{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook 9: \n",
    "\n",
    "This is an IPython notebook. Most of the code is composed of bash scripts, indicated by %%bash at the top of the cell, otherwise it is IPython code. This notebook includes code to download, assemble and analyze a published RADseq data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Notebook 9\n",
    "### Data set 9 (Ohomopterus)\n",
    "### Authors: Takahashi et al. 2014\n",
    "### Data Location: DRP001067"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Download the sequence data\n",
    "Sequence data for this study are archived on the NCBI sequence read archive (SRA). Below I read in SraRunTable.txt for this project which contains all of the information we need to download the data. \n",
    "\n",
    "+ Project DRA: DRA001025\n",
    "+ Study: DRP001067\n",
    "+ SRA link: http://trace.ddbj.nig.ac.jp/DRASearch/study?acc=DRP001067"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## make a new directory for this analysis\n",
    "mkdir -p empirical_9/fastq/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For each ERS (individuals) get all of the ERR (sequence file accessions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def wget_download_ddbj(SRR, outdir):\n",
    "    \"\"\" Python function to get sra data from ncbi and write to\n",
    "    outdir with a new name using bash call wget \"\"\"\n",
    "    \n",
    "    ## create a call string \n",
    "    call = \"wget -q -r -nH --cut-dirs=9 -P \"+outdir+\" \"+\\\n",
    "           \"ftp://ftp.ddbj.nig.ac.jp/ddbj_database/dra/sra/ByExp/\"+\\\n",
    "           \"sra/DRX/DRX011/DRX011{:03d}\".format(SRR)\n",
    "    \n",
    "    ## run wget call \n",
    "    ! $call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for ID in range(602,634):\n",
    "    wget_download_ddbj(ID, \"empirical_9/fastq/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we pass the SRR number and the sample name to the `wget_download` function so that the files are saved with their sample names. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 10248198 spots for empirical_9/fastq/DRR021959.sra\n",
      "Written 10248198 spots for empirical_9/fastq/DRR021959.sra\n",
      "Read 7839034 spots for empirical_9/fastq/DRR021960.sra\n",
      "Written 7839034 spots for empirical_9/fastq/DRR021960.sra\n",
      "Read 1132781 spots for empirical_9/fastq/DRR021961.sra\n",
      "Written 1132781 spots for empirical_9/fastq/DRR021961.sra\n",
      "Read 2609632 spots for empirical_9/fastq/DRR021962.sra\n",
      "Written 2609632 spots for empirical_9/fastq/DRR021962.sra\n",
      "Read 5077478 spots for empirical_9/fastq/DRR021963.sra\n",
      "Written 5077478 spots for empirical_9/fastq/DRR021963.sra\n",
      "Read 5173478 spots for empirical_9/fastq/DRR021964.sra\n",
      "Written 5173478 spots for empirical_9/fastq/DRR021964.sra\n",
      "Read 2077912 spots for empirical_9/fastq/DRR021965.sra\n",
      "Written 2077912 spots for empirical_9/fastq/DRR021965.sra\n",
      "Read 4930613 spots for empirical_9/fastq/DRR021966.sra\n",
      "Written 4930613 spots for empirical_9/fastq/DRR021966.sra\n",
      "Read 2494972 spots for empirical_9/fastq/DRR021967.sra\n",
      "Written 2494972 spots for empirical_9/fastq/DRR021967.sra\n",
      "Read 5276173 spots for empirical_9/fastq/DRR021968.sra\n",
      "Written 5276173 spots for empirical_9/fastq/DRR021968.sra\n",
      "Read 6316234 spots for empirical_9/fastq/DRR021969.sra\n",
      "Written 6316234 spots for empirical_9/fastq/DRR021969.sra\n",
      "Read 4871618 spots for empirical_9/fastq/DRR021970.sra\n",
      "Written 4871618 spots for empirical_9/fastq/DRR021970.sra\n",
      "Read 105160 spots for empirical_9/fastq/DRR021971.sra\n",
      "Written 105160 spots for empirical_9/fastq/DRR021971.sra\n",
      "Read 5521722 spots for empirical_9/fastq/DRR021972.sra\n",
      "Written 5521722 spots for empirical_9/fastq/DRR021972.sra\n",
      "Read 874466 spots for empirical_9/fastq/DRR021973.sra\n",
      "Written 874466 spots for empirical_9/fastq/DRR021973.sra\n",
      "Read 5376366 spots for empirical_9/fastq/DRR021974.sra\n",
      "Written 5376366 spots for empirical_9/fastq/DRR021974.sra\n",
      "Read 4715894 spots for empirical_9/fastq/DRR021975.sra\n",
      "Written 4715894 spots for empirical_9/fastq/DRR021975.sra\n",
      "Read 9091972 spots for empirical_9/fastq/DRR021976.sra\n",
      "Written 9091972 spots for empirical_9/fastq/DRR021976.sra\n",
      "Read 6609431 spots for empirical_9/fastq/DRR021977.sra\n",
      "Written 6609431 spots for empirical_9/fastq/DRR021977.sra\n",
      "Read 5827960 spots for empirical_9/fastq/DRR021978.sra\n",
      "Written 5827960 spots for empirical_9/fastq/DRR021978.sra\n",
      "Read 4742805 spots for empirical_9/fastq/DRR021979.sra\n",
      "Written 4742805 spots for empirical_9/fastq/DRR021979.sra\n",
      "Read 2231410 spots for empirical_9/fastq/DRR021980.sra\n",
      "Written 2231410 spots for empirical_9/fastq/DRR021980.sra\n",
      "Read 166474 spots for empirical_9/fastq/DRR021981.sra\n",
      "Written 166474 spots for empirical_9/fastq/DRR021981.sra\n",
      "Read 9263783 spots for empirical_9/fastq/DRR021982.sra\n",
      "Written 9263783 spots for empirical_9/fastq/DRR021982.sra\n",
      "Read 7912300 spots for empirical_9/fastq/DRR021983.sra\n",
      "Written 7912300 spots for empirical_9/fastq/DRR021983.sra\n",
      "Read 8731338 spots for empirical_9/fastq/DRR021984.sra\n",
      "Written 8731338 spots for empirical_9/fastq/DRR021984.sra\n",
      "Read 752632 spots for empirical_9/fastq/DRR021985.sra\n",
      "Written 752632 spots for empirical_9/fastq/DRR021985.sra\n",
      "Read 1482551 spots for empirical_9/fastq/DRR021986.sra\n",
      "Written 1482551 spots for empirical_9/fastq/DRR021986.sra\n",
      "Read 147164 spots for empirical_9/fastq/DRR021987.sra\n",
      "Written 147164 spots for empirical_9/fastq/DRR021987.sra\n",
      "Read 337046 spots for empirical_9/fastq/DRR021988.sra\n",
      "Written 337046 spots for empirical_9/fastq/DRR021988.sra\n",
      "Read 129310 spots for empirical_9/fastq/DRR021989.sra\n",
      "Written 129310 spots for empirical_9/fastq/DRR021989.sra\n",
      "Read 800590 spots for empirical_9/fastq/DRR021990.sra\n",
      "Written 800590 spots for empirical_9/fastq/DRR021990.sra\n",
      "Read 132868497 spots total\n",
      "Written 132868497 spots total\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "## convert sra files to fastq using fastq-dump tool\n",
    "## output as gzipped into the fastq directory\n",
    "fastq-dump --gzip -O empirical_9/fastq/ empirical_9/fastq/*.sra\n",
    "\n",
    "## remove .sra files\n",
    "rm empirical_9/fastq/*.sra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 11G\n",
      "-rw-rw-r-- 1 deren deren 801M Nov 28 18:32 DRR021959.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 618M Nov 28 18:32 DRR021960.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  91M Nov 28 18:32 DRR021961.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 206M Nov 28 18:32 DRR021962.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 399M Nov 28 18:32 DRR021963.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 408M Nov 28 18:32 DRR021964.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 167M Nov 28 18:32 DRR021965.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 387M Nov 28 18:32 DRR021966.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 197M Nov 28 18:32 DRR021967.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 418M Nov 28 18:32 DRR021968.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 499M Nov 28 18:32 DRR021969.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 386M Nov 28 18:32 DRR021970.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 8.4M Nov 28 18:32 DRR021971.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 439M Nov 28 18:32 DRR021972.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  70M Nov 28 18:32 DRR021973.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 434M Nov 28 18:32 DRR021974.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 375M Nov 28 18:32 DRR021975.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 713M Nov 28 18:32 DRR021976.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 520M Nov 28 18:32 DRR021977.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 473M Nov 28 18:32 DRR021978.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 373M Nov 28 18:32 DRR021979.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 179M Nov 28 18:32 DRR021980.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  14M Nov 28 18:32 DRR021981.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 727M Nov 28 18:32 DRR021982.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 625M Nov 28 18:32 DRR021983.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 697M Nov 28 18:32 DRR021984.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  60M Nov 28 18:32 DRR021985.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren 118M Nov 28 18:32 DRR021986.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  13M Nov 28 18:32 DRR021987.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  28M Nov 28 18:32 DRR021988.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  11M Nov 28 18:32 DRR021989.fastq.gz\n",
      "-rw-rw-r-- 1 deren deren  63M Nov 28 18:32 DRR021990.fastq.gz\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "ls -lh empirical_9/fastq/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a params file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyRAD 3.0.63\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pyrad --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tnew params.txt file created\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "## remove old params file if it exists\n",
    "rm params.txt \n",
    "\n",
    "## create a new default params file\n",
    "pyrad -n "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Note: \n",
    "The data here are from Illumina Casava <1.8, so the phred scores are offset by 64 instead of 33, so we use that in the params file below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## substitute new parameters into file\n",
    "sed -i '/## 1. /c\\empirical_9/           ## 1. working directory ' params.txt\n",
    "sed -i '/## 6. /c\\TGCAG                  ## 6. cutters ' params.txt\n",
    "sed -i '/## 7. /c\\20                     ## 7. N processors      ' params.txt\n",
    "sed -i '/## 9. /c\\6                      ## 9. NQual             ' params.txt\n",
    "sed -i '/## 10./c\\.85                    ## 10. clust threshold  ' params.txt\n",
    "sed -i '/## 12./c\\4                      ## 12. MinCov           ' params.txt\n",
    "sed -i '/## 13./c\\10                     ## 13. maxSH            ' params.txt\n",
    "sed -i '/## 14./c\\empirical_9_m4          ## 14. output name      ' params.txt\n",
    "sed -i '/## 18./c\\empirical_9/fastq/*.gz ## 18. data location    ' params.txt\n",
    "sed -i '/## 29./c\\2,2                    ## 29. trim overhang    ' params.txt\n",
    "sed -i '/## 30./c\\p,n,s                  ## 30. output formats   ' params.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==** parameter inputs for pyRAD version 3.0.63  **======================== affected step ==\r\n",
      "empirical_9/           ## 1. working directory \r\n",
      "./*.fastq.gz              ## 2. Loc. of non-demultiplexed files (if not line 18)  (s1)\r\n",
      "./*.barcodes              ## 3. Loc. of barcode file (if not line 18)             (s1)\r\n",
      "vsearch                   ## 4. command (or path) to call vsearch (or usearch)    (s3,s6)\r\n",
      "muscle                    ## 5. command (or path) to call muscle                  (s3,s7)\r\n",
      "TGCAG                  ## 6. cutters \r\n",
      "20                     ## 7. N processors      \r\n",
      "6                         ## 8. Mindepth: min coverage for a cluster              (s4,s5)\r\n",
      "6                      ## 9. NQual             \r\n",
      ".85                    ## 10. clust threshold  \r\n",
      "rad                       ## 11. Datatype: rad,gbs,pairgbs,pairddrad,(others:see docs)(all)\r\n",
      "4                      ## 12. MinCov           \r\n",
      "10                     ## 13. maxSH            \r\n",
      "empirical_9_m4          ## 14. output name      \r\n",
      "==== optional params below this line ===================================  affected step ==\r\n",
      "                       ## 15.opt.: select subset (prefix* only selector)            (s2-s7)\r\n",
      "                       ## 16.opt.: add-on (outgroup) taxa (list or prefix*)         (s6,s7)\r\n",
      "                       ## 17.opt.: exclude taxa (list or prefix*)                   (s7)\r\n",
      "empirical_9/fastq/*.gz ## 18. data location    \r\n",
      "                       ## 19.opt.: maxM: N mismatches in barcodes (def= 1)          (s1)\r\n",
      "                       ## 20.opt.: phred Qscore offset (def= 33)                    (s2)\r\n",
      "                       ## 21.opt.: filter: def=0=NQual 1=NQual+adapters. 2=strict   (s2)\r\n",
      "                       ## 22.opt.: a priori E,H (def= 0.001,0.01, if not estimated) (s5)\r\n",
      "                       ## 23.opt.: maxN: max Ns in a cons seq (def=5)               (s5)\r\n",
      "                       ## 24.opt.: maxH: max heterozyg. sites in cons seq (def=5)   (s5)\r\n",
      "                       ## 25.opt.: ploidy: max alleles in cons seq (def=2;see docs) (s4,s5)\r\n",
      "                       ## 26.opt.: maxSNPs: (def=100). Paired (def=100,100)         (s7)\r\n",
      "                       ## 27.opt.: maxIndels: within-clust,across-clust (def. 3,99) (s3,s7)\r\n",
      "                       ## 28.opt.: random number seed (def. 112233)              (s3,s6,s7)\r\n",
      "2,2                    ## 29. trim overhang    \r\n",
      "p,n,s                  ## 30. output formats   \r\n",
      "                       ## 31.opt.: maj. base call at depth>x<mindepth (def.x=mindepth) (s5)\r\n",
      "                       ## 32.opt.: keep trimmed reads (def=0). Enter min length.    (s2)\r\n",
      "                       ## 33.opt.: max stack size (int), def= max(500,mean+2*SD)    (s3)\r\n",
      "                       ## 34.opt.: minDerep: exclude dereps with <= N copies, def=1 (s3)\r\n",
      "                       ## 35.opt.: use hierarchical clustering (def.=0, 1=yes)      (s6)\r\n",
      "                       ## 36.opt.: repeat masking (def.=1='dust' method, 0=no)      (s3,s6)\r\n",
      "                       ## 37.opt.: vsearch max threads per job (def.=6; see docs)   (s3,s6)\r\n",
      "==== optional: list group/clade assignments below this line (see docs) ==================\r\n"
     ]
    }
   ],
   "source": [
    "cat params.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trimming the barcode\n",
    "In this data set the data were uploaded separated by sample, but with the barcode still attached to the sequences. The python code below will remove the 5bp barcode from each sequence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('DRR021982.fastq', '.gz')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.splitext(fil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "import itertools\n",
    "import gzip\n",
    "import os\n",
    "\n",
    "\n",
    "for infile in glob.glob(\"empirical_9/fastq/DRR*\"):\n",
    "    iofile = gzip.open(infile, 'rb')\n",
    "    dire, fil = os.path.split(infile)\n",
    "    fastq = os.path.splitext(fil)[0]\n",
    "    outhandle = os.path.join(dire, \"T_\"+fastq)\n",
    "    outfile = open(outhandle, 'wb')\n",
    "    \n",
    "    data = iter(iofile)\n",
    "    store = []\n",
    "    while 1:\n",
    "        try:\n",
    "            line = data.next()\n",
    "        except StopIteration:\n",
    "            break\n",
    "        if len(line) < 80:\n",
    "            store.append(line)\n",
    "        else:\n",
    "            store.append(line[5:])\n",
    "            \n",
    "        if len(store) == 10000:\n",
    "            outfile.write(\"\".join(store))\n",
    "            store = []\n",
    "            \n",
    "    iofile.close()\n",
    "    outfile.close()        \n",
    "    ! gzip $outhandle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Assemble in _pyrad_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "pyrad -p params.txt -s 234567 >> log.txt 2>&1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "sed -i '/## 12./c\\2                    ## 12. MinCov           ' params.txt\n",
    "sed -i '/## 14./c\\empirical_9_m2       ## 14. output name      ' params.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "pyrad -p params.txt -s 7 >> log.txt 2>&1  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "We are interested in the relationship between the amount of input (raw) data between any two samples, the average coverage they recover when clustered together, and the phylogenetic distances separating samples.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Raw data amounts\n",
    "The average number of raw reads per sample is 1.36M."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count         32.000000\n",
      "mean     3888871.375000\n",
      "std      2913183.441258\n",
      "min        98285.000000\n",
      "25%      1000709.500000\n",
      "50%      4518550.000000\n",
      "75%      5558420.000000\n",
      "max      9568142.000000\n",
      "Name: passed.total, dtype: float64\n",
      "\n",
      "most raw data in sample:\n",
      "0    T_DRR021959\n",
      "Name: sample , dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "## read in the data\n",
    "s2dat = pd.read_table(\"empirical_9/stats/s2.rawedit.txt\", header=0, nrows=32)\n",
    "\n",
    "## print summary stats\n",
    "print s2dat[\"passed.total\"].describe()\n",
    "\n",
    "## find which sample has the most raw data\n",
    "maxraw = s2dat[\"passed.total\"].max()\n",
    "print \"\\nmost raw data in sample:\"\n",
    "print s2dat['sample '][s2dat['passed.total']==maxraw]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at distributions of coverage\n",
    "pyrad v.3.0.63 outputs depth information for each sample which I read in here and plot. First let's ask which sample has the highest depth of coverage. The std of coverages is pretty low in this data set compared to several others. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "summary of means\n",
      "==================\n",
      "count     32.000000\n",
      "mean      57.046781\n",
      "std       37.276956\n",
      "min        5.436000\n",
      "25%       23.110250\n",
      "50%       65.631000\n",
      "75%       77.041000\n",
      "max      135.646000\n",
      "Name: dpt.me, dtype: float64\n",
      "\n",
      "summary of std\n",
      "==================\n",
      "count     32.000000\n",
      "mean     272.580687\n",
      "std      201.847071\n",
      "min       16.346000\n",
      "25%       94.865750\n",
      "50%      258.018000\n",
      "75%      399.662500\n",
      "max      690.205000\n",
      "Name: dpt.sd, dtype: float64\n",
      "\n",
      "summary of proportion lowdepth\n",
      "==================\n",
      "count    32.000000\n",
      "mean      0.188730\n",
      "std       0.161401\n",
      "min       0.074257\n",
      "25%       0.085030\n",
      "50%       0.114366\n",
      "75%       0.210204\n",
      "max       0.655746\n",
      "dtype: float64\n",
      "\n",
      "highest coverage in sample:\n",
      "7    T_DRR021966\n",
      "Name: taxa, dtype: object\n"
     ]
    }
   ],
   "source": [
    "## read in the s3 results\n",
    "s9dat = pd.read_table(\"empirical_9/stats/s3.clusters.txt\", header=0, nrows=33)\n",
    "\n",
    "## print summary stats\n",
    "print \"summary of means\\n==================\"\n",
    "print s9dat['dpt.me'].describe()\n",
    "\n",
    "## print summary stats\n",
    "print \"\\nsummary of std\\n==================\"\n",
    "print s9dat['dpt.sd'].describe()\n",
    "\n",
    "## print summary stats\n",
    "print \"\\nsummary of proportion lowdepth\\n==================\"\n",
    "print pd.Series(1-s9dat['d>5.tot']/s9dat[\"total\"]).describe()\n",
    "\n",
    "## find which sample has the greatest depth of retained loci\n",
    "max_hiprop = (s9dat[\"d>5.tot\"]/s9dat[\"total\"]).max()\n",
    "print \"\\nhighest coverage in sample:\"\n",
    "print s9dat['taxa'][s9dat['d>5.tot']/s9dat[\"total\"]==max_hiprop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "highest prop coverage in sample:\n",
      "7    T_DRR021966\n",
      "Name: taxa, dtype: object\n"
     ]
    }
   ],
   "source": [
    "maxprop =(s9dat['d>5.tot']/s9dat['total']).max()\n",
    "print \"\\nhighest prop coverage in sample:\"\n",
    "print s9dat['taxa'][s9dat['d>5.tot']/s9dat['total']==maxprop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Means for sample T_DRR021966\n",
      "72.1266330292 439.779736427\n",
      "77.7021948176 456.619346077\n"
     ]
    }
   ],
   "source": [
    "## print mean and std of coverage for the highest coverage sample\n",
    "with open(\"empirical_9/clust.85/T_DRR021966.depths\", 'rb') as indat:\n",
    "    depths = np.array(indat.read().strip().split(\",\"), dtype=int)\n",
    "    \n",
    "print \"Means for sample T_DRR021966\"\n",
    "print depths.mean(), depths.std()\n",
    "print depths[depths>5].mean(), depths[depths>5].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the coverage for the sample with highest mean coverage\n",
    "Green shows the loci that were discarded and orange the loci that were retained. The majority of data were discarded for being too low of coverage. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "<toyplot.canvas.Canvas at 0x7f91f2138050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import toyplot\n",
    "import toyplot.svg\n",
    "import numpy as np\n",
    "\n",
    "## read in the depth information for this sample\n",
    "with open(\"empirical_9/clust.85/T_DRR021966.depths\", 'rb') as indat:\n",
    "    depths = np.array(indat.read().strip().split(\",\"), dtype=int)\n",
    "    \n",
    "## make a barplot in Toyplot\n",
    "canvas = toyplot.Canvas(width=350, height=300)\n",
    "axes = canvas.axes(xlabel=\"Depth of coverage (N reads)\", \n",
    "                   ylabel=\"N loci\", \n",
    "                   label=\"dataset9/sample=T_DRR021966\")\n",
    "\n",
    "## select the loci with depth > 5 (kept)\n",
    "keeps = depths[depths>5]\n",
    "\n",
    "## plot kept and discarded loci\n",
    "edat = np.histogram(depths, range(30)) # density=True)\n",
    "kdat = np.histogram(keeps, range(30)) #, density=True)\n",
    "axes.bars(edat)\n",
    "axes.bars(kdat)\n",
    "\n",
    "#toyplot.svg.render(canvas, \"empirical_9_depthplot.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print final stats table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\r\n",
      "76963       ## loci with > minsp containing data\r\n",
      "76778       ## loci with > minsp containing data & paralogs removed\r\n",
      "76778       ## loci with > minsp containing data & paralogs removed & final filtering\r\n",
      "\r\n",
      "## number of loci recovered in final data set for each taxon.\r\n",
      "taxon\tnloci\r\n",
      "T_DRR021959\t40272\r\n",
      "T_DRR021960\t39500\r\n",
      "T_DRR021961\t29626\r\n",
      "T_DRR021962\t38304\r\n",
      "T_DRR021963\t40251\r\n",
      "T_DRR021964\t40440\r\n",
      "T_DRR021965\t37187\r\n",
      "T_DRR021966\t41059\r\n",
      "T_DRR021967\t38884\r\n",
      "T_DRR021968\t42626\r\n",
      "T_DRR021969\t43490\r\n",
      "T_DRR021970\t43303\r\n",
      "T_DRR021971\t5249\r\n",
      "T_DRR021972\t46589\r\n",
      "T_DRR021973\t29939\r\n",
      "T_DRR021974\t47628\r\n",
      "T_DRR021975\t46705\r\n",
      "T_DRR021976\t47963\r\n",
      "T_DRR021977\t47881\r\n",
      "T_DRR021978\t46731\r\n",
      "T_DRR021979\t46386\r\n",
      "T_DRR021980\t41322\r\n",
      "T_DRR021981\t8509\r\n",
      "T_DRR021982\t48193\r\n",
      "T_DRR021983\t47548\r\n",
      "T_DRR021984\t47715\r\n",
      "T_DRR021985\t27186\r\n",
      "T_DRR021986\t36910\r\n",
      "T_DRR021987\t6636\r\n",
      "T_DRR021988\t14945\r\n",
      "T_DRR021989\t6640\r\n",
      "T_DRR021990\t7699\r\n",
      "\r\n",
      "\r\n",
      "## nloci = number of loci with data for exactly ntaxa\r\n",
      "## ntotal = number of loci for which at least ntaxa have data\r\n",
      "ntaxa\tnloci\tsaved\tntotal\r\n",
      "1\t-\r\n",
      "2\t-\t\t-\r\n",
      "3\t-\t\t-\r\n",
      "4\t8714\t*\t76778\r\n",
      "5\t5780\t*\t68064\r\n",
      "6\t4454\t*\t62284\r\n",
      "7\t3488\t*\t57830\r\n",
      "8\t3019\t*\t54342\r\n",
      "9\t2568\t*\t51323\r\n",
      "10\t2381\t*\t48755\r\n",
      "11\t2254\t*\t46374\r\n",
      "12\t2153\t*\t44120\r\n",
      "13\t2052\t*\t41967\r\n",
      "14\t1941\t*\t39915\r\n",
      "15\t1933\t*\t37974\r\n",
      "16\t1927\t*\t36041\r\n",
      "17\t1955\t*\t34114\r\n",
      "18\t2016\t*\t32159\r\n",
      "19\t2150\t*\t30143\r\n",
      "20\t2367\t*\t27993\r\n",
      "21\t2645\t*\t25626\r\n",
      "22\t3061\t*\t22981\r\n",
      "23\t3563\t*\t19920\r\n",
      "24\t3957\t*\t16357\r\n",
      "25\t4030\t*\t12400\r\n",
      "26\t3789\t*\t8370\r\n",
      "27\t2673\t*\t4581\r\n",
      "28\t1357\t*\t1908\r\n",
      "29\t450\t*\t551\r\n",
      "30\t91\t*\t101\r\n",
      "31\t9\t*\t10\r\n",
      "32\t1\t*\t1\r\n",
      "\r\n",
      "\r\n",
      "## nvar = number of loci containing n variable sites (pis+autapomorphies).\r\n",
      "## sumvar = sum of variable sites (SNPs).\r\n",
      "## pis = number of loci containing n parsimony informative sites.\r\n",
      "## sumpis = sum of parsimony informative sites.\r\n",
      "\tnvar\tsumvar\tPIS\tsumPIS\r\n",
      "0\t967\t0\t8521\t0\r\n",
      "1\t1685\t1685\t8479\t8479\r\n",
      "2\t2050\t5785\t8315\t25109\r\n",
      "3\t2519\t13342\t7439\t47426\r\n",
      "4\t2935\t25082\t7082\t75754\r\n",
      "5\t3112\t40642\t6307\t107289\r\n",
      "6\t3429\t61216\t5747\t141771\r\n",
      "7\t3555\t86101\t5016\t176883\r\n",
      "8\t3723\t115885\t4372\t211859\r\n",
      "9\t3846\t150499\t3823\t246266\r\n",
      "10\t3664\t187139\t3017\t276436\r\n",
      "11\t3684\t227663\t2471\t303617\r\n",
      "12\t3663\t271619\t1940\t326897\r\n",
      "13\t3536\t317587\t1360\t344577\r\n",
      "14\t3428\t365579\t1016\t358801\r\n",
      "15\t3317\t415334\t705\t369376\r\n",
      "16\t3081\t464630\t466\t376832\r\n",
      "17\t2976\t515222\t287\t381711\r\n",
      "18\t2775\t565172\t175\t384861\r\n",
      "19\t2600\t614572\t95\t386666\r\n",
      "20\t2356\t661692\t62\t387906\r\n",
      "21\t2165\t707157\t36\t388662\r\n",
      "22\t1888\t748693\t21\t389124\r\n",
      "23\t1679\t787310\t13\t389423\r\n",
      "24\t1444\t821966\t7\t389591\r\n",
      "25\t1289\t854191\t3\t389666\r\n",
      "26\t1099\t882765\t0\t389666\r\n",
      "27\t890\t906795\t1\t389693\r\n",
      "28\t763\t928159\t1\t389721\r\n",
      "29\t609\t945820\t1\t389750\r\n",
      "30\t511\t961150\t0\t389750\r\n",
      "31\t370\t972620\t0\t389750\r\n",
      "32\t293\t981996\t0\t389750\r\n",
      "33\t272\t990972\t0\t389750\r\n",
      "34\t186\t997296\t0\t389750\r\n",
      "35\t132\t1001916\t0\t389750\r\n",
      "36\t98\t1005444\t0\t389750\r\n",
      "37\t49\t1007257\t0\t389750\r\n",
      "38\t55\t1009347\t0\t389750\r\n",
      "39\t31\t1010556\t0\t389750\r\n",
      "40\t19\t1011316\t0\t389750\r\n",
      "41\t18\t1012054\t0\t389750\r\n",
      "42\t7\t1012348\t0\t389750\r\n",
      "43\t6\t1012606\t0\t389750\r\n",
      "44\t1\t1012650\t0\t389750\r\n",
      "45\t1\t1012695\t0\t389750\r\n",
      "46\t1\t1012741\t0\t389750\r\n",
      "47\t0\t1012741\t0\t389750\r\n",
      "48\t0\t1012741\t0\t389750\r\n",
      "49\t0\t1012741\t0\t389750\r\n",
      "50\t0\t1012741\t0\t389750\r\n",
      "51\t0\t1012741\t0\t389750\r\n",
      "52\t1\t1012793\t0\t389750\r\n",
      "total var= 1012793\r\n",
      "total pis= 389750\r\n",
      "sampled unlinked SNPs= 75811\r\n",
      "sampled unlinked bi-allelic SNPs= 73051\r\n"
     ]
    }
   ],
   "source": [
    "cat empirical_9/stats/empirical_9_m4.stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "119626      ## loci with > minsp containing data\n",
      "119441      ## loci with > minsp containing data & paralogs removed\n",
      "119441      ## loci with > minsp containing data & paralogs removed & final filtering\n",
      "\n",
      "## number of loci recovered in final data set for each taxon.\n",
      "taxon\tnloci\n",
      "T_DRR021959\t45933\n",
      "T_DRR021960\t45128\n",
      "T_DRR021961\t33226\n",
      "T_DRR021962\t41997\n",
      "T_DRR021963\t45401\n",
      "T_DRR021964\t45882\n",
      "T_DRR021965\t40633\n",
      "T_DRR021966\t46239\n",
      "T_DRR021967\t43330\n",
      "T_DRR021968\t47995\n",
      "T_DRR021969\t47919\n",
      "T_DRR021970\t47490\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head -n 20 empirical_9/stats/empirical_9_m2.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Infer ML phylogeny in _raxml_ as an unrooted tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## raxml argumement w/ ...\n",
    "raxmlHPC-PTHREADS-AVX -f a -m GTRGAMMA -N 100 -x 12345 -p 12345 -T 20 \\\n",
    "                      -w /home/deren/Documents/RADmissing/empirical_9/ \\\n",
    "                      -n empirical_9_m4 -s empirical_9/outfiles/empirical_9_m4.phy\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## raxml argumement w/ ...\n",
    "raxmlHPC-PTHREADS-AVX -f a -m GTRGAMMA -N 100 -x 12345 -p 12345 -T 20 \\\n",
    "                      -w /home/deren/Documents/RADmissing/empirical_9/ \\\n",
    "                      -n empirical_9_m2 -s empirical_9/outfiles/empirical_9_m2.phy\n",
    "                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "This is RAxML version 8.0.16 released by Alexandros Stamatakis on March 21 2014.\n",
      "\n",
      "With greatly appreciated code contributions by:\n",
      "Andre Aberer      (HITS)\n",
      "Simon Berger      (HITS)\n",
      "Alexey Kozlov     (HITS)\n",
      "Nick Pattengale   (Sandia)\n",
      "Wayne Pfeiffer    (SDSC)\n",
      "Akifumi S. Tanabe (NRIFS)\n",
      "David Dao         (KIT)\n",
      "Charlie Taylor    (UF)\n",
      "\n",
      "\n",
      "Alignment has 818110 distinct alignment patterns\n",
      "\n",
      "Proportion of gaps and completely undetermined characters in this alignment: 54.64%\n",
      "\n",
      "RAxML rapid bootstrapping and subsequent ML search\n"
     ]
    }
   ],
   "source": [
    "%%bash \n",
    "head -n 20 empirical_9/RAxML_info.empirical_9_m4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash \n",
    "head -n 20 empirical_9/RAxML_info.empirical_9_m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the tree in R using `ape`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mpiaDwZCcnBwUFESXb9q0afXq1XROSUkJHXz55ZeZmZl/+tOfli5dmpCQ8Kgf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\nAAAAyQhYAAAAkhGwAAAAJPtfZcXGK3CKxzgAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -h 800 -w 800\n",
    "library(ape)\n",
    "tre <- read.tree(\"empirical_9/RAxML_bipartitions.empirical_9\")\n",
    "ltre <- ladderize(tre)\n",
    "\n",
    "par(mfrow=c(1,2))\n",
    "plot(ltre, use.edge.length=F)\n",
    "nodelabels(ltre$node.label)\n",
    "\n",
    "plot(ltre, type='u')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get phylo distances (GTRgamma dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] 0.04027079\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "mean(cophenetic.phylo(ltre))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}