run CLUMPP over 10 iterations at each level of K\n",
    "for i in range(2,9):\n",
    "    run_CLUMPP(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a stacked barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def structplot(infile, K, colorder=[], barorder=[]):\n",
    "    dat = open(infile).readlines()\n",
    "    D = OrderedDict()\n",
    "    for line in dat:\n",
    "        ll = line.strip().split()\n",
    "        vals = [float(i)*100 for i in ll[5:]]\n",
    "    \n",
    "        ## order of the bars\n",
    "        if not barorder:\n",
    "            D[ll[1]] = [vals[i] for i in range(K)]\n",
    "        else: \n",
    "            D[ll[1]] = [vals[i] for i in barorder]\n",
    "    \n",
    "    ## sort into the same order of individuals\n",
    "    ## as in the microsatellite data set of Cavender-Bares\n",
    "    sorteds = [11,16,21,25,\n",
    "               19,10,12,18,\n",
    "               17,14,15,13,\n",
    "               8,7,9,\n",
    "               24,4,20,5,6,\n",
    "               1,3,2,\n",
    "               22,23,\n",
    "               26,27]\n",
    "    \n",
    "    ## ordered color map\n",
    "    cmap = sns.color_palette(\"Set3\", 8)\n",
    "    #cmap = sns.dark_palette(\"skyblue\", 8, reverse=True)\n",
    "    if not colorder:\n",
    "        ncmap = [cmap[i] for i in range(K)]\n",
    "    else:\n",
    "        ncmap = [cmap[i] for i in colorder]\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10.,1.0))\n",
    "\n",
    "    ## normalize values to 100\n",
    "    for i in D:\n",
    "        D[i] = [100*(j/sum(D[i])) for j in D[i]]\n",
    "\n",
    "    ## make plot\n",
    "    for col in range(len(D.values()[0])):\n",
    "        if col > 0:\n",
    "            ax.bar(range(len(D.keys())), \n",
    "                   [D[str(i)][col] for i in sorteds],\n",
    "                   bottom = [sum(D[str(i)][:col]) for i in sorteds],\n",
    "                   color=ncmap[col], width=0.95, edgecolor='')       \n",
    "        else:\n",
    "            ax.bar(range(len(D.keys())), \n",
    "                   [D[str(i)][col] for i in sorteds],\n",
    "                   bottom = 0, color=ncmap[col], width=0.95, edgecolor=\"\")\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "\n",
    "    ll = [0, 3.95, 7.95, 11.95, 14.95,\n",
    "          19.95, 22.95, 26.95]\n",
    "    for l in ll:\n",
    "        ax.axvline(x=l, linewidth=1.5, color=\"#262626\")\n",
    "    ax.axhline(y=0.05, linewidth=1.3, color=\"#262626\")\n",
    "    ax.axhline(y=100, linewidth=1.3, color=\"#262626\")\n",
    "\n",
    "    ax.set_xlim(-0.,27.)\n",
    "    ax.set_ylim(-0.,100.)\n",
    "    \n",
    "    #ax.set_ylabel(\"Percent ancestry\")\n",
    "    ax.grid(b=0)\n",
    "    plt.savefig(\"figures/Fig2.RADstruct_\"+str(K)+\".svg\")\n",
    "    plt.savefig(\"figures/Fig2.RADstruct_\"+str(K)+\".png\", dpi=300)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "structplot(\"analysis_structure/RADr2.outfile\", 2, \n",
    "           colorder=[0,3], barorder = [1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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     "metadata": {},
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   "source": [
    "structplot(\"analysis_structure/RADr3.outfile\", 3, \n",
    "           colorder=[0,3,1], barorder = [0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "AFDef5Zydh/QhRK9AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9d07c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structplot(\"analysis_structure/RADr4.outfile\", 4, \n",
    "           colorder=[0,3,1,5], barorder = [3,2,0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "gg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9f65550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structplot(\"analysis_structure/RADr5.outfile\", 5, \n",
    "           colorder=[0,2,3,5,1], barorder = [1,0,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa006d90>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structplot(\"analysis_structure/RADr6.outfile\", 6, \n",
    "           colorder=[0,4,2,5,3,1], barorder = [0,1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structplot(\"analysis_structure/RADr7.outfile\", 7, \n",
    "           colorder=[0,5,6,3,2,4,1], barorder = [0,1,2,3,6,5,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f35550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "structplot(\"analysis_structure/RADr8.outfile\", 8,\n",
    "           colorder=[0,6,3,5,2,7,4,1], barorder = [7,6,4,5,2,0,1,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating optimal K\n",
    "The \"_f\" output files were zipped into the directory as analysis_structure/struct.zip which is in the `git` repository. This file was uploaded to the online resource [Structue Harvester](http://taylor0.biology.ucla.edu/structureHarvester/) which created the output files that are saved in the `git` repository in analysis_structure/Harvester.tar.gz. This analysis found K=3 as the optimal K value (highest Delta K) using the Evanno's K method, including the output table below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "## K\tReps\tMean LnP(K)\tStdev LnP(K)\tLn'(K)\t|Ln''(K)|\tDelta K\n",
    "## 2\t10\t-150180.520000\t758.978748\t—\t—\t—\n",
    "## 3\t10\t-132621.090000\t35.788218\t17559.430000\t26700.270000\t746.063129\n",
    "## 4\t10\t-141761.930000\t904.565336\t-9140.840000\t298432.520000\t329.918148\n",
    "## 5\t10\t-449335.290000\t240461.093921\t-307573.360000\t10843.300000\t0.045094\n",
    "## 6\t10\t-767751.950000\t1281470.488619\t-318416.660000\t2029640.650000\t1.583837\n",
    "## 7\t10\t-3115809.260000\t2635226.639916\t-2348057.310000\t344387.090000\t0.130686\n",
    "## 8\t10\t-5808253.660000\t3569473.653017\t-2692444.400000\t—\t—\n"
   ]
  }
 ],
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