{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Live oak introgression manuscript\n",
    "## Notebook 2: RAxML phylogenetic analyses  \n",
    "\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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## create directories for data assembly\n",
    "mkdir -p analysis_raxml\n",
    "mkdir -p analysis_bucky/locs  ## Bucky analysis was not included in manuscript\n",
    "mkdir -p figures/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------   \n",
    "\n",
    "## RAxML analyses\n",
    "\n",
    "Phylogenetic trees are inferred by maximum likelihood using raxml with bootstrap support inferred from 200 random starting trees. Input files are in phylip format containing concatenated RADseq loci output by PyRAD. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raxcmd = \"raxmlHPC-PTHREADS-AVX -f a\" +\\\n",
    "                               \" -m GTRGAMMA\"+\\\n",
    "                               \" -N 200 -x 12345 -p 12345 -T 20 -o NI,HE \"+\\\n",
    "                               \" -w /home/deren/Documents/Oaks/Virentes/analysis_raxml/ \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \"-s analysis_pyrad/outfiles/virentes_c85d6m4p5.phy -n virentes_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \"-s analysis_pyrad/outfiles/virentes_c85d6m20p5.phy -n virentes_m20p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \"-s analysis_pyrad/outfiles/MGV_c85d6m4p5.phy -n MGV_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \"-s analysis_pyrad/outfiles/MGV_c85d6m16p5.phy -n MGV_m16p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \"-s analysis_pyrad/outfiles/OS_c85d6m4p5.phy -n OS_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \"-s analysis_pyrad/outfiles/OS_c85d6m13p5.phy -n OS_m13p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \" -s analysis_pyrad/outfiles/FB_c85d6m4p5.phy -n FB_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \" -s analysis_pyrad/outfiles/FB_c85d6m12p5.phy -n FB_m12p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \" -s analysis_pyrad/outfiles/OSMGV_c85d6m4p5.phy -n OSMGV_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \" -s analysis_pyrad/outfiles/OSMGV_c85d6m20p5.phy -n OSMGV_m20p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \" -s analysis_pyrad/outfiles/FBMGV_c85d6m4p5.phy -n FBMGV_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \" -s analysis_pyrad/outfiles/FBMGV_c85d6m20p5.phy -n FBMGV_m20p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phy = \" -s analysis_pyrad/outfiles/FBOS_c85d6m4p5.phy -n FBOS_m4p5\"\n",
    "stderr = ! $raxcmd $phy\n",
    "phy = \" -s analysis_pyrad/outfiles/FBOS_c85d6m16p5.phy -n FBOS_m16p5\"\n",
    "stderr = ! $raxcmd $phy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------   \n",
    "## Assessing data/locus sharing in RAD data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import ete2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import glob\n",
    "from collections import OrderedDict, Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## read in the two full-taxa data sets\n",
    "infile4  = \"analysis_pyrad/outfiles/virentes_c85d6m4p5.loci\"\n",
    "infile20 = \"analysis_pyrad/outfiles/virentes_c85d6m20p5.loci\"\n",
    "loci4  = open(infile4).read().split(\"|\\n\")\n",
    "loci20 = open(infile20).read().split(\"|\\n\")\n",
    "\n",
    "## how many loci\n",
    "nloci4 = len(loci4) \n",
    "nloci20 = len(loci20)\n",
    "\n",
    "## read in a tree file for the order of tips\n",
    "tree = ete2.Tree(\"analysis_raxml/RAxML_bipartitions.virentes_m4p5\", format=2)\n",
    "\n",
    "## sort names by their order in the tree\n",
    "tree.ladderize()\n",
    "names = [i.strip() for i in tree.iter_leaf_names()]\n",
    "\n",
    "## create list of locus x sample\n",
    "def create_array(nsamp, nloci, loci):\n",
    "    lxs = np.zeros((nsamp, nloci))\n",
    "    for loc in xrange(len(loci)):\n",
    "        for seq in loci[loc].split(\"\\n\"):\n",
    "            if \">\" in seq:\n",
    "                lxs[names.index(seq.split()[0].replace(\">\",\"\")),loc] += 1\n",
    "    return lxs\n",
    "\n",
    "## create arrays for the two data sets\n",
    "lxs1 = create_array(len(names), nloci4, loci4)\n",
    "lxs2 = create_array(len(names), nloci20, loci20)\n",
    "\n",
    "## empty matrix\n",
    "share = np.zeros((len(names),len(names)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each column (locus) in lxs iterate over all pairs of samples and if both have data at that locus add 1 to their shared data in the matrix [share]. This fills the upper triangle of the matrix. I fill the lower triangle with shared data from the other data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## fill matrix share above diagonal (Allmin20 data set)\n",
    "for col in xrange(lxs2.shape[1]):\n",
    "    for samp1,samp2 in itertools.combinations(names,2):\n",
    "        if (lxs2[names.index(samp1),col] and lxs2[names.index(samp2),col]):\n",
    "            share[names.index(samp1),names.index(samp2)] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## fill matrix share below diagonal (Allmin4 data set)\n",
    "for col in xrange(lxs1.shape[1]):\n",
    "    for samp2,samp1 in itertools.combinations(names,2):\n",
    "        if (lxs1[names.index(samp1),col] and lxs1[names.index(samp2),col]):\n",
    "            share[names.index(samp1),names.index(samp2)] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "EkudYREREREpsdQZFhEREZESS51hERERESmx1BkWERERkRJLnWERERERKbHUGRYRERGREkudYRER\n",
       "EREpsdQZFhEREZES6/8DKMAF8smWkYMAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4a16610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,1, figsize=(15,10))\n",
    "cax = ax.imshow(share, interpolation='none', cmap='gist_yarg')\n",
    "ax.grid(True, color='white', lw=0.9)\n",
    "ax.get_xaxis().set_ticks([i-0.5 for i in range(35)])\n",
    "ax.get_xaxis().set_ticklabels([])\n",
    "ax.get_yaxis().set_ticks([i-0.5 for i in range(35)])\n",
    "ax.get_yaxis().set_ticklabels([])\n",
    "cb = plt.colorbar(cax);\n",
    "for t in cb.ax.get_yticklabels():\n",
    "     t.set_fontsize(20)\n",
    "f.savefig(\"figures/heatmap.svg\")\n",
    "f.savefig(\"figures/heatmap.png\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x30ebed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## get number of raw reads for each sample\n",
    "with open(\"analysis_pyrad/stats/s2.rawedit.txt\") as rawstat:\n",
    "    raws = rawstat.readlines()\n",
    "    \n",
    "dat = [(line.split(\"\\t\")[0],int(line.split(\"\\t\")[-1].strip())) for \\\n",
    "       line in raws if \"_\" in line]\n",
    "\n",
    "dat2 = OrderedDict()\n",
    "for t in names:\n",
    "    for j in dat:\n",
    "        if t == j[0].split(\"_\")[0]:\n",
    "            if t not in dat2:\n",
    "                dat2[t] = j[1]\n",
    "            else:\n",
    "                dat2[t] += j[1]\n",
    "dat = pd.DataFrame({\"raws\":pd.Series(dat2)})\n",
    "\n",
    "## make plot of raw reads\n",
    "f, ax = plt.subplots(1,1, figsize=(10,2))\n",
    "ax.bar(range(len(dat)),(dat.raws), color=\"#262626\")\n",
    "ax.grid('off')\n",
    "f.savefig(\"figures/bars.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------   \n",
    "\n",
    "### Do mantel correlation tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from skbio import DistanceMatrix\n",
    "from skbio.stats.distance import mantel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## func to measure jaccard's distance\n",
    "def jaccard(a, b):\n",
    "    c = a.intersection(b)\n",
    "    return float(len(c)) / (len(a) + len(b) - len(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Allmin4 data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## write matrices to file for loading into R for mantel test\n",
    "Pairs4 = OrderedDict()\n",
    "for samp1,samp2 in itertools.product(names,repeat=2):\n",
    "    if samp1 == samp2:\n",
    "        Pairs4[str(samp1+\"_\"+samp2)] = [0.,0.,0.]\n",
    "    else:    \n",
    "        jaccar = jaccard(set([i for i in range(lxs1.shape[1]) if \\\n",
    "                   lxs1[:,i][names.index(samp1)] == 1.]),\n",
    "                    set([i for i in range(lxs1.shape[1]) if \\\n",
    "                         lxs1[:,i][names.index(samp2)] == 1.]))\n",
    "        meanraw = np.mean([dat.raws[samp1],dat.raws[samp2]])\n",
    "        phydist = tree.get_distance(samp1,samp2)\n",
    "        Pairs4[str(samp1+\"_\"+samp2)] = [jaccar,meanraw,phydist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sharedist = DistanceMatrix(np.array([i[0] for i in Pairs4.values()]).reshape(34,34))\n",
    "rawdist = DistanceMatrix(np.array([i[1] for i in Pairs4.values()]).reshape(34,34))\n",
    "phydist = DistanceMatrix(np.array([round(i[2],7) for i in Pairs4.values()]).reshape(34,34))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shared loci ~ mean raw data\n",
      "coeff = 0.372246206797\n",
      "p-value = 0.0106\n"
     ]
    }
   ],
   "source": [
    "coeff, p_value, n = mantel(sharedist, rawdist, \n",
    "                           method='spearman', \n",
    "                           permutations=9999,\n",
    "                           alternative=\"two-sided\")\n",
    "print \"shared loci ~ mean raw data\"\n",
    "print \"coeff =\",coeff\n",
    "print \"p-value =\",p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shared loci ~ phylo dist\n",
      "coeff = -0.144901236203\n",
      "p-value = 0.2401\n"
     ]
    }
   ],
   "source": [
    "coeff, p_value, n = mantel(sharedist, phydist, \n",
    "                           method='spearman', \n",
    "                           permutations=9999,\n",
    "                           alternative=\"two-sided\")\n",
    "print \"shared loci ~ phylo dist\"\n",
    "print \"coeff =\",coeff\n",
    "print \"p-value =\", p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "BxA37axpRG72AAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15cd8610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,2, figsize=(8,4), sharey=True)\n",
    "\n",
    "## remove values on the diagonal\n",
    "r = rawdist.data[rawdist.data.nonzero()]\n",
    "s = sharedist.data[sharedist.data.nonzero()]\n",
    "p = phydist.data[phydist.data.nonzero()]\n",
    "\n",
    "ax[0].scatter(r/1e6,s, color=\"#262626\", alpha=0.3, s=50)\n",
    "ax[1].scatter(p,s, color=\"#262626\", alpha=0.3, s=50)\n",
    "\n",
    "ax[0].set_ylabel(\"Jaccard's distance of shared loci\", fontsize=14)\n",
    "ax[0].set_xlabel(\"Pair-wise mean raw reads (x10$^6$)\", fontsize=14)\n",
    "ax[1].set_xlabel(\"Pair-wise phylogenetic distance\", fontsize=14)\n",
    "\n",
    "f.savefig(\"figures/rawVphylo_min4.png\")\n",
    "#f.savefig(\"figures/rawVphylo_min4.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Allmin20 data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## write matrices to file for loading into R for mantel test\n",
    "Pairs20 = OrderedDict()\n",
    "for samp1,samp2 in itertools.product(names,repeat=2):\n",
    "    if samp1 == samp2:\n",
    "        Pairs20[str(samp1+\"_\"+samp2)] = [0.,0.,0.]\n",
    "    else:    \n",
    "        jaccar = jaccard(set([i for i in range(lxs2.shape[1]) if \\\n",
    "                   lxs2[:,i][names.index(samp1)] == 1.]),\n",
    "                    set([i for i in range(lxs2.shape[1]) if \\\n",
    "                         lxs2[:,i][names.index(samp2)] == 1.]))\n",
    "        meanraw = np.mean([dat.raws[samp1],dat.raws[samp2]])\n",
    "        phydist = tree.get_distance(samp1,samp2)\n",
    "        Pairs20[str(samp1+\"_\"+samp2)] = [jaccar,meanraw,phydist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sharedist20 = DistanceMatrix(np.array([i[0] for i in Pairs20.values()]).reshape(34,34))\n",
    "rawdist20 = DistanceMatrix(np.array([i[1] for i in Pairs20.values()]).reshape(34,34))\n",
    "phydist20 = DistanceMatrix(np.array([round(i[2],7) for i in Pairs20.values()]).reshape(34,34))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shared loci ~ mean raw data\n",
      "coeff = 0.478713410308\n",
      "p-value = 0.0028\n"
     ]
    }
   ],
   "source": [
    "coeff, p_value, n = mantel(sharedist, rawdist, \n",
    "                           method='spearman', \n",
    "                           permutations=9999,\n",
    "                           alternative=\"two-sided\")\n",
    "print \"shared loci ~ mean raw data\"\n",
    "print \"coeff =\",coeff\n",
    "print \"p-value =\",p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shared loci ~ phylo data\n",
      "coeff = 0.0869666708908\n",
      "p-value = 0.5273\n"
     ]
    }
   ],
   "source": [
    "coeff, p_value, n = mantel(sharedist, phydist, \n",
    "                           method='spearman', \n",
    "                           permutations=9999,\n",
    "                           alternative=\"two-sided\")\n",
    "print \"shared loci ~ phylo data\"\n",
    "print \"coeff =\",coeff\n",
    "print \"p-value =\",p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "8BkMBoPBeA1gDT6DwWAwGK8BrMFnMBgMBuM14P8BrXsYbPFFSSQAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15adbf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,2, figsize=(8,4), sharey=True)\n",
    "\n",
    "## remove values on the diagonal\n",
    "r = rawdist20.data[rawdist.data.nonzero()]\n",
    "s = sharedist20.data[sharedist.data.nonzero()]\n",
    "p = phydist20.data[phydist.data.nonzero()]\n",
    "\n",
    "ax[0].scatter(r/1e6,s, color=\"#262626\", alpha=0.3, s=50)\n",
    "ax[1].scatter(p,s, color=\"#262626\", alpha=0.3, s=50)\n",
    "\n",
    "ax[0].set_ylabel(\"Jaccard's distance of shared loci\", fontsize=14)\n",
    "ax[0].set_xlabel(\"Pair-wise mean raw reads (x10$^6$)\", fontsize=14)\n",
    "ax[1].set_xlabel(\"Pair-wise phylogenetic distance\", fontsize=14)\n",
    "\n",
    "f.savefig(\"figures/rawVphylo_min20.png\")\n",
    "#f.savefig(\"figures/rawVphylo.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BUCKy Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Dictionary to select one sample for each species\n",
    "\n",
    "D = {\"B\":\"BJSL25\",\n",
    "     \"Ft\":\"TXMD3\",\n",
    "     \"Fm\":\"MXED8\",\n",
    "     \"O\":\"CRL0030\",\n",
    "     \"S\":\"CUVN10\",\n",
    "     \"V\":\"FLBA140\",\n",
    "     \"G\":\"FLSF54\",\n",
    "     \"M\":\"FLSF47\",\n",
    "     \"out\":\"AR\"}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to write loci as mrbayes readable .nex files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def nexmake(D, N, outg):\n",
    "    outloc = open(\"analysis_bucky/locs/\"+`N`+\".nex\", 'w')\n",
    "    ## write header block\n",
    "    outloc.write(\"#NEXUS\\nbegin data;\\n\"+\\\n",
    "                 \"dimensions ntax=\"+`len(D)`+\" nchar=\"+`len(D.values()[0])`+\";\\n\"+\\\n",
    "                 \"format datatype=dna interleave=yes gap=-;\\n\"+\\\n",
    "                 \"matrix\\n\\n\")\n",
    "    ## write sequence data for locus loc\n",
    "    for i in D.items():\n",
    "        outloc.write( i[0][0:9]+\" \"*(10-len(i[0][0:9]))+\"\".join(i[1])+\"\\n\" )\n",
    "    ## write mrbayes block\n",
    "    outloc.write(\"\\n\\n    ;\\nend;\\n\"+\\\n",
    "                 \"\\n\\nbegin mrbayes;\\n\"+\\\n",
    "                 \"set autoclose=yes nowarn=yes;\\n\"+\\\n",
    "                 \"lset nst=2 rates=gamma;\\n\"+\\\n",
    "                 \"outgroup \"+outg+\";\\n\"+\\\n",
    "                 \"mcmc ngen=4000000 samplefreq=4000;\\n\"+\\\n",
    "                 \"sump burnin=1000000;\\n\"+\\\n",
    "                 \"sumt burnin=1000000;\\n\"+\\\n",
    "                 \"end;\")\n",
    "    outloc.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parse loci "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "828 loci kept\n"
     ]
    }
   ],
   "source": [
    "## In latter versions of pyrad one could use the\n",
    "## .alleles data file ('a' output from pyrad)\n",
    "infile = open(\"/home/deren/Documents/Oaks/Virentes/analysis_pyrad/outfiles/virentes_c85d6m4p5.loci\").read()\n",
    "loci = iter(infile.strip().split(\"|\\n\")[:])\n",
    "\n",
    "\n",
    "def unstruct(amb):\n",
    "    \" returns bases from ambiguity code\"\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 amb in D:\n",
    "        return D.get(amb)\n",
    "    else:\n",
    "        return [amb,amb]\n",
    "\n",
    "def resolveambig(subseq):\n",
    "    N = []\n",
    "    for col in subseq:\n",
    "        N.append([unstruct(i)[np.random.binomial(1,0.5)] for i in col])\n",
    "    return np.array(N)\n",
    "        \n",
    "\n",
    "def PIS(seqsamp):\n",
    "    ## check that locus is variable\n",
    "    pis = sum([Counter(col).most_common(2)[1][1] > 1 for col in seqsamp.T \\\n",
    "               if len(Counter(col).most_common(2))>1])\n",
    "    if pis >= 2:\n",
    "        return pis\n",
    "    else:\n",
    "        return 0    \n",
    "\n",
    "N = 0\n",
    "## create subsampled data set\n",
    "while 1:\n",
    "    try: loc = loci.next().split(\"\\n\")[:-1]\n",
    "    except StopIteration: break\n",
    "\n",
    "    ## if all tip samples have data in this locus\n",
    "    names = [i.split()[0].replace(\">\",\"\") for i in loc]\n",
    "    \n",
    "    ## check that locus has required samples for each subtree\n",
    "    if all([i in names for i in D.values()]):\n",
    "        seqs = np.array([list(i.split()[1]) for i in loc])\n",
    "        seqsamp = seqs[[names.index(tax) for tax in D.values()]]\n",
    "        seqsamp = resolveambig(seqsamp)\n",
    "        pis = PIS(seqsamp)    \n",
    "        if pis:\n",
    "            N += 1\n",
    "            ## remove invariable columns given this subsampling\n",
    "            keep = []\n",
    "            for n,col in enumerate(seqsamp.T):\n",
    "                if all([i not in [\"N\",\"-\"] for i in col]):\n",
    "                    keep.append(n)\n",
    "            subseq = seqsamp.T[keep].T\n",
    "            ## write to a nexus file\n",
    "            DD = dict(zip(D.keys(), [i.tostring() for i in subseq]))\n",
    "            nexmake(DD, N, \"out\")\n",
    "print N, 'loci kept'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a client to run mrbayes over multiple processors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": "markdown",
   "metadata": {},
   "source": [
    "### Create a list of input arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------\n",
      "828 \ttotal mrbayes analyses\n"
     ]
    }
   ],
   "source": [
    "## list of arguments to function single_mb\n",
    "args = []\n",
    "\n",
    "## create a new dir for log files\n",
    "! mkdir -p \"analysis_bucky\"/logs/\n",
    "\n",
    "## get all nex files from this dir\n",
    "files = glob.glob(\"analysis_bucky/locs/*.nex\")\n",
    "\n",
    "## put nex file as input and other as output location into args\n",
    "for ff in files:\n",
    "    num = ff.split(\"/\")[-1]\n",
    "    args.append([ff, \"analysis_bucky/logs/log.\"+str(num)])\n",
    "    \n",
    "print '------\\n',len(args), '\\ttotal mrbayes analyses'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## decorator adds parallel capability if run in IPython notebook\n",
    "@LOADBALANCE.parallel(block=True)\n",
    "def single_mb(arg):\n",
    "    infile, outloc = arg\n",
    "    ! mb \"$infile\" > \"$outloc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## run these jobs in parallel\n",
    "quiet = single_mb.map(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run mbsum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## create dir for bucky input files\n",
    "! mkdir -p analysis_bucky/ins/\n",
    "\n",
    "## iterate over dirs\n",
    "K = glob.glob(\"analysis_bucky/locs/*.nex\")\n",
    "nums = [int(i.split(\"/\")[-1].split('.nex')[0]) for i in K]\n",
    "\n",
    "## combine trees from the two replicate runs\n",
    "for n in range(1,max(nums)+1):\n",
    "    cmd = \"\"\" -o analysis_bucky/ins/%d.in \\\n",
    "              analysis_bucky/locs/%d.nex.run1.t \\\n",
    "              analysis_bucky/locs/%d.nex.run2.t \"\"\" % (n,n,n)\n",
    "    stderr = ! mbsum -n 101 $cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bayesian Untangling of Concordance Knots (applied to yeast and other organisms)\r\n",
      "BUCKy version 1.4.3, 9 Jul 2014\r\n",
      "Copyright (C) 2006-2014 by Bret Larget and Cecile Ane and Riley Larget\r\n",
      "\r\n",
      "This is free software; see the source for copying conditions.  There is NO\r\n",
      "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\r\n",
      "\r\n",
      "Usage: bucky [options] <input files>\r\n",
      "\r\n",
      "  Options:\r\n",
      "  Parameter                      | Usage                      | Default Value\r\n",
      "  -------------------------------------------------------------------\r\n",
      "  alpha                          | -a number                  | 1\r\n",
      "  # of runs                      | -k integer                 | 2\r\n",
      "  # of MCMC updates              | -n integer                 | 100000\r\n",
      "  # of chains                    | -c integer                 | 1\r\n",
      "  MCMCMC Rate                    | -r integer                 | 100\r\n",
      "  alpha multiplier               | -m number                  | 10\r\n",
      "  subsample rate                 | -s integer                 | 1\r\n",
      "  output root file name          | -o name                    | run1\r\n",
      "  input file list file           | -i filename                | \r\n",
      "  random seed 1                  | -s1 integer                | 1234\r\n",
      "  random seed 2                  | -s2 integer                | 5678\r\n",
      "  CF cutoff for display          | -cf number                 | 0.05\r\n",
      "  create sample file             | --create-sample-file       | false\r\n",
      "  create joint file              | --create-joint-file        | false\r\n",
      "  create single file             | --create-single-file       | false\r\n",
      "  use independence prior         | --use-independence-prior   | false\r\n",
      "  calculate pairs                | --calculate-pairs          | false\r\n",
      "  taxon set                      | -p prune-file              | common taxa\r\n",
      "  skip genes with fewer taxa     | -sg                        | false\r\n",
      "  use update groups              | --use-update-groups        | true\r\n",
      "  use update groups              | --do-not-use-update-groups | \r\n",
      "  Space optimization             | --opt-space                | false\r\n",
      "  do not build population tree?  | --no-population-tree       | false\r\n",
      "  grid-size for genomewide CF    | --genomewide-grid-size     | 1000\r\n",
      "  help                           | -h OR --help               |\r\n",
      "  version                        | --version                  |\r\n",
      "  -------------------------------------------------------------------\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "! bucky -h "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! bucky -a  1 -k 4 -c 4 -n 1000000 -o run1 analysis_bucky/ins/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! bucky -a  5 -k 4 -c 4 -n 1000000 -o run5 analysis_bucky/ins/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! bucky -a 20 -k 4 -c 4 -n 1000000 -o run20 analysis_bucky/ins/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! bucky --use-independence-prior -k 4 -n 1000000 -o runinf analysis_bucky/ins/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "translate\r\n",
      " 1 B,\r\n",
      " 2 Ft,\r\n",
      " 3 G,\r\n",
      " 4 M,\r\n",
      " 5 O,\r\n",
      " 6 S,\r\n",
      " 7 V,\r\n",
      " 8 Fm,\r\n",
      " 9 out;\r\n",
      "\r\n",
      "Population Tree:\r\n",
      "((((1,8),2),((3,4),7)),(5,6),9);\r\n",
      "\r\n",
      "Primary Concordance Tree Topology:\r\n",
      "((1,(2,8)),(((3,4),7),(5,6)),9);\r\n",
      "\r\n",
      "Population Tree, With Branch Lengths In Estimated Coalescent Units:\r\n",
      "((((1:10.000,8:10.000):0.086,2:10.000):0.308,((3:10.000,4:10.000):0.086,7:10.000):0.245):0.023,(5:10.000,6:10.000):0.395,9:10.000);\r\n",
      "\r\n",
      "Primary Concordance Tree with Sample Concordance Factors:\r\n",
      "((1:1.000,(2:1.000,8:1.000):0.206):0.219,(((3:1.000,4:1.000):0.199,7:1.000):0.136,(5:1.000,6:1.000):0.371):0.103,9:1.000);\r\n",
      "\r\n",
      "Four-way partitions in the Population Tree: sample-wide CF, coalescent units and Ties(if present)\r\n",
      "{1,2,5,6,8,9; 7|3; 4}\t0.388, 0.086,  \r\n",
      "{1; 8|2; 3,4,5,6,7,9}\t0.388, 0.086,  \r\n",
      "{1,2,3,4,7,8; 9|5; 6}\t0.551, 0.395,  \r\n",
      "{1,2,8; 5,6,9|3,4; 7}\t0.478, 0.245,  \r\n",
      "{1,8; 2|3,4,7; 5,6,9}\t0.510, 0.308,  \r\n",
      "{1,2,8; 3,4,7|5,6; 9}\t0.349, 0.023,  \r\n",
      "\r\n",
      "Splits in the Primary Concordance Tree: sample-wide and genome-wide mean CF (95% credibility), SD of mean sample-wide CF across runs\r\n",
      "{1,2,3,4,7,8,9|5,6} 0.371(0.326,0.429) 0.370(0.313,0.439)\t0.027\r\n",
      "{1,2,8|3,4,5,6,7,9} 0.219(0.174,0.262) 0.219(0.166,0.273)\t0.012\r\n",
      "{1,3,4,5,6,7,9|2,8} 0.206(0.147,0.280) 0.206(0.141,0.288)\t0.038\r\n",
      "{1,2,5,6,7,8,9|3,4} 0.199(0.156,0.240) 0.199(0.149,0.249)\t0.016\r\n",
      "{1,2,5,6,8,9|3,4,7} 0.136(0.105,0.167) 0.136(0.098,0.176)\t0.010\r\n",
      "{1,2,8,9|3,4,5,6,7} 0.103(0.054,0.164) 0.103(0.049,0.166)\t0.026\r\n",
      "\r\n",
      "Splits NOT in the Primary Concordance Tree but with estimated CF > 0.050:\r\n",
      "{1,8|2,3,4,5,6,7,9} 0.206(0.151,0.273) 0.206(0.145,0.281)\t0.032\r\n",
      "{1,2,3,5,6,8,9|4,7} 0.153(0.092,0.191) 0.153(0.088,0.201)\t0.009\r\n",
      "{1,2,4,5,6,8,9|3,7} 0.148(0.099,0.185) 0.148(0.094,0.194)\t0.012\r\n",
      "{1,2|3,4,5,6,7,8,9} 0.137(0.081,0.186) 0.137(0.077,0.194)\t0.029\r\n",
      "{1,2,3,4,7,8|5,6,9} 0.102(0.072,0.133) 0.102(0.067,0.141)\t0.015\r\n",
      "{1,9|2,3,4,5,6,7,8} 0.088(0.047,0.116) 0.088(0.046,0.123)\t0.009\r\n",
      "{1,2,5,6,8|3,4,7,9} 0.084(0.052,0.129) 0.084(0.047,0.134)\t0.009\r\n",
      "{1,2,3,4,5,6,8|7,9} 0.083(0.035,0.115) 0.083(0.033,0.123)\t0.011\r\n",
      "{1,2,3,4,5,7,8|6,9} 0.074(0.057,0.098) 0.074(0.050,0.104)\t0.004\r\n",
      "{1,2,3,4,6,7,8|5,9} 0.072(0.043,0.109) 0.072(0.040,0.113)\t0.014\r\n"
     ]
    }
   ],
   "source": [
    "! head -n 50 analysis_bucky/run1.concordance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! head -n 50 analysis_bucky/run5.concordance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! head -n 50 analysis_bucky/run20.concordance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "! head -n 50 analysis_bucky/runinf.concordance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R tree plots of all ML subtrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rmagic extension is already loaded. To reload it, use:\n",
      "  %reload_ext rmagic\n"
     ]
    }
   ],
   "source": [
    "%load_ext rmagic\n",
    "## requires that the Python package Rpy2 is installed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bucky trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "library(ape)\n",
    "\n",
    "alpha1 = read.tree(text=\"((1,(2,8)),(((3,4),7),(5,6)),9);\")\n",
    "alpha5 = read.tree(text=\"((((1,8),2),((3,4),7)),(5,6),9);\")\n",
    "alpha20 = read.tree(text=\"(((1,8),2),(((3,4),7),(5,6)),9);\")\n",
    "alphainf = read.tree(text=\"(((((1,8),2),(5,6)),(3,4)),7,9);\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "TkSuQmCC\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 400\n",
    "par(mfrow=c(2,2),\n",
    "    mar=c(2,1,1,2))\n",
    "plot(alpha1, edge.width=4, cex=2, adj=0.1)\n",
    "plot(alpha5, edge.width=4, cex=2, adj=0.1)\n",
    "plot(alpha20, edge.width=4, cex=2, adj=0.1)\n",
    "plot(alphainf, edge.width=4, cex=2, adj=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "SUVORK5CYII=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 600 -h 800\n",
    "library(ape)\n",
    "\n",
    "## save figure\n",
    "#svg(\"figures/tree_virentes_m4p5.svg\",height=8, width=6)\n",
    "#tre = read.tree(\"analysis_raxml/RAxML_bipartitions.virentes_m4p5\")\n",
    "#plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "#nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "#dev.off()\n",
    "\n",
    "## show figure\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.virentes_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "## save figure\n",
    "svg(\"figures/tree_virentes_m20p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.virentes_m20p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.virentes_m20p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -n\n",
    "svg(\"figures/cladogram.svg\", height=9, width=3)\n",
    "plot(ladderize(tre), edge.width=3, cex=0.75, adj=0.1, use.edge.length=F)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "png \n",
       "  2 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 300\n",
    "svg(\"figures/ingrouptree.svg\", height=9, width=3)\n",
    "tre <-  drop.tip(tre, c(\"HE\",\"NI\",\"CH\",\"DU\",\"DO\",\"AR\",\"EN\"))\n",
    "tre <- ladderize(tre)\n",
    "tre <- rotate(tre,28)\n",
    "tre <- rotate(tre,29)\n",
    "tre <- rotate(tre,30)\n",
    "plot(tre, edge.width=4, cex=0.75, adj=0.1)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_FBMGV_m4p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.FBMGV_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_FBMGV_m20p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.FBMGV_m20p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_OSMGV_m4p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.OSMGV_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_OSMGV_m20p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.OSMGV_m20p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "g5FVAAAAAElFTkSuQmCC\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_FBOS_m4p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.FBOS_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AABJRU5ErkJggg==\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_FBOS_m16p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.FBOS_m16p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "RK5CYII=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_MGV_m4p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.MGV_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "2wAAAABJRU5ErkJggg==\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_MGV_m16p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.MGV_m16p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "AAAASUVORK5CYII=\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_OS_m4p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.OS_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_OS_m13p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.OS_m13p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_FB_m4p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.FB_m4p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 500\n",
    "## save figure\n",
    "svg(\"figures/tree_FB_m12p5.svg\",height=8, width=6)\n",
    "tre = read.tree(\"analysis_raxml/RAxML_bipartitions.FB_m12p5\")\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)\n",
    "dev.off()\n",
    "\n",
    "## show figure\n",
    "plot(ladderize(tre), edge.width=4, cex=0.8, adj=0.1)\n",
    "nodelabels(tre$node.label, bg='grey', cex=0.8)"
   ]
  }
 ],
 "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": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}