{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# RADseq data simulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I simulated two trees to work with. One that is completely imbalanced (ladder-like) and one that is balanced (equal number tips descended from each node). I'm using the Python package `ete2` for most of the tree manipulations. This notebook was run in Python 2.7. You will also need the package rpy2 installed, as well as a working version of R with the package 'ape' to make tree plots later in the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## standard Python imports\n",
    "import glob\n",
    "import itertools\n",
    "from collections import OrderedDict, Counter\n",
    "\n",
    "## extra Python imports\n",
    "import rpy2              ## required for tree plotting\n",
    "import ete2              ## used for tree manipulation\n",
    "import egglib            ## used for coalescent simulations\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib\t1.5.1     \n",
      "numpy     \t1.11.0    \n",
      "pandas    \t0.18.0    \n",
      "ete2      \t2.3.10    \n",
      "rpy2      \t2.8.2     \n"
     ]
    }
   ],
   "source": [
    "## print versions\n",
    "for pkg in [matplotlib, np, pd, ete2, rpy2]:\n",
    "    print \"{:<10}\\t{:<10}\".\\\n",
    "    format(pkg.__name__, pkg.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulation software\n",
    "I wrote a program to simulate RAD-seq like sequence data which uses the python package egglib for coalescent simulations. Below will check that you have the relevant software installed. See here for simrrls installation: https://github.com/dereneaton/simrrls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## check simrrls package and requirements\n",
    "import egglib\n",
    "import simrrls\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "egglib 2.1.11\n",
      "simrrls 0.0.11\n"
     ]
    }
   ],
   "source": [
    "## print versions\n",
    "print 'egglib', egglib.version\n",
    "print 'simrrls', simrrls.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate trees for simulations\n",
    "\n",
    "### Make a balanced tree with 64 tips and tree length = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## base tree\n",
    "Tbal = ete2.Tree()\n",
    "\n",
    "## branch lengths\n",
    "bls = 1.\n",
    "\n",
    "## namer\n",
    "n = iter(('s'+str(i) for i in xrange(1,1500)))\n",
    "\n",
    "## first nodes\n",
    "n1 = Tbal.add_child(name=n.next(), dist=bls)\n",
    "n2 = Tbal.add_child(name=n.next(), dist=bls)\n",
    "\n",
    "## make balanced tree\n",
    "while len(Tbal.get_leaves()) < 64:\n",
    "    thisrep = Tbal.get_descendants()\n",
    "    for node in thisrep:\n",
    "        if len(node.get_children()) < 1:\n",
    "            node.add_child(name=n.next(), dist=bls)\n",
    "            node.add_child(name=n.next(), dist=bls)\n",
    "\n",
    "## Save newick string to file\n",
    "Tbal.write(outfile=\"Tbal.tre\", format=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "String representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((((((s63:1,s64:1)s31:1,(s65:1,s66:1)s32:1)s15:1,((s67:1,s68:1)s33:1,(s69:1,s70:1)s34:1)s16:1)s7:1,(((s71:1,s72:1)s35:1,(s73:1,s74:1)s36:1)s17:1,((s75:1,s76:1)s37:1,(s77:1,s78:1)s38:1)s18:1)s8:1)s3:1,((((s79:1,s80:1)s39:1,(s81:1,s82:1)s40:1)s19:1,((s83:1,s84:1)s41:1,(s85:1,s86:1)s42:1)s20:1)s9:1,(((s87:1,s88:1)s43:1,(s89:1,s90:1)s44:1)s21:1,((s91:1,s92:1)s45:1,(s93:1,s94:1)s46:1)s22:1)s10:1)s4:1)s1:1,(((((s95:1,s96:1)s47:1,(s97:1,s98:1)s48:1)s23:1,((s99:1,s100:1)s49:1,(s101:1,s102:1)s50:1)s24:1)s11:1,(((s103:1,s104:1)s51:1,(s105:1,s106:1)s52:1)s25:1,((s107:1,s108:1)s53:1,(s109:1,s110:1)s54:1)s26:1)s12:1)s5:1,((((s111:1,s112:1)s55:1,(s113:1,s114:1)s56:1)s27:1,((s115:1,s116:1)s57:1,(s117:1,s118:1)s58:1)s28:1)s13:1,(((s119:1,s120:1)s59:1,(s121:1,s122:1)s60:1)s29:1,((s123:1,s124:1)s61:1,(s125:1,s126:1)s62:1)s30:1)s14:1)s6:1)s2:1);"
     ]
    }
   ],
   "source": [
    "## newick string\n",
    "! cat Tbal.tre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## show tree, remove node circles\n",
    "#for node in Tbal.traverse():\n",
    "#    node.img_style[\"size\"] = 0\n",
    "#Tbal.render(\"%%inline\", h=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make an imbalanced tree of same treelength with 64 tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## base tree\n",
    "Timb = ete2.Tree()\n",
    "\n",
    "## namer\n",
    "n = iter(('s'+str(i) for i in range(1,5000)))\n",
    "\n",
    "## scale branches to match balanced treelength\n",
    "brlen = (bls*6.)/63\n",
    "\n",
    "## first nodes\n",
    "n1 = Timb.add_child(name=n.next(), dist=brlen)\n",
    "n2 = Timb.add_child(name=n.next(), dist=brlen)\n",
    "\n",
    "while len(Timb.get_leaves()) < 64:  \n",
    "    ## extend others\n",
    "    for tip in Timb.get_leaves()[:-1]:\n",
    "        tip.dist += brlen\n",
    "    ## extend the last node\n",
    "    Timb.get_leaves()[-1].add_child(name=n.next(), dist=brlen)\n",
    "    Timb.get_leaves()[-1].add_sister(name=n.next(), dist=brlen)\n",
    "\n",
    "## write to file\n",
    "Timb.write(outfile=\"Timb.tre\", format=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or copy the following string to a file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(s1:6,(s3:5.90476,(s5:5.80952,(s7:5.71429,(s9:5.61905,(s11:5.52381,(s13:5.42857,(s15:5.33333,(s17:5.2381,(s19:5.14286,(s21:5.04762,(s23:4.95238,(s25:4.85714,(s27:4.7619,(s29:4.66667,(s31:4.57143,(s33:4.47619,(s35:4.38095,(s37:4.28571,(s39:4.19048,(s41:4.09524,(s43:4,(s45:3.90476,(s47:3.80952,(s49:3.71429,(s51:3.61905,(s53:3.52381,(s55:3.42857,(s57:3.33333,(s59:3.2381,(s61:3.14286,(s63:3.04762,(s65:2.95238,(s67:2.85714,(s69:2.7619,(s71:2.66667,(s73:2.57143,(s75:2.47619,(s77:2.38095,(s79:2.28571,(s81:2.19048,(s83:2.09524,(s85:2,(s87:1.90476,(s89:1.80952,(s91:1.71429,(s93:1.61905,(s95:1.52381,(s97:1.42857,(s99:1.33333,(s101:1.2381,(s103:1.14286,(s105:1.04762,(s107:0.952381,(s109:0.857143,(s111:0.761905,(s113:0.666667,(s115:0.571429,(s117:0.47619,(s119:0.380952,(s121:0.285714,(s123:0.190476,(s125:0.0952381,s126:0.0952381)s124:0.0952381)s122:0.0952381)s120:0.0952381)s118:0.0952381)s116:0.0952381)s114:0.0952381)s112:0.0952381)s110:0.0952381)s108:0.0952381)s106:0.0952381)s104:0.0952381)s102:0.0952381)s100:0.0952381)s98:0.0952381)s96:0.0952381)s94:0.0952381)s92:0.0952381)s90:0.0952381)s88:0.0952381)s86:0.0952381)s84:0.0952381)s82:0.0952381)s80:0.0952381)s78:0.0952381)s76:0.0952381)s74:0.0952381)s72:0.0952381)s70:0.0952381)s68:0.0952381)s66:0.0952381)s64:0.0952381)s62:0.0952381)s60:0.0952381)s58:0.0952381)s56:0.0952381)s54:0.0952381)s52:0.0952381)s50:0.0952381)s48:0.0952381)s46:0.0952381)s44:0.0952381)s42:0.0952381)s40:0.0952381)s38:0.0952381)s36:0.0952381)s34:0.0952381)s32:0.0952381)s30:0.0952381)s28:0.0952381)s26:0.0952381)s24:0.0952381)s22:0.0952381)s20:0.0952381)s18:0.0952381)s16:0.0952381)s14:0.0952381)s12:0.0952381)s10:0.0952381)s8:0.0952381)s6:0.0952381)s4:0.0952381)s2:0.0952381);"
     ]
    }
   ],
   "source": [
    "! cat Timb.tre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## show tree, remove node circles\n",
    "#for node in Timb.traverse():\n",
    "#    node.img_style[\"size\"] = 0\n",
    "#Timb.render(\"%%inline\", h=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the trees are the same length (close enough)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([6.0]) treelength\n",
      "64 tips\n",
      "set([5.999999999999996]) treelength\n",
      "64 tips\n"
     ]
    }
   ],
   "source": [
    "print set([i.get_distance(Tbal) for i in Tbal]), 'treelength'\n",
    "print len(Tbal), 'tips'\n",
    "\n",
    "print set([i.get_distance(Timb) for i in Timb]), 'treelength'\n",
    "print len(Timb), 'tips'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An ultrametric topology of Viburnum w/ 64 tips\n",
    "This tree is inferred in notebook 3, and here it is scaled with penalized likelihood to be ultrametric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JvZTupNOMhftyljDpBOJKNfXS216YZHT1ZhhBI0SF00dWG7PuLYzDTjrNWNhU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\nYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 600\n",
    "library(ape)\n",
    "\n",
    "## make tree ultrametric using penalized likelihood\n",
    "Vtree <- read.tree(\"~/Dropbox/RAxML_bestTree.VIB_small_c85d6m4p99\")\n",
    "Utree <- drop.tip(Vtree, \"clemensiae_DRY6_PWS_2135\")\n",
    "Utree <- ladderize(chronopl(Utree, 0.5))\n",
    "\n",
    "## multiply bls so tree length=6 after dropping outgroup\n",
    "Utree$edge.length <- Utree$edge.length*6\n",
    "\n",
    "## save the new tree\n",
    "write.tree(Utree, \"Tvib.tre\")\n",
    "plot(Utree, cex=0.7, edge.width=2)\n",
    "add.scale.bar()\n",
    "#edgelabels(round(Utree$edge.length,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(((((((((((((anamensis_C6_PWS_2094:0.7975737351,sempervirens_combined:0.7975737351):0.38788117,setigerum:1.185454905):0.1159036148,((tashiori_D30_TET_YAH:0.9508943617,luzonicum_D27_9M_2005:0.9508943617):0.2224344758,erosum_D23_MJDJP_4:1.173328837):0.1280296823):0.1966874261,((parvifolium_D29_KFC_1953:0.9280851365,dilitatum_ELS45:0.9280851365):0.3392240969,wrightii_D31_MJDJP_1:1.267309233):0.2307367125):0.1683708168,japonicum_D26_WC_273:1.666416763):0.2285748893,integrifolium_D25_KFC_1946:1.894991652):0.8552902281,((betulifolium:1.406180911,muhalla_D28_WC_274:1.406180911):0.2814444291,foetidum_D24_KFC_1942:1.68762534):1.06265654):0.4191560518,(acerfolium_ELS88:2.103542008,orientale_DRY2_MJD_GEORGIA:2.103542008):1.065895924):0.2536372853,((((glaberrimum_D34_PWS_2323:1.145733296,vernicosum_D21_PWS_2123:1.145733296):0.4959207291,beccarii_combinedX:1.641654025):0.6376473347,sambucina_D20_PWS_2100:2.27930136):0.3829119791,(cylindricum_DRY1_WC_268:0.8292472406,coriaceum_combined:0.8292472406):1.832966098):0.7608618785):0.8509283234,(sargentii_RCW19:1.145837138,opulus_D6_WC_250:1.145837138):3.128166403):0.1755491402,(((acutifolium_DRY3_MEX_006:1.055259441,sulcatum_D9_MEX_003:1.055259441):0.3022564037,(triphyllum_D13_PWS_1783:0.6956223643,jamesonii_D12_PWS_1636:0.6956223643):0.6618934804):0.7545581918,(dentatum_ELS4:1.379325329,recognitum_AA_1471_83B:1.379325329):0.7327487076):2.337478644):0.1834757395,(((cinnamomifolium_PWS2105X:0.9443909619,propinquum_DRY4_WC_276:0.9443909619):0.1859467029,davidii_D32_WC_269:1.130337665):1.715940815,tinus_D33_WC_277:2.84627848):1.78674994):1.36697158,(((((((prunifolium_ELS57:0.4605954259,prunifolium_AA_22586A:0.4605954259):0.4436690703,rufidulum_ELS25:0.9042644962):0.525530087,lentago_ELS85:1.429794583):2.029772371,(cassinoides_ELS2:3.287159629,punctatum_D19_PWS_2097:3.287159629):0.1724073248):0.6538743879,(((veitchii:0.6523403244,rhytidophyllum:0.6523403244):1.024743851,lantan_combined:1.677084175):0.4005599057,((carlesii_D1_BP_001:0.4336007225,bitchuense_combined:0.4336007225):1.376780113,macrocephalum_D2_WC_284:1.810380836):0.2672632448):2.035797261):1.439283941,((((((((foetens_ERAD10:0.8212355504,grandiflorum_ERAD11_Wendy:0.8212355504):0.3328646861,farreri_RCW21:1.154100236):0.4228107657,suspensum_C5_MJD_111711:1.576911002):0.8113023509,erubescens_RCW36:2.388213353):0.3038880768,henryi_D22_WC_272:2.69210143):0.4106409896,(sieboldii_AA_616_6B:2.627864544,odoratissimum_combined:2.627864544):0.4748778756):0.7034334346,((plicatum_C1_MJDJP_12:1.677317667,hanceanum_D4_PWS_2195:1.677317667):0.4205302257,lutescens_D35_PWS_2077:2.097847893):1.708327961):0.9654371248,amplificatum_D3_SAN_156003:4.771612979):0.7811123034):0.1965029727,((((furcatum_combined:1.183437734,sympodiale_D18_KFC_1932:1.183437734):0.554269789,lantanoides_D15_Beartown_2:1.737707523):0.3948362082,nervosum_C4_PWS_2298:2.132543731):3.301651155,(taiwanianum_TW1_KFC_1952:1.319017626,urceolatum_MJD_Japan_8:1.319017626):4.11517726):0.3150333693):0.2507717451);\r\n"
     ]
    }
   ],
   "source": [
    "#### load TVib tree into Python and print newick string\n",
    "Tvib = ete2.Tree(\"Tvib.tre\")\n",
    "! cat Tvib.tre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulate sequence data on each tree\n",
    "Here I use the _simrrls_ program to simulate RADseq data on each input topology with locus dropout occurring with respect to phylogenetic distances. Find simrrls in my github profile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing tree shapes and sources of missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## balanced tree \n",
    "mkdir -p Tbal_rad_drop/\n",
    "mkdir -p Tbal_ddrad_drop/\n",
    "mkdir -p Tbal_rad_covfull/\n",
    "mkdir -p Tbal_rad_covlow/\n",
    "mkdir -p Tbal_rad_covmed/\n",
    "\n",
    "## imbalanced tree\n",
    "mkdir -p Timb_rad_drop/\n",
    "mkdir -p Timb_ddrad_drop/\n",
    "mkdir -p Timb_rad_covfull/\n",
    "mkdir -p Timb_rad_covlow/\n",
    "mkdir -p Timb_rad_covmed/\n",
    "\n",
    "## sims on empirical Viburnum topo\n",
    "mkdir -p Tvib_rad_drop/\n",
    "mkdir -p Tvib_ddrad_drop/\n",
    "mkdir -p Tvib_rad_covfull/\n",
    "mkdir -p Tvib_rad_covlow/\n",
    "mkdir -p Tvib_rad_covmed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show simrrls options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: simrrls [-h] [--version] [-o outname] [-mc dropout] [-ms dropout]\n",
      "               [-e error] [-f datatype] [-I indels] [-l length] [-L nLoci]\n",
      "               [-n Ninds] [-N Ne] [-t tree] [-u mu] [-df depthfunc]\n",
      "               [-dm depthmean] [-ds depthstd] [-c1 cut_1] [-c2 cut_2]\n",
      "               [-i1 min_insert] [-i2 max_insert] [-r1 seed_1] [-r2 seed_2]\n",
      "\n",
      "optional arguments:\n",
      "  -h, --help      show this help message and exit\n",
      "  --version       show program's version number and exit\n",
      "  -o outname      [str] output file name prefix (default 'out')\n",
      "  -mc dropout     [0/1] allelic dropout from mutation to cut sites (default 0)\n",
      "  -ms dropout     [0/1] allelic dropout from new cut sites in seq (default 0)\n",
      "  -e error        [float] sequencing error rate (default 0.0005)\n",
      "  -f datatype     [str] datatype (default rad) (options: rad, gbs, ddrad,\n",
      "                  pairddrad, pairgbs)\n",
      "  -I indels       [float] rate of indel mutations (default 0) ex: 0.001\n",
      "  -l length       [int] length of simulated sequences (default 100)\n",
      "  -L nLoci        [int] number of loci to simulate (default 100)\n",
      "  -n Ninds        [int] N individuals from each taxon (default 1)\n",
      "  -N Ne           [int] pop size (Ne for all lineages; default 5e5)\n",
      "  -t tree         [str] file name or newick string of ultrametric tree\n",
      "                  (default 12 taxon balanced tree w/ bls=1)\n",
      "  -u mu           [float] per site mutation rate (default 1e-9)\n",
      "  -df depthfunc   [str] model for sampling copies (default norm, other=exp)\n",
      "  -dm depthmean   [int] mean sampled copies in norm, 1/m for exp (default 10)\n",
      "  -ds depthstd    [int] stdev sampled copies, used with norm model (default 0)\n",
      "  -c1 cut_1       [str] restriction site 1 (default CTGCAG)\n",
      "  -c2 cut_2       [str] restriction site 1 (default CCGG)\n",
      "  -i1 min_insert  [int] total frag len = (2*l)+insert (default 200)\n",
      "  -i2 max_insert  [int] total frag len = (2*l)+insert (default 400)\n",
      "  -r1 seed_1      [int] random seed 1 (default 1234567)\n",
      "  -r2 seed_2      [int] random seed 2 (default 7654321)\n",
      "\n",
      "  Example usage: \n",
      "\n",
      "  simrrls -o test1 -N 1e6 -n 4 -L 1000       \n",
      "  simrrls -o test2 -f ddrad -c1 CCTGCAGG -c2 CCGG \n",
      "  simrrls -o test3 -f pairgbs -c1 CTGCAG -D 1 -i1 100 -i2 400 \n",
      "  echo \"((a:1,b:1):1,c:2);\" > treefile \n",
      "  simrrls -o test4 -t treefile -df norm -dm 5 -ds 1\n",
      "        \n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "simrrls -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate RAD data on different trees and with sampling\n",
    "Here I simulate 1000 loci on each tree. For each tree data are simulated in 5 ways. With and without data loss from mutation-disruption or low sequencing coverage, and as a rad data type (one cutter) and ddrad (two cutters). This will take about 10 minutes to run probably."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "for tree in Tbal Timb Tvib;\n",
    "  do \n",
    "    simrrls -mc 1 -ms 1 -t $tree.tre \\\n",
    "            -L 1000 -l 100 \\\n",
    "            -u 1e-9 -N 1e6 \\\n",
    "            -f rad -c1 CTGCAG \\\n",
    "            -o $tree\\_rad_drop/$tree\n",
    "            \n",
    "    simrrls -mc 1 -ms 1 -t $tree.tre \\\n",
    "            -L 1000 -l 100 \\\n",
    "            -u 1e-9 -N 1e6 \\\n",
    "            -f ddrad -c1 CTGCAG -c2 AATT \\\n",
    "            -o $tree\\_ddrad_drop/$tree        \n",
    "    \n",
    "    simrrls -mc 0 -ms 0 -t $tree.tre \\\n",
    "            -L 1000 -l 100 \\\n",
    "            -u 1e-9 -N 1e6 \\\n",
    "            -f rad -c1 CTGCAG \\\n",
    "            -o $tree\\_rad_covfull/$tree\n",
    "\n",
    "    simrrls -mc 0 -ms 0 -t $tree.tre \\\n",
    "            -L 1000 -l 100 \\\n",
    "            -u 1e-9 -N 1e6 \\\n",
    "            -f rad -c1 CTGCAG \\\n",
    "            -dm 5 -ds 5 \\\n",
    "            -o $tree\\_rad_covmed/$tree\n",
    "            \n",
    "    simrrls -mc 0 -ms 0 -t $tree.tre \\\n",
    "            -L 1000 -l 100 \\\n",
    "            -u 1e-9 -N 1e6 \\\n",
    "            -f rad -c1 CTGCAG \\\n",
    "            -dm 2 -ds 5 \\\n",
    "            -o $tree\\_rad_covlow/$tree\n",
    "done"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assemble data sets in _pyRAD_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyRAD 3.0.66\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "pyrad --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\tnew params.txt file created\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "## new params file (remove existing file if present)\n",
    "rm params.txt\n",
    "pyrad -n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## enter parameters into params file using sed\n",
    "sed -i '/## 1. /c\\Tbal_rad_drop       ## 1. working dir ' params.txt\n",
    "sed -i '/## 2. /c\\Tbal_rad_drop/*.gz  ## 2. data loc ' params.txt\n",
    "sed -i '/## 3. /c\\Tbal_rad_drop/*barcodes.txt  ## 3. Bcode ' params.txt\n",
    "sed -i '/## 6. /c\\TGCAG,AATT          ## 6. cutters ' params.txt\n",
    "sed -i '/## 7. /c\\20                  ## 7. Nproc ' params.txt\n",
    "sed -i '/## 10. /c\\.82                ## 10. clust thresh' params.txt\n",
    "sed -i '/## 11. /c\\rad                ## 11. datatype ' params.txt\n",
    "sed -i '/## 12. /c\\2                  ## 12. minCov ' params.txt\n",
    "sed -i '/## 13. /c\\10                  ## 13. maxSH' params.txt\n",
    "sed -i '/## 14. /c\\Tbal               ## 14. outname' params.txt\n",
    "sed -i '/## 24./c\\99                  ## 24. maxH' params.txt\n",
    "sed -i '/## 30./c\\n,p,s               ## 30. out format' params.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## IPython code to iterate over trees and coverages and run pyrad\n",
    "## sometimes this freezes when run in a jupyter notebook due \n",
    "## to problems with multiprocessing in notebooks. This is why my new\n",
    "## work with ipyrad uses ipyparallel instead of multiprocessing. \n",
    "\n",
    "for tree in ['Tbal', 'Timb', 'Tvib']:\n",
    "    for dtype in ['rad', 'ddrad']:\n",
    "        with open('params.txt', 'rb') as params:\n",
    "            pp = params.readlines()\n",
    "            pp[1] = \"{}_{}_drop                 ## 1. \\n\".format(tree, dtype)\n",
    "            pp[2] = \"{}_{}_drop/*.gz            ## 2. \\n\".format(tree, dtype)\n",
    "            pp[3] = \"{}_{}_drop/*barcodes.txt   ## 3. \\n\".format(tree, dtype)\n",
    "            pp[14] = \"{}                        ## 14. \\n\".format(tree)\n",
    "        with open('params.txt', 'wb') as params:\n",
    "            params.write(\"\".join(pp))\n",
    "        ## this calls pyrad as a bash script\n",
    "        ! pyrad -p params.txt >> log.txt 2>&1  \n",
    "          \n",
    "    for cov in ['full', 'med', 'low']:\n",
    "        with open('params.txt', 'rb') as params:\n",
    "            pp = params.readlines()\n",
    "            pp[1] = \"{}_rad_cov{}                ## 1. \\n\".format(tree, cov)\n",
    "            pp[2] = \"{}_rad_cov{}/*.gz           ## 2. \\n\".format(tree, cov)\n",
    "            pp[3] = \"{}_rad_cov{}/*barcodes.txt  ## 3. \\n\".format(tree, cov)\n",
    "            pp[14] = \"{}                         ## 14. \\n\".format(tree)     \n",
    "        with open('params.txt', 'wb') as params:\n",
    "            params.write(\"\".join(pp))    \n",
    "        ## this calls pyrad as a bash script            \n",
    "        ! pyrad -p params.txt >> log.txt 2>&1  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize data sharing on these trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getarray(locifile, tree):\n",
    "    \"\"\" parse the loci list and return a \n",
    "        presence/absence matrix ordered by \n",
    "        the tips on the tree\"\"\"\n",
    "    ## parse the loci file\n",
    "    loci = open(locifile).read().split(\"\\n//\")[:-1]\n",
    "    ## order (ladderize) the tree\n",
    "    tree.ladderize()\n",
    "    ## get tip names\n",
    "    names = tree.get_leaf_names()\n",
    "    ## make empty matrix\n",
    "    lxs = np.zeros((len(names), len(loci)))\n",
    "    ## fill the matrix\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][1:].rsplit(\"_\", 1)[0]),loc] += 1\n",
    "    return lxs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def countmatrix(lxsabove, lxsbelow, max=0):\n",
    "    \"\"\" fill a matrix with pairwise data sharing\n",
    "        between each pair of samples. You could put\n",
    "        in two different 'share' matrices to have\n",
    "        different results above and below the diagonal.\n",
    "        Can enter a max value to limit fill along diagonal.\n",
    "        \"\"\"\n",
    "    share = np.zeros((lxsabove.shape[0], \n",
    "                      lxsbelow.shape[0]))\n",
    "    ## fill above\n",
    "    names = range(lxsabove.shape[0])\n",
    "    for row in lxsabove:\n",
    "        for samp1,samp2 in itertools.combinations(names,2):\n",
    "            shared = lxsabove[samp1, lxsabove[samp2,]>0].sum()\n",
    "            share[samp1,samp2] = shared\n",
    "    ## fill below\n",
    "    for row in lxsbelow:\n",
    "        for samp2,samp1 in itertools.combinations(names,2):\n",
    "            shared = lxsabove[samp1, lxsabove[samp2,]>0].sum()\n",
    "            share[samp1,samp2] = shared\n",
    "    ## fill diagonal\n",
    "    if not max:\n",
    "        for row in range(len(names)):\n",
    "            share[row,row] = lxsabove[row,].sum()\n",
    "    else:\n",
    "        for row in range(len(names)):\n",
    "            share[row,row] = max\n",
    "    return share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plotSVGmatrix(share, outname):\n",
    "    surf = plt.pcolormesh(share, cmap=\"gist_yarg\")\n",
    "    dims = plt.axis('image')\n",
    "    surf.axes.get_xaxis().set_ticklabels([])\n",
    "    surf.axes.get_xaxis().set_ticks([])\n",
    "    surf.axes.get_yaxis().set_ticklabels([])\n",
    "    surf.axes.get_yaxis().set_ticks([])\n",
    "    ax = plt.gca()\n",
    "    ax.invert_yaxis()\n",
    "    plt.colorbar(surf, aspect=15)\n",
    "    if outname:\n",
    "        plt.savefig(outname+\".svg\")\n",
    "    \n",
    "    \n",
    "def fullplot(locifile, tree, outname=None):\n",
    "    lxsB = getarray(locifile, tree)\n",
    "    share = countmatrix(lxsB, lxsB)\n",
    "    plotSVGmatrix(share, outname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XwD8j87IlSTIVc5GXDcA1AG7u/n4zgGu7x8PPy5YkyeQy4rxsX+4e\nn1FKOQAApZT9EbGy+/vw87L1lm+qd2C9gOogWIek97EewOkydCCdibSp/O22aDCqu2Dcjn6X0MDV\n6Uzf+jweWy1zETCdDsvp7poyyJi5pAJ8rb5X/uZU/8hjOEgwfRd9oi4aqLaf9UY7d+6slF111VW1\nbeF63PjNhlFMSJSX7W+6P+k/zhn7M+QKKUlaTNMJ6Uc/+hHuueeeptX28rL9vnt+ICLOKKUciIhV\nAJ7o/j5wXrackJKkxTSdkC6//HJcfvnl/fNPf/rT7vJKXjZ08q99AMA/AHg/gH+i32+JiM+iI6rN\nPi9bbympYgUvYXWpy+JBU1EBqAaiV9zAcm40FQNZxHH5z7j+EydO1AagU1GITbdaxnU4r3QV9diE\nrUHY+Fzr5GW/Brrnd8TjBVTHSCMWMPq+nGjEfdfn8X3uvTqv++XLl9eWqac2t1t3+zvXBX4vvGsf\nqIqMbNpXEe3mm2/uH+umXI1YMAqGvbm2Ji/bPwD4akRcD2AfOpY1ZF62IaGTWpIwbjvIuDFHedme\nQmeSmur6zMuWJEmH1nlqJ0kyubRuQuq54TsxRmX2mZroeSms+hg2karuhMtcUgG9r851QV8i36fL\ndT53OhbdzsDnep/bzsBtc+Z0vY/b6czpLpmDixjp8t07U7u2xSVl4DI1vfNYuO/PfQOuTKM9Ms6N\ngfVGu3fvrpStXn3SJScjRnbIFVKStJickJIkGRtaNyH1RCBdWrudyizCqdnaeac2DfDuRCq39NX7\nuG0smqi4yCKHlnHfVTTh56k5nUVLZ5rV+3j8nPirIjY/T98JizjqeuECmPF7djnv9F26Ohltp3vP\nLq+ZEzvr6ld0rHnM+N+Cim9s2mcRDQB+8IMfTHndMMmY2knScuYi0uOwaN0KKUmSySUnpCRJxobW\nTUg987HL3T5I4PSmO9kHiZTYFF1qHzlypH/Meg4XxVDN91yH0/dwZEmgOmbaLmf25/ucfu7YsWOV\nMj53uidtpzPDcztdEgMX+H4QHQc/wwXyd/okbYvrQ9PvsWlUB62D9Ua7du2qvW82tG5CSpJkcskJ\nKUmSsSEnpCRJxoZJM/uHM2FGxOTYN5OkZZRSZrW8iYgH0ckEMhP2lVLOmc3zZ4KdkJIkSeaSyVrP\nJUnSanJCSpJkbMgJKUmSsSEnpCRJxoackJIkGRv+P1RM7eZKbiUkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec584090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Tbal_rad_drop/outfiles/Tbal.loci', Tbal, 'Tbal_drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TUriWRx2M2pBrQdnxIb7P9SUeocnm7cFgkjuk1tqzEbG1tfZ0RBwm6eaIuErS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\nf3IAuRluC5hiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec448350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Tbal_rad_covlow/outfiles/Tbal.loci', Tbal, 'Tbal_covlow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S+0spf6GZ5JA7fVN4zgNpSPdct0DO6+ZZ6kR4k1xq9T2eBz0K1E69wMqVK5sy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec6be910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Tbal_rad_covmed/outfiles/Tbal.loci', Tbal, 'Tbal_covmed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V9U5baCRNZClaaKrq+Dnm20IjaW7+CY2k5hlUkppnUElqXl/+KNmgknrMFZWk5hlUkppn\nUElqnkElqXkGlaTmGVSSmuflCZKa54pKUvP6ElT9WBdKGmmJWmhWJ7kqyY4kVyZZNTTfFhpJc1uC\nFpqfB84Crq6q5wBfwRYaSQuxFC00DNpmLuzmXAi8vntsC42k+S1RC81RVbUboKp2AUd2822hkTS/\naV6eMF8LzfDUqe20M29QbdmyZdr7lLQ07kryc4t87e5Rg2NaaHYnOaqqdic5GvhuN31qLTRZ5L3W\nJf0EmtVCcwVwMvA+4H+q6twk7wVWV9VZ3cn0i4CXMDjk+zJw/GIKHjz0k7QQo1pozgU+k+QM4C4G\n3/QxzRYaV1SSmue3fpKaZ1BJap5BJal5BpWk5hlUkppnUElqnkElqXkGlaTm/R/31GLe9Rb8BAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cebc92790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Tbal_rad_covfull/outfiles/Tbal.loci', Tbal, 'Tbal_covfull')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZrWds0taldr/B1bS9pvjNuiTn+tWSxiKNmszVjMxwe2rV436yiAYA11xzTXWs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\n6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cec51ef10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Timb_rad_drop/outfiles/Timb.loci', Timb, 'Timb_drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mre+ZXRDOO++8oox1jqp/5G9H9W78D0j1UjyeWiePoeom3RYXHmvVZ3F/mxJD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cebf3aa50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Tvib_rad_drop/outfiles/Tvib.loci', Tvib, 'Tvib_drop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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19kJE7GqtPR8RGyU9GBH/R9Lpkm6V9C9aay9GxAWjtq/U0ey1WyXdFxE7stja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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cebf2e410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fullplot('Tvib_rad_covlow/outfiles/Tvib.loci', Tvib, 'Tvib_covlow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The hierarchical distribution of informative sites\n",
    "First we re-calculate the pair-wise data sharing matrices for all species in each data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lxs_Tbal_droprad = getarray(\"Tbal_rad_drop/outfiles/Tbal.loci\", Tbal)\n",
    "lxs_Tbal_dropddrad = getarray(\"Tbal_ddrad_drop/outfiles/Tbal.loci\", Tbal)\n",
    "lxs_Tbal_covlow = getarray(\"Tbal_rad_covlow/outfiles/Tbal.loci\", Tbal)\n",
    "lxs_Tbal_covmed = getarray(\"Tbal_rad_covmed/outfiles/Tbal.loci\", Tbal)\n",
    "lxs_Tbal_covfull = getarray(\"Tbal_rad_covfull/outfiles/Tbal.loci\", Tbal)\n",
    "\n",
    "lxs_Timb_droprad = getarray(\"Timb_rad_drop/outfiles/Timb.loci\", Timb)\n",
    "lxs_Timb_dropddrad = getarray(\"Timb_ddrad_drop/outfiles/Timb.loci\", Timb)\n",
    "lxs_Timb_covlow = getarray(\"Timb_rad_covlow/outfiles/Timb.loci\", Timb)\n",
    "lxs_Timb_covmed = getarray(\"Timb_rad_covmed/outfiles/Timb.loci\", Timb)\n",
    "lxs_Timb_covfull = getarray(\"Timb_rad_covfull/outfiles/Timb.loci\", Timb)\n",
    "\n",
    "lxs_Tvib_droprad = getarray(\"Tvib_rad_drop/outfiles/Tvib.loci\", Tvib)\n",
    "lxs_Tvib_dropddrad = getarray(\"Tvib_ddrad_drop/outfiles/Tvib.loci\", Tvib)\n",
    "lxs_Tvib_covlow = getarray(\"Tvib_rad_covlow/outfiles/Tvib.loci\", Tvib)\n",
    "lxs_Tvib_covmed = getarray(\"Tvib_rad_covmed/outfiles/Tvib.loci\", Tvib)\n",
    "lxs_Tvib_covfull = getarray(\"Tvib_rad_covfull/outfiles/Tvib.loci\", Tvib)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A function to count loci for each bipartition (quartet-style)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def count_inf4(tree, matrix, node):\n",
    "    \"\"\" count the number of loci with data spanning \n",
    "    a given node in the tree \"\"\"\n",
    "    \n",
    "    ## get children of selected node\n",
    "    a, b = node.get_children()\n",
    "    \n",
    "    ## get tip descendents of a and b\n",
    "    tips_a = set(a.get_leaf_names())\n",
    "    tips_b = set(b.get_leaf_names())\n",
    "    \n",
    "    ## get every other tip (outgroups)\n",
    "    upone = node.up\n",
    "    if upone.is_root():\n",
    "        ch = upone.children\n",
    "        sis = [i for i in ch if i != node][0]\n",
    "        if sis.children:\n",
    "            tips_c = sis.children[0].get_leaf_names()\n",
    "            tips_d = sis.children[1].get_leaf_names()\n",
    "        else:\n",
    "            return 0\n",
    "    else:\n",
    "        upone = set(node.up.get_leaf_names())\n",
    "        tips_c = upone - tips_a - tips_b\n",
    "        tips_all = set(tree.get_leaf_names())\n",
    "        tips_d = tips_all - tips_a - tips_b - tips_c\n",
    "    \n",
    "    ## get indices in matrix for leaf tips\n",
    "    names = tree.get_leaf_names()\n",
    "    index_a = [names.index(i) for i in tips_a]\n",
    "    index_b = [names.index(i) for i in tips_b]\n",
    "    index_c = [names.index(i) for i in tips_c]\n",
    "    index_d = [names.index(i) for i in tips_d]\n",
    "   \n",
    "    ## how man loci are \"informative\"\n",
    "    inf = 0\n",
    "    for col in matrix.T:\n",
    "        hits_a = sum([col[i] for i in index_a])\n",
    "        hits_b = sum([col[i] for i in index_b])\n",
    "        hits_c = sum([col[i] for i in index_c])\n",
    "        hits_d = sum([col[i] for i in index_d])\n",
    "\n",
    "        if all([hits_a, hits_b, hits_c, hits_d]):\n",
    "                inf += 1 \n",
    "    return inf    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A function to write data to file for plotting\n",
    "Here I iterate over each node and apply _count\\_inf4_ which returns the number of loci that are informative for the subtending bipartition, and _count\\_snps_ which counts snps segregating at that bipartition. This takes a few minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def nodes_dat(tree, lxs, datfilename):\n",
    "    dat = []\n",
    "    for node in tree.traverse():\n",
    "        if not (node.is_leaf() or node.is_root()):\n",
    "            loci = count_inf4(tree, lxs, node)\n",
    "            dist = round(tree.get_distance(node),2)\n",
    "            dat.append([dist, loci])\n",
    "            node.name = \"%d\" % loci\n",
    "    \n",
    "    ## print tree with bls & node labels\n",
    "    tree.write(format=3,outfile=datfilename+\".tre\")\n",
    "    \n",
    "    ## print data to file\n",
    "    with open(datfilename, 'w') as outfile:\n",
    "        np.savetxt(outfile, np.array(dat), fmt=\"%.2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "## a new directory to store the data in\n",
    "mkdir -p analysis_counts2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "nodes_dat(Tbal, lxs_Tbal_droprad, \n",
    "          \"analysis_counts2/Tbal_droprad.dat3\")\n",
    "nodes_dat(Tbal, lxs_Tbal_dropddrad, \n",
    "          \"analysis_counts2/Tbal_dropddrad.dat3\")\n",
    "nodes_dat(Tbal, lxs_Tbal_covlow, \n",
    "          \"analysis_counts2/Tbal_covlow.dat3\")    \n",
    "nodes_dat(Tbal, lxs_Tbal_covmed, \n",
    "          \"analysis_counts2/Tbal_covmed.dat3\")\n",
    "nodes_dat(Tbal, lxs_Tbal_covfull,\n",
    "          \"analysis_counts2/Tbal_covfull.dat3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_dat(Timb, lxs_Timb_droprad, \n",
    "          \"analysis_counts2/Timb_droprad.dat3\")\n",
    "nodes_dat(Timb, lxs_Timb_dropddrad, \n",
    "          \"analysis_counts2/Timb_dropddrad.dat3\")\n",
    "nodes_dat(Timb, lxs_Timb_covlow, \n",
    "          \"analysis_counts2/Timb_covlow.dat3\")    \n",
    "nodes_dat(Timb, lxs_Timb_covmed, \n",
    "          \"analysis_counts2/Timb_covmed.dat3\")\n",
    "nodes_dat(Timb, lxs_Timb_covfull,\n",
    "          \"analysis_counts2/Timb_covfull.dat3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_dat(Tvib, lxs_Tvib_droprad, \n",
    "          \"analysis_counts2/Tvib_droprad.dat3\")  \n",
    "nodes_dat(Tvib, lxs_Tvib_dropddrad, \n",
    "          \"analysis_counts2/Tvib_dropddrad.dat3\")    \n",
    "nodes_dat(Tvib, lxs_Tvib_covlow, \n",
    "          \"analysis_counts2/Tvib_covlow.dat3\")    \n",
    "nodes_dat(Tvib, lxs_Tvib_covmed, \n",
    "          \"analysis_counts2/Tvib_covmed.dat3\")\n",
    "nodes_dat(Tvib, lxs_Tvib_covfull,\n",
    "          \"analysis_counts2/Tvib_covfull.dat3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the hierarchical distribution with the trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "library(ape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in the data to R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "## read in the data and factor results\n",
    "dat <- read.table(\"analysis_counts2/Tbal_droprad.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tbal_droprad_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tbal_dropddrad.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tbal_dropddrad_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tbal_covlow.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tbal_covlow_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tbal_covmed.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tbal_covmed_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tbal_covfull.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tbal_covfull_Lme <- with(dat, tapply(loci, depth, mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "## read in the data and factor results\n",
    "dat <- read.table(\"analysis_counts2/Timb_droprad.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Timb_droprad_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Timb_dropddrad.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Timb_dropddrad_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Timb_covlow.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Timb_covlow_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Timb_covmed.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Timb_covmed_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Timb_covfull.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Timb_covfull_Lme <- with(dat, tapply(loci, depth, mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "## read in the data and factor results\n",
    "dat <- read.table(\"analysis_counts2/Tvib_droprad.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tvib_droprad_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tvib_dropddrad.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tvib_dropddrad_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tvib_covlow.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tvib_covlow_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tvib_covmed.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tvib_covmed_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "dat <- read.table(\"analysis_counts2/Tvib_covfull.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "Tvib_covfull_Lme <- with(dat, tapply(loci, depth, mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1UIgSvFAODAoBg0LAoBAwKAQMCgGDQsCgEDAoBAwKAYNCwKAQMCgEDAoBg0LAoBAwKAQM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\nGBQCBoWAQSFgUAgYFALG/wGpFYqmtkgu4gAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 400\n",
    "\n",
    "#svg(\"box1.svg\", width=4, height=5)\n",
    "\n",
    "L = Tbal_droprad_Lme\n",
    "plot(L, xlim=c(0,6), ylim=c(575,1025), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "#abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Tbal_covfull_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Tbal_droprad_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#6E6E6E\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "#L = Tbal_dropddrad_Lme \n",
    "#df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "#points(df1, cex=3.5, pch=21, col=\"#6E6E6E\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "axis(side=1, at=seq(0,6,0.5), \n",
    "     labels=as.character(seq(6,0,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G2sQSA21ixSnOlpgMbWLFmRcm8abeaBMrzp81ibfSQ5tYccrvnSMvGdozSSBK4oUiqEMk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\nMSCEGBBCDAghBoQQA0KI8f+IDFKpdf7o+AAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 400\n",
    "\n",
    "#svg(\"box2.svg\", width=4, height=5)\n",
    "\n",
    "L = Tbal_covlow_Lme\n",
    "plot(L, xlim=c(0,6), ylim=c(575,1025), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Tbal_covfull_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Tbal_covmed_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "\n",
    "#L = Tbal_covlow_Lme\n",
    "#df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "#points(df1, cex=3.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "axis(side=1, at=seq(0,6,0.5), \n",
    "     labels=as.character(seq(6,0,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 400\n",
    "\n",
    "#svg(\"box3.svg\", width=4, height=5)\n",
    "\n",
    "## samples every 6th to make plot more readable\n",
    "\n",
    "L = Timb_droprad_Lme[c(3:62)]\n",
    "plot(L, xlim=c(0,6), ylim=c(575, 1025), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")#, yaxt=\"n\")\n",
    "#abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Timb_covfull_Lme[seq(3, 65, 6)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Timb_droprad_Lme[seq(3, 65, 6)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "axis(side=1, at=seq(0,6, 0.5), \n",
    "     labels=as.character(seq(6,0,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 400\n",
    "\n",
    "## samples every 6th to make plot more readable\n",
    "\n",
    "#svg(\"box4.svg\", width=4, height=5)\n",
    "L = Timb_droprad_Lme[c(3:62)]\n",
    "plot(L, xlim=c(0,6), ylim=c(575, 1025), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")#, yaxt=\"n\")\n",
    "#abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Timb_covfull_Lme[seq(3, 65, 6)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Timb_covmed_Lme[seq(3, 65, 6)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "lines(df1, type='l', lwd=2, lty=2)\n",
    "points(df1, cex=2.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "axis(side=1, at=seq(0,6, 0.5), \n",
    "     labels=as.character(seq(6,0,by=-0.5)), cex.axis=1.25)\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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deAfw/f4BM/jQ04JRkKx3GFI8gNesExL4D1ANB7tYvh/CZ2IU4qG3091saE1P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\nkyBGBpMgRgaTIEaG/wfiAVfP33TQTgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 400 -h 500\n",
    "\n",
    "#svg(\"Sourcemissing.svg\", height=10, width=8)\n",
    "#svg(\"Sourcemissing.svg\", height=7, width=5.33)\n",
    "\n",
    "mat2 = matrix(c(1,1,1,4,4,4,7,7,7,  \n",
    "                1,1,1,4,4,4,7,7,7,\n",
    "                2,2,2,5,5,5,8,8,8,\n",
    "                3,3,3,6,6,6,9,9,9),\n",
    "                4,9, byrow=TRUE)\n",
    "layout(mat2)\n",
    "par(mar=c(1,1,0,1), \n",
    "    oma=c(2,2,1,0))\n",
    "\n",
    "#########################################################\n",
    "tre <- read.tree(\"Tbal.tre\")\n",
    "plot(tre, show.tip.label=F,\n",
    "     edge.width=2.5, type='p', \n",
    "     x.lim=c(0,6))\n",
    "\n",
    "####  \n",
    "L = Tbal_droprad_Lme\n",
    "plot(L, xlim=c(0,6), ylim=c(-25,1200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Tbal_covfull_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Tbal_droprad_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "L = Tbal_dropddrad_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "####\n",
    "L = Tbal_covlow_Lme\n",
    "plot(L, xlim=c(0,6), ylim=c(-25,1200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Tbal_covfull_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Tbal_covmed_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "L = Tbal_covlow_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "axis(side=1, at=seq(0,6,0.5), \n",
    "     labels=as.character(seq(6,0,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "box()\n",
    "\n",
    "##########################################################\n",
    "tre <- read.tree(\"Timb.tre\")\n",
    "plot(tre, show.tip.label=F,\n",
    "     edge.width=2.5, type='p', \n",
    "     x.lim=c(0,6))\n",
    "\n",
    "####\n",
    "L = Timb_droprad_Lme[c(3:62)]\n",
    "plot(L, xlim=c(0,6), ylim=c(-25,1200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\", yaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Timb_covfull_Lme[c(3:62)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Timb_droprad_Lme[c(3:62)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "L = Timb_dropddrad_Lme[c(3:62)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "####\n",
    "L = Timb_covlow_Lme[c(3:62)] \n",
    "plot(L, xlim=c(0,6), ylim=c(-25,1200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\", yaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Timb_covfull_Lme[c(3:62)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Timb_covmed_Lme[c(3:62)]\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "L = Timb_covlow_Lme[c(3:62)] \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "\n",
    "##\n",
    "axis(side=1, at=seq(0,6,0.5), \n",
    "     labels=as.character(seq(6,0,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "#########################################################\n",
    "tre <- read.tree(\"Tvib.tre\")\n",
    "plot(tre, show.tip.label=F,\n",
    "     edge.width=2.5, type='p', \n",
    "     x.lim=c(0,6))\n",
    "\n",
    "####\n",
    "L = Tvib_droprad_Lme\n",
    "plot(L, xlim=c(0,6.25), ylim=c(-25,1200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\", yaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Tvib_covfull_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Tvib_droprad_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "L = Tvib_dropddrad_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "####\n",
    "\n",
    "plot(L, xlim=c(0,6.25), ylim=c(-25,1200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\", yaxt=\"n\")\n",
    "abline(h=1000, lwd=2, col=\"#6E6E6E\", lty=\"dotted\")\n",
    "\n",
    "L = Tvib_covfull_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#262626\", lwd=0.75)\n",
    "\n",
    "L = Tvib_covmed_Lme \n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#6E6E6E\", lwd=0.75)\n",
    "\n",
    "L = Tvib_covlow_Lme\n",
    "df1 = data.frame(as.numeric(names(L)),as.numeric(L))\n",
    "points(df1, cex=1.5, pch=21, col=\"#262626\", bg=\"#D3D3D3\", lwd=0.75)\n",
    "\n",
    "box()\n",
    "##\n",
    "axis(side=1, at=seq(0,6,1), \n",
    "     labels=as.character(seq(6,0,by=-1)), cex.axis=1.25)\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empirical data (full & half depth)\n",
    "Here I am grabbing the assembled empirical data from notebook_1 (Viburnum) to compare the effect of sequencing coverage with the results we see when simulating data on that tree. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Tvib2 = Tvib.copy()\n",
    "for node in Tvib2:\n",
    "    node.name = node.name+\"_0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'acutifolium_DRY3_MEX' is not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-59562b793f2b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## full size data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#lxs_EmpVib_full = getarray(\"/home/deren/Dropbox/RADexplore/EmpVib/vib_full_64tip_c85d6m4p99.loci\", Tvib)#, dropind=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlxs_EmpVib_half\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/home/deren/Dropbox/RADexplore/EmpVib/vib_half_64tip_c85d6m4p99.loci\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTvib\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m#, dropind=1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-30-282cdcd80fbf>\u001b[0m in \u001b[0;36mgetarray\u001b[0;34m(locifile, tree)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloci\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m\">\"\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseq\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                 \u001b[0mlxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mlxs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'acutifolium_DRY3_MEX' is not in list"
     ]
    }
   ],
   "source": [
    "## full size data\n",
    "lxs_EmpVib_full = getarray(\"/home/deren/Dropbox/RADexplore/EmpVib/vib_full_64tip_c85d6m4p99.loci\", Tvib)#, dropind=1)\n",
    "lxs_EmpVib_half = getarray(\"/home/deren/Dropbox/RADexplore/EmpVib/vib_half_64tip_c85d6m4p99.loci\", Tvib)#, dropind=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "share_full = countmatrix(lxs_EmpVib_full,lxs_EmpVib_full)\n",
    "plotSVGmatrix(share_full, \"EmpVib_full\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "share_half = countmatrix(lxs_EmpVib_half,lxs_EmpVib_half)\n",
    "plotSVGmatrix(share_half, \"EmpVib_half\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nodes_dat(Tvib, lxs_EmpVib_half, \n",
    "          \"analysis_counts/Tvib_Emp_half.dat3\")\n",
    "nodes_dat(Tvib, lxs_EmpVib_full, \n",
    "          \"analysis_counts/Tvib_Emp_full.dat3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "## read in the data and factor results\n",
    "dat <- read.table(\"analysis_counts/Tvib_Emp_full.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "EmpVib_full_Lme <- with(dat, tapply(loci, depth, mean))\n",
    "\n",
    "## read in the data and factor results\n",
    "dat <- read.table(\"analysis_counts/Tvib_Emp_half.dat3\", \n",
    "                  header=F, col.names=c('depth','loci'))\n",
    "dat[,1] <- as.factor(dat[,1])\n",
    "EmpVib_half_Lme <- with(dat, tapply(loci, depth, mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "EmpVib_half_Lme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R -w 200 -h 400\n",
    "\n",
    "#svg(\"EmpVib_fullvhalf3.svg\", height=6, width=2.5)\n",
    "\n",
    "mat2 <- matrix(c(1,1,1,2),byrow=TRUE)\n",
    "layout(mat2)\n",
    "par(mar=c(1,1,0,1), \n",
    "    oma=c(2,2,1,0))\n",
    "\n",
    "#########################################################\n",
    "#tre <- read.tree(\"Tvib.tre\")\n",
    "#plot(tre, show.tip.label=F,\n",
    "#     edge.width=2.5, type='p', \n",
    "#     x.lim=c(0,2.25))\n",
    "Vtre <- read.tree(\"analysis_counts/EmpVib_full.dat3.tre\")\n",
    "plot(Vtre, cex=0.6, adj=0.05, x.lim=c(0,2.25),\n",
    "     edge.width=2.5, type='p',show.tip.label=FALSE)\n",
    "nodelabels(pch=20, col=\"black\",\n",
    "           cex=as.integer(Vtre$node.label)/7500)\n",
    "\n",
    "####\n",
    "L = EmpVib_half_Lme\n",
    "plot(L, xlim=c(0,2.25), ylim=c(-25,50200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "df1 = data.frame(as.numeric(names(L)),\n",
    "                 as.numeric(L))\n",
    "points(df1, cex=1.25, pch=21, col=\"red\", bg=\"red\")\n",
    "\n",
    "####\n",
    "L = EmpVib_full_Lme\n",
    "#plot(L, xlim=c(0,2.25), ylim=c(-25,50200), \n",
    "#     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=0, lwd=2, col=\"gray\", lty=\"dotted\")\n",
    "df1 = data.frame(as.numeric(names(L)),\n",
    "                 as.numeric(L))\n",
    "points(df1, cex=1.25, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R -w 200 -h 400\n",
    "\n",
    "#svg(\"fullvhalf3.svg\", height=4.5, width=4)\n",
    "\n",
    "####\n",
    "L = EmpVib_full_Lme\n",
    "plot(L, xlim=c(0,2.25), ylim=c(-25,50200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=0, lwd=2, col=\"gray\", lty=\"dotted\")\n",
    "df1 = data.frame(as.numeric(names(L)),\n",
    "                 as.numeric(L))\n",
    "points(df1, cex=1.25, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "\n",
    "\n",
    "dev.off()\n",
    "\n",
    "svg(\"fullonly.svg\", height=4.5, width=4)\n",
    "####\n",
    "L = EmpVib_half_Lme\n",
    "plot(L, xlim=c(0,2.25), ylim=c(-25,50200), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "df1 = data.frame(as.numeric(names(L)),\n",
    "                 as.numeric(L))\n",
    "points(df1, cex=1.25, pch=21, col=\"red\", bg=\"red\")\n",
    "\n",
    "####\n",
    "L = EmpVib_full_Lme\n",
    "abline(h=0, lwd=2, col=\"gray\", lty=\"dotted\")\n",
    "df1 = data.frame(as.numeric(names(L)),\n",
    "                 as.numeric(L))\n",
    "points(df1, cex=1.25, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "data.frame(cbind(median(EmpVib_full_Lme),\n",
    "           median(EmpVib_half_Lme)),\n",
    "           cbind(mean(EmpVib_full_Lme),\n",
    "           mean(EmpVib_half_Lme)),\n",
    "     col.names=c(\"full\",\"half\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "svg('hist.svg', width=4.25, height=4)\n",
    "hist(rnorm(10000), col=\"grey\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R -w 300 -h 600\n",
    "\n",
    "Vtre <- read.tree(\"analysis_counts/EmpVib_full.dat3.tre\")\n",
    "svg(\"EmpVib_full_nodes.svg\", height=6, width=3)\n",
    "plot(Vtre, cex=0.6, adj=0.05,\n",
    "     edge.width=3, show.tip.label=FALSE)\n",
    "nodelabels(pch=20, col=\"black\",\n",
    "           cex=as.integer(Vtre$node.label)/10000)\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R -w 300 -h 600\n",
    "\n",
    "#svg(\"locisnpsdepth.svg\", height=8, width=4)\n",
    "#pdf(\"locisnpsdepth.pdf\", height=8, width=4)\n",
    "\n",
    "mat2 <- matrix(c(1,1,1,5,5,5,\n",
    "                 1,1,1,5,5,5,\n",
    "                 2,2,2,6,6,6,\n",
    "                 3,3,3,7,7,7,\n",
    "                 4,4,4,8,8,8), \n",
    "                 5,6, byrow=TRUE)\n",
    "layout(mat2)\n",
    "par(mar=c(1,1,0,1), \n",
    "    oma=c(2,2,1,0))\n",
    "tre <- read.tree(\"Tbal.tre\")\n",
    "plot(tre, show.tip.label=F,\n",
    "     edge.width=2.5, type='p', \n",
    "     x.lim=c(-0.25,2.75))\n",
    "\n",
    "##-------------------------------\n",
    "## Plot full data locus sharing\n",
    "x = seq(1.5,5.5)\n",
    "y = Tbal_full_Lme#[1:6]\n",
    "s = Tbal_full_Lsd#[1:6]\n",
    "plot(x, y, xlim=c(1,7.2), ylim=c(-25,3100), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=(seq(0,1000,200)), lwd=1.5, col=\"gray\", lty=\"dotted\")\n",
    "points(x,y, cex=1.25, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "lines(x,y, lwd=2, col=\"#262626\")\n",
    "segments(x, y-s, x, y+s, col=\"#262626\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "x = seq(1.5,5.5)\n",
    "y = Tbal_full_Sme#[2:6]\n",
    "s = Tbal_full_Ssd#[2:6]\n",
    "lines(x, y, lwd=2, col=\"darkgrey\")\n",
    "points(x, y, cex=1.25, pch=21, col=\"darkgrey\", bg=\"darkgrey\")\n",
    "segments(x, y-s, x, y+s, col=\"darkgrey\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "##--------------------------------\n",
    "## Plot drop data locus sharing\n",
    "x = seq(1.5,5.5)\n",
    "y = Tbal_drop_Lme\n",
    "s = Tbal_drop_Lsd\n",
    "plot(x, y, xlim=c(1,7.2), ylim=c(-25,3100), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=(seq(0,1000,200)), lwd=1.5, col=\"gray\", lty=\"dotted\")\n",
    "points(x,y, cex=1.25, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "lines(x,y, lwd=2, col=\"#262626\")\n",
    "segments(x, y-s, x, y+s, col=\"#262626\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "x = seq(1.5,5.5)\n",
    "y = Tbal_drop_Sme\n",
    "s = Tbal_drop_Ssd\n",
    "lines(x, y, lwd=2, col=\"darkgrey\")\n",
    "points(x, y, cex=1.25, pch=21, col=\"darkgrey\", bg=\"darkgrey\")\n",
    "segments(x, y-s, x, y+s, col=\"darkgrey\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "##------------------------------\n",
    "## Plot cov data locus sharing\n",
    "x = seq(1.5,5.5)\n",
    "y = Tbal_cov_Lme\n",
    "s = Tbal_cov_Lsd\n",
    "plot(x, y, xlim=c(1,7.2), ylim=c(-25,3100), \n",
    "     cex.axis=1.25, type='n', xaxt=\"n\")\n",
    "abline(h=(seq(0,1000,200)), lwd=1.5, col=\"gray\", lty=\"dotted\")\n",
    "points(x,y, cex=1.25, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "lines(x,y, lwd=2, col=\"#262626\")\n",
    "segments(x, y-s, x, y+s, col=\"#262626\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "x = seq(1.5,5.5)\n",
    "y = Tbal_cov_Sme\n",
    "s = Tbal_cov_Ssd\n",
    "lines(x, y, lwd=2, col=\"darkgrey\")\n",
    "points(x, y, cex=1.25, pch=21, col=\"darkgrey\", bg=\"darkgrey\")\n",
    "segments(x, y-s, x, y+s, col=\"darkgrey\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "axis(side=1, at=seq(1.1,8.1,1), \n",
    "     labels=as.character(seq(3,-0.5,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "###########################################\n",
    "###########################################\n",
    "tre <- read.tree(\"Timb.tre\")\n",
    "plot(tre, show.tip.label=F, \n",
    "     edge.width=2, type='p')\n",
    "##------------------------------------\n",
    "## Plot full data locus sharing\n",
    "x = seq(2,62)\n",
    "y = Timb_full_Lme[2:62]\n",
    "s = Timb_full_Lsd[2:62]\n",
    "plot(x, y, xlim=c(1,65), ylim=c(-25,3100), \n",
    "     cex.axis=1.25, type='n', yaxt=\"n\", xaxt=\"n\")\n",
    "abline(h=(seq(0,1000,200)), lwd=1.5, col=\"gray\", lty=\"dotted\")\n",
    "points(x,y, cex=1, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "lines(x,y, lwd=2, col=\"#262626\")\n",
    "segments(x, y-s, x, y+s, col=\"#262626\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "y = Timb_full_Sme[2:62]\n",
    "s = Timb_full_Ssd[2:62]\n",
    "lines(x, y, lwd=2, col=\"darkgrey\")\n",
    "#points(x, y, cex=1, pch=21, col=\"darkgrey\", bg=\"darkgrey\")\n",
    "segments(x, y-s, x, y+s, col=\"darkgrey\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "##----------------------------------\n",
    "## Plot full data locus sharing\n",
    "x = seq(2,62)\n",
    "y = Timb_drop_Lme[2:62]\n",
    "s = Timb_drop_Lsd[2:62]\n",
    "plot(x, y, xlim=c(1,65), ylim=c(-25,3100), \n",
    "     cex.axis=1.25, type='n', yaxt=\"n\", xaxt=\"n\")\n",
    "abline(h=(seq(0,1000,200)), lwd=1.5, col=\"gray\", lty=\"dotted\")\n",
    "points(x,y, cex=1, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "lines(x,y, lwd=2, col=\"#262626\")\n",
    "segments(x, y-s, x, y+s, col=\"#262626\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "y = Timb_drop_Sme[2:62]\n",
    "s = Timb_drop_Ssd[2:62]\n",
    "lines(x, y, lwd=2, col=\"darkgrey\")\n",
    "#points(x, y, cex=1, pch=21, col=\"darkgrey\", bg=\"darkgrey\")\n",
    "segments(x, y-s, x, y+s)\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "##-----------------------------------\n",
    "## Plot drop data locus sharing\n",
    "x = seq(2,62)\n",
    "y = Timb_cov_Lme[2:62]\n",
    "s = Timb_cov_Lsd[2:62]\n",
    "plot(x, y,\n",
    "     xlim=c(1,65), ylim=c(-20,3100), \n",
    "     cex=1, cex.axis=1.25,\n",
    "     pch=21, bg=\"#262626\", xaxt=\"n\", yaxt=\"n\")\n",
    "abline(h=(seq(0,1000,200)), lwd=1.5, col=\"gray\", lty=\"dotted\")\n",
    "points(x,y, cex=1, pch=21, col=\"#262626\", bg=\"#262626\")\n",
    "lines(x,y, lwd=2, col=\"#262626\")\n",
    "segments(x, y-s, x, y+s, col=\"#262626\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "\n",
    "y = Timb_cov_Sme[2:62]\n",
    "s = Timb_cov_Ssd[2:62]\n",
    "lines(x, y, lwd=2, col=\"darkgrey\")\n",
    "#points(x, y, cex=1, pch=21, col=\"darkgrey\", bg=\"darkgrey\")\n",
    "segments(x, y-s, x, y+s, col=\"darkgrey\")\n",
    "epsilon = 0.02\n",
    "segments(x-epsilon,y-s,x+epsilon,y-s)\n",
    "segments(x-epsilon,y+s,x+epsilon,y+s)\n",
    "axis(side=1, at=seq(2.1,72,10), \n",
    "     labels=as.character(seq(3,0,by=-0.5)), cex.axis=1.25)\n",
    "\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOt nodes on tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lxs_EmpVib_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_nodes_to_tree(tree,lxs,treefilename):\n",
    "    for node in tree.traverse():\n",
    "        if not node.is_leaf():\n",
    "            inf = count_inf4(tree, lxs, node)\n",
    "            node.name = \"%d\" % inf\n",
    "    \n",
    "    ## print tree with bls & node labels\n",
    "    tree.write(format=3,outfile=treefilename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "write_nodes_to_tree(Tvib, lxs_EmpVib_full, \"Tvib_full_nodes.tre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%R -w 400 -h 500\n",
    "tre <- read.tree(\"loci_Tbal_cov\")\n",
    "plot(tre)#, show.tip.label=F, edge.width=2.5)\n",
    "#nodelabels(pch=21, \n",
    "#           bg=\"#262626\",\n",
    "#           cex=as.integer(tre$node.label)/150)\n",
    "nodelabels(tre$node.label, bg='grey', cex=1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data sharing by sub-sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How much data are shared by a random N samples, and how much data are shared across the deepest bipartition for 2+N samples. _Also how many SNPs?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def counts(lxs, minr, maxr, maxi):\n",
    "    ## data store\n",
    "    data = np.zeros((maxr+1-minr,maxi))\n",
    "    \n",
    "    for sample in range(minr, maxr+1):\n",
    "        g = itertools.combinations(range(maxr), sample)\n",
    "        i = 0\n",
    "        while i<maxi:\n",
    "            try: \n",
    "                gsamp = g.next()\n",
    "            except StopIteration:\n",
    "                break\n",
    "            shared = sum(lxs[gsamp,:].sum(axis=0) == len(gsamp))\n",
    "            data[sample-minr,i] = shared\n",
    "            i += 1\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Dbal = counts(lxs_Tbal_drop, 4, 32, 1000)\n",
    "Dimb = counts(lxs_Timb_drop, 4, 32, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Dbal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def counts2(lxs, minr, maxr, maxi):\n",
    "    ## data store\n",
    "    data = np.zeros(((maxr+1-minr)*maxi, 3))\n",
    "    count = 0\n",
    "    for sample in range(minr, maxr+1):\n",
    "        g = itertools.combinations(range(maxr), sample)\n",
    "        i = 0\n",
    "        while i<maxi:\n",
    "            try: \n",
    "                gsamp = g.next()\n",
    "            except StopIteration:\n",
    "                break\n",
    "            shared = sum(lxs[gsamp,:].sum(axis=0) == len(gsamp))\n",
    "            datum = [sample, float(shared), i+1]\n",
    "            data[count] = datum\n",
    "            i += 1\n",
    "            count += 1\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Dimb1 = counts2(lxs_Timb_full, 4, 32, 100)\n",
    "Dimb2 = counts2(lxs_Timb_drop, 4, 32, 100)\n",
    "Dimb3 = counts2(lxs_Timb_cov, 4, 32, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Dbal1 = counts2(lxs_Tbal_full, 4, 32, 100)\n",
    "Dbal2 = counts2(lxs_Tbal_drop, 4, 32, 100)\n",
    "Dbal3 = counts2(lxs_Tbal_cov, 4, 32, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def saveto(D, outname):\n",
    "    dd = pd.DataFrame({\"time\":[i[0] for i in D],\n",
    "                       \"loci\":[i[1] for i in D],\n",
    "                       \"idx\":[i[2] for i in D],\n",
    "                       \"tree\":[\"Timb\" for _ in D]})\n",
    "    dd.to_csv(outname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saveto(Dimb1, \"Dimb1.dat\")\n",
    "saveto(Dimb2, \"Dimb2.dat\")\n",
    "saveto(Dimb3, \"Dimb3.dat\")\n",
    "saveto(Dbal1, \"Dbal1.dat\")\n",
    "saveto(Dbal2, \"Dbal2.dat\")\n",
    "saveto(Dbal3, \"Dbal3.dat\")"
   ]
  }
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