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  "name": "",
  "signature": "sha256:da23179cc87873266918434c4ead66eca7e6218fd311b2a0df46a939dbcc80fb"
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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Viburnum RADseq project\n",
      "### Notebook 4: Analysis of _V. lantanoides & relatives\n",
      "\n",
      "-----------------------   \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## load libraries for running R\n",
      "%load_ext rmagic\n",
      "\n",
      "## for embedding figures\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "## a directory for putting figures & results into\n",
      "! mkdir -p project_lantanoides/figures/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### The data set\n",
      "This analysis will use the data sets produced in [Notebook2](link). We are interested in testing for introgression between lineages. We will use the \".loci\" output file to perform D-statistic tests in _pyRAD_, and use the \".treemix.gz\" output to test introgression in _TreeMix_. \n",
      "\n",
      "Below is the tree inferred from this data set in _RAxML_. \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -w 400 -h 400\n",
      "library(ape)\n",
      "## 65,115 loci\n",
      "## 5.6M bp alignment\n",
      "## 44% missing data\n",
      "lant_m4 <- read.tree(\"project_lantanoides/analyses_raxml/RAxML_bipartitions.lantanoides_c88d6m4p3\")\n",
      "tre <- ladderize(lant_m4)\n",
      "plot(tre, edge.width=2)\n",
      "nodelabels(tre$node.label, bg='grey')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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c/lqUw3BCWkl5uLnpbS3w72gxDMeVjzvWHo60KyMhOaGC3AP76sp5OKmI3JBlJ5uXjv/w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\nCqEMFEIZKIQyUAhloBDK+H/s0H9HmuD9AgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## D-statistic tests  \n",
      "\n",
      "First we will setup the input file with parameters and topologies over which to measure `D`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "## make a new directory for D-statistic tests\n",
      "mkdir -p project_lantanoides/analysis_dtests/\n",
      "\n",
      "## generate template params file\n",
      "~/Dropbox/pyrad-github/pyRAD -D > project_lantanoides/analysis_dtests/template.D\n",
      "\n",
      "## look at template\n",
      "cat project_lantanoides/analysis_dtests/template.D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200                          ## N bootstrap replicates\n",
        "test.loci                    ## loc/path to input .loci file\n",
        "dstats/test1_res             ## output file path/name (no suffix)\n",
        "4                            ## which test: 4,part,foil,foilalt\n",
        "2                            ## N cores (execute jobs [lines below] in parallel\n",
        "0                            ## output ABBA/BABA loci to files (0=no,1,2=verbose)\n",
        "0                            ## output bootstrap Ds to files (0=no,1=yes)\n",
        "-----------------------------------------------------------\n"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "## substitute new values into the template\n",
      "sed -i \"/## loc/c\\project_lantanoides/outfiles/lantanoides_c88d6m4p3.loci  ## loc/path \" project_lantanoides/analysis_dtests/template.D\n",
      "sed -i \"/## output file/c\\project_lantanoides/analysis_dtests/result  ## output file \" project_lantanoides/analysis_dtests/template.D\n",
      "sed -i \"/## N cores/c\\16                           ## N cores \" project_lantanoides/analysis_dtests/template.D\n",
      "\n",
      "## look at template\n",
      "cat project_lantanoides/analysis_dtests/template.D"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200                          ## N bootstrap replicates\n",
        "project_lantanoides/outfiles/lantanoides_c88d6m4p3.loci  ## loc/path \n",
        "project_lantanoides/analysis_dtests/result  ## output file \n",
        "4                            ## which test: 4,part,foil,foilalt\n",
        "16                           ## N cores \n",
        "0                            ## output ABBA/BABA loci to files (0=no,1,2=verbose)\n",
        "0                            ## output bootstrap Ds to files (0=no,1=yes)\n",
        "-----------------------------------------------------------\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## put samples into groups (taxa)\n",
      "outgroups = [\"plicatum_C1_MJDJP_12\", \"carlesii_D1_BP_001\", \"foetidum_D24_KFC_1942\"]\n",
      "nervosum = [\"nervosum_C4_PWS_2298\", \"nervosum_C2_PWS_2288_2289\"]\n",
      "sympodiale = [\"sympodiale_D18_KFC_1932\"]\n",
      "furcatum = [\"furcatum_combined\"]\n",
      "lantanoides = [\"lantanoides_D15_Beartown_2\", \"lantanoides_D14_Beartown_1\",\n",
      "               \"lantanoides_D_17_Mohawk_3\",\"lantanoides_D16_Mohawk_2\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makeD4(P1,P2,P3,O,num,name,template):\n",
      "    stringout = \"\"\n",
      "    tests = []\n",
      "    i = 1\n",
      "    for p1 in P1: \n",
      "        for p2 in P2:\n",
      "            for p3 in P3:\n",
      "                if p1 != p2:\n",
      "                    if set([p1,p2,p3]) not in tests:\n",
      "                        stringout += \"[%s]\\t[%s]\\t[%s]\\t[%s]\\t## test %i.%i\\n\" % (p1,p2,p3,\",\".join(O),num,i)\n",
      "                        i += 1\n",
      "                        tests.append(set([p1,p2,p3]))\n",
      "    ! head -n 8 $template > \"project_lantanoides/analysis_dtests/input.\"$num\".\"$name\".txt\"\n",
      "    ! echo \"$stringout\" >> \"project_lantanoides/analysis_dtests/input.\"$num\".\"$name\".txt\"\n",
      "    ostring = \"/## output file/c\\\\project_lantanoides/analysis_dtests/out.\"+str(num)+\".\"+str(name)+\"      ## output file \"\n",
      "    ! sed -i \"$ostring\" \"project_lantanoides/analysis_dtests/input.\"$num\".\"$name\".txt\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "##\n",
      "makeD4(lantanoides,lantanoides,furcatum,outgroups,1,\n",
      "       \"LLF\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "makeD4(lantanoides,lantanoides,nervosum,outgroups,2,\n",
      "       \"LLN\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "makeD4(lantanoides,lantanoides,sympodiale,outgroups,3,\n",
      "       \"LLS\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "##\n",
      "makeD4(lantanoides,furcatum,nervosum,outgroups,4,\n",
      "       \"LFN\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "makeD4(lantanoides,sympodiale,nervosum,outgroups,5,\n",
      "       \"LFS\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "##\n",
      "makeD4(nervosum,nervosum,lantanoides,outgroups,6,\n",
      "       \"NNL\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "makeD4(nervosum,nervosum,sympodiale,outgroups,7,\n",
      "       \"NNS\",\"project_lantanoides/analysis_dtests/template.D\")\n",
      "makeD4(nervosum,nervosum,furcatum,outgroups,8,\n",
      "       \"NNF\",\"project_lantanoides/analysis_dtests/template.D\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "cat project_lantanoides/analysis_dtests/input.1.LLF.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200                          ## N bootstrap replicates\n",
        "project_lantanoides/outfiles/lantanoides_c88d6m4p3.loci  ## loc/path \n",
        "project_lantanoides/analysis_dtests/out.1.LLF      ## output file \n",
        "4                            ## which test: 4,part,foil,foilalt\n",
        "16                           ## N cores \n",
        "0                            ## output ABBA/BABA loci to files (0=no,1,2=verbose)\n",
        "0                            ## output bootstrap Ds to files (0=no,1=yes)\n",
        "-----------------------------------------------------------\n",
        "[lantanoides_D15_Beartown_2]\t[lantanoides_D14_Beartown_1]\t[furcatum_combined]\t[plicatum_C1_MJDJP_12,carlesii_D1_BP_001,foetidum_D24_KFC_1942]\t## test 1.1\n",
        "[lantanoides_D15_Beartown_2]\t[lantanoides_D_17_Mohawk_3]\t[furcatum_combined]\t[plicatum_C1_MJDJP_12,carlesii_D1_BP_001,foetidum_D24_KFC_1942]\t## test 1.2\n",
        "[lantanoides_D15_Beartown_2]\t[lantanoides_D16_Mohawk_2]\t[furcatum_combined]\t[plicatum_C1_MJDJP_12,carlesii_D1_BP_001,foetidum_D24_KFC_1942]\t## test 1.3\n",
        "[lantanoides_D14_Beartown_1]\t[lantanoides_D_17_Mohawk_3]\t[furcatum_combined]\t[plicatum_C1_MJDJP_12,carlesii_D1_BP_001,foetidum_D24_KFC_1942]\t## test 1.4\n",
        "[lantanoides_D14_Beartown_1]\t[lantanoides_D16_Mohawk_2]\t[furcatum_combined]\t[plicatum_C1_MJDJP_12,carlesii_D1_BP_001,foetidum_D24_KFC_1942]\t## test 1.5\n",
        "[lantanoides_D_17_Mohawk_3]\t[lantanoides_D16_Mohawk_2]\t[furcatum_combined]\t[plicatum_C1_MJDJP_12,carlesii_D1_BP_001,foetidum_D24_KFC_1942]\t## test 1.6\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## run tests\n",
      "import glob\n",
      "for test in range(1,9):\n",
      "    t = glob.glob(\"project_lantanoides/analysis_dtests/input.\"+str(test)+\".*\")[0]\n",
      "    print \"running\",t\n",
      "    stderr = ! ~/Dropbox/pyrad-github/pyRAD -d $t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.1.LLF.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.2.LLN.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.3.LLS.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.4.LFN.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.5.LFS.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.6.NNL.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.7.NNS.txt\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "running project_lantanoides/analysis_dtests/input.8.NNF.txt\n"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash \n",
      "echo -e \"\\n==== test 1 === furcatum into lantanoides?\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.1.LLF.D4.txt\n",
      "echo -e \"\\n==== test 2 === nervosum into lantanoides?\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.2.LLN.D4.txt\n",
      "echo -e \"\\n==== test 3 === sympodiale into lantanoides?\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.3.LLS.D4.txt\n",
      "echo -e \"\\n==== test 4 === nervosum into lant v furc\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.4.LFN.D4.txt\n",
      "echo -e \"\\n==== test 5 === sympodiale into lant v furc\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.5.LFS.D4.txt\n",
      "echo -e \"\\n==== test 6 === lantanoides into nervosum\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.6.NNL.D4.txt\n",
      "echo -e \"\\n==== test 7 === sympodiale into nervosum\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.7.NNS.D4.txt\n",
      "echo -e \"\\n==== test 8 === furcatum into nervosum\"\n",
      "cut -f5-13 project_lantanoides/analysis_dtests/out.8.NNF.D4.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "==== test 1 === furcatum into lantanoides?\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.002\t0.053\t0.03\t146.25\t146.75\t11307\t200\t0.03\ttest 1.1\n",
        "-0.064\t0.055\t1.16\t218.62\t192.38\t11845\t200\t0.04\ttest 1.2\n",
        "0.040\t0.054\t0.74\t159.50\t172.75\t11527\t200\t0.03\ttest 1.3\n",
        "-0.113\t0.039\t2.87\t357.62\t284.88\t19209\t200\t0.04\ttest 1.4\n",
        "0.019\t0.066\t0.29\t164.38\t170.88\t10989\t200\t0.03\ttest 1.5\n",
        "0.130\t0.036\t3.59\t268.62\t349.12\t19423\t200\t0.04\ttest 1.6\n",
        "\n",
        "==== test 2 === nervosum into lantanoides?\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.058\t0.059\t0.99\t153.88\t172.88\t11739\t200\t0.03\ttest 2.1\n",
        "0.041\t0.063\t0.66\t121.88\t132.38\t10909\t200\t0.03\ttest 2.2\n",
        "-0.126\t0.054\t2.32\t219.00\t170.00\t12250\t200\t0.03\ttest 2.3\n",
        "-0.054\t0.063\t0.85\t189.12\t169.88\t11431\t200\t0.03\ttest 2.4\n",
        "0.001\t0.055\t0.01\t178.88\t179.12\t11866\t200\t0.03\ttest 2.5\n",
        "-0.050\t0.066\t0.75\t153.00\t138.50\t10949\t200\t0.03\ttest 2.6\n",
        "-0.180\t0.054\t3.34\t220.62\t153.38\t11723\t200\t0.03\ttest 2.7\n",
        "-0.101\t0.053\t1.90\t189.50\t154.75\t10933\t200\t0.03\ttest 2.8\n",
        "-0.039\t0.050\t0.78\t172.38\t159.38\t11319\t200\t0.03\ttest 2.9\n",
        "-0.036\t0.055\t0.66\t147.50\t137.25\t10479\t200\t0.03\ttest 2.10\n",
        "0.091\t0.046\t1.99\t163.62\t196.38\t11818\t200\t0.03\ttest 2.11\n",
        "0.031\t0.060\t0.52\t163.00\t173.50\t11058\t200\t0.03\ttest 2.12\n",
        "\n",
        "==== test 3 === sympodiale into lantanoides?\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.022\t0.039\t0.55\t242.38\t253.12\t19262\t200\t0.03\ttest 3.1\n",
        "-0.064\t0.039\t1.64\t363.25\t319.50\t20148\t200\t0.04\ttest 3.2\n",
        "0.004\t0.041\t0.10\t291.50\t294.00\t19538\t200\t0.03\ttest 3.3\n",
        "-0.113\t0.038\t2.95\t357.62\t284.88\t19209\t200\t0.04\ttest 3.4\n",
        "0.013\t0.045\t0.28\t282.00\t289.25\t18618\t200\t0.03\ttest 3.5\n",
        "0.130\t0.039\t3.36\t268.62\t349.12\t19423\t200\t0.04\ttest 3.6\n",
        "\n",
        "==== test 4 === nervosum into lant v furc\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.016\t0.043\t0.39\t455.00\t470.25\t6884\t200\t0.12\ttest 4.1\n",
        "0.104\t0.043\t2.43\t381.00\t469.50\t6292\t200\t0.12\ttest 4.2\n",
        "0.043\t0.039\t1.12\t425.25\t463.50\t6584\t200\t0.12\ttest 4.3\n",
        "0.078\t0.043\t1.82\t390.88\t457.12\t6085\t200\t0.12\ttest 4.4\n",
        "0.056\t0.040\t1.38\t454.12\t507.88\t6869\t200\t0.13\ttest 4.5\n",
        "0.072\t0.039\t1.85\t428.38\t494.88\t6357\t200\t0.13\ttest 4.6\n",
        "0.099\t0.036\t2.72\t413.50\t504.25\t6670\t200\t0.13\ttest 4.7\n",
        "0.064\t0.042\t1.54\t390.25\t443.75\t6084\t200\t0.12\ttest 4.8\n",
        "\n",
        "==== test 5 === sympodiale into lant v furc\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.034\t0.030\t1.16\t741.62\t794.38\t11538\t200\t0.12\ttest 5.1\n",
        "0.044\t0.029\t1.51\t705.25\t770.75\t10743\t200\t0.13\ttest 5.2\n",
        "0.030\t0.029\t1.04\t707.12\t750.88\t10968\t200\t0.13\ttest 5.3\n",
        "0.057\t0.034\t1.68\t676.88\t758.12\t10243\t200\t0.13\ttest 5.4\n",
        "0.059\t0.028\t2.12\t726.50\t817.25\t11459\t200\t0.13\ttest 5.5\n",
        "0.094\t0.029\t3.20\t689.12\t831.62\t10770\t200\t0.13\ttest 5.6\n",
        "0.056\t0.030\t1.89\t684.75\t766.00\t11128\t200\t0.12\ttest 5.7\n",
        "0.068\t0.031\t2.19\t647.38\t741.12\t10331\t200\t0.12\ttest 5.8\n",
        "\n",
        "==== test 6 === lantanoides into nervosum\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.022\t0.052\t0.42\t201.62\t210.62\t6918\t200\t0.06\ttest 6.1\n",
        "-0.003\t0.055\t0.06\t215.62\t214.12\t6615\t200\t0.06\ttest 6.2\n",
        "0.013\t0.057\t0.24\t211.50\t217.25\t6944\t200\t0.06\ttest 6.3\n",
        "0.013\t0.056\t0.24\t213.38\t219.12\t6676\t200\t0.06\ttest 6.4\n",
        "\n",
        "==== test 7 === sympodiale into nervosum\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.004\t0.053\t0.07\t224.12\t225.88\t6867\t200\t0.06\ttest 7.1\n",
        "\n",
        "==== test 8 === furcatum into nervosum\n",
        "D\tstd(D)\tZ\tBABA\tABBA\tnloci\tnboot\tpdisc\tnotes\n",
        "0.003\t0.068\t0.05\t151.50\t152.50\t4078\t200\t0.07\ttest 8.1\n"
       ]
      }
     ],
     "prompt_number": 133
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Treemix analyses"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "mkdir -p project_lantanoides/analysis_treemix/"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "echo -e \"nerv\\nfurc\\nlant\\nsymp\\noutg\" > project_lantanoides/analysis_treemix/poporder\n",
      "\n",
      "## no admixture\n",
      "treemix -i project_lantanoides/outfiles/testing_c88d6m10p3.treemix.gz \\\n",
      "        -root outg \\\n",
      "        -k 1 \\\n",
      "        -o project_lantanoides/analysis_treemix/SNPs_m0 > project_lantanoides/analysis_treemix/logSm0\n",
      "        \n",
      "## 1 admixture edge\n",
      "treemix -i project_lantanoides/outfiles/testing_c88d6m10p3.treemix.gz \\\n",
      "        -root outg \\\n",
      "        -k 1 \\\n",
      "        -m 1 \\\n",
      "        -o project_lantanoides/analysis_treemix/SNPs_m1 > project_lantanoides/analysis_treemix/logSm1\n",
      "\n",
      "## 2 admixture edges\n",
      "treemix -i project_lantanoides/outfiles/testing_c88d6m10p3.treemix.gz \\\n",
      "        -root outg \\\n",
      "        -k 1 \\\n",
      "        -m 2 \\\n",
      "        -o project_lantanoides/analysis_treemix/SNPs_m2 > project_lantanoides/analysis_treemix/logSm2        \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "source(\"~/local/src/treemix/src/plotting_funcs.R\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -h 400 -w 800\n",
      "par(mfrow=c(1,2));\n",
      "plot_tree(\"project_lantanoides/analysis_treemix/SNPs_m0\", lwd=2)\n",
      "plot_resid(\"project_lantanoides/analysis_treemix/SNPs_m0\",\n",
      "           \"project_lantanoides/analysis_treemix/poporder\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "  V1   V2       V3      V4      V5 V6 V7 V8 V9 V10\n",
        "1  1 outg NOT_ROOT NOT_MIG     TIP 33 NA NA NA  NA\n",
        "2  2 <NA> NOT_ROOT NOT_MIG NOT_TIP 32  3  1 16   2\n",
        "3  3 lant NOT_ROOT NOT_MIG     TIP  2 NA NA NA  NA\n",
        "4  4 symp NOT_ROOT NOT_MIG     TIP 16 NA NA NA  NA\n",
        "5 15 furc NOT_ROOT NOT_MIG     TIP 16 NA NA NA  NA\n",
        "6 16 <NA> NOT_ROOT NOT_MIG NOT_TIP  2  4  1 15   1\n",
        "7 31 nerv NOT_ROOT NOT_MIG     TIP 32 NA NA NA  NA\n",
        "8 32 <NA> NOT_ROOT NOT_MIG NOT_TIP 33 31  1  2   3\n",
        "9 33 <NA>     ROOT NOT_MIG NOT_TIP 33  1  1 32   4\n",
        "                                                                                                                 V11\n",
        "1                                                                                                     outg:0.0737969\n",
        "2                                              (lant:0.0397762,(symp:0.0306949,furc:0.0355318):0.0185119):0.00736161\n",
        "3                                                                                                     lant:0.0397762\n",
        "4                                                                                                     symp:0.0306949\n",
        "5                                                                                                     furc:0.0355318\n",
        "6                                                                          (symp:0.0306949,furc:0.0355318):0.0185119\n",
        "7                                                                                                     nerv:0.0432739\n",
        "8                   (nerv:0.0432739,(lant:0.0397762,(symp:0.0306949,furc:0.0355318):0.0185119):0.00736161):0.0737969\n",
        "9 (outg:0.0737969,(nerv:0.0432739,(lant:0.0397762,(symp:0.0306949,furc:0.0355318):0.0185119):0.00736161):0.0737969);\n",
        "           x   y ymin ymax\n",
        "1 0.07379690 0.9  0.8  1.0\n",
        "2 0.08115851 0.4  0.0  0.6\n",
        "3 0.12093471 0.5  0.4  0.6\n",
        "4 0.13036531 0.3  0.2  0.4\n",
        "5 0.13520221 0.1  0.0  0.2\n",
        "6 0.09967041 0.2  0.0  0.4\n",
        "7 0.11707080 0.7  0.6  0.8\n",
        "8 0.07379690 0.6  0.0  0.8\n",
        "9 0.00000000 0.8  0.0  1.0\n",
        "[1] 0.0737969 0.1209347 0.1303653 0.1352022 0.1170708\n",
        "[1] 0.003\n",
        "[1] \"mse 0.001844796\"\n",
        "[1] 0.001844796\n",
        "[1] \"here\"\n",
        "[1] \"1 1 0\"\n",
        "[1] \"2 1 0.000854859999999999\"\n",
        "[1] \"2 2 0\"\n",
        "[1] \"3 1 -0.00070636\"\n",
        "[1] \"3 2 -0.00052454\"\n",
        "[1] \"3 3 0\"\n",
        "[1] \"4 1 -0.000148549999999999\"\n",
        "[1] \"4 2 0\"\n",
        "[1] \"4 3 0.00052454\"\n",
        "[1] \"4 4 0\"\n",
        "[1] \"5 1 0\"\n",
        "[1] \"5 2 -0.000330400000000002\"\n",
        "[1] \"5 3 0.000706400000000003\"\n",
        "[1] \"5 4 -0.000375899999999998\"\n",
        "[1] \"5 5 0\"\n",
        " [1] \"#FF0000\" \"#FF0C00\" \"#FF1900\" \"#FF2500\" \"#FF3200\" \"#FF3E00\" \"#FF4B00\"\n",
        " [8] \"#FF5700\" \"#FF6400\" \"#FF7000\" \"#FF7D00\" \"#FF8900\" \"#FF9600\" \"#FFA200\"\n",
        "[15] \"#FFAA00\" \"#FFB100\" \"#FFB800\" \"#FFBF00\" \"#FFC600\" \"#FFCC00\" \"#FFD300\"\n",
        "[22] \"#FFDA00\" \"#FFE100\" \"#FFE800\" \"#FFEF00\" \"#FFF500\" \"#FFFC00\" \"#FFFF0C\"\n",
        "[29] \"#FFFF20\" \"#FFFF33\" \"#FFFF47\" \"#FFFF5A\" \"#FFFF6D\" \"#FFFF81\" \"#FFFF94\"\n",
        "[36] \"#FFFFA7\" \"#FFFFBB\" \"#FFFFCE\" \"#FFFFE1\" \"#FFFFF5\" \"#F5FFF5\" \"#E1FFE1\"\n",
        "[43] \"#CEFFCE\" \"#BBFFBB\" \"#A7FFA7\" \"#94FF94\" \"#81FF81\" \"#6DFF6D\" \"#5AFF5A\"\n",
        "[50] \"#47FF47\" \"#33FF33\" \"#20FF20\" \"#0CFF0C\" \"#00F806\" \"#00E519\" \"#00D12D\"\n",
        "[57] \"#00BE40\" \"#00AB53\" \"#009767\" \"#00847A\" \"#00708E\" \"#005DA1\" \"#004AB4\"\n",
        "[64] \"#0036C8\" \"#0023DB\" \"#0010EE\" \"#0000FB\" \"#0000E8\" \"#0000D5\" \"#0000C1\"\n",
        "[71] \"#0000AE\" \"#00009A\" \"#000087\" \"#000074\" \"#000060\" \"#00004D\" \"#00003A\"\n",
        "[78] \"#000026\" \"#000013\" \"#000000\"\n",
        " [1] 0.500 0.505 0.510 0.515 0.520 0.525 0.530 0.535 0.540 0.545 0.550 0.555\n",
        "[13] 0.560 0.565 0.570 0.575 0.580 0.585 0.590 0.595 0.600 0.605 0.610 0.615\n",
        "[25] 0.620 0.625 0.630 0.635 0.640 0.645 0.650 0.655 0.660 0.665 0.670 0.675\n",
        "[37] 0.680 0.685 0.690 0.695 0.700 0.705 0.710 0.715 0.720 0.725 0.730 0.735\n",
        "[49] 0.740 0.745 0.750 0.755 0.760 0.765 0.770 0.775 0.780 0.785 0.790 0.795\n",
        "[61] 0.800 0.805 0.810 0.815 0.820 0.825 0.830 0.835 0.840 0.845 0.850 0.855\n",
        "[73] 0.860 0.865 0.870 0.875 0.880 0.885 0.890 0.895 0.900\n",
        "         nerv        furc        lant        symp       outg\n",
        "1  0.00000000  0.00085486 -0.00070636 -0.00014855  0.0000000\n",
        "2  0.00085486  0.00000000 -0.00052454  0.00000000 -0.0003304\n",
        "3 -0.00070636 -0.00052454  0.00000000  0.00052454  0.0007064\n",
        "4 -0.00014855  0.00000000  0.00052454  0.00000000 -0.0003759\n",
        "5  0.00000000 -0.00033040  0.00070640 -0.00037590  0.0000000\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U19cLN2DW0dEJCAhobW3dsWNHcXFxfHy8h4eHqqqqGN8LAKB7EC5wRfGBM1iU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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -h 400 -w 800\n",
      "par(mfrow=c(1,2))\n",
      "plot_tree(\"project_lantanoides/analysis_treemix/SNPs_m1\", lwd=2)\n",
      "plot_resid(\"project_lantanoides/analysis_treemix/SNPs_m1\",\n",
      "           \"project_lantanoides/analysis_treemix/poporder\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "   V1   V2       V3      V4      V5 V6 V7 V8 V9 V10\n",
        "1   1 outg NOT_ROOT NOT_MIG     TIP 33 NA NA NA  NA\n",
        "2   2 <NA> NOT_ROOT NOT_MIG NOT_TIP 32  3  1 16   2\n",
        "3   3 lant NOT_ROOT NOT_MIG     TIP  2 NA NA NA  NA\n",
        "4   4 symp NOT_ROOT NOT_MIG     TIP 16 NA NA NA  NA\n",
        "5  15 furc NOT_ROOT NOT_MIG     TIP 16 NA NA NA  NA\n",
        "6  16 <NA> NOT_ROOT NOT_MIG NOT_TIP  2  4  1 15   1\n",
        "7  31 nerv NOT_ROOT NOT_MIG     TIP 32 NA NA NA  NA\n",
        "8  32 <NA> NOT_ROOT NOT_MIG NOT_TIP 33 31  1  2   3\n",
        "9  33 <NA>     ROOT NOT_MIG NOT_TIP 33  1  1 32   4\n",
        "10 34 <NA> NOT_ROOT     MIG NOT_TIP 16 15 NA NA  NA\n",
        "                                                                                                                 V11\n",
        "1                                                                                                     outg:0.0727818\n",
        "2                                               (lant:0.0387222,(symp:0.030191,furc:0.0360352):0.0195665):0.00904034\n",
        "3                                                                                                     lant:0.0387222\n",
        "4                                                                                                      symp:0.030191\n",
        "5                                                                                                     furc:0.0360352\n",
        "6                                                                           (symp:0.030191,furc:0.0360352):0.0195665\n",
        "7                                                                                                     nerv:0.0520042\n",
        "8                    (nerv:0.0520042,(lant:0.0387222,(symp:0.030191,furc:0.0360352):0.0195665):0.00904034):0.0727818\n",
        "9  (outg:0.0727818,(nerv:0.0520042,(lant:0.0387222,(symp:0.030191,furc:0.0360352):0.0195665):0.00904034):0.0727818);\n",
        "10                                                                                                              <NA>\n",
        "            x   y ymin ymax\n",
        "1  0.07278180 0.9  0.8  1.0\n",
        "2  0.08182214 0.4  0.0  0.6\n",
        "3  0.12054434 0.5  0.4  0.6\n",
        "4  0.13157964 0.3  0.2  0.4\n",
        "5  0.13742384 0.1  0.0  0.2\n",
        "6  0.10138864 0.2  0.0  0.4\n",
        "7  0.11784481 0.7  0.6  0.8\n",
        "8  0.07278180 0.6  0.0  0.8\n",
        "9  0.00000000 0.8  0.0  1.0\n",
        "10 0.12324604  NA  0.0  0.2\n",
        "[1] \"0.606558 0.10138864 0.13742384\"\n",
        "[1] 0.0727818 0.1205443 0.1315796 0.1374238 0.1178448\n",
        "[1] 0.003\n",
        "[1] \"mse 0.001844796\"\n",
        "[1] 0.001844796\n",
        "[1] \"here\"\n",
        "[1] \"1 1 0\"\n",
        "[1] \"2 1 0\"\n",
        "[1] \"2 2 0\"\n",
        "[1] \"3 1 -3.81999999999987e-06\"\n",
        "[1] \"3 2 -0.000272590000000001\"\n",
        "[1] \"3 3 0\"\n",
        "[1] \"4 1 3.81999999999987e-06\"\n",
        "[1] \"4 2 -2.70000000000131e-07\"\n",
        "[1] \"4 3 0.00027285\"\n",
        "[1] \"4 4 0\"\n",
        "[1] \"5 1 0\"\n",
        "[1] \"5 2 0.0002729\"\n",
        "[1] \"5 3 3.60000000000291e-06\"\n",
        "[1] \"5 4 -0.0002764\"\n",
        "[1] \"5 5 0\"\n",
        " [1] \"#FF0000\" \"#FF0C00\" \"#FF1900\" \"#FF2500\" \"#FF3200\" \"#FF3E00\" \"#FF4B00\"\n",
        " [8] \"#FF5700\" \"#FF6400\" \"#FF7000\" \"#FF7D00\" \"#FF8900\" \"#FF9600\" \"#FFA200\"\n",
        "[15] \"#FFAA00\" \"#FFB100\" \"#FFB800\" \"#FFBF00\" \"#FFC600\" \"#FFCC00\" \"#FFD300\"\n",
        "[22] \"#FFDA00\" \"#FFE100\" \"#FFE800\" \"#FFEF00\" \"#FFF500\" \"#FFFC00\" \"#FFFF0C\"\n",
        "[29] \"#FFFF20\" \"#FFFF33\" \"#FFFF47\" \"#FFFF5A\" \"#FFFF6D\" \"#FFFF81\" \"#FFFF94\"\n",
        "[36] \"#FFFFA7\" \"#FFFFBB\" \"#FFFFCE\" \"#FFFFE1\" \"#FFFFF5\" \"#F5FFF5\" \"#E1FFE1\"\n",
        "[43] \"#CEFFCE\" \"#BBFFBB\" \"#A7FFA7\" \"#94FF94\" \"#81FF81\" \"#6DFF6D\" \"#5AFF5A\"\n",
        "[50] \"#47FF47\" \"#33FF33\" \"#20FF20\" \"#0CFF0C\" \"#00F806\" \"#00E519\" \"#00D12D\"\n",
        "[57] \"#00BE40\" \"#00AB53\" \"#009767\" \"#00847A\" \"#00708E\" \"#005DA1\" \"#004AB4\"\n",
        "[64] \"#0036C8\" \"#0023DB\" \"#0010EE\" \"#0000FB\" \"#0000E8\" \"#0000D5\" \"#0000C1\"\n",
        "[71] \"#0000AE\" \"#00009A\" \"#000087\" \"#000074\" \"#000060\" \"#00004D\" \"#00003A\"\n",
        "[78] \"#000026\" \"#000013\" \"#000000\"\n",
        " [1] 0.500 0.505 0.510 0.515 0.520 0.525 0.530 0.535 0.540 0.545 0.550 0.555\n",
        "[13] 0.560 0.565 0.570 0.575 0.580 0.585 0.590 0.595 0.600 0.605 0.610 0.615\n",
        "[25] 0.620 0.625 0.630 0.635 0.640 0.645 0.650 0.655 0.660 0.665 0.670 0.675\n",
        "[37] 0.680 0.685 0.690 0.695 0.700 0.705 0.710 0.715 0.720 0.725 0.730 0.735\n",
        "[49] 0.740 0.745 0.750 0.755 0.760 0.765 0.770 0.775 0.780 0.785 0.790 0.795\n",
        "[61] 0.800 0.805 0.810 0.815 0.820 0.825 0.830 0.835 0.840 0.845 0.850 0.855\n",
        "[73] 0.860 0.865 0.870 0.875 0.880 0.885 0.890 0.895 0.900\n",
        "       nerv        furc        lant        symp       outg\n",
        "1  0.00e+00  0.00000000 -0.00000382  0.00000382  0.0000000\n",
        "2  0.00e+00  0.00000000 -0.00027259 -0.00000027  0.0002729\n",
        "3 -3.82e-06 -0.00027259  0.00000000  0.00027285  0.0000036\n",
        "4  3.82e-06 -0.00000027  0.00027285  0.00000000 -0.0002764\n",
        "5  0.00e+00  0.00027290  0.00000360 -0.00027640  0.0000000\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAE0QsIBmNTXN6uqnqffBevp0cteuStKtiRE6XMASaWlpSUhICAoKio2N7dy584IFC1av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\nYII=\n"
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -h 400 -w 800\n",
      "par(mfrow=c(1,2))\n",
      "plot_tree(\"project_lantanoides/analysis_treemix/SNPs_m2\", lwd=2)\n",
      "plot_resid(\"project_lantanoides/analysis_treemix/SNPs_m2\",\n",
      "           \"project_lantanoides/analysis_treemix/poporder\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "   V1   V2       V3      V4      V5 V6 V7 V8 V9 V10\n",
        "1   1 outg NOT_ROOT NOT_MIG     TIP 33 NA NA NA  NA\n",
        "2   2 <NA> NOT_ROOT NOT_MIG NOT_TIP 32  3  1 16   2\n",
        "3   3 lant NOT_ROOT NOT_MIG     TIP  2 NA NA NA  NA\n",
        "4   4 symp NOT_ROOT NOT_MIG     TIP 16 NA NA NA  NA\n",
        "5  15 furc NOT_ROOT NOT_MIG     TIP 16 NA NA NA  NA\n",
        "6  16 <NA> NOT_ROOT NOT_MIG NOT_TIP  2  4  1 15   1\n",
        "7  31 nerv NOT_ROOT NOT_MIG     TIP 32 NA NA NA  NA\n",
        "8  32 <NA> NOT_ROOT NOT_MIG NOT_TIP 33 31  1  2   3\n",
        "9  33 <NA>     ROOT NOT_MIG NOT_TIP 33 32  4  1   1\n",
        "10 34 <NA> NOT_ROOT     MIG NOT_TIP 16 15 NA NA  NA\n",
        "11 49 <NA> NOT_ROOT     MIG NOT_TIP 33  1 NA NA  NA\n",
        "                                                                                                                  V11\n",
        "1                                                                                                       outg:0.145462\n",
        "2                                                (lant:0.0381693,(symp:0.0270886,furc:0.0491967):0.022676):0.00966781\n",
        "3                                                                                                      lant:0.0381693\n",
        "4                                                                                                      symp:0.0270886\n",
        "5                                                                                                      furc:0.0491967\n",
        "6                                                                            (symp:0.0270886,furc:0.0491967):0.022676\n",
        "7                                                                                                      nerv:0.0524267\n",
        "8                   (nerv:0.0524267,(lant:0.0381693,(symp:0.0270886,furc:0.0491967):0.022676):0.00966781):1.96937e-05\n",
        "9  ((nerv:0.0524267,(lant:0.0381693,(symp:0.0270886,furc:0.0491967):0.022676):0.00966781):1.96937e-05,outg:0.145462);\n",
        "10                                                                                                               <NA>\n",
        "11                                                                                                               <NA>\n",
        "              x   y ymin ymax\n",
        "1  0.1454616937 0.1  0.0  0.2\n",
        "2  0.0096875037 0.6  0.2  0.8\n",
        "3  0.0478568037 0.7  0.6  0.8\n",
        "4  0.0594521037 0.5  0.4  0.6\n",
        "5  0.0710897561 0.3  0.2  0.4\n",
        "6  0.0323635037 0.4  0.2  0.6\n",
        "7  0.0451364298 0.9  0.8  1.0\n",
        "8  0.0000196937 0.8  0.2  1.0\n",
        "9  0.0000000000 0.2  0.0  1.0\n",
        "10 0.0474027348  NA  0.2  0.4\n",
        "11 0.0000196937  NA  0.0  0.2\n",
        "[1] \"0.388348 0.0323635037 0.0710897561100534\"\n",
        "[1] \"0.000135387 0 0.1454616937\"\n",
        "[1] 0.14546169 0.04785680 0.05945210 0.07108976 0.04513643\n",
        "[1] 0.003\n",
        "[1] \"mse 0.001844796\"\n",
        "[1] 0.001844796\n",
        "[1] \"here\"\n",
        "[1] \"1 1 0\"\n",
        "[1] \"2 1 0\"\n",
        "[1] \"2 2 0\"\n",
        "[1] \"3 1 9.99999999994061e-09\"\n",
        "[1] \"3 2 0\"\n",
        "[1] \"3 3 0\"\n",
        "[1] \"4 1 -9.99999999994061e-09\"\n",
        "[1] \"4 2 0\"\n",
        "[1] \"4 3 0\"\n",
        "[1] \"4 4 0\"\n",
        "[1] \"5 1 0\"\n",
        "[1] \"5 2 0\"\n",
        "[1] \"5 3 0\"\n",
        "[1] \"5 4 9.99999999994061e-08\"\n",
        "[1] \"5 5 0\"\n",
        " [1] \"#FF0000\" \"#FF0C00\" \"#FF1900\" \"#FF2500\" \"#FF3200\" \"#FF3E00\" \"#FF4B00\"\n",
        " [8] \"#FF5700\" \"#FF6400\" \"#FF7000\" \"#FF7D00\" \"#FF8900\" \"#FF9600\" \"#FFA200\"\n",
        "[15] \"#FFAA00\" \"#FFB100\" \"#FFB800\" \"#FFBF00\" \"#FFC600\" \"#FFCC00\" \"#FFD300\"\n",
        "[22] \"#FFDA00\" \"#FFE100\" \"#FFE800\" \"#FFEF00\" \"#FFF500\" \"#FFFC00\" \"#FFFF0C\"\n",
        "[29] \"#FFFF20\" \"#FFFF33\" \"#FFFF47\" \"#FFFF5A\" \"#FFFF6D\" \"#FFFF81\" \"#FFFF94\"\n",
        "[36] \"#FFFFA7\" \"#FFFFBB\" \"#FFFFCE\" \"#FFFFE1\" \"#FFFFF5\" \"#F5FFF5\" \"#E1FFE1\"\n",
        "[43] \"#CEFFCE\" \"#BBFFBB\" \"#A7FFA7\" \"#94FF94\" \"#81FF81\" \"#6DFF6D\" \"#5AFF5A\"\n",
        "[50] \"#47FF47\" \"#33FF33\" \"#20FF20\" \"#0CFF0C\" \"#00F806\" \"#00E519\" \"#00D12D\"\n",
        "[57] \"#00BE40\" \"#00AB53\" \"#009767\" \"#00847A\" \"#00708E\" \"#005DA1\" \"#004AB4\"\n",
        "[64] \"#0036C8\" \"#0023DB\" \"#0010EE\" \"#0000FB\" \"#0000E8\" \"#0000D5\" \"#0000C1\"\n",
        "[71] \"#0000AE\" \"#00009A\" \"#000087\" \"#000074\" \"#000060\" \"#00004D\" \"#00003A\"\n",
        "[78] \"#000026\" \"#000013\" \"#000000\"\n",
        " [1] 0.500 0.505 0.510 0.515 0.520 0.525 0.530 0.535 0.540 0.545 0.550 0.555\n",
        "[13] 0.560 0.565 0.570 0.575 0.580 0.585 0.590 0.595 0.600 0.605 0.610 0.615\n",
        "[25] 0.620 0.625 0.630 0.635 0.640 0.645 0.650 0.655 0.660 0.665 0.670 0.675\n",
        "[37] 0.680 0.685 0.690 0.695 0.700 0.705 0.710 0.715 0.720 0.725 0.730 0.735\n",
        "[49] 0.740 0.745 0.750 0.755 0.760 0.765 0.770 0.775 0.780 0.785 0.790 0.795\n",
        "[61] 0.800 0.805 0.810 0.815 0.820 0.825 0.830 0.835 0.840 0.845 0.850 0.855\n",
        "[73] 0.860 0.865 0.870 0.875 0.880 0.885 0.890 0.895 0.900\n",
        "    nerv furc  lant   symp  outg\n",
        "1  0e+00    0 1e-08 -1e-08 0e+00\n",
        "2  0e+00    0 0e+00  0e+00 0e+00\n",
        "3  1e-08    0 0e+00  0e+00 0e+00\n",
        "4 -1e-08    0 0e+00  0e+00 1e-07\n",
        "5  0e+00    0 0e+00  1e-07 0e+00\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BHiwAAGBJlps941Y5rPEtYMmdg4PD8uXLDx48GBkZyXUtrAm0SYwlQgAAYEHS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\nrkJggg==\n"
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}